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Perspectives on drought and temperature variability for the southwestern United States from a new hydro-isotopic network

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Perspectives on drought and temperature variability for the southwestern United States from a new hydro-isotopic network

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Content PERSPECTIVES ON DROUGHT AND TEMPERATURE V ARIABILITY FOR THE
SOUTHWESTERN UNITED STATES FROM A NEW HYDRO-ISOTOPIC NETWORK
by
Max B. Berkelhammer
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(GEOLOGICAL SCIENCES)
December 2010
Copyright 2010 Max B. Berkelhammer
Epigraph
There is no such thing as a long piece of work, except one that you dare not start.
-Charles Baudelaire
ii
Acknowledgements
I would like to express my profound gratitude to the many individuals who have contributed to the
completion of this work. It has been an honor and pleasure to interact and work intimately with so
many gifted and kind people for the past five years.
Namely, Dr. Lowell Stott, who will be a life-long friend and mentor. Miguel Rincon whose immense
patience allowed me to finish this work despite arduous and frustrating battles with various instru-
ments. Melanie Gerault who’s genius and warmth was my greatest inspiration. My various lab
mates over the years including Andres Martinez, Reetta Saikku, Deborah Khider, Mengfan Zhu and
especially Patrick Horan who was unlucky enough to have to share an office with me for the last 4
years. Ashish Sinha and Mark Bernstein for critical discussion on topics other than what is included
in this thesis.
My dear friends including (but not limited to) Byron Kahr, Ryan Adlaf, Nizar Wattad and John
Nixon. My parents, especially my father who showed enough interest in my work to follow me into
caves in remote corners of India .
Lastly, I need to thank Kei Yoshimura for providing the IsoGSM data and helping me to run my first
climate model simulations, Tom Harlan who taught me about cross-dating, Kevin Anchukaitis who
hosted me at the Tree Ring Lab, Chris Lehmann from the National Atmospheric Deposition pro-
gram for providing the precipitation samples, and my thesis committee, Donal Manahan and Julien
Emile-Geay for providing critical feedback.
iii
TableofContents
Epigraph ii
Acknowledgements iii
ListofFigures vii
Abstract xvi
Chapter1 Introduction 1
1.1 Climate forecasts for southwestern US . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Tree-ring drought reconstructions . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Tree-ring temperature reconstructions . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Climatic information from hydro-isotopes . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Chapter2 Atmosphericcirculationandtheisotopiccompositionofprecipitationover
thewesternUS 10
2.1 Introductory note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 Locations and Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 Analytical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.3 Numerical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.4 A Lagrangian Assumption . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.1 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.2 Regional and Synoptic Controls . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.3 Mesoscale Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4.4 Water-tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.5 Deuterium-excess gradients . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5.1 Isotopes and 21
st
century hydrologic changes . . . . . . . . . . . . . . . . 38
2.5.2 Isoscapes and Proxy reconstructions . . . . . . . . . . . . . . . . . . . . . 42
2.6 Isotopic controls at Inland Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
iv
Chapter3 Testingmodels of themechanistic controlson the isotopiccomposition of
celluloseusingintra-annualsampling 50
3.1 Introductory note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3.1 Age model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3.2 Analytical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4 Site descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.4.1 White Mountains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.4.2 Almagre Mountains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.5 Cellulose Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.5.1 The Source Water Term . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.5.2 The leaf water term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.5.3 Model parameters-White Mountains . . . . . . . . . . . . . . . . . . . . . 67
3.5.4 Model parameters-Almagre Mountains . . . . . . . . . . . . . . . . . . . 68
3.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.6.1 White Mountains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.6.2 Almagre Mountain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.7.1 White Mountains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.7.2 Validation of Model Assumptions . . . . . . . . . . . . . . . . . . . . . . 75
3.7.3 Almagre Mountains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
3.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Chapter4 A growth-independent temperature reconstruction for the southwestern
UnitedStates 81
4.1 Introductory note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.2.1 Tree Cellulose Paleothermometry . . . . . . . . . . . . . . . . . . . . . . 86
4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.5.1 Tree ring response to temperature . . . . . . . . . . . . . . . . . . . . . . 96
4.5.2 Hypothesis testing with a paleo-gcm simulation . . . . . . . . . . . . . . . 100
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
Chapter5 Insights into the mechanisms that generate drought in the southwestern
USderivedfromtheisotopiccompositionoftree-ringcellulose 103
5.1 Introductory note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.2.1 Hydroclimatology of the southwestern US . . . . . . . . . . . . . . . . . . 106
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.4.1 White Mountain Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.4.2 Alta Peak Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
v
5.4.3 Geochemical modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.5.1 Isotope relation to drought . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.5.2 Low Frequency 20
th
century trends . . . . . . . . . . . . . . . . . . . . . 124
5.5.3 The 19
th
Century Transition . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Chapter6 EnigmaticisotopicresponsestoGreenlandInterstadialsincavesfromthe
southwesternUS 135
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
6.3 Fort Stanton and Cave of the Bells . . . . . . . . . . . . . . . . . . . . . . . . . . 140
6.4 Isotopic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.5.1 Timing and Shape of Events . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.5.2 Response between North Atlantic and Southwestern US . . . . . . . . . . 150
6.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
References 157
Appendix 180
vi
ListofFigures
1.1 Gridded correlation coefficient (r) between d
18
O of precipitation and surface tem-
perature and precipitation amount. Data used are from (Yoshimura et al., 2008). . 7
1.2 Photo of White Mountain Bristlecone Pine stands during the June 2008. This photo
serves to provide some visual reference to the reader on the environment at which
the trees discussed in Chapters 3 and 5 grow. . . . . . . . . . . . . . . . . . . . . 9
2.1 Map showing all isotopic monitoring sites referred to in this chapter. . . . . . . . 16
2.2 Relationship of d
18
O and dD (Local Meteoric Water Line) for all measurements
made for this study. The slope for each site is indicated on the figure. The global
average slope is 8 and the low values as observed at the DV site indicate evaporative
enrichment of the sample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 Storms following a latitudinal transect (top), an altitudinal transect (center) and dxs.
Landfalling storms produce increasingly depletedd
18
O with increasing latitude and
altitude. dxs does not display a strong altitude effect, but does follow latitude. At
each site a probability distribution function using a normal kernel density estimator
was fitted to the data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Monthly distributions of storm events at all sites for oxygen and dxs. The isotopic
composition of each storm is corrected to the average of the site and then box plots
with quartiles are made for each month. While there is a seasonal cycle where mean
values in the summer are higher than the winter, the distribution of individual storms
overlaps almost completely between months. Deuterium-excess has a more defined
seasonality, with extremely negative values occurring during the summer months.
Outlier values are marked by open circles. . . . . . . . . . . . . . . . . . . . . . . 25
2.5 Comparison betweend
18
O values of measured storm event and their predicted val-
ues based on the IsoGSM simulation. The mean isotopic values were subtracted
from each dataset to correct for the coarse topography in the model simulation (left
panel). The colors are used to denote the different sites. The distribution of the same
storm events shown in the left panel, bins are 1‰ and the line is the best fit Gaussian
distribution (right panel). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
vii
2.6 Time evolution ofd
18
O of precipitable water (left column), precipitation rate (mid-
dle column), and atmospheric specific humidity (right column) during a sequence
of the most enriched (top row) and depleted (bottom row) storm events. The mean
value for the different storms are shown as a bold gray line with circle markers.
All values were taken from the IsoGSM simulation. Time 0 represents an arbitrary
beginning point before precipitation began to fall moving forward in 6-hour time
steps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.7 Difference between latent heat flux (w/m
2
) for the enriched and depleted compos-
ites. High latent heat flux from just offshore is a common feature of the most
enriched events. Data for latent heat is from the North American Regional Reanal-
ysis dataset (Mesinger et al., 2006). . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.8 Top panels (left and right) show the precipitation rates in six hour time steps as two
isotopically enriched storm events strike the western US. The bottom panels show
vertical cross sections of the isotopic composition of water vapor as the storms pass
over the region. The figure shows how the maximum isotopic changes (on the order
8-10 ) occur between the 800-700 mb levels. . . . . . . . . . . . . . . . . . . . . 29
2.9 A plan view of the average 850 mb wind fields during the most depleted (left) and
enriched (center) events and the difference between the two vector fields (right).
Scale bar shows the length of a 10 m/s vector. . . . . . . . . . . . . . . . . . . . . 30
2.10 Isotopic concentrations ofd
18
O of water vapor during depleted (left) and enriched
(right) events. Water vapor is taken for the 850 mb level. Colors show isotopic
anomalies relative to the field in view while contours show absolute isotopic con-
centration relative to VSMOW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.11 Correlation coefficient between annually average vertically integrated meridional
moisture flux and amount weighted d
18
O of precipitation over the southwestern
US. Contours indicate correlations that are significant at the 95% confidence based
on a Student’s T-test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.12 Correlation coefficients between meridional moisture flux and the d
18
O of vapor
for a vertical cross section of the Pacific between 120-180
o
W (bottom). Correlation
coefficients between vertical velocity (omega) and thed
18
O of vapor for a vertical
cross section of the Pacific between 120-180
o
W (top). . . . . . . . . . . . . . . . 34
2.13 A cross section across the Pacific basin with contours showing the average isotopic
concentration of water vapor and colors showing vertical velocities (positive values
are dark gray and negative values are orange). . . . . . . . . . . . . . . . . . . . . 35
viii
2.14 The relationship between the relative percentage of tagged water in the atmospheric
column over southwestern US and the isotopic composition of the integrated water
column (left). All days associated with storm events were selected from the figure
on the left showing the coherent relationship between tagged water and the isotopic
composition of water during landfalling frontal storms. . . . . . . . . . . . . . . . 35
2.15 A composite of tagged water concentration for a series of isotopically enriched
(right) and depleted (left) storm events. The concentration of tagged water in the
atmospheric column is taken as the ratio of the mass of tagged water to total water
(e.g. specific humidity). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.16 Plan view of the 850mb wind fields and relative percentage of tagged water in the
atmospheric column during the atmospheric river event on January 22, 2005. . . . 36
2.17 Precipitation rate (kg of water/m
2
) during storms included in the high (left), low
(middle) and average (right) dxs gradient events. The figure emphasizes that the
storms influenced the entire coast. . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.18 Vertically integrated meridional moisture flux (kg/m) during storms included in the
high (left) and low (middle) dxs gradient events and the difference between the two
composites (right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.19 Latent heat flux (W/m
2
) during storms included in the high (left) and low (middle)
dxs gradient events and the difference between the two composites (right). . . . . 40
2.20 Average annual 850 mb geopotential height (m) and wind vector anomalies from
NCEP 2 Reanalysis (Kanamitsu et al., 2002) during 1989, 1998 and 2003 (left to
right, top row) and the isotopic anomalies in precipitation associated with these
same years (bottom row). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.21 Isotopic composition of 112 individual storm events between 1994-1998 from the
Global Network of Isotopes in Precipitation event station in the Pawnee Grasslands
in Colorado plotted against surface temperatures during the events. The right panel
shows the slopes of the regression after storms have been binned by season and
prevailing trajectory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.22 Slope of monthly integrated d
18
O
p
and temperature at the 39
o
N and 105
o
W grid
point in the IsoGSM (Yoshimura et al., 2008), Echam4 (Hoffmann et al., 1998) and
GissE (Schmidt et al., 2007) isotope-enabled GCM simulations. The relationship is
highly significant in all three models and the slopes are nearly equivalent though the
intercept is larger in the nudged IsoGSM simulation than observed in the other two
models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
ix
2.23 Probability Distribution Functions ofDd
18
O-DT for sliding 5-year windows encom-
passing the entire length of the simulations from preceding Figure. Positive (nega-
tive) values indicate 5-year windows in which the slope was steeper (shallower) than
the mean slope calculated from the entire dataset. The slope stays within 5% of the
mean slope more than 90% of the time. . . . . . . . . . . . . . . . . . . . . . . . 47
2.24 Lagrangian back trajectory analysis for the coastal and inland sites. The latitudinal
gradient depicted for the coastal sites serves as a first order predictor for the isotopic
composition while the more varied trajectory at the inland sites does not serve the
same function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.1 Scan of a Bristlecone Pine tree core used for isotopic analysis. The dark bands are
called ”late wood” and form at the end of the growing season because of increased
cell density. The growth orientation is up. The colored lines are used to show the
sampling strategy used in this chapter. Each ring is approximately 1mm. . . . . . 55
3.2 Photo of the rotary microtome at USC used for slicing samples. The screen on the
left of the photo is being fed from the microscope, which is focussed on the mounted
core. Individual wood cells can be seen on the screen as light circles. In this image
the growth direction is down and to the right. . . . . . . . . . . . . . . . . . . . . 56
3.3 A highly schematicized representation of the leaf and soil systems where movement
of water is denoted by arrows and the process labeled. Steady-state theoretical iso-
topic profiles (enriched to the left) that arise from the phase change and diffusion
processes are included. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.4 Parameters input into the geochemical model described above to model the intra-
annual cellulosic cycle. Uncertainty envelope is a 1s window generated from daily
instrumental climate data from the nearby Crooked Creek meteorological station. . 69
3.5 Raw isotopic measurements for each of the three wood sections. The non-growing
season hiatus were removed from the age model to reduce long empty spaces between
years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.6 All measurements included in Figure 3.5, with each datum having been normalized
to a common age model and corrected as an anomaly relative to the mean value for
the entire year. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.7 A third order polynomial fit to each of the time periods (left panel). Uncertainty
envelope is ones. Box plots of the annual isotopic standard deviation from each of
the time windows (center panel). Same as in the left panel except each cycle was
corrected to a common variance to compare simply the shape of the cycles (right
panel). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
x
3.8 Modeled isotopic composition at the White Mountain site using the parameters
shown in Figure 3.4. Three simulations with a normal, shortened and elongated
growing season were conducted and 100 random iterations from the three simula-
tions are used to illustrate the results. . . . . . . . . . . . . . . . . . . . . . . . . 73
3.9 Correlation matrix between modeled and measured isotopic cycles. . . . . . . . . 73
3.10 Intra-annual isotopic measurements from the Almagre Mountain site. A spline fit
has been included. Note that because it was not possible to sample all rings at this
resolution some years were excluded and thus the x-axis is not evenly-spaced. . . 74
3.11 The mean isotopic value was removed to generate a composite intra-annual cycle
(left), with a 1s envelope shown in gray. A modeled cycle based on the average
climate conditions during this time interval is shown in red alongside the measured
cycle (grey). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.1 Complete time series from the Almagre Mountain site generated from separate
tree cores (blue and green). A low-pass filtered (18 years) timeseries of the mean
between cores is shown in black. The pink region denotes the instrumental period
used to calibrate the response betweend
18
O
c
and temperature. . . . . . . . . . . . 89
4.2 A map showing the correlation coefficient (r) between d
18
O
c
and surface temper-
atures from the CRUTEM3 temperature dataset between 1900-2001. The purple
line roughly encompasses the area where the correlation betweend
18
O
c
and surface
temperature is significant (p=0.01). The Almagre site is labelled A, B is the frost
ring chronology (Brunstein, 1996), C is Pawnee National Grasslands Global Net-
work of Isotopes in Precipitation site, D is the tree ring temperature reconstruction
from (Salzer & Kipfmueller, 2005), E is the tree ring temperature reconstruction
from (Biondi et al., 1999), F is the isotope-based temperature reconstruction from
(Edwards et al., 2008), and the dotted line encompassing G is the composite tem-
perature reconstruction based on wood density from (Briffa et al., 1992). . . . . . 90
4.3 Correlation coefficients between monthly average temperature andd
18
O
c
from the
CRUTEM3 temperature dataset at the 39
o
N 105
o
W grid point. The final column,
shows the correlation coefficient against mean annual temperatures. The p-values
for each of the correlation coefficients are shown in the bottom panel. . . . . . . . 91
4.4 Timeseries showing instrumental temperatures from the nearby Canon City meteo-
rological station (gray) against ring widths (left) andd
18
O
c
(center) all of which are
presented as an anomaly relative to the 20
th
century mean. The three timeseries are
shown together in the right column. The ring width chronology only extends until
1983, which is why it is truncated relative to the other records. . . . . . . . . . . . 91
xi
4.5 Relationship between surface temperature and the isotopic composition of individ-
ual storm events from the Pawnee Grasslands GNIP station (left). The slope of
the relationship between temperature and the isotopic composition of annual cel-
lulose (green), intra-annual cellulose (red) and precipitation (purple) (center). The
envelope shows the 90% confidence band around the slope. Intra-annual isotopic
measurements (purple) with a 1s error envelope, modeled isotopic composition of
cellulose from Chapter 3 (red) and seasonal temperature cycle (green). . . . . . . 92
4.6 Lomb-scargle periodogram of thed
18
O
c
timeseries based on the methods described
in (Schulz & Mudelsee, 2002) using a Welch window and 3 overlapping segments.
The confidence interval was generated using a 2000 iteration Monte Carlo simula-
tion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.7 Wavelet analysis of thed
18
O
c
timeseries using a Morlet window based off the code
from (Torrence & Compo, 1998). The black line shows regions where the power is
significant. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.8 Time series of temperature reconstructions from the co-located width (purple) and
isotope-based (green) techniques. Both records are shown as anomalies relative to
the 1960-1990 average temperatures. The gray region highlights the discrepancy
between the two records. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.9 Time series of low-pass filtered temperature reconstructions from the co-located
width (purple) and isotope-based (green) techniques alongside the composite global
temperature reconstruction from (Mann et al., 2008) with the uncertainty in the latter
shown as gray shading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.10 Frost ring frequency record from (Brunstein, 1996). In this plot an increase in frost
ring frequency is caused by cooler conditions and thus the axis is reversed. The
number of frost rings is reported as an anomaly relative to the average number of
frost rings per decade during the 20
th
century. . . . . . . . . . . . . . . . . . . . . 96
4.11 A compilation of temperature records, which include tree ring widths (left column)
and do not include tree-ring widths (right column). All records are specific to the
western US except the northern Hemisphere composite record from (Mann et al.,
2008). Each record is reported using the same temperature scale as the original
authors except (Edwards et al., 2008), where I show the uncorrected isotopic time-
series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.12 Evolutive response function between weekly temperature and ring widths. The ordi-
nate is time during the growing season with the markers noting 30 day intervals
starting at the bottom with May 1 and ending at the top with September 1. Cor-
relation coefficients were calculated for sliding ten year windows during the 20
th
century using daily temperature data from the Canon City meteorological station.
Only positive response functions are shown. . . . . . . . . . . . . . . . . . . . . 99
xii
4.13 The residual between the isotope and width-based (green) and density and width-
based (blue) temperature estimates. The gray line is the difference between MAT
and June temperature anomalies relative to the 1960-1990 temperatures from the
ECHO-G climate simulation, which has been smoothed using a Stineman function. 100
5.1 Photograph of Bristlecone Pine slab highlighting the quasi-exponential decay in ring
size as a function of increasing tree radius. . . . . . . . . . . . . . . . . . . . . . 111
5.2 Timeseries of multiple BcP chronologies spanning modern (right) to AD 1500 (left).
Certain sections were lost either due to instrumentation era of insufficient material. 113
5.3 Correlation coefficients between 3-year running mean of zonal geostrophic winds
derived from the Trenberth SLP dataset and the BcP record (top) and correlation
against the Kaplan SST dataset (bottom). . . . . . . . . . . . . . . . . . . . . . . 114
5.4 Cellulosicd
18
O from the White Mountain site (A) compared to indices of annually-
averaged Atmospheric (North Pacific Index, B) and Oceanic (Pacific Decadal Oscil-
lation, C) indices of climate variability. Cellulose (red) and PDO (blue) records have
been smoothed with a 3-year moving average filter. The NPI record is shown on a
reverse scale where up denotes an equatorward shift in storm tracks driven by SLP
anomalies in the Aleutian Low region. . . . . . . . . . . . . . . . . . . . . . . . . 115
5.5 Cellulosicd
18
O from the Alta Peak site for three separate trees. . . . . . . . . . . 116
5.6 Timeseries of measured BcP and Alta (red and green) and modeled (gray) annual
isotopic values. The modeled values were calculated based on the equations and
methodologies discussed in Chapter 2 except done using integrated growing season
values as opposed to daily or weekly time steps. . . . . . . . . . . . . . . . . . . 117
5.7 Cellulosic d
18
O for the White Mountains (green) and rainfall anomalies averaged
from California Climate Division 4, 5 and 7 during the 20
th
century (blue). Pre-
vious multi-year droughts are denoted by gray bands while the current drought is
delineated by the pink band. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.8 Comparison of SST anomalies from the Kaplan dataset during the 1950’s drought
and the latter stages of the current drought. . . . . . . . . . . . . . . . . . . . . . 121
5.9 PDSI from (Dai et al., 2004) during the two major droughts of the early and mid
20
th
century compared to the current PDSI anomalies. . . . . . . . . . . . . . . . 122
5.10 The isotopic composition of water vapor over grid points along the North Pacific
Basin from the IsoGSM simulation. The black line is used to show the areal average. 122
5.11 Schematic representation of how the water budget varies from an isotopic perspec-
tive during previous droughts, the current drought and theoretically the situation if
a La Ni ˜ na-like drought were to occur amidst the current state. . . . . . . . . . . . 123
xiii
5.12 Comparison of isotopic trends from sites in the Pacific Northwest (Mt Logan and
Eclipse (Fisher et al., 2004), Jellybean Lake (Anderson et al., 2005), Mica Lake
(Schiff et al., 2009), Lakes (Hu et al., 2001)) and the southwestern US. All Pacific
Northwest sites were normalized and timeseries were interpolated to a common
decadal timescale. Colored dots on the map correspond to the locations of the sites
used in the composite timeseries. . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.13 Correlation (r) between zonal geostrophic winds and time for the Trenberth and
HadSLP datasets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.14 Cross wavelet coherence using a morlet wave between PDSI reconstruction from
(Cook et al., 2007) and the BcP annual chronology. Periods of significant coherence
(95%) are denoted with the bold black line. The arrows indicate the phase angle of
the relationship with arrows pointing right denoting in-phase coherence, and to the
left indicating anti-phase. Power is reported in normalized units and the veil, shows
the cone of influence, under which power needs to be treated with caution. Analysis
is done using the algorith from (Grinsted et al., 2004). . . . . . . . . . . . . . . . 129
5.15 Timeseries of other circulation proxies from the Pacific Basin. Top is the BcP
chronology from this study, center is the upwelling/wind proxy from the Santa Mon-
ica Basin from (Holsten et al., 2004) and bottom is the ice core d
18
O chronology
from Mount Logan (Fisher et al., 2004). All records have been smoothed (colored
line) using a running mean filter. . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.1 Time series of NGRIP (Svensson et al., 2008), Cave of the Bells (Wagner et al.,
2010), and Fort Stanton (Asmerom et al., 2010). All shown on independent timescales
described in the original publications . . . . . . . . . . . . . . . . . . . . . . . . 141
6.2 Slope between amount-weighted monthly modeled isotopic composition of precipi-
tation and precipitation amount (normalized) at the grid point nearest to the Cave of
the Bells Site for winter months between 1979-2008 (top). Slope between amount-
weighted monthly modeled isotopic composition of precipitation and monthly tem-
perature at the grid point nearest to the Fort Stanton site (bottom). . . . . . . . . . 144
6.3 Shape of the 9 DO events at each site, where each event was centered at its peak and
the mean cycle calculated (color). The well-defined sawtooth in the NGRIP record
erodes quite substantially in the speleothem records. . . . . . . . . . . . . . . . . 146
6.4 The ”average” interstadial event generated from the average of 9 events from the
NGRIP record (blue) alongside the peaks of each of the events at the two sites (red
is CoB and green is Stanton). The error bars are the uncertainty of the timing of
the peak based on the U/Th ages. The height of each of the peaks is based on the
magnitude of the event. This is to show that the relative lead or lag is not a function
of the size of the event. The CoB peaks tend to scatter just before the NGRIP events
while the Stanton events tend to lag the NGRIP events. . . . . . . . . . . . . . . . 147
xiv
6.5 Lomb-scargle periodogram for the three time series using the original age models
with no linear interpolation performed. Analysis was done using a Welch window
and the dotted line denotes the 99% confidence interval based on a 5000 iteration
Monte Carlo simulation against a red noise background (Schulz & Mudelsee, 2002).
The commonly cited 1500 year cycle is the NGRIP record is marked on each of the
records. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6.6 Coherence and phase angle between the CoB and NGRIP records using the multi-
taper coherence method. The gray bar denotes to 1500 year cycle, which is coherent
in both records and the phase angle suggests the two are nearly in phase. . . . . . 150
6.7 A cross wavelet coherency analysis between the NGRIP and CoB (left) and NGRIP
and Stanton (right). Significance coherency is shown in red tones, with significant
regions denoted by a heavy black line. The arrows are used to show the phasing
with arrows to the right indicating in phase behavior. . . . . . . . . . . . . . . . . 151
6.8 Linear fit between 9 DO events in NGRIP and CoB (red) and Fort Stanton (purple)
caves. The fit is improves if DO event 9 is removed from CoB record. Both fits are
high significant and the slopes are statistically different. . . . . . . . . . . . . . . 152
xv
Abstract
Late Holocene paleoclimatology of the southwestern United States has been reconstructed largely
through the analysis of ring width variability from a network of gridded tree chronologies. Trees
commonly respond to moisture stress in this semi-arid environment providing a spatially coherent
annually-resolved record of PDSI variability. At a handful of high altitude sites, trees are thermally
stressed, providing a record of temperature variability. This thesis addresses two prominent ques-
tions that arise from the tree ring network; 1) The precipitation history highlights that sustained
hydroclimate epochs are an ubiquitous aspect of the regions climate history though it is not fea-
sible with the existing tree ring network alone to partition whether drought (or pluvial) regimes
arise through a singular forcing mechanism or can be delineated into dynamical types. 2) The
response between tree growth and temperature variability is poorly understood and because these
records are some of the few available mid latitude temperature records, an independent assessment
of their response to temperature is required. To address these questions, isotopic chronologies from
Bristlecone and Foxtail Pine trees in the White Mountains, Almagre Mountains and Sierra Nevada
are presented. Because the isotopic composition of cellulose responds not distinctly to moisture
availability but rather to the isotopic composition of precipitation, the isotope records provide infor-
mation on atmospheric circulation and temperature that is independent from the co-located drought
and temperature reconstructions.
To directly test the relationship between atmospheric circulation and the isotopic composition of
precipitation in the southwestern US, I develop a catalog of 120 individual storm events striking
the west coast over a 5-year period. The cause of isotopic variability is assessed using an isotope-
enabled GCM simulation that has been nudged to Reanalysis fields. The results from this analysis
show that changes in meridional moisture flux from the low latitudes leave a tangible mark on pre-
cipitation in the region. This relationship can theoretically be quantified by a linear relationship
xvi
between the modeled isotopic composition of precipitation and the relative percentage of low lati-
tude tagged water that is delivered with the storm system. The controls on the isotopic variability of
precipitation change substantially moving eastward into the North American Monsoon region where
moisture is not delivered by large frontal storms but rather through localized convection. In these
regions, variability of the moisture source is subdued and isotopic variability arises principally as a
function of depth of convection, which leads to a close correlation between temperature andd
18
O.
Mechanistic constraints are placed on the cause of isotopic variability in the cellulose chronologies
using a forward modeling approach where meteorological data and the isotopic composition of soil
water and vapor from an ensemble of isotope-enabled GCM simulations are fed into a geochemical
model that captures the isotopic fractionation associated with the biogeochemical processes in the
tree prior to cellulose metabolism. At sites where precipitation is predominantly from winter pre-
cipitation the intra-ring isotopic cycles are driven largely by the relative humidity and temperature
at the leaf boundary while the higher amplitude interannual variability arises from changes in the
trees source water. Variations in the shape of the cycle reflect not only differences in growing season
climate but also changes in the length of the growing season. At sites where moisture is predomi-
nantly from summer rains, the cycle directly tracks the isotopic composition of precipitation during
the growing season.
The isotope chronology from the Almagre Mountains in Colorado shares little covariance with the
more western sites, because it relies predominately on monsoonal moisture whose isotopic compo-
sition tracks temperature. The record from this sites provides a 500-year reconstruction of growing
season temperatures for this region. Growing season temperatures in the southern Rocky Moun-
tains display a high degree of multi-decadal variability between the 18
th
-20
th
century but only sub-
dued variability prior (15
th
-18
th
centuries). The temperature reconstruction presented here differs
markedly from the tree-ring width based temperature reconstruction from the same site but agrees
with regional temperature reconstructions based on instrumental temperature records and tree ring
density. Because the relationship between temperature and the isotopic composition of precipitation
xvii
is stable, I interpret the difference between the two reconstructions to be evidence that the ring-width
based reconstruction is biased by non-stationary relationships between temperature and growth.
Isotopic chronologies from Bristlecone Pines in the White Mountains show distinctive minima dur-
ing each of the multi-year droughts of the 20
th
century, suggesting that drought is sustained by a
selective loss of low latitude moisture. This is dynamically consistent with an understanding that
each of the major 20
th
century droughts was driven by cooler conditions in the eastern Tropical
Pacific and a northward shift in the storm track. The exception to the isotope-aridity relationship
is during the current drought, which has been associated with rising isotopic values and therefore
appears dynamically distinct. Rising temperatures in the mid troposphere over the North Pacific
during the current decade has diminished the pressure gradient between the subtropics and extrat-
ropics and has therefore reduced the convergence of the depleted northerly moisture source. Prior to
the 20
th
century there is a dramatic change in the isotopic composition of precipitation at the White
Mountain site. The cause of this is hypothesized to be an increase in low latitude moisture associ-
ated with cooler Northern Hemispheric conditions, which led to a more southerly storm track that
consistently tapped into the enriched tropical moisture pool. Dramatic isotopic shifts in ice cores
and lake sediments from the North Pacific confirm a major change in the midlatitude storm track at
the terminus of the Little Ice Age.
A brief consideration at the end of this thesis is given to recently published isotopic chronologies
from stalagmites in the southwestern US that span the last Glacial Period. While these records show
high amplitude millennial variability in this region during the last Glacial Period, the enigmatic
discrepancies between two nearby records, are taken as evidence of overwhelming kinetic effects
that likely obscure the exact nature of the climatic changes that occurred in the southwestern US
during Greenland Interstadials.
xviii
Chapter1
Introduction
1.1 ClimateforecastsforsouthwesternUS
In 1994, Scott Stine published the paper, Extreme and Persistent drought in California during
mediaeval time, where he documents the presence of tree stumps rooted in situ deep below the
waters of Mono, Walker and Tenaya Lakes in Eastern California and Nevada (Stine, 1994). The
presence of these buried stumps indicates that lake levels had dropped sufficiently low during
certain periods of recent history, to expose these depths of the lake bottom to the atmosphere. The
alarming aspect of this finding was that some of these stumps had over 200 annual growth rings,
implying that severe drought had gripped the region for multiple centuries. This study serves
as a haunting depiction that the semi-arid southwestern US is vulnerable to stark hydroclimatic
extremes and leads to an obvious concern regarding the impacts that such an event would have on
today’s municipal and agricultural systems in the western US. The ambitious and complex water
distribution infrastructure in California relies heavily on the same snow melt that feeds the lakes of
Stine’s megadroughts, in fact it is specifically the catchment of Mono Lake that William Mulholland
targeted in the development of the Los Angeles aqueduct. While, there is no adequate recent analog
to consider the impact of a comparable event today, the noted synchrony between megadroughts
and collapse of Puebloan civilizations serves as some indication of the cultural sensitivity in this
region to water availability (MacDonald & Tingstad, 2007; Cook et al., 2007; Benson, 2009).
It is likely that the southwestern US will undergo a long term drying during the upcoming
century (Seager et al., 2007b). Changes in the radiative balance and the associated warming of the
troposphere will lead to a rise in global atmospheric humidity and a poleward shift in the latitude
of the storm track, which acting in concert will bring about the projected drying (Held & Soden,
1
2006; Yin, 2005; Salathe, 2006). The impact of the drying on agricultural and municipal water
resources of the region will be exacerbated by localized feedbacks associated with the warming
trend, including a shift in the proportion of rain and snow and an earlier onset of snowmelt each
year (Stewart et al., 2005; Cayan et al., 2001). These predictions will be slowly borne out over the
coming century amidst additional higher frequency and amplitude variability that will periodically
enhance or alleviate the trend towards a more arid southwestern US.
Transient droughts in the southwestern US are part of a symmetrical hemispheric response
to cooling in the eastern Tropical Pacific, which leads to wave-driven shifts in the locations of the
quasi-permanent high pressure cells. These changes influence the trajectory of the jet stream and
consequently the prevailing pathway of storms. The cool anomalies in the eastern tropical Pacific
can persist for multiple years and have been called upon as the mechanism that triggered each of
the major droughts of the instrumental record (Seager, 2007; Seager et al., 2003; Burgman et al.,
2010; Herweijer et al., 2006). Additional drought forcing is also likely rooted in SST variability in
the North Atlantic (McCabe et al., 2004) though the mechanism that brings about this empirical
relationship is not currently understood. Unlike the long term drying, multi-annual and decadal
hydroclimatic and temperature variability in the western US is not consistently reproduced between
21
st
century forecasts and it is thus unclear how variability on these timescales will aggravate or
alleviate the predicted long term drying trend. Therefore, further characterization of the natural
modes of variability that bring about the hydroclimatic anomalies, which can be accomplished
through coupling proxy reconstructions and hindcast modeling are beneficial in elucidating the
relevant dynamics (Herweijer et al., 2006; Seager et al., 2007a; Graham & Hughes, 2007).
2
1.2 Tree-ringdroughtreconstructions
The southwestern US is coincidentally endowed with a widespread occurrence of some of the
world’s oldest living trees (Schulman, 1958; Brunstein & Yamaguchi, 1992; LaMarche & Mooney,
1967; Lamarche, 1978). Because tree growth in semi-arid regions is often limited by the availability
of water, year to year variations in growth enable a depiction of drought over the entire region for
nearly 1,000 years and in some instances such as the White Mountains, many thousands of years
(Fritts, 1969). The gridded network of tree ring chronologies from the western US (North American
Drought Atlas), depicts high amplitude year to year precipitation variability characteristic of both
stochastic processes and the slightly more predictable influence that ENSO events have on the
region (Cook et al., 2007). The gridded drought record also depicts lower frequency decadal and
arguably centennial epochs where the region is characteristically dry or wet (Cook et al., 1999). The
tree rings for example not only depict the major droughts recorded in the Mono Lake tree stumps
(Stine, 1994) but also a general recurrence of numerous severe multidecadal wet and dry intervals.
It is an ongoing effort to use the tree-ring hydroclimate database to understand the mechanisms
responsible for bringing about severe drought. One approach which has been adopted is to use the
spatial pattern of droughts to elucidate the mechanisms responsible. Woodhouse et al. (2009) uses
Empirical Orthogonal Functions of reconstructed drought patterns to clearly distinguish distinctive
drought modes. Many of the large droughts display a classic dipole pattern with an anomalously
wet Pacific Northwest and a dry southwestern US, which is similar to the pattern that emerges
during annual ENSO events (Cook et al., 2007; Cayan et al., 1998). The spatial footprint of a
drought will also indicate drought epicenters, which is useful in distinguishing where the peak
impact should be found. Recently, Cook et al. (2009) distinguishes a unique spatial characteristic
of the Dust Bowl drought, which was linked to feedbacks associated with dust over the continental
US and its impacts on radiative forcing. Spatial comparisons, thus can elucidate subtle dynamics
that distinguish one event from the next.
3
An additional approach to understanding drought mechanisms is through targeted proxy
analysis of teleconnection hotspots. For example, by generating Sea Surface Temperature (SST)
reconstructions in the eastern Tropical Pacific or North Atlantic, it is is possible to fingerprint
if drought was associated with an anomaly in one of these locations. A nice discussion on this
approach is found in Conroy et al. (2009), who distinguish large western North American droughts
that were associated with both Atlantic and Pacific SST anomaly patterns. The results from this
analysis are consistent with the spatial analysis of droughts, in that there appear to be multiple
mechanisms that bring about drought and delineating a single origin is not likely feasible. The
challenge with the teleconnection approach is that it requires SST proxies with nearly absolute
dates. Consider that severe drought may last for 2-3 decades and thus fingerprinting an SST
anomaly that brought about this drying would require a sediment record of near equal resolution
and age control. While a few records of this type are available (Cobb et al., 2003), such records are
decidedly rare. Furthermore, the co-occurrence of an SST anomaly with a drought event, does not
necessarily imply causality.
An additional approach is through hindcast modeling, where boundary conditions are
constrained through proxies and it is tested whether the atmospheric dynamics resulting from
these initialized conditions are sufficient to trigger the spatial extent and severity of the proxy
reconstructed drought (Graham & Hughes, 2007). This is an emerging approach, whose challenge
is the appropriate delineation of paleo-boundary conditions, which are often only constrained
through a few available records. Feng et al. (2008) for example, show that in order to replicate
the medieval megadroughts, consideration must be given to both the Atlantic and Pacific SST
patterns, while Seager et al. (2007a); Graham & Hughes (2007) find it is possible to replicate
drought intensity only through defining conditions in the tropical Pacific. The choice of where to
develop new proxy records for developing boundary conditions in hindcast models is guided by
4
instrumental observations of locations with important teleconnections, but still it is possible that
influential regions may have sparse or non-existent proxy data.
1.3 Tree-ringtemperaturereconstructions
While much of the focus on tree ring width in the region has been on reconstructing precipitation
patterns, tree-ring width variability has also been used to depict temperature variability in the
southwestern US (Scuderi, 1993; Graumlich, 1993). Drought reconstructions are more ubiquitous
because trees are principally water-stressed in semi-arid regions, though at some rare sites, tree
growth has been shown to be more sensitive to temperature variability (LaMarche & Stockton,
1974). Temperature-sensitive sites appear to emerge only under rare environmental stress that
occur along the upper treeline where minimum tenmeratures remain near to the minimum threshold
required for xylogenesis (LaMarche & Stockton, 1974; Rossi et al., 2008). Salzer et al. (2009),
show very clearly how stands of trees in the White Mountains of California, which may only be
separated by a few 100 meters have entirely unique responses to temperature. These sorts of rather
fortuitous and highly localized relationships between temperature and growth have led to varying
degrees of skepticism regarding the use of this proxy to reconstruct temperature. Details of this
debate will be provided in Chapter 4. Despite the inherent uncertainties that may arise from using
tree rings to reconstruct temperature, they are one of the few available temperature proxies, outside
of the polar regions and thus are an important source of information on understanding temperature
trends prior to the instrumental period.
5
1.4 Climaticinformationfromhydro-isotopes
The accumulation and flux of the isotopologues of water throughout the ocean-atmosphere system
provides a useful tracer for key processes in the hydrological cycle and have been used for decades
to understand meteorological or climatological phenomenon (Craig & Gordon, 1965; Dansgaard,
1964; Rozanski et al., 1993; Gonfiantini et al., 2001; Lawrence et al., 1982). If we trace an air parcel
from its origin at the marine surface, the vapor will have an isotopic signature characteristic of the
temperature and humidity that prevailed when the moisture evaporated following the Craig-Gordon
Model (Craig & Gordon, 1965; Merlivat & Jouzel, 1979) As this air parcel moves through the
atmosphere, the isotopic composition of its vapor evolves as a function of condensation, which
progressively depletes the air mass in heavy isotopes and through entrainment of newly evaporated
water or mixing with other air masses. The isotopic composition of an air mass at a given moment
in time therefore bears information on numerous intreacting processes that have affected this air
mass. From a global perspective, evaporation of water into the atmosphere occurs principally in
the tropics. As air moves poleward as part of the global overturning circulation, the air becomes
progressively depleted leading to a latitudinal gradient in the isotopic composition of vapor,
that largely tracks the meridional temperature gradient. This zonal isotopic gradient is the most
prominent spatial feature of the global distribution of water isotopes and provides an important
depiction of the global hydrological cycle.
The spatial patterns of isotopic variability are useful in highlighting global atmospheric pro-
cesses but in order to use isotopic variability to reconstruct past oceanic or atmospheric processes,
it is critical to understand what influences the isotopic composition at one location in the time
domain. In Figure 1.1, I show the results of a point-by-point correlation (r) between the isotopic
composition of precipitation and temperature (right) and precipitation amount (left) during a thirty-
year simulation of a global climate model that includes isotopic tracers (Yoshimura et al., 2008).
In the left hand panel, we observe that throughout much of the low latitudes, there is a negative
relationship between the isotopic composition of precipitation and precipitation amount. This is
referred to generally as the ”amount effect” and implies that over time, changes in the isotopic
6
Figure 1.1: Gridded correlation coefficient (r) between d
18
O of precipitation and surface temperature
and precipitation amount. Data used are from (Yoshimura et al., 2008).
composition of precipitation at a given grid point will reflect changes in how much precipitation has
fallen there (Lee et al., 2008a). On the opposing panel, I show the correlation between the isotopic
composition of precipitation and surface temperature. Here we see that the positive relationships
between surface temperature and the isotopic composition of precipitation are restricted to the high
latitudes (Rozanski et al., 1993). Therefore time series of the isotopic composition of precipitation
would contain information on temperature variability at this given location.
The ”amount effect” and ”temperature effect” have proven to be incredibly useful relationships
in understanding past climatic variability. Ice core records, from Greenland and Antarctica depict
systematic shifts in Earth’s temperature over the recent 800 thousand years while corresponding
isotopic records from low latitude sites derived, from Loess sequences and speleothems, show cor-
responding changes in hydrology that accompanied these temperature changes (Wang et al., 1999;
Jouzel et al., 1987). What is also obvious from Figure 1.1, is that the southwestern US falls between
the regions where the isotopic composition of precipitation characteristically responds to either
precipitation amount or surface temperatures. Indeed, the lack of a systematic relationship between
the isotopic composition of precipitation and surface conditions throughout the midlatitudes has led
to a lack of quantitative paleoclimatic information from these regions.
7
Therefore one of the first challenges in this thesis is filling in the ”correlation gap” in the mid-
latitudes that would enable meaningful climatic information to be generated from isotopic recon-
structions from these regions. The analysis relies heavily on a series of fundamental changes which
are currently underway in the field of isotope hydrology. Firstly, there is an increasing effort to
include isotope tracers in global climate simulations (Noone & Simmonds, 2002; Hoffmann et al.,
1998; Lee et al., 2007). This opens up opportunities to explore the causes of isotopic variability that
are too complex to explore with linear regression models. Secondly, there has been a fundamental a
change in the instrumentation of isotope hydrology, which has led to increasingly routine measure-
ments. Particularly, the development of commercial cavity ring down spectroscopy has opened up
the door to make increasingly large numbers of measurements of both water and vapor samples at
relatively low cost, which means the number of measurements available to understand the climatic
controls on mid latitude precipitation are growing exponentially.
1.5 ThesisOutline
In this thesis, I will explore two primary questions. Firstly, I will attempt to shed light on the mech-
anisms that drive drought in this region, which is motivated by a hope to improve hydroclimatic
forecasts for the region. Secondly, I will provide a test of the relationship between surface tempera-
ture and tree growth at sites where growth is believed to be ”temperature-sensitive”. This is relevant
both from an improved understanding regional temperature variability as it pertains to for example
the timing of snow melt, but also because of the significance there records play in global temper-
ature reconstructions (Mann et al., 1998, 1999, 2008, 2009). In order to address these questions,
I revisit some of the ancient Bristlecone Pine tree ring chronologies of California and Colorado
(LaMarche & Stockton, 1974) but as opposed to measurements of ring width, I use variations in the
isotopic composition of cellulose from these trees. This complements the existing climatic informa-
tion derived from growth variability but is inherently independent. The thesis is a compilation of
mostly published work or work that has been submitted for publication and so at times the chapters
8
Figure1.2: Photo of White Mountain Bristlecone Pine stands during the June 2008. This photo serves to
provide some visual reference to the reader on the environment at which the trees discussed in Chapters
3 and 5 grow.
will come across as independent entities. However, the common theme is the utility of isotope prox-
ies in improving our understanding of the mechanisms that drive climatic variability in this region
and perhaps across the mid latitudes. Chapters 2 and 3 are calibration studies where I visit the
mechanistic controls on the isotopic composition of precipitation in the western US and the isotope
systematics of tree ring cellulose in Bristlecone Pine trees. Chapters 4 and 5 are case studies where
I present isotopic time series which shed light on temperature and drought variability respectively.
In Chapter 6, I explore isotopic records from speleothems in the southwestern US to refine climatic
information that can be garnered from these proxies by utilizing the information presented in Chap-
ters 2-5. The work presented here in many ways is intended to motivate extending the network of
isotopic records presented further back in time and include other sites to improve the spatial density.
So as opposed to being a ”closed book”, each chapter intends to plant seeds for continued studies.
The long term goal of this project is the development of a true network akin to the Isonet project
currently underway in Europe (Treydte et al., 2007).
9
Chapter2
Atmosphericcirculationandtheisotopic
compositionofprecipitationoverthewesternUS
SUMMARY
In this chapter I present a description of the controls on the isotopic composition of precip-
itation along the west coast of the United States. Previous efforts to delineate the dominant
climatological influences on the isotopic composition of precipitation for this region have been
hampered by both a lack of empirical observations and a model that can be used to parse the
complexities associated with moisture transport in extratropical cyclones. To address the for-
mer issue, I have developed a network of sites spanning a latitudinal transect of the west coast
of the US. I use this network to validate the robustness of an isotope-enabled General Cir-
culation Model with a spectral nudging routine, which allows for direct comparison between
individual storm events and and their modeled equivalents. The model shows that efficient
poleward transport of tropical moisture within certain frontal storm systems is responsible
for driving precipitation towards extremely enriched isotopic values. The mass of low lati-
tude moisture that precipitates over the western US is a cumulative function of both seasonal
and interannual changes in the rigor of meridional circulation and the dynamics of individual
synoptic storm systems. A theoretical test of this hypothesis is conducted through a numer-
ical painted water exercise where water evaporated from the tropical Pacific is released into
extratropical cyclones and its evolution traced. An additional test is presented where a third
isotopic variable called deuterium excess, which describes the relative proportion ofd
18
O to
dD in a water mass, is used as a proxy for where moisture in the storm initially evaporated
from. The deuterium excess analysis is used to delineate two families of storms, one group
which is dominated by moisture convergence along the storm’s trajectory and another group
10
which behaves with river-like characteristics, which is to say that it conserves the isotopic
composition of its source. It is this latter family of storms that are principally responsible for
the convergence of tropical moisture to the western US and consequently enriched isotopic
values. The complexity of moisture source controls on the isotopic composition of precipi-
tation at the coastal sites is contrasted against isotopic measurements from central Colorado,
which are overwhelmingly dominated by conditions that prevailed during condensation and
can be described with a simple linear relationship between surface temperature andd
18
O.
2.1 Introductorynote
The contents of this chapter have been published in:
Berkelhammer, M., Stott, L., Yoshimura, K., Johnson, K., and Sinha, A. (submitted to Cli-
mate Dynamics). Synoptic and mesoscale controls on the isotopic composition of precipitation in
the southwestern US.
2.2 Introduction
The isotopic composition of precipitation at a given site reflects a summation of remote and local
processes that can affect contributions of moisture from different source locations, rainout along
the storm trajectory and conditions that prevail during condensation (Dansgaard, 1964). Isotopic
records thus capture an integrated signal of synoptic and mesoscale atmospheric processes. In
tropical and high latitude settings, the complex multivariate signal can often be reduced to a simple
univariate linear regression model leading to climate reconstructions from archived precipitation
that largely reflect a single climate variable. In the polar regions for example, the isotopic
composition of precipitation tracks latitudinal variations in the moisture source, which varies
with hemispheric temperatures and consequently allows for reconstructions of broadly regional
or even global temperatures from high latitude ice cores (Noone, 2008; Jouzel et al., 1997). The
11
robustness of high latitude ice core temperature reconstructions is not simply a product of a direct
physical relationship between local conditions and the isotopic composition of precipitation but
rather arises because the atmospheric overturning circulation, which drives the isotopic variability,
is tightly linked with hemispheric temperatures (Hendricks et al., 2000; Kavanaugh & Cuffey, 2003).
In mid latitude and subtropical locations, isotopes also track large-scale atmospheric circula-
tion patterns. However, circulation in the subtropics has a characteristically more complex
relationship with local and regional surface climate and thus, direct univariate regression models
between the local climate and isotope ratios are typically not robust (Alley & Cuffey, 2001; Fricke
& O’Neil, 1999). The difficulties in calibrating isotopic records from mid latitude and subtropical
locations has resulted in a lack of quantitative paleoclimate information from these regions that
would be a valuable asset in studies that attempt to link low and high latitude ocean and atmospheric
dynamics. Therefore, partitioning the controls on isotopic variability would provide opportunities
to develop new records and reinterpret existing ones.
In a seminal study of the isotopic composition of precipitation from the southwestern United
States, Friedman et al. (1992) suggested that storm trajectories were likely the leading cause
of isotopic variability in precipitation. Their conclusions were based largely on conjecture (by
their own admission) because their analysis utilized 6-month integrated precipitation samples and
thus lacked sufficient resolution to properly explore direct relationships between individual storm
trajectories and the isotopic composition of precipitation. Benson & Klieforth (1989) working in
the Yucca Mountain region of Nevada and Friedman et al. (2002) working in Utah and Nevada
both presented event-scale isotopic values to address the shortcomings of seasonal and monthly
sampling. Consistent between these studies was the finding that there is a range of storm to
storm variations that is on the order of 20‰ for d
18
O and 180‰ for dD. The wide range of
isotopic variability was attributed to two primary factors; trajectory, and the depth of atmospheric
convection. Friedman et al. (2002) addressed the latter effect (depth of convection), showing that
storms associated with high vertical wind shear generated isotopically more-depleted rainfall.
12
Their data loosely fit a Rayleigh curve where the heavier isotopologues of water are preferentially
distilled from the vapor phase as the air cools adiabatically upon ascent to higher altitudes. Recently,
(Coplen et al., 2008) provided a more rigorous test of this idea by making isotopic measurements
every 15 minutes during a single storm that struck the coast of California. They argued that
during air mass ascent isotopic values dropped and subsequently rose as the air mass descended to
warmer atmospheric levels. This is consistent with the closed-system pseudo-adiabatic Rayleigh
distillation model of (Gonfiantini et al., 2001) where the isotopic composition of the precipitate is
a function of condensation temperature (equivalent in this case with height) and the extent of rainout.
The findings of Coplen et al. (2008), suggest that each storm is a discrete closed system that
has been initialized with a different integrated water vapor (IWV) content. In this chapter, I consider
whether or not the most important influence on isotopic variability between storms is attributable
to differences in the isotopic composition of the moisture source incorporated into the large frontal
storms that deliver most of the annual precipitation budget to the western US. There have been
other event-scale studies for the western US including those by Benson & Klieforth (1989) and
Friedman et al. (2002), which have considered this question but their studies were conducted in
the Great Basin, which Ingraham & Taylor (1991) have argued represents a quasi-closed isotopic
system. This would imply that changes in the isotopic composition of water vapor over the Pacific
that arise from variations in sea surface temperatures or from atmospheric circulation changes
have only secondary influence on the isotope hydrology within the basin because of large dilution
effects and mixing with recycled evapotranspired water (Ingraham & Taylor, 1991; Eltahir & Bras,
1996). Therefore, the present study is unique because data is presented from locations that are
situated to provide a more direct conduit to moisture advected from the Pacific, which allows
for the examination of how changes in vapor source are manifest in the isotopic composition of
precipitation that falls along the west coast of the US.
The isotopic composition of IWV has typically been estimated by assuming a source region
for the vapor using trajectory analysis and then calculating the isotopic composition of vapor that
13
would have evaporated from seawater in those source regions, if values for sea surface temperature
and humidity are known and an assumption is made that evaporation takes place in isotopic equi-
librium with the sea surface and the vapor diffuses through an unsaturated boundary layer (Craig
& Gordon, 1965; Wright et al., 2001; Yamanaka et al., 2002). The initial isotopic composition
of vapor would be modified following a Rayleigh model, as the vapor both loses and gains water
through processes of mixing and rainout (Hendricks et al., 2000). Recent satellite estimates of
the isotopic composition of water vapor provide valuable validations of this theoretical model
(Frankenberg et al., 2009; Worden et al., 2007). Given the inherent spatial and temporal complexi-
ties associated with the evolution of the vapor source, isotope-enabled General Circulation Models
(GCM) provide a tool to interpret the cause of isotopic variability that minimizes the reliance on
a priori assumptions about the source region (Henderson-Sellers et al., 2006; Hoffmann et al., 2000).
The efficacy of a GCM model simulation to capture the distribution of stable water isotopo-
logues throughout the atmosphere depends greatly on its ability to accurately depict the isotopic
fractionations that occur during phase changes and then to capture how the water vapor is moved
through the atmosphere, both vertically and horizontally. The former task has been well constrained
and validated with numerous model-empirical inter-comparisons (Hoffmann et al., 2000; Noone
& Simmonds, 2002). Yoshimura et al. (2008) were able to improve the representation of atmo-
spheric circulation by prescribing small-scale atmospheric processes with an Atmospheric General
Circulation Model while synoptic-scale patterns were constrained by the lower-resolution NCEP
Reanalysis II dataset, which captures the large synoptic scale system behavior that is associated
with precipitation over the western US (Neiman et al., 2008; Kanamitsu et al., 2002).
In the first section of this chapter I present 240 event-scale stable isotope measurements
(d
18
O and dD) for precipitated water from 96 individual storm events that struck the southwest
coast of the Unites States between 2001-2005. A basic description of this dataset is provided to
orient the reader to characteristics of storm to storm isotopic variability in the region. The catalog is
sufficient in size to produce a validation of the IsoGSM simulations representation of precipitation
14
over the region and consequently allow for an investigation of vapor source evolution (e.g. mixing
and rainout) prior to storms making landfall. From this analysis, a hypothesis is presented that
the isotopic composition of a precipitation event can be predicted based on the latitudinal origin
of a storm and the dynamics of a storm, which determine how much of the initial moisture source
is transported within the system (hereafter storm efficiency). This hypothesis is tested with a
numerical experiment, in which passive water molecules that do not fractionate during phase
changes are included in a nudged GCM simulation, a painted water experiment. An additional test
of this hypothesis is done using a third isotopic parameter called deuterium excess. To conduct this
test, a catalog of isotopic measurements from individual storm events striking northern California
and Washington are presented. In this analysis, a series of storms that swept along the entire west
coast (e.g. southern California to northern Washington) are selected from the complete catalog. It is
shown that a certain population of these storms produce the same deuterium excess values along the
entire west coast. This serves as empirical evidence of a high degree of efficiency in these systems.
The chapter concludes with a short discussion on published isotopic values from an inland station
in Colorado. The isotopic composition of precipitation from the central United States is shown
to be almost exclusively controlled by the temperature during condensation and can therefore be
described with a simple univariate regression model between surface temperature and the isotopic
composition of precipitation. The contrasting isotopic controls on coastal and inland sites is then
discussed.
2.3 Methods
2.3.1 LocationsandSamples
Precipitation samples used in the study were provided from the National Atmospheric Deposition
Program (NADP) archives. Previous work has shown that the collection and archiving protocol used
by the NADP is adequate for isotopic analysis (Harvey, 2001, 2005; Vachon et al., 2007; Welker,
2000). The samples were collected from 2001-2005 at six sites shown in Figure 2.1.
15
-130
-130
-125
-125
-120
-120
-115
-115
-110
-110
-105
-105
30 30
35 35
40 40
45 45
50 50
Hopland
Pinnacles
Sequoia
Joshua Tree
Death Valley
Destruction Island
Olympic
Pawnee
Santa Maria
Figure2.1: Map showing all isotopic monitoring sites referred to in this chapter.
16
2.3.2 AnalyticalTechniques
All water samples from the Joshua Tree, Pinnacles, Sequoia and Death Valley sites were analyzed
by a continuous flow method using a Thermofinnigan TC/EA and Delta Plus XP mass spectrometer.
The 0.5ml water samples were injected into He carrier gas and carried in vapor form to the TC/EA
reduction furnace where the water undergoes a pyrolysis reaction at 1400
o
C (H
2
O + C) H
2
+
CO). The reaction products are separated by a Gas Chromatograph column and analyzed directly.
Data are corrected using 5 in-house and certified standards. The precision is0.2‰ ford
18
O and
2.0‰ fordD. The methods are very similar to those described in detail in (Sharp et al., 2001).
All water samples from the Washington and Hopland sites were measured using Cavity Ringdown
Spectroscopy with a Picarro L1102. Approximately 0.5ml of water is injected into a vaporizer that
is maintained at 140
o
C. The vapor is introduced into a cavity on a stream of dry nitrogen gas where
a tuned laser is passed through the vapor sample. Because the different isotopologues of water have
unique absorption spectra, the relative abundance of the different water isotopes will affect how
much of the energy from the laser is absorbed. This is translated into a molecular abundance, which
is corrected to a standard ‰ isotope ratio through comparison against repeated measurements of
known isotopic standards (International Atomic Energy Agency’s, VSMOW, GNIP and VSLAP).
Each sample is measured 8 consecutive times. The first 3 samples are rejected because of slight
memory effects between samples and the average of the last 5 samples are used as the reported
result. After every 8 samples, an in-house isotopic standard is measured to ensure that stability of
the measurements. The uncertainty is less than0.2‰ for oxygen and1.0‰ for hydrogen. A
complete list of results is included in the Appendix.
2.3.3 NumericalModel
The principal tool used to interpret the isotopic variability of precipitation is the IsoGSM model
outputs from Yoshimura et al. (2008), which provide the isotopic composition for surface waters
and atmospheric vapor at 6-hour resolution on a 2.5x2.5
o
global grid from 1979-2008. The model
simulation was generated by fitting isotope tracers into the Experimental Climate Prediction Centers
17
Global Spectral Model with prescribed SSTs. The simulation was nudged to historical reanalysis
data, which allows for direct comparison between model outputs and historical isotopic observa-
tions on event-timescales, analogous to previous model validations using monthly integrated Global
Network of Isotopes in Precipitation samples or ice core data (Yoshimura et al., 2008; Schneider
& Noone, 2007; Lee et al., 2007). Further details of the methodology used to generate the model
outputs can be found in the original publication (Yoshimura et al., 2008). An additional experiment
was conducted for this study where I included passive water tracers in the same nudged simula-
tion. In these experiments the evaporative flux of water from a specified region is labelled following
an approach that is computationally analogous to the isotope-enabled simulations, except in these
experiments labelled water is assigned a fractionation factor (a) of 1. Therefore, the molecules
behave the same as regular water during phase changes and diffusion. These experiments thus
enable water to be conservatively traced through the hydrological cycle. The method is similar to
that described by (Kelley, 2003).
2.3.4 ALagrangianAssumption
Implicit in the following discussion is that isotopic variability between storm events can be described
from a Lagrangian perspective where the vapor mixture at a given point point can be traced back
to a probabilistic source region. In its pure form, the Lagrangian equations track the movement of
an infinitesimally small particle through a three-dimensional fluid field (Draxler & Rolph, 2003).
The particle is theoretically non-reactive (conservative) and therefore its position in time will be
determined exclusively by the potential flow of the fluid matrix. There is a considerable literature
on the use of Lagrangian physics to describe the isotopic composition of atmospheric moisture
(for example: Pfahl & Wernli (2008, 2009); Sodemann et al. (2008) and references therein) but
nonetheless its use still requires a certain appreciation of some fundamental assumptions regarding
the chemistry that underly the reactions of water molecules in the atmosphere and the appropriate
choice in defining the state of the atmosphere. It should be noted that for discussing the isotopic
variability in the atmosphere, the most common alternative to the Lagrangian framework is the Eule-
rian coordinate system, where molecules are not tracked through the atmosphere but rather the flow
18
potential is calculated at fixed points in time. This approach has been discussed in Noone (2008).
The choice of coordinate system will not lead to any substantial differences in the interpretation of
isotopic variability but rather the two have distinct utilities depending on the scale of the question.
Because I am attempting to make an analysis of specific storm events with time scales of hours to
days, it is simpler to discuss isotopic variability from a Lagrangian perspective. This same approach
would become increasingly difficult if the goal was trying to define the isotopic composition of the
state of the atmosphere on long time scales, because the magnitude of the probable source region
would expand rapidly with time. For such studies, a Eulerian coordinate system is clearly preferable.
The choice of the Lagrangian coordinate system comes with uncertainties that are rooted in
three general locations 1) a proper definition of the state of the atmosphere on synoptic scales, 2)
the depiction of subgrid-scale physics and 3) the kinetic fractionation between water isotopologues
that occurs during phase changes. The atmospheric state was defined for this exercise using
the Global Spectral Model, where the primitive equations are solved using spherical harmonics
and then transformed onto a Gaussian grid (Yulaeva et al., 2008). It is generally accepted by
quasi-geostrophic theory that this approach will result in an accurate depiction of atmospheric flow
at the synoptic scale if boundary conditions are properly defined (Peixoto & Oort, 1992). The
atmospheric state in this model is subsequently ”nudged” at each time-step to temperature and
wind fields based on the closest approximation of the actual atmospheric state, which is defined
by Reanalysis fields that incorporate instrumental measurements into a fully realized atmospheric
circulation model (Kanamitsu et al., 2002). This technique effectively addresses the definition
of proper boundary conditions. While this procedure will produce fields that are correct from a
synoptic scale, it is possible that smaller-scale processes that play an active role within the storm
track are not properly represented.
In addition, it is not possible to define many of the critical sub-grid processes which require
parametrization schemes. Perhaps most important for understanding of water vapor transport
would be the convection scheme (a relaxed Arakawa-Schubert scheme) and cloud parametrization.
19
Consider an air parcel moving through the atmosphere, its water content will change as convection
entrains moisture into the air and will lose moisture during the formation of clouds. These processes
are therefore clearly important but at the current time must be represented only with parametriza-
tion. The choice of how to represent these processes is typically made in order to maximize the
relationship between the model outputs and observed surface conditions. Because mid and upper
tropospheric moisture in the extratropics is not well-defined instrumentally (i.e. there are few
physical measurements of tropospheric moisture content over the ocean and there are questions
regarding the appropriate algorithm for satellite data in both cloudy and low moisture regions), it is
not clear what the consequence of different parameterization schemes are on accurately depicting
moisture fields in this region, which lies in the pathway of the mid latitude storm track. Recent
discussion on this topic from both an isotopic and non-isotopic perspective can be found in Lee
et al. (2009a) and Sherwood et al. (2010) respectively.
The final consideration must be given to kinetic fractionation that occurs during phase change,
particularly with respect to the behavior at the sea-air interface. The Craig-Gordon model (Craig &
Gordon, 1965), has been shown to be immensely effective at depicting the isotopic composition of
the vapor flux from the ocean surface. However, the model is sensitive to two principal parameters
that are not well constrained, which are 1) the sensitivity of the kinetic fractionation factor to wind
speed, which evolves non-linearly according to (Merlivat & Jouzel, 1979) and 2) the difference
between the ocean skin temperature (where evaporation actually occurs) and SST measurements,
which integrate some depth into the ocean. For example, the IsoGSM model utilizes the relation-
ship between wind speed and kinetic fractionation according to Merlivat & Jouzel (1979), while
Pfahl & Wernli (2009) showed recently that by removing the influence of wind speed on kinetic
fractionation, one may more accurately capture the isotopic composition of the evaporative flux.
This study is indeed being pursued amidst active research on many of the topics discussed
in the above paragraphs. Improvements in instrumentation will undoubtedly improve the current
isotopic and non-isotopic depiction of the atmospheric state. This will allow for opportunities to
20
make more meaningful model benchmarks and lead to enhanced depictions of poorly understood
physical processes. However, it is also important to state that it is the presence of these very sources
of uncertainty that motivate this work. Particularly, this study is driven by the dearth of knowledge
on how moisture is transported in the mid latitude storm track.
2.4 Results
The local meteoric water line (LMWL) for each of the sites is consistent with that of previous
studies from the western US (Benson & Klieforth, 1989; Friedman et al., 2002; Ingraham &
Taylor, 1991; Smith et al., 1992) (Figure 2.2). The slopes of the LMWL are not statistically
different between the Sequoia (SEKI), Pinnacles (Pinn), Joshua Tree (JT), Hopland (HO) or
Olympic (Ol) sites however, the slope is notably smaller at the Death Valley (DV) site, which
is indicative of evaporation that occurs either while the precipitation fell or after it reached
the collector (Clark & Fritz, 1997). This is not unexpected given the extreme aridity at this
site. On the basis of these results we infer that post-precipitation evaporation did not affect the
integrity of samples from the SEKI, PINN, HO , OL or JT sites. Because evaporation bias cannot be
ruled out for the DV data, the results from this location are not included in the subsequent discussion.
Although this study is concerned principally with the cause of temporal variations in the
isotopic composition of precipitation, spatial patterns can be informative in highlighting the
mechanisms that drive isotopic variability (Bowen & Revenaugh, 2003). In Figure 2.3, probability
distribution functions are shown for each of the sites, highlighting how average isotopic values
decrease with latitude and altitude consistent with (Bowen & Revenaugh, 2003).
While precipitation is heavily weighted towards the winter months at these sites, seasonal
isotopic variability is also informative in highlighting the mechanisms that drive isotopic variability.
For example, Feng et al. (2009) using a global dataset argue that isotopic seasonality reflect shifts
in the latitude of the subtropical high pressure zones. There is a seasonal isotopic cycle at each of
21
the sites, consistent with Friedman et al. (1992), but it is also noted that there is a wide distribution
of isotopic values within each month (Figure 2.4). This result illustrates that although the mean
isotopic value for all storms during the winter months are more depleted than the summer months,
the isotopic intra-storm range of values during any single month encompasses values during any
other month of the year. Therefore substantial isotopic variability arises from mechanisms other
than that which drives the seasonal cycle. The seasonal deuterium-excess (dxs) cycle (Figure 2.4)
defined as:
dxs=dDd
18
O 8 (2.1)
shows stable values for most of the year with a sharp decline during the summer months.
The sharp decline in dxs during the summer months arises from continental-sourced moisture from
localized summer convective storms, which have anomalously low dxs values (Welker, 2000).
2.4.1 ModelValidation
In order to validate the IsoGSM model’s ability to accurately depict isotopic variability at an event
scale, I compared thed
18
O of each measured value from a storm event with their model-predicted
d
18
O value. If a measured isotopic value represented precipitation from three consecutive days
of rainfall, I identified these three model days and calculated the amount-weighted isotopic value.
Event-scale sampling produces an episodic dataset and thus this approach is only effective if the
model is able to produce isotopic estimates at the correct time storms make landfall. Previous
work has demonstrated that NCEP Reanalysis data is effective in reproducing accurate estimates of
precipitation within the western US (Neiman et al., 2008) and there are very few instances when an
isotopic measurement was made, which had not been simulated by the model. The only necessary
adjustment to directly compare the model-simulated values with the measured values arose because
of the effects of elevation, which the model’s coarse topographic resolution does not resolve well
in complex orographic regions like the western US. As a consequence, the modeled values are
positively offset from measured values by an average of 1.8‰, at the higher altitude sites (SEKI and
JT). Despite this, the comparison between measured isotopic values and the corresponding IsoGSM
22
-150
-100
-50
-20-15 -10-50
δ
18
O
HO (m=8.0)
DV (m=6.7)
JT (m=7.8)
OL (m=8.3)
Pinn (m=7.7)
SEKI (m=7.5)
-10
δD
Figure 2.2: Relationship ofd
18
O anddD (Local Meteoric Water Line) for all measurements made for
this study. The slope for each site is indicated on the figure. The global average slope is 8 and the low
values as observed at the DV site indicate evaporative enrichment of the sample.
isotopic values shown in Figure 2.5 demonstrates how well the model simulates the storm-to-storm
isotopic variability. The correlation between the isotopic composition of 96 measured and modeled
storm events is high (adjusted r
2
=0.50), providing confidence that IsoGSM captures the critical
processes associated with synoptic moisture transport in the mid-latitude storm track.
2.4.2 RegionalandSynopticControls
To determine what mechanisms control the storm to storm isotopic variability, I selected a suite of
storm events from the observational and model simulated data base that were associated with the
most isotopically enriched and depleted precipitation (selected storms are denoted in the Appendix).
This subset includes only storms that made landfall and passed over the region shown in Figure 2.1.
Timeseries of areally-averaged precipitation rate, precipitable water and the isotopic composition
of precipitation are generated for the entire region encompassing the sample sites for each of
23
0.00
0.05
0.10
0.15
0.20
-20 -15 -10 -5 0
HO (39oN)
OL (48oN)
Pinn (36oN
0.00
0.05
0.10
0.15
0.20
-25 -20 -15 -10 -5 0
Pinn (317 m)
SEKI (1902 m)
δ
18
O
0.00
0.02
0.04
0.06
0.08
0.10
0.12
-10 -5 0 5 10 15 20 25 30
HO
Pinn
SEKI
OL
dxs
δ
18
O as a function of latitude
δ
18
O as a function of altitude
dxs as a function of latitude
Figure2.3: Storms following a latitudinal transect (top), an altitudinal transect (center) and dxs. Land-
falling storms produce increasingly depleted d
18
O with increasing latitude and altitude. dxs does not
display a strong altitude effect, but does follow latitude. At each site a probability distribution function
using a normal kernel density estimator was fitted to the data.
the selected storms and then pooled together to generate a composite sequence of atmospheric
conditions that occur when enriched and depleted systems made landfall. As each storm system
approached the western US, there was a sharp increase in the precipitable water content, which
coincided with marked changes in the isotopic composition of the moisture in the atmospheric
column (Figure 2.6 panels A and D). In the model simulation, the isotopic composition of the water
column rises or falls by as much as 8‰ relative to the background moisture (i.e. prior to and after
the storm system passes through). As the precipitation rate fell to zero, the amount of water in the
atmospheric column and its isotopic composition returned to their background values (Figure 2.6).
(Coplen et al., 2008) argue that the isotopic variability within precipitation events reflects changes
in the temperature at which condensation occurs and thus isotopic values could be driven positive
24
-15
-10
-5.0
0.0
5.0
10
15

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Month Month
Anomaly (‰)
Monthly δ
18
O distribution Monthly dxs distribution
Figure2.4: Monthly distributions of storm events at all sites for oxygen and dxs. The isotopic compo-
sition of each storm is corrected to the average of the site and then box plots with quartiles are made for
each month. While there is a seasonal cycle where mean values in the summer are higher than the winter,
the distribution of individual storms overlaps almost completely between months. Deuterium-excess has
a more defined seasonality, with extremely negative values occurring during the summer months. Outlier
values are marked by open circles.
-10
-5
0
5
10
Modeled δ
18
O (anomaly)
Measured δ
18
O (anomaly)
Slope: 0.97
Intercept: 0.01
R: 0.72
-10 -5 0 5 10
0
10
20
30
40
50
Measured
IsoGSM
Frequency
Isotopic Anomaly
-12 -8 -4 0 4 8 12
Figure2.5: Comparison betweend
18
O values of measured storm event and their predicted values based
on the IsoGSM simulation. The mean isotopic values were subtracted from each dataset to correct for
the coarse topography in the model simulation (left panel). The colors are used to denote the different
sites. The distribution of the same storm events shown in the left panel, bins are 1‰ and the line is the
best fit Gaussian distribution (right panel).
25
-27
-24
-21
-18
-15
0 6 12 18 24 30 36 40
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0 6 12 18 24 30 36 40
-30
-27
-24
-21
-18
-15
0 6 12 18 24 30 36 40
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 6 12 18 24 30 36 40
5
10
15
20
25
30
35
0 6 12 18 24 30 36 40
10
15
20
25
30
35
0 6 12 18 24 30 36 40
δ
18
O(vapor)
δ
18
O(vapor)
Specific Humidity (kg/m
2
) Specific Humidity (kg/m
2
)
Precipitation Rate (kg/m
2
/s
2
) Precipitation Rate (kg/m
2
/s
2
)
Columnar Water Vapor Precipitation Rate Precipitable Water
Time (hrs) Time (hrs)
Time (hrs)
A
B C
D E F
Figure 2.6: Time evolution ofd
18
O of precipitable water (left column), precipitation rate (middle col-
umn), and atmospheric specific humidity (right column) during a sequence of the most enriched (top
row) and depleted (bottom row) storm events. The mean value for the different storms are shown as a
bold gray line with circle markers. All values were taken from the IsoGSM simulation. Time 0 represents
an arbitrary beginning point before precipitation began to fall moving forward in 6-hour time steps.
or negative with negligible antecedent changes in the isotopic composition of the integrated water
column. The results of the analysis shown in Figure 2.6 imply that the range of storm to storm
variability actually reflects comprehensive and heterogeneous changes in the isotopic composition
of the entire water column and not variations in the magnitude of isotopic fractionation during
condensation.
The convergence of moisture that saturates the atmospheric column includes vapor derived
from a wide radius surrounding the frontal storm center. The isotopic changes in the atmospheric
column (Figure 2.6, left column) thus track the evolving mixture of water that is entrained locally
26
and from remote moisture sources that has been transported by the storm system (Trenberth et al.,
2003). It is not feasible to isolate the proportional contributions of the local and remote moisture
to the integrated water vapor column. Hence, I consider whether there is a difference in local
moisture fluxes associated with the isotopically enriched and the isotopically depleted storms and
then consider how wind fields and moisture transport influences the source and relative contribution
of remote moisture. In figure 2.7, it is shown that the enriched storm events are associated with
higher surface latent heat flux (taken as a proxy for evaporation) just offshore. Thus, a portion of
the isotopic difference between the enriched and depleted columns can be attributed to increased
evaporation from the underlying local coastal water prior to landfall. This is consistent with what
would be predicted using a Rayleigh distillation model where local (i.e. younger) waters are
predictably more isotopically enriched (Feng et al., 2009; Noone, 2008; Clark & Fritz, 1997) and
the increased flux of this source would thus shift the column towards more isotopically enriched
values. A higher latent heat flux is indicative of warmer SST conditions, which additionally
reduces the magnitude of isotopic fractionation during surface evaporation (Majoube, 1971). The
fractionation effects at the ocean-atmosphere interface coupled with simply the increase in this
local moisture source appear therefore to shift the isotopic composition of the near-surface vapor
and the subsequent precipitation towards more enriched values.
To distinguish the relative influences that locally entrained and remotely advected moisture
have on storm to storm changes in the integrated water column, I deconvolve the atmospheric
column into discrete pressure horizons and identify the height in the atmospheric column at which
isotopic changes were most pronounced during the composite enriched and depleted events (Figure
2.8). The most pronounced isotopic changes occur not along the surface but rather between
the 800-700hPa pressure surfaces. The large isotopic changes at these heights cannot likely be
attributed to the entrainment of moisture from below. Instead, they reflect moisture that is being
advected into the region by winds aloft. This observation is consistent with theoretical discussions
in Trenberth et al. (2007) who suggest that up to 70% of the moisture associated with major
27
landfalling storms in the subtropics and mid-latitudes is composed of vapor from distant sources.
Similarly, experiments with tagged water molecules in an ensemble of GCM simulations, show
that midlatitude cyclones, characteristic of the major rainfall events in the western US, may be
drawing on water from a radius of over 25
o
latitude (Kelley, 2003). Bao et al. (2006) test this idea
by looking at moisture convergence and moisture trajectories in a subset of land-falling storms and
provide more empirical evidence for the presence of remote moisture sources in certain systems that
make landfall along the west coast of the US. Thus, based on the magnitude and height at which
maximum isotopic changes occur within the atmospheric column and the independent tagging
experiments (Kelley, 2003) as well as the empirical moisture convergence studies (Bao et al., 2006),
it is concluded that the principal mechanism affecting the storm to storm water column isotope
budget is convergence of moisture from distant sources rather than variable moisture flux from local
terrestrial or marine surfaces.
In figures 2.9 and 2.10 I show both the mean prevailing wind fields and d
18
O of integrated
water vapor over the Pacific during the composite (6 events) of the most enriched and depleted
storm events from Figure 2.6. Isotopically depleted storms are associated with a high pressure
anomaly that is centered near 30
o
N over the central Pacific (Figure 2.9). This generates strong
low level northerly flow along the west coast of the US, which advects isotopically depleted
moisture from the Aleutian Low region over the southwestern US. This is in clear contrast with the
isotopically enriched storms that are characterized by more zonal flow and a high pressure center
that is located further south near 25
o
N (Figure 2.9). By subtracting the composite wind vectors
from one another it is evident that anomalous southwesterly flow is characteristic of the most
enriched isotopic storm events (Figure 2.9). The prevailing southwesterly winds lead to a wide
swath of isotopically enriched moisture that stretches from the central Pacific, near Hawaii, to the
southwestern US (Figure 2.10). The isotopes appear to provide a tracer of the poleward flux of low
latitude moisture and confirm the suggestion of both Dettinger et al. (2004) and Bao et al. (2006)
that southwesterly storms do indeed tap into and export tropical water vapor to the western US.
28
Figure2.7: Difference between latent heat flux (w/m
2
) for the enriched and depleted composites. High
latent heat flux from just offshore is a common feature of the most enriched events. Data for latent heat
is from the North American Regional Reanalysis dataset (Mesinger et al., 2006).
Figure 2.8: Top panels (left and right) show the precipitation rates in six hour time steps as two iso-
topically enriched storm events strike the western US. The bottom panels show vertical cross sections of
the isotopic composition of water vapor as the storms pass over the region. The figure shows how the
maximum isotopic changes (on the order 8-10 ) occur between the 800-700 mb levels.
29
Depleted storms Enriched storms Enriched-Depleted
Figure2.9: A plan view of the average 850 mb wind fields during the most depleted (left) and enriched
(center) events and the difference between the two vector fields (right). Scale bar shows the length of a
10 m/s vector.
Figure2.10: Isotopic concentrations ofd
18
O of water vapor during depleted (left) and enriched (right)
events. Water vapor is taken for the 850 mb level. Colors show isotopic anomalies relative to the field in
view while contours show absolute isotopic concentration relative to VSMOW.
The isotopic plume portrayed in the right panel of Figure 2.10 suggests that the vapor trans-
ported by the most enriched storms have the capacity to conserve the isotopic composition of mois-
ture from their low latitude source region. However, water vapor is typically short-lived in the
30
atmosphere (9 days) and thus a high rate of turnover from entrainment and condensation would typ-
ically obscure the capacity of isotopes to behave as a conservative tracer. Because the analysis on
individual storms was based on only a relatively small population whose dynamical behavior may
well be aberrant, further validation of the isotopic influence of distant moisture sources is accom-
plished by calculating the correlation coefficient between annually-averaged d
18
O of precipitation
over southern California and gridded meridional moisture flux. This analysis takes advantage of the
longer timeseries available from the IsoGSM simulation and allows for an assessment of whether
the relationships inferred on an event-scale are stable on timescales relevant to proxy records (e.g.
annually-resolved). I find that meridional moisture flux from across the tropical Pacific is correlated
with the isotopic composition of precipitation in southern California (Figure 2.11), which suggests
this region would be particularly sensitive to ocean-atmosphere changes in this region. In addition,
increased meridional moisture flux along the southwesterly storm track, leads to enriched isotopic
values. I also note negative correlations betweend
18
O of precipitation and meridional moisture flux
on the west side of the Pacific Basin consistent with the enhanced southerly flow west of 150
o
W
during depleted storm events as presented in the left panel of Figure 2.9.
2.4.3 MesoscaleControls
Although the event scale analysis helps to to delineate specific moisture source regions that have
direct influences on the isotopic composition of landfalling precipitation, because of the wide
radius that mid latitude cyclones draw moisture from, it is critical also to consider how mesoscale
circulation with seasonal to interannual timescales influence the distribution of water isotopologues
across the basin. To assess this, I regress 6-hourly values for the isotopic composition of water vapor
from the IsoGSM simulation against meridional and vertical moisture flux (Figure 2.12, top and
bottom panels respectively). In the lower troposphere between 10
o
N and 40
o
N there is a strongly
positive correlation between meridional moisture flux and the isotopic composition of vapor. In both
model and satellite-derived estimates of lower tropospheric water vapor, there is a steep latitudinal
gradient in the isotopic composition of water vapor across this region, withd
18
O
vapor
dropping by
approximately 10‰ over these latitudes. The positive correlation between meridional flow and
31
Figure2.11: Correlation coefficient between annually average vertically integrated meridional moisture
flux and amount weighted d
18
O of precipitation over the southwestern US. Contours indicate correla-
tions that are significant at the 95% confidence based on a Student’s T-test.
the isotopic composition of water vapor is thus interpreted to largely reflect poleward transport of
enriched vapor by transient eddies, which are the principal mechanism to drive meridional moisture
flux at these latitudes (Peixoto & Oort, 1992). Poleward of 40
o
, the relationship weakens until it is
no longer significant and eventually reverses sign. An increase in meridional moisture flux is thus
actually associated with depleted isotopic values in the high northern latitudes of the Pacific Basin.
The lack of a positive correlation could arise because of an absence of any measurable isotopic
gradient in the high latitudes of the north Pacific (Frankenberg et al., 2009; Craig & Gordon, 1965)
and from the fact that meridional moisture transport is minimal north of the subtropics (Peixoto &
Oort, 1992). It is not clear why the relationship actually reverses sign and does not simply dissipate.
This may result from the influence that humidity or skin temperature changes have on the isotopic
composition of the evaporative flux, which do directly influence the meridional moisture transport.
32
Figures 2.12 and 2.13 illustrate how overturning circulation influences the isotopic composi-
tion of water vapor over the Pacific basin. Vertical velocity is considered to exert an important
control on the isotopic composition of atmospheric moisture by influencing ascent (subsidence)
of isotopically enriched (depleted) moisture (Feng et al., 2009; Noone, 2008). In the IsoGSM
simulation, this circulation behavior is manifest in isotopic contours that loosely follow the
boundaries between rising and subsiding air masses (Figure 2.13). To characterize the influence
that vertical mixing has on the isotopic composition of vapor, I calculate point-by-point correlations
between vertical velocity and the isotopic composition of precipitation (Figure 2.12). Regions of
dominantly rising air (0-10
o
N and 45-60
o
N) display a positive correlation between the isotopic
composition of water vapor and vertical velocity. A similar relationship was noted by (Feng et al.,
2009) and can be explained as arising from the fact that by suppressing convection and rainout, the
moisture is on average more enriched. The opposite relationship can be observed in the subtropics
where increased subsidence leads to isotopic values that are depleted (e.g. a negative relationship
between vertical velocity and vapor). Therefore, more vigorous overturning (e.g. anomalous ascent
at the low latitudes and descent in the subtropics) would collectively lead to depleted isotopic values
across much of the Pacific basin.
2.4.4 Water-tagging
A further analysis of the source controls on the d
18
O of precipitation is conducted by tagging all
waters evaporated from the tropical Pacific between 0-20
o
N and 120-180
o
W. In this way water
molecules can be conservatively tracked through the atmosphere. In order to quantify the relation-
ship between the percentage of low latitude moisture during a precipitation and its isotopic com-
position, the isotopic composition of the water column is plotted against the percentage of tagged
water in the column (Figure 2.14). There is a complex and poorly defined relationship between the
two on a 6-hourly basis, suggesting the the isotopic composition of the atmospheric column cannot
be described as a simple product of moisture source origin. However, when days in which storm
events strike are selected from the simulation, a far more coherent picture emerges. The absence or
presence of low latitude moisture accounts for greater than 50% of the isotopic variability. Although
33
Figure 2.12: Correlation coefficients between meridional moisture flux and the d
18
O of vapor for a
vertical cross section of the Pacific between 120-180
o
W (bottom). Correlation coefficients between
vertical velocity (omega) and thed
18
O of vapor for a vertical cross section of the Pacific between 120-
180
o
W (top).
the slope of this relationship is presented as only a tentative result, it suggests that a loss of 20%
of the tagged moisture would result in a 2‰ depletion of the average isotopic composition of the
water column (Figure 2.14). In Figure 2.15, I show a plan view of the fraction of tagged water in the
atmospheric column during the composite of enriched (right) and depleted (left) storm events. This
figure is comparable to Figure 2.10 except showing tagged water as opposed to the isotopic com-
position of the water column. The results depict that up to 40-50% of the moisture in the enriched
storm events striking the western US are derived from a remote southwesterly source (Figure 2.15).
For illustrative purposes the atmospheric river event from January 22, 2005 is shown (Neiman et al.,
2008), where the moisture making landfall is composed of approximately 70% tagged moisture.
34
Figure2.13: A cross section across the Pacific basin with contours showing the average isotopic concen-
tration of water vapor and colors showing vertical velocities (positive values are dark gray and negative
values are orange).
70%
0% 10% 20% 30% 40% 50% 60% 70%
-10
-20
-30
-40
-50
δ
18
O
-22
-20
-18
-16
-14
-12
-10
0% 20% 40% 60% 70% 50% 30% 10%
Percentage of tagged Water
R
2
=0.52
Percentage of tagged Water
δ
18
O
Figure2.14: The relationship between the relative percentage of tagged water in the atmospheric column
over southwestern US and the isotopic composition of the integrated water column (left). All days
associated with storm events were selected from the figure on the left showing the coherent relationship
between tagged water and the isotopic composition of water during landfalling frontal storms.
35
Fraction of tracer
Figure2.15: A composite of tagged water concentration for a series of isotopically enriched (right) and
depleted (left) storm events. The concentration of tagged water in the atmospheric column is taken as
the ratio of the mass of tagged water to total water (e.g. specific humidity).
Figure 2.16: Plan view of the 850mb wind fields and relative percentage of tagged water in the atmo-
spheric column during the atmospheric river event on January 22, 2005.
36
2.4.5 Deuterium-excessgradients
Deuterium-excess (equation 2.1), is considered a more robust tracer of moisture source region than
either d
18
O or dD individually. This is because the deuterium excess (dxs) value of a water mass
is effected most strongly by the differing degrees of kinetic enrichment between the isotopologues
of water during evaporation (Merlivat & Jouzel, 1979). The moisture evaporated from the source
region is thus marked by the temperature and humidity of the marine atmosphere from where
the moisture originated. Processes occurring during water mass evolution (i.e. condensation) are
considered to occur in near equilibrium conditions and therefore impose only a small change in the
deuterium excess characteristic of the water mass (Merlivat & Jouzel, 1979). On average there is
an approximately 4‰ gradient in dxs values between northern Washington and central California
based on monitoring from the Global Network of Isotopes in Precipitation sites in Santa Maria, CA
and Destruction Island off the coast of Washington. This gradient reflects not surprisingly, a more
southerly source for the moisture in southern California. As an empirical test of the results of the
tagged water simulation, which suggested a highly efficient transport of remote moisture in certain
storm systems, a series of 20 storm events from 2001-2003 that swept along the entire west of the
US are selected from the catalog and the dxs gradient is calculated (dxs
Cali f ornia
-dxs
Washington
). The
mean dxs gradient of all storms (3.8‰) is found to be similar to the gradient which was calculated
from the monthly mean gradient GNIP values (4‰). From a storm to storm perspective, the dxs
gradient can differ quite substantially from the mean, with some storms displaying no measurable
dxs difference between the northern and southern sites while other storms producing values that
are a factor of 2 greater than the mean. To interpret the cause of the variability of storm to storm
dxs gradients, I explore the atmospheric conditions that prevailed during the high and low gradient
storms using the North American Regional Reanalysis dataset (Mesinger et al., 2006).
In Figure 2.17, I show the precipitation rate during storms that displayed a high (left), low
(right) and normal (left) dxs gradient. In Figure 2.18, I show the vertically integrated meridional
moisture flux for the high (left) and low (middle) gradient storms. The difference between the two
depict the dominant poleward moisture flux that prevails during the low gradient storms. This is
37
interpreted to reflect that low dxs gradients arise because of a common moisture source along the
entire west coast as opposed to more localized moisture from the nearby coast. In Figure 2.19, a
similar analysis is done to look at latent heat flux as a means to identify different moisture source
regions. The analysis does not present a clear depiction of moisture source locations but suggests
that low dxs gradient are associated with increased evaporation from an equatorward source while
high dxs gradients are associated with high latent heat flux north of 35
o
.
The analysis presented suffers a bit from a small dataset used to generate composites and
also added analytical uncertainty because dxs is a second order parameter and thus accumulates
uncertainty from both the d
18
O and dD measurements. The analysis of both meridional moisture
flux and latent heat flux suggest that storms with an absence of any measurable dxs gradient arise
from a common low latitude moisture source. This would be akin to storms along the entire west
coast showing high amounts of tagged water as depicted in Figures 2.15 and 2.16. The contrary can
be stated of storms with high dxs gradient where entrainment of moisture leads to storms having a
dxs signature that reflects their latitude and corresponding zonal dxs value Although this analysis is
based on a small data set, it portrays a satisfying test of the results from the numerical modeling
exercises.
2.5 Discussion
2.5.1 Isotopesand21
st
centuryhydrologicchanges
A complete description of how ocean and atmospheric variability influences the stable isotope
composition of precipitation over the western US has been hampered by a lack of isotopic
measurements from discrete storm events and an analysis of how rainout and mixing from source to
sink influences the isotopic composition of the moisture. The strong correlation between observed
and simulated isotopic values for storm water provides an important validation of IsoGSMs (and
by corollary NCEP 2 Reanalysis’) ability to simulate a moisture budget for southern California.
The correlation between observed and modeled isotopic variability is robust despite the fact that
38
Figure2.17: Precipitation rate (kg of water/m
2
) during storms included in the high (left), low (middle)
and average (right) dxs gradient events. The figure emphasizes that the storms influenced the entire
coast.
convective processes that entrain water into the storm system and raindrop physics are not well
depicted in this type of model (Risi et al., 2008; Lee et al., 2009b). Continued efforts to refine the
analytical representation of these processes will undoubtedly improve isotope simulations of this
kind, though for regions where moisture convergence is predominately the result of large-scale
frontal systems, the major processes are adequately represented. This finding is important because
accurate depictions of moisture transport associated with storms in coupled models is seen as an
obstacle in efforts to improve 21
st
century hydrologic forecasts for semi-arid subtropical regions
(Seager et al., 2007b). While isotopic tracers have proven important in the representation of the
hydrological cycles in GCM simulations at seasonal or intra-annual timescales, the results presented
here illustrate the utility of using isotope tracers for simulations of event-scale processes that
involve complex condensation and mixing processes.
39
Figure 2.18: Vertically integrated meridional moisture flux (kg/m) during storms included in the high
(left) and low (middle) dxs gradient events and the difference between the two composites (right).
Figure 2.19: Latent heat flux (W/m
2
) during storms included in the high (left) and low (middle) dxs
gradient events and the difference between the two composites (right).
40
Not surprisingly, there are indeed important regional controls on the isotopic variability of
individual storms that arise because of nearshore sea surface temperature variability (Figure 2.7).
Warmer SSTs reduce the fractionation between ocean water and vapor and also increase the
entrainment of locally evaporated waters (Majoube, 1971; Craig & Gordon, 1965). The local effects
however are not adequate to explain why the isotopic composition of precipitation is driven to such
extreme values. To account for the full range of variability, consideration of the influences associ-
ated with basin-scale atmospheric circulation are required. The large isotopic variability between
storms arises principally from the mechanism initially proposed but not tested by Friedman et al.
(1992), which is the advection of moisture from source regions of distinct isotopic composition.
I find that precipitation over California that is isotopically enriched arises from storms that tap
into subtropical and tropical moisture sources and have trajectories that are more direct than those
systems which take a circuitous route around the middle latitude quasi-permanent high pressure
cells (Dettinger et al., 2004).
Large cyclonic storms draw on moisture from a wide radius, and thus a simple delineation
of the source region neglects the complex mixing that occurs as these storms evolve. On a
basin-wide scale, low-level meridional moisture flux will enrich the vapor sources across the
subtropics and into the mid latitudes. Low frequency changes in eddy-driven poleward moisture
transport would consequently shift the average isotopic value of storms towards values that are
enriched relative to time of reduced overturning. In contrast, an increased vigor of overturning
circulation, would deplete the vapor fields across the region most storms pass through and shift the
average isotopic value of precipitation towards more depleted values.
Simulations of how the concentration of water vapor in the atmosphere responds to radiative
forcing has been investigated by numerous authors (Schneider et al., 2010; Hall & Manabe, 1999;
Held & Soden, 2006) and provide a test bed to consider the regional isotopic response to a global
climate perturbation. Climate projections for the 21
st
century for example, predict a northward
41
shift in the mid-latitude storm track with a slight reduction in the latitudinal temperature gradient
(Rind et al., 2001; Yin, 2005; Vecchi et al., 2006). If these changes in circulation were acting
alone, a poleward shift in the storm track would lead to a reduction in the isotopic composition of
precipitation during the 21
st
century for the midlatitudes by increasing (decreasing) the relative
contributions of water from northerly (southerly) storms. However, an increase in the water
holding capacity of warmer air over the tropics could enhance moisture transport from the tropics
to the extratropics albeit with a slope slightly shallower than the global rise in humidity (Lorenz
& DeWeaver, 2007), which would in principal offset the isotopic impact that would arise from
the poleward shift in the storm tracks. To the extent that these changes have already begun, a
compilation of isotopic records from midlatitude sites would aid in assessing if the hydrological
changes in the subtropical high pressure zones as predicted by GCMs driven with rising greenhouse
gas concentration are indeed taking place (Seager et al., 2007b; Hoerling & Kumar, 2003). For
example, rising isotopic values of precipitation in the subtropics and mid latitudes, would indicate
that the poleward shift in the storm track is being offset by changes in the water holding capacity of
the tropical atmosphere. On the contrary, if the isotopic values document a long term decline, this
would provide evidence of a reduced presence of low latitude moisture to the region. Delineating,
which of these two scenarios is underway is consequential not only to regional aridity forecasts but
also to assessing the response of the global hydrological cycle to increased radiative forcing.
2.5.2 IsoscapesandProxyreconstructions
When the multiple competing influences on the isotopic composition of precipitation along the west
coast of the US are considered, it is clear that caution must be exercised in interpreting isotopic
variability from this region in terms of a single climate variable. I offer a preliminary discussion,
which will be elaborated on more fully in Chapter 5, which is to consider spatial isotopic patterns
across the west coast by integrating multiple records into isotopic networks or isoscapes (Bowen
et al., 2009), which can be used to deconvolve controls associated with local to synoptic variability
42
from basin-scale changes. An example of this is illustrated in Figure 2.20, where enriched
(depleted) isotopic values occur across the entire west coast of the US in years with a strong (weak)
and westward shifted Aleutian Low and anomalous southerly (northerly) flow (Figure 2.20, left and
right panels) while an isotopic dipole pattern emerges, with enriched values south of 45
o
during
years when there is a strong eastward-shifted Aleutian Low and anomalously strong Westerly winds
(Figure 2.20, center panel). Therefore, a latitudinal transect of proxy reconstructions from along the
western US could be used to delineate between paleo-circulation patterns that generate basinwide
or dipole-like patterns. The interpretation of isotopic variability in western North America in terms
of specific atmospheric modes was suggested by (Birks & Edwards, 2009) who find that the Pacific
North American pattern (a leading mode of low frequency pressure anomalies over the North
Pacific) is a good predictor of isotopic values in central Canada. Their approach attempts to fit
isotopic variability into an established climate mode with well-documented teleconnection patterns.
Longer simulations would allow for more rigorous approaches to defining spatial isotopic patterns
(i.e. Empirical Orthogonal Function analysis of isotopic fields) and test whether isotopic modes are
truly analogous to climate modes defined through SLP or SST patterns. For example, (Field, 2010)
show that the isotopic composition of precipitation over Europe appears related to a NAO-like mode
but with centers of action that are distinct than those defined strictly through SLP patterns.
2.6 IsotopiccontrolsatInlandSites
Discussion thus far has focussed on near coastal sites which are more sensitive to moisture advected
directly from the Pacific. Sites further inland have been shown to have a strongly linear relationship
with surface temperatures (Vachon et al., 2007; Harvey, 2001). This is because multiple marine
and continental moisture sources mix together and tend to subdue variability that would arise form
changes in the moisture source (Friedman et al., 2002). Therefore changes in the temperature at
which condensation occur serve as the principal control of isotopic varibaility. Using the GNIP
event station in the Pawnee Grasslands of Colorado, I show the presence of a dominant relationship
43
between surface temperature and the isotopic composition of 102 precipitation events that fell
between 1994-1998 (Figure 2.21). To test if this relationship is influenced by source region, I cluster
storms by their prevailing source region using Lagrangian Trajectory analysis (Draxler & Rolph,
2003). 72-hour back trajectories that were initialized at a height of 1000 m above the ground surface
were run with NCEP Reanalysis atmospheric boundary conditions were calculated for each storm
(Figure 2.24). Storm were classified as either North, Northeast, Northwest, Southeast, Southwest,
or South. The slope and goodness of fit between temperature and the isotopic composition of
precipitation (Dd
18
O-T) is not degraded between storms of different origins (Figure 2.21), which
suggests fundamentally that the isotopic controls are dominated by local processes.
The event data spans only 4 years, so as a test of the long term stability of the relationship
between the isotopic composition of precipitation and surface temperature, I utilize the isotopic
composition of monthly isotopic values from the ECHAM 4 (Hoffmann et al., 1998), GissE
(Schmidt et al., 2007) and IsoGSM (Yoshimura et al., 2008) model outputs. The ECHAM4 and
GissE simulations were not nudged but were run with prescribed 20
th
century SSTs, which permits
a test of how sensitiveDd
18
O-T is to perturbations in SST patterns. TheDd
18
O-T was calculated
at the Pawnee Grid Point using the complete time series of each model (Figure 2.22) and then
Dd
18
O-T was calculated for randomly shuffled 5-year windows during the 20
th
century using
a Monte Carlo simulation to assess the stability of the Dd
18
O-T relationship. The mean slope
was subtracted from the slope calculated for each 5 year window and probability distribution
functions of the slope anomalies were calculated for each of the 3 models. In 90% of the iterations,
the calculated Dd
18
O-T is within 5% of the cumulative 20
th
century slope (Figure 2.23), which
suggests an exceptionally stable relationship between temperature and the isotopic composition of
precipitation at this site.
2.7 Conclusions
This chapter presents a new catalog of 367 isotopic measurements of storm events striking sites
along the west coast of the US. The isotope data is used to provide a test of the representation
44
Figure2.20: Average annual 850 mb geopotential height (m) and wind vector anomalies from NCEP 2
Reanalysis (Kanamitsu et al., 2002) during 1989, 1998 and 2003 (left to right, top row) and the isotopic
anomalies in precipitation associated with these same years (bottom row).
of water vapor transport in the Experimental Climate Prediction Center’s Global Spectral Model,
which has been isotope-enabled (IsoGSM). The model is able to reproduce the large range of
storm-to-storm stable isotope variability in precipitation, suggesting that it accurately captures
the moisture transport that is associated with large frontal storms that constitute most of the
annual water budget across the southwestern United States. I document large shifts in the isotopic
composition of atmospheric water vapor when storms make landfall, which points to convergence
of remote moisture sources that are isotopically distinct from moisture evaporated from the adjacent
coastal ocean. Southwesterly storms are associated with the most enriched isotope values, as they
tap into isotopically enriched low latitude moisture sources that is transported poleward. This is
45
-30
-25
-20
-15
-10
-5
0
5
-20 -10
0. 2
0. 4
0. 6
0. 8
1. 0
1. 2
All events NNW SSW NDJFM AMJ JASO
Δδ
18
O-T
Temperature (C)
δ
18
O
p
10 0 20 30
Figure 2.21: Isotopic composition of 112 individual storm events between 1994-1998 from the Global
Network of Isotopes in Precipitation event station in the Pawnee Grasslands in Colorado plotted against
surface temperatures during the events. The right panel shows the slopes of the regression after storms
have been binned by season and prevailing trajectory.
most clearly depicted during atmospheric rivers that originate in the central tropical Pacific and
carry significant amounts of isotopically distinct tropical moisture. Additional isotopic variability
can arise because of changes in the isotopic composition of moisture within the source regions
themselves in response to meridional and vertical moisture flux. Nonetheless, it is the distinctive
difference in isotopic composition of low and high latitude atmospheric moisture that provide the
dominant control on the isotopic value of rain and snow that falls over the western US. Empirical
and numerical tests of the role of source water changes in defining the isotopic composition of
precipitation are conducted by including passive water tracers in the tropical source region and by
looking at deuterium excess gradients along a latitudinal transect. Both of the experiments support
the interpretation that remote tropical moisture can make up a significant percentage of the moisture
delivered by storms and its presence is causally related to event-scale isotopic variability.
46
Figure 2.22: Slope of monthly integrated d
18
O
p
and temperature at the 39
o
N and 105
o
W grid point
in the IsoGSM (Yoshimura et al., 2008), Echam4 (Hoffmann et al., 1998) and GissE (Schmidt et al.,
2007) isotope-enabled GCM simulations. The relationship is highly significant in all three models and
the slopes are nearly equivalent though the intercept is larger in the nudged IsoGSM simulation than
observed in the other two models.
0
2
4
6
8
-0.3 -0.2 -0.1 00.1 0.20.3
Isogsm
Echam4
GissE
∆δ
18
O/ ∆T
(anomaly)
Density
Figure2.23: Probability Distribution Functions ofDd
18
O-DT for sliding 5-year windows encompassing
the entire length of the simulations from preceding Figure. Positive (negative) values indicate 5-year
windows in which the slope was steeper (shallower) than the mean slope calculated from the entire
dataset. The slope stays within 5% of the mean slope more than 90% of the time.
47
Isotopic changes in moisture over the western US can be used to distinguish different atmo-
spheric processes that have influenced the regional moisture budget in the past, which could
be important in assessing how the hydrologic system may behave in the future. For example,
despite model to model discrepancies in how the tropical ocean-atmosphere system responds to
increased radiative forcing, the consensus result is a drying of the subtropical high zones during
the 21
st
century as a consequence of increasing water-holding capacity of the atmosphere, which
leads to drying in regions such as the subtropics that experience persistent moisture divergence
(Seager et al., 2007b). If rising humidity is synchronous with dynamical changes in poleward
eddy-driven moisture transport from the tropics, than the rate and severity of drying could be
more (or less) severe than predicted from the GCM ensemble. The isotopic response to increased
evaporation is distinct from the isotopic signal of an actual change in poleward moisture flux.
Modern and proxy-reconstructed records of atmospheric d
18
O can be used to investigate how
these two competing processes are currently unfolding and have varied in the past in response to
known modes of ocean and atmospheric variability, such as La Nina-like cooling in eastern tropical
Pacific. This would provide a basis for assessing the mechanisms responsible for aberrant surface
conditions in the past and improve long-term hydroclimatic forecasts.
In conclusion I refer the reader to Figure 2.24, where I show the trajectories for each storm
striking central California during 2001-2005 (purple). The storms originate along a latitudinal
continuum, which was originally proposed by (Friedman et al., 1992) to provide the dominant
isotopic controls on the region. This depiction is helpful in understanding storm to storm isotopic
variability but not fully satisfying, as mixing dynamics between storms can lead to isotopic
differences between two storms that follow the same basic trajectory. Trajectories of storms striking
the inland site in Colorado are actually more diverse than along the coast, but they ultimately
have only a muted isotopic influence because it is the local temperatures and their influence on
fractionation during condensation that provide the dominant controls. In Chapters 4 and 5 I will
48
-150˚
-150˚
-120˚
-120˚
-90˚
-90˚
-60˚
-60˚
20˚ 20˚
40˚ 40˚
60˚ 60˚
30
Figure2.24: Lagrangian back trajectory analysis for the coastal and inland sites. The latitudinal gradient
depicted for the coastal sites serves as a first order predictor for the isotopic composition while the more
varied trajectory at the inland sites does not serve the same function.
discuss new proxy records both from inland sites and along the coast. The different isotopic controls
between these two regions leads to different climate information that can be garnered from these
two records.
49
Chapter3
Testingmodelsofthemechanisticcontrolsonthe
isotopiccompositionofcelluloseusing
intra-annualsampling
SUMMARY
In this chapter I present results from a series of numerical and analytical experiments designed
to elucidate the environmental factors that determine the amplitude and shape of seasonal iso-
topic variability in tree rings from sites across the southwestern US. Tree cores spanning var-
ious time slices throughout the late Holocene were selected from Pinus longaevae and Pinus
aristata that grew at high altitude sites in the White Mountains of California and Almagre
Mountains of Colorado, respectively. Along the growth axis of each annual ring, the isotopic
composition of the cellulose follows a well-defined parabolic trend with depleted values at
the beginning and ends of the growing season and enriched values during the middle of the
growing season. The shape and amplitude of this cycle is surprisingly constant at all sites even
during intervals of time that were known to be affected by severe shortages and excesses of
precipitation. A schematic model is introduced to understand the climatic cause of this cycle
where cellulose begins to be laid down in the late spring when temperatures exceed a critical
temperature threshold that permits active photosynthesis and cellulose ceases to form during
the late summer when either the soil is fully desiccated or the temperatures drop below the
critical temperature threshold. A numerical model of this process is used to understand how
growing season changes in humidity, temperature and source water inputs to the tree interact
to generate this well-defined cycle. In the simplest iteration of the model, water in the soil
is assumed to remain isotopically constant and photosynthates are formed and immediately
50
metabolized into cellulose. More complex iterations of the model are presented that account
for a dynamic soil water pool that varies with inputs from summer precipitation and loss of
moisture through evaporation. At the White Mountain site, the simplest model with a water
pool that is isotopically static best replicates the observed cycle, thus the seasonal variabil-
ity is interpreted to exclusively reflect changes in the fractionation of water isotopologues
from the leaf as a response to growing season changes in relative humidity and temperature.
This result sheds light on some critical phenological and hydrological features of this system.
Firstly, water loss from the soil is done exclusively by transpiration (e.g. not evaporation) and
secondly, growth terminates each year before late summer convective precipitation begins.
On the contrary, in order to model the isotopic cycle at the Almagre site, it is found that a
dynamic source water pool is required. At this location, precipitation is monsoonal in nature
and therefore the soil water is constantly evolving during the growing season through inputs of
newly precipitated moisture. Changes in the water pool serve as the dominant control on the
isotopic variability. High-resolution isotopic measurements of mid latitude tree species have
hitherto never been presented because the narrow growth rings normally preclude analysis of
such minute samples. The results in this chapter show the utility of this type of experiment
in understanding the multivariate and site-specific climatic signals embedded in the isotopic
composition of cellulose.
3.1 Introductorynote
The contents of this chapter have been published in:
Berkelhammer, M. & Stott, L., 2009. Modeled and observed intra-ring d
18
O cycles in late
holocene bristlecone pine tree samples, Chemical Geology.
Berkelhammer, M. & Stott, L., submitted to Nature. A growth-independent temperature
reconstruction for the southwestern US.
51
3.2 Introduction
The d
18
O of tree-ring cellulose can be a valuable archive of local and regional environmental
conditions. However, attempts to correlate changes in cellulosic d
18
O with an isolated environ-
mental variable have failed to produce consistent results. This is primarily because the isotopic
composition of cellulose integrates both the isotopic ratio of the tree’s source water and the series
of isotopic exchange reactions and fractionations that take place at the leaf-atmosphere interface
and during the synthesis of cellulose (Burk & Stuiver, 1981; McCarroll & Loader, 2004; Treydte
et al., 2006). The atmospheric processes that affect the tree’s source water and the local climate
or meteorological conditions affecting the leaf-atmosphere interface need not be coupled and thus
each may vary independently of the other. There have been a handful of efforts to separate these
dueling influences by assessing the isotopic composition of intermediate photosynthetic products
(Gessler et al., 2007; Hill et al., 1995) or by focusing on the isotopic ratio of specific carbon groups
(Sternberg et al., 2007). However, with respect to the rapid generation of long chronologies for
paleoclimate interpretation, isolating targeted environmental information from thed
18
O of tree-ring
cellulose remains an important challenge.
Much of the isotope work on tree cellulose has focused on regions where growth is compla-
cent such as European oak chronologies (Treydte et al., 2007) or where the development of
ring-width chronologies have been unsuccessful, as in large regions of the Tropics (Evans &
Schrag, 2004; Poussart et al., 2004). Even in locations where traditional dendroclimatological
reconstructions have been successful, isotopic chronologies have the capacity to capture low-
frequency signals that are otherwise subtracted out of the tree-ring width chronology as part of
the standardization procedure (Cook et al., 1995; Gagen et al., 2007). Furthermore, because large
numbers of overlapping tree-ring series are needed to produce a significant climate-growth time
series, rare sections of chronologies such as very old or subfossil components cannot be used.
Isotopic timeseries require less replication (McCarroll & Loader, 2004; Robertson et al., 1997),
which opens up the potential to utilize rare sequences (Jahren & Sternberg, 2003; Robertson et al.,
2007).
52
An additional advantage in working with isotopic data from tree-rings is the option to make
measurements at subannual resolution. The generation of records at this resolution has proven to be
quite useful for deriving information at seasonal or even weekly timescales. Barbour et al. (2002),
working with Pinus radiata from New Zealand, Verheyden et al. (2004) working with Rhizophora
mucronata from Kenya and Jahren & Sternberg (2003) working with Metasequoia from the Eocene
Arctic all show strong intra-ring cyclicity that is associated with growing season changes in water
availability and humidity. Miller et al. (2006) showed that by focusing on the latewood component
of a Pinus palustris chronology from southeastern United States one can capture isolated source
water pulse events associated with landfalling tropical cyclones. Working with tropical trees from
Indonesia, Poussart et al. (2004) showed that an annual cycle associated with the amount effect can
be useful in dating ringless tropical species. While this literature indicates the breadth of applica-
tions from intra-ring measurements, it also implies the interpretation of intra-ring isotope variability
depends greatly on the species, regional climate or local meteorology. Evans (2007) recently used
a heuristic modeling approach to replicate the observed annuald
18
O cycle in Hyeronima alcornei-
des from Costa Rica. In locations where sufficient instrumental data is available to parameterize
a geochemical model, this method can help to unify interpretations across diverse ecological regions.
In this chapter I build off the aforementioned literature by investigating how local environ-
mental factors that are responsible for the fractionation of H
2
18
O at the air-leaf interface and
non-local influences that control the source water d
18
O combine to produce an intraseasonal
d
18
O signature in high altitude pine species at sites in western North America. In the case of
Pinus longaevae from the White Mountains, this is important because cellulose synthesis takes
place during the summer months whereas the water these trees utilize for cellulose synthesis is
precipitated primarily during winter. It is shown that because precipitation and growth occurs in
different seasons, it is feasible to isolate the growing season climatic variables (temperature and
humidity) from the wintertime atmospheric circulation patterns that establish the isotopic compo-
sition of source water. This site is contrasted against measurements made on Pinus aristata from
53
the Almagre Mountains in central Colorado where moisture is monsoonal and therefore growth
and precipitation occur simultaneously during the summer months. Although some discussion is
presented on direct paleoclimatic information from these measurements (for example, growing
season length during the Medieval Climate Anomaly), the chapter is intended to test the efficacy
of mechanistic models used to describe the isotopic composition of cellulose. This will introduce
the reader to the basic equations and exchange reactions that determine the isotopic composition of
cellulose and will be used in subsequent chapters to improve the interpretation of multi-centennial
paleoclimate records derived from annually resolved cellulose samples.
3.3 Methods
The rings of high altitude trees in the western United States are often less than 0.05 mm and thus
to conduct this experiment it required scouring the wood archives at the Laboratory of Tree Ring
Research at University of Arizona and from a locally maintained archive for appropriately wide-
ringed samples. All cores used in this study were mounted onto a rotary microtome fitted with a
binocular microscope to aid in identifying ring boundaries. The microtome was set to produce 35
mm slices, which was found to be an optimal width to generate a sample that is a few cells wide.
In order to produce sufficient material for cellulose extraction and mass spectrometry three adjacent
35mm slices were combined. With this sampling strategy it was possible to produce between 6-13
continuous samples per ring (Figures 3.1 and 3.2).
3.3.1 Agemodel
In order to assign an annual date for each sample, ring widths were measured and cross dated
against the published chronology. For the Almagre site (39
o
N, -105
o
W), widths were measured
using a WinDendro digital image analysis system at the University of Guelph and cross-dated
against the published chronology from this site following typical pattern-matching methods (Cook,
1985). The chronology is available for download at the International Tree Ring Database (Almagre
Mountain B, uploaded by D.A. Graybill). The rings chosen for the intra-annual analysis were all
taken from the last 40 years of the chronology to allow for direct comparison against instrumental
54
Figure 3.1: Scan of a Bristlecone Pine tree core used for isotopic analysis. The dark bands are called
”late wood” and form at the end of the growing season because of increased cell density. The growth
orientation is up. The colored lines are used to show the sampling strategy used in this chapter. Each
ring is approximately 1mm.
records. At the White Mountain site (37
o
N, -117
o
W) there was an absence of any wood samples
from the 20
th
century that had adequate ring widths to accommodate this type of high resolution
analysis. Thus, samples were selected from older sections of the chronology where wider widths
were available. In addition to selecting samples of adequate ring with, I also selected samples from
periods of time for which other independent proxies suggest the climate was different to serve as
a test of the sensitivity of the annual isotopic cycle to sustained periods of aridity or pluviality.
The samples hereafter referred to as Vo and MCA are from remnant wood pieces and Modn was
taken from a living tree in the saddle adjacent to the Methuselah Walk Trail. It was not possible to
generate a statistically significant fit between the ring width pattern in this sample and the master
chronology for this site. Instead the sample was radiocarbon dated at the Keck AMS Facility
55
Figure3.2: Photo of the rotary microtome at USC used for slicing samples. The screen on the left of the
photo is being fed from the microscope, which is focussed on the mounted core. Individual wood cells
can be seen on the screen as light circles. In this image the growth direction is down and to the right.
at University of California, Irvine. The
14
C samples were pretreated using the Acid-Base-Acid
method, combusted to CO
2
and graphitized before being measured (Stuiver et al., 1998). This
yielded an inner date of 135 BC corrected to calendar year (2,100 years old) with an uncertainty
of 20 years (Reimer et al., 2004).
The MCA sample was cut from a remnant wood piece from the same slope from which Vo
was obtained. This sample was dated against the Methuselah Walk master chronology (available
for download from the International Tree Ring Database) using the pattern-matching algorithm
available with the Cross Date software. The sample dates from AD 1080-1140. I selected the period
from AD 1106 to 1122 for sampling because of the presence of a consistent unbroken sequence of
wider rings. The recent sample (Modn) was cored from a living tree in the saddle adjacent to the
Methuselah Walk trail and was dated using the Cross Date program. The period from AD 1827 to
56
1834 was selected as it contained the only sequence of rings that were suitably wide enough to be
sub-sampled.
Fritts (1969) measured growth patterns of Bristlecone Pines using a dendrograph and found
that greater than 90% of cellulosic growth occurs at a constant growth rate during the peak of the
growing season. This empirical study agrees with attempts to model cambial growth in these species
based on the kinetic and physiological limitations on cell growth (Vaganov et al., 2006). With the
assumption of a linear growth rate, dates are assigned to each subannual sample using the following
equation:
age=[n+J+(t=h)m] 1 (3.1)
where n is the measured calendar year based on the cross-dating, J is the ordinal day that
growth begins divided by 365, t is the fraction of the year that growth occurs, h is the sample
number and m is the total number of samples in the ring. Thus if 8 samples were taken from a
ring dated from AD 1987, the 3rd sample would have been given a date of AD 1987.51. The date
assigned to each sample is the middle date of approximately a one week window in which this
narrow slice of cellulose was produced.
3.3.2 Analyticalmethods
a-cellulose was extracted from the whole wood according to the methods originally developed by
Brendel et al. (2000) and later modified for speed and small samples by Evans & Schrag (2004)
with thorough verification by Anchukaitis et al. (2008). The protocol is available for download
at http://ic.ltrr.arizona.edu/Contrib.html. The method involves heating the samples in a nitric and
acetic acid bath until the lignins and resins have completely dissolved and then using repeated water,
ethanol and acetone washes to fully cleanse the remaining a-cellulose component. No significant
modification to this method were made except for the inclusion of a vortexing step before and after
the acid wash. Because the wood samples were not milled but extracted from thin slices, the added
57
vortexing step allowed for the samples to readily break apart into fibrous material. Although there
is ongoing discussion regarding the preferable method to extract pure a-cellulose (Anchukaitis
et al., 2008; Boettger et al., 2007; Gaudinski et al., 2005), this method was chosen because it is the
only published methodology that readily accommodates samples of such a minute mass.
The isotopic composition of the extracted cellulose was measured using standard pyrolysis
and continuous flow mass spectrometry. Although measurements of thed
18
O of organic materials
by these methods has been routine for over a decade (Werner et al., 1996), the ongoing non-trivial
challenge has been in developing a method that consistently converts all the oxygen in the organic
matter into a single gaseous species which, reduces potential biases associated with fractionation
of oxygen between different gas species (e.g. water and carbon dioxide). It is ideal to convert all
of the oxygen and hydrogen atoms into carbon monoxide (CO) and hydrogen gas (H
2
), though in
order to favor their production (without the use of a catalyst) temperatures of greater than 1400
o
C
must be reached (following the Boudouard equilibrium). Even at these high temperatures, the
pyrolysis reaction yields trace amounts of water, carbon dioxide and elemental carbon, which
can be filtered out of the gas stream or simply diluted with additional helium gas. Therefore
a substrate and vessel for the reaction are required that can withstand extreme temperatures for
days at a time without itself producing carbon monoxide that would interfere with the sample signal.
For the Almagre samples, the isotopic composition of the cellulose was measured using a
Thermofinnigan TC/EA with a zero-blank autosampler and a Delta Advantage isotope ratio mass
spectrometer. The following approach was employed; samples were weighed to0.25 mg and
loaded into silver capsules and dropped into a ceramic column whose temperature was continuously
maintained at temperatures between 1400-1415
o
C. At these temperatures the oxygen atoms in the
aluminosilicate disassociate from the ceramic and react with the excess carbon to produce small
amounts of CO. In order to avoid this background CO interfering with the sample signal, the
column must be lined with a glassy carbon sleeve to separate the reaction site (where cellulose is
pyrolyzed) from the oxygen being produced from the heated ceramic. The column is packed with
58
glassy carbon chips from the bottom to the hottest place in the furnace which forces the sample to
cease its free-fall and consistently pyrolyze at temperatures of 1400-1415
o
C. The reaction products
travel through a stainless steel capillary and into a 0.5m 5
˚
A Gas Chromatograph column where
the gas species (hydrogen and carbon monoxide) are separated based on their mass. The reaction
products are then introduced into a Thermofinnigan Conflo reference box where they are diluted
with helium before being bled into a Delta Advantage isotope ratio mass spectrometer through a
silica capillary.
During the analysis, two reference peaks of hydrogen are measured in sequence followed by
the hydrogen sample pulse. For reasons discussed in detail in Chapter 5, the hydrogen (dD) mea-
surements were made but will not be explicitly discussed. Following the hydrogen measurement,
a peak jump is performed to allow for the continuous measurement of CO from the same sample.
After the CO sample is fed into the mass spectrometer, two reference pulses of CO are measured.
The sample peaks are converted to d units relative to VSMOW by integrating the area under the
peaks and comparing the product of this integral to the reference pulses, which are corrected to an
isotopic ratio through repeated measurement of the following known isotopic standards; IAEA C3
(32.2‰), Benzoic Acid (23.6‰), IAEA Sucrose (36.4‰), and Sigma Cellulose (26.4‰). A typical
run consisted of 36 samples and 12 standards, which are used to correct for any drift that may occur
during the process of a run. After measurement of 100 samples, the reactor is cooled down and a
small graphite crucible used to collect the elemental carbon and melted silver is removed. After
300-400 samples are measured, the entire column is cleaned and re-packed. With this configuration,
it is possible to routinely maintain an analytical precision of less than 0.3‰ d
18
O and 1.0‰ for
dD.
The isotopic measurements for the White Mountain Bristlecone Pine site were done prior to
the acquisition of the Themofinnigan instrument. These measurements were conducted on a Flash
HT Elemental Analyzer with a Costech autosampler and a VG Instruments Isoprime Isotope Ratio
59
Mass Spectrometer. The same basic methodology was employed except the reaction was done in
a ceramic column that was simply packed to the hottest place without any additional carbon liners.
The reaction products were than passed through a magnesium perchlorate trap to remove water and
carbon dioxide. The sample gas then entered a reference box where it was diluted with helium
before being bled into the mass spectrometer through a silica capillary. Only measurement ofd
18
O
was conducted and this was done by measuring a single CO reference gas pulse prior to the sample
peak arriving. Because this method did not use any crucibles or liners it was routine to measure
100’s of samples before changing the reaction column. With this configuration, it was possible to
maintain an analytical precision of0.3‰ ford
18
O.
3.4 Sitedescriptions
3.4.1 WhiteMountains
Wood samples were collected from the Methuselah Walk region (37.3
o
N, 117.2
o
W, and 3,075
m) during the summer of 2007. The region is dry much of year, receiving an average of
24.5 cm of rainfall, over 75% of which falls between the months of November and March
(ftp://www.wmrs.edu/weather/). The trees grow on steep slopes with a thin soil horizon (30 cm)
that is classified as a minimal calcisol (Fritts, 1969). Typically, the roots of Bristlecone Pine tree
are concentrated in the upper 5-18 cm of the thin soil and are virtually absent below 30 cm where
an impenetrable carbonate layer is consistently found (Fritts, 1969). Bristlecone Pine trees in this
region are most common on shaded dolomitic substrates where soil moisture is higher than in the
adjacent darker sandstone substrates (Wright, 1963). The trees along the Methuselah Walk are
noted for their extreme longevity, some exceeding 4,000 years in age. Because of the aridity at
this site and the resinous nature of the wood, remnant pieces of wood can sit on the ground for
thousands of years undergoing minimal diagenesis (LaMarche & Mooney, 1967).
60
The climate of eastern California during the late Holocene has been fairly well-characterized
through the dendroclimatologic analysis of long-lived trees, lake sediments and assorted historical
and archeological records (Benson et al., 2003; Salzer et al., 2009; Hughes & Graumlich, 2000;
Cook et al., 2004; Graumlich, 1993). The Modn (AD 1827-1834) sample falls within a period of
time that was cooler than the 20
th
century across much of the Northern Hemisphere; the period
known as the Little Ice Age (Jones & Mann, 2004). Tree-ring records from the White Mountains
and adjacent Sierra Nevada indicate the presence of moist and potentially cool conditions during
the Little Ice Age (LaMarche & Stockton, 1974; Scuderi, 1993). It also appears that much of the
multidecadal hydroclimatic variability that is evident in the instrumental period was subdued or
nearly absent during this period (Berkelhammer & Stott, 2008; Gedalof & Smith, 2001; MacDonald
& Case, 2005). The hydroclimate contrast between the LIA and the 20
th
century has been attributed
to changes in trade wind strength, a southerly migration of the mid-latitude jet stream, expansion
of Arctic sea-ice extent, a shift in ENSO statistics or some combination of these factors (D’Arrigo
et al., 2005; Mann et al., 2005; Sewall & Sloan, 2004; Zhao & Moore, 2006). The Modn sample
grew during a decadal-length pluvial within this time period (Cook et al., 2007). Benson et al.
(2003) also identified this as a period of high lake levels in Mono Lake.
The MCA (AD 1106-1123) sample comes from a sequence that grew during the Medieval
Climate Anomaly ( AD 800-1300), a period of generally warmer temperatures across much of the
Northern Hemisphere. Regionally however, there appears to be considerable temporal and spatial
heterogeneity with regards to the extent of warmth during this era. Regional evidence suggests
that there may have been modest warming in the Sierra Nevada Mountains during this period
(Graumlich, 1993; Scuderi, 1993) yet, tree ring records from the White Mountains have also been
interpreted to reflect no such warm anomaly (Salzer et al., 2009). It is clear however, from multiple
lines of evidence that this region did experience severe multidecadal droughts during this period
(Graham et al., 2007). Two of the most severe droughts were centered around AD 800-900 and
AD 1250. Occurring between these two droughts there was an extended pluvial (AD 1080-1130),
which is the time period during which the MCA sample grew. The pluvial period was marked by a
61
decrease in the frequency of forest fires and raised lake levels across much of California (Graham
et al., 2007). In the White Mountain Bristlecone Pine tree ring chronology, this time period is
marked by a sharp decrease ind
13
C values and an increase in ring widths (Graybill & Funkhouser,
1995; Leavitt, 1994).
Prior to 1,000 years ago, increased uncertainties in the climatic reconstructions make it
difficult to constrain exactly what conditions were like in eastern California when the Vo sample
grew. Mensing et al. (2004) suggests the presence of a severe drought between 2500-2000 cal
yr. B.P. based on a pollen record from Pyramid Lake, Nevada. Similar conditions appear to have
occurred in the White Mountains based on the change in tree line elevations at that time (LaMarche,
1973). Notwithstanding the uncertainties that exist in the climate reconstructions and the small
dating uncertainties, the evidence suggests that the Vo sample grew within a period of extended
aridity.
3.4.2 AlmagreMountains
The Almagre Mountain site is located at 39.5
o
N, 105.2
o
W at an elevation of 3,450 m altitude.
The site is sparsely vegetated and the trees are found growing on small patches of thin calcisol
soil. Because of its high altitude, minimum temperature are only above freezing between May-
September, which constrains the length of the growing season (Brunstein, 1996). Unlike sites further
west, precipitation is loosely bimodal at this location with a small number of winter storms bringing
snowfall and a larger precipitation peak in mid summer during the growing season (Ingraham &
Taylor, 1991). The summer rains are often referred to as the North American Monsoon because
they are driven by a reversal of moist low level winds from the Gulf of California and Mexico onto
the continent (Higgins & Shi, 2001). Unlike the more classical monsoons of Asia and Africa, the
North American Monsoon is categorized by highly localized and sporadic convective rainfall. The
rings analyzed in this chapter all came from the most recent 40 years, so unlike the samples from the
62
White Mountain site where proxy data was used to infer the background growing season conditions,
the climate can be directly inferred from instrumental records.
3.5 CelluloseModel
Thed
18
O composition of tree-ring cellulose can be described with Equation 2, from (Roden et al.,
2000) wheree is the biochemical fractionation factor (27‰) associated with the exchange between
carbonyl groups and the substrate water, f is the fraction of oxygen in the cellulose molecule that
is allowed to exchange with xylem water during the synthesis of cellulose and the source and leaf
subscripts refer to the soil water and leaf water respectively.
d
18
O
cell
= f(d
18
O
source
+e)+(1 f)(d
18
O
lea f
+e) (3.2)
3.5.1 TheSourceWaterTerm
The first term in the equation defines the isotopic composition of the trees source water pool (i.e.
soil water). The soil horizon can be separated hydrologically into an unsaturated zone where
water is in the vapor form overlaying a saturated zone where the pore spaces are occupied by
liquid water. Water is evaporated from the saturated zone into the unsaturated zone and the vapor
diffuses upward towards the surface. The interface between the two (the evaporative head) becomes
isotopically enriched following the Craig-Gordon Model, where the lighter isotopologues of water
evaporate in equilibrium conditions and diffuse upwards through the semi-porous soil medium
(Zimmermann et al., 1967). The enriched moisture at the evaporative head propagates downward
into the soil following fickian diffusion (Barnes & Allison, 1983). This leads to an isotopic maxima
at the evaporative interface with vapor (water) becoming progressively depleted at an exponential
rate with increasing distance upwards (downwards) (Allison, 1982). A steady-state schematic
representation of a hypothetical soil profile is shown in the left panel of figure 3.3.
63
δ
18
O
Precipitation
Percolation
unsaturated saturated
Evaporative head
Evaporation/
transpiration
Convection
Leaf
Boundary
Layer
Unsaturated
Atmosphere
Transpiration
δ
18
O
Soil System
Fluxes Isotope profile
Leaf System
Fluxes Isotope profile
α
∗
Figure3.3: A highly schematicized representation of the leaf and soil systems where movement of water
is denoted by arrows and the process labeled. Steady-state theoretical isotopic profiles (enriched to the
left) that arise from the phase change and diffusion processes are included.
The isotopic composition of soil water has been modeled by (Barnes & Allison, 1983; Zim-
mermann et al., 1967; Allison, 1982) using the following series of equations:
(d
18
O
z
d
18
O
res
)=(d
18
O
sur f
d
18
O
res
)exp(z=ˆ z) (3.3)
ˆ z= D

=E and D

= ptD (3.4)
a

R=[(1 h
a
)sR
res
+ h
a
R
a
] (3.5)
In Equation 3.3, the difference between the isotopic composition of soil water at some depth
z relative to the moisture at a sufficient depth below the surface where the water is isotopically
stable (res), is a function of 1) the isotopic composition of the surface water, surf, which has been
modified following the Craig-Gordon Equation, and 2) the physical characteristics of the soil (e.g.
porosity). Equation 3.4 describes the diffusion of the enriched water in the soil horizon, where E
is the evaporation rate and D* is the effective diffusivity defined by the porosity (p), tortuosity (t)
and the coefficient of diffusion (D). Equation 3.5 is lastly used to solve for the isotopic composition
64
of the moisture at the evaporative head and it is simply another representation of the Craig-Gordon
Equation, witha

being the equilibrium fractionation factor between liquid and vapor water, h
a
is
the relative humidity of the unsaturated soil atmosphere, R
a
is the isotopic composition of the soil
humidity and s is the kinetic fractionation associated with the phase change between liquid and
vapor. The exponential structure that results from solving this series of equations (shown in Figure
3.3) becomes reset when precipitation is flushed through the soil.
The above equations describe only the process of water loss through evaporation. Water
however is also lost by transpiration, which is non-fractionating (i.e. water entering the root
system is isotopically identical to the water in the surrounding substrate (Zimmermann et al.,
1967)). Therefore if water is exclusively lost through transpiration, the soil water is identical to
the precipitation input while evaporation leads to an overall enrichment of the soil moisture with
a defined vertical structure following equations 3.3-3.5. In both the Almagre Mountain and White
Mountain sites, the soil horizons are shallow with a maximum depth of less than 1 m, which is fixed
by an impervious calcite layer (Fritts, 1969). An assumption can therefore be made that the roots
are distributed evenly throughout the soil column and thus the source water does not have to be
assumed to come from a specific depth but can be estimated by solving for the integrated isotopic
composition of the entire water column. The climatology of the the White Mountain site indicates
that during the growing season less than 15% of total annual precipitation falls and thus inputs to the
soil horizon would be sparse. Therefore the isotopic composition of the soil water pool is modeled
such that there is an initial isotopic pool with no inputs and water is lost during the growing season
either through transpiration of evaporation. At the Almagre site, stochastic inputs to the soil during
the growing season are derived with the use an isotope-enabled GCM (Yoshimura et al., 2008).
Between precipitation events moisture is lost either by way of transpiration or evaporation but
unlike the White Mountain site, a mature steady-state isotopic profile does not likely develop.
65
An added source of complexity with respect to the isotopic composition of the source water
for trees, which will only theoretically be considered, involves the idea that soil water is actually
composed of distinct mobile and non-mobile constituents (Tang & Feng, 2001). The former, rapidly
flushes through the soil following a precipitation event while the latter can be stored in small pores
in the soil for long periods of time after the the precipitation has infiltrated. Recently, (Brooks
et al., 2010) show that precipitation which follows a long dry period tends to occupy the micro-pore
spaces while subsequent moisture constitutes the the mobile water pool. This can lead to a scenario
where the two pools of water in the soil are isotopically distinct. Therefore, if trees are selectively
drawing from one pool as suggested by Brooks et al. (2010); Dawson & Ehleringer (1991), than the
isotopic composition of the integrated soil water column would not be representative of the tree’s
source water.
3.5.2 Theleafwaterterm
The second part of Equation 3.2 is used to describe the enrichment of the leaf water that occurs by
way of evaporation through the stomata. This process is described quantitatively by Equation 3.6,
based on the Craig-Gordon Model (Craig & Gordon, 1965), which has been modified for leaves by
(Flanagan et al., 1991):
R
cell
=a

[a
k
R(
e
i
e
s
e
i
)+a
kb
R
source
(
e
s
e
a
e
i
)+ R
a
(
e
a
e
i
)] (3.6)
In this equation R is used to describe the ratio of
18
O to
16
O as opposed to the more common
d notation used in the rest of the paper. a

is the temperature-dependent equilibrium fractionation
factor between liquid water and vapor (see Equation 3.7). a
k
is the fractionation factor associ-
ated with the diffusion of oxygen isotopes through air and a
kb
is the fractionation factor through
the leaf-atmosphere boundary layer which are both held constant at 1.032 and 1.021 respectively
(Roden et al., 2000). e
a
refers to the ambient vapor pressure (kPa), which varies as a function of air
66
temperature and relative humidity. e
i
is the leaf vapor pressure (kPa), which varies with the tem-
perature of the leaf. It is assumed that the difference between leaf temperature and air temperature
remains constant. Although (Linacre, 1967) points out that the relationship between leaf and air
temperature may not be static, recent findings by (Helliker & Richter, 2008) indicate that trees may
act to regulate their leaf temperature within a very small window. There is no empirical evidence to
challenge these contradictory claims at this site and thus a conservative approach is applied, leaving
this term constant. e
s
is the vapor pressure (kPa) at the leaf surface, which varies as a function of
barometric pressure (held constant by altitude), temperature, humidity, and stomatal conductance.
Full explanations of the equations used to derive these terms can be found in (Roden et al., 2000) and
references therein. The r
a
term is the isotopic composition of the humidity. I have assumed for the
model experiments at the White Mountain site that thed
18
O of the humidity remains in equilibrium
with the source water, which has been held constant. The equilibrium fraction factor will however
vary with temperature according to Equation 3.7 from (Majoube, 1971).
a =
1137
T
2

0:4156
T
0:00207 (3.7)
For the Almagre Mountain site, the isotopic composition of humidity is taken from the isotope-
enabled GCM experiments of (Yoshimura et al., 2008). Because this region receives monsoonal
storms throughout the summer, it was more critical to estimate the isotopic composition of humidity
from a model that would account for advection of moisture from further afield sources than simply
equilibration with recycled moisture.
3.5.3 Modelparameters-WhiteMountains
The growing season for these trees was measured by Fritts (1969); Mooney et al. (1966); Wright
(1963) during numerous field campaigns in the 1960’s. These authors find growth to occur from late
June to early August. As a starting hypothesis, the model is initialized using average daily humidity
and temperature values over this time interval from the Crooked Creek station (37
o
N 118
o
W and
elevation 3,100 m), which is located less than 12 km from the Methuselah Walk site. Alternative
model runs are conducted in which: 1) the growing season is extended by 10 days on both ends and,
67
2) shortened by 10 days in the same manner. Continuous daily climate measurements are available
from 1981-1999 from the White Mountain Research Station. Using the average temperature and
humidity values from this station for each day a Monte Carlo simulation is conducted to generate
1000 growing seasons, to assess uncertainties in the humidity, temperature andd
18
O of atmospheric
humidity cycle. The instrumental data do not exhibit a Gaussian distribution, so the Monte Carlo
simulation assumes a uniform distribution with a 2s range around the mean daily value. The
results and the 90% confidence intervals are shown in Figure 3.4. Transpiration data was gathered
by (Wright, 1963) and was reported relative to dry leaf weight, which was normalized and used to
estimate stomatal conductance. The daily growing season stomatal conductance cycle is shown in
the last panel of Figure 3.4. Daily estimates for each variable were input into the model. Uncertainty
in the modeled growing season annual cycle was estimated using a 1000 iteration Monte Carlo
simulation.
3.5.4 Modelparameters-AlmagreMountains
The isotopic model at the Almagre was run in a similar fashion except because the measurements
were made on samples from the instrumental period, the inputs could be taken directly from the
climate data with no need to address uncertainties in the input values. Temperature, relative humid-
ity, barometric pressure, the isotopic composition of the source water and the isotopic composition
of atmospheric vapor were taken from the North American Regional Reanalysis model (Mesinger
et al., 2006) and the isotopic parameters were taken from the nudged isotope-enabled GCM simu-
lation from (Yoshimura et al., 2008). The surface reservoir parameter used as the source water for
the tree is solved following equations 3.3-3.6 described above, where soil moisture is evaporated or
lost through transpiration developing an enriched evaporative head near the surface. All parameters
were input at 6-hourly time steps for all of July through August and the seasonal cycle was modeled
for all 15 years in which intra-annual measurements of the isotopic composition of the cellulose
were made (1979, 1981, 1984, 1985, 1987, 1988, 1990, 1993, 1996, 1997,1998, 1999, 2000, and
68
0.007
0.0075
0.008
0.0085
0.009
0.0095
0.01
0.0105
0.011
7-J un 14-J un 21-J un 28-J un 5-J ul 12-J ul 19-J ul 26-J ul 2-Aug
mol•m
-2
•s
-1
-5
0
5
10
7-J un 21-J un 5-J ul 19-J ul 2-Aug 16-Aug 30-Aug
30
40
50
60
70
7-J un 21-J un 5-J ul 19-J ul 2-Aug 16-Aug 30-Aug
-29.5
-29
-28.5
-28
-27.5
-27
7-J un 21-J un 5-J ul 19-J ul 2-Aug 16-Aug 30-Aug
Temperature (C)
Relative Humidity (%)
δ
18
O
Temperature
Conductance
Relative Humidity
Isotopic composition of vapor
Figure 3.4: Parameters input into the geochemical model described above to model the intra-annual
cellulosic cycle. Uncertainty envelope is a 1s window generated from daily instrumental climate data
from the nearby Crooked Creek meteorological station.
2003). The outputs were then reduced to the same resolution as the measured values (i.e. 6 samples
per year) and a mean growing season isotopic cycle was calculated by taking the average of the 15
modeled seasonal isotopic cycles.
3.6 Results
3.6.1 WhiteMountains
The raw isotopic composition for each time series is shown in Figures 3.5. Because this study was
not concerned with inter-annual changes, only a short discussion of long term trends are presented.
In general, the average mean isotopic value of each time series was close to one another and similar,
69
32
34
36
38
40
1107 1109 1111 1113 1115 1117 1119 1121 1828 1830 1832 1834
Calendar year
δ
18
O
Vo MCA
Modn
~165 BC +/- 20 years
}
1 year
Figure 3.5: Raw isotopic measurements for each of the three wood sections. The non-growing season
hiatus were removed from the age model to reduce long empty spaces between years.
though slightly more enriched than the pre-20
th
century values found by Berkelhammer & Stott
(2008). The Vo sample shows a steady enrichment of approximately 4‰ during the latter part
of the record (Figure 3.5). This event is similar to the decadal-length oscillations observed by
Berkelhammer & Stott (2008) that were attributed to the changes in the isotopic composition of the
source water. A feature of similar temporal duration and magnitude is observed in the MCA sample
results. Of the three samples, the Modn sample exhibits the smallest isotopic variability and there
is an absence of any notable multi-year features. There is a hiatus in this record from the end of
1830 to the beginning sample of 1831. It was not possible to slice definitively along the boundary
between these two years and these samples were therefore not included.
To compare within-ring (seasonal) isotopic cycles between years and between the three time
periods, the averaged
18
O value was calculated for each year and this values subtracted from each
sample. This served the purpose of normalizing each annual cycle around a mean of 0. A date
was then assigned to each sample based upon its relative position in the ring (between 0-1). The
individuald
18
O results were then composited according to their relative position within a ring. In
this way, samples that are from the same part of the ring (e.g. early, middle, late) have the same time
coordinate (Figure 3.6). Plotting the subannual results together in this way highlights the general
similarity between the annual cycles during the three time periods. A correlation analysis between
these three periods, after interpolating to a common temporal resolution of ten samples per ring,
70
-3
-2
-1
0
1
2
3
δ
18
O (Anomaly)
Vo
MCA
Modn
21-Jun 28-Jun5-J ul 12-Jul 19-Jul 26-Jul
Figure 3.6: All measurements included in Figure 3.5, with each datum having been normalized to a
common age model and corrected as an anomaly relative to the mean value for the entire year.
indicates the MCA correlates strongly with both Modn and V o (r
2
of 0.86 and 0.45, respectively)
but V o and Modn are not strongly correlated.
A 3
rd
order polynomial is fit to each of the composite intervals to further illustrate how the
average annual cycle in d
18
O compares between the sections. This order polynomial was chosen
because it was found that there was no observable increase in the r
2
value by increasing the
polynomial order. An uncertainty cloud surrounding the polynomial fit was generated using the
average residual between the data and best-fit regression results for each time slice. The amplitude
of both V o and MCA are very similar (1.1‰) while Modn has a smaller average amplitude
(0.77‰). The significance of this difference is tested using a Student’s T-test (Figure 3.7) and the
difference is not statistically significant (at the 95% confidence interval).
A second series of polynomial equations is fitted to the raw data after each time period was
adjusted to a common variance (Figure 3.7). This allows a more direct comparison between the
71
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
21-Jun 28-Jun5-J ul 12-Jul 19-Jul 26-Jul
-1.0
0.0
1.0
2.0
21-Jun 28-Jun5-J ul 12-Jul 19-Jul 26-Jul
1.6
1.4
1.2
1.0
.8
.6
.4
.2
Intra-annual Standard Deviations
MCA Vo Modn
1.5
0.5
-0.5
-1.5
Figure3.7: A third order polynomial fit to each of the time periods (left panel). Uncertainty envelope is
ones. Box plots of the annual isotopic standard deviation from each of the time windows (center panel).
Same as in the left panel except each cycle was corrected to a common variance to compare simply the
shape of the cycles (right panel).
shapes of the three curves. The shapes of MCA and Modn are similar while the V o cycle has a
unique shape whereby 1) the inflection point occurs earlier in the growing season and 2) there
appears to be a second period of enrichment at the end of the growing season.
The simulated seasonal cycle derived from the model outputs, adjusted to a mean of zero, are
shown in Figure 3.8. A best fit third-order polynomial is fit to the first 100 iterations of the 1000
iteration simulation along with the mean fit of the entire population. Each simulation predicts a
growing season cycle with an amplitude of approximately 2‰. The values become enriched during
the first part of the growing season and then experience a period of depletion that is slid earlier as the
growing season is elongated. When the growing season reaches a critical length, a second period of
enrichment occurs. The correlations between the modeled and observed results are shown in Figure
3.9. Significant correlations at the 95% confidence interval are shaded gray.
3.6.2 AlmagreMountain
The complete intra-annual measurements from the Almagre Mountain are shown in Figure 3.10 and
reveal the presence of a consistent seasonal isotopic cycle that is of a slightly larger magnitude than
the one observed at the White Mountain site. There are a few notable features in the record including
72
-3
-2
-1
0
1
2
18-Jun 25-Jun2-J ul 9-Jul 16-Jul
-3
-2
-1
0
1
2
21-Jun 28-Jun5-J ul 12-Jul 19-Jul 26-Jul
-2
-1
0
1
2
11-Jun 25-Jun9-J ul 23-Jul6-Aug
δ
18
O (anomaly)
Control Truncated Extended
Figure 3.8: Modeled isotopic composition at the White Mountain site using the parameters shown in
Figure 3.4. Three simulations with a normal, shortened and elongated growing season were conducted
and 100 random iterations from the three simulations are used to illustrate the results.
Modn Vo MCA Truncate Elongate Control
Modn
Vo 0.03
MCA 0.86 0.41
Truncate 0.85 0.11 0.78
Elongate 0.40 0.37 0.56 0.36
Control 0.92 0.19 0.92 0.85 0.27
Figure3.9: Correlation matrix between modeled and measured isotopic cycles.
a slight long-term isotopic decline during the course of the record. In addition, it is clear that the
magnitude of the isotopic cycle varies slightly between years. For example, there is a near absence
of any cycle during 1977 and 2000 while the cycle us quite large during 1971 and 1987. Using
a similar method as done at the White Mountain site, a composite seasonal cycle is generated by
normalizing each year to a zero mean and adding all the values together (Figure 3.11). The modeled
annual cycle, which is the mean of 15 modeled years, closely follows the measured values.
73
29
30
31
32
33
34
35
36
37
1971 1972 1977 1978 1979 1984 1987 1989 1993 1996 1997 1998 1999 2000
Calendar Year
δ
18
O
Figure3.10: Intra-annual isotopic measurements from the Almagre Mountain site. A spline fit has been
included. Note that because it was not possible to sample all rings at this resolution some years were
excluded and thus the x-axis is not evenly-spaced.
-1.5
-1
-0.5
0
0.5
1
1.5
00.2 0.40.6 0.81
δ
18
O(anomaly)
Fraction of Growing Season
30.5
31
31.5
32
32.5
33
33.5
34
34.5
00.2 0.40.6 0.81
Fraction of Growing Season
δ
18
O(anomaly)
Figure 3.11: The mean isotopic value was removed to generate a composite intra-annual cycle (left),
with a 1s envelope shown in gray. A modeled cycle based on the average climate conditions during this
time interval is shown in red alongside the measured cycle (grey).
3.7 Discussion
3.7.1 WhiteMountains
It is clear from our results that there is a preferred intra-annual isotopic cycle, which has a well-
defined parabolic form. The cycle is prevalent in almost all the years measured irrespective of the
mean annual isotopic value or ring-width. One of the goals of this study was to critically assess
the extent to which changes in intra-annual variability might influence the isotopic composition of
a whole-ring sample. For the studies presented in Chapters 4 and 5, I generate multi-centennial
74
records by taking a single integrated annual cellulose sample however, a concern is that if the intra-
annual cycle is unstable it would influence the whole-ring annually integrated value. Such a change
would not inherently be a problem but would need to be adequately addressed in order to appropri-
ately interpret inter-annual or lower frequency trends. The results from this study indicate there is
a persistent seasonal cycle manifest in the whole-ring samples across a wide range of ring widths
and hydroclimatic conditions. Consequently, changes in the seasonal cycle itself that would accom-
pany a large range of environmental conditions are not likely to have a significant influence on the
interannual variability that is derived from whole-ring samples from the Bristlecone Pine. This is
quantitatively verified by integrating the area under the best-fit polynomial curves and finding no
significant difference between the area under the curves from these distinct eras.
3.7.2 ValidationofModelAssumptions
It is shown that a comparable cycle can be simulated by initializing a geochemical model with
the average growing season climate conditions that prevailed during the late 20
th
century. This sug-
gests fundamentally, that the inputs into the model closely approximate the geochemical phenomena
occurring during the genesis of photosynthates and cellulose metabolism. A few implicit assump-
tions were made during this modeling exercise that can now be discussed in light of the results.
• It was assumed that the distribution of daily climate conditions during the late 20
th
century
growing season were representative of long-term conditions at this site. This appears to be the
case and suggests at least to a first-order that growing season climate has exhibited relative
stability through the Holocene time slices sampled here.
• The geochemical model was run with a constant source water term based on the assumption
that there were no inputs to the soil during the dry growing season. The findings suggest
this was a reasonable simplification, as the influence that changes in daily temperature and
humidity have on the fractionations occurring at the leaf boundary seem to be sufficient to
capture the observed variability. The fact that the model simulates a seasonal isotope cycle
that is in close agreement with the observed values while keeping the isotopic composition of
the source water constant suggests that the trees are drawing on an isotopically homogenized
75
water pool and thus the bulk of water loss from the root zone is by way of transpiration. It is
hypothesized that any changes in source water during the growing season would result in an
enrichment of the water pool either by mixing with enriched summer precipitation, which is
substantially enriched relative to the annual average (Friedman et al., 1992) or by evaporative
enrichment (Zimmermann et al., 1967). In no instance do I observe such a trend, and there is
no foreseeable mechanism to change the isotopic composition of the soil water that would
not lead to a steady enrichment.
Alternatively, it is worthwhile to consider if changes in source water may not influ-
ence the shape of the seasonal cycle because the exchange between source water and
carbohydrates during the synthesis of cellulose is far smaller than predicted by Roden et al.
(2000) (i.e. the f -value in equation 3.2 is incorrect). In other words, source water changes
have little influence on the isotopic composition of the cellulose. This idea can be rejected
because the large interannual variations observed during this study and those which will be
discussed in Chapter 5 and in Berkelhammer & Stott (2008) far exceed isotopic changes that
could be generated even by temperature and humidity extremes.
A final consideration on the choice of an appropriate source water term revolves around the
idea that if the trees are drawing on an immobile/fixed soil water pool, their source water
would remain constant despite isotopic changes in the mobile soil water constituent. This is
a curious idea and indeed plausible though further analysis of the soil, leaf and xylem water
pools following Brooks et al. (2010) would be required to test this.
• I neglected to fully parameterize all known variables that affect the isotopic composition of
photosynthate production because data was not available. For example, the leaf boundary
conditions and the difference between leaf temperature and ambient temperatures were held
constant. While the model is not particularly sensitive to either variable, it is likely that these
variables do in fact evolve slightly during the summer months owing either to changes in wind
conditions (Drake et al., 1970) or seasonal changes in radiation (Jaggi et al., 2003). Recently
76
Helliker & Richter (2008) in a study that analyzed a large survey of cellulosic d
18
O from
varying climatic zones suggested that photosynthesis occurs within a very narrow range of
leaf temperatures. It is not clear if this stability arises from a direct physiologic capacity for
trees to self-regulate their leaf temperatures through transpiration or that photosynthesis is
only conducted when leaf temperature are externally brought within this fixed temperature
range. Irrespective of the causative mechanism, this would not only stabilize the temperature-
dependent fractionations that are associated with the formation of photosynthates but would
also maintain a constant saturation vapor pressure inside the leaf, which would affect the
leaf-atmosphere humidity gradient. Our data is not entirely consistent with their findings
because indeed seasonal temperature variations at the leaf boundary played a critical role in
the efficacy of the model to replicate the empirical results.
Although the general patterns between time slices and model runs were consistent, there are
some observable differences that may be attributable to the distinct climatic conditions during these
three time slices. The distinctions between annual cycles can be classified in terms of both shape
and amplitude. It is found that Modn has a distinctly smaller amplitude than either of the other
two sections (Figure 3.7). This could be caused both by shortening/shifting of the growing season
or by a reduction in summer climate variability. Previous paleoclimatic reconstructions suggest
this period was moist, which would likely result in a delay in the time when the soil desiccates
to the point of retarding growth and consequently an elongated growing season. However, the
combination of both cooler and wetter conditions and the possibility that snow pack prevailed
deeper into the summer would have delayed the beginning of growth leading to a shorter growing
season. The reduced intra-annual amplitude favors the latter scenario, which would be a slightly
shortened LIA growing season. The observed Modn cycle correlates most significantly with the
controlled model run where a growing season that is comparable to today’s is used and thus suggests
that while there may have been slight changes in the length of the growing season it was likely
comparable to today’s.
77
In terms of shape (Figure 3.7), the V o sample emerges as quite distinct from the other samples.
The measured cycle from this period correlates significantly with a model run with an elongated
growing season. Mensing et al. (2004) and LaMarche (1973) both suggest this period was notably
arid and thus it is hypothesized that this would reduce available water and lead to soil desiccation
earlier in the summer. However, if this period was not only drier but also warmer, growth may
have begun earlier in the summer and allowed for an extended growing season even given a lack of
available late summer water. Growing season length is most sensitive to the timing of the onset of
bloom (Cayan et al., 2001) and therefore the warmth of this period is hypothesized to have led to an
extended growing season and the anomalous intra-annual isotopic cycle observed.
The samples taken from the LIA and MCA display an excellent correlation (Figure 3.9)
between a model using a growing season length and prevailing climate that is very similar to
today’s. The fit degrades slightly if the growing season is truncated and more so if the growing
season is lengthened. If these small time slices are representative of these climatic eras, which is
admittedly a gross assumption given the amplitude of decadal climate variability in this region, it
suggests a growing season length similar to today’s prevailed during both the Little Ice Age and
Medieval Climate Anomaly.
3.7.3 AlmagreMountains
The intra annual cycle at the Almagre Mountain site is consistent in both shape and amplitude
with that observed at the White Mountain site. Because these samples were all taken from the
instrumental period, less hypothetical consideration needs to be given to the choice of inputs to
the model. In the monsoonal climate of Colorado, when the model was run with a constant source
water term as in the White Mountain site, there was an absence of any observable intra-annual
cycle (not shown). Thus it was clear from the onset that consideration needed to be given to how
the source water for these trees evolved during the growing season. I use a soil water reservoir for
the trees that accounts for loss of water through evaporation and transpiration between storm events
and then mixing of new water into the soil matrix following precipitation events. Although there are
78
flashes of precipitation during the growing season these do not appear as spikes in the cellulose as
has been observed in some sites (Miller et al., 2006), because likely the mixing of new water with
that already in the soil leads to a natural smoothing term. The isotopic composition of precipitation
events is closely correlated with temperature in this region (see Chapter 2) and thus not surprisingly
the isotopic composition of the cellulose closely tracks the seasonal temperature cycle. There is
apparently very little lag between the temperature cycle and the cellulosic response implying a fast
turnover of soil waters and limited use of reserved photosynthates for cellulose metabolism.
3.8 Conclusions
This study presents evidence for a pervasive intra-annual d
18
O cycle in Bristlecone Pine (Pinus
aristata and Pinus longaevae tree rings across a range of hydroclimatic conditions. The measure-
ments were made at near-weekly resolution and suggest the intraseasonal isotope cycle manifests as
a parabolic function with an inflection point occurring just prior to the middle of the growing season.
Critical to the discussions presented in Chapters 4 and 5, the cycle is very stable and therefore its
variability does not likely have any measurable impact on isotopic samples that are generated from
integrating the cellulose from an entire annual ring into one sample. A geochemical model that
captures the observed cycle was parameterized with daily growing season humidity, temperature,
stomatal conductance, d
18
O or soil water and humidity. The results suggest fundamentally that at
the White Mountain site where precipitation is predominantly during the winter season, the cycle
arises from growing season changes in humidity and temperature while the trees draw upon an
isotopically stable source water pool. From this we conclude that the trees utilize a homogenous
water pool that accumulates during the wetter winter season and that water loss from the soil
horizon is caused predominantly by transpiration, which does not affect the isotopic composition
of the residual water pool. Although the cycle at the Almagre site looks similar on the surface, it
can only be captured if I include a source water term that accounts for inputs from monsoonal rains
whose isotopic composition is tightly coupled to surface temperatures.
79
I note subtle differences at the White Mountain site between the isotopic cycles generated
during drought and pluvial periods, which indicates that the structure of the cycle may complacently
respond to low frequency changes in mean climatic conditions. By varying the length of the
modeled growing season in the model simulations, it is shown that this can notably impact the
correlation between modeled and observed results. For two of the three time slices the best fit
between model and observations occurred when using a growing season that is similar to today’s
while in the third instance it was necessary to elongate the growing season to generate a significant
correlation. The maximum observed intra-annual variability is less than 2‰, which given that the
growing season essentially captures the extreme range of temperature and moisture conditions that
these trees can produce cellulose under, it suggests this range is close to the maximum isotopic
shift that can be attributed to humidity and temperature changes alone. Thus, the wide range of
interannually variability observed in this study and previously by Berkelhammer & Stott (2008)
must be attributed to changes in the source water pool caused by year to year changes in winter
storm conditions.
80
Chapter4
Agrowth-independenttemperature
reconstructionforthesouthwesternUnited
States
SUMMARY
Tree-ring width records are one of the few high-resolution terrestrial temperature proxies
available from the middle latitudes, yet the mechanistic nature of the relationship between
temperature and growth is poorly understood. In this chapter, a 500-year replicated tempera-
ture reconstruction based on thed
18
O of cellulose from temperature-sensitive Rocky Moun-
tain Bristlecone Pine trees from the Almagre Mountains of Colorado is presented. At this site,
the isotopic composition of cellulose is positively correlated against growing season, winter
and mean annual temperatures from local and regional instrumental temperature records. The
slope of the best-fit linear relationship between temperature and cellulose is not statistically
different from the slope derived from the relationship between temperature andd
18
O of pre-
cipitation from a nearby Global Network of Isotopes in Precipitation station, which suggests
that the temperature response function arises largely from a direct relationship between cellu-
lose and changes in the isotopic composition of precipitation. Because the isotope-based ther-
mometry is unaffected by growth behavior, this new reconstruction is entirely independent of
the temperature reconstructions based on the ring widths of these same trees and can therefore
be used to empirically test whether the relationship between growth and temperature is stable
over the past 5 centuries. The new isotope-based temperature reconstruction shows significant
decadal and multidecadal power over the last 5 centuries but clearly lacks the long-term 20
th
81
century warming characteristic of composite Northern Hemisphere trends. This result con-
tradicts a co-located temperature reconstruction, which was derived from an assumed linear
relationship between tree ring width and surface temperature variability at high-altitude sites
in the western United States. Prior to the 20
th
century calibration period, there is a systematic
0.5
o
C cold-bias in the width-based paleotemperature estimates, which leads to an overesti-
mation of the uniqueness of the 20
th
century with respect to the previous 4 centuries. Other
independent regional temperature reconstructions based on early instrumental records, wood
density and frost ring frequency corroborate the presence of this acute non-linear response
between temperature and width. The results presented here provide the first direct evidence of
a pre-20
th
century bias (or divergence) in tree growth response to temperature and the findings
necessitate similar assessments at other sites where quantitative paleotemperature estimates
have been derived from tree growth variability.
4.1 Introductorynote
The contents of this chapter have been published in:
Berkelhammer, M. & Stott, L., submitted to Nature. A growth-independent temperature
reconstruction for the southwestern US.
4.2 Introduction
Over the past decade there has been a concerted effort to develop robust millennium-length
global-scale reconstructions of Earth’s surface temperature variability. Such records can be used
in conjunction with estimates of past greenhouse gas concentrations, solar forcing, and volcanic
emissions to quantify the sensitivity of the Earths climate system to changes in radiative forcing
(Hegerl et al., 2006; Crowley, 2000). Accurate forecasts of Earth’s temperatures for the coming
century rely heavily on a well-constrained estimate of this parameter.
82
Global surface temperature reconstructions are derived by compositing regional proxy arrays
into a hemispheric or global estimate using any number of statistical approaches that aim to
maximize the information about large-scale temperature features from a sparse and noisy network
of proxy temperatures (Lee et al., 2008b). A review of this literature is beyond the scope of this
chapter, and the reader is thus referred to examples and reviews of the various approaches in the
following texts; Rutherford et al. (2005); Jones et al. (2003); D’Arrigo et al. (2006); Lee et al.
(2008b); Mann et al. (2008); Esper et al. (2005); Moberg et al. (2005).
While the reconstructions don’t entirely agree with respect to aspects of the higher frequency
variability, they all converge on a common solution that warming during the late 20
th
century is
anomalous relative to the past millennium (Lee et al., 2008b; Mann et al., 2008; Hegerl et al., 2006;
Moberg et al., 2005). The agreement between the various approaches provides confidence that
the solution is not unique to any single statistical technique as posited by (McIntyre & McKitrick,
2005) however, the overall strength of the assertion still suffers from its reliance on a sparse
density of sites prior to the instrumental era and uncertainties regarding whether proxy calibrations
derived from 20
th
century climate records are stationary. With respect to the latter issue, composite
hemispheric reconstructions benefit from utilizing a diversity of proxy types that mitigate biases
(e.g. loss of low frequency signals or seasonal-weighting) in any one record or family of proxies
(Mann & Schmidt, 2003; Jones et al., 2003). Because high-resolution terrestrial temperature
proxies are rarified over the tropics and middle latitudes, much of the reconstructed history of
surface temperature variability prior to the instrumental period has been derived from tree-ring
width records. There are some inherent risks in the reliance on this single proxy for temperature
reconstructions. Firstly, there is a requirement to use some variant of a low-pass filter to remove
trends related to non-climatic (geometrical) aspects of tree growth processes, which removes some
component of the low frequency signal (Cook et al., 1995). Secondly, the controls on tree growth
are multivariate and many factors that are not inherently coupled or linearly-related to climate such
83
as forest dynamics, soil nutrients, parasitism, and atmospheric chemistry can all influence tree
growth (Fritts, 2001).
Some of these issues have been addressed in the literature. For example, with respect to
biases in the frequency domain, (Moberg et al., 2005) deconvolves tree-ring temperature proxies
into their frequency components using a wavelet transformation and then reconstructs high and
low frequency temperature variability separately. In addition, modified approaches to applying a
growth curve correction such as the Regional Curve Standardization (Briffa et al., 1995) improve
on the methods necessary for removal of some low frequency power from tree ring-based climate
reconstructions (Cook et al., 1995). There are other potential non-statistical issues that complicate
a potentially useful relationship between tree growth and surface temperature such as the CO
2
Fertilization Effect, which describes a presumed positive response of tree growth to increased
partial pressures of carbon dioxide. It was originally proposed that the anomalous growth of
trees at high altitude sites in western North America over the last century was evidence of the
Fertilization Effect (Graybill & Idso, 1993; Lamarche et al., 1986). The argument was that at high
altitudes where atmospheric gasses are rarified, plant growth would be limited by a lack of available
carbon dioxide. The increased availability of this resource during the recent century therefore led
to unprecedented growth. While this is theoretically satisfying and appears to be observable in
greenhouse environments (Idso & Kimball, 1993), there is simply little evidence on a global scale
that the CO
2
Fertilization Effect has had any measurable impact on tree growth (Jacoby & DArrigo,
1997). Specific efforts have been made to address this with respect to the anomalous late 20
th
century growth of Bristlecone Pine trees by Salzer et al. (2009). These authors argue that because
the lapse rate of carbon dioxide with altitude is spatially uniform, than if the Fertilization Effect
was the cause of recent growth patterns its signature would be prevalent at all sites above a fixed
height in the atmosphere. Anomalous late 20
th
century growth appears at upper tree lines sites in
eastern California, Nevada, Arizona, and Colorado however the altitude of upper tree line varies
considerably between these sites depending on the mean temperature and aridity. Therefore, it
84
is unlikely that is the availability of carbon dioxide, is driving enhanced 20
th
century growth and
(Salzer et al., 2009) conclude, that anomalous temperatures must be the responsible for the recent
unprecedented growth trends.
A remaining enigma with respect to the inferred relationship between tree growth and
temperature has to do with the Divergence Problem, which describes a phenomenon where trees
at high northern latitudes respond positively to temperature variability during the early part but
nor the latter part of this century (Wilson et al., 2007; D’Arrigo et al., 2006). The Divergence
Problem is a vexing conundrum with respect to quantitative dendroclimatology because it suggests
that there may be an upper limit of temperatures that trees are likely to systematically respond
to. Interestingly, because this upper temperature limit is close to today’s temperatures, it implies
potentially that this proxy is not capable of assessing whether current temperatures are in fact
truly unique over the last 1-2 thousand years. The Divergence Problem highlights very clearly the
non-linear or non-stationary (it is not clear which is responsible) nature of the response between tree
growth and temperature and because its spatial extent and mechanistic origins are not yet known, it
hangs a cloud of uncertainty over tree ring paleothermometry.
In an attempt to entirely circumvent the potential issues associated with tree rings as proxies
for temperature, Mann et al. (2008) generated a composite temperature reconstruction without tree
ring records. Mann et al. (2008) argue that irrespective of the presence or absence of tree rings, the
outcome of the reconstruction is largely the same. However, their results show that with the removal
of tree ring records, the reconstruction becomes far more sensitive to the choice of statistical
compositing technique. For example, when the error-in-variables approach is used, which is the
preferable method to accommodate for uncertainty in both the predictor and predictand (Hegerl
et al., 2006), there are periods of time when the presence of tree ring records leads to a 0.5
o
C
cold-bias in the reconstruction. A difference of 0.5
o
C is quite substantial when considering global
85
mean temperatures (e.g. the warming since the mid 19
th
century is only 0.7
o
-0.8
o
C). Therefore,
the assertion that the reconstructions are unaffected by tree ring records is only true in a qualitative
sense. Furthermore, because of the lack of alternative proxies, the network becomes exceedingly
sparse when the tree ring arrays are removed, and it is thus preferable to not remove tree ring
records but rather refine our understanding of the information they contain.
In this chapter an empirical test of the stability between Bristlecone Pine tree growth and
surface temperature is presented. Width based temperature records from this species are included
in a large number of the global temperature reconstructions (Mann et al., 1998, 1999; Jones et al.,
2003; Rutherford et al., 2005; Mann et al., 2008) and have emerged at the center of many of the
controversies regarding the robustness of tree ring paleothermometry and therefore they are an obvi-
ous target for this analysis. In order to test the robustness of this record, an independently-derived
and process-based temperature reconstruction from the d
18
O of cellulose (hereafter d
18
O
c
) that is
co-located with the width-based reconstruction (WBT) is generated. The new record allows for a
critical assessment of the long-term stability of the regression between growth and temperature and
thus sheds light on the reliability of these records in providing an estimate of how pre-instrumental
temperatures compare to current conditions.
4.2.1 TreeCellulosePaleothermometry
A systematic relationship between d
18
O
c
and surface temperature arises because of the rapid
isotopic response of cellulose to changes in the tree’s source water (Miller et al., 2006; Dawson &
Ehleringer, 1993; Deniro & Cooper, 1989) which, occurs as a result of isotopic exchange between
xylem water and sugars during the metabolism of cellulose (Hill et al., 1995; Sternberg, 2009).
Please refer to Chapter 3, for more details of the biogeochemical processes that influence the
exchange between xylem water and photosynthates during cellulose metabolism. In regions where
the isotopic composition of precipitation (hereafterd
18
O
p
) varies systematically with temperature,
86
cellulose can be used much like an ice core to derive surface temperatures. Although this rela-
tionship has been documented at numerous sites across the globe (Burk & Stuiver, 1981; Libby &
Pandolfi, 1977; Feng & Epstein, 1994), the challenge in usingd
18
O
c
as a paleothermometer arises
becaused
18
O
p
is not linearly related to temperature in many locations and also that cellulose incor-
porates a secondary isotopic signal that arises from environmental conditions at the leaf boundary,
which can influence the isotopic composition of the photosynthates involved in the metabolism of
cellulose. To use a tree-ring isotope approach to reconstruct past surface temperatures it is required
that a location is found where both the relationship between d
18
O
p
and surface temperatures is
stable and also a tree species whose photosynthetic/physiological mechanisms will not strongly
influence the variability ofd
18
O
c
.
Rocky Mountain Bristlecone Pines (Pinus aristata) at the Almagre Mountains in central
Colorado (3,450 amsl, 39
o
N, -105
o
W) emerged as one of only a few sites globally where
paleo-temperatures have been derived from ring widths (Mann et al., 2008; LaMarche & Stockton,
1974) and also met critical criteria that suggested it would be appropriate to independently
reconstruct temperature from d
18
O
c
. The trees at this site are shallow-rooted and found on thin
well-flushed soils, which assures they are relying on water that was not precipitated during prior
years. Previous work on intra-annual isotopic cycles from this species and the closely related
Great Basin Bristlecone Pine (Pinus longaevae) (see Chapter 3 and Berkelhammer & Stott (2009))
suggests there is little year-to-year carry-over of photosynthates, which would otherwise obscure
high frequency variability. Lastly, the trees have a short growing season, which minimizes the range
of environmental conditions leaves are exposed to during active photosynthesis.
As stated in Chapter 2, precipitation events in this region are highly correlated with surface
temperatures (r=0.81) and when events are binned by either season or prevailing trajectory, the slope
(Dd
18
O-T) and strength of the correlation is retained. This yields empirical evidence thatd
18
O
p
is
87
strongly controlled by local conditions and not changes in the upstream moisture source. The reader
is referred to Chapter 2, regarding tests on the stability of this relationship based on an ensemble of
isotope-enabled climate simulations.
4.3 Methods
The wood samples for isotopic analysis were obtained with a 5mm increment borer and the widths
were measured using a WinDendro digital image analysis system at University of Guelph. The
cores were dated against the published chronology from this site, which is available for download
at the International Tree Ring Database. In subsequent discussion there is reference made to
the ring width chronology from this site, which refers to the published chronology not the width
measurements, which were used to make an age model for the the samples used for isotopic analysis.
An annually resolved, replicated isotopic chronology from AD 1500-2005 was generated by
slicing selected cores into annual samples and extracting thea-cellulose component from the wood.
Isotopic measurements were done with standard pyrolysis and continuous flow mass spectrometry
as described in Chapter 3 and the results from the multiple cores were averaged together to produce
a composite chronology (Figure 4.1).
4.4 Results
The isotopic chronology was correlated against monthly and mean annual temperatures (MAT)
between 1901-2002 from the CRUTEM3 gridded temperature dataset to establish a calibration
curve (Figures 4.2). The record correlates positively not only with the local grid point but also
with grid points across a wide region of the United States east of the Rocky Mountain (Figure
4.2), which suggests it is a record of regional significance. The correlation is positive during all
months of the year with distinctive peaks in June-July (the growing season) and early winter when
snow pack accumulates (Figure 4.3). The highest correlation coefficient is achieved whend
18
O
c
is
regressed against MAT (r=0.54) and therefore this relationship is used to generate a univariate linear
88
28
29
30
31
32
33
34
35
36
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
δ
18
O
c
Calendar Year
Figure 4.1: Complete time series from the Almagre Mountain site generated from separate tree cores
(blue and green). A low-pass filtered (18 years) timeseries of the mean between cores is shown in
black. The pink region denotes the instrumental period used to calibrate the response between d
18
O
c
and temperature.
regression model to convertd
18
O
c
into MAT anomalies relative to the 1960-1990 mean value. The
isotope chronology also correlates significantly against the temperature timeseries from the nearest
long meteorological station record (Canon City, CO) (Figure 4.4). The slope of the regression
between d
18
O
c
and MAT (0.77‰/
o
C) is not statistically different from the slope of the regression
between d
18
O
p
and temperature (0.70‰/
o
C) (Figure 4.5). This is taken to be validation that the
correlation between d
18
O
c
and MAT is a response to the temperature-dependent fractionation that
controls the isotopic composition of regional precipitation (i.e. the tree’s source water) (Figure 4.5).
The inherent response ofd
18
O
c
to local temperature is further validated by the intra-seasonal
isotopic measurements, which were presented in Chapter 3. Within each ring there is a well-defined
parabolicd
18
O
c
trend, which closely tracks the seasonal temperature cycle at this site (Figure 4.5).
The slope of the regression between intra-ringd
18
O
c
and temperature is similar with that which is
derived from the annually-integratedd
18
O
c
and MAT (Figure 4.5), further indicating that thed
18
O
c
cycle arises because d
18
O
p
tracks the growing season temperature cycle and leaves a rapid mark
ond
18
O
c
. This is corroborated through geochemical modeling where the timing and amplitude of
89
D
A
B
C
E
F
G
Figure4.2: A map showing the correlation coefficient (r) betweend
18
O
c
and surface temperatures from
the CRUTEM3 temperature dataset between 1900-2001. The purple line roughly encompasses the area
where the correlation betweend
18
O
c
and surface temperature is significant (p=0.01). The Almagre site
is labelled A, B is the frost ring chronology (Brunstein, 1996), C is Pawnee National Grasslands Global
Network of Isotopes in Precipitation site, D is the tree ring temperature reconstruction from (Salzer &
Kipfmueller, 2005), E is the tree ring temperature reconstruction from (Biondi et al., 1999), F is the
isotope-based temperature reconstruction from (Edwards et al., 2008), and the dotted line encompassing
G is the composite temperature reconstruction based on wood density from (Briffa et al., 1992).
the intra-annual cycle is replicated if the moisture source for the xylem water is derived from an
isotope-enabled GCM simulation (Figure 4.5) (Chapter 3).
4.5 Discussion
The 500-year temperature reconstruction derived from the isotope paleothemometry (Figure 4.8)
exhibits significant power in the decadal and multidecadal band (Figure 4.6 and 4.7). Notably,
there is a significant peak (greater than 99% confidence) at 11.6 years, which is common amongst
90
0.0
0.1
0.2
0.3
0.4
0.5
Correlation Coefficient
10
-10
10
-5
10
0
p-value
0.6
J-M
F-A
M-M
A-J
M-J
J-A
J-S
A-O
S-N
O-D
N-J
D-F
An2
An
Figure 4.3: Correlation coefficients between monthly average temperature and d
18
O
c
from the
CRUTEM3 temperature dataset at the 39
o
N 105
o
W grid point. The final column, shows the correla-
tion coefficient against mean annual temperatures. The p-values for each of the correlation coefficients
are shown in the bottom panel.
-2
-1
0
1
2
3
-3
-2
-1
0
1
2
3
4
1880 1900 1920 1940 1960 1980 2000
Mean Annual Temperature (C)
δ
18
O Cellulose
-2
-1
0
1
2
3
1880 1900 1920 1940 1960 1980
Mean Annual Temperature (C)
-2
-1
0
1
2
3
4
Ring Width Standard
Mean Annual Temperature (C)
-2
-1
0
1
2
3
1880 1900 1920 1940 1960 1980 2000
Figure4.4: Timeseries showing instrumental temperatures from the nearby Canon City meteorological
station (gray) against ring widths (left) and d
18
O
c
(center) all of which are presented as an anomaly
relative to the 20
th
century mean. The three timeseries are shown together in the right column. The ring
width chronology only extends until 1983, which is why it is truncated relative to the other records.
91
00.2 0.40.6 0.81
Fraction of Growing Season
35
34
33
32
31
30
284
282
280
278
276
274
272
Modeled
Temperature
Temperature (K)
δ
18
O
Measured
-2
-1
0
1
2
-2 -1 2
Δδ
18
O
cell
/ Δt
Δδ
18
O
pcp
/ Δt
-30
-25
-20
-15
-10
-5
0
5
-15 -10 -5 10 15 20 25 0 1
Temperature Anomaly (K)
δ
18
O Anomaly
δ
18
O Anomaly
0 5
Temperature (K)
Δδ
18
O
cell
/ Δt (intra)
Northeast
Local
Southwest
Cumulative
Figure4.5: Relationship between surface temperature and the isotopic composition of individual storm
events from the Pawnee Grasslands GNIP station (left). The slope of the relationship between temper-
ature and the isotopic composition of annual cellulose (green), intra-annual cellulose (red) and precip-
itation (purple) (center). The envelope shows the 90% confidence band around the slope. Intra-annual
isotopic measurements (purple) with a 1s error envelope, modeled isotopic composition of cellulose
from Chapter 3 (red) and seasonal temperature cycle (green).
climate records, and often associated with the 11-year sunspot cycle (Lean et al., 1995). Additional
significant peaks are found in the 70-80 year range and in the high frequency (3-7 years) domain.
The latter is often associated with ENSO variability, which has a documented impact on the
temperature and precipitation of this region (Higgins & Shi, 2001). Multidecadal climate variability
in the southwestern US has been well documented by previous authors (McCabe et al., 2004).
Potential drivers of this variability include Pacific Decadal Variability (PDV) (Mantua & Hare,
2002) and Atlantic Multidecadal Variability (AMV) (Enfield et al., 2001). The spectral power
of the PDV does not manifest as a narrow stable band but it is usually associated with higher
frequency variability (20-30 years) than is observed in the Almagre Mountain record. It would
be more likely, albeit with caution, to relate the observed 70-80 year power to the basinwide mode
of Atlantic multidecadal variability referred to typically as the Atlantic Multidecadal Oscillation
(Gray et al., 2004). The origins or stability of this cycle is a topic of continued inquiry though
modeling exercises have found that internal ocean-atmosphere dynamics can generate an analogous
dynamical feature (Park & Latif, 2008). No attempt is made here to causally relate the 70-80 year
cycle in the Almagre record to AMV , I rather simply point out that the two share similar spectral
characteristics. Wavelet analysis using a morlet wavelet (Torrence & Compo, 1998), is used to
92
0
2
4
6
8
10
12
14
10 100
Power (dB)
95% CI
70-80 yrs
11.6 yrs
3-8 yrs
Figure 4.6: Lomb-scargle periodogram of the d
18
O
c
timeseries based on the methods described in
(Schulz & Mudelsee, 2002) using a Welch window and 3 overlapping segments. The confidence interval
was generated using a 2000 iteration Monte Carlo simulation.
show that the multidecadal power is actually intermittent in the record (Figure 4.7). The presence of
multidecadal power entirely ceases in the early 19
th
century and reappears in the mid 17
th
century.
This is similar to the wavelet analysis of the AMV reconstruction presented in Gray et al. (2004),
who note a breakdown in multidecadal variability during the 18
th
century and when the power
reappears during the 18
th
century it is at a higher frequency.
The most prominent aspect of this temperature reconstruction is its lack of a long-term
warming trend between the mid 19
th
century and the present. This is in stark contrast to the
co-located reconstruction of MATs derived from ring widths, which closely track the northern
hemisphere temperature reconstruction over the past 5 centuries (Figures 4.8 and 4.9). During the
instrumental calibration period, the isotopes and widths follow each other and consequently both
can be positively calibrated against the same instrumental dataset (Figure 4.4). However, in the mid
19
th
century, when instrumental records cease to available, the width-based temperatures (WBT)
suggest a sustained cooling that is not replicated in the isotope-based temperatures (IBT). After this
divergence, there remains a0.5
o
C discrepancy between the two records through the duration of
the overlapping period (AD 1500-1850) (Figure 4.8).
93
Figure 4.7: Wavelet analysis of the d
18
O
c
timeseries using a Morlet window based off the code from
(Torrence & Compo, 1998). The black line shows regions where the power is significant.
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Temperature Anomaly
Calendar Year (AD)
Figure4.8: Time series of temperature reconstructions from the co-located width (purple) and isotope-
based (green) techniques. Both records are shown as anomalies relative to the 1960-1990 average tem-
peratures. The gray region highlights the discrepancy between the two records.
The incongruity between these two independent reconstructions is consistently sustained at
a significant level, which suggests a systematic bias in one or both of these co-located records.
To reconcile this issue, I turn to other available temperature reconstructions from the western
94
-1.0
-0.5
0.0
0.5
1.0
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Calendar Year (AD)
Temperature (anomaly)
Isotope Recon. (20-yr low pass)
Width Recon. (20-yr low pass)
Northern Hemisphere
Figure4.9: Time series of low-pass filtered temperature reconstructions from the co-located width (pur-
ple) and isotope-based (green) techniques alongside the composite global temperature reconstruction
from (Mann et al., 2008) with the uncertainty in the latter shown as gray shading
US (Figures 4.10 and 4.11). A compilation of early instrumental records from the region that
extend back to AD 1830 indicate that between 1850-1870 surface temperatures were on average
as warm as those during the 1930’s-1960’s (Wahl & Lawson, 1970). This is consistent with the
IBT, which indicates no negligible temperature difference between these two periods, while the
WBT suggests conditions were greater than 0.5
o
C cooler during this period than during any part
of the 20
th
century. Another independent temperature estimate form this same site comes from
a frost ring chronology developed by (Brunstein, 1996). The frequency of frost rings cannot be
quantitatively transferred into a temperature estimate but provides a relative history of temperature.
The frost ring chronology (Figure 4.10) indicates that the frequency of growing season frost events
(anomalous cooling) were in fact more common during the 20
th
century than through most of the
Little Ice Age (LIA), with the exception of a brief cool interval during the early 19
th
century. Other
regional climate records based on wood density (Briffa et al., 1992) and d
18
O
c
from the Northern
Rockies (Edwards et al., 2008) also lack a distinct difference between the 20
th
century and LIA. It
95
1600 1700 1800 1900 2000
−20
0
20
40
60
Number of Frost Rings (anomaly)
warm
cold
1500
Figure 4.10: Frost ring frequency record from (Brunstein, 1996). In this plot an increase in frost ring
frequency is caused by cooler conditions and thus the axis is reversed. The number of frost rings is
reported as an anomaly relative to the average number of frost rings per decade during the 20
th
century.
is exclusively the width-based reconstructions from the region that point to anomalous warming
since the mid 20
th
century (Figure 4.11).
4.5.1 Treeringresponsetotemperature
On the basis of both the kinetic constraints on the calibration betweend
18
O
c
and temperature and
corroboration with independent proxies (historical, isotopic and frost ring-occurrence), it is inferred
that the narrowing of pre-20
th
century ring widths as observed at the Almagre Mountain site, and
perhaps across high elevation sites in western north America, overestimates the magnitude of the
20
th
century temperature anomaly relative to the past 400 years. Non-climatic mechanisms as
discussed above such as CO
2
Fertilization have previously been considered to affect tree ring width
records and bias the distinction between 20
th
and pre-20
th
century ring widths. But in light of the
findings of (Salzer et al., 2009) this cannot be considered a viable explanation for the anomalous
ring widths of the 20
th
century. A critical question therefore is why would the response function
between tree growth and temperature differ between the 20
th
and pre-20
th
centuries.
96
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Mixed-Northern Hemisphere (Mann et al., 2008)
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Width-Colorado (LaMarche and Stockton, 1976)
13.5
14
14.5
15
15.5
16
16.5
17
17.5
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Width-New Mexico (Salzer and Kipfmueller, 2005)
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Width-Composite western US (D’Arrigo et al 2007)
Density-Regional (Briffa et al., 1992)
-1
-0.5
0
0.5
1
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
-3
-2
-1
0
1
2
3
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Width-Idaho (Biondi et al., 1999)
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Isotope-Colorado (this study)
-3
-2
-1
0
1
2
3
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Isotope-Canadian Rockies (Edwards et al., 2008)*
Figure 4.11: A compilation of temperature records, which include tree ring widths (left column) and
do not include tree-ring widths (right column). All records are specific to the western US except the
northern Hemisphere composite record from (Mann et al., 2008). Each record is reported using the same
temperature scale as the original authors except (Edwards et al., 2008), where I show the uncorrected
isotopic timeseries.
At locations in western North America where tree growth is considered temperature-sensitive,
ring-width variability is thought to reflect a combination of both a direct physiological response
to temperature, which influences the rate of cambial growth and also a response to changes in the
length of the growing season, which is essentially a measure of the amount of time that conditions
are above the temperature threshold where active photosynthesis occurs (LaMarche & Stockton,
1974; Fritts, 1969; Vaganov et al., 2006). The positive correlation between annual growth and
97
temperature therefore arises as a sum of these two distinct mechanisms. As an attempt to separate
the response of tree growth to growing season length versus a direct cambial response to warmer
temperatures, an evolutive correlation analysis is conducted whereby weekly temperatures from the
growing season are correlated against the ring width from that year (Figure 4.12). In this way it
is possible to isolate specific windows of time during the growing season when temperatures have
a strong influence on annual width (Vaganov et al., 2006). The results from this analysis suggest
that total annual growth (ring width) is most sensitive to temperatures during a distinct period
early on in the growing season and then a second window during the peak of the growing season
(Figure 4.12). I hypothesize, the positive relationship between early growing season temperatures
and ring width arises from the influence of changes in the length of the growing season while
the correlation with peak growing season temperatures reflects a direct cambial growth response
to warmer temperature. Neither of these responses are stable throughout the 20
th
century. The
response of the annual growth band to peak growing season temperatures is intermittently present
throughout the instrumental period while the response to early growing season temperatures only
emerges prior to 1940.
Isotopic and density-based thermometry both directly record growing season temperature
while ring widths incorporate the added response to changes in the growing season length, which
has no known influence on either density or the isotopic composition of cellulose. To reconcile the
ring width record with the isotope and ring density history, it is suggested that the narrower widths
evident in the pre-20
th
century are indicative of a shorter growing season during the LIA, while
MATs were in fact similar to those of the 20
th
century.
Growing season length is a poorly constrained metric because there are few existing observa-
tional records of when cambial growth actually begins owing to the obvious instrumental challenge
of monitoring in situ tree growth. Fortunately, phenology networks of ”bloom time” from across
98

1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975
0.2 0.4 0.6
Peak
Growing Season
End
Early
Correlation Coefficient
Calendar Year (AD)
10 11 12 ann.
Figure 4.12: Evolutive response function between weekly temperature and ring widths. The ordinate
is time during the growing season with the markers noting 30 day intervals starting at the bottom with
May 1 and ending at the top with September 1. Correlation coefficients were calculated for sliding ten
year windows during the 20
th
century using daily temperature data from the Canon City meteorological
station. Only positive response functions are shown.
the western US provide constraints on the climatic features associated with changes in the onset of
growth. Phenological studies suggest that years with late blooms are characterized by cool spring
conditions arising principally from negative geopotential height anomalies over the continental
high latitudes of North America (Cayan et al., 2001). The incursion of cool arctic air masses that
result from this pressure anomaly, yield a spatially coherent delay in the onset of plant growth
across much of western North America and would thus result in narrower rings over much of
western North America. Numerous independent proxy records based on ice cores (Fisher et al.,
2004; Moore et al., 2002; Kreutz et al., 1997), marine sediment cores (Holsten et al., 2004), tree
ring cellulose (Edwards et al., 2008; Berkelhammer & Stott, 2008), tree ring widths (Trouet &
Taylor, 2009) and corals (Hendy et al., 2002) all suggest that atmospheric circulation patterns
were markedly distinct during the LIA relative to those of the 20
th
century. Specifically, as Trouet
& Taylor (2009) point out, a protracted negative Pacific North American pattern (i.e. sustained
negative geopotential height anomalies across continental North America during the LIA), which
99
Calendar Year (AD)
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
ΔTemperature
Δiso-width
Δdensity-width
ΔMAT-june (ECHO-G)
Figure 4.13: The residual between the isotope and width-based (green) and density and width-based
(blue) temperature estimates. The gray line is the difference between MAT and June temperature
anomalies relative to the 1960-1990 temperatures from the ECHO-G climate simulation, which has been
smoothed using a Stineman function.
may have led to a delayed onset of spring. In addition, if there was an equatorward shift of the polar
vortex as suggested by Kreutz et al. (1997), this potentially could have had pointed affects on spring
conditions though a specific analysis of the seasonal extent of the polar vortex during the LIA has
yet to be conducted.
4.5.2 Hypothesistestingwithapaleo-gcmsimulation
As a theoretical test of this hypothesis, I draw upon outputs from the ECHO-G 1000 year climate
model simulation (Zorita et al., 2003) to explore how growing season length may have varied in
the past 5 centuries relative to mean annual temperatures. The GCM experiment was conducted
by coupling the ECHAM4 atmospheric model with the HOPE-G ocean model. The model was
forced by historical greenhouse gas concentrations, solar irradiance estimates from (Crowley, 2000;
Lean et al., 1995), and volcanic aerosols emissions from (Crowley, 2000). Previous studies, have
shown the utility of this model simulation for validating and testing proxy reconstructions (Zorita
et al., 2003; Moberg et al., 2005). Some concerns have been raised about the quality of the data
100
in the early part of the simulation (Osborn et al., 2006). The problematic data is believed to have
arisen because the simulation was initialized after a 100-year control simulation with modern
forcings, which are notably distinct from pre-industrial forcings (Osborn et al., 2006). Therefore,
the early part of the simulation needed an extended period to stabilize. However, because I am only
concerned with the most recent 5 centuries, the problematic data in the beginning of the simulation
need not be considered.
The assumption is made that isotopes and wood density track mean annual temperatures while
widths are more selectively responding to conditions in the early summer that influence the timing
of the growth. Therefore the residual between the two records (Figure 4.13), would thus track how
early growing season temperature vary with respect to mean annual temperatures. To approximate
this in the climate model, I calculate the residual between the regionally-averaged June and MAT
anomalies (Figure 4.13). The month of June is chosen to reflect the early portion of the growing
season at the Almagre site. The results from the coupled GCM simulation predict that a temperature
reconstruction weighted towards early summer temperatures would produce a cold-bias during the
LIA that is of the same magnitude as the observed residual between the IBT and WBT (Figure
4.13). In addition, the model simulation closely replicates multidecadal-scale features in the proxy
record such as reduced residuals between annual and early summer temperatures during the mid
18
th
century and a loss of any consistent discrepancy after the mid 19
th
century. The similarity
between the low-resolution GCM simulation and proxy data is taken as evidence of a sustained
atmospheric dynamic during the LIA that persistently shortened the length of the growing season in
this region while having a minimal impact on mean growing season or annual temperatures.
101
4.6 Conclusions
The findings presented in this chapter indicate that contrary to the inferences of previous studies,
the reduced ring widths in trees from the Almagre site prior to the mid 19
th
century are not likely
indicative of mean annual or peak growing season temperature anomalies. Because of the inherent
complexity of the relationship between temperature and tree growth it is not surprising that some
unstable behavior would arise though it was assumed that such behavior would not radically alter the
temperature history derived from tree ring widths. Consequently, there has been no previous effort to
thoroughly test whether over long periods of time (multiple centuries) trees truly respond systemati-
cally to temperature variability. The non-stationary nature of the ring width-temperature relationship
shown in this chapter is rather remarkable and could not have been appreciated until there was an
independent test, which the isotope paleothermometry provides. It is acknowledged that indeed the
isotope based reconstruction is not without its own sources of uncertainty including the potential that
the relationship between the isotopic composition of precipitation and temperature may have varied
or the role that changes in humidity may have had in influencing the isotopic composition of the
cellulose. Although these cannot be directly assessed at the current time, the agreement between the
isotope-based reconstruction and records based on totally independent techniques lend confidence
to this new reconstruction. The width based record, only shares similarities with other width-based
records. Because of the complex site-specific nature of tree growth response to temperature, the
results presented in this chapter are only applicable to the Almagre site. However, because of the
striking magnitude of the bias in the ring-width temperature reconstruction and because many other
chronologies from this species growing in similar environments are central to composite global
temperature reconstructions, the results clearly necessitate similar independent tests at other sites. If
such non-stationary relationships are evident from other tree-ring derived surface temperature esti-
mates at sites across the subtropics and midlatitudes, it may call for a re-evaluation of the canonical
northern hemisphere surface temperature history.
102
Chapter5
Insightsintothemechanismsthatgenerate
droughtinthesouthwesternUSderivedfromthe
isotopiccompositionoftree-ringcellulose
SUMMARY
Annually-resolved cellulosic d
18
O and dD from Foxtail (Alta) and Bristlecone Pine (BcP)
trees in the Sierra Nevada and White Mountains are presented in this chapter. At these
locations, the isotopic composition of precipitation and consequently the cellulose varies in
response to synoptic atmospheric circulation patterns, which influence the isotopic compo-
sition of the moisture transported to the region within large frontal storm systems. During
the 20
th
century, the BcP timeseries shows prominent decadal-length periods where the iso-
topic composition of precipitation is depleted relative to the mean. These excursions are
synchronous with each of the major multi-year droughts of the 20
th
century, which suggests
that atmospheric circulation patterns during drought episodes are such that there is a selec-
tive loss of low-latitude moisture sources (e.g. enriched moisture sources) in the precipitation
budget. Prior work has identified a distinctive dynamic associated with each of the major
droughts of the 20
th
century, which is anomalous cooling in the eastern tropical Pacific that
leads to a poleward shift in the storm track and drying in the subtropics and moistening of
the temperature latitudes. The isotopic budget derived from the cellulose implies the pole-
ward shift of the storm tracks leads to an approximate 30% loss of low latitude moisture.
The southwestern US has been in the grips of a drought for the last decade and interestingly
the isotopic composition of precipitation as documented both in the cellulose proxy and from
isotope-enabled GCM simulations, show the current drought has been associated with rising
103
isotopic values in the precipitation. This suggests the current drought is sustained through an
alternative mechanism than its predecessors and indeed the climatology of the current decade
does not appear to have the same characteristic cooling in the eastern Tropical Pacific. Prior
to the 20
th
century, the White Mountain site shows an isotopic enrichment of striking mag-
nitude. A similar shift though of a smaller magnitude is also documented in the ice core
record from Mt. Logan in Northwest Canada, which provides evidence for rather profound
basinwide change in atmospheric circulation during the mid 19
th
to early 20
th
century. This
isotopic shift has been interpreted to reflect a shift in the source water though its magnitude is
so large that other factors such as local hydrologic or even physiological changes might also
have exacerbated the isotopic transition.
5.1 Introductorynote
The contents of this chapter have been published in:
Berkelhammer, M. B. & Stott, L. D., 2008. Recent and dramatic changes in pacific storm
trajectories recorded in d
18
O from bristlecone pine tree ring cellulose, Geochemistry Geophysics
Geosystems, 9.
5.2 Introduction
A near consensus of the state-of-the-art global climate models predict that over the course of the
next century the subtropical high pressure zones in the northern and southern hemispheres will
dry (Seager et al., 2007b). This portends a transition towards an even more arid baseline for the
southwestern US, which accompanied by a shift in the timing of snowmelt, a change in the relative
proportion of rain and snow and rising populations will further strain an already-strained water bud-
get (Stewart et al., 2004; Cayan et al., 2001; Dettinger & Cayan, 1995; Dettinger et al., 2004; Snyder
et al., 2004). The drying of the subtropical high zones arises in model simulations principally as
104
a product of the following two mechanisms: 1) Atmospheric humidity will rise globally over the
coming century following the rising tropospheric temperatures (Clausius-Clapeyron relationship)
(Lorenz & DeWeaver, 2007), which will not lead to a uniform rise in precipitation but rather to
increased moisture convergence in the stationary low pressure regions and an increase in moisture
divergence in regions of persistent high pressures (Held & Soden, 2006). In other words, the wet
regions will becomes wetter while the dry regions become drier. This occurs because air will on
average carry more moisture, and thus regions where flow is naturally ”away”, more moisture will
be lost through the mean flow. 2) Rising temperatures will lead to a poleward displacement of the
sinking arm of the Hadley Cell, which will shift the winter storm track away from the southwestern
US (Salathe, 2006; Yin, 2005). The two drying mechanisms are offset slightly by an increase in
eddy-mediated moisture convergence, which is from transient high frequency (non-mean flow)
perturbations. It is the balance between these three phenomena, which will ultimately determine
the hydroclimate state of the western US over the coming century. While it is likely that other
mechanisms such as vegetation feedbacks or changes in the recurrence frequency or amplitude
of ENSO events could indeed be relevant, the principal focus in this chapter is on large scale
circulation features that appear in a consensus of models and thus can be discussed with increased
confidence. Because there are model-to-model differences in the relative contributions of the
factors influencing the predicted hydroclimate changes, there remains uncertainty regarding both
the timing and severity of the predicted drying. Opportunities are therefore available to improve our
understanding of how the competing influences will unfold in the coming century.
Although there are no historical analogs for the 21
st
century, certainly we can improve our
understanding of the predictions through an analysis of the factors that have influenced the water
budget of the region in the past with a focus on both the recent decade which has been most severely
influenced by rising global temperatures and also during known severe drought events (e.g. the
Dust Bowl). There is a rich literature on the dynamical mechanisms driving previous droughts
(Schubert et al., 2009; Seager, 2007; Burgman et al., 2010; Herweijer et al., 2006; Cook et al.,
2009), and in this chapter I will build off this existing work by presenting information on secular
105
variability of the isotopic composition of precipitation over recent centuries derived from tree-ring
cellulose. Viewing the water budget from an isotopic perspective is novel and useful in delineating
the different mechanisms that have been instrumental in driving previous droughts. A particular
focus will be on highlighting aspects of the current drought that shed light on the extent to which
the projections towards a new arid baseline have begun to be borne out.
5.2.1 HydroclimatologyofthesouthwesternUS
One of the most striking features of the instrumental precipitation record from the southwestern
US is the high amplitude of year to year variability. Consider for example the wettest year in Los
Angeles (1884) received 102 cm of precipitation while the driest year (1960) received only 12.2 cm
of rain (National Weather Service). The end members thus span nearly a factor of 10 range. Because
annual rainfall in this semi-arid region is so heavily dependent on a small number of individual
storm events, some of the year to year variability can be attributed to simply stochastic behavior.
However, it is widely documented that on interannual time scales much of the extreme behavior
is tied to El Nino-Southern Oscillation (ENSO) (Cayan et al., 1998). The tropical-extratropical
teleconnection is invoked principally, through a dynamical poleward (equatorward) adjustment of
the midlatitude storm track in response to anomalous cooling (warming) in the eastern tropical
Pacific. When the storm track shifts poleward, it results in a decreased likelihood that a storm will
make landfall in the southwestern US. Instrumental and proxy records suggest an approximate
3-7 year recurrence frequency of ENSO events though it is likely that there are lower frequency
cycles generating protracted periods of time where ENSO events are more frequent or of a greater
magnitude (Cook et al., 2007; McCabe & Dettinger, 1999; Stott, 2002; Cobb et al., 2003; Conroy
et al., 2008; D’Arrigo et al., 2005). The impact of ENSO on the hydroclimate of southwestern US
(i.e. the teleconnection) is highly non-stationary owing to interactions with other hemispheric and
regional modes of climate variability including, but not limited to, the Pacific Decadal Oscillation
and North Pacific Index, the Atlantic Multidecadal Oscillation and related factors such as the mean
106
location of the mid-latitude jet stream (Biondi et al., 2001; McCabe et al., 2001; Cole & Cook,
1998; Cole et al., 2002).
Interannual variability clearly dominates the instrumental record, which gives it an inherently
whitened appearance though amidst this, there are clearly observable lower frequency hydroclimatic
modes with decadal to multidecadal timescales (Cayan et al., 1999). While singular wet or dry years
can be detrimental to water management, it is the lower frequency (decadal and longer) variability
that is most critical to long term infrastructural planning. Sustained patterns of SST and SLP
anomalies are primarily called upon to explain the protracted wet and dry periods in the historical
record. Notable in this respect is the Pacific Decadal Oscillation (PDO), which over the most recent
millennium has been called upon as one of the primary drivers of decadal to multidecadal climate
variability in the region (McCabe et al., 2004). Despite having a transient spectra, the PDO is
believed to be a persistent pattern of ocean/atmosphere climate variability (Benson et al., 2003;
Biondi et al., 2001; D’Arrigo et al., 2001; Gedalof & Smith, 2001; Mantua & Hare, 2002). The
PDO is described as a warm-cold oscillation of ocean surface temperatures, north of 20
o
N in the
Pacific (Mantua et al., 1997) driven by an interplay between the subtropical gyre and the Aleutian
Low. The depth (strength) of the low pressure system in the North Pacific responds to changes
in the strength of the westerly winds, affecting the latitude of Pacific storm tracks. The positive
(negative) state is associated with a southerly (northerly) shift in the storm tracks, resembling a
sustained El Nino-like (La Nina-like) ocean-atmosphere pattern in the extratropical north Pacific
(Liu & Alexander, 2007; Mantua & Hare, 2002; Deser et al., 2004).
Like ENSO, the PDO has opposing hydroclimatic influences on the Pacific Northwest and
Southwest and thus by combining proxy records along a latitudinal transect of the western US it
has been possible to reconstruct the system back in time multiple centuries (Biondi et al., 2001;
Gedalof & Smith, 2001; MacDonald & Case, 2005). Tree-ring width and lake level reconstructions
107
of the PDO suggest that prior to the terminus of the Little Ice Age in the middle of the 19
th
century,
the PDO had both a smaller amplitude and higher frequency (Biondi et al., 2001; Gray et al., 2007;
Hidalgo, 2004; MacDonald & Case, 2005; Wilson et al., 2007). The toggling of the system between
higher and lower frequency types of variability as observed in the proxy record is consistent with
the behavior of the Pacific ocean-atmosphere dynamics in coupled GCM experiments (Latif, 2001).
A spatially similar mode is the North Pacific Index (NPI), which is defined purely through an
atmospheric metric (SLP) (Trenberth & Hurrell, 1994). The NPI generates a similar effect on the
pathway of the mid-latitude jet stream by way of the development of a sustained high pressure
blocking cell in the North Pacific, which modifies the course of storms approaching western North
America. While the hydroclimatic impacts of the NPI are largely the same as the PDO, it appears to
have a distinctly higher frequency timescale than the PDO, which is not surprising because of the
longer memory associated with oceanic conditions (Trenberth & Hurrell, 1994).
Hydrologic changes across the western United States may also be influenced by ocean and
atmospheric changes in the Atlantic basin (McCabe et al., 2004). Instrumental and proxy records
as well as model simulations all suggest that the North Atlantic is dominated by a60-year basin
wide warm-cold oscillation referred to as the Atlantic Multidecadal Oscillation (Gray et al., 2004;
McCabe et al., 2004; Knight et al., 2006; Park & Latif, 2008). The simplest explanation for this
teleconnection is that the warming of the North Atlantic basin leads to persistent high pressure
anomalies over southwestern US and consequently generally drier conditions across the region
(McCabe et al., 2004; Knight et al., 2006). However, it is possible that there are other more
circuitous mechanisms including perhaps the influence of the North Atlantic on the tropical Pacific
that may play a role in this observed relationship. Thus on decadal to multidecadal timescales,
hydroclimatic variability in the southwestern United States can be described as a sum of constructive
and destructive interactions between ENSO, PDO (and related Pacific Decadal Variability) and
AMO. The resonance of these different modes can lead to large climate changes or regime shifts
(Biondi et al., 2001; Minobe, 1999). A common finding between studies is the presence of stark
108
changes in the north Pacific climate during the mid-1940’s, mid-1970’s and perhaps a recent regime
shift within the last decade (Biondi et al., 2001; Minobe, 1999; Swanson & Tsonis, 2009).
The literature depicts a hydroclimatology defined through a complex interaction of demarcated
climate modes each with distinct timescales and spatial characteristics. It is beneficial to consider
the question of what drives hydroclimatic variability from a more fundamental perspective. For
example, (Seager et al., 2005, 2003) and (Herweijer et al., 2006) argue that much of the observed
and reconstructed precipitation variability can be attributed to ocean-atmospheric dynamics in the
tropical Pacific and skillful hydroclimatic predictions will come out of an improved understanding
of ocean-atmosphere dynamics in the tropical Pacific. In the spirit of this perspective, the following
chapter makes no effort to reconstruct a specific atmosphere/ocean index but rather presents a
more generalized depiction of the water budget for the southwestern US over the recent centuries.
Following the discussion presented in Chapter 2, I interpret changes in the isotopic composition
of cellulose (or precipitation) to depict changes in sources of moisture that make up the annual
precipitation budget. These changes can be tied specifically to patterns of moisture transport in the
North Pacific though not inherently linked to any specific pre-demarcated index.
5.3 Methods
The methodologies used in this chapter do not differ substantially from those presented in the
previous chapters. The cellulose samples from the White Mountain site were collected from two
trees in the White Mountains of California just east of the National Forest Services Shulman Grove
Visitor Center along the Methuselah Walk Trail (37.3
o
N, 118.15
o
W at 3,075m). This site is largely
undisturbed with limited evidence for anthropogenic land-use or hydrologic changes. Samples were
collected using a 5 mm increment borer in April and July of 2006 from living trees that appeared
to be of similar age. The cores were sanded to reveal their annual rings and cores from the first
109
tree were dated with the assistance of Tom Harlan from the Laboratory of Tree Ring Research at
the University of Arizona using the Methuselah Walk ring-width chronology available from the
International Tree Ring Database (Ferguson, 1969). The dating was done using the Cross Date
program. The cores chosen for this study were from trees that had a greater than 80% correlation
with the master chronology for this grove and had no missing rings back through the last five
centuries.
Working under a microscope, each core was sliced into annual samples using a scalpel or a
rotary microtome. Isotopic analysis was performed with a Carlo Erba elemental analyzer affixed
to a VG instruments IsoPrime Isotope Ratio Mass Spectrometer. The furnace was maintained at
a temperature of 1060
o
C. To facilitate the complete conversion of cellulose to CO at these lower
temperatures (relative to those in Chapters 3 and 4), nickel carbon wool was placed at the reaction
sites and served as a catalyst for the pyrolysis reaction. The water and CO
2
products were filtered
out of the helium stream using a magnesium perchlorate trap and the CO was then introduced into
the mass spectrometer through a silica capillary. Packed reaction columns were typically replaced
every 300-500 samples. When reaction columns were replaced, it was observed that between
10-20 standards were required to be run before consistent stable isotopic values were produced.
A need to condition columns through pyrolysis of organic samples has been noted by previous
researchers (Robertson et al., 1997). It is hypothesized that the need for conditioning arises because
of elemental carbon produced as a byproduct of the pyrolysis reaction provides excess reactive
carbon at the reaction site, which further catalyzes the reaction. Thus once a critical amount of free
elemental carbon is available at the reaction site, all the oxygen in the cellulose is converted to CO,
which results in consistent isotopic measurements.
The Foxtail Pine samples were gathered at the Alta Peak site (36.6
o
N, 118.6
o
at 3,350 m) during
the summer of 2008. These samples were collected within the Sequoia National Park boundary
and thus permission to gather these samples was provided under a National Park Service sampling
110
10 years
10 years
10 years
1 cm
Figure5.1: Photograph of Bristlecone Pine slab highlighting the quasi-exponential decay in ring size as
a function of increasing tree radius.
permit. The widths were measured at the Lamont Doherty Tree ring lab and dated against the
published Boreal Peak chronology. The trees sampled ranged in age from 600-1200 years old, and
a series of three cores were selected from the group that lacked any missing 20
th
century rings were
chosen for analysis. Isotope measurements were done with the identical techniques used to generate
the Almagre Mountain chronology described in Chapter 4. Just a series of preliminary isotopic
measurements are presented from this site.
5.4 Results
5.4.1 WhiteMountainSite
Figure 5.2 shows the annually-resolved continuous time series of d
18
O from the White Mountain
site (hereafter BcP) back to AD 1500 for two separate trees. The correlation between the two
series is0.8, a value which is inflated partially as an artifact of the large magnitude transition in
the mid 19
th
century. The correlations during sliding 20-year intervals ranges from between 0.68
(1960-1980) to 0.48 (1920-1940). The high degree of correlation between the two isotopic records
validates our use of smaller sample sizes than is typically used in such studies (Treydte et al.,
2006). The Bristlecone Pine d
18
O record is characterized by a naturally smoothed appearance,
which contradicts the noisy high frequency variability in the surface hydroclimatology record. It
can therefore be assumed that either as a function of some mixing of waters in the soil column or
through year to year storage of photosynthates, some high frequency variability is lost.
111
One can split the chronology into three discrete regimes: 1500-1860, 1860-1910 and 1910-
2004. Between 1910-2004 the record exhibits a distinctive bidecadal oscillation that is punctuated
by positive isotopic excursions centered around 1920, 1940, 1960 and 1980. With the exception
of the 1920’s peak, the excursions have become progressively smaller in magnitude towards the
present-day. Between 1860-1910, the most pronounced feature of the 500 year-long record appears,
which is a major isotopic enrichment. The oxygen isotope values prior to 1857 are on average
close to 10‰ higher than values during the 20
th
century (Figure 5.2). This shift in isotopic values
occurred in a series of step-like events with the majority of change occurring as part of a rapid
excursion in the beginning of the 20
th
century. These steps occur in roughly 20-year intervals
and could be identified as a continued, albeit subdued, manifestation of the bidecadal oscillation
observable during the 20
th
century. The d
18
O values show increasingly less variability prior to
the middle of the 19
th
century, with average values hovering near 31‰ through the beginning of
the 16
th
century. Spectral analysis of the entire dataset using a Fast Fourier Transform, indicate
the presence of a power in the near 20-year band. A similar analysis for the period between
1860-1500 indicates the presence of quasi-decadal power (8-12 year period). Thus, the predominant
periodicities changed during the middle of the 19
th
century to a system dominated by higher
frequency and lower amplitude variability.
The modeling results from Chapter 3 suggest that isotopic variability of the magnitude
observed for the decadal features during the 20
th
century exceeds that which could likely be gen-
erated by temperature or humidity changes alone. It is therefore assumed that the principal driver
of this variability is changes in the isotopic composition of the source water, which in this region
is driven by shifts in the storm track (see Chapter 2). When the 20
th
century component of the
BcP chronology (see Figure 5.4) is correlated against gridded zonal geostrophic wind fields derived
from the HadSLP dataset, it is observed that stronger zonal winds poleward of 45
o
leads to depleted
112
15
20
25
30
35
1550 1650 1750 1850 1950
BcP4_1
BcP2_4
Calendar Year (AD)
δ
18
O
(VSMOW)

Figure5.2: Timeseries of multiple BcP chronologies spanning modern (right) to AD 1500 (left). Certain
sections were lost either due to instrumentation era of insufficient material.
isotopic values. This is consistent with the notion that anomalous poleward shifts in the zonal wind
fields lead to depleted isotopic values (Figure 5.3). The changes in atmospheric circulation lead to
a scenario where frontal storms are populated with moisture sourced from a higher latitude (more
depleted) region. The inverse correlation between the North Pacific Index and the cellulosic d
18
O
provide another useful way to visualize the relationship between atmospheric circulation in the north
Pacific and isotopic values of cellulose (Figure 5.4). The correlation between BcP and gridded SST
fields from the HadSST dataset reveal a negative correlation between SSTs in the central North
Pacific and positive correlations with near coastal SSTs and those along the southwesterly storm
track into the central tropical/subtropical Pacific (Figure 5.3). The negative relationship is centered
around the same region where maximum increases in zonal winds influence the isotopic composi-
tion of the cellulose. I interpret this relationship as arising because of feedbacks that occur between
increases in wind speed and SSTs (Latif et al., 1994). Therefore the negative relationship between
cellulose and SSTs from this region is not causal but is an artifact of the correlation between atmo-
spheric circulation and moisture source. The positive correlation of BCP with coastal SSTs and
SSTs along the southwesterly storm track is indicative of the fact that increased latent heat flux from
these regions leads to isotopically enriched precipitation over the southwestern US (see Figure 2.6).
113
Figure 5.3: Correlation coefficients between 3-year running mean of zonal geostrophic winds derived
from the Trenberth SLP dataset and the BcP record (top) and correlation against the Kaplan SST dataset
(bottom).
114
6
8
10
12
-1.5
-1
-0.5
0
0.5
1
1.5
18
20
22
24
26
1920 1940 1960 1980 2000
δ
18
O
VSMOW
σ
A
B
C
Figure5.4: Cellulosicd
18
O from the White Mountain site (A) compared to indices of annually-averaged
Atmospheric (North Pacific Index, B) and Oceanic (Pacific Decadal Oscillation, C) indices of climate
variability. Cellulose (red) and PDO (blue) records have been smoothed with a 3-year moving average
filter. The NPI record is shown on a reverse scale where up denotes an equatorward shift in storm tracks
driven by SLP anomalies in the Aleutian Low region.
5.4.2 AltaPeakSite
The triplicatedd
18
O timeseries’ from the Alta Peak site covering the last thirty years are shown in
Figure 5.5. These data are presented only as preliminary results. Like the White Mountain site,
the cellulosicd
18
O at the Alta Peak site shows a 1-2‰ depletion in the early 1990’s followed by a
recent rise in isotopic values of 1-2‰ relative to the mean of this period. The brevity of this record
precludes any further discussion on the trends or characteristics of this record.
115
1980 1985 1990 1995 2000 2005
27
28
29
30
31
δ
18
O
Year
Figure5.5: Cellulosicd
18
O from the Alta Peak site for three separate trees.
5.4.3 Geochemicalmodeling
Following the equations presented in Chapter 3, I model the predicted annually integrated isotopic
values for each of the last 30 years. The calculation is done using integrated growing season humid-
ity, temperature, barometric pressure and the isotopic composition of atmospheric humidity and the
mass-weighted average isotopic composition of winter time precipitation. All inputs to the model
were taken from the IsoGSM simulation whose climatology is nudged to NCEP 2 Reanalysis fields
(Yoshimura et al., 2008; Kanamitsu et al., 2002) (Figure 5.6). The model predicted cellulosicd
18
O
values closely follow the observed values. Both model and measurements show a minima in the late
1980’s through early 1990’s followed by a long term rise towards the present. The higher frequency
variability in the Alta Peak record is well replicated by the model however, the lack of year to year
variability in the White Mountain site degrades its fit with the model data. This is further indicative
of a natural low-pass filter of either a biological or hydrological origin at the White Mountain site.
116
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1980 1985 1990 1995 2000 2005
Modeled
Foxtail
White Mts.
Calendar Year
δ
18
O Anomaly
Figure 5.6: Timeseries of measured BcP and Alta (red and green) and modeled (gray) annual isotopic
values. The modeled values were calculated based on the equations and methodologies discussed in
Chapter 2 except done using integrated growing season values as opposed to daily or weekly time steps.
5.5 Discussion
5.5.1 Isotoperelationtodrought
During the instrumental period each of the major multi-year droughts that struck the southwestern
US was accompanied by a 1-3‰ depletion in the isotopic composition of precipitation (Figure
5.7). It is important to emphasize that if the isotopic composition of source water during droughts
remained stable, the drier conditions and lower relative humidities would drive the isotopic value
of photosynthates and consequently the cellulose towards higher isotopic values. Because we
see isotopic minima during droughts, it unequivocally demonstrates a change in the isotopic
composition of the source water. Multiyear droughts in the southwestern US have been related to
shifts in the latitudinal variations of the storm track (Cole et al., 2002), and the positive correlation
between cellulosic d
18
O and droughts reflect the shared response of the isotopic composition of
precipitation and precipitation amount to changes in the storm track. The absence of a positive
117
relationship between precipitation and the isotopic composition of cellulose in the higher frequency
domain likely arises as a product of the fact that year to year variations in precipitation are stochastic
in nature and not simply a product of the probabilistic relationship between mean storm track
latitude and land falling storm frequency and also the nature of this proxy, which does not capture
high frequency variability well. In chapter 1 (Figure 2.14), it is shown that there is a 0.06‰ change
in the isotopic composition of precipitation for every 1% change in the percentage of low latitude
moisture advected with a storm system. This relationship suggests that each of the major droughts
of the 20
th
century were coincident with an approximate 30% loss of low latitude moisture. The
slope presented is tentative because it is based on a short simulation but the results indicate that the
absence of this specific constituent of the water budget is enough to account for a substantial portion
of the moisture deficit during these major drought intervals. For example, if a drought involved a
40% reduction in precipitation and a 2‰ depletion in isotopic values, than an entire 75% (30% out
of the 40%) of the moisture deficit would have to be derived from a specific loss of moisture con-
verging from the low latitudes. This presents a new perspective on how water budgets change during
multiyear drought, which have typically been viewed in terms of cumulative deficits without specific
attention paid to the possibility that a specific constituent of the moisture budget was being removed.
A feature that is consistent in the cellulose records, is a recent trend towards rising isotopic
values. This isotopic shift is occurring despite the fact that the region has been in the grips of a
drought since early in 1999 with only brief hiatuses in 2005 and 2010 (Cook et al., 2010; Seager
et al., 2009; Hoerling & Kumar, 2003). From an isotopic perspective, it would appear that the
current drought is anomalous with respect to its instrumental predecessors. In Figure 5.8, the SST
pattern during the 1950’s drought is shown to emphasize the very well-defined cooling in the eastern
Tropical Pacific that triggered this drought and each of the droughts of the instrumental period
(Herweijer et al., 2006; Seager, 2007; Cook et al., 2007). Between 1999-2001 a similar SST pattern
prevailed (not shown), and based on this observation it had been noted that the turn-of-the-century
drought was dynamically comparable to each of the major multi-year instrumental droughts
(Hoerling & Kumar, 2003; Seager et al., 2007b). After this characteristic SST anomaly pattern
118
subsided in 2002 (Figure 5.8), the drought continued more or less unabated. The sustained period
of aridity that has most recently affected the southwestern US thus cannot be linked to the same
SST anomaly pattern.
The uniqueness of the current drought can also be appreciated from an analysis of its spatial
extent (Figure 5.9). Each of the large preceding droughts had a well-defined dipole pattern (wet
in the north and dry in the south), which is a characteristic hydroclimate footprint of the poleward
shift in the storm track. The current drought lacks this same spatial pattern. Therefore, it is not only
the isotopes that suggest something fundamentally unique about this current drought (Figures 5.9
and 5.10).
I argue based on these observations that the rising isotopic values over the last decade are
tracking a distinct drought-forming atmospheric dynamic. One way to reconcile the observed water
deficit coupled with rising isotopic values would be through a loss of high latitude moisture, which
acts to characteristically deplete the isotopic composition of storm events. The past decade has seen
anomalously high sea level pressure (SLP) at mid-latitudes over the north Pacific in concert with a
progressive atmospheric warming in this region. It is proposed that coupled shifts in tropospheric
temperatures and pressure gradients in the north Pacific are acting to reduce the convergence of
northerly-sourced moisture to the region. It remains a topic of continued inquiry to understand
how moisture transport in the northerly storms are currently unfolding during this current drought.
For example, it is not clear whether the drying and rising isotopic values mark a decrease in the
number of landfalling storms arriving from northerly origins or alternatively a reduction in the
moisture carrying capacity of these systems. An added product of rising temperature over the North
Pacific is that the isotopic values in the vapor over the Pacific basin are becoming increasingly
enriched (Figure 5.10). Thus, the rising values of the normally-depleted high latitude vapor source
119
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
-2
-1
0
1
2
3
δ
18
O
(anomaly)
2
3
4
5
6 Precip. (inches)
Year
Figure 5.7: Cellulosic d
18
O for the White Mountains (green) and rainfall anomalies averaged from
California Climate Division 4, 5 and 7 during the 20
th
century (blue). Previous multi-year droughts are
denoted by gray bands while the current drought is delineated by the pink band.
synchronous with a loss of this moisture source would act to exacerbate the isotopic enrichment
over the western US. A schematic summary of this idea is presented in Figure 5.11, where 4
moisture budget scenarios are presented. Normally, the water budget is weighted towards moisture
derived from the northern midlatitudes, bringing characteristically depleted moisture to the region.
I argue that this budget is changed during large drought episodes by decreasing the presence of the
low latitude moisture, leading to both a moisture deficit and precipitation that is on average more
depleted. The budget of the current drought opposes its predecessors though a loss of this higher
latitude moisture. A final hypothetical budget is presented on the bottom of Figure 5.11, where a
historically ”normal” drought is superimposed on the current scenario.
It is tempting to ask whether the current drought represents an early manifestation of the
predicted-transition towards a more arid southwestern US. The shift towards more arid conditions is
120
Figure 5.8: Comparison of SST anomalies from the Kaplan dataset during the 1950’s drought and the
latter stages of the current drought.
predicted to arise principally from a reduction in moisture convergence from mean flow and a rise
in specific humidity. Following the methods of Seager et al. (2007b), I calculate the relative contri-
bution of annual moisture convergence over the last 30 years from mean flow, changes in specific
humidity and eddy-moisture transport in the NCEP 2 Reanalysis model (calculations included in
Appendix). The former two are calculated using vertically integrated mean monthly wind speed and
specific humidity fields, while the latter is calculated using only contributions from transient flow.
Using the same ”southwestern US” delineation of Seager et al. (2007b), I calculate how contribu-
tions to moisture convergence from these three sources during the current decade compares to the
last 30 years (i.e. the full length of the simulation). The contribution from humidity has decreased
121
Figure5.9: PDSI from (Dai et al., 2004) during the two major droughts of the early and mid 20
th
century
compared to the current PDSI anomalies.
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1984 1988 1992 1996 2000 2004 2008
Year
δ
18
O
(anomaly)
Figure 5.10: The isotopic composition of water vapor over grid points along the North Pacific Basin
from the IsoGSM simulation. The black line is used to show the areal average.
122
Low-latitude
enriched source
High-latitude
depleted source
Typical drought
Current drought
Latitude/Isotopic composition
Normal
Future drought (?)
Figure 5.11: Schematic representation of how the water budget varies from an isotopic perspective
during previous droughts, the current drought and theoretically the situation if a La Ni ˜ na-like drought
were to occur amidst the current state.
over the past decade consistent with what is predicted to occur over the 21
st
century but the mean
flow contribution has risen and eddy-mediated contributions have decreased both of which oppose
the prediction. If we consider this result from an isotopic perspective, changes in humidity would
have no impact on the isotopic budget and the isotopic influence of eddy-mediated flow is not pre-
dictable, but changes in mean-flow, synonymous with shifts in the latitude of the storm track, would
lead to an isotopic depletion following the rationale discussed in this chapter and Chapter 1. There-
fore, the rising isotopic values are consistent with the fact that moisture convergence from mean
flow has been rising however, based on this analysis alone, it is not possible to conclude whether
the current drought is an early manifestation of the shift towards a new arid baseline. Continued
efforts to pursue this topic will include longer nudged simulations with passive and isotopic tracers
that will capture the major droughts of the mid 20
th
and a mored detailed characterization of the
moisture budget of the current decade.
123
5.5.2 LowFrequency20
th
centurytrends
In addition to the decadal scale isotopic variability in the BcP record, there is a weak long term
trend towards more depleted values during the 20
th
century, which reverses direction only during
the last decade (Figure 5.12). This trend is corroborated by a similar long term trend from a
stalagmite in Sequoia National Park (Glynn et al., unpublished) (Figure 5.12). The interpretation of
stalagmite records, will be addressed in detail in the following chapter but it will be considered for
this discussion to principally be a proxy for the isotopic composition of precipitation. An age model
for this record was constrained by identifying the peak in radiocarbon during the 1950’s associated
with nuclear testing and then assuming a linear growth rate from this single tie point. The long
term isotopic depletion in both sites from the southwestern US opposes the trend observed from
a compilation produced from a suite of sites in the Pacific Northwest (Figure 5.12). The Pacific
Northwest suite includes a diatom d
18
O record from Mica Like in Alaska (Schiff et al., 2009),
d
18
O records of sedimentary calcite from Jellybean Lake in the Yukon (Anderson et al., 2005) and
a lake compilation from western Alaska (Hu et al., 2001). The isotopic compilation of lake water
in general reflects not only changes in precipitation but also evaporation. The records included
in this composite were chosen from lakes identified by their authors as not being influenced by
evaporation, which was concluded from the observation that the isotopic composition of the lake
water falls on the Global meteoric Water Line whereas evaporatively-influenced lakes fall along a
shallowerd
18
O-dD slope. In addition to the lake records, I include thed
18
O record from the Mount
Logan Ice core in western Canada (Fisher et al., 2004). The records were interpolated to a common
decadal time scale using the age models presented by their authors.
As discussed by Feng et al. (2009), from a global perspective, variations in the isotopic composition
of precipitation can be viewed in terms of the distance between moisture source and sink. This is
explained by a Rayleigh Distillation model, where moisture becomes depleted with increasing age
(i.e. progressive loss of enriched moisture). Feng et al. (2009) argue that the evaporation maximum
along the subtropical high zones (STHs), is the principal extratropical atmospheric moisture source.
Therefore seasonal or longer term isotopic trends arise because of changing distance from the
124
STHs. A poleward shift in the latitude of the STH would bring the moisture source closer to the
Pacific Northwest and lead to an enrichment in the isotopic composition of precipitation in this
region. This atmospheric transition would simultaneously lead to a depletion of the precipitation
in the Southwestern US, akin to the depletions that occur from a poleward shift in the storm track
during transient drought events. The opposing trends therefore imply a 20
th
century poleward shift
in the latitude of the subtropical high pressure zones.
In Figure 5.13 I show the regression of the zonal component of the geostrophic wind field
against time. The regression coefficient are weak because of substantial year to year and decadal
variability of atmospheric wind fields. The analysis does suggest a strengthening of both westerly
winds (positive correlation in the mid latitudes) and trade winds (negative trend in the subtropics)
over the course of the 20
th
century. The maxima of rising zonal wind fields is near 50
o
N which
is taken as an indicator of a poleward shift in the storm track. This is consistent with the results
of Archer & Caldeira (2008) who assessed trends in the latitude and strength of the jet streams.
Authors of the studies included in the Pacific Northwest composite interpret the isotopic variability
in their records as reflecting changes in the latitude of the moisture source, and the interpretation
presented here does not diverge from their own presentation. While the composite trend in the
Pacific Northwest is very well-defined, additional records from the southwestern US would be
needed to further confirm a long term trend amidst the higher frequency variability. It would be
curious to test whether the long term opposition between Pacific Northwest and southwestern US
records are manifest on annual or multi-annual timescales, which would provide a way to clearly
distinguish hydroclimate variability driven by latitudinal variations of the storm track. However, the
current records from the Pacific Northwest do not afford the resolution to pursue this comparison.
5.5.3 The19
th
CenturyTransition
Beginning in the mid 19
th
century, the isotopic chronology at the White Mountain site undergoes a
steep step-like isotopic depletion. This change predates instrumental monitoring in the region and
thus other available regional proxies are used to consider the environmental changes that drove the
125
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
CRC
BcP
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Mt. Logan
Eclipse
Jellybean Lake
Mica Lake
Lakes
Calendar Year
δ
18
O
(normalized)
δ
18
O
(normalized)
-160
-160
-140
-140
-120
-120
40
40
60
60
Figure5.12: Comparison of isotopic trends from sites in the Pacific Northwest (Mt Logan and Eclipse
(Fisher et al., 2004), Jellybean Lake (Anderson et al., 2005), Mica Lake (Schiff et al., 2009), Lakes (Hu
et al., 2001)) and the southwestern US. All Pacific Northwest sites were normalized and timeseries were
interpolated to a common decadal timescale. Colored dots on the map correspond to the locations of the
sites used in the composite timeseries.
126
Figure 5.13: Correlation (r) between zonal geostrophic winds and time for the Trenberth and HadSLP
datasets.
127
isotopic shift. Because of the observed relationship between multiyear drought and cellulosicd
18
O,
a principal place to start looking would be in finding a suitably severe regional hydroclimate change.
Indeed, one of the largest and most sustained shifts in regional hydroclimatology of the western
US occurred between AD 1850 and 1915 (Cook et al., 2007, 2004), which marked the transition
between a generally more moist Little Ice Age in the western US relative to the current century. A
similar, though less protracted change, is captured in ad
13
C chronology from tree-ring cellulose in
the southwestern United States (Leavitt & Long, 1989). The change is most pronounced from 1900
to 1905, which represents the single most severe drought pentad since 1790. The ring widths from
the Methuselah Walk site generated directly from where the isotopic chronology was developed,
echo the presence of local hydroclimatic change but clearly do not show a transition that scales with
the magnitude of the isotopic changes in cellulosicd
18
O. This suggests implicitly that the inferred
relationship between drought and the isotopic composition of cellulose that prevailed during the
instrumental period cannot be called upon exclusively to explain this shift. Another perspective on
this change can be observed through cross wavelet coherency between the reconstructed Palmer
Drought Severity Index (PDSI) (Cook et al., 2007) from this site and the tree ring d
18
O. This
analysis is done following the methods of Grinsted et al. (2004) and shows the coherency in
the multidecadal band between drought and isotopes during the 20
th
century as discussed above
however, prior to the mid 19
th
century this relationship tails off and disappears (Figure 5.14).
Thus, while there is evidence of modest hydroclimatic changes during the isotopic transition,
clearly other factors which influenced the isotopic composition of the principal moisture sources
or storm dynamic must have been influential. Efforts to look at Pacific decadal variability
by Gedalof & Smith (2001); Gray et al. (2007); Hidalgo (2004); MacDonald & Case (2005);
Mann et al. (1995) have identified the middle of the 20
th
century as a period when the multi-
decadal climate variability of the 20
th
and late 20
th
centuries were supplanted by higher frequency
and lower amplitude modes, which is indeed what happens the spectral properties of the BcP record.
128
Period
1589 1639 1689 1739 1789 1839 1889 1939 1989
4
8
16
32
64
128
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Year
Figure 5.14: Cross wavelet coherence using a morlet wave between PDSI reconstruction from (Cook
et al., 2007) and the BcP annual chronology. Periods of significant coherence (95%) are denoted with
the bold black line. The arrows indicate the phase angle of the relationship with arrows pointing right
denoting in-phase coherence, and to the left indicating anti-phase. Power is reported in normalized units
and the veil, shows the cone of influence, under which power needs to be treated with caution. Analysis
is done using the algorith from (Grinsted et al., 2004).
Another example of this mid-19
th
century climate shift is reflected in the marine proxy records
from the Santa Monica and Santa Barbara Basins off the western coast of southern California.
Here the sediments that are deposited on the sea floor are laminated, reflecting the absence of
bottom burrowing organisms that would otherwise disturb the laminated sedimentation. In the
Santa Barbara Basin the laminations are deposited annually as a light (low density) and dark
(higher density) couplet (Lange et al., 1997). In the Santa Monica Basin the high-lower density
layers are deposited interannually (Christensen et al., 1994). In both basins the extent of laminated
sediments has varied over time. Prior to about 500 years ago the sediments within these basins
were bioturbated and the laminated sediments were not preserved as they are today. At the end of
the LIA the centers of these basins began to undergo a transition to laminated sedimentation and
the extent of laminated sediments has expanded progressively outward from the center of the basins
(Christensen et al., 1994; Hagadorn et al., 1995). In the most recent phase of sedimentation change,
the transition from bioturbated to laminated sedimentation began in the middle of the 19
th
century
in close correspondence with the transition observed in the BcP record. The mid 19
th
century
129
expansion of laminated sediments is attributed to a progressive increase in carbon flux to the sea
floor and enhanced carbon oxidation rates that depleted bottom waters of oxygen resulting from
enhanced northerly winds that drive upwelling along the continental margin (Holsten et al., 2004).
Historical records from the Santa Barbara Basin provide further evidence for this transition in the
wind fields as sailors noted changes in the extent of kelp forests and storm trajectories during this
period (Schimmelmann et al., 1992). The nearby sediment records therefore indicate a fundamental
shift in wind and oceanic circulation synchronous with this large isotopic change (Figure 5.15).
Ice core records from the Dasuopu Glacier in China (Thompson et al., 2003; Zhao & Moore,
2006), Mount Logan and adjacent ice core and lake records from northwestern Canada (Fisher
et al., 2004; Moore et al., 2002) and the Fremont Glacier in Wyoming (Naftz et al., 1996) all show
major inflection points occurring during the middle of the 19
th
century (Figure 5.15). Accumulation
totals from the former records show a decrease in snow accumulation since the end of the Little Ice
Age, which has been interpreted to be the result of weaker Hadley Cell circulation (Zhao & Moore,
2006). d
18
O and d-excess measured from the North Pacific ice core records also indicate dramatic
shifts at the end of the Little Ice age, which suggest that this region experienced a major change in
source water accompanying this shift in accumulation rates. The authors interpreted this to be the
result of an isolation of the North Pacific during the Little Ice Age, which prevented the intrusion of
tropical and subtropical moisture sources from reaching northwestern Canada (Fisher et al., 2004).
The influence of sea ice retreat on this regional climatic transition is not explicitly addressed in
the analysis of the Mt. Logan record though modeling studies suggest that the sea ice retreat at
the end of the Little Ice Age would have profound influences on atmospheric circulation across
the north Pacific (Sewall & Sloan, 2004). Broadly speaking, the middle of the 19
th
century was a
time of significant environmental change across the Pacific Basin, with not only a rise in global
temperatures, glacier and sea ice retreat, but also on atmospheric circulation patterns as manifest in
changes in precipitation patterns and storm trajectories.
130
-35
-34
-33
-32
-31
-30
-29
1600 1650 1700 1750 1800 1850 1900 1950 2000
15
20
25
30
35
-2.6
-2.4
-2.2
-2
-1.8
-1.6
-1.4
-1.2
δ
18
O
VSMOW
Δδ
13
C
VPDB
δ
18
O
VSMOW
Calendar Year
Figure5.15: Timeseries of other circulation proxies from the Pacific Basin. Top is the BcP chronology
from this study, center is the upwelling/wind proxy from the Santa Monica Basin from (Holsten et al.,
2004) and bottom is the ice cored
18
O chronology from Mount Logan (Fisher et al., 2004). All records
have been smoothed (colored line) using a running mean filter.
131
The isotopic transition in the BcP record is remarkably large and GCM models run with
isotopic tracers using 20
th
century boundary conditions cannot replicate anything of remotely
this magnitude (Yoshimura et al., 2008; Hoffmann et al., 1998). Therefore, it is not possible to
identify any modern analog for this transition. The only way to theoretically drive such a shift
in the isotopic composition of precipitation would be that all precipitation delivered to the region
would be through storms that exist on the far isotopic end members of the individual storm events
presented in Chapter 2. There is a greater than 10‰ range of isotopic variability between storms,
so if it is assumed that the most enriched end members became the norm, than indeed a marked
isotopic shift in precipitation would be feasible. The equatorward shift in the storm track driven by
cooler LIA conditions in the high northern latitudes, would have increased the landfalling frequency
of the extremely enriched tropically-sourced storm events, but this interpretation would require an
absence of any northerly storms from falling on the region, because this precipitation would shift
the isotopic balance back towards its current baseline. If a change in the frequency of southwesterly
storm frequency was coupled to changes in the isotopic composition of the vapor in the moisture
source regions themselves, it could further exacerbate the extent of isotopic variability. There is
currently no singularly satisfactory explanation that could reconcile the magnitude of the isotopic
change without calling upon profound global climatic or regional hydroclimatic mechanisms that
would be abundantly clear in other records. For example, land-falling atmospheric river events,
which I have shown can bring extremely enriched moisture to the region, are also accompanied
by major flooding (Ralph et al., 2006). Thus if the frequency of these events were to increase
dramatically, evidence of major flooding should be observable yet there is no definitive evidence
for this. An alternative explanation would be a shift in the seasonality of precipitation towards
more summer precipitation, which has been called upon to explain isotopic variability in other
records from the southwestern US on longer timescales (Asmerom et al., 2010). However, the
research on intra-annual isotopic cycles during the LIA (Chapter 3), show a lack of evidence for
summer moisture having an influence on the isotopic value of cellulose. The isotopic shift does
mark a significant change in the characteristics of the source water utilized by these trees during
132
this time interval. Atmospheric circulation changes are the obvious culprit based on previously
discussed results, but exactly the nature of these changes and the extent to which other biological or
hydrological factors played a role in exacerbating this shift in cellulose remains a topic of continued
inquiry.
5.6 Conclusions
Annually resolved records of the isotopic composition of precipitation from sites in the southwestern
US depict a coherent picture of storm track variability during the instrumental period. The poleward
shifts in the storm track lead to isotopic depletions caused by frontal storms tapping into poleward
moisture sources. The transient shifts in the storm track are driven by anomalous cooling in the east-
ern Tropical Pacific, which leads not only to isotopic depletions but also to precipitation anomalies.
The results suggest a tight bond between the convergence of low latitude moisture and drought in
the southwestern US. This relationship appears to break down over the most recent decade where
a sustained drought has been associated with rising isotopic values. The isotopic trends as well as
the spatial pattern of this current drought and SST anomaly patterns all agree on a dynamic distinct
from the preceding droughts as driving this event. It is tempting to suggest that the uniqueness of
the current drought is evidence that it is early evidence of the predicted transition towards a drier
southwestern US. Though it is not clear that the rising isotopic values signify a dynamic that is
entirely consistent with the mechanisms predicted to dry the southwest during the coming century.
Namely, moisture convergence from mean flow has been rising (or at least remaining stable) during
the current decade. Evidence to confirm this would require a more thorough analysis of the moisture
budget during previous droughts, which is not directly feasible without tracer simulations that have
been conducted during previous events of the mid 20
th
century. The major isotopic transition during
the mid 19
th
to early 20
th
marks a fundamental change in the coherent relationship between isotopes
and drought during the instrumental period. The magnitude of the change is enigmatically large
and cannot be explained by linearly extrapolating from the relationship observed between atmo-
spheric circulation and the isotopic composition of precipitation from observations and simulations
133
using 20
th
century boundary conditions. Understanding the meaning of this result clearly remains
an ongoing challenge.
134
Chapter6
EnigmaticisotopicresponsestoGreenland
InterstadialsincavesfromthesouthwesternUS
SUMMARY
Recently published isotopic records from speleothems in New Mexico and Arizona provide
an opportunity to consider the isotopic and climatic response of the southwestern US to the
abrupt climatic shifts that characterized the high latitudes during the Glacial Period (Green-
land Interstadials). Both records show the presence of millennial scale features that could
represent manifestations of the Greenland Interstadials. An analysis of the shape, timing and
phasing of these features in the southwestern US, provide convincing evidence that at one site,
there is a clear and in-phase response between Greenland climate shifts and the southwestern
US while at the other nearby site the relationship is far more ambiguous. Based on the results
from one site where the Greenland-southwestern US relationship is well-defined, I attempt to
quantify the climate response of the region to Greenland Interstadial events. Using numerous
combinations of instrumental relationships between the isotopic composition of precipitation
and surface climate, I cannot generate a satisfying depiction of the climate shifts at this time
that agree with other available hydroclimate records from lakes and thermal records from
Noble Gas concentrations in paleo-groundwaters. The analysis implies that overwhelming
kinetic influences on one of the stalagmites obscures quantitative paleoclimate estimates. The
other record might be a useful climate record but without additional constraints on the sea-
sonality of precipitation, only qualitative estimates are presented.
135
6.1 Introduction
In this final chapter I will review some additional published isotopic records from the southwestern
US. The discussion is a clear departure from previous chapters as it will focus on isotopic records
covering the last glacial period. These older records must be viewed in a different light because of
uncertainties in their age control and a lack of quantitative constraints on the calibration between
isotopic variability and climate during glacial boundary conditions. Nonetheless, the glacial records
provide insight into climatic/isotopic variability as a response to large climatic perturbations prior
to the quiescence of the Holocene.
During the last Glacial period (110ka-10ka), the high northern latitudes underwent a series
of 24 abrupt millennial-scale climate changes where temperatures in the polar regions rapidly
warmed by as much as 14
o
(Lang et al., 1999) and then slowly cooled back to glacial baseline
temperatures (Dansgaard et al., 1993). Considerable attention has been given to these events,
because their abruptness serves as a prominent example of the dynamic nature of the Earth’s climate
system. The warming events known as Dansgaard-Oeschger (DO) cycles or Greenland Interstadials
(GI) appear to have a quasi-periodic nature with a 1,470 year recurrence cycle (Bond et al., 2001).
The regularity of these events invokes an external forcing mechanism (deterministic) and it has
been been argued that these features are paced by oscillations in solar variability (Bond et al., 1993;
Schulz & Mudelsee, 2002; Braun et al., 2005). Other analyses of the periodicity of the interstadial
events suggest these events are inherently stochastic owing to a lack of evidence for a stationary
oscillatory signal (Fleitmann et al., 2009; Wunsch, 2000).
The Interstadial events are most clearly documented in the polar ice records and because of
rigorous efforts to model the Dd
18
O/T relationship, the magnitude of the temperature signal in
the high latitudes is fairly well constrained (Cuffey et al., 1994). Evidence from around the globe
suggests that the millennial climate events were not likely confined to the polar regions. Marine
sediment records from the North Pacific (Hendy & Kennett, 2000; Kotilainen & Shackleton, 1995),
tropical Pacific (Saikku et al., 2009; Stott, 2002), Arabian Sea (Schulz et al., 1998) and throughout
136
the Atlantic Basin (Bond et al., 1993) all show prominent millennial-scale temperature, salinity and
productivity changes which have a common shape and timing to the polar climate events. Terrestrial
records derived from lake sediments, speleothems, glacial extent and high altitude ice cores also
confirm similar abrupt events during this period (Benson et al., 2003; Wang et al., 2008; Fleitmann
et al., 2009; Leuschner & Sirocko, 2000; Heusser, 1998; V oelker et al., 2002). The ubiquitous pres-
ence of these millennial saw-toothed features in the geological record allow for a general statement
regarding their global impact but the exact nature of regional climates responses are not well under-
stood. One avenue of continued research on this topic involves an analysis of the phasing between
polar and subpolar signals, which is critical to distinguishing the climate mechanisms that modulate
the signal around the globe (Fleitmann et al., 2009; Burns et al., 2003; Genty et al., 2003; Wang
et al., 2006). A second avenue of research, which will be discussed in this chapter, involves further
efforts to quantitatively calibrate the proxy changes associated with these events (Lewis et al., 2010).
The emergence of speleothem records from sites around the globe has begin to fill in proxy
gaps through this time interval in many regions where previously little climatic information was
available. Speleothem proxies can be interpreted not unlike the isotopic composition of cellulose
in that they both largely reflect shifts in the isotopic composition of soil water. In the case of
speleothems, precipitation (which carries an atmospheric/climatic signal) percolates into the soil
and dissolves carbon dioxide as it flows downward into the underlying karst. Upon reaching
the cave system, the water has a higher concentration of dissolved carbon dioxide than the cave
atmosphere which drives degassing of the drip water (McDermott, 2004). This results in water
which is supersaturated with respect to carbonate and precipitation of calcium carbonate begins.
During this process, equilibrium isotopic exchange between the carbonate and the water embeds
the calcite with the isotopic composition of the water. The isotopic composition of the calcite can
therefore be used as a proxy for the isotopic composition of water that drips into the cave, which is
presumed to be controlled to a first-order by changes in the isotopic composition of the overlying
precipitation (McDermott, 2004). Additional, sources of isotopic variability in stalagmites include
the temperature at which the calcite forms, which controls the fractionation factor between
137
carbonate and water, and kinetic fractionation that occurs during rapid calcite formation (Kim &
O’Neil, 1997). The added sources of isotopic variability, which are not inherently related to the
isotopic composition of precipitation, presents intrinsic challenges to the use of these records as
paleoclimate archives as will be discussed later in this chapter.
The rising prominence of these records can be attributed in part to the fact that they can be
absolutely dated (i.e. not tied to another age model) using radiometric techniques, and therefore,
have the potential to address global phasing of Interstadial events (Genty et al., 2003). The
overwhelming conundrum in using speleothem records is how best to interpret the meaning of
the d
18
O variability. As discussed throughout the previous chapters, the complex atmospheric
circulation signal embedded in the isotopic composition of precipitation in many mid latitude
sites makes interpreting even modern records with the potential for good instrumental calibration
challenging. The issue of quantitatively calibrating an ice-aged speleothem is immensely onerous
in considering the uncertainties in how glacial boundary conditions might influence the response
function between the isotopic composition of precipitation and a climatic perturbation (i.e. a shift
in the storm track). Furthermore, the records must be interpreted amidst a tenuous assumption that
there have been no major perturbations in the hydrology of the karstic system or humidity changes
in the cave system that might drive kinetic fractionation.
In this chapter, I utilize two stalagmite records that were recently published from the Fort
Stanton Cave in New Mexcio (Asmerom et al., 2010) and Cave of the Bells in Arizona (Wagner
et al., 2010). The records are approximately 100 miles apart from one another, have comparable
age control and overlap for a 30,000 year period in which 9 Interstadial events occurred in the polar
latitudes. With the exception of these two new records, there are no other available time series with
the resolution necessary to assess how the southwestern US responded to the Greenland Interstadial
events. From the few available records, it can tentatively be inferred only that the region became
relatively drier and warmer during the interstadial events (Benson et al., 2003; Clark & Fritz, 1997).
After providing a brief introduction to these records and a review of how they have been interpreted,
138
I will assess the phasing of the climate response in the southwestern US relative to the interstadial
events in the North Atlantic. This topic is critical to understanding if the North Atlantic climate
changes were teleconnected to the region principally through an oceanic or atmospheric mechanism
or alternatively, that the abrupt events in the southwest have no causal relationship to the North
Atlantic events. This topic was not addressed by the authors who assumed the response between
the southwestern US and Greenland was instantaneous. Secondly, I will estimate a response
function between the North Atlantic and the Southwestern US by comparing the magnitude of each
Interstadial event to the isotopic response it generates in the southwestern US. In light of these two
analyses I will highlight how some peculiar differences between the Fort Stanton and Cave of the
Bell records shed light on precipitation and temperature changes over the southwestern US during
the abrupt North Atlantic climate events during the last glacial period.
6.2 Methods
The isotopic measurements and dating of these records is fully discussed in the original publications
and no modifications to the published age models are made (Wagner et al., 2010; Asmerom
et al., 2010). In addition to the Fort Stanton (FS) and Cave of the Bells (CoB) records, I use the
Greenland Ice d
18
O record on the age model of (Svensson et al., 2008) (NGRIP) to assess the
absolute timing and magnitude of the interstadial events in Greenland. I calculated the timing of
the beginning and end of each of the DO events with the use of a ramp function, which provides
an objective approach to identifying the beginning of the interstadial transitions and the timing
of the event’s peak. This calculation is done using the algorithm from (Mudelsee, 2000) and I
implement the procedure closely following the approach of (Fleitmann et al., 2009). I did not
independently calculate the peaks in the NGRIP record but utilized the interstadial delineations
from (Svensson et al., 2008). After distinguishing the events, each interstadial was placed on
a timescale relative to the North Atlantic event. In this way, the center of each Interstadial in
Greenland was given an age of 0 and negative (positive) ages indicate years prior to (after) the peak.
The corresponding events in the southwestern US are given dates relative to the North Atlantic
counterpart based on the difference between the absolute independently-derived ages. With this
139
analysis, if the peak of a southwestern interstadial had a negative (positive) age, it implies that
the response in the southwestern US lead (lagged) the corresponding North Atlantic event. This
provides one method to assess the phase relationship between Greenland and the southwestern US.
Additional analyses to test the issue of phasing is done in the spectral domain. Spectral analysis
of each record was calculated on raw time series’ using a Lomb-Scargle periodogram (Schulz &
Mudelsee, 2002) and phase and coherence was tested on interpolated time series (50 year time
steps) using a multi-taper coherence analysis based on the methods developed by Peter Huybers
(http://www.people.fas.harvard.edu/ phuybers/Mfiles/index.html). Lastly, wavelet coherence was
used to look at the stationarity of these phase relationships.
6.3 FortStantonandCaveoftheBells
The Fort Stanton Cave is located at 33.6
o
N and -106
o
W. The authors performed 1200 isotopic
measurements along the length of this stalagmite and the age model was generated by linearly
interpolating between 68 U/Th dates, which had an average uncertainty of150-200 years (Figure
6.1). The resolution of this record is approximately 37 years/sample. The authors did not monitor
drip water or the precipitation above the cave, but it was assumed that the isotopic composition
of precipitation in this area is correlated with temperature, which is consistent with the analysis
presented from the nearby Almagre Mountain site in Colorado (see Chapter 2). Based on both the
observation that cave humidity stayed very near to 100% throughout a single year of monitoring
and the absence of a relationship betweend
18
O andd
13
C, the authors assume kinetic fractionation
did not likely have a large influence on the isotopic variability (Asmerom et al., 2010). The abrupt
shifts in the Fort Stanton chronology are interpreted to arise from two primary influences, the
temperature influence on the isotopic composition of precipitation and changes in the seasonality
of precipitation. In this region precipitation is roughly split between summer monsoon and winter
frontal storm coming off the North Pacific. A reduction in the percentage of annual moisture
from winter storms or conversely an increase in monsoon rainfall would both act to drive the
140
-11
-10
-9
-8
-7 -12.0
-11.0
-10.0
-9.0
-46
-44
-42
-40
-38
32000 36000 40000 44000 48000 52000
Age (BP)
δ
18
O
δ
18
O
δ
18
O
NGRIP
CoB
Stanton
Figure6.1: Time series of NGRIP (Svensson et al., 2008), Cave of the Bells (Wagner et al., 2010), and
Fort Stanton (Asmerom et al., 2010). All shown on independent timescales described in the original
publications
annually-weighted isotopic composition of precipitation higher. These two positive influences are
offset by the fact that warmer temperatures reduce the fractionation factor between calcite and
water and consequently drive the isotopic composition of calcite down during warmer periods. The
record is cumulatively taken as evidence of a warmer climate and a reduction in moisture delivered
from winter storms as a consequence of a poleward shift in the storm track. These changes are
assumed to occur more or less in phase with changes in the North Atlantic.
The Cave of the Bells is located at 31
o
N and -110
o
W outside of Tucson, Arizona (Wagner
et al., 2010). The record was generated from approximately 1200 isotopic measurements and
141
the age model was made by linear interpolation between 62 U/Th with uncertainty of around
150-200 years. The record has a similar resolution as the Fort Stanton cave though its average
isotopic composition is about 1-2‰ more depleted and shows roughly half the range of variability.
Monitoring of precipitation from nearby Tucson indicates that at this site, the isotopic composition
of precipitation is driven by changes in the amount of precipitation. The authors find a weak
correlation between the isotopic composition of precipitation and temperature during any season.
Monitoring of drip water in this cave for multiple years indicates that the drip water is isotopically
stable (no seasonality) and is very similar to the isotopic composition of winter precipitation. It is
assumed from this observation that monsoonal moisture is lost through evapotranspiration and does
not percolate into the cave system. The abrupt changes in the Cave of the Bells record is taken to
exclusively represent changes in the amount of winter precipitation that fell. A similar equatorward
shift of the storm track is discussed as the driver of the aridity during the interstadial events. A lack
of correlation betweend
18
O andd
13
C are taken as evidence for a lack of kinetic enrichment of the
calcite (Wagner et al., 2010).
6.4 IsotopicModeling
I performed an independent analysis on the surface climate controls of the isotopic composition of
precipitation by correlating monthly temperatures and precipitation against the isotopic composition
of precipitation between 1979-2008 from the IsoGSM simulation (Yoshimura et al., 2008). The
model is useful in providing a longer and continuous dataset by which to assess the cause of isotopic
variability and because of the nudging routine, the simulation is more accurate than other isotope-
GCM simulations at simulating observed isotopic variability (Yoshimura et al., 2008). At the Cave
of the Bells sites, the GCM simulation confirms the findings of (Wagner et al., 2010) which is an
absence of any correlation between the isotopic composition of precipitation and temperature and
a negative relationship between the isotopic composition of precipitation and precipitation amount
(Figure 6.2). The negative relationship between precipitation amount and the isotopic composition
142
of precipitation is often referred to as the amount effect, which describes a mechanistically-complex
but statistically simple relationship that prevails in some low latitude and continental locations
(Rozanski et al., 1993). The amount effect arises because of both raindrop processes (exchange with
atmospheric moisture below the cloud base and evaporation) and through larger scale atmospheric
processes, which lead to the progressive depletion of an air mass with increasing precipitation (dis-
tillation) (Lee & Fung, 2008). At this site under current climatic conditions, a50% reduction in
winter precipitation amounts would result in a 1‰ drop in the isotopic composition of precipitation.
Although the Fort Stanton site is only 100 miles away, the analysis suggests a uniquely dif-
ferent relationship between the isotopic composition of precipitation and local climate. Here, the
isotopic composition of precipitation displays a weak relationship to surface temperature but no
apparent relationship to precipitation amount. The noisy relationship between surface temperature
and the isotopic composition of precipitation yields a best-fit linear slope of 0.52‰/
o
C for winter
precipitation and 0.47‰/
o
C for summer precipitation (Figure 6.2). This is slightly smaller than
the slope assumed by (Asmerom et al., 2010) who used the average middle latitude relationship
between surface temperature and the isotopic composition of precipitation from (Rozanski et al.,
1993). Because winter and summer storms contribute equally to the annual budget, I take an
average between the winter and summer slopes as an estimate of the cumulative temperature effect
at this site.
6.5 Results
6.5.1 TimingandShapeofEvents
A ramp function (Mudelsee, 2000) was used to identify the beginning and peak of each of the 9
Greenland Interstadial events identified a priori by (Wagner et al., 2010; Asmerom et al., 2010)
in the Fort Stanton and Cave of the Bells records. In Figure 6.3, I show a stack of each of the
interstadial events after their age has been centered around the peak of the event and an arithmetic
mean ”Interstadial Event” is calculated. In comparing the mean shape of the response function at the
143
-10
-8
-6
-4
-2
0
2
4
6
-6 -4 -2 0246
Precip at Fort Stanton
M=0.50
R=0.32
Monthly ONDJ Temperature (C)
Monthly-weighted δ
18
O
-8
-6
-4
-2
0
2
4
6
-2 -1 0123 4
M=-1.0
R=0.47
Monthly NDJF Precipitation (Normalized)
Monthly-weighted δ
18
O
Precip at Fort Stanton
Figure6.2: Slope between amount-weighted monthly modeled isotopic composition of precipitation and
precipitation amount (normalized) at the grid point nearest to the Cave of the Bells Site for winter months
between 1979-2008 (top). Slope between amount-weighted monthly modeled isotopic composition of
precipitation and monthly temperature at the grid point nearest to the Fort Stanton site (bottom).
144
two cave sites, it is clear there are some fundamental differences relative to the well-defined abrupt
rise/shallow decline of the Greenland events. The cave records display a noisy, quasi-parabolic
shape. If indeed the millennial-features in the cave records are triggered by changes in the North
Atlantic, the difference in shape could be attributed to the following: 1) a slow climatic transition in
response to the rapid high altitude warming 2) multiple competing climatic factors that obscure the
predicted sawtooth pattern 3) an intrinsic aspect of the mixing of waters in the overlying karst that
naturally incorporates waters of differing ages into the flow or 4) non-linear aspects of the proxy
records driven by kinetic effects.
The timing of the peak of each of the interstadial events in the southwestern US is compared
against the timing of the NGRIP events (Figure 6.4). The Interstadials at Cave of the Bells
consistently lead the corresponding event in the North Atlantic sometimes by as much as 1,000
years. The exact magnitude of this lead varies considerably between events, and when considering
the age uncertainties and the spread between events, it cannot be stated with certainty that the lead
is in fact significant. Therefore, on the basis of this analysis, the peaks of the millennial features
of the Cave of the Bells site appear approximately in phase with the Greenland Interstadials. On
the contrary, a similar exercise with the Fort Stanton Cave shows a persistently lagged response
between the Greenland Interstadials and the isotopic peak. The lag is on average approximately 500
years and is significant within one standard deviation around the mean of the 9 lag times. This was
a rather surprising result as these two caves from the same general region show unique temporal
relationships in their response to what is assumed to be the same forcing. It should be noted that the
U/Th-derived ages for each of these events was generated in different labs, and it is indeed possible
that the differences of the timing of these events arise purely as an analytical artifact. In light of the
results, it might be worth addressing this discrepancy through a lab intercomparison study but this
is not a topic to be addressed any further in this chapter. Another important source of uncertainty is
that a population of only 9 overlapping interstadials was used and a greater number of events would
145
-44
-43
-42
-41
-40
-39
-38
-11
-10
-9
-8
-7
-1200 -1000 -800 -600 -400 -200 0 200
-11.0
-10.5
-10.0
-9.5
-11.5
δ
18
O
δ
18
O
δ
18
O
Year Relative to Peak
Figure 6.3: Shape of the 9 DO events at each site, where each event was centered at its peak and the
mean cycle calculated (color). The well-defined sawtooth in the NGRIP record erodes quite substantially
in the speleothem records.
146
-44
-42
-40
-38
-36
-34
-1200 -800 -400 0 400 800 1200
Years Relative to Greenland Peak
CoB
Stanton
δ
18
O
Figure 6.4: The ”average” interstadial event generated from the average of 9 events from the NGRIP
record (blue) alongside the peaks of each of the events at the two sites (red is CoB and green is Stanton).
The error bars are the uncertainty of the timing of the peak based on the U/Th ages. The height of each
of the peaks is based on the magnitude of the event. This is to show that the relative lead or lag is not a
function of the size of the event. The CoB peaks tend to scatter just before the NGRIP events while the
Stanton events tend to lag the NGRIP events.
be needed to constrain the actual magnitude of the lead at the Cave of the Bells site and the lag at
the Fort Stanton site.
A further series of analyses are conducted to look at the spectral properties of these two
records. For each of the records, a Lomb-Scargle periodogram is calculated using a Welch Window
and 3 overlapping segments. We tested the null hypothesis red-noise background with no significant
periodic components by means of Monte Carlo realizations of a first-order autoregressive process
for unevenly spaced time series (Schulz & Mudelsee, 2002). The NGRIP record displays the
well-characterized1500-year cycle but lacks any other significant power in the millennial to
multi-centennial range (Figure 6.5). The Cave of the Bell sites shows a similar though broader
peak with nearly the same period. There are additional significant peaks in the (multi)centennial
band that do not appear in the North Atlantic record. The power spectrum of the Fort Stanton site
has a much different character than either of the other two sites. Focussing on the window near
147
the1500-year cycle, there is significant power in this range, but not a singularly defined peak as
at the other sites. The Fort Stanton record in fact shows significant power throughout almost the
entire spectrum. This analysis alone, highlights differences in the nature of the variability between
the speleothem records. The complexity of the power spectrum at the Fort Stanton site suggests
multiple competing influences on the isotopic variability that might reflect real climatic signals or
alternatively mineralogical or isotope-kinetic effects.
Coherency and phase angle of the records from both cave sites is tested against the NGRIP
record (Figure 6.6). To do this test, I interpolated the records to a common 50-year age model.
Comparison of the time series between raw and interpolated records, suggest this had only minor
influences on the variability of the records. The Fort Stanton record had no significant coherent
bands with the NGRIP record (not shown) while the Cave of the Bells record has a coherent peak
near 1500 years. An analysis of the phase angle shows that with 95% confidence, the two are in
phase in this window (Figure 6.6). Cross wavelet analysis is used to test the stationarity of the
phasing (Figure 6.7) following Grinsted et al. (2004). There is broad low frequency coherence in
both records with the North Atlantic but only the Cave of the Bells records shows consistently
in-phase variability.
The suite of analyses does not lead to strong feeling of confidence that the Fort Stanton
record is systematically related to the North Atlantic. There are clearly millennial scale features
in the record, but the exact temporal relationship with interstadials is ambiguous. The Cave of
the Bells site displays a more clearly defined and temporally comprehensible relationship with
the Greenland Interstadials. It is interesting to note these differences because in all likelihood
the actual climate at these two nearby sites responded similarly to the Glacial Interstadials. The
coherent relationship between Cave of the Bells and North Atlantic could be taken as confirmation
of a rapid atmospheric bridge (jet stream response) that was proposed by (Wagner et al., 2010) to
link the southwestern US climate to the North Atlantic events. The more complex and potentially
148
10
4
100 1000
10
3
10
2
10
1
10
0
10
-1
100 1000
10
3
10
2
10
1
10
0
10
-1
100 1000
10
4
10
3
10
2
10
1
10
0
10
-1
Frequency
Power (dB) Power (dB)
Power (dB)
~1500 year
NGRIP
CoB
Stanton
Figure 6.5: Lomb-scargle periodogram for the three time series using the original age models with no
linear interpolation performed. Analysis was done using a Welch window and the dotted line denotes
the 99% confidence interval based on a 5000 iteration Monte Carlo simulation against a red noise back-
ground (Schulz & Mudelsee, 2002). The commonly cited 1500 year cycle is the NGRIP record is marked
on each of the records.
149
0.01 0.02 0.03 0.04 0.05 0.06 0.07
0
0.2
0.4
0.6
0.8
1
Coherence
0.01 0.02 0.03 0.04 0.05 0.06 0.07
−100
0
100
frequency
Phase Angle
Figure 6.6: Coherence and phase angle between the CoB and NGRIP records using the multi-taper
coherence method. The gray bar denotes to 1500 year cycle, which is coherent in both records and the
phase angle suggests the two are nearly in phase.
significant lag at Fort Stanton, if taken to be causal (which will be further addressed), would imply
a mechanism that delayed the response. Probable mechanisms to delay the response would be an
oceanic bridge (Fleitmann et al., 2009; Winograd et al., 2006), hydrology or slowly varying regional
changes such as forest dynamics or groundwater changes.
6.5.2 ResponsebetweenNorthAtlanticandSouthwesternUS
An alternative test of the relationship between the North Atlantic and southwestern US is considered
by calculating the slope of the magnitude of the Greenland Interstadials with their southwestern
US equivalent. In this analysis it is assumed that if the two are causally related than a large
interstadial would lead to a large isotopic response in the southwestern US. This relationship could
be linear or alternatively non-linear, for example where the impact of a Greenland interstadial on
the southwestern US only occurs above a certain threshold. In Figure 6.7 I show the relationship
between the Greenland Interstadials and those at the Fort Stanton site. The correlation coefficient
150
Period
35350 37850 39350 41850 43350 45850 47350 49850 52350
4
8
16
32
64
128
256
512
1024
Year (bp)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
35350 37850 39350 41850 43350 45850 47350 49850 52350
Year (bp)
Power
Figure 6.7: A cross wavelet coherency analysis between the NGRIP and CoB (left) and NGRIP and
Stanton (right). Significance coherency is shown in red tones, with significant regions denoted by a
heavy black line. The arrows are used to show the phasing with arrows to the right indicating in phase
behavior.
is 0.8 with a slope of 0.64‰/‰. The Cave of the Bells record has a similarly good fit (0.77) if
one event (DO 13) is removed, otherwise the fit is reduced to 0.50. The slope at the Cave of the
Bells site is notably smaller (0.4‰/‰) than observed at the Fort Stanton site. This analysis leads to
increased confidence that the millennial events at the Cave of the Bells site are causally linked to
the Greenland Interstadials. The analysis also suggests potentially a causal relationship between the
North Atlantic and the Fort Stanton despite the lack of a convincing relationship based on analyses
of the spectral domain. The difference between the slopes at the two sites however provides yet
another enigmatic aspect of the distinction between these two nearby records.
6.6 Discussion
It is rare to have the fortune of two proxy records so near to one another of the same type that are
independently-dated and overlapping for multiple thousands of years. I will start by discussing an
idealized perspective that the millennial isotopic events in the these two records are exclusively
tracking changes in precipitation amount and temperature. At the Cave of the Bells site, temperature
151
0.5
1
1.5
2
2.5
3
3.5
4
4.5
44555.566.577.5 8
DO Magnitude Greenland (per mille)
DO Magnitude Southwest (per mille)
m=0.27
r=0.50
m=0.40
r=0.77
m=0.64
r=0.80
Stanton
CoB
Figure 6.8: Linear fit between 9 DO events in NGRIP and CoB (red) and Fort Stanton (purple) caves.
The fit is improves if DO event 9 is removed from CoB record. Both fits are high significant and the
slopes are statistically different.
has no apparent influence on the isotopic composition of precipitation. Therefore the influence of
temperature on the isotopic composition of the calcite is only through changes in the fractionation
factor between water and calcite, which is fixed at -0.24‰/
o
C (Kim & O’Neil, 1997). The influence
of precipitation at Cave of the Bells is predicted based on the amount effect relationship shown in
Figure 6.2. At the Fort Stanton site the temperature relationship arises from the correlation shown
in Figure 6.2, which is offset by -0.24‰/
o
C (Kim & O’Neil, 1997). This leads to a cumulative
temperature influence of 0.26‰/
o
C. There is no observable amount effect at the Fort Stanton site,
however, Asmerom et al. (2010) suggest that because of the seasonal difference between winter
and summer season precipitation, a loss of winter precipitation would lead to an increase in the
isotopic composition of precipitation. The gradient between summer and winter values is 7‰,
which leads to a 0.9‰ decrease in the isotopic composition of precipitation for every 10% loss in
152
winter precipitation. Using these relationships with uncertainty estimated from the instrumental
calibration, I write a series of equations shown below to solve for temperature and precipitation
during an average-sized interglacial (3.7‰ at Fort Stanton and 1.2‰ at Cave of the Bells).
3:7h=[(0:5 0:15) 0:24)xT)+((0:9 0:2)xP)] (6.1)
1:2h=[(0:24xT)+((0:2 0:1)xP)] (6.2)
Using this series of equations (6.1 for Fort Stanton and 6.2 or Cave of the Bells) where T
is mean temperature and P is a 10% change in winter precipitation, I estimate that the millennial
oscillations in the record, were associated with a 0.39.5
o
decrease in temperature coupled with a
439% decrease in winter precipitation. The reduction in precipitation is consistent in magnitude
as predicted by (Benson et al., 2003) based on lake sediments. There are no existing constraints on
the temperature changes in the region during Interstadials and a lack of temperature response could
be a real possibility. However, the glacial-interglacial temperature difference in the southwestern
US based on Noble Gas measurements suggest a 5-6
o
temperature change (Stute et al., 1992). If
the interstadial events are considered transient analogs to glacial-interglacial transitions, than a lack
of temperature change doesn’t seem probable. If I fix the temperature change at 5-6
o
, based on
the Noble Gas estimates, than the predicted decrease in precipitation based on the modern amount
affect relationships would predict a 30% loss of winter moisture at the Fort Stanton site with a
160% loss of winter moisture at the Cave of the Bells site. This is clearly an unreasonable scenario
and even any values approaching this would have to be rejected because there is a minimum amount
of percolation that would be required to continue stalagmite growth.
153
In these equations an assumption is made that monsoon moisture presents a fixed contribu-
tion to the moisture budget. As a first order attempt to generate a more realistic depiction of the
climate change in the region during interstadial events, more thorough constraints on precipitation
seasonality would be required. Both authors, assume a fixed contribution of monsoonal rains
however, it would be curious to test if there is a dynamical relationship between North American
Monsoon strength and the surface temperature. The dynamics of the North American Monsoon
are not well understood, but if rising surface temperature led to an increase in the strength of the
summer monsoon than the slope of the ”amount effect”, which assumed a stable contribution of
summer moisture, would have to be tied to the temperature term and steepened considerably at both
sites. This would require the addition of terms to account for both summer and winter contributions,
the latter of which would vary as a function of surface temperature. This system of equations would
no longer be solvable without constraints on additional variables. The role of monsoonal moisture
in driving isotopic variability at the Cave of the Bells would be particularly complex because
monsoon moisture currently does not percolate into the system. Therefore its presence over time
would be intermittent and likely would only occur when a certain threshold of the system is crossed.
This would add a highly non-linear aspect of the isotopic variability at this site.
An additional consideration needs to be given to the isotopic variability that would arise due
to kinetic fractionation. The likelihood of kinetic effects would increase with drying conditions,
and thus as the system begins to dry out and isotopic values rise, along the trajectory of the amount
effect, the possibility for a nonlinearity could arise from non-equilibrium processes. This could be
addressed through analyses of the relationship between d
18
O and d
13
C during the peaks of these
millennial events. The authors have not yet made d
13
C data available to conduct this analysis. I
assume, a priori that kinetic effects do play a significant role at least in the variability of the Fort
Stanton site. The peaks are almost double the magnitude of the analogous peaks at the Cave of
the Bells and considerably strange aspects of the record including the timing of the peaks and the
noisy power spectrum suggest the presence of variability that may not be climatically related. The
154
presence of such effects, unfortunately, deeply hinder the potential to make quantitative climatic
estimates using this proxy approach.
6.7 Conclusions
New isotopic chronologies from nearby caves in New Mexico and Arizona provide an opportunity
to assess the climatic response of the southwestern US to Interstadial events. Upon exploring some
basic aspects of these two nearby records, it is clear that there are some distinctions between the
records, which would not be expected based on their nearby proximity. The Cave of the Bells
site shows numerous signs that it responds predictably to North Atlantic forcing though it is far
more ambiguous whether the millennial feature at the Fort Stanton site are of the same nature.
When the two records are used to try and reconstruct the thermal and hydroclimatic changes during
these intervals, the results suggests no temperature change but a precipitation drop consistent with
previous estimates. This needs to be considered a real possibility, in the absence of any additional
information to directly contradict it. When independent temperature estimates are a priori assumed
based on the regional glacial-interglacial temperature difference, the precipitation response is
demonstrably larger and not feasible. I hypothesize that two additional terms would be needed to
come to a more satisfying climate reconstruction. One would involve the strength of the North
American Monsoon and whether it is dynamically linked to regional surface temperature variability
and the second would involve kinetic effects that would accompany a severe drying. The former
term would require climatic modeling of the North American Monsoon system but because of the
localized nature of the convective-driven precipitation in this system, its response to climate forcing
has thus far not been consistently modeled. The latter term could be addressed using evolutive
correlation analysis between d
18
O and d
13
C particularly to see if the slope changes (i.e. kinetic
enrichment) during periods when it was presumed to be dry.
155
Speleothem records are emerging at a rapid rate but as this study emphasizes their utility
requires some challenging issues that must be overcome. For example, at this site the presence of
multiple nearby records shed important light on peculiar isotopic behavior on one of these records
that otherwise could not have been appreciated. Secondly, additional constraints for example on
cave temperature can now be provided through noble gas paleothermometry of fluid inclusions
(Kluge et al., 2008). If these measurements become routine, than they should be regularly generated
alongside thed
18
O variability to remove uncertainties regarding the temperature-dependence of the
fractionation between calcite and water. Lastly, kinetic effects are usually considered unimportant
if the relationship between all pairedd
18
O andd
13
C measurements is not significant. However, it
is more critical to test for kinetic effects intermittently throughout the record as opposed to cumu-
latively. In semi-arid records, such affects would be most detrimental during the dry (enriched)
intervals and it is during these time intervals where kinetic effects should be rigorously tested for.
156
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Appendix
*******************Roden Model for IsoGSM***************
*This script is designed to be used with the GrADS software program.
Inputs and parameters are
specific to the IsoGSM simulation (Yoshimura et
al. (2008).
The script could be modified to used with other isotope-enabled simulations.
’reinit’
’open /pgb
m
on s1:ctl
0
opensout puts f romisotopesimulations(atmospheric)
0
open = f lx
m
on s1:ctl
0
opensout puts f romisotopesimulations( f lux)
*****This section is where user defines some things about their tree******
llon=-118.8 ***longitude of site
llat=36.6 ***latitude of site
styr=1980 ***start year
edyr=2008 ***end year
stt=(styr-1979)*12+1
edt=(edyr-1979)*12+12
’set lat ’llat”
’set lon ’llon”
’set t ’stt’ ’edt
180
’set z 1’
**define first month of growing season, enter month after ”=”
gsbn=6
’define gsb=’gsbn
**define last month of growing season, enter month after ”=”
gsen=9
’define gse=’gsen
**definition of ”water year”, which spans from the month after the
**end of the growing
**season to the end of the next growing season
wybn=gsen-11
wyen=gsbn
’define wyb=gse-11’
’define wye=gsb’
**define stomatal conductance (mol*m-2*s-1)
’define sc=0.0001’
**define boundary layer conductance (mol*m-2*s-1)
’define bc=0.0004’
*define difference between air temperature and leaf temperature
’define dt=6’
*this section should not be modifed by user!
****fractionation factors from Majoube et al., 1971
’define ak=1.032’
’define kb=1.021’
’define ro=0.0020052’
181
’define ff=27’
’define fre=42/100’
*define growing season temperature
’define tmp1=ave(tmpprs, t+’gsbn-1’, t+’gsen-1’)’
’define tmp=tmp1-273’
*define growing season temperature at leaf surface
’define atmp=tmp+dt’
*define growing season relative humidity
’define rh=ave(rhprs, t+’gsbn-1’, t+’gsen-1’)’
*define growing season barometric pressure
’define bp=(ave(pressfc, t+’gsbn-1’, t+’gsen-1’))/1000’
*define average growing season atmospheric water del18O value
’set dfile 2’
’set lat ’llat”
’set lon ’llon”
’set t ’stt’ ’edt
’define dl=(pwat1/pwatclm-1)’
’define dl1=dl*1000’
’define dv=ave(dl1, t+’gsbn-1’, t+’gsen-1’)’
**define weighted average source water isotopic value, which is
**the amount-weighted del18O value from the end of the previous
182
**year’s growing season to the end of the current growing season
’define sw1=(prate1/pratesfc-1)’
’define sw2=sw1*1000’
’define sw3=sw2*pratesfc’
’define sw4=sum(sw3, t+’wybn-1’, t+’wybn+10’)’
’define sw5=sum(pratesfc, t+’wybn-1’, t+’wybn+10’)’
’define sw6=sw4/sw5’
’define sw=ave(sw6, t+’wybn-1’, t+’wybn+10’)’
’set dfile 1’
*Calculate Leaf vapor pressure (ei) in kPa
’define ei=(101325*EXP((((-0.1299*(1-(373.16/(273.16+atmp)))-0.6445)*
(1-(373.16/(273.16+atmp)))-1.976)*(1-(373.16/(273.16+atmp)))
+13.3185)*(1-(373.16/(273.16+atmp)))))/1000’
*Calculate saturation vapor pressure (esat) in kPa
’define esat=(101325*EXP((((-0.1299*(1-(373.16/(273.16+tmp)))-0.6445)*
(1-(373.16/(273.16+tmp)))-1.976)*(1-(373.16/(273.16+tmp)))
+13.3185)*(1-(373.16/(273.16+tmp)))))/1000’
*calculate ambient vapor pressure (ea) in kPa
’define ea=(rh/100)*esat’
*Calculate leaf conductance of water vapor (gi) in mol*m-2*s-1
’define gi=1/(1/sc+1/bc)’
*calculate leaf transpiration (et) in mol*m-2*s-1
’define et=((ei-ea)/bp)*gi’
*calculate leaf water vapor fraction (wi) as a molar fraction
’define wi=ei/bp’
*calculate ambient water vapor (wa) as a molar fraction
183
’define wa=ea/bp’
*calculate leaf surface water vapor (ws) as a molar fraction
’define ws=((sc*wi)-et*(1 -wi/2))/(sc - et/2)’
*calculate vapor pressure at leaf surface (es) in kPa
’define es=ws*bp’
*calculate temperature-dependent equilibrium fractionation
*factor between vapor and liquid water at leaf surface (ef)
’define ef=EXP((1.137*1000/(273.16+atmp)*(273.16+atmp))-
(0.4156/(273.16+atmp))-0.0020667)’
*calculate isotopic composition of source water as an isotopic ratio, R-value (rsw)
’define rsw=ro*(1+sw/1000)’
*calculate isotopic composition of atmospheric water vapor as an isotopic ratio,
R-value, (ratm)
’define ratm=ro*(1+dv/1000)’
*calculate isotopic composition of leaf water as an isotopic ratio, R-value (lho)
’define lho=1*((ak*ratm*((ei-es)/ei))+(kb*ratm*(es-ea)/ei)+(rsw*ea/ei))’
*calculate delta value for leaf water (ldho)
’define ldho=((lho/ro)-1)*1000’
*calculate delta value for annual integrated cellulose (celld)
’define celld=fre*(sw+ff)+(1-fre)*(ldho+ff)’
’set t ’stt’ ’edt
’set missconn on’
****display reconstructed cellulose
’d skip(celld,12)’
******Moisture Divergence from various contributions using IsoGSM*****
184
***This script is intended to replicate the analysis of Seager et al., 2007
where the precipitation deficit is separated
into eddy, humiditiy and mean flow contributions. We use the model
*outputs from the ECPC GSM model to perform this calculation
***Choose Model type
run=s1
’reinit’
***Open model outputs (trans is transients, pgb is atmospheric and flx is surface flux)
’open /home/kyoshimura/IsoGSM1/pgbmon-’run’.ctl’
’open /home/kyoshimura/IsoGSM1/transmon-’run’.ctl’
’open /home/kyoshimura/IsoGSM1/flxmon-’run’.ctl’
*** define region
’set lat -20 70’
’set lon -190 70’
***define atmospheric levels
’set z 1 17’
***define first and last months of season (annual =1-12)
’set t 1 12’
***Define climatology for specific humidity and u and v winds
’qbarp=ave(spfh,t+0,t=240,12)’
’ubarp=ave(ugrdprs,t+0,t=240,12)’
185
’vbarp=ave(vgrdprs,t+0,t=240,12)’
’modify qbarp seasonal’
’modify ubarp seasonal’
’modify vbarp seasonal’
’set z 1’
’set t 1 360’
***Define divergence integrated through the atmospheric column
*** Where ”a” is contribution from mean flow, ”b” is contribution from humidity and
”c” is contribution from transient eddies
’a=-hdivg(vint(pressfc/100,(ugrdprs-ubarp)*qbarp,10),vint(pressfc/100,
(vgrdprs-vbarp)*qbarp,10))’
’b=-hdivg(vint(pressfc/100,(spfh-qbarp)*ubarp,10),vint(pressfc/100,
(spfh-qbarp)*vbarp,10))’
’c=-hdivg(vint(pressfc/100,uq.2-ugrdprs*spfh,10),vint(pressfc/100,
vq.2-vgrdprs*spfh,10))’
’set z 1’
’set t 1’
’c’
***Aesthetic choices....
’set gxout shaded’
’set clevs -100 -80 -60 -40 30 -20 -10 -5 5 10 20 30 40 60 80 100’
***This sections displays the mean flow contribution
’d ave(a,t=241,t=360)*60*60*24*100’
’run cbarn’
186
’set gxout contour’
’set ccolor 1’
’set clevs -100 -80 -60 -40 30 -20 -10 -5 5 10 20 30 40 60 80 100’
***This section shows precipitation deficit and mean flow of recent 10 years
*** relative to previous 20 years
’display
(ave(pratesfc.3-lhtflsfc.3/2500000,t=241,t=360)
-ave(pratesfc.3-lhtflsfc.3/2500000,t=1,t=240))*60*60*24*100’
’set lat 0’
’set lon 0’
’display
ave(aave(maskout(lterp(a,landsfc.3),landsfc.3-1)
,lon=-125,lon=-95,lat=25,lat=40),t=241,t=360)*60*60*24’
res=sublin(result,2)
anum=subwrd(res,4)
’draw title A Mean Circulation Contribution [1e-2 mm/d] (2008-1999) -
(1979-1998) ‘3D‘0a=’anum
***Print figure if you want it for safe-keeping
’print -R seagerfig4a.eps’
’! ps2img.sh seagerfig4a.eps seagerfig4a.gif’
’c’
***This sections displays the specific humidity contribution
’set grads off’
’set lat -10 60’
187
’set lon -180 60’
’set gxout shaded’
’set clevs -100 -80 -60 -40 30 -20 -10 -5 5 10 20 30 40 60 80 100’
’d ave(b,t=241,t=360)*60*60*24*100’
’run cbarn’
’set gxout contour’
’set ccolor 1’
’set clevs -100 -80 -60 -40 30 -20 -10 -5 5 10 20 30 40 60 80 100’
***This section shows precipitation deficit and specific humidity
changes of recent 10 years relative to previous 20 years
’display
(ave(pratesfc.3-lhtflsfc.3/2500000,t=241,t=360)
-ave(pratesfc.3-lhtflsfc.3/2500000,t=1,t=240))*60*60*24*100’
’set lat 0’
’set lon 0’
’display
ave(aave(maskout(lterp(b,landsfc.3),landsfc.3-1),
lon=-125,lon=-95,lat=25,lat=40),t=241,t=360)*60*60*24’
res=sublin(result,2)
anum=subwrd(res,4)
’draw title B Humidity
Contribution to Mean [1e-2 mm/d] (2008-1999) - (1979-1998) ‘3D‘0b=’anum
***Print figure if you want it for safe-keeping
’print -R seagerfig4b.eps’
’! ps2img.sh seagerfig4b.eps seagerfig4b.gif’
’c’
***This sections displays the transient eddy contribution
188
’set grads off’
’set lat -10 60’
’set lon -180 60’
’set gxout shaded’
’set clevs -100 -80 -60 -40 30 -20 -10 -5 5 10 20 30 40 60 80 100’
***This section shows precipitation deficit and eddy
transport changes of recent 10 years
*** relative to previous 20 years
’display
(ave(c,t=241,t=360)-ave(c,t=1,t=240))*60*60*24*100’
’run cbarn’
’set gxout contour’
’set ccolor 1’
’set clevs -100 -80 -60 -40 30 -20 -10 -5 5 10 20 30 40 60 80 100’
’display
(ave(pratesfc.3-lhtflsfc.3/2500000,t=241,t=360)-
ave(pratesfc.3-lhtflsfc.3/2500000,t=1,t=240))*60*60*24*100’
’set lat 0’
’set lon 0’
’display
(ave(aave(maskout(lterp(c,landsfc.3),landsfc.3-1),
lon=-125,lon=-95,lat=25,lat=40),t=241,t=360)
-ave(aave(maskout(lterp(c,landsfc.3),landsfc.3-1),
lon=-125,lon=-95,lat=25,lat=40),t=1,t=240))*60*60*24’
res=sublin(result,3)
anum=subwrd(res,4)
’draw title C Transient Contribution [1e-2 mm/d]
(2008-1999) - (1979-1998) ‘3D‘0c=’anum
189
***Print figure if you want it for safe-keeping
’print -R seagerfig4c.eps’
’! ps2img.sh seagerfig4c.eps seagerfig4c.gif’
190
SUPPLEMENTARY DATA TABLE
MAXBERKELHAMMER
TABLE1. IsotopicResultsfromWhiteMountainBristleconePine
Date Sampletype SampleID BeamSize(nA) weight(mg) d18O Instrument
6/21/06 Std. Baker 8.586504 0.533 29.47715 IsoPrime
6/21/06 Std. Baker 9.976476 0.628 29.74044 IsoPrime
6/21/06 Std. Baker 4.767247 0.335 29.39369 IsoPrime
6/21/06 Std. Baker 4.948038 0.339 29.40591 IsoPrime
6/21/06 Std. Baker 6.673579 0.435 29.4634 IsoPrime
6/21/06 4 2 2006 4.464519 0.344 20.70733 IsoPrime
7/13/06 4 2 2006 8.306839 0.552 19.67008 IsoPrime
7/6/06 4 1 2005 7.424889 0.534 18.65483 IsoPrime
7/13/06 4 2 2005 6.859134 0.46 20.94614 IsoPrime
7/6/06 4 1 2004 6.570584 0.46 18.14872 IsoPrime
7/14/06 4 1 2004 7.163906 0.508 19.46501 IsoPrime
7/6/06 4 1 2003 7.424889 0.534 18.65483 IsoPrime
6/21/06 Std. Baker 7.089727 0.407 29.05518 IsoPrime
6/21/06 Std. Baker 9.33565 0.591 29.34316 IsoPrime
6/21/06 Std. Baker 8.031091 0.531 29.40905 IsoPrime
7/14/06 4 1 2003 7.163906 0.508 19.46501 IsoPrime
7/7/06 4 1 2002 6.467578 0.475 20.70533 IsoPrime
7/7/06 4 1 2000 7.099361 0.507 18.78772 IsoPrime
7/8/06 4 1 1999 8.91424 0.605 20.54343 IsoPrime
7/14/06 4 1 1999 5.547372 0.404 19.68662 IsoPrime
7/8/06 4 1 1998 6.019676 0.434 18.11589 IsoPrime
7/10/06 4 1 1997 7.317852 0.52 16.54999 IsoPrime
7/10/06 4 1 1996 7.166183 0.502 18.83199 IsoPrime
7/14/06 4 1 1996 7.822323 0.549 19.43666 IsoPrime
6/21/06 Std. Baker 6.601152 0.431 29.44619 IsoPrime
6/21/06 Std. Baker 6.665466 0.456 29.60681 IsoPrime
7/11/06 4 1 1995 6.553135 0.467 17.05198 IsoPrime
7/13/06 4 1 1995 5.893516 0.429 17.64022 IsoPrime
7/11/06 4 1 1994 7.765586 0.539 17.87628 IsoPrime
7/11/06 4 1 1993 8.583963 0.59 16.70384 IsoPrime
7/6/06 4 1 1992 4.847945 0.364 17.51967 IsoPrime
7/7/06 4 1 1991 6.895243 0.495 16.91145 IsoPrime
1
2 MAXBERKELHAMMER
7/14/06 4 1 1991 7.317902 0.518 17.65258 IsoPrime
7/7/06 4 1 1990 7.39116 0.529 17.77241 IsoPrime
7/14/06 4 1 1990 6.028017 0.428 17.15634 IsoPrime
6/22/06 Std. Baker 8.996744 0.595 29.7137 IsoPrime
6/22/06 Std. Baker 9.519956 0.602 29.50398 IsoPrime
6/22/06 Std. Baker 8.137299 0.528 29.5127 IsoPrime
7/14/06 4 1 1989 6.121799 0.432 18.80544 IsoPrime
7/7/06 4 1 1988 6.570278 0.467 17.73543 IsoPrime
7/14/06 4 1 1988 6.191445 0.441 18.35076 IsoPrime
7/8/06 4 1 1987 6.873235 0.504 19.16943 IsoPrime
6/22/06 2 4 1986 6.2797 0.472 18.48163 IsoPrime
7/13/06 2 4 1986 7.278005 0.492 18.5425 IsoPrime
6/22/06 Std. Baker 8.383714 0.531 28.63815 IsoPrime
6/22/06 Std. Baker 8.898747 0.56 28.97744 IsoPrime
6/22/06 Std. Baker 11.026 0.674 29.47191 IsoPrime
6/22/06 Std. Baker 7.029113 0.473 29.54054 IsoPrime
6/22/06 Std. Baker 8.28678 0.541 29.56203 IsoPrime
7/8/06 4 1 1986 7.000675 0.5 19.47535 IsoPrime
7/13/06 4 1 1986 6.685005 0.47 19.81663 IsoPrime
7/14/06 4 1 1986 6.003977 0.42 19.91971 IsoPrime
6/22/06 2 4 1985 6.631196 0.502 18.01267 IsoPrime
7/10/06 4 1 1985 7.716414 0.534 18.81885 IsoPrime
7/12/06 4 1 1985 7.821311 0.54 19.80513 IsoPrime
6/23/06 2 4 1984 5.366797 0.427 17.82609 IsoPrime
7/10/06 4 1 1984 6.444734 0.466 19.52846 IsoPrime
6/22/06 Std. Baker 18.44936 0.531 19.081 IsoPrime
6/22/06 Std. Baker 8.700382 0.566 20.292 IsoPrime
6/23/06 2 4 1983 6.239492 0.488 17.4622 IsoPrime
7/11/06 4 1 1983 5.703041 0.417 19.03808 IsoPrime
6/23/06 2 4 1982 7.282687 0.532 17.25253 IsoPrime
7/11/06 4 1 1982 6.313947 0.453 19.07009 IsoPrime
6/23/06 2 4 1981 5.868231 0.441 18.29134 IsoPrime
7/11/06 4 1 1981 5.990614 0.423 19.91944 IsoPrime
6/21/06 2 4 1980 6.13619 0.461 20.307 IsoPrime
7/6/06 2 4 1980 7.359041 0.511 19.29303 IsoPrime
6/22/06 Std. Baker 8.687483 0.558 29.59265 IsoPrime
7/6/06 4 1 1980 6.139898 0.43 21.94981 IsoPrime
6/22/06 2 4 1979 8.6434 0.634 17.735 IsoPrime
7/7/06 4 1 1979 7.346809 0.515 21.12327 IsoPrime
6/21/06 2 4 1978 6.790073 0.516 18.146 IsoPrime
7/7/06 4 1 1978 7.84967 0.55 21.02984 IsoPrime
6/21/06 2 4 1977 6.117056 0.443 19.00216 IsoPrime
7/7/06 2 4 1977 7.946007 0.556 18.002 IsoPrime
SUPPLEMENTARY DATA TABLE 3
6/22/06 2 4 1976 7.382492 0.55 18.094 IsoPrime
6/23/06 Std. Baker 7.049935 0.473 29.59059 IsoPrime
6/23/06 Std. Baker 6.817648 0.493 29.92119 IsoPrime
6/23/06 Std. Baker 8.68979 0.565 29.68776 IsoPrime
7/7/06 4 1 1976 8.119429 0.572 19.33307 IsoPrime
6/22/06 2 4 1975 8.418544 0.618 17.85 IsoPrime
7/8/06 4 1 1975 5.770003 0.416 19.61471 IsoPrime
6/22/06 2 4 1974 5.013746 0.404 17.616 IsoPrime
7/8/06 4 1 1974 7.030001 0.493 19.92706 IsoPrime
6/22/06 2 4 1973 7.624517 0.533 19.66818 IsoPrime
7/10/06 4 1 1973 8.398055 0.567 18.95165 IsoPrime
6/23/06 Std. Baker 7.537832 0.528 29.81346 IsoPrime
6/23/06 Std. Baker 8.1119 0.514 27.8418 IsoPrime
6/23/06 Std. Baker 8.102786 0.513 28.18006 IsoPrime
6/23/06 Std. Baker 7.150295 0.464 28.39479 IsoPrime
6/23/06 Std. Baker 7.777709 0.493 28.23063 IsoPrime
6/23/06 Std. Baker 9.049502 0.569 28.34298 IsoPrime
6/23/06 2 4 1972 8.475755 0.63 18.58184 IsoPrime
7/10/06 4 1 1972 6.677368 467 18.8891 IsoPrime
6/23/06 2 4 1971 7.445838 0.549 19.09585 IsoPrime
7/11/06 4 1 1971 6.501471 0.467 19.502 IsoPrime
7/13/06 4 1 1971 6.158608 0.442 19.48468 IsoPrime
6/23/06 2 4 1970 6.982832 0.524 18.7037 IsoPrime
7/11/06 4 1 1970 7.764029 0.571 20.05371 IsoPrime
6/23/06 2 4 1969 8.502641 0.605 16.18722 IsoPrime
6/23/06 Std. Baker 9.59463 0.603 28.33551 IsoPrime
6/23/06 Std. Baker 6.706904 0.434 28.6577 IsoPrime
6/23/06 Std. Baker 9.088083 0.569 28.78572 IsoPrime
7/6/06 2 4 1969 7.626683 0.521 16.323 IsoPrime
7/11/06 4 1 1969 7.854298 0.545 19.79718 IsoPrime
6/21/06 2 4 1968 7.489069 0.552 19.44672 IsoPrime
7/6/06 2 4 1968 7.986795 0.554 17.58 IsoPrime
7/6/06 4 1 1968 5.984872 0.425 18.47858 IsoPrime
6/22/06 2 4 1967 6.483489 0.49 16.168 IsoPrime
6/23/06 Std. Baker 7.605408 0.491 28.81026 IsoPrime
6/23/06 Std. Baker 9.510623 0.434 29.25275 IsoPrime
6/23/06 Std. Baker 7.137791 0.472 29.12186 IsoPrime
7/6/06 2 4 1967 7.340161 0.513 16.36033 IsoPrime
7/7/06 4 1 1967 6.66064 0.488 17.83625 IsoPrime
6/21/06 2 4 1966 5.834252 0.592 18.694 IsoPrime
7/7/06 4 1 1966 7.692054 0.553 19.34495 IsoPrime
6/21/06 2 4 1965 7.416061 0.545 18.657 IsoPrime
7/7/06 2 4 1965 4.481332 0.535 18.41773 IsoPrime
4 MAXBERKELHAMMER
6/22/06 2 4 1964 7.540122 0.566 18.703 IsoPrime
7/7/06 4 1 1964 7.293364 0.511 18.52729 IsoPrime
6/24/06 Std. Baker 9.634344 0.614 28.52342 IsoPrime
6/24/06 Std. Baker 6.409374 0.458 28.70999 IsoPrime
6/24/06 Std. Baker 6.981375 0.458 28.8419 IsoPrime
6/22/06 2 4 1963 6.577368 0.501 18.983 IsoPrime
6/22/06 2 4 1962 6.046862 0.491 20.81902 IsoPrime
7/8/06 4 1 1962 6.810834 0.482 21.14621 IsoPrime
6/22/06 2 4 1961 6.973163 0.525 22.39833 IsoPrime
6/23/06 2 4 1961 7.045071 0.528 21.404 IsoPrime
7/8/06 4 1 1961 6.69069 0.479 22.104 IsoPrime
6/24/06 Std. Baker 7.954308 0.51 28.21794 IsoPrime
6/24/06 Std. Baker 6.585761 0.433 28.64091 IsoPrime
6/24/06 Std. Baker 7.006769 0.465 28.90883 IsoPrime
7/5/06 Std. Baker 9.806775 0.606 27.47903 IsoPrime
7/5/06 Std. Baker 6.79569 0.433 27.30246 IsoPrime
7/5/06 Std. Baker 6.757949 0.433 27.37992 IsoPrime
7/5/06 Std. Baker 6.789699 0.437 27.50739 IsoPrime
7/5/06 Std. Baker 9.460674 0.575 28.29752 IsoPrime
7/5/06 Std. Baker 8.475962 0.516 27.94662 IsoPrime
7/6/06 Std. Baker 7.461792 0.454 28.25505 IsoPrime
7/6/06 Std. Baker 8.038823 0.486 27.57511 IsoPrime
7/6/06 Std. Baker 9.309558 0.546 28.17507 IsoPrime
7/6/06 Std. Baker 9.290287 0.555 28.33154 IsoPrime
7/6/06 Std. Baker 9.480956 0.578 28.71155 IsoPrime
7/6/06 Std. Baker 7.027847 0.435 28.4929 IsoPrime
7/6/06 Std. Baker 7.709873 0.491 28.46126 IsoPrime
6/23/06 2 4 1960 7.921169 0.602 20.483 IsoPrime
7/10/06 4 1 1960 6.353622 0.473 22.27567 IsoPrime
6/23/06 2 4 1959 5.597152 0.426 19.821 IsoPrime
7/10/06 4 1 1959 5.906169 0.432 24.68013 IsoPrime
7/12/06 4 1 1959 8.379436 0.559 24.49652 IsoPrime
6/23/06 2 4 1958 7.829941 0.583 18.232 IsoPrime
7/11/06 4 1 1958 7.75343 0.547 19.15278 IsoPrime
6/21/06 2 4 1957 7.830722 0.568 18.958 IsoPrime
7/6/06 Std. Baker 8.599885 0.557 28.47258 IsoPrime
7/6/06 Std. Baker 8.646718 0.529 28.51668 IsoPrime
7/6/06 Std. Baker 6.507047 0.415 28.44199 IsoPrime
7/11/06 4 1 1957 6.036018 0.434 18.18494 IsoPrime
6/22/06 2 4 1956 6.839292 0.506 17.515 IsoPrime
7/11/06 4 1 1956 7.791168 0.549 18.43164 IsoPrime
6/21/06 2 4 1955 6.085645 0.467 19.081 IsoPrime
7/6/06 4 1 1955 6.223844 0.445 18.881 IsoPrime
SUPPLEMENTARY DATA TABLE 5
6/21/06 2 4 1954 6.22342 0.468 20.292 IsoPrime
7/7/06 4 1 1954 8.178783 0.586 18.21469 IsoPrime
6/22/06 2 4 1953 6.831983 0.515 20.42951 IsoPrime
7/6/06 Std. Baker 9.490079 0.579 29.36484 IsoPrime
7/6/06 Std. Baker 8.371794 0.522 28.65213 IsoPrime
7/6/06 Std. Baker 9.353086 0.58 28.53838 IsoPrime
7/7/06 4 1 1953 5.616469 0.421 19.23769 IsoPrime
6/22/06 2 4 1952 7.896573 0.594 20.41611 IsoPrime
7/6/06 2 4 1952 6.729352 0.48 19.17821 IsoPrime
7/7/06 2 4 1952 6.341457 0.478 18.32923 IsoPrime
6/22/06 2 4 1951 7.101604 0.515 16.748 IsoPrime
7/7/06 4 1 1951 6.649928 0.48 18.99307 IsoPrime
6/22/06 2 4 1950 5.739037 0.441 19.158 IsoPrime
7/6/06 2 4 1950 6.993702 0.482 18.98931 IsoPrime
7/7/06 Std. Baker 7.294669 0.472 27.95165 IsoPrime
7/7/06 Std. Baker 7.056717 0.457 28.33764 IsoPrime
7/7/06 Std. Baker 8.197872 0.516 28.62444 IsoPrime
7/8/06 4 1 1950 8.192452 0.586 19.78236 IsoPrime
6/23/06 2 4 1949 8.262568 0.601 18.614 IsoPrime
7/10/06 4 1 1949 6.69957 0.465 20.11674 IsoPrime
7/12/06 4 1 1949 7.639929 0.508 21.45094 IsoPrime
6/23/06 2 4 1948 6.551098 0.488 18.716 IsoPrime
7/7/06 Std. Baker 8.056349 0.504 28.16605 IsoPrime
7/7/06 Std. Baker 6.966717 0.453 28.501 IsoPrime
7/7/06 Std. Baker 7.951327 0.499 27.79244 IsoPrime
7/7/06 Std. Baker 8.416391 0.519 28.23611 IsoPrime
7/7/06 Std. Baker 8.911166 0.551 28.35508 IsoPrime
7/7/06 Std. Baker 8.096626 0.509 28.64235 IsoPrime
7/7/06 Std. Baker 8.752307 0.589 28.41002 IsoPrime
7/10/06 4 1 1948 6.39126 0.45 19.84914 IsoPrime
6/23/06 2 4 1947 6.237007 0.421 19.412 IsoPrime
7/10/06 4 1 1947 6.421407 0.443 21.38196 IsoPrime
7/12/06 4 1 1947 7.445253 0.505 22.17023 IsoPrime
6/22/06 2 4 1946 7.223844 0.612 21.61059 IsoPrime
6/23/06 2 4 1946 6.130325 0.428 22.66525 IsoPrime
7/10/06 2 4 1946 7.269893 0.523 21.59001 IsoPrime
7/11/06 4 1 1946 7.196426 0.498 21.02357 IsoPrime
7/7/06 Std. Baker 6.394597 0.416 28.31601 IsoPrime
7/7/06 Std. Baker 7.496264 0.488 28.38405 IsoPrime
7/7/06 Std. Baker 9.718087 0.604 28.59446 IsoPrime
6/21/06 2 4 1945 7.318328 0.5 24.26713 IsoPrime
7/11/06 4 1 1945 7.505193 0.502 21.16527 IsoPrime
6/22/06 2 4 1944 5.988988 0.437 20.483 IsoPrime
6 MAXBERKELHAMMER
7/11/06 4 1 1944 6.972008 0.475 22.50294 IsoPrime
6/21/06 2 4 1943 6.844264 0.489 22.08657 IsoPrime
7/6/06 4 1 1943 9.383721 0.591 22.46471 IsoPrime
6/21/06 2 4 1942 7.942713 0.577 21.57803 IsoPrime
7/7/06 4 1 1942 6.677152 0.466 23.31788 IsoPrime
7/7/06 Std. Baker 8.696009 0.539 28.34553 IsoPrime
7/7/06 Std. Baker 10.76058 0.538 28.46198 IsoPrime
7/7/06 Std. Baker 7.12198 0.466 29.45168 IsoPrime
6/22/06 2 4 1941 9.426062 0.649 21.66742 IsoPrime
7/7/06 2 4 1941 6.922873 0.475 24.62969 IsoPrime
7/13/06 4 1 1941 6.361209 0.431 24.3506 IsoPrime
7/7/06 4 1 1940 6.576236 0.474 24.31504 IsoPrime
6/22/06 2 4 1939 8.195738 0.577 21.27874 IsoPrime
7/7/06 4 1 1939 7.158238 0.492 21.78056 IsoPrime
6/22/06 2 4 1938 7.569372 0.547 18.181 IsoPrime
7/8/06 4 1 1938 6.376321 0.536 19.85388 IsoPrime
7/8/06 Std. Baker 6.979963 0.439 28.79133 IsoPrime
7/8/06 Std. Baker 8.61453 0.529 29.13304 IsoPrime
7/8/06 Std. Baker 6.646011 0.431 28.39203 IsoPrime
6/23/06 2 4 1937 6.766178 0.495 19.529 IsoPrime
7/10/06 4 1 1937 6.289068 0.441 17.67186 IsoPrime
7/13/06 4 1 1937 6.08247 0.433 17.40541 IsoPrime
6/23/06 2 4 1936 6.476448 0.478 23.2703 IsoPrime
7/13/06 2 4 1936 7.795183 0.516 22.51257 IsoPrime
7/10/06 4 1 1936 6.763252 0.481 18.53286 IsoPrime
7/8/06 Std. Baker 8.341381 0.515 27.7954 IsoPrime
7/8/06 Std. Baker 7.37107 0.462 28.66277 IsoPrime
7/13/06 4 1 1936 7.014927 0.49 18.52373 IsoPrime
6/23/06 2 4 1935 7.683159 0.542 22.04144 IsoPrime
7/13/06 2 4 1935 6.003857 0.414 22.19297 IsoPrime
7/10/06 4 1 1935 5.898378 0.422 17.22409 IsoPrime
7/12/06 4 1 1935 6.412443 0.466 18.91588 IsoPrime
7/13/06 4 1 1935 5.999591 0.438 18.8679 IsoPrime
6/23/06 2 4 1934 5.974297 0.424 23.60163 IsoPrime
7/11/06 4 1 1934 7.797851 0.545 19.69625 IsoPrime
7/10/06 Std. Baker 7.357061 0.465 28.18973 IsoPrime
7/10/06 Std. Baker 8.692767 0.538 28.34465 IsoPrime
7/10/06 Std. Baker 7.254176 0.507 28.0534 IsoPrime
6/23/06 2 4 1933 6.782561 0.482 18.58837 IsoPrime
7/13/06 2 4 1933 7.32606 0.506 18.301 IsoPrime
7/11/06 4 1 1933 6.273559 0.434 19.67425 IsoPrime
7/13/06 4 1 1933 7.150302 0.506 17.71092 IsoPrime
9/18/06 4 1 1933 5.061981 0.197 17.62339 IsoPrime
SUPPLEMENTARY DATA TABLE 7
6/23/06 2 4 1932 7.380802 0.519 17.34491 IsoPrime
7/11/06 4 1 1932 7.143916 0.489 18.84749 IsoPrime
7/13/06 4 1 1932 8.364832 0.575 19.74686 IsoPrime
7/10/06 Std. Baker 5.074029 0.492 28.33106 IsoPrime
7/10/06 Std. Baker 7.549546 0.46 28.31976 IsoPrime
6/23/06 2 4 1931 7.460229 0.527 17.91776 IsoPrime
7/6/06 4 1 1931 7.792755 0.522 19.55922 IsoPrime
6/23/06 2 4 1930 8.089402 0.566 18.84922 IsoPrime
7/7/06 4 1 1930 7.584818 0.538 18.6167 IsoPrime
6/23/06 2 4 1929 6.022317 0.454 18.48148 IsoPrime
7/7/06 2 4 1929 7.508458 0.546 18.081 IsoPrime
6/24/06 2 4 1928 7.142009 0.525 19.95 IsoPrime
7/7/06 4 1 1928 8.217096 0.564 19.71941 IsoPrime
7/11/06 Std. Baker 8.774164 0.54 27.52021 IsoPrime
7/11/06 Std. Baker 6.836825 0.433 27.94374 IsoPrime
7/11/06 Std. Baker 7.37987 0.459 28.05464 IsoPrime
6/24/06 2 4 1927 6.731328 0.502 18.964 IsoPrime
7/7/06 4 1 1927 6.749577 0.472 19.91124 IsoPrime
6/24/06 2 4 1926 6.475046 0.479 18.258 IsoPrime
7/8/06 4 1 1926 7.018101 0.489 19.71811 IsoPrime
6/24/06 2 4 1925 8.048239 0.577 17.972 IsoPrime
7/10/06 4 1 1925 7.821631 0.527 18.66166 IsoPrime
7/11/06 Std. Baker 9.416914 0.573 28.06797 IsoPrime
7/11/06 Std. Baker 8.031758 0.508 27.09043 IsoPrime
7/11/06 Std. Baker 8.731413 0.528 28.14704 IsoPrime
7/11/06 Std. Baker 7.941078 0.494 27.26878 IsoPrime
7/11/06 Std. Baker 7.368287 0.459 27.1404 IsoPrime
7/11/06 Std. Baker 9.242946 0.554 27.98019 IsoPrime
7/11/06 Std. Baker 7.676665 0.472 28.1808 IsoPrime
7/11/06 Std. Baker 6.448664 0.402 28.40024 IsoPrime
6/24/06 2 4 1924 5.980208 0.447 19.802 IsoPrime
7/10/06 4 1 1924 6.815939 0.463 19.19264 IsoPrime
6/24/06 2 4 1923 6.887379 0.521 19.50395 IsoPrime
7/11/06 4 1 1923 6.687341 0.459 20.85065 IsoPrime
7/13/06 4 1 1923 6.419355 0.454 20.87376 IsoPrime
6/24/06 2 4 1922 8.254253 0.595 17.03901 IsoPrime
7/11/06 4 1 1922 6.274415 0.441 18.53972 IsoPrime
6/24/06 2 4 1921 6.849637 0.503 17.72585 IsoPrime
7/11/06 Std. Baker 8.960671 0.55 28.03806 IsoPrime
7/11/06 Std. Baker 8.288454 0.52 28.24959 IsoPrime
7/11/06 Std. Baker 7.203477 0.475 28.29725 IsoPrime
7/11/06 4 1 1921 6.499279 0.445 18.99385 IsoPrime
6/24/06 2 4 1920 6.83363 0.502 19.36421 IsoPrime
8 MAXBERKELHAMMER
7/11/06 4 1 1920 6.965871 0.471 21.09656 IsoPrime
6/24/06 2 4 1919 6.208868 0.47 19.20812 IsoPrime
7/6/06 4 1 1919 7.273087 0.486 19.87213 IsoPrime
6/24/06 2 4 1918 6.509467 0.493 18.995 IsoPrime
7/7/06 4 1 1918 7.621873 0.541 17.49461 IsoPrime
6/24/06 2 4 1917 7.723333 0.567 14.98699 IsoPrime
7/11/06 Std. Baker 7.796513 0.477 28.66143 IsoPrime
7/11/06 Std. Baker 6.955345 0.433 28.66539 IsoPrime
7/11/06 Std. Baker 10.16011 0.6 28.69617 IsoPrime
7/7/06 2 4 1917 7.306319 0.505 16.71671 IsoPrime
6/24/06 2 4 1916 7.903536 0.569 16.97967 IsoPrime
7/6/06 2 4 1916 7.666363 0.53 17.34848 IsoPrime
7/7/06 4 1 1916 6.26371 0.439 17.67008 IsoPrime
7/12/06 Std. Baker 8.354573 0.545 28.21244 IsoPrime
7/12/06 Std. Baker 7.439376 0.466 28.16302 IsoPrime
7/12/06 Std. Baker 6.745733 0.427 28.30237 IsoPrime
7/12/06 2 4 1915 6.863816 0.468 23.41244 IsoPrime
7/7/06 4 1 1915 6.317337 0.469 17.60512 IsoPrime
7/12/06 Std. Baker 7.185518 0.453 28.48205 IsoPrime
7/12/06 Std. Baker 7.446503 0.472 28.57004 IsoPrime
7/12/06 Std. Baker 6.835087 0.445 28.27271 IsoPrime
7/12/06 Std. Baker 8.129649 0.498 27.23983 IsoPrime
7/12/06 Std. Baker 6.581195 0.407 27.68984 IsoPrime
7/12/06 Std. Baker 8.560561 0.524 27.99076 IsoPrime
7/12/06 Std. Baker 6.87188 0.438 27.98174 IsoPrime
7/12/06 Std. Baker 7.304574 0.453 28.16023 IsoPrime
7/12/06 2 4 1914 6.807947 0.467 24.45733 IsoPrime
7/8/06 4 1 1914 6.406009 0.456 18.23322 IsoPrime
7/12/06 2 4 1913 7.843245 0.54 27.35384 IsoPrime
7/10/06 4 1 1913 6.336547 0.438 17.79018 IsoPrime
7/12/06 2 4 1912 7.624327 0.503 27.08324 IsoPrime
7/10/06 4 1 1912 7.508815 0.499 21.54675 IsoPrime
7/12/06 2 4 1911 6.439812 0.442 27.56211 IsoPrime
7/12/06 Std. Baker 9.398324 0.571 28.37975 IsoPrime
7/12/06 Std. Baker 8.717641 0.531 28.51294 IsoPrime
7/12/06 Std. Baker 8.287475 0.508 28.61844 IsoPrime
7/11/06 4 1 1911 6.500921 0.453 21.43545 IsoPrime
7/13/06 4 1 1911 8.139843 0.552 21.48579 IsoPrime
7/12/06 2 4 1910 6.630137 0.439 29.51667 IsoPrime
7/11/06 4 1 1910 6.870523 0.462 22.78985 IsoPrime
7/12/06 2 4 1909 6.253661 0.414 26.08394 IsoPrime
7/11/06 4 1 1909 8.039598 0.524 26.51227 IsoPrime
7/12/06 Std. Baker 7.430345 0.454 28.54633 IsoPrime
SUPPLEMENTARY DATA TABLE 9
7/12/06 Std. Baker 8.00372 0.489 28.74126 IsoPrime
7/12/06 Std. Baker 8.434012 0.506 29.23232 IsoPrime
7/13/06 Std. Baker 8.216859 0.508 28.05735 IsoPrime
7/13/06 Std. Baker 6.805919 0.428 28.16708 IsoPrime
7/13/06 Std. Baker 8.09552 0.502 28.32635 IsoPrime
7/13/06 Std. Baker 6.559742 0.423 27.76395 IsoPrime
7/13/06 Std. Baker 6.96785 0.443 27.84783 IsoPrime
7/13/06 Std. Baker 8.688849 0.533 28.12054 IsoPrime
7/13/06 Std. Baker 9.415678 0.568 28.1012 IsoPrime
7/12/06 2 4 1908 6.036167 0.409 29.08586 IsoPrime
7/11/06 4 1 1908 7.148545 0.46 26.816 IsoPrime
7/14/06 4 1 1908 7.191319 0.477 26.0501 IsoPrime
7/12/06 2 4 1907 6.191643 0.411 30.1782 IsoPrime
7/11/06 4 1 1907 6.911471 0.446 27.97354 IsoPrime
7/12/06 2 4 1906 6.028048 0.456 26.34139 IsoPrime
7/11/06 4 1 1906 7.535028 0.486 24.51081 IsoPrime
7/14/06 4 1 1906 7.89227 0.527 24.476 IsoPrime
7/13/06 Std. Baker 8.298659 0.506 27.99933 IsoPrime
7/13/06 Std. Baker 7.885828 0.486 28.12597 IsoPrime
7/13/06 Std. Baker 7.353221 0.454 28.29503 IsoPrime
7/12/06 2 4 1905 7.661458 0.513 27.31356 IsoPrime
7/11/06 4 1 1905 7.09627 0.465 26.09046 IsoPrime
9/9/06 4 1 1905 8.296052 0.334 25.77 IsoPrime
7/11/06 4 1 1904 6.379532 0.429 30.31065 IsoPrime
7/14/06 4 1 1904 7.73784 0.52 28.305 IsoPrime
9/18/06 4 1 1904 10.8511 0.397 27.63897 IsoPrime
7/12/06 2 4 1903 6.955383 0.464 28.81565 IsoPrime
7/11/06 4 1 1903 6.298949 0.429 28.0079 IsoPrime
7/13/06 Std. Baker 6.797015 0.433 27.81567 IsoPrime
7/14/06 Std. Baker 8.809376 0.536 28.17938 IsoPrime
7/14/06 Std. Baker 7.096432 0.451 27.96008 IsoPrime
9/9/06 4 1 1903 5.893911 0.24 23.505 IsoPrime
9/18/06 4 1 1903 6.362515 0.237 25.01914 IsoPrime
7/12/06 2 4 1902 6.979309 0.459 27.75456 IsoPrime
9/21/06 2 4 1902 6.763481 0.246 29.94926 IsoPrime
7/14/06 Std. Baker 9.817928 0.594 27.96546 IsoPrime
7/14/06 Std. Baker 8.966516 0.548 28.23269 IsoPrime
7/14/06 Std. Baker 8.200535 0.517 28.17268 IsoPrime
7/11/06 4 1 1902 7.166593 0.485 24.38394 IsoPrime
7/14/06 4 1 1902 6.542729 0.447 22.23515 IsoPrime
9/19/06 4 1 1902 7.528959 0.289 22.77345 IsoPrime
7/12/06 2 4 1901 6.99132 0.456 28.89326 IsoPrime
7/11/06 4 1 1901 6.468414 0.449 20.62813 IsoPrime
10 MAXBERKELHAMMER
9/13/06 4 1 1901 9.544546 0.384 21.93841 IsoPrime
7/14/06 Std. Baker 7.118726 0.444 28.21844 IsoPrime
7/14/06 Std. Baker 8.428333 0.51 27.91345 IsoPrime
7/14/06 Std. Baker 8.398158 0.505 28.89133 IsoPrime
9/7/06 Std. Baker 7.036456 0.28 29.68698 IsoPrime
9/7/06 Std. Baker 5.768822 0.224 29.24098 IsoPrime
7/12/06 2 4 1900 8.684138 0.558 31.05103 IsoPrime
9/21/06 2 4 1900 9.935394 0.377 30.949 IsoPrime
7/12/06 4 1 1900 5.972401 0.417 21.37453 IsoPrime
7/14/06 4 1 1900 8.216781 0.549 21.79333 IsoPrime
9/13/06 4 1 1900 5.072915 0.218 20.70737 IsoPrime
9/7/06 Std. Baker 9.082631 0.346 27.81377 IsoPrime
7/12/06 2 4 1899 7.302957 0.468 30.25077 IsoPrime
7/12/06 4 1 1899 6.353574 0.436 23.87225 IsoPrime
9/13/06 4 1 1899 7.481447 0.296 25.7269 IsoPrime
7/12/06 2 4 1898 7.931002 0.503 28.57102 IsoPrime
7/12/06 4 1 1898 9.247856 0.593 26.96072 IsoPrime
9/19/06 2 4 1897 5.665766 0.218 29.05669 IsoPrime
9/21/06 2 4 1897 6.107373 0.223 32.88072 IsoPrime
7/12/06 4 1 1897 8.493343 0.56 26.41804 IsoPrime
7/14/06 4 1 1897 7.802924 0.507 26.61832 IsoPrime
9/19/06 2 4 1896 7.743027 0.284 30.20567 IsoPrime
7/12/06 4 1 1896 7.598722 0.498 26.98009 IsoPrime
9/9/06 4 1 1896 5.007441 0.202 25.88577 IsoPrime
9/19/06 2 4 1895 8.277328 0.294 29.97222 IsoPrime
9/13/06 4 1 1895 6.618104 0.271 28.79186 IsoPrime
9/19/06 2 4 1894 6.417515 0.241 29.15844 IsoPrime
9/9/06 4 1 1894 6.849893 0.282 26.90434 IsoPrime
9/13/06 4 1 1894 6.436008 0.26 26.31959 IsoPrime
9/8/06 4 1 1893 6.77796 0.28 28.35082 IsoPrime
9/9/06 4 1 1893 7.490373 0.307 27.62869 IsoPrime
9/13/06 4 1 1893 5.495719 0.23 28.56679 IsoPrime
9/8/06 Std. Baker 9.47149 0.356 28.64208 IsoPrime
9/8/06 Std. Baker 6.60599 0.257 27.84189 IsoPrime
9/8/06 Std. Baker 9.775323 0.346 27.74245 IsoPrime
9/8/06 Std. Baker 9.603451 0.352 28.14214 IsoPrime
9/8/06 Std. Baker 7.38847 0.28 29.27081 IsoPrime
9/8/06 Std. Baker 8.482131 0.314 28.29944 IsoPrime
9/19/06 2 4 1892 7.203716 0.253 27.65727 IsoPrime
9/8/06 4 1 1892 6.839009 0.281 28.44981 IsoPrime
9/12/06 4 1 1892 5.484115 0.22 27.19795 IsoPrime
9/19/06 4 1 1891 6.301952 0.239 27.02217 IsoPrime
9/20/06 2 4 1890 7.440094 0.271 32.87406 IsoPrime
SUPPLEMENTARY DATA TABLE 11
9/8/06 Std. Baker 6.088696 0.226 28.59773 IsoPrime
9/8/06 Std. Baker 9.24381 0.337 28.12237 IsoPrime
9/8/06 Std. Baker 9.71675 0.359 28.94096 IsoPrime
9/8/06 4 1 1890 6.930402 0.272 27.76933 IsoPrime
9/20/06 2 4 1889 5.806781 0.213 31.61053 IsoPrime
9/8/06 4 1 1889 6.531637 0.265 28.74338 IsoPrime
9/20/06 2 4 1888 8.491116 0.304 29.60986 IsoPrime
9/20/06 2 4 1887 7.763246 0.274 27.98917 IsoPrime
9/8/06 Std. Baker 6.321122 0.239 28.82469 IsoPrime
9/9/06 Std. Baker 7.279628 0.277 28.56852 IsoPrime
9/20/06 2 4 1886 8.60667 0.306 31.95636 IsoPrime
9/8/06 4 1 1886 5.990644 0.25 30.05487 IsoPrime
9/13/06 4 1 1886 4.742891 0.19 26.93919 IsoPrime
9/19/06 2 4 1885 7.428984 0.279 30.19173 IsoPrime
9/8/06 4 1 1885 6.186038 0.246 29.58841 IsoPrime
9/9/06 Std. Baker 7.946453 0.298 28.36727 IsoPrime
9/9/06 Std. Baker 7.384852 0.281 28.05918 IsoPrime
9/9/06 Std. Baker 9.392412 0.351 28.63727 IsoPrime
9/12/06 Std. Baker 8.504743 0.305 27.36217 IsoPrime
9/12/06 Std. Baker 9.770556 0.35 28.83558 IsoPrime
9/12/06 Std. Baker 6.365469 0.253 28.2495 IsoPrime
9/12/06 Std. Baker 8.962683 0.327 28.81644 IsoPrime
9/12/06 Std. Baker 7.520538 0.311 27.96821 IsoPrime
9/19/06 2 4 1884 5.361195 0.196 30.15158 IsoPrime
9/8/06 4 1 1884 6.320036 0.259 23.45172 IsoPrime
9/19/06 2 4 1883 5.522207 0.205 30.46375 IsoPrime
9/19/06 4 1 1883 7.289005 0.262 30.86682 IsoPrime
9/19/06 2 4 1882 10.82344 0.382 30.89369 IsoPrime
9/12/06 Std. Baker 8.120926 0.31 29.31289 IsoPrime
9/12/06 Std. Baker 8.549312 0.333 29.09278 IsoPrime
9/8/06 4 1 1882 4.857617 0.202 27.57144 IsoPrime
9/19/06 2 4 1881 5.108166 0.182 28.89742 IsoPrime
9/21/06 2 4 1881 6.00258 0.23 29.53292 IsoPrime
9/8/06 4 1 1881 5.190439 0.203 26.63893 IsoPrime
9/18/06 4 1 1881 5.672677 0.217 28.30106 IsoPrime
9/13/06 Std. Baker 8.056983 0.299 29.06405 IsoPrime
9/13/06 Std. Baker 8.856845 0.33 29.21888 IsoPrime
9/13/06 Std. Baker 8.819411 0.325 29.07806 IsoPrime
9/19/06 2 4 1880 5.978634 0.214 33.0025 IsoPrime
9/20/06 2 4 1880 8.736033 0.328 29.73714 IsoPrime
9/21/06 2 4 1880 3.89039 0.143 31.56124 IsoPrime
9/20/06 2 4 1879 5.40957 0.216 29.5014 IsoPrime
9/8/06 4 1 1879 5.809522 0.24 29.12613 IsoPrime
12 MAXBERKELHAMMER
9/13/06 Std. Baker 6.599287 0.25 29.96017 IsoPrime
9/13/06 Std. Baker 5.640653 0.315 27.84654 IsoPrime
9/13/06 Std. Baker 8.552692 0.314 28.69878 IsoPrime
9/20/06 2 4 1878 6.38816 0.234 28.92199 IsoPrime
9/20/06 2 4 1877 10.19905 0.366 28.7227 IsoPrime
9/8/06 4 1 1877 6.23249 0.244 27.9566 IsoPrime
9/20/06 2 4 1876 8.478432 0.322 29.62033 IsoPrime
9/13/06 Std. Baker 8.840124 0.32 29.35545 IsoPrime
9/18/06 Std. Baker 10.70952 0.371 28.22515 IsoPrime
9/18/06 Std. Baker 10.12522 0.369 28.77532 IsoPrime
9/18/06 Std. Baker 8.094626 0.275 29.17305 IsoPrime
9/18/06 Std. Baker 6.560422 0.237 28.93066 IsoPrime
9/18/06 Std. Baker 6.430466 0.218 29.00348 IsoPrime
9/7/06 4 1 1876 6.825801 0.268 31.86921 IsoPrime
9/13/06 4 1 1876 5.979716 0.224 28.92578 IsoPrime
9/20/06 2 4 1875 9.432766 0.34 30.92732 IsoPrime
9/21/06 2 4 1875 7.248441 0.268 31.22356 IsoPrime
9/18/06 Std. Baker 9.742024 0.335 29.57011 IsoPrime
9/18/06 Std. Baker 8.115332 0.286 29.40255 IsoPrime
9/20/06 2 4 1874 10.33466 0.367 28.11198 IsoPrime
9/8/06 4 1 1874 6.388641 0.248 26.92902 IsoPrime
9/12/06 4 1 1874 8.073176 0.311 28.41378 IsoPrime
9/19/06 2 4 1873 9.083421 0.357 28.92182 IsoPrime
9/19/06 Std. Baker 8.024939 0.287 29.5533 IsoPrime
9/19/06 Std. Baker 7.421223 0.318 29.39285 IsoPrime
9/19/06 Std. Baker 9.532903 0.328 29.36246 IsoPrime
9/8/06 4 1 1873 6.529233 0.265 28.94547 IsoPrime
9/12/06 4 1 1873 3.038041 0.12 28.35884 IsoPrime
9/19/06 2 4 1872 5.565825 0.209 29.39002 IsoPrime
9/7/06 4 1 1872 6.354636 0.26 28.97206 IsoPrime
9/8/06 4 1 1872 8.154535 0.331 29.22758 IsoPrime
9/19/06 Std. Baker 5.491708 0.2 29.99353 IsoPrime
9/19/06 Std. Baker 5.76131 0.205 29.83851 IsoPrime
9/19/06 Std. Baker 6.817178 0.242 29.39544 IsoPrime
9/12/06 4 1 1872 2.363492 0.097 23.54178 IsoPrime
9/19/06 2 4 1871 7.034891 0.264 29.81531 IsoPrime
9/8/06 4 1 1871 5.83144 0.241 30.25031 IsoPrime
10/20/06 4 1 1871 8.240894 0.262 31.46148 IsoPrime
9/19/06 2 4 1870 7.735122 0.289 30.68619 IsoPrime
9/19/06 Std. Baker 7.810667 0.269 29.93414 IsoPrime
9/21/06 2 4 1870 7.662721 0.293 31.48853 IsoPrime
9/8/06 4 1 1870 7.275795 0.306 32.05716 IsoPrime
9/8/06 4 1 1870 7.91412 0.326 30.41127 IsoPrime
SUPPLEMENTARY DATA TABLE 13
9/19/06 2 4 1869 5.516007 0.21 29.23866 IsoPrime
9/8/06 4 1 1869 8.546227 0.338 29.55474 IsoPrime
10/20/06 4 1 1869 8.871244 0.284 31.20639 IsoPrime
9/19/06 Std. Baker 8.534934 0.295 28.51694 IsoPrime
9/19/06 Std. Baker 8.234791 0.288 29.07633 IsoPrime
9/19/06 Std. Baker 6.770401 0.23 29.30966 IsoPrime
9/19/06 Std. Baker 5.54266 0.197 28.86562 IsoPrime
9/19/06 Std. Baker 6.579835 0.222 29.60333 IsoPrime
9/19/06 2 4 1868 6.87897 0.257 27.38519 IsoPrime
9/20/06 2 4 1868 6.875282 0.269 26.28427 IsoPrime
9/8/06 4 1 1868 8.273929 0.338 24.35112 IsoPrime
9/19/06 Std. Baker 6.956 0.237 29.46534 IsoPrime
9/13/06 4 1 1868 8.72859 0.343 27.57694 IsoPrime
10/21/06 4 1 1868 8.134357 0.271 27.15807 IsoPrime
9/19/06 2 4 1867 8.23173 0.294 31.931 IsoPrime
9/20/06 2 4 1867 6.85248 0.254 31.13788 IsoPrime
9/8/06 4 1 1867 9.436609 0.391 30.07794 IsoPrime
9/20/06 Std. Baker 9.38551 0.31 29.38262 IsoPrime
9/20/06 Std. Baker 9.291736 0.318 29.90798 IsoPrime
9/8/06 4 1 1867 10.04139 0.394 27.61383 IsoPrime
10/21/06 4 1 1867 9.050345 0.287 30.12875 IsoPrime
9/20/06 2 4 1866 8.955532 0.327 32.86542 IsoPrime
9/8/06 4 1 1866 8.335754 0.277 31.74885 IsoPrime
9/13/06 4 1 1866 8.190136 0.312 29.3681 IsoPrime
9/20/06 Std. Baker 7.661261 0.287 29.66858 IsoPrime
9/20/06 Std. Baker 8.33998 0.285 28.62012 IsoPrime
9/20/06 2 4 1865 7.542081 0.281 30.33257 IsoPrime
9/8/06 4 1 1865 8.992856 0.362 31.44474 IsoPrime
10/21/06 4 1 1865 7.56694 0.231 32.16801 IsoPrime
9/20/06 2 4 1864 5.790004 0.216 30.33672 IsoPrime
9/7/06 4 1 1864 8.924143 0.355 31.59247 IsoPrime
9/8/06 4 1 1864 6.568644 0.259 28.6422 IsoPrime
9/20/06 Std. Baker 7.477593 0.267 29.0468 IsoPrime
9/20/06 Std. Baker 8.200581 0.279 29.41875 IsoPrime
9/20/06 Std. Baker 8.245083 0.285 29.25062 IsoPrime
9/20/06 Std. Baker 8.522105 0.285 27.40171 IsoPrime
9/20/06 Std. Baker 10.0741 0.324 28.65843 IsoPrime
9/20/06 Std. Baker 10.04559 0.329 28.92675 IsoPrime
9/20/06 Std. Baker 8.165161 0.288 28.77154 IsoPrime
9/20/06 Std. Baker 9.070365 0.308 29.45642 IsoPrime
9/13/06 4 1 1864 6.171208 0.247 36.34978 IsoPrime
10/21/06 4 1 1864 8.267315 0.265 31.78006 IsoPrime
9/20/06 2 4 1863 6.756542 0.244 31.33804 IsoPrime
14 MAXBERKELHAMMER
9/21/06 2 4 1863 6.685432 0.253 32.84575 IsoPrime
9/8/06 4 1 1863 8.931681 0.357 31.84379 IsoPrime
9/21/06 Std. Baker 8.453865 0.308 29.64087 IsoPrime
9/8/06 4 1 1863 8.429244 0.336 32.55038 IsoPrime
10/21/06 4 1 1863 9.769726 0.298 31.87308 IsoPrime
9/20/06 2 4 1862 8.395958 0.304 30.0933 IsoPrime
9/7/06 4 1 1862 5.882581 0.245 31.54896 IsoPrime
9/8/06 4 1 1862 8.302427 0.322 26.89074 IsoPrime
9/21/06 Std. Baker 6.234593 0.215 29.5404 IsoPrime
9/13/06 4 1 1862 8.135412 0.321 24.86813 IsoPrime
9/19/06 2 4 1861 9.551943 0.36 30.55899 IsoPrime
9/8/06 4 1 1861 6.890635 0.286 31.11696 IsoPrime
9/13/06 4 1 1861 6.51721 0.253 30.47151 IsoPrime
9/19/06 2 4 1860 5.475295 0.214 28.19566 IsoPrime
9/21/06 Std. Baker 6.183323 0.218 29.12109 IsoPrime
9/21/06 Std. Baker 7.112299 0.24 29.11569 IsoPrime
10/20/06 Std. Baker 10.86798 0.351 29.50208 IsoPrime
10/20/06 Std. Baker 5.476608 0.18 28.61766 IsoPrime
10/20/06 Std. Baker 6.05242 0.193 27.96693 IsoPrime
10/20/06 Std. Baker 9.359502 0.2869 29.04269 IsoPrime
10/20/06 Std. Baker 6.932606 0.215 28.27857 IsoPrime
9/21/06 2 4 1860 8.210395 0.307 29.78469 IsoPrime
9/7/06 4 1 1860 9.159115 0.371 33.67008 IsoPrime
9/8/06 4 1 1860 9.100734 0.364 31.26893 IsoPrime
9/13/06 4 1 1860 7.034886 0.282 30.87415 IsoPrime
4/14/08 2 4 1859 8.894661 0.221 35.29106 IsoPrime
10/20/06 Std. Baker 7.416047 0.22 28.82344 IsoPrime
10/20/06 Std. Baker 6.572756 0.249 28.31827 IsoPrime
10/20/06 Std. Baker 8.852572 0.262 29.3263 IsoPrime
4/15/08 2 4 1859 7.776157 0.193 33.65276 IsoPrime
4/16/08 2 4 1859 7.6941 0.198 32.52391 IsoPrime
4/16/08 2 4 1859 6.613535 0.18 32.51436 IsoPrime
4/16/08 2 4 1859 7.250677 0.18 32.96632 IsoPrime
10/20/06 4 1 1859 8.862601 0.28 34.20313 IsoPrime
10/20/06 Std. Baker 9.521987 0.275 29.37361 IsoPrime
10/20/06 Std. Baker 7.088577 0.208 28.57374 IsoPrime
10/21/06 Std. Baker 7.485183 0.223 29.20965 IsoPrime
10/20/06 4 1 1859 9.306921 0.293 34.04093 IsoPrime
4/14/08 2 4 1858 8.010233 0.191 35.17726 IsoPrime
4/16/08 2 4 1858 7.261242 0.184 34.48461 IsoPrime
10/21/06 4 1 1858 6.516264 0.218 32.15904 IsoPrime
10/21/06 4 1 1858 7.453902 0.246 33.43256 IsoPrime
10/21/06 Std. Baker 10.95996 0.317 29.89696 IsoPrime
SUPPLEMENTARY DATA TABLE 15
10/21/06 Std. Baker 8.582144 0.255 29.55271 IsoPrime
4/14/08 2 4 1857 8.126047 0.2 36.09996 IsoPrime
4/16/08 2 4 1857 8.618358 0.229 35.584 IsoPrime
4/14/08 2 4 1856 9.140762 0.222 33.12063 IsoPrime
4/15/08 2 4 1856 7.565674 0.202 32.68034 IsoPrime
10/22/06 4 1 1856 8.524657 0.281 33.43358 IsoPrime
10/21/06 Std. Baker 9.425176 0.271 29.85195 IsoPrime
10/23/06 4 1 1856 6.732149 0.218 34.47847 IsoPrime
4/14/08 2 4 1855 7.433066 0.183 31.98246 IsoPrime
4/16/08 2 4 1855 7.186066 0.19 31.13967 IsoPrime
10/23/06 4 1 1855 9.093489 0.293 32.70633 IsoPrime
10/23/06 4 1 1855 6.390605 0.208 33.11264 IsoPrime
4/14/08 2 4 1854 9.015361 0.219 31.84456 IsoPrime
10/22/06 Std. Baker 7.385922 0.254 27.58774 IsoPrime
10/22/06 Std. Baker 6.679202 0.222 27.69806 IsoPrime
10/22/06 Std. Baker 7.346984 0.235 28.56155 IsoPrime
10/22/06 Std. Baker 7.780865 0.24 28.48469 IsoPrime
10/22/06 Std. Baker 8.695786 0.268 29.18443 IsoPrime
12/13/06 4 1 1854 7.403418 0.219 29.94238 IsoPrime
2/1/07 4 1 1854 10.5639 0.302 30.34063 IsoPrime
4/14/08 2 4 1853 8.278666 0.201 31.46941 IsoPrime
4/15/08 2 4 1853 7.643294 0.188 32.40953 IsoPrime
4/16/08 2 4 1853 8.076887 0.212 30.70663 IsoPrime
12/13/06 4 1 1853 7.12253 0.208 29.79829 IsoPrime
2/1/07 4 1 1853 7.462358 0.205 30.91238 IsoPrime
4/14/08 2 4 1852 8.946623 0.212 32.05793 IsoPrime
4/14/08 2 4 1851 7.569846 0.188 31.24663 IsoPrime
4/16/08 2 4 1851 6.944719 0.183 30.38961 IsoPrime
10/23/06 Std. Baker 8.810869 0.259 29.89327 IsoPrime
4/14/08 2 4 1850 8.339039 0.202 30.49188 IsoPrime
4/16/08 2 4 1850 7.085334 0.185 30.46209 IsoPrime
1/12/07 4 1 1850 6.908421 0.216 29.97114 IsoPrime
4/15/08 2 4 1849 8.774069 0.214 31.86415 IsoPrime
4/16/08 2 4 1849 7.737636 0.199 32.41524 IsoPrime
01/12/07 4 1 1849 8.624024 0.255 30.26326 IsoPrime
4/15/08 2 4 1848 7.622147 0.184 33.35733 IsoPrime
4/16/08 2 4 1848 8.284419 0.183 33.38651 IsoPrime
01/12/07 4 1 1848 8.531922 0.244 31.41512 IsoPrime
4/15/08 2 4 1847 7.539267 0.18 34.01048 IsoPrime
10/27/06 Std. Baker 8.01764 0.26 28.72132 IsoPrime
10/27/06 Std. Baker 6.752651 0.22 29.39011 IsoPrime
10/27/06 Std. Baker 9.693329 0.318 29.56379 IsoPrime
10/27/06 Std. Baker 7.682467 0.261 29.02616 IsoPrime
16 MAXBERKELHAMMER
10/27/06 Std. Baker 8.028575 0.282 29.246 IsoPrime
10/27/06 Std. Baker 9.656284 0.344 29.93001 IsoPrime
10/27/06 Std. Baker 7.964095 0.292 29.21883 IsoPrime
10/27/06 Std. Baker 7.842951 0.301 29.9586 IsoPrime
10/28/06 Std. Baker 7.136615 0.319 29.85077 IsoPrime
10/28/06 Std. Baker 1.500241 0.309 28.43209 IsoPrime
10/28/06 Std. Baker 7.379143 0.316 29.72928 IsoPrime
11/12/06 Std. Baker 12.21774 0 27.9519 IsoPrime
11/12/06 Std. Baker 11.07786 0 28.22177 IsoPrime
11/12/06 Std. Baker 9.303354 0.254 28.21094 IsoPrime
11/12/06 Std. Baker 8.956726 0.242 28.25459 IsoPrime
11/12/06 Std. Baker 8.435849 0.234 29.15963 IsoPrime
11/13/06 Std. Baker 10.37927 0.282 28.99217 IsoPrime
11/13/06 Std. Baker 8.530801 0.23 29.10942 IsoPrime
11/13/06 Std. Baker 8.535616 0.234 28.89924 IsoPrime
11/13/06 Std. Baker 7.632703 0.208 28.70695 IsoPrime
11/13/06 Std. Baker 9.106075 0.244 28.935 IsoPrime
11/13/06 Std. Baker 7.059278 0.197 29.55148 IsoPrime
11/13/06 Std. Baker 6.908927 0.191 29.05413 IsoPrime
11/13/06 Std. Baker 10.75093 0.293 29.07514 IsoPrime
11/13/06 Std. Baker 8.965635 0.249 29.11243 IsoPrime
11/13/06 Std. Baker 6.838703 0.183 28.8221 IsoPrime
11/13/06 Std. Baker 10.86574 0.298 29.15866 IsoPrime
11/16/06 Std. Baker 12.33872 0.333 28.05657 IsoPrime
11/16/06 Std. Baker 7.24627 0.192 28.67835 IsoPrime
11/16/06 Std. Baker 7.626288 0.209 28.65045 IsoPrime
11/17/06 Std. Baker 8.926944 0.244 28.72093 IsoPrime
11/17/06 Lf LfA 11.49878 0.339 36.10815 IsoPrime
11/17/06 Lf LfB 9.073357 0.288 36.31827 IsoPrime
11/17/06 Lf LfC 9.895706 0.318 35.36125 IsoPrime
11/17/06 Lf LfA 8.825539 0.263 37.53127 IsoPrime
11/17/06 Lf LfB 4.752584 0.146 36.70868 IsoPrime
11/17/06 Lf LfC 8.190187 0.261 34.73794 IsoPrime
11/17/06 Std. Baker 12.49521 0.338 29.58602 IsoPrime
11/17/06 Std. Baker 6.966769 0.184 29.13921 IsoPrime
11/17/06 Std. Baker 9.025252 0.252 29.15532 IsoPrime
11/17/06 Lf LfA 10.48714 0.319 37.12141 IsoPrime
11/17/06 Lf LfB 10.19054 0.314 37.96302 IsoPrime
11/17/06 Std. Baker 5.820623 0.158 29.39209 IsoPrime
11/17/06 Std. Baker 7.812801 0.211 28.943 IsoPrime
11/17/06 Std. Baker 8.546868 0.206 29.09961 IsoPrime
12/11/06 Std. Baker 6.568044 0.179 29.89159 IsoPrime
12/11/06 Std. Baker 10.26125 0.276 29.60835 IsoPrime
SUPPLEMENTARY DATA TABLE 17
12/12/06 Std. Baker 9.852209 0.26 29.96838 IsoPrime
12/12/06 Std. Baker 5.749942 0.165 29.09046 IsoPrime
12/12/06 Std. Baker 7.678661 0.204 29.23623 IsoPrime
12/12/06 Std. Baker 6.484203 0.17 29.73015 IsoPrime
12/12/06 Std. Baker 7.432609 0.2 29.39591 IsoPrime
12/12/06 Std. Baker 8.690593 0.228 29.74617 IsoPrime
12/12/06 Std. Baker 9.951969 0.261 29.68834 IsoPrime
12/12/06 Std. Baker 8.349634 0.219 29.64806 IsoPrime
12/12/06 Lf 2 3 3 6.744447 0.185 38.82549 IsoPrime
12/12/06 Lf 2 5 4 6.10766 0.17 37.55185 IsoPrime
12/12/06 Lf 2 3 1 9.992001 0.286 38.60022 IsoPrime
12/12/06 Lf 2 1 1 9.639229 0.297 36.26431 IsoPrime
12/12/06 Lf 2 1 2 8.52342 0.26 35.16955 IsoPrime
12/12/06 Lf 2 1 3 7.078567 0.217 35.41006 IsoPrime
12/12/06 Lf 2 1 4 7.709777 0.234 33.70572 IsoPrime
12/12/06 Lf 2 1 5 10.20116 0.31 35.73057 IsoPrime
12/12/06 Std. Baker 7.463587 0.197 28.77994 IsoPrime
12/12/06 Lf 2 2 1 6.330765 0.198 35.50911 IsoPrime
12/12/06 Lf 2 2 2 10.23607 0.31 35.54611 IsoPrime
12/12/06 Lf 2 2 3 5.173752 0.16 37.0759 IsoPrime
12/12/06 Lf 2 2 4 6.36347 0.197 35.99203 IsoPrime
12/12/06 Lf 2 2 5 6.028022 0.195 35.86961 IsoPrime
12/12/06 Lf 2 4 1 3.78994 0.121 36.664 IsoPrime
12/12/06 Lf 2 4 2 7.764333 0.24 39.97378 IsoPrime
12/12/06 Lf 2 4 3 10.15397 0.317 37.62547 IsoPrime
12/12/06 Std. Baker 6.859079 0.182 29.3411 IsoPrime
12/12/06 Std. Baker 11.48942 0.309 29.97233 IsoPrime
12/12/06 Lf 2 4 4 5.969438 0.206 36.35696 IsoPrime
12/12/06 Lf 2 4 5 6.779908 0.218 36.92976 IsoPrime
3/12/08 IAEA 13 9.304354 0.261 32.96923 IsoPrime
12/13/06 Std. Baker 8.138346 0.208 28.92707 IsoPrime
12/13/06 Std. Baker 11.53 0.297 28.3906 IsoPrime
3/11/08 IAEA 7 7.247943 0.212 32.96692 IsoPrime
12/13/06 Std. Baker 8.52139 0.219 28.94787 IsoPrime
12/13/06 Std. Baker 8.752482 0.229 29.16498 IsoPrime
12/13/06 Std. Baker 8.86173 0.232 28.74769 IsoPrime
12/13/06 Std. Baker 5.871683 0.153 29.13688 IsoPrime
01/13/07 4 1 1847 9.478418 0.281 31.96143 IsoPrime
4/15/08 2 4 1846 8.997487 0.217 34.3908 IsoPrime
10/20/06 4 1 1846 8.306804 0.267 32.04272 IsoPrime
10/20/06 4 1 1846 7.489572 0.236 31.93111 IsoPrime
4/15/08 2 4 1845 6.506661 0.211 33.34748 IsoPrime
4/15/08 2 4 1845 7.752668 0.22 33.22176 IsoPrime
18 MAXBERKELHAMMER
10/21/06 4 1 1845 5.994579 0.196 34.10186 IsoPrime
4/15/08 2 4 1844 9.682795 0.232 34.20054 IsoPrime
12/13/06 Std. Baker 8.706161 0.233 27.56254 IsoPrime
12/13/06 Std. Baker 7.114136 0.187 27.50302 IsoPrime
12/13/06 Std. Baker 10.83929 0.296 27.93631 IsoPrime
10/23/06 4 1 1844 7.016146 0.234 32.5523 IsoPrime
10/23/06 4 1 1844 11.03996 0.348 33.14157 IsoPrime
4/15/08 2 4 1843 8.645643 0.207 33.44114 IsoPrime
10/23/06 4 1 1843 8.492772 0.276 34.32214 IsoPrime
10/23/06 4 1 1843 8.806387 0.284 34.07339 IsoPrime
12/13/06 4 1 1842 7.118342 0.211 31.80647 IsoPrime
1/11/07 Std. Baker 7.015095 0.195 28.45723 IsoPrime
1/11/07 Std. Baker 7.938083 0.224 28.82998 IsoPrime
1/11/07 Std. Baker 9.357936 0.264 28.63315 IsoPrime
1/11/07 Std. Baker 7.838399 0.22 28.8172 IsoPrime
1/11/07 Std. Baker 7.851417 0.23 29.03739 IsoPrime
1/11/07 Std. Baker 4.516332 0.132 29.00324 IsoPrime
1/11/07 Std. Baker 5.979789 0.182 29.61619 IsoPrime
1/11/07 Std. Baker 6.879093 0.198 28.93127 IsoPrime
1/11/07 Std. Baker 8.459453 0.24 28.8449 IsoPrime
1/11/07 Std. Baker 9.46476 0.275 28.61637 IsoPrime
1/11/07 Std. Baker 6.294291 0.176 28.78628 IsoPrime
1/11/07 Std. Baker 6.724334 0.189 28.70583 IsoPrime
1/11/07 Std. Baker 7.721049 0.217 28.51178 IsoPrime
1/11/07 Std. Baker 9.545575 0.274 28.52372 IsoPrime
1/11/07 Std. Baker 8.82209 0.275 28.83565 IsoPrime
1/11/07 Std. Baker 8.141666 0.231 28.71096 IsoPrime
1/11/07 Std. Baker 7.509606 0.211 28.87998 IsoPrime
1/11/07 Std. Baker 9.126524 0.256 29.01067 IsoPrime
2/1/07 4 1 1842 7.797348 0.212 32.02671 IsoPrime
4/15/08 2 4 1841 7.531338 0.197 32.31392 IsoPrime
1/12/07 Std. Baker 9.382105 0.266 28.60049 IsoPrime
1/12/07 Std. Baker 6.838382 0.189 29.06921 IsoPrime
1/12/07 Std. Baker 7.781854 0.218 28.98827 IsoPrime
12/13/06 4 1 1841 7.454666 0.217 32.32539 IsoPrime
2/1/07 4 1 1841 7.90814 0.22 32.90493 IsoPrime
4/15/08 2 4 1840 7.156733 0.182 31.24081 IsoPrime
4/22/08 2 4 1840 7.380837 0.182 30.9675 IsoPrime
4/15/08 2 4 1839 8.816159 0.219 32.46514 IsoPrime
1/12/07 4 1 1838 9.271002 0.296 29.25862 IsoPrime
4/15/08 2 4 1838 7.978975 0.193 33.92788 IsoPrime
01/12/07 4 1 1837 8.777238 0.256 32.55915 IsoPrime
1/12/07 Std. Baker 7.140911 0.198 29.28595 IsoPrime
SUPPLEMENTARY DATA TABLE 19
1/12/07 Std. Baker 7.591637 0.213 29.09789 IsoPrime
01/12/07 Std. Baker 8.656052 0.259 27.6491 IsoPrime
01/12/07 Std. Baker 8.532207 0.238 28.20302 IsoPrime
01/12/07 Std. Baker 8.677514 0.246 28.41682 IsoPrime
01/12/07 Std. Baker 8.871263 0.245 28.42825 IsoPrime
01/12/07 Std. Baker 8.054247 0.222 28.95672 IsoPrime
01/12/07 Std. Baker 9.117175 0.252 28.76699 IsoPrime
4/15/08 2 4 1837 8.6534 0.211 33.81025 IsoPrime
01/12/07 4 1 1836 8.31817 0.254 34.16135 IsoPrime
4/15/08 2 4 1836 8.848255 0.212 32.2075 IsoPrime
4/16/08 2 4 1836 4.837464 0.135 32.57 IsoPrime
01/13/07 4 1 1835 8.01833 0.232 34.05835 IsoPrime
4/15/08 2 4 1835 8.490914 0.21 32.46497 IsoPrime
4/16/08 2 4 1835 7.732368 0.206 32.14768 IsoPrime
10/20/06 4 1 1834 6.286727 0.202 30.90526 IsoPrime
01/12/07 Std. Baker 9.75969 0.274 29.34412 IsoPrime
01/12/07 Std. Baker 6.780173 0.183 29.72525 IsoPrime
01/12/07 Std. Baker 9.15381 0.143 29.31865 IsoPrime
10/20/06 4 1 1834 10.19586 0.311 32.59432 IsoPrime
4/15/08 2 4 1834 8.506046 0.211 33.24679 IsoPrime
4/15/08 2 4 1834 7.883749 0.192 31.93221 IsoPrime
10/21/06 4 1 1833 15.00655 0.444 33.33391 IsoPrime
10/21/06 4 1 1833 8.615153 0.285 31.78108 IsoPrime
4/15/08 2 4 1833 8.109035 0.2 32.47132 IsoPrime
4/16/08 2 4 1833 7.424963 0.225 32.25476 IsoPrime
10/23/06 4 1 1832 8.410337 0.272 31.62194 IsoPrime
4/18/08 IAEA 20 8.928271 0.228 32.96466 IsoPrime
01/13/07 Std. Baker 9.379168 0.259 29.51379 IsoPrime
01/13/07 Std. Baker 9.847453 0.275 29.51485 IsoPrime
10/23/06 4 1 1832 7.13659 0.232 31.74767 IsoPrime
4/15/08 2 4 1832 9.024475 0.22 31.65389 IsoPrime
12/13/06 4 1 1831 7.446196 0.215 31.07274 IsoPrime
2/1/07 4 1 1831 8.413606 0.238 31.0695 IsoPrime
4/15/08 2 4 1831 8.023328 0.197 32.48396 IsoPrime
12/13/06 4 1 1830 7.152131 0.212 31.13661 IsoPrime
2/1/07 4 1 1830 8.370376 0.238 31.84084 IsoPrime
4/15/08 2 4 1830 8.18647 0.2 33.60957 IsoPrime
01/13/07 Std. Baker 7.833076 0.213 29.97028 IsoPrime
01/13/07 Std. Baker 9.16717 0.251 29.48725 IsoPrime
01/13/07 Std. Baker 10.03253 0.274 29.17658 IsoPrime
12/13/06 4 1 1829 8.189003 0.234 32.91348 IsoPrime
2/1/07 4 1 1829 10.09609 0.281 32.51577 IsoPrime
4/15/08 2 4 1829 8.248232 0.2 30.57906 IsoPrime
20 MAXBERKELHAMMER
5/6/08 IAEA 10 7.649967 0 32.9622 IsoPrime
01/13/07 Std. Baker 7.968873 0.221 29.83268 IsoPrime
01/13/07 Std. Baker 8.625023 0.25 29.3477 IsoPrime
1/15/07 Std. Baker 7.17285 0.202 27.78701 IsoPrime
1/15/07 Std. Baker 8.701535 0.242 27.89948 IsoPrime
1/15/07 Std. Baker 10.27639 0.291 27.81858 IsoPrime
1/15/07 Std. Baker 8.730759 0.252 28.46068 IsoPrime
1/15/07 Std. Baker 9.033793 0.248 28.6251 IsoPrime
1/15/07 Std. Baker 9.965581 0.277 28.19318 IsoPrime
4/22/08 2 4 1829 8.459207 0.206 31.39829 IsoPrime
4/15/08 2 4 1828 8.607091 0.211 31.90008 IsoPrime
4/15/08 2 4 1828 8.731655 0.213 32.80017 IsoPrime
4/16/08 2 4 1828 7.772456 0.194 31.69547 IsoPrime
1/11/07 4 1 1827 1.174485 0.154 30.88893 IsoPrime
4/15/08 2 4 1827 8.11882 0.2 31.34229 IsoPrime
1/12/07 4 1 1826 8.131287 0.249 30.04603 IsoPrime
4/15/08 2 4 1826 8.100556 0.199 31.40378 IsoPrime
1/15/07 Std. Baker 9.894527 0.27 28.87573 IsoPrime
1/15/07 Std. Baker 6.597422 0.182 29.42417 IsoPrime
1/15/07 Std. Baker 8.512233 0.236 29.23788 IsoPrime
01/12/07 4 1 1825 7.995227 0.229 32.63172 IsoPrime
4/15/08 2 4 1825 8.760319 0.213 31.27986 IsoPrime
01/12/07 4 1 1824 8.092366 0.236 32.99838 IsoPrime
4/15/08 2 4 1824 7.536021 0.185 32.45186 IsoPrime
4/16/08 2 4 1824 8.970387 0.228 32.54517 IsoPrime
01/13/07 4 1 1823 7.363141 0.219 32.90196 IsoPrime
4/17/08 2 4 1823 9.091349 0.236 33.25195 IsoPrime
10/20/06 4 1 1822 8.484703 0.273 30.25856 IsoPrime
1/16/07 Std. Baker 6.965994 0.187 29.87773 IsoPrime
1/16/07 Std. Baker 8.969913 0.245 29.17419 IsoPrime
1/16/07 Std. Baker 9.246289 0.254 29.00148 IsoPrime
1/20/07 Std. Baker 7.569628 0.217 28.58429 IsoPrime
10/20/06 4 1 1822 8.595986 0.279 30.1786 IsoPrime
4/17/08 2 4 1822 8.502119 0.215 32.38418 IsoPrime
10/21/06 4 1 1821 6.986768 0.225 31.01638 IsoPrime
10/21/06 4 1 1821 8.09672 0.274 31.16588 IsoPrime
4/17/08 2 4 1821 8.644458 0.218 33.29055 IsoPrime
4/17/08 2 4 1821 8.160421 0.208 33.58759 IsoPrime
10/23/06 4 1 1820 7.165279 0.246 32.1561 IsoPrime
1/21/07 Std. Baker 6.156329 0.185 28.66592 IsoPrime
1/21/07 Std. Baker 9.335265 0.264 28.33054 IsoPrime
1/21/07 Std. Baker 10.10638 0.283 28.46941 IsoPrime
10/23/06 4 1 1820 8.689303 0.287 31.43813 IsoPrime
SUPPLEMENTARY DATA TABLE 21
4/17/08 2 4 1820 7.858635 0.191 32.38945 IsoPrime
12/13/06 4 1 1819 7.418847 0.215 31.58158 IsoPrime
2/1/07 4 1 1819 6.206082 0.173 31.83365 IsoPrime
4/17/08 2 4 1819 7.506348 0.195 32.81891 IsoPrime
4/17/08 2 4 1819 9.805535 0.236 32.16992 IsoPrime
12/13/06 4 1 1818 8.935172 0.273 28.81965 IsoPrime
2/1/07 4 1 1818 9.17844 0.262 29.64987 IsoPrime
1/21/07 Std. Baker 6.68285 0.186 28.6149 IsoPrime
1/21/07 Std. Baker 10.12682 0.285 28.62049 IsoPrime
1/21/07 Std. Baker 8.282485 0.241 29.00144 IsoPrime
1/21/07 Std. Baker 10.1597 0.286 28.1561 IsoPrime
1/21/07 Std. Baker 10.27926 0.293 28.34497 IsoPrime
1/21/07 Std. Baker 7.623992 0.208 28.07246 IsoPrime
1/21/07 Std. Baker 8.612948 0.242 28.52923 IsoPrime
1/21/07 Std. Baker 7.42965 0.208 28.41061 IsoPrime
4/17/08 2 4 1818 7.993444 0.209 31.96927 IsoPrime
12/13/06 4 1 1817 10.13076 0.289 29.52501 IsoPrime
2/1/07 4 1 1817 7.498155 0.213 30.03348 IsoPrime
4/17/08 2 4 1817 7.22282 0.193 32.80701 IsoPrime
4/17/08 2 4 1817 7.367374 0.196 33.72335 IsoPrime
2/2/07 4 1 1816 8.766421 0.245 31.40671 IsoPrime
4/17/08 2 4 1816 7.500672 0.19 32.22187 IsoPrime
1/12/07 4 1 1815 6.340834 0.202 31.0542 IsoPrime
1/21/07 Std. Baker 8.58518 0.23 28.57286 IsoPrime
1/21/07 Std. Baker 10.36233 0.284 29.32561 IsoPrime
1/21/07 Std. Baker 8.35096 0.225 27.6869 IsoPrime
4/17/08 2 4 1815 7.195378 0.186 32.39507 IsoPrime
1/12/07 4 1 1814 6.701744 0.203 33.08972 IsoPrime
4/17/08 2 4 1814 7.909866 0.202 32.98318 IsoPrime
01/12/07 4 1 1813 4.364894 0.126 34.66733 IsoPrime
01/12/07 4 1 1812 8.427118 0.244 32.65175 IsoPrime
4/18/08 2 4 1812 7.896264 0.203 30.423 IsoPrime
4/22/08 2 4 1812 8.944851 0.222 30.98649 IsoPrime
01/13/07 4 1 1811 9.189385 0.253 30.42809 IsoPrime
1/22/07 Std. Baker 8.792628 0.249 29.10461 IsoPrime
1/22/07 Std. Baker 7.33915 0.195 28.86922 IsoPrime
1/22/07 Std. Baker 7.34833 0.204 28.84786 IsoPrime
1/22/07 Std. Baker 8.683967 0.228 28.00325 IsoPrime
1/22/07 Std. Baker 6.743261 0.175 27.92424 IsoPrime
1/22/07 Std. Baker 8.379363 0.25 28.16915 IsoPrime
1/22/07 Std. Baker 11.38267 0.3 28.63958 IsoPrime
1/22/07 Std. Baker 7.430679 0.193 28.2453 IsoPrime
1/22/07 Std. Baker 8.608076 0.22 28.46309 IsoPrime
22 MAXBERKELHAMMER
1/22/07 Std. Baker 7.825038 0.227 28.63807 IsoPrime
4/18/08 2 4 1811 7.54014 0.196 33.00599 IsoPrime
10/21/06 4 1 1810 10.00689 0.311 32.97871 IsoPrime
10/21/06 4 1 1810 8.443707 0.27 32.54069 IsoPrime
4/18/08 2 4 1810 7.129226 0.181 33.89979 IsoPrime
10/22/06 4 1 1809 10.39103 0.347 30.7007 IsoPrime
10/23/06 4 1 1809 8.102627 0.269 31.23701 IsoPrime
4/18/08 2 4 1809 6.539441 0.168 34.26602 IsoPrime
4/18/08 2 4 1809 6.578937 0.174 34.54967 IsoPrime
1/23/07 Std. Baker 11.43838 0.294 28.99173 IsoPrime
1/23/07 Std. Baker 10.83796 0.286 28.95372 IsoPrime
1/23/07 Std. Baker 8.641494 0.22 28.87622 IsoPrime
10/23/06 4 1 1808 6.125544 0.208 31.90248 IsoPrime
10/23/06 4 1 1808 8.074501 0.267 32.10665 IsoPrime
4/18/08 2 4 1808 7.29365 0.189 34.47978 IsoPrime
12/13/06 4 1 1807 7.301579 0.221 31.03687 IsoPrime
2/1/07 4 1 1807 10.30177 0.298 31.22204 IsoPrime
2/2/07 4 1 1807 7.761115 0.224 31.68656 IsoPrime
1/23/07 Std. Baker 11.95721 0.308 29.1337 IsoPrime
1/23/07 Std. Baker 7.406248 0.202 29.19109 IsoPrime
1/23/07 Std. Baker 8.075369 0.21 28.98498 IsoPrime
1/24/07 Std. Baker 7.269322 0.185 28.18533 IsoPrime
1/24/07 Std. Baker 7.879924 0.196 28.0248 IsoPrime
1/24/07 Std. Baker 8.635305 0.239 28.47867 IsoPrime
1/24/07 Std. Baker 9.822476 0.249 28.61849 IsoPrime
4/18/08 2 4 1807 8.92642 0.227 34.58198 IsoPrime
12/13/06 4 1 1806 7.155049 0.213 31.76856 IsoPrime
2/1/07 4 1 1806 10.71017 0.307 31.52766 IsoPrime
4/17/08 2 4 1806 8.197094 0.205 33.21029 IsoPrime
4/17/08 2 4 1806 9.104649 0.235 33.81185 IsoPrime
4/18/08 2 4 1805 8.451952 0.214 32.61406 IsoPrime
2/2/07 4 1 1804 8.705625 0.248 31.32919 IsoPrime
1/24/07 Std. Baker 10.03496 0.254 28.95324 IsoPrime
1/24/07 Std. Baker 8.719779 0.219 28.96636 IsoPrime
1/24/07 Std. Baker 7.811366 0.199 29.02667 IsoPrime
4/18/08 2 4 1804 7.397627 0.191 31.93673 IsoPrime
1/12/07 4 1 1803 7.274023 0.232 30.55351 IsoPrime
4/18/08 2 4 1803 8.027958 0.201 31.22097 IsoPrime
1/12/07 4 1 1802 7.3198 0.235 29.3859 IsoPrime
4/18/08 2 4 1802 7.949806 0.199 31.96492 IsoPrime
4/18/08 2 4 1802 7.57926 0.195 32.51401 IsoPrime
01/12/07 4 1 1801 7.962102 0.232 32.00207 IsoPrime
4/21/08 2 4 1801 8.628996 0.218 33.35274 IsoPrime
SUPPLEMENTARY DATA TABLE 23
1/25/07 Std. Baker 7.608941 0.194 29.12838 IsoPrime
1/25/07 Std. Baker 9.579568 0.238 29.00524 IsoPrime
1/25/07 Std. Baker 8.349395 0.213 29.18242 IsoPrime
01/12/07 4 1 1800 8.978391 0.262 33.29139 IsoPrime
4/21/08 2 4 1800 7.421736 0.189 31.21 IsoPrime
4/22/08 2 4 1800 9.058837 0.228 31.36747 IsoPrime
01/13/07 4 1 1799 7.231731 0.218 32.26428 IsoPrime
4/18/08 2 4 1799 6.899351 0.183 32.88815 IsoPrime
10/21/06 4 1 1798 8.813904 0.277 32.33241 IsoPrime
1/25/07 Std. Baker 9.633518 0.241 28.9734 IsoPrime
1/25/07 Std. Baker 9.011255 0.226 28.8371 IsoPrime
2/1/07 Std. Baker 9.05552 0.23 28.13797 IsoPrime
2/1/07 Std. Baker 9.043449 0.231 28.46108 IsoPrime
2/1/07 Std. Baker 9.349889 0.239 28.51097 IsoPrime
2/1/07 Std. Baker 10.92771 0.282 28.61793 IsoPrime
2/1/07 Std. Baker 8.331057 0.21 28.69899 IsoPrime
2/1/07 Std. Baker 12.20774 0.324 28.63423 IsoPrime
2/1/07 Std. Baker 6.19327 0.155 28.12925 IsoPrime
2/1/07 Std. Baker 7.204544 0.191 28.73053 IsoPrime
10/21/06 4 1 1798 6.613571 0.212 31.51166 IsoPrime
4/18/08 2 4 1798 7.507319 0.201 33.35517 IsoPrime
4/18/08 2 4 1798 8.890018 0.227 32.66663 IsoPrime
10/22/06 4 1 1797 7.745898 0.263 31.49536 IsoPrime
10/23/06 4 1 1797 6.162485 0.207 30.85918 IsoPrime
4/18/08 2 4 1797 7.34568 0.187 33.99465 IsoPrime
10/23/06 4 1 1796 7.912615 0.255 32.06348 IsoPrime
10/23/06 4 1 1796 10.5332 0.337 32.12622 IsoPrime
2/1/07 Std. Baker 10.1808 0.261 29.02754 IsoPrime
2/1/07 Std. Baker 8.216942 0.211 29.02109 IsoPrime
2/1/07 Std. Baker 7.374236 0.19 28.92763 IsoPrime
4/18/08 2 4 1796 8.174158 0.222 33.95661 IsoPrime
12/13/06 4 1 1795 8.673796 0.263 30.43093 IsoPrime
2/1/07 4 1 1795 7.9812 0.226 31.11137 IsoPrime
4/20/08 2 4 1795 7.601816 0.185 31.94286 IsoPrime
12/13/06 4 1 1794 8.635271 0.262 29.99196 IsoPrime
2/1/07 4 1 1794 9.549341 0.269 30.7598 IsoPrime
4/20/08 2 4 1794 7.225593 0.179 32.24033 IsoPrime
2/1/07 Std. Baker 7.457105 0.194 29.29557 IsoPrime
2/1/07 Std. Baker 8.87111 0.235 28.90825 IsoPrime
2/1/07 Std. Baker 9.307573 0.237 29.0829 IsoPrime
4/20/08 2 4 1794 8.200305 0.208 32.56436 IsoPrime
2/1/07 4 1 1793 9.950823 0.284 30.08132 IsoPrime
4/20/08 2 4 1793 8.530836 0.214 32.00131 IsoPrime
24 MAXBERKELHAMMER
2/2/07 Std. Baker 8.720208 0.223 29.25724 IsoPrime
2/2/07 Std. Baker 6.930561 0.184 29.39905 IsoPrime
2/2/07 Std. Baker 10.27421 0.278 29.02101 IsoPrime
2/7/07 Std. Baker 8.905984 0.233 27.64515 IsoPrime
2/7/07 Std. Baker 11.06138 0.287 28.16263 IsoPrime
2/7/07 Std. Baker 11.25826 0.294 28.20255 IsoPrime
2/7/07 Std. Baker 10.47258 0.287 28.49449 IsoPrime
2/7/07 Std. Baker 9.371471 0.246 28.93473 IsoPrime
2/7/07 Std. Baker 7.313604 0.192 28.67688 IsoPrime
2/7/07 Std. Baker 8.511695 0.219 29.08581 IsoPrime
2/7/07 Std. Baker 8.003796 0.207 28.55877 IsoPrime
2/7/07 Std. Baker 8.398687 0.215 28.72021 IsoPrime
2/1/07 4 1 1792 10.67565 0.311 29.04992 IsoPrime
4/20/08 2 4 1792 7.99988 0.197 31.97697 IsoPrime
4/20/08 2 4 1792 8.097927 0.199 31.59312 IsoPrime
1/12/07 4 1 1791 5.92692 0.198 30.40868 IsoPrime
4/20/08 2 4 1791 8.871695 0.222 32.54056 IsoPrime
1/12/07 4 1 1790 6.368114 0.197 30.7814 IsoPrime
4/20/08 2 4 1790 8.084122 0.203 32.19894 IsoPrime
01/12/07 4 1 1789 8.812948 0.262 31.88583 IsoPrime
2/8/07 Std. Baker 10.12833 0.273 28.63408 IsoPrime
2/8/07 Std. Baker 9.57744 0.259 28.72024 IsoPrime
4/20/08 2 4 1789 8.20323 0.199 31.05581 IsoPrime
4/20/08 2 4 1789 7.592577 0.179 31.21928 IsoPrime
01/13/07 4 1 1788 6.769461 0.203 31.64982 IsoPrime
4/20/08 2 4 1788 8.477455 0.213 31.82536 IsoPrime
01/13/07 4 1 1787 9.145166 0.271 30.73009 IsoPrime
4/30/08 2 4 1787 7.159473 0.177 33.2554 IsoPrime
01/13/07 4 1 1786 8.130834 0.244 32.25747 IsoPrime
2/8/07 Std. Baker 7.610289 0.21 28.16325 IsoPrime
2/8/07 Std. Baker 8.350002 0.215 27.97106 IsoPrime
2/8/07 Std. Baker 10.55147 0.293 27.97918 IsoPrime
4/30/08 2 4 1786 7.160967 0.188 33.88901 IsoPrime
01/13/07 4 1 1785 9.12311 0.265 33.07921 IsoPrime
4/30/08 2 4 1785 7.175966 0.185 33.71941 IsoPrime
4/30/08 2 4 1785 7.410952 0.197 34.16501 IsoPrime
1/15/07 4 1 1784 7.940472 0.229 32.65934 IsoPrime
4/30/08 2 4 1784 7.851698 0.196 32.8732 IsoPrime
1/16/07 4 1 1783 9.736529 0.289 32.39067 IsoPrime
4/30/08 2 4 1783 7.284046 0.185 33.37316 IsoPrime
2/8/07 Std. Baker 9.884898 0.267 28.77055 IsoPrime
2/8/07 Std. Baker 10.41432 0.279 28.99885 IsoPrime
2/8/07 Std. Baker 11.59656 0.307 28.84536 IsoPrime
SUPPLEMENTARY DATA TABLE 25
2/11/08 IAEA 9 8.240897 0.16 32.95548 IsoPrime
4/29/08 IAEA 14 7.163084 0 32.94919 IsoPrime
2/4/08 IAEA 6 6.358567 0.164 32.94723 IsoPrime
2/18/08 IAEA 4 6.145371 0.168 32.94515 IsoPrime
2/13/08 IAEA 12 7.936302 0.202 32.93929 IsoPrime
4/18/08 IAEA 15 9.041043 0.235 32.92921 IsoPrime
1/28/08 Sigma 1 8.635372 0.164 26.65588 IsoPrime
1/28/08 V0 3 6 7.62533 0.149 37.12427 IsoPrime
1/28/08 V0 3 7 7.369564 0.147 35.74401 IsoPrime
1/28/08 V0 3 8 7.99729 0.158 34.94234 IsoPrime
1/28/08 V0 3 9 8.337323 0.164 35.77993 IsoPrime
1/28/08 V0 4 1 5.984861 0.124 35.51276 IsoPrime
2/16/08 IAEA 11 6.46003 0.163 32.92779 IsoPrime
2/5/08 IAEA 7 6.287474 0.155 32.92475 IsoPrime
4/14/08 IAEA 4 8.384839 0.207 32.91366 IsoPrime
2/4/08 Blank 4 2 0.001463 0.136 - IsoPrime
2/4/08 V0 4 2 5.515556 0.109 33.93801 IsoPrime
2/4/08 V0 4 3 4.480378 0.116 33.90343 IsoPrime
2/4/08 V0 4 4 4.520077 0.144 33.83272 IsoPrime
2/4/08 V0 4 5 5.463185 0.151 32.74066 IsoPrime
2/4/08 V0 4 6 5.803201 0.161 33.90047 IsoPrime
2/4/08 V0 4 78 6.241832 0.203 32.89237 IsoPrime
1/28/08 IAEA 7 10.56109 0.199 32.88472 IsoPrime
4/18/08 IAEA 17 8.398477 0.214 32.87473 IsoPrime
3/20/08 IAEA 7 9.119008 0.199 32.87335 IsoPrime
2/4/08 V0 3 6 6.972817 0.172 36.41808 IsoPrime
2/4/08 V0 3 8 5.894912 0.15 33.08849 IsoPrime
2/4/08 V0 4 9 6.348878 0.159 32.78787 IsoPrime
2/4/08 V0 4 10 5.289192 0.129 33.2881 IsoPrime
2/4/08 V0 5 1 5.78563 0.147 35.22103 IsoPrime
2/4/08 V0 5 2 4.774632 0.118 31.01836 IsoPrime
2/4/08 V0 5 3 6.048199 0.152 35.77875 IsoPrime
2/4/08 V0 5 4 4.856794 0.122 36.07022 IsoPrime
3/15/08 IAEA 8 10.36463 0.256 32.87083 IsoPrime
4/20/08 IAEA 8 7.580046 0.193 32.8622 IsoPrime
2/13/08 IAEA 11 7.845757 0.202 32.85011 IsoPrime
2/5/08 V0 5 5 5.488914 0.138 35.96168 IsoPrime
2/5/08 V0 5 6 6.078026 0.159 35.5086 IsoPrime
2/5/08 V0 5 7 6.930209 0.178 34.51117 IsoPrime
2/5/08 V0 5 8 6.955996 0.183 35.14633 IsoPrime
2/5/08 V0 5 9 6.66852 0.173 34.41463 IsoPrime
2/5/08 V0 5 10 7.232591 0.185 33.84695 IsoPrime
2/19/08 IAEA 10 5.80981 0.151 32.82935 IsoPrime
26 MAXBERKELHAMMER
2/4/08 IAEA 5 7.11014 0.185 32.82555 IsoPrime
2/19/08 IAEA 4 6.415061 0.162 32.81316 IsoPrime
3/16/08 IAEA 12 10.84395 0.258 32.79062 IsoPrime
3/11/08 IAEA 6 7.260174 0.219 32.78461 IsoPrime
9/8/06 IAEA 15 8.544161 0.331 32.77195 IsoPrime
2/5/08 V0 6 1 6.761405 0.159 32.59637 IsoPrime
2/5/08 V0 6 2 7.726852 0.193 33.40229 IsoPrime
2/5/08 V0 6 3 5.883642 0.144 35.33158 IsoPrime
2/5/08 V0 6 4 6.089859 0.145 33.78839 IsoPrime
2/5/08 V0 5 7 5.84253 0.143 35.00463 IsoPrime
2/5/08 V0 6 5 6.429599 0.156 33.79381 IsoPrime
2/5/08 V0 6 6 6.338258 0.155 32.80837 IsoPrime
2/5/08 V0 6 7 5.072529 0.129 33.17215 IsoPrime
2/6/08 IAEA 7 5.538886 0.138 32.75033 IsoPrime
4/16/08 IAEA 7 9.412285 0.201 32.74706 IsoPrime
2/12/08 IAEA 4 5.78991 0.138 32.74484 IsoPrime
2/5/08 V0 6 8 6.429655 0.162 32.68735 IsoPrime
2/5/08 V0 4 10 6.950197 0.169 33.34918 IsoPrime
2/5/08 V0 7 1 7.356092 0.179 34.99076 IsoPrime
2/5/08 V0 7 2 6.209466 0.153 35.18563 IsoPrime
2/5/08 V0 7 3 7.671635 0.188 34.57278 IsoPrime
2/6/08 V0 7 4 6.581878 0.162 34.31809 IsoPrime
4/16/08 IAEA 2 7.142545 0.197 32.74445 IsoPrime
2/6/08 IAEA 8 6.017858 0.148 32.71875 IsoPrime
4/16/08 IAEA 9 7.419228 0.201 32.71375 IsoPrime
2/6/08 V0 7 5 6.327738 0.144 32.71182 IsoPrime
2/6/08 V0 7 6 7.666082 0.185 32.83229 IsoPrime
2/6/08 V0 7 7 4.580126 0.171 33.33549 IsoPrime
2/6/08 V0 7 8 5.75436 0.142 33.87449 IsoPrime
2/6/08 V0 7 9 6.235257 0.154 34.34648 IsoPrime
2/6/08 V0 3 9 6.208943 0.152 34.54996 IsoPrime
2/6/08 V0 6 4 6.722139 0.174 33.99366 IsoPrime
2/6/08 V0 6 8 5.865067 0.145 32.0921 IsoPrime
2/6/08 V0 7 3 5.457097 0.133 34.24782 IsoPrime
4/14/08 IAEA 5 7.866453 0.192 32.71088 IsoPrime
2/18/08 IAEA 6 7.914965 0.187 32.70498 IsoPrime
4/21/08 IAEA 13 7.862403 0.202 32.70136 IsoPrime
4/25/08 IAEA 14 8.583373 0.173 32.67744 IsoPrime
2/13/08 IAEA 13 6.488989 0.167 32.6726 IsoPrime
3/18/08 IAEA 6 8.570254 0.226 32.6672 IsoPrime
4/16/08 IAEA 8 8.710984 0.224 32.66281 IsoPrime
2/16/08 IAEA 14 6.640263 0.159 32.66062 IsoPrime
2/20/08 IAEA 12 7.054267 0.204 32.65788 IsoPrime
SUPPLEMENTARY DATA TABLE 27
2/11/08 V0 8 1 6.265758 0.17 34.78356 IsoPrime
2/11/08 V0 8 2 6.853109 0.128 35.52274 IsoPrime
2/11/08 V0 8 3 5.193311 0.185 34.88867 IsoPrime
2/11/08 V0 8 4 7.474435 0.184 35.00793 IsoPrime
2/11/08 V0 8 5 7.312217 0.175 35.38137 IsoPrime
2/11/08 V0 5 11 7.258803 0.182 32.56733 IsoPrime
2/11/08 V0 8 6 7.427547 0.147 35.15802 IsoPrime
4/16/08 IAEA 4 8.930965 0.218 32.6338 IsoPrime
2/17/08 IAEA 3 7.995777 0.218 32.63066 IsoPrime
3/12/08 IAEA 11 8.579852 0.236 32.61673 IsoPrime
3/21/08 IAEA 6 8.914221 0.226 32.61508 IsoPrime
2/11/08 V0 8 7 6.943495 0.17 33.78929 IsoPrime
2/11/08 V0 8 8 7.80885 0.195 33.2701 IsoPrime
2/11/08 V0 8 9 7.455546 0.177 33.58225 IsoPrime
2/11/08 V0 9 1 6.824203 0.169 34.43481 IsoPrime
2/11/08 V0 9 2 7.98245 0.196 34.21541 IsoPrime
2/11/08 V0 9 3 6.526727 0.154 32.53256 IsoPrime
2/11/08 V0 9 4 7.913775 0.193 33.07805 IsoPrime
2/11/08 V0 9 5 6.868433 0.169 33.27403 IsoPrime
3/27/08 IAEA 1 9.277104 0.216 32.61415 IsoPrime
4/27/08 IAEA 28 8.645494 0.219 32.60619 IsoPrime
2/15/08 IAEA 8 5.132994 0.125 32.60461 IsoPrime
2/12/08 Pat 1 6.293323 0.17 27.42639 IsoPrime
2/12/08 Pat 2 7.192325 0.195 25.92086 IsoPrime
2/12/08 V0 9 6 7.484409 0.177 32.88634 IsoPrime
2/12/08 V0 9 7 7.484681 0.169 32.29756 IsoPrime
2/12/08 V0 9 8 7.360689 0.196 34.30671 IsoPrime
2/12/08 V0 9 5 7.253707 0.154 33.10413 IsoPrime
2/12/08 V0 8 6 7.845792 0.193 34.88201 IsoPrime
4/25/08 IAEA 10 8.708782 0.162 32.60269 IsoPrime
4/26/08 IAEA 24 7.796678 0 32.59241 IsoPrime
4/21/08 IAEA 12 9.13691 0.232 32.59235 IsoPrime
2/12/08 V0 10 1 9.072959 0.176 35.83992 IsoPrime
2/12/08 V0 10 2 5.79998 0.163 36.2697 IsoPrime
2/12/08 V0 10 3 7.925512 0.167 36.48278 IsoPrime
2/12/08 V0 10 4 5.702771 0.162 36.35089 IsoPrime
2/12/08 V0 10 5 7.106812 0.184 35.82048 IsoPrime
2/12/08 V0 10 6 7.129859 0.179 34.88143 IsoPrime
2/12/08 V0 10 7 7.354568 0.183 34.4786 IsoPrime
2/12/08 V0 10 8 7.732451 0.192 34.87385 IsoPrime
4/25/08 IAEA 12 6.926353 0.162 32.58908 IsoPrime
2/19/08 IAEA 6 7.063855 0.179 32.5885 IsoPrime
3/11/08 IAEA 2 8.35314 0.264 32.5876 IsoPrime
28 MAXBERKELHAMMER
39558.49 IAEA 1 7.781122 0.199 32.5825 IsoPrime
2/19/08 IAEA 2 7.381008 0.191 32.58094 IsoPrime
2/12/08 Vo 10 9 7.800754 0.207 35.73957 IsoPrime
2/12/08 Vo 10 10 6.372272 0.156 36.08942 IsoPrime
2/12/08 Vo 10 7 9.149402 0.236 34.77786 IsoPrime
2/12/08 Vo 10 9 7.635534 0.194 35.76547 IsoPrime
2/12/08 Vo 10 2 7.177105 0.181 36.36944 IsoPrime
2/12/08 Vo 7 6 8.806581 0.223 36.3793 IsoPrime
2/12/08 Vo 9 4 7.302848 0.188 33.9489 IsoPrime
2/12/08 Vo 8 2 6.265634 0.158 37.02679 IsoPrime
2/17/08 IAEA 5 7.700715 0.199 32.57934 IsoPrime
3/26/08 IAEA 6 8.244665 0.201 32.57441 IsoPrime
3/27/08 IAEA 9 8.51188 0.208 32.56285 IsoPrime
2/12/08 Vo 12 1 6.758096 0.174 36.41658 IsoPrime
2/12/08 Vo 12 2 5.292242 0.134 34.82109 IsoPrime
2/12/08 Vo 12 3 7.609191 0.192 36.93917 IsoPrime
2/12/08 Vo 12 4 7.680382 0.192 34.51348 IsoPrime
2/12/08 Vo 12 5 7.318096 0.187 35.03236 IsoPrime
2/12/08 Vo 12 6 6.096974 0.159 34.31408 IsoPrime
2/13/08 Vo 12 7 6.794223 0.169 33.13795 IsoPrime
2/13/08 Vo 12 8 7.011593 0.175 33.30053 IsoPrime
2/19/08 IAEA 11 5.815247 0.147 32.56194 IsoPrime
3/21/08 IAEA 2 7.553518 0.202 32.55665 IsoPrime
5/5/08 IAEA 6 7.894249 0.17 32.54923 IsoPrime
2/13/08 Vo 12 3 7.918187 0.206 35.69608 IsoPrime
2/13/08 Vo 12 8 7.211604 0.176 34.45785 IsoPrime
2/13/08 Vo 8 8 6.714756 0.176 31.44064 IsoPrime
2/13/08 Vo 9 6 6.506943 0.17 32.82315 IsoPrime
2/13/08 Vo 13 1 5.254642 0.132 34.06695 IsoPrime
2/13/08 Vo 13 2 6.781493 0.168 36.94544 IsoPrime
2/13/08 Vo 13 3 6.534742 0.172 37.80394 IsoPrime
2/13/08 Vo 13 4 6.494629 0.154 37.82358 IsoPrime
4/30/08 IAEA 16 8.475216 0.215 32.54887 IsoPrime
2/19/08 IAEA 14 6.546347 0.17 32.54053 IsoPrime
3/21/08 IAEA 3 8.262743 0.208 32.52782 IsoPrime
2/13/08 Vo 13 5 6.970952 0.185 37.4015 IsoPrime
2/13/08 Vo 13 6 8.086465 0.208 36.61349 IsoPrime
2/13/08 Vo 13 7 7.884865 0.2 33.58958 IsoPrime
2/13/08 Vo 13 8 6.66286 0.165 34.57113 IsoPrime
2/13/08 Vo 13 2 7.065025 0.183 36.44806 IsoPrime
2/13/08 Vo 13 6 5.891584 0.149 36.52629 IsoPrime
2/13/08 Vo 13 8 6.7923 0.175 33.70539 IsoPrime
2/13/08 Vo 13 3 5.641706 0.139 38.17305 IsoPrime
SUPPLEMENTARY DATA TABLE 29
5/5/08 IAEA 8 8.373266 0 32.51459 IsoPrime
4/17/08 IAEA 10 8.922102 0.226 32.51422 IsoPrime
3/21/08 IAEA 8 9.98958 0.2 32.51165 IsoPrime
4/22/08 IAEA 18 8.123663 0.209 32.50852 IsoPrime
2/17/08 IAEA 4 5.256441 0.142 32.50378 IsoPrime
3/27/08 IAEA 10 7.798218 0.191 32.49934 IsoPrime
2/4/08 IAEA 4 7.623262 0.187 32.49521 IsoPrime
4/17/08 IAEA 10 7.14139 0.187 32.4946 IsoPrime
4/14/08 IAEA 6 8.086508 0.2 32.49371 IsoPrime
2/15/08 Vo 14 1 5.86604 0.153 34.47359 IsoPrime
2/15/08 Vo 14 2 6.331737 0.169 37.09073 IsoPrime
2/15/08 Vo 14 3 6.622261 0.182 37.86768 IsoPrime
2/15/08 Vo 14 4 7.981176 0.218 36.8865 IsoPrime
2/15/08 Vo 14 5 7.442391 0.198 36.35037 IsoPrime
2/15/08 Vo 14 6 7.211135 0.189 36.1007 IsoPrime
2/15/08 Vo 14 7 6.05371 0.152 36.14618 IsoPrime
2/15/08 Vo 14 8 8.506957 0.225 36.4758 IsoPrime
4/14/08 IAEA 3 7.467825 0.185 32.48334 IsoPrime
2/11/08 IAEA 1 6.432391 0.181 32.48219 IsoPrime
3/12/08 IAEA 12 7.721472 0.208 32.47813 IsoPrime
2/15/08 Vo 15 1 6.527226 0.167 37.00974 IsoPrime
2/15/08 Vo 15 2 6.720782 0.167 39.45613 IsoPrime
2/15/08 Vo 15 3 8.291888 0.22 40.51936 IsoPrime
2/15/08 Vo 15 4 7.504972 0.191 38.96598 IsoPrime
2/15/08 Vo 15 5 7.060451 0.183 37.01137 IsoPrime
2/16/08 Vo 15 6 7.631036 0.196 37.77627 IsoPrime
2/16/08 Vo 16 1 7.512378 0.201 37.24231 IsoPrime
2/16/08 Vo 16 2 7.613595 0.194 37.66138 IsoPrime
5/5/08 IAEA 7 8.854907 0 32.47522 IsoPrime
4/28/08 IAEA 1 9.727797 0 32.47308 IsoPrime
2/11/08 IAEA 6 6.733894 0.171 32.47088 IsoPrime
2/16/08 Vo 16 3 6.432244 0.159 38.03183 IsoPrime
2/16/08 Vo 16 4 7.484763 0.192 37.21916 IsoPrime
2/16/08 Vo 14 3 7.286958 0.193 37.73008 IsoPrime
2/16/08 Vo 14 6 6.442049 0.164 35.56583 IsoPrime
2/16/08 Vo 14 8 8.225597 0.21 35.8333 IsoPrime
2/16/08 Vo 15 5 7.037151 0.178 36.85888 IsoPrime
2/16/08 Vo 15 2 3.903812 0.1 39.24038 IsoPrime
2/16/08 Vo 6 7 7.019969 0.179 32.07957 IsoPrime
4/14/08 IAEA 7 7.950854 0.194 32.46652 IsoPrime
2/17/08 IAEA 6 6.818467 0.184 32.46104 IsoPrime
4/29/08 IAEA 11 8.815207 0 32.45562 IsoPrime
2/18/08 IAEA 8 7.8174 0.2 32.45494 IsoPrime
30 MAXBERKELHAMMER
2/17/08 Vo 16 5 6.85443 0.191 36.77771 IsoPrime
2/17/08 Vo 17 1 4.507631 0.122 36.83319 IsoPrime
2/17/08 Vo 17 2 6.240098 0.172 38.37837 IsoPrime
2/17/08 Vo 17 3 6.860131 0.193 38.96498 IsoPrime
2/17/08 Vo 17 4 6.526909 0.176 38.58113 IsoPrime
2/17/08 Vo 17 5 6.93853 0.191 37.40755 IsoPrime
2/17/08 Vo 17 6 7.488151 0.204 36.51395 IsoPrime
3/11/08 IAEA 1 7.414318 0 32.45358 IsoPrime
5/1/08 IAEA 21 6.640535 0.172 32.453 IsoPrime
3/21/08 IAEA 9 9.05992 0.238 32.44907 IsoPrime
2/17/08 Vo 17 7 6.488261 0.171 36.64169 IsoPrime
2/17/08 Vo 17 8 6.658674 0.172 35.58493 IsoPrime
2/17/08 Vo 17 9 6.200207 0.171 34.45521 IsoPrime
2/17/08 Vo 17 10 7.27851 0.194 34.29767 IsoPrime
2/17/08 Vo 17 2 6.057432 0.16 39.02578 IsoPrime
2/18/08 Vo 17 5 7.941631 0.21 36.99229 IsoPrime
2/18/08 Vo 17 9 7.189037 0.195 33.60592 IsoPrime
4/30/08 IAEA 18 8.658694 0.222 32.43184 IsoPrime
4/16/08 IAEA 8 7.338051 0.192 32.42864 IsoPrime
5/2/08 IAEA 36 6.431124 0 32.41392 IsoPrime
2/4/08 IAEA 3 4.865704 0.12 32.40871 IsoPrime
3/15/08 IAEA 3 8.774772 0.25 32.40295 IsoPrime
4/29/08 IAEA 15 7.557142 0 32.39583 IsoPrime
2/18/08 Vo 18 1 6.096712 0.177 37.6314 IsoPrime
2/18/08 Vo 18 2 6.313975 0.164 38.2718 IsoPrime
2/18/08 Vo 18 3 5.619973 0.158 39.29291 IsoPrime
2/18/08 Vo 18 4 8.107176 0.224 37.16834 IsoPrime
2/18/08 Vo 18 5 6.310439 0.167 38.3921 IsoPrime
2/19/08 Vo 18 6 7.374666 0.197 38.57982 IsoPrime
2/19/08 Vo 18 7 6.214242 0.171 39.01342 IsoPrime
2/19/08 Vo 18 8 6.555044 0.177 37.63484 IsoPrime
2/19/08 Vo 18 9 7.486188 0.197 36.08997 IsoPrime
4/26/08 IAEA 19 8.95422 0 32.39502 IsoPrime
4/15/08 IAEA 8 8.940728 0.22 32.39183 IsoPrime
2/15/08 IAEA 5 6.953115 0.187 32.38992 IsoPrime
2/19/08 Vo 19 1 7.202894 0.197 37.88376 IsoPrime
2/19/08 Vo 19 2 6.63037 0.183 38.79581 IsoPrime
2/19/08 Vo 19 3 6.179911 0.158 38.08047 IsoPrime
2/19/08 Vo 19 4 6.72352 0.181 37.24768 IsoPrime
2/19/08 Vo 20 1 6.842524 0.183 36.8328 IsoPrime
2/19/08 Vo 20 2 6.773123 0.181 39.38963 IsoPrime
2/19/08 Vo 20 3 8.71425 0.235 39.3593 IsoPrime
2/19/08 Vo 20 4 7.020772 0.184 39.08004 IsoPrime
SUPPLEMENTARY DATA TABLE 31
4/30/08 IAEA 7 8.02961 0.187 32.38953 IsoPrime
1/28/08 IAEA 4 8.33461 0.159 32.38528 IsoPrime
3/18/08 IAEA 2 8.76167 0.222 32.38273 IsoPrime
2/19/08 Vo 20 5 7.083106 0.185 37.83256 IsoPrime
2/19/08 Vo 20 6 7.519955 0.194 36.04569 IsoPrime
2/19/08 Vo 20 7 8.685388 0.237 36.3161 IsoPrime
2/19/08 Vo 20 8 6.951487 0.229 37.53637 IsoPrime
2/19/08 IAEA 9 5.597134 0.142 32.38212 IsoPrime
3/10/08 IAEA 9 9.151518 0.226 32.37815 IsoPrime
4/30/08 IAEA 14 6.94146 0.187 32.37237 IsoPrime
2/4/08 IAEA 2 5.480855 0.203 32.36566 IsoPrime
4/26/08 IAEA 17 7.607174 0 32.36175 IsoPrime
4/29/08 IAEA 6 7.407216 0.22 32.35897 IsoPrime
2/20/08 IAEA 10 6.212948 0.199 32.35566 IsoPrime
4/18/08 IAEA 16 7.898466 0.205 32.35527 IsoPrime
2/19/08 Vo 21 1 7.743527 0.204 37.29506 IsoPrime
2/19/08 Vo 21 2 8.753148 0.223 36.80486 IsoPrime
2/19/08 Vo 21 3 7.853131 0.217 35.43634 IsoPrime
2/19/08 Vo 21 4 6.364129 0.174 34.47026 IsoPrime
2/19/08 Vo 21 5 7.513663 0.198 33.28283 IsoPrime
2/19/08 Vo 21 6 7.683627 0.2 32.03924 IsoPrime
2/19/08 Vo 21 7 8.308328 0.215 34.51812 IsoPrime
2/19/08 Vo 21 8 8.138801 0.216 34.01907 IsoPrime
5/5/08 IAEA 5 6.878264 0.17 32.35224 IsoPrime
5/1/08 IAEA 26 8.191097 0 32.34989 IsoPrime
2/15/08 IAEA 3 6.224725 0.17 32.34563 IsoPrime
2/19/08 Vo 18 2 7.539918 0.196 38.49792 IsoPrime
2/20/08 Vo 18 6 4.68493 0.12 37.91756 IsoPrime
2/20/08 Vo 18 8 6.589035 0.177 37.57041 IsoPrime
2/20/08 Vo 20 2 7.322794 0.189 38.90496 IsoPrime
2/20/08 Vo 19 4 7.467636 0.194 37.08117 IsoPrime
2/20/08 Vo 20 1 7.286197 0.187 37.17133 IsoPrime
2/20/08 Vo 20 9 8.092244 0.212 36.58949 IsoPrime
2/20/08 Vo 20 5 6.769525 0.172 36.59826 IsoPrime
4/15/08 IAEA 12 8.782564 0.212 32.34434 IsoPrime
1/28/08 IAEA 6 10.48152 0.207 32.34333 IsoPrime
4/16/08 IAEA 6 9.127863 0.234 32.34323 IsoPrime
2/20/08 Vo 16 4 6.703527 0.182 38.20928 IsoPrime
2/20/08 Vo 15 3 6.087325 0.156 41.1144 IsoPrime
2/20/08 Vo 7 4 6.444643 0.166 33.89706 IsoPrime
2/20/08 Vo 5 6 6.137391 0.159 34.74525 IsoPrime
2/20/08 Vo 9 8 7.603481 0.197 33.90141 IsoPrime
2/20/08 Vo 9 4 5.01383 0.125 32.58487 IsoPrime
32 MAXBERKELHAMMER
2/20/08 Vo 10 1 6.448271 0.1765 37.24275 IsoPrime
2/20/08 Vo 10 8 8.295724 0.213 35.78029 IsoPrime
2/15/08 IAEA 2 7.312721 0.204 32.3369 IsoPrime
4/16/08 IAEA 5 8.109713 0.203 32.32783 IsoPrime
4/16/08 IAEA 9 7.972278 0.212 32.32689 IsoPrime
3/9/08 Vo2 1106 1 9.590468 0.239 32.02205 IsoPrime
3/9/08 Vo2 1106 2 8.989573 0.223 31.97406 IsoPrime
3/9/08 Vo2 1106 3 9.509799 0.242 32.93076 IsoPrime
3/9/08 Vo2 1106 4 7.946035 0.2 32.98766 IsoPrime
3/9/08 Vo2 1106 5 7.636522 0.199 34.05067 IsoPrime
3/9/08 Vo2 1106 6 7.660385 0.193 33.94517 IsoPrime
3/9/08 Vo2 1106 7 8.354953 0.214 34.46272 IsoPrime
3/9/08 Vo2 1106 8 9.100006 0.234 35.68364 IsoPrime
3/9/08 Vo2 1106 9 10.75216 0.285 37.05164 IsoPrime
3/9/08 Vo2 1106 10 9.836511 0.261 34.66121 IsoPrime
4/22/08 IAEA 2 6.734485 0.17 32.31944 IsoPrime
4/29/08 IAEA 12 8.253842 0 32.31887 IsoPrime
2/12/08 IAEA 6 8.513522 0.221 32.31868 IsoPrime
3/9/08 Vo2 1106 11 9.893687 0.255 33.72832 IsoPrime
3/9/08 Vo2 1106 12 9.106764 0.23 34.46622 IsoPrime
3/9/08 Vo2 1106 13 10.08299 0.264 36.59274 IsoPrime
3/9/08 Vo2 1107 1 9.716334 0.243 35.49219 IsoPrime
3/9/08 Vo2 1107 2 11.71463 0.309 37.36034 IsoPrime
3/9/08 Vo2 1107 3 9.137005 0.246 35.60251 IsoPrime
3/9/08 Vo2 1107 4 10.07746 0.272 36.08212 IsoPrime
3/10/08 Vo2 1107 5 10.49701 0.279 35.7142 IsoPrime
3/10/08 Vo2 1107 6 10.86205 0.28 35.01594 IsoPrime
5/1/08 IAEA 28 9.011938 0 32.31824 IsoPrime
2/16/08 Sucrose 2 6.61606 0.152 35.90891 IsoPrime
3/11/08 IAEA 5 7.157618 0.199 32.3168 IsoPrime
3/10/08 Vo2 1107 8 7.347516 0.279 34.91772 IsoPrime
3/10/08 Vo2 1107 9 9.995796 0.26 33.8444 IsoPrime
3/10/08 Vo2 1108 1 10.17076 0.262 34.24378 IsoPrime
3/10/08 Vo2 1108 2 9.167709 0.232 34.68098 IsoPrime
3/10/08 Vo2 1108 3 9.626776 0.243 36.72715 IsoPrime
3/10/08 Vo2 1108 4 9.88854 0.266 37.7253 IsoPrime
3/10/08 Vo2 1108 5 10.90563 0.28 37.62856 IsoPrime
3/10/08 Vo2 1108 6 9.458256 0.25 35.05715 IsoPrime
3/10/08 Vo2 1108 7 10.72974 0.27 35.22594 IsoPrime
3/10/08 Vo2 1108 6 12.20888 0.324 37.65452 IsoPrime
3/21/08 Sucrose 1 8.761166 0.189 36.04569 IsoPrime
5/5/08 Sucrose 2 10.45799 0.219 36.1025 IsoPrime
4/21/08 IAEA 15 7.602583 0.185 32.31655 IsoPrime
SUPPLEMENTARY DATA TABLE 33
3/10/08 Vo2 1108 10 9.693391 0.25 34.55253 IsoPrime
3/10/08 Vo2 1107 3 10.69233 0.271 36.94618 IsoPrime
3/10/08 Vo2 1108 6 9.346693 0.234 35.16203 IsoPrime
4/25/08 IAEA 5 8.069662 0.191 32.31454 IsoPrime
4/25/08 IAEA 7 7.8784 0.235 32.31184 IsoPrime
3/11/08 IAEA 3 7.948815 0.256 32.30256 IsoPrime
4/22/08 IAEA 1 8.364623 0.222 32.30143 IsoPrime
4/14/08 Sucrose 1 7.94122 0.183 36.13921 IsoPrime
3/16/08 IAEA 11 9.6992 0.245 32.30011 IsoPrime
3/11/08 Vo2 1107 1 7.723259 0.218 35.9863 IsoPrime
3/11/08 Vo2 1107 2 7.177787 0.21 34.39179 IsoPrime
3/11/08 Vo2 1107 5 6.709458 0.199 34.50674 IsoPrime
3/11/08 Vo2 1107 8 7.246212 0.205 32.47837 IsoPrime
3/11/08 Vo2 1107 9 8.216564 0.239 33.12936 IsoPrime
3/11/08 Vo2 1108 1 6.963824 0.201 33.27494 IsoPrime
3/11/08 Vo2 1108 3 7.280008 0.222 35.62789 IsoPrime
3/11/08 Vo2 1108 5 7.063582 0.203 36.09315 IsoPrime
3/11/08 Vo2 1108 7 8.137156 0.238 34.47869 IsoPrime
4/21/08 IAEA 14 7.443078 0.192 32.2995 IsoPrime
4/15/08 IAEA 10 9.103588 0.223 32.29293 IsoPrime
5/2/08 IAEA 34 6.610044 0 32.29185 IsoPrime
3/11/08 Vo2 1109 1 8.091163 0.223 34.01137 IsoPrime
3/11/08 Vo2 1109 2 9.485201 0.267 34.82649 IsoPrime
3/11/08 Vo2 1109 3 7.764757 0.224 35.34297 IsoPrime
3/12/08 Vo2 1109 4 8.732746 0.241 35.97359 IsoPrime
3/12/08 Vo2 1109 5 7.914743 0.226 32.82704 IsoPrime
3/12/08 Vo2 1109 6 8.113891 0.219 35.80201 IsoPrime
3/12/08 Vo2 1109 7 8.24425 0.239 36.42556 IsoPrime
3/12/08 Vo2 1109 8 8.208735 0.223 36.4584 IsoPrime
3/12/08 Vo2 1110 1 9.214171 0.256 36.97149 IsoPrime
3/12/08 Vo2 1110 2 8.770263 0.255 37.90921 IsoPrime
2/15/08 IAEA 4 7.231917 0.196 32.27438 IsoPrime
4/18/08 IAEA 19 7.607219 0.196 32.27304 IsoPrime
4/26/08 IAEA 16 9.045854 0 32.27272 IsoPrime
3/12/08 Vo2 1110 3 8.09238 0.229 38.86683 IsoPrime
3/12/08 Vo2 1110 4 8.222521 0.229 37.16197 IsoPrime
3/12/08 Vo2 1110 5 7.90567 0.212 36.115 IsoPrime
3/12/08 Vo2 1110 6 8.14518 0.211 37.40903 IsoPrime
3/12/08 Vo2 1110 7 8.121882 0.22 38.01219 IsoPrime
3/12/08 Vo2 1110 8 9.474888 0.262 38.4892 IsoPrime
3/12/08 Vo2 1110 9 9.348248 0.272 37.36352 IsoPrime
3/12/08 Vo2 1111 1 9.086226 0.241 37.04643 IsoPrime
3/12/08 Vo2 1111 2 9.005717 0.235 37.92367 IsoPrime
34 MAXBERKELHAMMER
3/12/08 Vo2 1111 3 9.239858 0.246 39.80064 IsoPrime
2/12/08 IAEA 19 7.090038 0.173 32.26815 IsoPrime
3/27/08 IAEA 6 - - 32.25953 IsoPrime
2/19/08 IAEA 15 7.48363 0.199 32.25732 IsoPrime
4/16/08 IAEA 3 6.658523 0.174 32.25709 IsoPrime
3/12/08 IAEA 15 9.560436 0.255 32.25673 IsoPrime
3/12/08 Vo2 1111 5 8.864597 0.235 38.03288 IsoPrime
2/12/08 IAEA 17 7.28184 0.155 32.25571 IsoPrime
3/12/08 Vo2 1111 6 7.845951 0.216 38.09009 IsoPrime
3/12/08 Vo2 1111 7 9.541365 0.273 39.61963 IsoPrime
3/12/08 Vo2 1111 8 9.562078 0.259 38.19678 IsoPrime
3/12/08 Vo2 1111 9 8.408079 0.24 38.56041 IsoPrime
3/12/08 Vo2 1111 10 8.364003 0.234 37.81169 IsoPrime
3/12/08 Vo2 1111 11 8.745394 0.232 38.02465 IsoPrime
3/12/08 Vo2 1109 5 8.692336 0.249 36.48629 IsoPrime
3/12/08 Vo2 1111 3 8.990524 0.243 39.07014 IsoPrime
3/12/08 Vo2 1111 4 7.889156 0.211 38.15561 IsoPrime
2/12/08 IAEA 18 5.756346 0.16 32.25229 IsoPrime
3/12/08 IAEA 8 7.408325 0.204 32.25213 IsoPrime
3/26/08 IAEA 1 9.972145 0.239 32.24829 IsoPrime
4/30/08 IAEA 12 8.093443 0.187 32.2461 IsoPrime
4/17/08 IAEA 11 7.095256 0.177 32.24575 IsoPrime
5/1/08 IAEA 23 8.577902 0 32.24398 IsoPrime
3/27/08 Sucrose 4 8.443858 0.188 36.1735 IsoPrime
4/29/08 Sucrose 3 9.550723 0.22 36.19545 IsoPrime
3/15/08 Sigma 1 7.809071 0.214 25.45539 IsoPrime
3/15/08 Sigma 2 8.489506 0.204 26.8415 IsoPrime
3/15/08 Vo2 1112 1 9.484967 0.24 35.90341 IsoPrime
3/15/08 Vo2 1112 2 8.97294 0.221 35.3719 IsoPrime
3/15/08 Vo2 1112 3 11.35724 0.282 37.03184 IsoPrime
3/15/08 Vo2 1112 4 10.45205 0.269 36.59884 IsoPrime
3/15/08 Vo2 1112 5 8.879004 0.22 35.39614 IsoPrime
3/15/08 Vo2 1112 6 8.521931 0.224 34.87408 IsoPrime
3/15/08 Vo2 1112 7 9.244148 0.231 33.11536 IsoPrime
3/15/08 Vo2 1112 8 9.561633 0.252 32.39194 IsoPrime
3/15/08 Vo2 1112 9 8.581648 0.214 32.11463 IsoPrime
3/15/08 Vo2 1112 10 9.169387 0.231 33.42809 IsoPrime
3/15/08 Vo2 1113 1 9.08006 0.23 34.58394 IsoPrime
3/15/08 Vo2 1113 2 9.262986 0.249 36.01246 IsoPrime
3/15/08 Vo2 1113 3 7.740146 0.244 35.49846 IsoPrime
3/15/08 Vo2 1113 5 7.73723 0.24 36.83308 IsoPrime
3/27/08 IAEA 4 10.13815 0.225 32.24126 IsoPrime
4/22/08 IAEA 17 8.381527 0.209 32.24075 IsoPrime
SUPPLEMENTARY DATA TABLE 35
2/13/08 IAEA 7 7.398996 0.19 32.23806 IsoPrime
3/15/08 Vo2 1113 6 9.198736 0.2 37.15603 IsoPrime
3/15/08 Vo2 1113 7 7.458418 0.2 35.86006 IsoPrime
3/15/08 Vo2 1113 8 9.260709 0.23 36.5107 IsoPrime
3/15/08 Vo2 1113 9 7.585017 0.194 35.00985 IsoPrime
3/15/08 Vo2 1114 1 8.484758 0.219 33.31825 IsoPrime
3/15/08 Vo2 1114 2 8.27087 0.213 33.29243 IsoPrime
3/15/08 Vo2 1114 3 7.642628 0.23 34.44571 IsoPrime
3/15/08 Vo2 1114 4 7.420487 0.182 33.30544 IsoPrime
3/16/08 Vo2 1114 5 8.914537 0.193 34.34399 IsoPrime
3/16/08 Vo2 1114 6 9.087136 0.205 33.17954 IsoPrime
3/16/08 Vo2 1114 7 7.629482 0.208 31.93621 IsoPrime
3/16/08 Vo2 1114 8 7.623881 0.192 31.70049 IsoPrime
3/16/08 Vo2 1114 9 7.809055 0.2 32.28436 IsoPrime
3/16/08 Vo2 1114 10 9.008779 0.222 32.01433 IsoPrime
5/2/08 IAEA 32 7.093996 0 32.22235 IsoPrime
4/17/08 IAEA 14 7.908578 0.2 32.22175 IsoPrime
3/21/08 IAEA 1 6.859218 0.186 32.22121 IsoPrime
3/16/08 Vo2 1115 1 9.288305 0.236 32.40033 IsoPrime
3/16/08 Vo2 1115 2 8.1174 0.208 32.84742 IsoPrime
3/16/08 Vo2 1115 3 9.097213 0.217 34.10057 IsoPrime
3/16/08 Vo2 1115 4 8.149587 0.204 34.87633 IsoPrime
3/16/08 Vo2 1115 5 8.837433 0.237 35.57155 IsoPrime
3/16/08 Vo2 1115 6 8.750261 0.208 35.54965 IsoPrime
3/16/08 Vo2 1115 8 8.262422 0.224 35.10307 IsoPrime
3/16/08 Vo2 1115 9 8.583876 0.221 34.90186 IsoPrime
3/16/08 Vo2 1115 10 9.462186 0.241 34.50691 IsoPrime
4/30/08 IAEA 9 7.161481 0.187 32.21997 IsoPrime
3/12/08 IAEA 10 7.666493 0.206 32.2176 IsoPrime
4/15/08 IAEA 11 7.874153 0.192 32.21225 IsoPrime
5/2/08 Sucrose 8 7.222275 0 36.1958 IsoPrime
3/16/08 Sigma 3 7.867623 0.201 26.64089 IsoPrime
3/16/08 Sigma 4 7.756607 0.191 26.22936 IsoPrime
4/22/08 IAEA 19 7.454856 0.213 32.212 IsoPrime
3/18/08 Vo2 1116 1 7.031034 0.181 33.02087 IsoPrime
3/18/08 Vo2 1116 2 7.486284 0.194 34.55823 IsoPrime
3/18/08 Vo2 1116 3 8.840545 0.212 35.60995 IsoPrime
3/18/08 Vo2 1116 4 8.242848 0.211 36.68401 IsoPrime
3/18/08 Vo2 1116 5 8.47298 0.216 36.79016 IsoPrime
3/18/08 Vo2 1116 6 9.230264 0.237 36.72046 IsoPrime
3/18/08 Vo2 1116 7 8.915257 0.222 36.56986 IsoPrime
3/18/08 Vo2 1116 8 8.505387 0.217 34.93154 IsoPrime
3/18/08 Vo2 1116 9 8.35297 0.198 33.87067 IsoPrime
36 MAXBERKELHAMMER
4/28/08 IAEA 10 8.483091 0 32.21038 IsoPrime
3/20/08 IAEA 5 8.966251 0.219 32.20675 IsoPrime
4/21/08 IAEA 10 9.111201 0.227 32.20621 IsoPrime
3/18/08 Vo2 1117 1 8.182906 0.209 34.51739 IsoPrime
3/18/08 Vo2 1117 2 8.644014 0.215 35.50804 IsoPrime
3/18/08 Vo2 1117 3 8.674118 0.214 36.99215 IsoPrime
3/18/08 Vo2 1117 4 9.175705 0.211 37.5804 IsoPrime
3/18/08 Vo2 1117 5 7.983606 0.2 36.49499 IsoPrime
3/18/08 Vo2 1117 6 8.011086 0.2 35.83749 IsoPrime
3/18/08 Vo2 1117 7 7.915759 0.2 35.76106 IsoPrime
3/18/08 Vo2 1117 8 8.934384 0.237 35.08668 IsoPrime
3/18/08 Vo2 1117 9 9.123464 0.23 34.40803 IsoPrime
4/25/08 IAEA 8 8.919198 0.185 32.197 IsoPrime
5/1/08 IAEA 30 8.221949 0 32.19379 IsoPrime
4/21/08 IAEA 11 7.274165 0.184 32.19073 IsoPrime
3/18/08 Vo2 1118 1 8.514417 0.225 34.42606 IsoPrime
3/19/08 Vo2 1118 4 8.532409 0.22 34.68062 IsoPrime
3/19/08 Vo2 1118 5 7.881783 0.195 34.83054 IsoPrime
3/19/08 Vo2 1119 1 6.617946 0.171 33.23305 IsoPrime
3/19/08 Vo2 1119 2 9.228949 0.224 34.18048 IsoPrime
3/19/08 Vo2 1119 3 8.313653 0.217 33.42743 IsoPrime
3/19/08 Vo2 1119 4 8.877512 0.22 33.36193 IsoPrime
3/19/08 Vo2 1119 5 7.98786 0.202 31.47372 IsoPrime
3/19/08 Vo2 1120 1 8.170771 0.206 30.64002 IsoPrime
3/19/08 Vo2 1120 2 8.487259 0.212 33.54206 IsoPrime
3/19/08 Vo2 1120 3 8.27157 0.21 33.53832 IsoPrime
3/19/08 Vo2 1120 4 7.85686 0.198 33.66229 IsoPrime
3/19/08 Vo2 1120 5 9.118418 0.237 31.88531 IsoPrime
2/19/08 IAEA 7 7.227927 0.182 32.18387 IsoPrime
4/20/08 IAEA 7 8.113472 0.205 32.18338 IsoPrime
3/26/08 Sucrose 1 9.259447 0.204 36.2051 IsoPrime
IsoPrime
4/21/08 IAEA 9 9.01261 0.209 32.18256 IsoPrime
2/11/08 IAEA 13 6.572432 0.155 32.18166 IsoPrime
5/6/08 IAEA 12 7.467166 0 32.17947 IsoPrime
3/21/08 m 1 1 8.284949 0.209 35.77418 IsoPrime
3/21/08 m 1 2 7.608692 0.192 35.99036 IsoPrime
3/21/08 m 1 3 8.698671 0.225 37.11999 IsoPrime
3/21/08 m 1 4 8.907585 0.227 36.63192 IsoPrime
3/21/08 m 1 5 7.031674 0.186 34.79096 IsoPrime
3/21/08 m 1 6 8.607772 0.215 35.39878 IsoPrime
3/21/08 m 2 1 7.994083 0.209 35.08107 IsoPrime
3/21/08 m 2 2 8.77094 0.219 35.21098 IsoPrime
SUPPLEMENTARY DATA TABLE 37
3/21/08 m 2 3 7.508577 0.199 35.50142 IsoPrime
3/21/08 m 2 4 9.045447 0.228 35.32998 IsoPrime
3/21/08 m 2 5 8.2731 0.216 34.89217 IsoPrime
3/21/08 m 2 6 8.314144 0.212 35.01249 IsoPrime
3/21/08 m 2 7 9.16516 0.229 34.75872 IsoPrime
5/1/08 IAEA 27 7.338628 0 32.17842 IsoPrime
4/15/08 IAEA 16 7.370718 0.177 32.17684 IsoPrime
4/20/08 IAEA 4 7.893091 0.179 32.17633 IsoPrime
3/21/08 m 3 1 9.522876 0.24 35.49733 IsoPrime
3/21/08 m 3 2 9.100155 0.231 35.15716 IsoPrime
3/21/08 m 3 3 7.57008 0.19 35.01199 IsoPrime
3/21/08 m 3 4 7.674597 0.199 36.52466 IsoPrime
3/21/08 m 3 5 8.326289 0.224 35.52973 IsoPrime
3/21/08 m 3 6 8.550037 0.22 36.04964 IsoPrime
3/21/08 m 4 1 8.727197 0.203 36.17888 IsoPrime
3/21/08 m 4 2 6.637608 0.17 36.26743 IsoPrime
3/21/08 m 4 3 8.867262 0.236 37.43931 IsoPrime
3/21/08 m 4 4 8.018699 0.21 36.89723 IsoPrime
3/21/08 m 4 5 8.597337 0.22 37.21986 IsoPrime
3/21/08 m 4 6 8.181591 0.211 36.18484 IsoPrime
4/15/08 IAEA 17 7.478442 0.183 32.1603 IsoPrime
2/16/08 IAEA 13 6.107558 0.148 32.1568 IsoPrime
4/15/08 IAEA 13 7.189203 0.195 32.15656 IsoPrime
3/21/08 m 5 1 8.867099 0.217 34.50504 IsoPrime
3/22/08 m 5 2 8.164821 0.215 32.51879 IsoPrime
3/22/08 m 5 3 7.752377 0.208 32.46436 IsoPrime
3/22/08 m 5 4 8.0068 0.216 33.73869 IsoPrime
3/22/08 m 5 5 6.955344 0.181 32.57043 IsoPrime
3/22/08 m 6 1 7.94731 0.215 33.60569 IsoPrime
3/22/08 m 6 2 7.427355 0.192 34.49672 IsoPrime
3/22/08 m 6 3 7.636566 0.206 35.05856 IsoPrime
3/22/08 m 6 4 7.956369 0.213 34.25171 IsoPrime
3/22/08 m 6 5 9.300602 0.233 33.47319 IsoPrime
2/12/08 IAEA 16 6.793234 0.161 32.15476 IsoPrime
4/16/08 IAEA 7 8.977319 0.192 32.15141 IsoPrime
4/28/08 IAEA 8 8.559844 0 32.14768 IsoPrime
4/16/08 Sucrose 1 6.931605 0.157 36.27488 IsoPrime
3/27/08 Sucrose 5 10.54674 0.234 36.28487 IsoPrime
2/12/08 IAEA 3 7.318603 0.2 32.14649 IsoPrime
4/22/08 IAEA 3 7.934482 0.193 32.14613 IsoPrime
3/27/08 IAEA 3 10.15485 0.225 32.14502 IsoPrime
5/3/08 IAEA 37 8.031538 0 32.14361 IsoPrime
9/21/06 IAEA 7 7.735144 0.282 32.14276 IsoPrime
38 MAXBERKELHAMMER
3/20/08 Vo2 1121 1 8.31154 0.212 32.83648 IsoPrime
3/20/08 Vo2 1121 2 8.141661 0.2 34.2432 IsoPrime
3/20/08 Vo2 1121 3 7.751075 0.199 34.57097 IsoPrime
3/20/08 Vo2 1121 4 8.656194 0.201 35.07852 IsoPrime
3/20/08 Vo2 1121 5 7.606659 0.2 33.38075 IsoPrime
3/20/08 Vo2 1121 6 9.098898 0.22 33.05676 IsoPrime
3/20/08 Vo2 1122 1 8.466608 0.204 32.80954 IsoPrime
3/20/08 Vo2 1122 2 8.913786 0.22 35.51546 IsoPrime
3/20/08 Vo2 1122 3 7.858118 0.19 35.76979 IsoPrime
3/20/08 Vo2 1122 4 9.559557 0.234 36.75636 IsoPrime
3/20/08 Vo2 1122 5 8.606634 0.208 37.11476 IsoPrime
3/20/08 Vo2 1122 6 8.523637 0.225 36.77131 IsoPrime
3/20/08 Vo2 1122 8 6.30677 0.178 35.22734 IsoPrime
3/20/08 Vo2 1122 9 9.015523 0.223 35.01425 IsoPrime
4/27/08 IAEA 25 9.03475 0.217 32.14198 IsoPrime
2/5/08 IAEA 8 5.396995 0.135 32.14126 IsoPrime
3/20/08 Vo2 unk 8.70387 0.232 37.08428 IsoPrime
3/20/08 Vo2 1118 2 7.149047 0.218 33.43534 IsoPrime
3/20/08 Vo2 1118 3 9.328217 0.19 34.88938 IsoPrime
3/20/08 Vo2 1120 1 8.201506 0.23 31.40982 IsoPrime
3/20/08 Vo2 1116 1 9.026355 0.208 34.4718 IsoPrime
3/20/08 Vo2 1114 4 7.345334 0.217 34.24985 IsoPrime
3/20/08 Vo2 1113 6 8.225662 0.183 37.69815 IsoPrime
3/20/08 Vo2 1112 3 9.186749 0.194 36.45525 IsoPrime
3/20/08 Vo2 1112 4 8.315661 0.226 35.92046 IsoPrime
3/20/08 Vo2 1111 3 8.046063 0.207 37.20914 IsoPrime
3/20/08 Vo2 1106 1 9.015928 0.193 31.78149 IsoPrime
3/20/08 Vo2 1106 4 8.670168 0.229 32.86512 IsoPrime
3/21/08 Vo2 1106 7 8.649208 0.213 35.3015 IsoPrime
3/21/08 Vo2 1106 10 8.086469 0.215 32.52515 IsoPrime
3/21/08 Vo2 1106 13 8.061593 0.199 34.14627 IsoPrime
4/26/08 IAEA 18 8.518843 0 32.14122 IsoPrime
4/25/08 IAEA 11 7.264071 0.162 32.13934 IsoPrime
4/28/08 IAEA 9 9.205966 0 32.13649 IsoPrime
4/29/08 Sucrose 1 6.972276 0.22 36.28545 IsoPrime
2/16/08 Sucrose 3 9.953218 0.247 36.28562 IsoPrime
3/15/08 IAEA 2 8.068382 0.218 32.13523 IsoPrime
3/27/08 IAEA 8 7.870421 0.196 32.12983 IsoPrime
4/16/08 IAEA 4 7.962497 0.203 32.12949 IsoPrime
5/5/08 Sucrose 1 10.42001 0.181 36.28709 IsoPrime
4/15/08 Sucrose 4 7.440525 0.174 36.29272 IsoPrime
4/15/08 Sucrose 5 7.893697 0.177 36.30791 IsoPrime
1/28/08 IAEA 1 7.971052 0.154 32.12925 IsoPrime
SUPPLEMENTARY DATA TABLE 39
3/26/08 Vo2 1106 8.868379 0.203 34.80239 IsoPrime
3/26/08 Vo2 1106 13 9.084193 0.22 35.37609 IsoPrime
3/26/08 Vo2 1106 11 8.25355 0.203 32.62087 IsoPrime
3/26/08 Vo2 1107 2 7.85474 0.189 35.21404 IsoPrime
3/26/08 Vo2 1108 6 9.205154 0.224 35.34433 IsoPrime
3/26/08 Vo2 1111 4 8.822256 0.217 38.61915 IsoPrime
3/26/08 Vo2 1111 5 8.176518 0.19 37.94048 IsoPrime
3/26/08 Vo2 1111 6 8.746127 0.202 37.57507 IsoPrime
3/26/08 Vo2 1114 4 9.437079 0.224 34.75715 IsoPrime
3/26/08 Vo2 1114 10 8.980021 0.211 31.46818 IsoPrime
2/15/08 IAEA 9 6.954556 0.184 32.1227 IsoPrime
3/22/08 IAEA 11 9.1124 0.215 32.1182 IsoPrime
3/21/08 IAEA 5 9.221766 0.239 32.11374 IsoPrime
3/26/08 m 5 2 9.152185 0.226 34.50172 IsoPrime
3/26/08 m 5 3 7.331124 0.188 33.50988 IsoPrime
3/26/08 m 5 4 8.722272 0.207 32.30072 IsoPrime
3/26/08 m 5 5 9.078307 0.218 33.4715 IsoPrime
3/26/08 m 3 4 8.539895 0.203 37.01132 IsoPrime
3/26/08 m 3 6 8.347719 0.203 37.06935 IsoPrime
3/27/08 m 7 1 7.528132 0.182 33.13556 IsoPrime
3/27/08 m 7 2 9.037896 0.21 33.95754 IsoPrime
3/27/08 m 7 3 8.694273 0.214 35.19031 IsoPrime
3/27/08 m 7 4 9.769551 0.233 35.33876 IsoPrime
3/27/08 m 7 5 8.154543 0.21 35.13821 IsoPrime
3/27/08 Vo2 1110 5 8.621298 0.218 37.37852 IsoPrime
3/27/08 Vo2 1110 8 9.680276 0.229 37.31625 IsoPrime
3/27/08 Vo2 1110 2 8.959978 0.214 36.81931 IsoPrime
5/3/08 IAEA 39 7.04901 0 32.113 IsoPrime
4/16/08 IAEA 3 7.328098 0.18 32.1111 IsoPrime
2/11/08 IAEA 12 6.515146 0.161 32.11028 IsoPrime
4/14/08 Sucrose 3 8.344723 0.184 36.31322 IsoPrime
2/19/08 Sucrose 3 10.09088 0.242 36.34132 IsoPrime
3/18/08 IAEA 3 9.184311 0.23 32.10458 IsoPrime
4/22/08 Sucrose 3 7.763464 0.182 36.34401 IsoPrime
3/27/08 M 4 5 9.745068 0.208 36.44965 IsoPrime
3/27/08 M 4 6 8.614066 0.203 36.43836 IsoPrime
3/27/08 M 2 7 8.056034 0.198 35.39725 IsoPrime
3/27/08 M 6 1 8.325556 0.22 33.9788 IsoPrime
3/27/08 M 6 3 7.914059 0.225 35.82162 IsoPrime
3/27/08 M 6 5 8.078328 0.188 33.49866 IsoPrime
3/27/08 M 5 5 8.446533 0.225 33.96348 IsoPrime
3/27/08 M 7 5 9.050098 0.225 34.82925 IsoPrime
3/20/08 IAEA 4 8.383686 0.191 32.10003 IsoPrime
40 MAXBERKELHAMMER
2/5/08 IAEA 8 5.709393 0.138 32.10001 IsoPrime
3/26/08 IAEA 3 9.543862 0.204 32.0911 IsoPrime
3/27/08 M 2 2 8.403344 0.225 34.64353 IsoPrime
3/27/08 M 1 6 8.330558 0.225 34.91967 IsoPrime
2/15/08 IAEA 6 5.125169 0.135 32.08825 IsoPrime
3/22/08 IAEA 10 8.284014 0.202 32.08678 IsoPrime
5/5/08 IAEA 2 7.272214 0.228 32.08595 IsoPrime
2/5/08 IAEA 1 5.800672 0.137 32.08503 IsoPrime
3/26/08 IAEA 5 8.623137 0.223 32.08438 IsoPrime
1/28/08 IAEA 5 9.429082 0.183 32.08321 IsoPrime
2/5/08 IAEA 7 6.063501 0.152 32.07884 IsoPrime
3/27/08 IAEA 2 9.729365 0.188 32.07419 IsoPrime
2/15/08 IAEA 7 5.300673 0.139 32.07029 IsoPrime
4/25/08 Sucrose 2 6.867191 0.179 36.35184 IsoPrime
1/20/07 4 1 1782 7.137823 0.218 31.5279 IsoPrime
4/30/08 2 4 1782 7.444362 0.189 31.91924 IsoPrime
1/21/07 4 1 1781 6.22241 0.181 30.95543 IsoPrime
4/30/08 2 4 1781 6.840034 0.179 32.08568 IsoPrime
4/30/08 2 4 1781 6.990164 0.179 33.04518 IsoPrime
5/1/08 2 4 1781 8.852913 0.223 32.97123 IsoPrime
1/21/07 4 1 1780 6.875302 0.199 31.19309 IsoPrime
4/30/08 2 4 1780 8.267505 0.209 33.36602 IsoPrime
1/22/07 4 1 1779 9.319625 0.27 30.83144 IsoPrime
4/30/08 2 4 1779 7.63403 0.194 32.17151 IsoPrime
4/14/08 Sucrose 2 7.327714 0.168 36.39741 IsoPrime
3/11/08 Sucrose 1 8.865388 0.249 36.39997 IsoPrime
4/30/08 IAEA 17 7.508628 0.185 32.0701 IsoPrime
1/22/07 4 1 1778 8.064659 0.221 31.50034 IsoPrime
4/30/08 2 4 1778 9.08626 0.225 33.196 IsoPrime
1/23/07 4 1 1777 8.117825 0.226 32.56443 IsoPrime
4/30/08 2 4 1777 8.040268 0.199 33.60764 IsoPrime
4/30/08 2 4 1777 7.309293 0.195 33.49995 IsoPrime
1/24/07 4 1 1776 8.591693 0.234 32.86951 IsoPrime
4/30/08 2 4 1776 7.895035 0.187 32.99568 IsoPrime
1/20/07 4 1 1775 6.723243 0.213 31.1335 IsoPrime
4/30/08 2 4 1775 6.639478 0.168 31.75935 IsoPrime
01/13/07 4 1 1774 8.372827 0.242 32.69645 IsoPrime
2/5/08 IAEA 3 6.344244 0.164 32.0647 IsoPrime
4/15/08 IAEA 14 8.320201 0.199 32.06348 IsoPrime
2/13/08 IAEA 8 6.567131 0.165 32.06206 IsoPrime
4/30/08 2 4 1774 7.184162 0.184 31.17492 IsoPrime
01/13/07 4 1 1773 8.858345 0.256 32.97952 IsoPrime
4/30/08 2 4 1773 8.140565 0.199 32.50857 IsoPrime
SUPPLEMENTARY DATA TABLE 41
1/15/07 4 1 1772 6.95348 0.211 29.85921 IsoPrime
4/30/08 2 4 1772 7.110032 0.183 32.75786 IsoPrime
4/30/08 2 4 1772 8.362601 0.208 32.09865 IsoPrime
1/21/07 4 1 1771 5.196835 0.168 28.89463 IsoPrime
4/30/08 2 4 1771 7.847437 0.189 32.12591 IsoPrime
1/21/07 4 1 1770 6.840135 0.2 31.99634 IsoPrime
2/16/08 IAEA 15 7.707569 0.198 32.06204 IsoPrime
5/1/08 IAEA 22 6.56105 0 32.05042 IsoPrime
4/30/08 IAEA 8 6.786554 0.187 32.04926 IsoPrime
4/30/08 2 4 1770 7.570078 0.205 31.73124 IsoPrime
1/24/07 4 1 1769 7.258727 0.2 32.0759 IsoPrime
4/30/08 2 4 1769 6.625419 0.18 31.61094 IsoPrime
1/22/07 4 1 1768 8.754761 0.254 29.73152 IsoPrime
4/30/08 2 4 1768 7.646753 0.209 30.42864 IsoPrime
1/23/07 4 1 1767 8.106262 0.232 30.82026 IsoPrime
4/30/08 2 4 1767 8.413151 0.211 31.1036 IsoPrime
4/30/08 2 4 1767 7.264008 0.18 30.28191 IsoPrime
1/23/07 4 1 1766 9.799902 0.28 29.38091 IsoPrime
4/26/08 IAEA 20 8.253676 0 32.04907 IsoPrime
4/22/08 IAEA 4 7.093938 0.183 32.04231 IsoPrime
4/29/08 IAEA 13 7.911249 0 32.03933 IsoPrime
4/30/08 2 4 1766 8.610064 0.214 32.67135 IsoPrime
1/24/07 4 1 1765 9.216506 0.259 28.92387 IsoPrime
4/30/08 2 4 1765 8.036964 0.207 33.48398 IsoPrime
2/5/08 IAEA 9 6.345982 0.155 32.03882 IsoPrime
4/25/08 Sucrose 1 5.717074 0.211 36.40873 IsoPrime
3/19/08 Sucrose 10 9.508847 0.23 36.41888 IsoPrime
4/16/08 IAEA 6 7.486453 0.186 32.03708 IsoPrime
3/12/08 IAEA 16 6.684359 0.178 32.03397 IsoPrime
4/29/08 IAEA 3 7.877121 0.188 32.03263 IsoPrime
4/20/08 IAEA 5 9.651785 0.235 32.01896 IsoPrime
4/27/08 IAEA 26 8.808803 0.209 32.01838 IsoPrime
5/2/08 IAEA 33 8.942017 0 32.01638 IsoPrime
5/1/08 IAEA 24 6.743377 0 32.01557 IsoPrime
1/25/07 4 1 1764 7.245483 0.202 31.94011 IsoPrime
4/30/08 2 4 1764 8.32068 0.211 33.40349 IsoPrime
01/13/07 4 1 1763 8.865588 0.251 33.59637 IsoPrime
4/25/08 2 4 1763 6.088556 0.162 35.22891 IsoPrime
4/26/08 2 4 1763 7.021353 0.177 34.23418 IsoPrime
01/13/07 4 1 1762 8.214482 0.241 34.35841 IsoPrime
4/25/08 2 4 1762 7.132449 0.185 32.97011 IsoPrime
1/15/07 4 1 1761 6.336276 0.192 33.8902 IsoPrime
4/26/08 IAEA 23 6.330347 0.193 32.01528 IsoPrime
42 MAXBERKELHAMMER
4/20/08 IAEA 6 7.235087 0.179 32.00588 IsoPrime
5/5/08 Sucrose 4 7.297913 0 36.42506 IsoPrime
4/25/08 IAEA 13 8.750673 0.219 31.99805 IsoPrime
2/6/08 IAEA 11 5.735849 0.137 31.99443 IsoPrime
2/19/08 IAEA 5 7.797431 0.208 31.98986 IsoPrime
2/19/08 Sucrose 2 9.137106 0.217 36.43975 IsoPrime
2/5/08 IAEA 6 5.915348 0.144 31.98883 IsoPrime
5/6/08 IAEA 13 6.573611 0 31.98735 IsoPrime
2/16/08 IAEA 12 6.100491 0.157 31.98432 IsoPrime
4/25/08 2 4 1761 8.675955 0.211 32.3824 IsoPrime
1/20/07 4 1 1760 8.084637 0.259 29.50744 IsoPrime
4/25/08 2 4 1760 9.630778 0.235 32.53401 IsoPrime
4/25/08 2 4 1760 7.786365 0.189 32.9792 IsoPrime
1/21/07 4 1 1759 9.026297 0.26 31.61556 IsoPrime
4/25/08 2 4 1759 8.772551 0.214 30.93482 IsoPrime
1/21/07 4 1 1758 8.24959 0.243 31.25156 IsoPrime
4/25/08 2 4 1758 8.31812 0.199 33.09108 IsoPrime
3/15/08 IAEA 1 8.406301 0.238 31.98404 IsoPrime
2/11/08 IAEA 8 8.118298 0.214 31.9814 IsoPrime
2/18/08 IAEA 9 5.736667 0.142 31.97787 IsoPrime
1/21/07 4 1 1757 8.630668 0.246 30.77385 IsoPrime
1/22/07 4 1 1756 7.28502 0.202 30.89964 IsoPrime
4/25/08 2 4 1756 8.393447 0.203 32.75991 IsoPrime
1/23/07 4 1 1755 7.668056 0.221 30.93371 IsoPrime
4/25/08 2 4 1755 6.742454 0.177 33.66597 IsoPrime
1/24/07 4 1 1754 8.557881 0.237 29.56978 IsoPrime
4/25/08 2 4 1754 7.747327 0.183 30.82267 IsoPrime
1/24/07 4 1 1753 5.07333 0.135 30.98365 IsoPrime
4/25/08 2 4 1753 8.701552 0.201 33.07778 IsoPrime
4/25/08 2 4 1753 9.200516 0.22 33.60767 IsoPrime
1/25/07 4 1 1752 9.497983 0.254 32.28778 IsoPrime
2/11/08 IAEA 11 6.695622 0.166 31.96124 IsoPrime
4/17/08 Sucrose 6 7.722855 0.174 36.44217 IsoPrime
5/2/08 Sucrose 6 5.936511 0 36.46365 IsoPrime
01/13/07 4 1 1751 9.180681 0.271 32.12328 IsoPrime
4/25/08 2 4 1751 7.278168 0.195 33.81764 IsoPrime
1/15/07 4 1 1750 9.855967 0.28 31.92325 IsoPrime
1/21/07 4 1 1750 10.8026 0.319 31.09938 IsoPrime
4/25/08 2 4 1750 7.998849 0.196 32.4296 IsoPrime
1/15/07 4 1 1749 7.76936 0.215 32.78342 IsoPrime
4/25/08 2 4 1749 8.335297 0.202 32.07607 IsoPrime
1/20/07 4 1 1748 9.71587 0.299 31.91297 IsoPrime
4/25/08 2 4 1748 7.183881 0.177 33.33032 IsoPrime
SUPPLEMENTARY DATA TABLE 43
4/26/08 2 4 1748 7.485375 0.197 33.60842 IsoPrime
1/21/07 4 1 1747 7.558473 0.239 30.9196 IsoPrime
4/26/08 2 4 1747 7.644501 0.199 32.24149 IsoPrime
1/21/07 4 1 1746 8.586527 0.253 31.19678 IsoPrime
4/17/08 Sucrose 5 7.38826 0.18 36.46883 IsoPrime
2/15/08 Sucrose 1 7.691735 0.223 36.47727 IsoPrime
3/26/08 Sucrose 3 9.712976 0.22 36.51385 IsoPrime
4/26/08 2 4 1746 8.978715 0.215 29.20072 IsoPrime
2/11/08 IAEA 5 6.853703 0.18 31.95933 IsoPrime
2/11/08 IAEA 3 6.344942 0.168 31.95646 IsoPrime
1/21/07 4 1 1745 8.91397 0.259 29.56244 IsoPrime
4/26/08 2 4 1745 7.837982 0.19 30.64264 IsoPrime
1/22/07 4 1 1744 7.976815 0.22 31.16833 IsoPrime
4/26/08 2 4 1744 7.996465 0.192 31.23641 IsoPrime
4/26/08 2 4 1744 8.657635 0.209 31.35783 IsoPrime
1/23/07 4 1 1743 9.002352 0.248 31.99769 IsoPrime
4/26/08 2 4 1743 8.28167 0.199 31.21369 IsoPrime
1/24/07 4 1 1742 8.278745 0.223 30.93605 IsoPrime
4/26/08 2 4 1742 7.198182 0.182 32.08245 IsoPrime
1/24/07 4 1 1741 7.696394 0.196 31.02397 IsoPrime
1/25/07 4 1 1740 8.740997 0.228 31.70649 IsoPrime
4/26/08 2 4 1740 8.48292 0.206 31.82993 IsoPrime
2/6/08 IAEA 10 4.695974 0.115 31.95571 IsoPrime
3/11/08 IAEA 4 6.808978 0.199 31.95443 IsoPrime
5/1/08 IAEA 19 8.746446 0.209 31.95397 IsoPrime
4/26/08 2 4 1740 9.485055 0.229 31.96143 IsoPrime
01/13/07 4 1 1739 8.047275 0.228 32.70316 IsoPrime
4/26/08 2 4 1739 7.18505 0.186 32.94719 IsoPrime
1/15/07 4 1 1738 7.194814 0.211 31.49435 IsoPrime
4/26/08 2 4 1738 6.446025 0.173 32.28566 IsoPrime
1/15/07 4 1 1737 8.011381 0.23 30.71905 IsoPrime
4/26/08 2 4 1737 8.460084 0.207 31.10528 IsoPrime
1/20/07 4 1 1736 6.909089 0.213 30.25898 IsoPrime
4/26/08 2 4 1736 8.863531 0.214 31.67967 IsoPrime
1/21/07 4 1 1735 8.418144 0.242 30.39043 IsoPrime
4/26/08 2 4 1735 6.95423 0.179 32.72469 IsoPrime
4/26/08 2 4 1735 9.449238 0.22 29.75595 IsoPrime
4/25/08 IAEA 15 7.731568 0.167 31.95295 IsoPrime
3/16/08 IAEA 10 9.134188 0.238 31.95243 IsoPrime
2/11/08 IAEA 4 6.282959 0.167 31.95076 IsoPrime
4/26/08 2 4 1735 7.227709 0.173 27.92479 IsoPrime
1/21/07 4 1 1734 9.026279 0.262 28.31949 IsoPrime
4/26/08 2 4 1734 8.498973 0.209 32.78711 IsoPrime
44 MAXBERKELHAMMER
1/21/07 4 1 1733 6.066533 0.175 30.8838 IsoPrime
1/25/07 4 1 1733 9.144817 0.248 31.533 IsoPrime
4/26/08 2 4 1733 9.299266 0.226 32.00288 IsoPrime
1/22/07 4 1 1732 7.238784 0.199 31.90104 IsoPrime
4/26/08 2 4 1732 8.102142 0.195 32.13724 IsoPrime
1/23/07 4 1 1731 10.48484 0.283 31.40293 IsoPrime
4/26/08 2 4 1731 8.070681 0.197 32.9714 IsoPrime
3/16/08 IAEA 15 8.438907 0.213 31.94522 IsoPrime
3/22/08 IAEA 12 9.295677 0.235 31.94479 IsoPrime
4/14/08 IAEA 2 7.446648 0.18 31.94405 IsoPrime
3/19/08 IAEA 8 9.549522 0.236 31.93657 IsoPrime
3/20/08 IAEA 3 8.296646 0.201 31.93259 IsoPrime
4/20/08 IAEA 3 8.534976 0.235 31.9309 IsoPrime
4/27/08 IAEA 29 7.466929 0.179 31.92559 IsoPrime
3/21/08 Sucrose 2 10.86675 0.199 36.52815 IsoPrime
3/27/08 Sucrose 1 10.26994 0.201 36.53745 IsoPrime
3/18/08 IAEA 5 8.020538 0.193 31.92528 IsoPrime
1/24/07 4 1 1730 10.98113 0.304 31.0068 IsoPrime
4/26/08 2 4 1730 7.053975 0.173 31.86549 IsoPrime
1/24/07 4 1 1729 8.380103 0.227 31.02166 IsoPrime
4/26/08 2 4 1729 8.488437 0.216 33.85252 IsoPrime
4/26/08 2 4 1729 8.130948 0.199 33.35556 IsoPrime
1/25/07 4 1 1728 9.398282 0.251 33.71197 IsoPrime
4/26/08 2 4 1728 9.542758 0.233 33.54226 IsoPrime
01/13/07 4 1 1727 7.708455 0.223 31.65189 IsoPrime
4/26/08 2 4 1727 8.401883 0.202 30.97707 IsoPrime
1/15/07 4 1 1726 8.03581 0.233 31.2893 IsoPrime
4/26/08 2 4 1726 6.668552 0.175 31.83945 IsoPrime
4/26/08 IAEA 21 8.413723 0.194 31.91165 IsoPrime
3/21/08 IAEA 4 8.697469 0.21 31.89924 IsoPrime
3/18/08 IAEA 1 7.816224 0.199 31.89909 IsoPrime
4/26/08 2 4 1726 8.014456 0.192 31.21952 IsoPrime
1/15/07 4 1 1725 8.879716 0.257 31.68491 IsoPrime
4/26/08 2 4 1725 8.589761 0.209 31.41025 IsoPrime
1/20/07 4 1 1724 5.330892 0.209 27.85961 IsoPrime
4/26/08 2 4 1724 7.203531 0.18 31.73318 IsoPrime
1/21/07 4 1 1723 8.521546 0.247 31.67069 IsoPrime
4/26/08 2 4 1723 9.058827 0.216 32.99323 IsoPrime
1/21/07 4 1 1722 7.13127 0.218 31.32107 IsoPrime
4/26/08 2 4 1722 8.291177 0.19 33.07572 IsoPrime
4/26/08 2 4 1722 8.150708 0.195 33.11601 IsoPrime
1/21/07 4 1 1721 8.591362 0.25 30.89542 IsoPrime
2/17/08 Sucrose 1 4.71899 0.114 36.55893 IsoPrime
SUPPLEMENTARY DATA TABLE 45
5/1/08 Sucrose 4 9.856724 0 36.55904 IsoPrime
4/26/08 2 4 1721 7.561992 0.185 31.92868 IsoPrime
1/22/07 4 1 1720 11.68911 0.321 29.95512 IsoPrime
4/26/08 2 4 1720 8.593755 0.222 31.4975 IsoPrime
1/23/07 4 1 1719 8.640928 0.234 30.68206 IsoPrime
4/26/08 2 4 1719 8.48006 0.216 31.06445 IsoPrime
1/24/07 4 1 1718 8.282876 0.216 29.7214 IsoPrime
4/26/08 2 4 1718 6.780432 0.174 32.50299 IsoPrime
4/26/08 2 4 1718 7.687151 0.185 31.40471 IsoPrime
1/24/07 4 1 1717 9.623587 0.265 29.9885 IsoPrime
4/26/08 2 4 1717 8.490299 0.204 32.96407 IsoPrime
1/25/07 4 1 1716 8.546869 0.231 31.64315 IsoPrime
4/27/08 2 4 1716 7.453135 0.189 34.17841 IsoPrime
3/20/08 IAEA 2 8.881374 0.222 31.88601 IsoPrime
3/20/08 IAEA 1 9.043025 0.22 31.87786 IsoPrime
3/15/08 IAEA 5 7.444483 0.181 31.87681 IsoPrime
1/15/07 4 1 1715 6.375763 0.186 32.45028 IsoPrime
4/27/08 2 4 1715 7.513233 0.195 33.76547 IsoPrime
1/15/07 4 1 1714 8.466274 0.25 31.52426 IsoPrime
4/27/08 2 4 1714 7.822758 0.193 32.13928 IsoPrime
4/27/08 2 4 1714 8.903269 0.225 32.51163 IsoPrime
1/15/07 4 1 1713 7.738332 0.236 29.48825 IsoPrime
1/20/07 4 1 1712 6.797472 0.22 28.81526 IsoPrime
4/27/08 2 4 1712 7.041865 0.159 31.79907 IsoPrime
1/21/07 4 1 1711 8.053396 0.253 26.58626 IsoPrime
1/25/07 4 1 1711 7.583533 0.209 28.13129 IsoPrime
1/21/07 4 1 1710 7.127269 0.204 29.44158 IsoPrime
4/27/08 2 4 1710 6.848637 0.176 32.01928 IsoPrime
4/16/08 IAEA 2 8.218307 0.203 31.87538 IsoPrime
4/25/08 IAEA 3 8.207755 0.201 31.86964 IsoPrime
5/5/08 Sucrose 3 8.472631 0.17 36.56662 IsoPrime
1/21/07 4 1 1709 8.784297 0.251 30.67623 IsoPrime
4/27/08 2 4 1709 7.730732 0.199 32.58393 IsoPrime
4/27/08 2 4 1709 7.567514 0.188 31.96402 IsoPrime
1/22/07 4 1 1708 8.833792 0.246 31.70097 IsoPrime
4/27/08 2 4 1708 8.290355 0.2 32.52984 IsoPrime
1/23/07 4 1 1707 8.302131 0.231 32.36327 IsoPrime
4/27/08 2 4 1707 7.357195 0.177 32.40005 IsoPrime
1/24/07 4 1 1706 7.342611 0.199 31.56583 IsoPrime
4/27/08 2 4 1706 3.971048 0.097 34.89329 IsoPrime
1/24/07 4 1 1705 8.101007 0.221 30.11784 IsoPrime
2/13/08 IAEA 14 7.002459 0.182 31.86843 IsoPrime
4/16/08 IAEA 5 7.386743 0.193 31.86757 IsoPrime
46 MAXBERKELHAMMER
5/6/08 IAEA 11 7.298448 0 31.86473 IsoPrime
4/27/08 2 4 1705 9.33463 0.231 31.64447 IsoPrime
1/25/07 4 1 1704 8.5232 0.231 30.24539 IsoPrime
4/27/08 2 4 1704 8.557712 0.208 31.30798 IsoPrime
1/15/07 4 1 1703 8.29882 0.238 30.47827 IsoPrime
4/27/08 2 4 1703 9.829719 0.233 31.75686 IsoPrime
1/15/07 4 1 1702 7.263056 0.244 30.56502 IsoPrime
4/27/08 2 4 1702 8.908126 0.214 31.81912 IsoPrime
1/15/07 4 1 1701 7.301965 0.224 31.13439 IsoPrime
4/27/08 2 4 1701 8.370935 0.208 33.06873 IsoPrime
4/27/08 2 4 1701 7.801667 0.185 33.24246 IsoPrime
1/20/07 4 1 1700 9.098606 0.286 30.23705 IsoPrime
4/27/08 2 4 1700 7.628699 0.194 32.19613 IsoPrime
2/19/08 IAEA 8 6.358493 0.189 31.86434 IsoPrime
2/16/08 IAEA 10 7.822814 0.208 31.85421 IsoPrime
5/1/08 IAEA 20 8.347285 0.209 31.85275 IsoPrime
1/21/07 4 1 1699 9.057529 0.28 29.39453 IsoPrime
4/27/08 2 4 1699 9.201785 0.22 31.6959 IsoPrime
1/21/07 4 1 1698 8.210516 0.241 30.26069 IsoPrime
4/27/08 2 4 1698 7.918648 0.202 31.65373 IsoPrime
4/27/08 2 4 1698 7.49119 0.19 31.83871 IsoPrime
4/28/08 2 4 1697 7.992842 0.177 31.7292 IsoPrime
4/28/08 2 4 1697 9.595445 0.21 32.39742 IsoPrime
4/28/08 2 4 1696 9.462928 0.211 30.80785 IsoPrime
4/30/08 2 4 1696 7.982571 0.206 30.9213 IsoPrime
4/29/08 IAEA 4 7.808318 0.22 31.84731 IsoPrime
3/27/08 IAEA 7 - - 31.84623 IsoPrime
3/15/08 Sucrose 1 9.903729 0.233 36.56707 IsoPrime
4/28/08 2 4 1695 9.691325 0.223 31.58072 IsoPrime
4/28/08 2 4 1694 8.364845 0.187 31.93022 IsoPrime
4/28/08 2 4 1693 8.001015 0.185 32.60372 IsoPrime
4/28/08 2 4 1692 8.104267 0.184 32.4253 IsoPrime
4/28/08 2 4 1691 8.349283 0.19 31.29586 IsoPrime
4/30/08 2 4 1691 5.899323 0.15 30.73377 IsoPrime
4/28/08 2 4 1690 8.248652 0.185 31.63215 IsoPrime
4/28/08 2 4 1690 7.890198 0.177 31.10167 IsoPrime
4/28/08 2 4 1689 7.996815 0.183 31.48697 IsoPrime
4/28/08 2 4 1688 7.961697 0.184 31.9298 IsoPrime
3/16/08 Sucrose 3 9.843868 0.246 36.6032 IsoPrime
4/21/08 Sucrose 5 8.292266 0.194 36.60578 IsoPrime
2/19/08 IAEA 8 8.218193 0.225 31.8418 IsoPrime
2/19/08 IAEA 3 7.263273 0.183 31.83882 IsoPrime
3/26/08 IAEA 2 8.598833 0.228 31.83415 IsoPrime
SUPPLEMENTARY DATA TABLE 47
5/3/08 IAEA 42 7.878227 0 31.83046 IsoPrime
4/16/08 IAEA 1 8.094987 0.186 31.82757 IsoPrime
2/11/08 Sucrose 1 8.429652 0.205 36.61364 IsoPrime
4/30/08 IAEA 10 6.679222 0.187 31.82488 IsoPrime
4/22/08 Sucrose 1 8.297149 0.174 36.62032 IsoPrime
4/15/08 IAEA 9 7.231312 0.181 31.82425 IsoPrime
3/9/08 IAEA 2 8.653006 0.273 31.82327 IsoPrime
4/28/08 2 4 1687 7.809053 0.183 30.87931 IsoPrime
5/6/08 IAEA 9 7.863 0 31.80539 IsoPrime
2/12/08 IAEA 14 7.213535 0.176 31.80069 IsoPrime
3/15/08 IAEA 6 7.934044 0.21 31.7952 IsoPrime
4/29/08 2 4 1686 7.086678 0.169 32.01504 IsoPrime
4/29/08 2 4 1685 9.032549 0.214 31.32085 IsoPrime
4/29/08 2 4 1684 8.29987 0.197 29.30061 IsoPrime
4/29/08 2 4 1683 8.804695 0.207 30.86339 IsoPrime
4/29/08 2 4 1683 8.220638 0.193 31.67316 IsoPrime
4/30/08 2 4 1683 8.024011 0.204 30.80218 IsoPrime
4/29/08 2 4 1682 7.566307 0.176 31.73057 IsoPrime
4/29/08 2 4 1681 7.803104 0.192 30.70427 IsoPrime
4/28/08 2 4 1680 8.381666 0.196 31.27133 IsoPrime
4/28/08 2 4 1680 8.301031 0.202 30.5708 IsoPrime
4/28/08 2 4 1679 7.857275 0.177 31.32287 IsoPrime
1/28/08 IAEA 3 8.465474 0.162 31.79137 IsoPrime
4/30/08 IAEA 11 8.170992 0.187 31.78986 IsoPrime
3/26/08 IAEA 4 8.020527 0.198 31.78128 IsoPrime
4/28/08 2 4 1678 9.432732 0.204 31.2593 IsoPrime
4/28/08 2 4 1678 8.060753 0.199 30.89786 IsoPrime
4/29/08 2 4 1677 8.398873 0.196 32.33871 IsoPrime
4/29/08 2 4 1676 8.198414 0.192 32.47118 IsoPrime
4/29/08 2 4 1674 8.986837 0.206 31.55363 IsoPrime
4/29/08 2 4 1674 8.170878 0.192 31.54295 IsoPrime
4/28/08 2 4 1673 8.335556 0.201 30.74326 IsoPrime
4/28/08 2 4 1673 7.762231 0.22 30.57164 IsoPrime
4/28/08 2 4 1672 8.484302 0.219 31.5226 IsoPrime
4/28/08 2 4 1671 8.537207 0.173 32.83545 IsoPrime
4/28/08 2 4 1670 7.609854 0.167 32.79273 IsoPrime
3/15/08 IAEA 7 7.861282 0.219 31.77844 IsoPrime
4/25/08 IAEA 2 9.299353 0.224 31.7718 IsoPrime
4/26/08 Sucrose 3 8.261686 0 36.62719 IsoPrime
4/28/08 2 4 1669 8.925423 0.195 32.25186 IsoPrime
4/29/08 2 4 1668 7.839891 0.186 31.88323 IsoPrime
4/29/08 2 4 1667 8.618675 0.194 31.72039 IsoPrime
4/30/08 2 4 1666 7.274265 0.182 31.28071 IsoPrime
48 MAXBERKELHAMMER
4/29/08 2 4 1665 7.971862 0.179 31.55853 IsoPrime
4/17/08 4 1 1664 8.629796 0.209 31.44568 IsoPrime
4/22/08 4 1 1664 8.160747 0.198 31.38557 IsoPrime
4/29/08 2 4 1664 8.762979 0.213 31.963 IsoPrime
4/29/08 2 4 1663 9.182541 0.216 29.64482 IsoPrime
4/29/08 2 4 1662 7.601275 0.181 31.26058 IsoPrime
4/29/08 2 4 1662 8.411005 0.205 29.81396 IsoPrime
3/9/08 IAEA 1 8.602036 0 31.76257 IsoPrime
4/26/08 IAEA 22 8.549984 0.198 31.7541 IsoPrime
2/12/08 IAEA 2 7.348128 0.2 31.75282 IsoPrime
4/26/08 Sucrose 5 9.350751 0.22 36.64265 IsoPrime
3/22/08 Sucrose 1 10.62899 0.235 36.65725 IsoPrime
4/30/08 2 4 1662 7.085202 0.173 30.93797 IsoPrime
4/29/08 2 4 1661 7.473771 0.181 33.56041 IsoPrime
4/30/08 2 4 1661 6.831729 0.184 32.92658 IsoPrime
4/29/08 2 4 1660 9.823223 0.219 31.4994 IsoPrime
4/29/08 2 4 1660 8.125317 0.193 32.46979 IsoPrime
4/30/08 2 4 1660 8.864134 0.22 31.65212 IsoPrime
4/29/08 2 4 1659 7.39442 0.182 31.66837 IsoPrime
4/29/08 2 4 1658 9.592615 0.228 30.93195 IsoPrime
4/29/08 2 4 1658 8.45728 0.207 31.48105 IsoPrime
4/29/08 2 4 1657 7.964715 0.197 33.59478 IsoPrime
5/1/08 IAEA 29 7.86981 0 31.74549 IsoPrime
3/12/08 IAEA 14 9.555479 0.252 31.7414 IsoPrime
2/5/08 IAEA 2 5.470368 0.127 31.727 IsoPrime
4/29/08 2 4 1656 8.362837 0.205 33.79542 IsoPrime
4/29/08 2 4 1655 8.842448 0.222 33.03847 IsoPrime
4/29/08 2 4 1654 8.578822 0.209 31.61476 IsoPrime
4/29/08 2 4 1653 8.349731 0.201 31.43501 IsoPrime
4/29/08 2 4 1652 7.316103 0.189 33.21395 IsoPrime
4/29/08 2 4 1651 8.040379 0.199 33.01798 IsoPrime
4/29/08 2 4 1651 7.162476 0.184 32.5754 IsoPrime
4/30/08 2 4 1650 7.703576 0.187 31.56579 IsoPrime
4/30/08 2 4 1649 7.641799 0.194 30.87968 IsoPrime
4/30/08 2 4 1648 8.860777 0.215 30.95639 IsoPrime
4/30/08 2 4 1647 8.142229 0.206 30.42682 IsoPrime
4/30/08 2 4 1646 7.672244 0.186 29.61682 IsoPrime
4/17/08 Sucrose 3 8.896755 0.213 36.67098 IsoPrime
9/7/06 IAEA 7 9.353487 0.35 31.7242 IsoPrime
5/2/08 IAEA 31 6.442483 0 31.72045 IsoPrime
4/30/08 2 4 1645 8.539369 0.218 30.67363 IsoPrime
5/1/08 2 4 1645 7.884223 0.202 31.67973 IsoPrime
5/1/08 2 4 1644 6.697635 0.174 30.70969 IsoPrime
SUPPLEMENTARY DATA TABLE 49
5/1/08 2 4 1643 7.225976 0.177 31.08111 IsoPrime
5/1/08 2 4 1643 7.802579 0.19 30.89284 IsoPrime
4/17/08 4 1 1642 7.457051 0.188 31.08283 IsoPrime
4/17/08 4 1 1642 8.042186 0.202 31.44979 IsoPrime
5/1/08 2 4 1642 8.511985 0.215 31.5974 IsoPrime
4/17/08 4 1 1641 9.096982 0.23 31.96919 IsoPrime
5/1/08 2 4 1641 7.006283 0.182 30.1508 IsoPrime
5/1/08 2 4 1640 6.502721 0.189 31.43013 IsoPrime
5/1/08 2 4 1639 7.411767 0.197 33.21639 IsoPrime
2/19/08 IAEA 12 7.029522 0.187 31.7153 IsoPrime
4/30/08 IAEA 15 6.984661 0.187 31.70026 IsoPrime
4/17/08 4 1 1638 8.624705 0.217 32.1778 IsoPrime
4/17/08 4 1 1638 7.658332 0.191 31.12699 IsoPrime
4/17/08 4 1 1638 7.171183 0.187 32.18878 IsoPrime
5/1/08 2 4 1638 7.146833 0.179 37.31583 IsoPrime
5/1/08 2 4 1638 5.144064 0.134 33.06213 IsoPrime
4/17/08 4 1 1637 8.211476 0.202 33.64731 IsoPrime
5/1/08 2 4 1637 7.178015 0.182 32.92502 IsoPrime
5/1/08 2 4 1636 6.19642 0.164 32.16158 IsoPrime
5/1/08 2 4 1636 7.196415 0.18 31.77703 IsoPrime
4/27/08 IAEA 30 6.848449 0.161 31.69634 IsoPrime
5/1/08 IAEA 25 7.444343 0 31.69398 IsoPrime
2/11/08 IAEA 2 6.945932 0.183 31.68662 IsoPrime
4/25/08 IAEA 6 9.715009 0.165 31.68353 IsoPrime
4/28/08 IAEA 3 10.42401 0.223 31.68312 IsoPrime
9/7/06 IAEA 8 8.245965 0.315 31.67851 IsoPrime
3/18/08 IAEA 7 9.109521 0.239 31.67438 IsoPrime
2/12/08 IAEA 1 7.375746 0.2 31.67342 IsoPrime
3/18/08 IAEA 4 7.740878 0.21 31.66119 IsoPrime
4/17/08 4 1 1635 7.733922 0.191 32.39693 IsoPrime
4/17/08 4 1 1635 7.098518 0.182 32.51127 IsoPrime
4/17/08 4 1 1635 7.965196 0.205 33.12703 IsoPrime
5/1/08 2 4 1635 6.635376 0.165 32.38786 IsoPrime
4/17/08 4 1 1634 8.070745 0.214 33.39139 IsoPrime
5/1/08 2 4 1634 6.359959 0.168 34.16345 IsoPrime
5/1/08 2 4 1634 7.347684 0.2 34.01702 IsoPrime
4/17/08 4 1 1633 7.103337 0.236 33.64238 IsoPrime
4/17/08 4 1 1633 8.032476 0.18 34.08467 IsoPrime
5/1/08 2 4 1633 8.831085 0.224 33.42379 IsoPrime
5/1/08 2 4 1633 6.291435 0.168 33.54418 IsoPrime
4/17/08 4 1 1632 7.959985 0.18 33.54719 IsoPrime
2/19/08 IAEA 16 8.272778 0.219 31.65497 IsoPrime
4/30/08 IAEA 13 8.29077 0.187 31.64535 IsoPrime
50 MAXBERKELHAMMER
3/27/08 IAEA 5 - - 31.62845 IsoPrime
5/1/08 2 4 1632 7.93815 0.196 32.4846 IsoPrime
5/1/08 2 4 1632 6.408654 0.168 33.25906 IsoPrime
4/17/08 4 1 1631 7.461752 0.185 32.76429 IsoPrime
5/1/08 2 4 1631 6.284144 0.171 33.49086 IsoPrime
4/17/08 4 1 1630 7.531256 0.184 31.19692 IsoPrime
4/17/08 4 1 1630 7.466495 0.203 30.52015 IsoPrime
5/1/08 2 4 1630 8.54644 0.209 30.28518 IsoPrime
4/17/08 4 1 1629 7.508378 0.19 31.62303 IsoPrime
4/17/08 4 1 1629 8.431684 0.199 31.77394 IsoPrime
5/1/08 2 4 1629 8.006661 0.199 31.18671 IsoPrime
5/1/08 2 4 1629 6.871638 0.175 30.64142 IsoPrime
4/17/08 4 1 1628 7.900342 0.194 30.34849 IsoPrime
4/29/08 IAEA 5 8.754737 0.22 31.62379 IsoPrime
3/15/08 IAEA 4 7.139404 0.172 31.62155 IsoPrime
4/21/08 IAEA 16 8.15714 0.195 31.61508 IsoPrime
4/22/08 4 1 1628 8.216713 0.202 30.23447 IsoPrime
5/1/08 2 4 1628 7.930515 0.197 29.98206 IsoPrime
4/17/08 4 1 1627 8.52968 0.204 33.54125 IsoPrime
4/17/08 4 1 1627 8.586096 0.228 32.63886 IsoPrime
4/17/08 4 1 1627 7.852354 0.199 30.80981 IsoPrime
5/1/08 2 4 1627 8.408739 0.216 28.9132 IsoPrime
5/1/08 2 4 1626 9.092464 0.218 29.1244 IsoPrime
4/20/08 4 1 1625 6.812948 0.175 29.92807 IsoPrime
5/1/08 2 4 1625 7.016325 0.179 29.24628 IsoPrime
4/20/08 4 1 1624 7.845409 0.196 29.30208 IsoPrime
4/20/08 4 1 1624 7.8756 0.199 28.9985 IsoPrime
5/1/08 2 4 1624 7.598979 0.188 29.47033 IsoPrime
2/12/08 IAEA 15 6.513051 0.169 31.60882 IsoPrime
3/16/08 IAEA 14 8.134524 0.211 31.59957 IsoPrime
3/19/08 IAEA 9 7.637108 0.199 31.59746 IsoPrime
4/20/08 4 1 1623 9.076557 0.224 28.60584 IsoPrime
5/1/08 2 4 1623 7.180418 0.184 29.77644 IsoPrime
4/20/08 4 1 1622 7.903221 0.204 30.32061 IsoPrime
5/1/08 2 4 1622 7.684421 0.196 31.21467 IsoPrime
4/20/08 4 1 1621 7.329322 0.195 31.58375 IsoPrime
4/20/08 4 1 1621 8.346851 0 32.2154 IsoPrime
5/1/08 2 4 1621 8.310955 0.207 30.82256 IsoPrime
5/1/08 2 4 1621 7.699467 0.195 31.37859 IsoPrime
4/21/08 4 1 1620 8.846131 0.22 31.3422 IsoPrime
5/1/08 2 4 1620 8.183731 0.202 32.09384 IsoPrime
4/21/08 4 1 1619 9.230095 0.229 32.78774 IsoPrime
4/21/08 4 1 1619 7.261536 0.183 33.19101 IsoPrime
SUPPLEMENTARY DATA TABLE 51
1/28/08 IAEA 8 10.14424 0.192 31.59414 IsoPrime
2/13/08 IAEA 15 6.822116 0.178 31.59303 IsoPrime
2/4/08 IAEA 1 7.54336 0.181 31.58295 IsoPrime
3/22/08 Sucrose 2 9.84787 0.244 36.717 IsoPrime
2/13/08 Sucrose 2 7.734772 0.184 36.74618 IsoPrime
4/21/08 Sucrose 4 8.356134 0.203 36.76477 IsoPrime
4/29/08 BenD 1 5.255175 0.22 23.33748 IsoPrime
4/29/08 BenD 2 5.425771 0.22 23.02707 IsoPrime
4/29/08 BenD 3 5.91368 0.22 22.91666 IsoPrime
9/8/06 IAEA 10 6.203755 0.242 31.5483 IsoPrime
4/20/08 IAEA 2 7.34599 0.188 31.545 IsoPrime
4/28/08 IAEA 4 8.611823 0.18 31.5308 IsoPrime
5/1/08 2 4 1619 8.908058 0.215 30.84717 IsoPrime
4/21/08 4 1 1618 8.194574 0.201 31.96473 IsoPrime
4/21/08 4 1 1618 8.643928 0.217 33.48952 IsoPrime
4/21/08 4 1 1618 7.397601 0.182 32.53057 IsoPrime
5/1/08 2 4 1618 7.24271 0.19 29.90977 IsoPrime
4/21/08 4 1 1617 8.305083 0.203 29.30433 IsoPrime
4/21/08 4 1 1617 7.568138 0.185 28.87045 IsoPrime
5/1/08 2 4 1617 6.638229 0.167 30.30216 IsoPrime
4/21/08 4 1 1616 7.148214 0.187 30.80121 IsoPrime
3/15/08 IAEA 9 8.318735 0.213 31.49363 IsoPrime
3/20/08 IAEA 6 7.835477 3 31.49165 IsoPrime
2/18/08 IAEA 7 7.444248 0.182 31.47714 IsoPrime
5/2/08 2 4 1616 8.419224 0.219 32.24846 IsoPrime
4/21/08 4 1 1615 7.666667 0.187 30.58558 IsoPrime
5/2/08 2 4 1615 2.130643 0.05 32.41343 IsoPrime
4/21/08 4 1 1614 7.262994 0.184 30.28592 IsoPrime
4/21/08 4 1 1614 9.311576 0.232 31.03053 IsoPrime
5/2/08 2 4 1614 8.428775 0.209 31.94934 IsoPrime
4/21/08 4 1 1613 7.786616 0.199 31.77951 IsoPrime
5/2/08 2 4 1613 8.215918 0.207 30.26449 IsoPrime
5/2/08 2 4 1613 7.37615 0.186 31.52041 IsoPrime
5/2/08 2 4 1613 7.879911 0.19 31.96282 IsoPrime
5/2/08 IAEA 35 7.605885 0 31.45915 IsoPrime
2/13/08 IAEA 10 6.759913 0.185 31.44837 IsoPrime
4/28/08 IAEA 2 8.886082 0.185 31.41052 IsoPrime
4/21/08 4 1 1612 7.81918 0.2 31.87933 IsoPrime
4/21/08 4 1 1612 8.414349 0.211 31.61253 IsoPrime
5/2/08 2 4 1612 7.282496 0.182 31.00053 IsoPrime
4/21/08 4 1 1611 7.625495 0.188 31.39947 IsoPrime
5/2/08 2 4 1611 7.159442 0.187 31.15009 IsoPrime
4/21/08 4 1 1610 8.509859 0.215 31.03214 IsoPrime
52 MAXBERKELHAMMER
5/2/08 2 4 1610 6.825289 0.174 30.29709 IsoPrime
5/2/08 2 4 1610 7.019431 0.175 30.62582 IsoPrime
4/21/08 4 1 1609 7.012897 0.193 31.03782 IsoPrime
5/2/08 2 4 1609 7.719861 0.2 30.41268 IsoPrime
4/21/08 4 1 1608 8.771191 0.222 30.39884 IsoPrime
4/21/08 4 1 1608 5.720708 0.149 30.83364 IsoPrime
4/28/08 IAEA 6 9.262761 0.199 31.40575 IsoPrime
2/20/08 IAEA 9 7.53646 0.164 31.37608 IsoPrime
4/28/08 IAEA 7 8.229948 0.178 31.35752 IsoPrime
5/2/08 2 4 1608 7.013759 0.171 30.5153 IsoPrime
4/21/08 4 1 1607 8.296382 0.219 30.17307 IsoPrime
5/2/08 2 4 1607 6.508754 0.17 31.18127 IsoPrime
4/21/08 4 1 1606 7.923813 0.199 30.93465 IsoPrime
4/21/08 4 1 1606 8.78339 0.22 30.40642 IsoPrime
5/2/08 2 4 1606 8.199414 0.217 30.81522 IsoPrime
4/21/08 4 1 1605 9.173124 0.227 30.55226 IsoPrime
5/2/08 2 4 1605 8.147415 0.2 31.09448 IsoPrime
5/2/08 2 4 1605 7.601279 0.189 31.00762 IsoPrime
4/21/08 4 1 1604 7.421553 0.184 30.98431 IsoPrime
4/21/08 4 1 1604 7.725767 0.201 31.22147 IsoPrime
5/2/08 2 4 1604 6.950412 0.177 31.33224 IsoPrime
2/5/08 IAEA 4 5.824262 0.146 31.34883 IsoPrime
4/29/08 IAEA 2 7.925909 0.188 31.33696 IsoPrime
5/5/08 IAEA 1 7.563978 0 31.32851 IsoPrime
4/21/08 4 1 1603 7.541694 0.198 31.7607 IsoPrime
5/2/08 2 4 1603 7.166731 0.189 32.38081 IsoPrime
4/21/08 4 1 1602 7.522094 0.193 30.7996 IsoPrime
5/2/08 2 4 1602 7.415977 0.183 31.31648 IsoPrime
4/21/08 4 1 1601 8.307055 0.209 32.17906 IsoPrime
4/21/08 4 1 1601 7.498053 0.187 32.02573 IsoPrime
5/2/08 2 4 1601 7.231035 0.181 30.4632 IsoPrime
5/2/08 2 4 1601 7.280551 0.178 30.22429 IsoPrime
4/21/08 4 1 1600 8.042387 0.21 32.63552 IsoPrime
5/2/08 2 4 1600 8.115636 0.201 31.2002 IsoPrime
4/21/08 4 1 1599 8.661624 0.209 30.72246 IsoPrime
4/22/08 4 1 1599 8.29354 0.204 29.59115 IsoPrime
5/3/08 IAEA 40 8.779249 0 31.31707 IsoPrime
2/11/08 IAEA 7 6.091927 0.205 31.30268 IsoPrime
9/20/06 IAEA 6 8.464025 0.302 31.28107 IsoPrime
5/2/08 2 4 1599 7.197874 0.18 32.69968 IsoPrime
4/22/08 4 1 1598 7.526162 0.188 30.6047 IsoPrime
5/2/08 2 4 1598 8.155386 0.203 32.14244 IsoPrime
4/21/08 4 1 1597 7.665399 0.201 32.86582 IsoPrime
SUPPLEMENTARY DATA TABLE 53
4/22/08 4 1 1597 9.195141 0.228 33.0005 IsoPrime
5/2/08 2 4 1597 7.332551 0.179 31.73789 IsoPrime
4/21/08 4 1 1596 7.600393 0.193 31.96174 IsoPrime
5/2/08 2 4 1596 8.541164 0.227 32.00765 IsoPrime
4/21/08 4 1 1595 8.269327 0.194 32.48204 IsoPrime
5/2/08 2 4 1595 6.506472 0.162 31.65669 IsoPrime
5/2/08 2 4 1595 8.065092 0.194 31.12938 IsoPrime
4/21/08 4 1 1594 7.338124 0.205 31.90057 IsoPrime
5/2/08 2 4 1594 8.323975 0.193 29.54061 IsoPrime
3/21/08 IAEA 7 8.42416 0.206 31.27924 IsoPrime
3/21/08 IAEA 9 7.075917 0.19 31.27052 IsoPrime
9/8/06 IAEA 16 9.348549 0.357 31.24459 IsoPrime
4/21/08 4 1 1593 7.404751 0.199 30.27205 IsoPrime
5/2/08 2 4 1593 7.880906 0.21 31.44927 IsoPrime
4/22/08 4 1 1592 7.094249 0.186 29.41258 IsoPrime
4/22/08 4 1 1592 6.988647 0.199 30.51692 IsoPrime
5/2/08 2 4 1592 6.34965 0.175 33.19672 IsoPrime
4/22/08 4 1 1591 7.17557 0.185 32.58446 IsoPrime
4/22/08 4 1 1590 8.096604 0.179 32.49889 IsoPrime
5/2/08 2 4 1590 7.830654 0.192 30.29581 IsoPrime
10/21/06 IAEA 19 12.20474 0.359 31.21714 IsoPrime
10/21/06 IAEA 18 12.191 0.359 31.19739 IsoPrime
12/11/06 IAEA 11 7.544843 0.199 31.15569 IsoPrime
4/17/08 Sucrose 4 7.703015 0.189 36.81552 IsoPrime
3/26/08 Sucrose 2 10.20588 0.212 36.82617 IsoPrime
5/1/08 BenD 4 6.402517 0 23.30073 IsoPrime
5/1/08 BenD 5 6.941728 0 23.63247 IsoPrime
4/22/08 4 1 1589 7.21573 0.206 31.34813 IsoPrime
4/22/08 4 1 1589 7.737981 0.186 31.72872 IsoPrime
5/2/08 2 4 1589 7.360361 0.191 32.15795 IsoPrime
4/22/08 4 1 1588 7.950809 0.209 30.44798 IsoPrime
5/2/08 2 4 1588 7.969262 0.195 30.33735 IsoPrime
4/22/08 4 1 1587 8.983658 0.18 30.6567 IsoPrime
5/2/08 2 4 1587 9.140516 0.221 32.07861 IsoPrime
4/22/08 4 1 1586 7.695126 0.223 31.48075 IsoPrime
5/2/08 2 4 1586 7.538799 0.198 32.95671 IsoPrime
5/2/08 2 4 1586 7.628991 0.192 32.11073 IsoPrime
4/22/08 4 1 1585 9.016362 0.19 32.20623 IsoPrime
4/22/08 4 1 1585 8.529008 0.227 32.48216 IsoPrime
4/29/08 IAEA 1 6.5642 0.16 31.15127 IsoPrime
9/21/06 IAEA 9 11.448 0.392 31.1113 IsoPrime
6/21/06 IAEA 11 8.153036 0.52 31.09055 IsoPrime
5/2/08 2 4 1585 6.596346 0.168 31.21423 IsoPrime
54 MAXBERKELHAMMER
4/22/08 4 1 1584 7.724669 0.19 30.34819 IsoPrime
5/2/08 2 4 1584 7.927656 0.189 29.86626 IsoPrime
4/22/08 4 1 1583 8.076233 0.194 29.1226 IsoPrime
5/2/08 2 4 1583 7.289908 0.192 30.63526 IsoPrime
4/22/08 4 1 1582 7.49498 0.203 30.15582 IsoPrime
4/22/08 4 1 1582 7.893192 0.189 29.84921 IsoPrime
5/2/08 2 4 1582 7.354171 0.185 30.56887 IsoPrime
5/2/08 2 4 1582 8.13581 0.211 31.80473 IsoPrime
4/22/08 4 1 1581 8.94496 0.201 30.48943 IsoPrime
5/2/08 2 4 1581 8.442383 0.213 30.12047 IsoPrime
4/22/08 4 1 1580 7.343111 0.227 31.78913 IsoPrime
12/12/06 IAEA 13 7.754488 0.208 31.07069 IsoPrime
5/5/08 IAEA 3 6.882751 0.207 31.05901 IsoPrime
9/13/06 IAEA 16 9.131187 0.33 31.05717 IsoPrime
5/3/08 2 4 1579 7.467048 0.208 32.02628 IsoPrime
4/22/08 4 1 1578 8.241014 0.19 30.73048 IsoPrime
5/3/08 2 4 1578 7.613421 0.197 32.74118 IsoPrime
5/6/08 2 4 1578 7.28328 0.178 32.33727 IsoPrime
4/22/08 4 1 1577 7.072816 0.208 32.73603 IsoPrime
4/22/08 4 1 1577 8.838634 0.175 33.01985 IsoPrime
5/3/08 2 4 1577 7.555909 0.194 32.28938 IsoPrime
5/3/08 2 4 1577 7.200393 0.187 31.61377 IsoPrime
5/3/08 2 4 1576 7.735865 0.191 31.31522 IsoPrime
5/3/08 2 4 1574 7.164492 0.184 32.80018 IsoPrime
5/6/08 2 4 1574 7.650121 0.185 33.28392 IsoPrime
5/3/08 2 4 1573 7.899047 0.19 31.46616 IsoPrime
5/5/08 IAEA 4 7.59576 0.17 31.04867 IsoPrime
3/21/08 IAEA 8 6.939135 0.184 31.04751 IsoPrime
4/28/08 IAEA 5 7.651914 0.164 31.03559 IsoPrime
5/3/08 2 4 1573 7.593773 0.196 31.92493 IsoPrime
5/3/08 2 4 1572 7.014391 0.179 30.64649 IsoPrime
5/3/08 2 4 1571 6.640922 0.173 30.10891 IsoPrime
5/3/08 2 4 1570 8.700532 0.218 30.70886 IsoPrime
5/6/08 2 4 1570 7.044622 0.182 31.78636 IsoPrime
5/3/08 2 4 1569 8.403658 0.211 30.1962 IsoPrime
5/3/08 2 4 1569 6.80262 0.17 30.16317 IsoPrime
5/3/08 2 4 1567 8.352589 0.209 32.06158 IsoPrime
5/6/08 2 4 1567 7.937804 0.204 31.69201 IsoPrime
5/3/08 2 4 1566 8.064541 0.201 31.69769 IsoPrime
5/3/08 2 4 1564 7.092016 0.182 30.6376 IsoPrime
4/21/08 Sucrose 3 8.278704 0.196 36.87619 IsoPrime
3/10/08 Sucrose 2 9.512268 0.21 36.8787 IsoPrime
5/5/08 2 4 1564 8.323169 0.17 30.10812 IsoPrime
SUPPLEMENTARY DATA TABLE 55
5/6/08 2 4 1564 8.43077 0.209 31.30386 IsoPrime
5/5/08 2 4 1563 7.946691 0.197 32.09454 IsoPrime
5/5/08 2 4 1563 8.51652 0.211 31.76515 IsoPrime
5/5/08 2 4 1562 7.399467 0.179 32.60128 IsoPrime
5/5/08 2 4 1561 8.780092 0.219 30.37293 IsoPrime
5/5/08 2 4 1560 6.958299 0.175 30.87326 IsoPrime
5/5/08 2 4 1559 6.937425 0.177 30.61994 IsoPrime
5/5/08 2 4 1558 7.614731 0.189 30.02512 IsoPrime
5/5/08 2 4 1558 7.46553 0.192 30.103 IsoPrime
5/5/08 2 4 1557 7.290313 0.183 31.05396 IsoPrime
5/5/08 2 4 1556 8.129611 0.197 30.84969 IsoPrime
9/8/06 IAEA 9 6.969293 0.273 31.02466 IsoPrime
9/8/06 IAEA 11 9.934873 0.381 31.00642 IsoPrime
5/3/08 IAEA 38 6.913131 0 30.99218 IsoPrime
5/5/08 2 4 1555 7.811637 0.191 30.08187 IsoPrime
5/6/08 2 4 1554 7.727423 0.189 30.44176 IsoPrime
5/6/08 2 4 1553 6.727838 0.168 31.40921 IsoPrime
5/6/08 2 4 1553 5.125308 0.19 30.77652 IsoPrime
5/6/08 2 4 1552 8.134328 0.195 32.03901 IsoPrime
5/6/08 2 4 1551 8.096519 0.196 32.38761 IsoPrime
5/6/08 2 4 1550 7.790982 0.189 33.29991 IsoPrime
5/6/08 2 4 1549 7.380417 0.174 29.3507 IsoPrime
5/6/08 2 4 1549 8.378383 0.204 30.74119 IsoPrime
5/6/08 2 4 1548 6.694993 0.161 30.51977 IsoPrime
5/6/08 2 4 1547 7.925003 0.209 32.0205 IsoPrime
5/6/08 2 4 1546 8.356934 0.201 32.23328 IsoPrime
3/9/08 IAEA 5 7.599451 0.199 30.99178 IsoPrime
3/9/08 IAEA 4 6.691174 0.169 30.9898 IsoPrime
10/21/06 IAEA 20 10.67228 0.305 30.92583 IsoPrime
3/21/08 IAEA 10 7.256009 0.226 30.92137 IsoPrime
5/5/08 BenD 1 9.893489 0.182 22.92473 IsoPrime
9/8/06 IAEA 13 9.091641 0.347 30.90499 IsoPrime
9/12/06 IAEA 8 7.267825 0.309 30.85597 IsoPrime
9/8/06 IAEA 19 6.409609 0.248 30.84048 IsoPrime
5/5/08 BenD 2 7.289372 0.17 23.42618 IsoPrime
2/13/08 IAEA 9 6.44866 0.159 30.81336 IsoPrime
3/26/08 IAEA 7 7.964946 0.194 30.80042 IsoPrime
5/6/08 2 4 1545 8.140524 0.194 32.79625 IsoPrime
5/6/08 2 4 1544 7.860443 0.198 33.66082 IsoPrime
5/6/08 2 4 1544 6.242573 0.163 33.50272 IsoPrime
10/23/06 IAEA 9 9.416375 0.279 30.77665 IsoPrime
9/13/06 IAEA 15 9.485613 0.348 30.71897 IsoPrime
10/23/06 IAEA 16 9.74746 0.308 30.71451 IsoPrime
56 MAXBERKELHAMMER
9/7/06 IAEA 3 8.01693 0.306 30.68962 IsoPrime
9/7/06 IAEA 2 9.416942 0.358 30.61275 IsoPrime
12/11/06 IAEA 9 7.918695 0.212 30.60093 IsoPrime
12/11/06 IAEA 8 7.755582 0.218 30.59577 IsoPrime
5/6/08 2 4 1543 8.285023 0.2 32.06589 IsoPrime
5/6/08 2 4 1541 7.945184 0.197 31.73384 IsoPrime
5/6/08 2 4 1541 8.233097 0.193 30.75421 IsoPrime
5/6/08 2 4 1540 7.673158 0.187 30.88032 IsoPrime
5/6/08 2 4 1539 9.31562 0.226 31.42618 IsoPrime
TABLE2. Isotopiccompositionofprecipitationsamples
SiteName Elevation(M) Latitude Longitude Out dD dO
Hopland 253.0 39.0 -123.0 36893.0 -71.2 -9.8
Olympic 182.0 47.9 -123.9 36893.5 -64.3 -8.9
Sequoia 1902.0 36.6 -118.8 36900.0 -135.7 -18.0
Hopland 253.0 39.0 -123.0 36900.5 -71.9 -9.8
Olympic 182.0 47.9 -123.9 36900.6 -54.0 -8.0
Hopland 253.0 39.0 -123.0 36904.0 -57.2 -8.8
Pinnacles 317.0 36.5 -121.2 36907.0 -72.3 -10.7
JoshuaTree 1239.0 34.1 -116.4 36907.0 -77.4 -11.4
Sequoia 1902.0 36.6 -118.8 36907.0 -123.3 -17.0
DeathValley 125.0 36.6 -117.0 36907.0 -80.8 -11.3
Hopland 253.0 39.0 -123.0 36907.5 -60.6 -9.0
Olympic 182.0 47.9 -123.9 36907.6 -89.7 -11.9
Olympic 182.0 47.9 -123.9 36913.6 -93.1 -12.5
Hopland 253.0 39.0 -123.0 36914.0 -63.1 -9.6
Pinnacles 317.0 36.5 -121.2 36921.0 -40.7 -7.4
JoshuaTree 1239.0 34.1 -116.4 36921.0 -141.3 -19.3
Sequoia 1902.0 36.6 -118.8 36921.0 -89.7 -13.4
DeathValley 125.0 36.6 -117.0 36921.0 -84.3 -12.3
Hopland 253.0 39.0 -123.0 36921.5 -63.4 -9.7
Olympic 182.0 47.9 -123.9 36921.5 -81.2 -11.6
Hopland 253.0 39.0 -123.0 36928.0 -71.1 -10.9
Olympic 182.0 47.9 -123.9 36928.8 -69.4 -10.0
Sequoia 1902.0 36.6 -118.8 36935.0 -72.8 -11.6
DeathValley 125.0 36.6 -117.0 36935.0 -83.8 -11.8
Pinnacles 317.0 36.5 -121.2 36935.0 -62.7 -9.8
JoshuaTree 1239.0 34.1 -116.4 36935.0 -80.2 -12.0
Hopland 253.0 39.0 -123.0 36935.5 -72.1 -11.0
Olympic 182.0 47.9 -123.9 36935.5 -132.1 -17.5
Pinnacles 317.0 36.5 -121.2 36942.0 -52.9 -8.4
SUPPLEMENTARY DATA TABLE 57
JoshuaTree 1239.0 34.1 -116.4 36942.0 -97.5 -13.6
Sequoia 1902.0 36.6 -118.8 36942.0 -95.0 -13.9
DeathValley 125.0 36.6 -117.0 36942.0 -105.5 -14.9
Hopland 253.0 39.0 -123.0 36942.0 -72.1 -10.2
Hopland 253.0 39.0 -123.0 36942.5 -58.2 -8.8
Olympic 182.0 47.9 -123.9 36942.6 -120.6 -16.1
Pinnacles 317.0 36.5 -121.2 36949.0 -78.6 -10.9
JoshuaTree 1239.0 34.1 -116.4 36949.0 -147.0 -19.8
Sequoia 1902.0 36.6 -118.8 36949.0 -143.7 -18.9
DeathValley 125.0 36.6 -117.0 36949.0 -114.9 -15.3
Hopland 253.0 39.0 -123.0 36949.0 -61.6 -8.8
Hopland 253.0 39.0 -123.0 36949.5 -73.4 -10.4
Olympic 182.0 47.9 -123.9 36949.5 -72.4 -9.9
Pinnacles 317.0 36.5 -121.2 36956.0 -67.0 -9.9
JoshuaTree 1239.0 34.1 -116.4 36956.0 -135.0 -18.0
Sequoia 1902.0 36.6 -118.8 36956.0 -105.1 -13.9
DeathValley 125.0 36.6 -117.0 36956.0 -129.2 -15.8
Hopland 253.0 39.0 -123.0 36956.5 -61.9 -8.7
JoshuaTree 1239.0 34.1 -116.4 36963.0 -84.8 -12.4
Sequoia 1902.0 36.6 -118.8 36963.0 -83.0 -11.8
Olympic 182.0 47.9 -123.9 36963.7 -77.4 -10.7
Hopland 253.0 39.0 -123.0 36970.0 -15.3 -3.4
Olympic 182.0 47.9 -123.9 36970.5 -62.7 -8.9
Pinnacles 317.0 36.5 -121.2 36977.0 -9.4 -2.9
Sequoia 1902.0 36.6 -118.8 36977.0 -21.9 -4.6
Hopland 253.0 39.0 -123.0 36977.5 -15.5 -3.3
Olympic 182.0 47.9 -123.9 36977.7 -48.0 -7.2
Hopland 253.0 39.0 -123.0 36984.0 -42.6 -6.5
Olympic 182.0 47.9 -123.9 36984.6 -80.4 -11.0
JoshuaTree 1239.0 34.1 -116.4 36991.0 -53.4 -8.5
Pinnacles 317.0 36.5 -121.2 36991.0 -53.8 -7.9
Hopland 253.0 39.0 -123.0 36991.4 -43.6 -6.6
Hopland 253.0 39.0 -123.0 36997.0 -69.3 -10.2
Olympic 182.0 47.9 -123.9 36998.5 -82.2 -11.3
JoshuaTree 1239.0 34.1 -116.4 37005.0 -69.4 -10.0
Sequoia 1902.0 36.6 -118.8 37005.0 -85.8 -12.3
Hopland 253.0 39.0 -123.0 37005.4 -70.1 -10.1
Olympic 182.0 47.9 -123.9 37026.5 -111.5 -13.8
Olympic 182.0 47.9 -123.9 37033.4 -129.2 -16.6
Olympic 182.0 47.9 -123.9 37040.5 -54.2 -7.9
Olympic 182.0 47.9 -123.9 37047.5 -74.8 -10.2
Olympic 182.0 47.9 -123.9 37054.6 -83.1 -11.1
Olympic 182.0 47.9 -123.9 37068.7 -64.6 -8.5
58 MAXBERKELHAMMER
Olympic 182.0 47.9 -123.9 37075.4 -79.2 -10.0
Sequoia 1902.0 36.6 -118.8 37082.0 -23.1 -3.6
DeathValley 125.0 36.6 -117.0 37082.0 -17.5 -2.6
Olympic 182.0 47.9 -123.9 37103.4 -63.9 -8.5
Olympic 182.0 47.9 -123.9 37110.4 -63.0 -8.5
Olympic 182.0 47.9 -123.9 37124.4 -51.5 -6.7
Olympic 182.0 47.9 -123.9 37131.5 -66.2 -9.1
DeathValley 125.0 36.6 -117.0 37138.0 -39.8 -3.5
Olympic 182.0 47.9 -123.9 37138.4 -23.2 -3.8
Hopland 253.0 39.0 -123.0 37152.0 -31.7 -3.3
Olympic 182.0 47.9 -123.9 37158.6 -34.4 -5.3
Pinnacles 317.0 36.5 -121.2 37159.0 -67.9 -8.8
Hopland 253.0 39.0 -123.0 37159.4 -33.2 -3.4
Olympic 182.0 47.9 -123.9 37167.5 -55.9 -8.0
Olympic 182.0 47.9 -123.9 37173.4 -29.1 -4.8
Olympic 182.0 47.9 -123.9 37180.4 -41.3 -6.6
Hopland 253.0 39.0 -123.0 37187.0 -56.7 -8.5
Olympic 182.0 47.9 -123.9 37187.4 -55.0 -8.4
Sequoia 1902.0 36.6 -118.8 37194.0 -108.5 -13.4
Hopland 253.0 39.0 -123.0 37194.0 -74.2 -10.2
Olympic 182.0 47.9 -123.9 37194.5 -72.7 -10.5
Hopland 253.0 39.0 -123.0 37194.5 -57.4 -8.5
Sequoia 1902.0 36.6 -118.8 37201.0 -100.1 -13.9
Hopland 253.0 39.0 -123.0 37201.0 -58.6 -8.6
Hopland 253.0 39.0 -123.0 37201.6 -74.9 -10.2
Sequoia 1902.0 36.6 -118.8 37208.0 -48.2 -7.7
Hopland 253.0 39.0 -123.0 37208.0 -23.1 -4.7
Hopland 253.0 39.0 -123.0 37208.5 -59.1 -8.6
Olympic 182.0 47.9 -123.9 37209.0 -90.9 -11.9
Pinnacles 317.0 36.5 -121.2 37209.0 -41.7 -6.7
Hopland 253.0 39.0 -123.0 37215.0 -129.4 -16.4
Hopland 253.0 39.0 -123.0 37215.5 -23.2 -4.8
Olympic 182.0 47.9 -123.9 37215.6 -70.2 -9.8
Pinnacles 317.0 36.5 -121.2 37221.0 -31.1 -5.4
JoshuaTree 1239.0 34.1 -116.4 37222.0 -62.3 -8.8
Sequoia 1902.0 36.6 -118.8 37222.0 -65.2 -9.8
Hopland 253.0 39.0 -123.0 37222.0 -56.6 -9.0
Hopland 253.0 39.0 -123.0 37222.5 -31.9 -5.3
Olympic 182.0 47.9 -123.9 37222.6 -65.1 -9.7
Pinnacles 317.0 36.5 -121.2 37228.0 -24.7 -5.4
Sequoia 1902.0 36.6 -118.8 37229.0 -72.5 -11.1
Hopland 253.0 39.0 -123.0 37229.0 -46.3 -7.1
Hopland 253.0 39.0 -123.0 37229.5 -57.4 -9.0
SUPPLEMENTARY DATA TABLE 59
Pinnacles 317.0 36.5 -121.2 37236.0 -50.0 -8.1
Sequoia 1902.0 36.6 -118.8 37236.0 -69.5 -10.8
Hopland 253.0 39.0 -123.0 37236.0 -52.1 -7.9
Hopland 253.0 39.0 -123.0 37236.5 -47.2 -7.3
Olympic 182.0 47.9 -123.9 37236.5 -79.0 -11.5
Olympic 182.0 47.9 -123.9 37241.5 -70.2 -9.7
JoshuaTree 1239.0 34.1 -116.4 37243.0 -48.2 -7.9
Sequoia 1902.0 36.6 -118.8 37243.0 -94.7 -13.5
Hopland 253.0 39.0 -123.0 37243.0 -42.8 -7.2
Olympic 182.0 47.9 -123.9 37243.5 -73.8 -10.1
Hopland 253.0 39.0 -123.0 37243.5 -52.3 -7.9
Pinnacles 317.0 36.5 -121.2 37244.0 -46.9 -7.1
JoshuaTree 1239.0 34.1 -116.4 37251.0 -90.7 -13.3
Sequoia 1902.0 36.6 -118.8 37251.0 -82.0 -12.4
Pinnacles 317.0 36.5 -121.2 37251.0 -50.8 -8.4
Hopland 253.0 39.0 -123.0 37251.5 -42.7 -7.2
Olympic 182.0 47.9 -123.9 37252.5 -63.8 -10.1
Pinnacles 317.0 36.5 -121.2 37256.0 -45.4 -6.1
Sequoia 1902.0 36.6 -118.8 37258.0 -113.5 -15.2
Olympic 182.0 47.9 -123.9 37258.0 -99.5 -12.5
Hopland 253.0 39.0 -123.0 37258.0 -48.8 -7.3
Sequoia 1902.0 36.6 -118.8 37264.0 -74.8 -11.2
Hopland 253.0 39.0 -123.0 37264.0 -26.8 -5.0
Olympic 182.0 47.9 -123.9 37264.0 -65.2 -9.1
Pinnacles 317.0 36.5 -121.2 37265.0 -30.3 -5.7
Hopland 253.0 39.0 -123.0 37278.0 -25.6 -4.7
Olympic 182.0 47.9 -123.9 37278.0 -68.0 -10.3
Sequoia 1902.0 36.6 -118.8 37285.0 -124.7 -16.7
Pinnacles 317.0 36.5 -121.2 37285.0 -31.4 -5.6
Hopland 253.0 39.0 -123.0 37285.0 -61.4 -8.7
Olympic 182.0 47.9 -123.9 37285.0 -113.4 -15.4
Sequoia 1902.0 36.6 -118.8 37292.0 -70.5 -10.8
Pinnacles 317.0 36.5 -121.2 37298.0 -12.1 -3.3
Pinnacles 317.0 36.5 -121.2 37306.0 -37.6 -6.7
Sequoia 1902.0 36.6 -118.8 37306.0 -100.8 -15.5
Hopland 253.0 39.0 -123.0 37306.0 -64.4 -9.6
Olympic 182.0 47.9 -123.9 37306.0 -77.6 -10.6
Olympic 182.0 47.9 -123.9 37313.0 -85.7 -11.6
Hopland 253.0 39.0 -123.0 37313.0 -17.3 -3.1
Pinnacles 317.0 36.5 -121.2 37327.0 -46.5 -6.6
Sequoia 1902.0 36.6 -118.8 37327.0 -73.0 -10.7
Hopland 253.0 39.0 -123.0 37327.0 -24.2 -5.0
Olympic 182.0 47.9 -123.9 37327.0 -65.1 -9.5
60 MAXBERKELHAMMER
Pinnacles 317.0 36.5 -121.2 37334.0 -75.1 -10.9
Sequoia 1902.0 36.6 -118.8 37334.0 -87.0 -12.7
Pinnacles 317.0 36.5 -121.2 37341.0 -27.1 -5.2
Sequoia 1902.0 36.6 -118.8 37341.0 -65.1 -9.9
Hopland 253.0 39.0 -123.0 37341.0 -48.8 -7.6
Olympic 182.0 47.9 -123.9 37341.0 -55.5 -8.6
Pinnacles 317.0 36.5 -121.2 37369.0 -13.9 -3.7
Sequoia 1902.0 36.6 -118.8 37369.0 -44.9 -7.8
Pinnacles 317.0 36.5 -121.2 37376.0 -53.9 -7.6
JoshuaTree 1239.0 34.1 -116.4 37376.0 -59.2 -8.5
Olympic 182.0 47.9 -123.9 37376.0 -95.1 -12.1
Hopland 253.0 39.0 -123.0 37376.0 -38.2 -6.3
Olympic 182.0 47.9 -123.9 37376.0 -42.5 -7.4
Sequoia 1902.0 36.6 -118.8 37383.0 -60.5 -9.1
Pinnacles 317.0 36.5 -121.2 37397.0 -44.1 -6.8
Sequoia 1902.0 36.6 -118.8 37397.0 -54.9 -8.7
Hopland 253.0 39.0 -123.0 37397.0 -47.3 -7.4
Olympic 182.0 47.9 -123.9 37397.0 -63.6 -8.4
JoshuaTree 1239.0 34.1 -116.4 37509.0 -71.1 -7.8
Sequoia 1902.0 36.6 -118.8 37537.0 -14.2 2.2
Olympic 182.0 47.9 -123.9 37569.0 -67.5 -10.2
Pinnacles 317.0 36.5 -121.2 37572.0 -22.2 -4.1
Sequoia 1902.0 36.6 -118.8 37572.0 -59.6 -9.1
Hopland 253.0 39.0 -123.0 37572.0 -38.0 -6.4
Olympic 182.0 47.9 -123.9 37572.0 -56.4 -8.7
JoshuaTree 1239.0 34.1 -116.4 37593.0 -52.9 -8.3
Pinnacles 317.0 36.5 -121.2 37607.0 -23.8 -5.1
JoshuaTree 1239.0 34.1 -116.4 37607.0 -59.1 -8.6
Sequoia 1902.0 36.6 -118.8 37607.0 -55.6 -9.3
Sequoia 1902.0 36.6 -118.8 37613.0 -60.1 -10.2
JoshuaTree 1239.0 34.1 -116.4 37614.0 -87.0 -13.1
DeathValley 125.0 36.6 -117.0 37614.0 -38.3 -6.4
Sequoia 1902.0 36.6 -118.8 37621.0 -82.4 -12.4
Pinnacles 317.0 36.5 -121.2 37621.0 -53.2 -8.7
Olympic 182.0 47.9 -123.9 37627.0 -72.9 -10.0
Hopland 253.0 39.0 -123.0 37628.0 -37.8 -6.8
Pinnacles 317.0 36.5 -121.2 37635.0 -31.4 -5.7
Hopland 253.0 39.0 -123.0 37635.0 -34.0 -5.9
Olympic 182.0 47.9 -123.9 37636.0 -101.0 -13.7
Olympic 182.0 47.9 -123.9 37642.0 -53.3 -7.6
Hopland 253.0 39.0 -123.0 37649.0 -22.7 -4.4
Olympic 182.0 47.9 -123.9 37649.0 -58.4 -8.2
Olympic 182.0 47.9 -123.9 37656.0 -48.3 -7.2
SUPPLEMENTARY DATA TABLE 61
JoshuaTree 1239.0 34.1 -116.4 37663.0 -87.2 -11.9
Pinnacles 317.0 36.5 -121.2 37670.0 -70.3 -10.3
JoshuaTree 1239.0 34.1 -116.4 37670.0 -119.7 -15.7
DeathValley 125.0 36.6 -117.0 37670.0 -130.3 -17.3
Hopland 253.0 39.0 -123.0 37670.0 -102.2 -14.0
Olympic 182.0 47.9 -123.9 37670.0 -85.8 -12.2
Sequoia 1902.0 36.6 -118.8 37671.0 -141.4 -19.0
Pinnacles 317.0 36.5 -121.2 37677.0 -35.0 -5.6
JoshuaTree 1239.0 34.1 -116.4 37677.0 -86.0 -12.4
Sequoia 1902.0 36.6 -118.8 37677.0 -96.3 -13.4
Hopland 253.0 39.0 -123.0 37677.0 -62.4 -8.8
Olympic 182.0 47.9 -123.9 37678.0 -56.1 -8.2
Pinnacles 317.0 36.5 -121.2 37684.0 -93.6 -12.5
JoshuaTree 1239.0 34.1 -116.4 37684.0 -95.6 -13.4
Sequoia 1902.0 36.6 -118.8 37684.0 -88.7 -12.7
DeathValley 125.0 36.6 -117.0 37684.0 -88.9 -10.8
Olympic 182.0 47.9 -123.9 37684.0 -72.7 -10.0
Olympic 182.0 47.9 -123.9 37691.0 -59.0 -8.9
Olympic 182.0 47.9 -123.9 37693.0 -87.2 -11.4
Pinnacles 317.0 36.5 -121.2 37698.0 -28.8 -4.8
JoshuaTree 1239.0 34.1 -116.4 37698.0 -79.9 -11.1
Sequoia 1902.0 36.6 -118.8 37698.0 -61.8 -9.7
DeathValley 125.0 36.6 -117.0 37698.0 -44.4 -4.3
Hopland 253.0 39.0 -123.0 37698.0 -32.2 -5.4
Hopland 253.0 39.0 -123.0 37705.0 -17.1 -3.9
Olympic 182.0 47.9 -123.9 37705.0 -84.7 -11.4
Pinnacles 317.0 36.5 -121.2 37713.0 -40.0 -5.4
Olympic 182.0 47.9 -123.9 37713.0 -59.4 -8.9
Sequoia 1902.0 36.6 -118.8 37719.0 -61.7 -10.0
Hopland 253.0 39.0 -123.0 37719.0 -42.4 -7.7
Olympic 182.0 47.9 -123.9 37719.0 -80.1 -11.3
Pinnacles 317.0 36.5 -121.2 37720.0 -20.4 -4.6
JoshuaTree 1239.0 34.1 -116.4 37726.0 -59.6 -8.9
Sequoia 1902.0 36.6 -118.8 37726.0 -69.9 -11.0
DeathValley 125.0 36.6 -117.0 37726.0 -121.3 -15.8
Pinnacles 317.0 36.5 -121.2 37726.0 -28.6 -5.7
Hopland 253.0 39.0 -123.0 37726.0 -39.3 -7.0
Olympic 182.0 47.9 -123.9 37726.0 -73.7 -10.8
Sequoia 1902.0 36.6 -118.8 37733.0 -62.3 -9.7
Hopland 253.0 39.0 -123.0 37733.0 -46.2 -7.5
Olympic 182.0 47.9 -123.9 37733.0 -61.1 -9.0
Pinnacles 317.0 36.5 -121.2 37734.0 -35.6 -6.3
Sequoia 1902.0 36.6 -118.8 37740.0 -57.7 -9.1
62 MAXBERKELHAMMER
Pinnacles 317.0 36.5 -121.2 37740.0 -16.2 -3.9
Hopland 253.0 39.0 -123.0 37740.0 -48.7 -7.8
Olympic 182.0 47.9 -123.9 37745.0 -67.2 -9.5
Pinnacles 317.0 36.5 -121.2 37747.0 -41.7 -6.8
Sequoia 1902.0 36.6 -118.8 37747.0 -85.7 -12.5
Hopland 253.0 39.0 -123.0 37747.0 -57.5 -8.1
Olympic 182.0 47.9 -123.9 37747.0 -99.5 -13.8
Sequoia 1902.0 36.6 -118.8 37754.0 -59.8 -9.5
Hopland 253.0 39.0 -123.0 37754.0 -64.0 -8.6
Sequoia 1902.0 36.6 -118.8 37761.0 -76.5 -10.6
Olympic 182.0 47.9 -123.9 37761.0 -60.9 -9.5
Olympic 182.0 47.9 -123.9 37768.0 -42.2 -5.8
Olympic 182.0 47.9 -123.9 37775.0 -12.9 -2.1
Olympic 182.0 47.9 -123.9 37789.0 -35.1 -5.6
Olympic 182.0 47.9 -123.9 37796.0 -75.6 -10.1
Olympic 182.0 47.9 -123.9 37803.0 -88.6 -11.8
JoshuaTree 1239.0 34.1 -116.4 37831.0 -10.8 -1.5
Sequoia 1902.0 36.6 -118.8 37831.0 3.0 0.9
Sequoia 1902.0 36.6 -118.8 37838.0 -32.7 -5.1
Olympic 182.0 47.9 -123.9 37845.0 -44.6 -6.2
Pinnacles 317.0 36.5 -121.2 37859.0 -44.8 -5.5
JoshuaTree 1239.0 34.1 -116.4 37859.0 -31.4 -5.0
JoshuaTree 1239.0 34.1 -116.4 37873.0 -50.1 -7.4
Olympic 182.0 47.9 -123.9 37873.0 -59.0 -8.1
Olympic 182.0 47.9 -123.9 37880.0 -40.5 -6.1
Olympic 182.0 47.9 -123.9 37887.0 -30.5 -5.2
Olympic 182.0 47.9 -123.9 37903.0 -51.5 -8.1
Olympic 182.0 47.9 -123.9 37915.0 -41.1 -6.2
Olympic 182.0 47.9 -123.9 37922.0 -37.6 -6.5
Pinnacles 317.0 36.5 -121.2 37929.0 -51.5 -9.0
Olympic 182.0 47.9 -123.9 37929.0 -28.5 -4.9
Sequoia 1902.0 36.6 -118.8 37930.0 -78.1 -12.5
Pinnacles 317.0 36.5 -121.2 37936.0 -35.8 -6.4
Olympic 182.0 47.9 -123.9 37936.0 -59.1 -8.6
Sequoia 1902.0 36.6 -118.8 37937.0 -73.8 -11.5
Pinnacles 317.0 36.5 -121.2 37943.0 -5.1 -3.1
JoshuaTree 1239.0 34.1 -116.4 37943.0 -95.9 -14.1
Sequoia 1902.0 36.6 -118.8 37943.0 -150.5 -20.6
DeathValley 125.0 36.6 -117.0 37943.0 -93.8 -12.3
Olympic 182.0 47.9 -123.9 37943.0 -49.6 -7.8
Olympic 182.0 47.9 -123.9 37950.0 -65.2 -9.9
Olympic 182.0 47.9 -123.9 37957.0 -102.9 -13.5
Pinnacles 317.0 36.5 -121.2 37964.0 -20.6 -3.0
SUPPLEMENTARY DATA TABLE 63
Olympic 182.0 47.9 -123.9 37964.0 -79.2 -11.2
Pinnacles 317.0 36.5 -121.2 37971.0 -33.1 -5.9
Olympic 182.0 47.9 -123.9 37977.0 -69.6 -10.0
Pinnacles 317.0 36.5 -121.2 37978.0 -28.6 -3.8
Pinnacles 317.0 36.5 -121.2 37985.0 -60.2 -9.7
JoshuaTree 1239.0 34.1 -116.4 37985.0 -53.3 -7.9
Pinnacles 317.0 36.5 -121.2 37992.0 -54.6 -8.2
Pinnacles 317.0 36.5 -121.2 38013.0 -51.1 -7.5
JoshuaTree 1239.0 34.1 -116.4 38020.0 -98.3 -13.6
Pinnacles 317.0 36.5 -121.2 38020.0 -48.6 -7.7
Sequoia 1902.0 36.6 -118.8 38020.0 -92.2 -13.0
Pinnacles 317.0 36.5 -121.2 38027.0 -43.9 -6.8
Sequoia 1902.0 36.6 -118.8 38034.0 -81.9 -11.5
Pinnacles 317.0 36.5 -121.2 38034.0 -24.2 -2.7
JoshuaTree 1239.0 34.1 -116.4 38041.0 -111.9 -15.3
DeathValley 125.0 36.6 -117.0 38041.0 -111.2 -14.7
Sequoia 1902.0 36.6 -118.8 38041.0 -108.5 -15.1
Pinnacles 317.0 36.5 -121.2 38041.0 -78.7 -10.7
JoshuaTree 1239.0 34.1 -116.4 38048.0 -69.5 -10.1
Pinnacles 317.0 36.5 -121.2 38048.0 -48.7 -7.4
Sequoia 1902.0 36.6 -118.8 38048.0 -80.0 -11.8
JoshuaTree 1239.0 34.1 -116.4 38055.0 -104.2 -15.0
Sequoia 1902.0 36.6 -118.8 38076.0 -101.3 -13.3
Pinnacles 317.0 36.5 -121.2 38076.0 -38.7 -5.3
DeathValley 125.0 36.6 -117.0 38083.0 -74.3 -8.1
Sequoia 1902.0 36.6 -118.8 38097.0 -56.2 -8.8
DeathValley 125.0 36.6 -117.0 38160.0 -61.3 -3.6
DeathValley 125.0 36.6 -117.0 38244.0 -68.0 -7.7
Pinnacles 317.0 36.5 -121.2 38279.0 -46.3 -7.6
Sequoia 1902.0 36.6 -118.8 38279.0 -53.9 -8.8
Pinnacles 317.0 36.5 -121.2 38286.0 -47.0 -8.0
Sequoia 1902.0 36.6 -118.8 38286.0 -56.7 -9.7
JoshuaTree 1239.0 34.1 -116.4 38286.0 -50.0 -7.7
JoshuaTree 1239.0 34.1 -116.4 38293.0 -92.9 -13.7
DeathValley 125.0 36.6 -117.0 38300.0 -82.8 -11.7
Sequoia 1902.0 36.6 -118.8 38300.0 -95.6 -14.1
JoshuaTree 1239.0 34.1 -116.4 38314.0 -87.4 -13.1
Sequoia 1902.0 36.6 -118.8 38321.0 -71.4 -10.3
Pinnacles 317.0 36.5 -121.2 38328.0 -65.3 -10.1
Sequoia 1902.0 36.6 -118.8 38328.0 -125.6 -17.3
JoshuaTree 1239.0 34.1 -116.4 38328.0 -51.2 -9.5
Sequoia 1902.0 36.6 -118.8 38335.0 -107.3 -15.1
Sequoia 1902.0 36.6 -118.8 38349.0 -165.0 -21.8
64 MAXBERKELHAMMER
JoshuaTree 1239.0 34.1 -116.4 38349.0 -38.2 -7.5
Pinnacles 317.0 36.5 -121.2 38349.0 -104.1 -14.7
JoshuaTree 1239.0 34.1 -116.4 38356.0 -60.9 -9.8
DeathValley 125.0 36.6 -117.0 38356.0 -88.1 -12.7
Pinnacles 317.0 36.5 -121.2 38356.0 -41.4 -7.4
Sequoia 1902.0 36.6 -118.8 38356.0 -87.8 -13.2
JoshuaTree 1239.0 34.1 -116.4 38363.0 -81.0 -11.3
DeathValley 125.0 36.6 -117.0 38363.0 -80.1 -10.3
Pinnacles 317.0 36.5 -121.2 38363.0 -45.1 -7.1
Sequoia 1902.0 36.6 -118.8 38363.0 -76.8 -11.6
Pinnacles 317.0 36.5 -121.2 38370.0 -50.6 -7.9
DeathValley 125.0 36.6 -117.0 38384.0 -65.1 -7.7
Pinnacles 317.0 36.5 -121.2 38384.0 -19.0 -4.0
Sequoia 1902.0 36.6 -118.8 38384.0 -64.7 -10.0
JoshuaTree 1239.0 34.1 -116.4 38384.0 -35.6 -6.0
Sequoia 1902.0 36.6 -118.8 38391.0 -36.3 -6.4
DeathValley 125.0 36.6 -117.0 38398.0 -135.7 -16.2
Sequoia 1902.0 36.6 -118.8 38398.0 -134.7 -17.9
Pinnacles 317.0 36.5 -121.2 38398.0 -69.3 -10.1
JoshuaTree 1239.0 34.1 -116.4 38398.0 -154.2 -20.4
DeathValley 125.0 36.6 -117.0 38405.0 -63.4 -8.4
Pinnacles 317.0 36.5 -121.2 38405.0 -61.9 -9.1
Sequoia 1902.0 36.6 -118.8 38405.0 -111.3 -15.5
JoshuaTree 1239.0 34.1 -116.4 38405.0 -75.8 -10.9
DeathValley 125.0 36.6 -117.0 38412.0 -57.7 -7.2
Pinnacles 317.0 36.5 -121.2 38412.0 -46.1 -7.6
Sequoia 1902.0 36.6 -118.8 38412.0 -90.9 -12.9
JoshuaTree 1239.0 34.1 -116.4 38412.0 -101.2 -14.0
JoshuaTree 1239.0 34.1 -116.4 38419.0 -68.3 -10.2
Sequoia 1902.0 36.6 -118.8 38419.0 -87.9 -12.2
Pinnacles 317.0 36.5 -121.2 38419.0 -42.7 -6.3
Pinnacles 317.0 36.5 -121.2 38433.0 -16.5 -3.7
Sequoia 1902.0 36.6 -118.8 38433.0 -59.6 -9.3
Sequoia 1902.0 36.6 -118.8 38440.0 -81.3 -11.6
JoshuaTree 1239.0 34.1 -116.4 38440.0 -50.3 -7.9
Pinnacles 317.0 36.5 -121.2 38440.0 -47.4 -7.0
Pinnacles 317.0 36.5 -121.2 38447.0 -45.1 -7.3
Sequoia 1902.0 36.6 -118.8 38447.0 -60.6 -9.7
Pinnacles 317.0 36.5 -121.2 38454.0 -10.4 -2.3
Sequoia 1902.0 36.6 -118.8 38454.0 -51.9 -8.1
Sequoia 1902.0 36.6 -118.8 38468.0 -44.0 -7.5
Pinnacles 317.0 36.5 -121.2 38468.0 -13.1 -3.3
JoshuaTree 1239.0 34.1 -116.4 38475.0 -62.6 -9.4
SUPPLEMENTARY DATA TABLE 65
Pinnacles 317.0 36.5 -121.2 38475.0 -44.9 -7.4
Sequoia 1902.0 36.6 -118.8 38475.0 -73.1 -11.2
Pinnacles 317.0 36.5 -121.2 38482.0 -25.8 -4.3
Sequoia 1902.0 36.6 -118.8 38482.0 -83.6 -12.2
Sequoia 1902.0 36.6 -118.8 38489.0 -76.8 -10.7
JoshuaTree 1239.0 34.1 -116.4 38559.0 -96.3 -13.1
JoshuaTree 1239.0 34.1 -116.4 38573.0 -45.3 -6.7
Sequoia 1902.0 36.6 -118.8 38580.0 -41.4 -6.0
JoshuaTree 1239.0 34.1 -116.4 38580.0 -47.1 -7.6
Sequoia 1902.0 36.6 -118.8 38622.0 -45.4 -6.9
JoshuaTree 1239.0 34.1 -116.4 38643.0 -46.7 -8.4
Sequoia 1902.0 36.6 -118.8 38651.0 -63.8 -9.7
Sequoia 1902.0 36.6 -118.8 38657.0 -31.9 -5.2
Pinnacles 317.0 36.5 -121.2 38671.0 -48.8 -7.4
Sequoia 1902.0 36.6 -118.8 38671.0 -67.5 -10.3
Sequoia 1902.0 36.6 -118.8 38685.0 -60.5 -8.7
Pinnacles 317.0 36.5 -121.2 38685.0 -20.0 -2.9
Sequoia 1902.0 36.6 -118.8 38692.0 -86.1 -12.5
Pinnacles 317.0 36.5 -121.2 38692.0 -49.1 -7.1
Sequoia 1902.0 36.6 -118.8 38699.0 -84.3 -11.4
Sequoia 1902.0 36.6 -118.8 38706.0 -64.5 -10.6
Pinnacles 317.0 36.5 -121.2 38706.0 -31.8 -6.2
Pinnacles 317.0 36.5 -121.2 38713.0 -41.8 -6.6
Sequoia 1902.0 36.6 -118.8 38713.0 -99.2 -14.3
TABLE3. IsotopiccompositionofAlmagreMt. Cellulose
Date(numeric) Type SampleID Beamheight Weight d18O dD
39624.6 IAEA 1.0 9.4 0.2 30.8
39604.4 IAEA 1.0 7.4 0.2 30.9
39604.4 IAEA 2.0 7.5 0.2 31.8
39604.4 IAEA 3.0 8.9 0.2 31.9
39604.5 IAEA 4.0 6.2 0.2 31.4
39604.5 24b 2007.0 7.8 0.2 27.3
39604.5 24b 2006.0 8.0 0.2 29.9
39604.5 24b 2004.0 7.0 0.2 32.9
39604.5 24b 2004.0 6.5 0.2 29.0
39604.6 24b 2003.0 7.6 0.2 32.9
39604.6 24b 2002.0 7.0 0.2 34.6
39604.6 24b 2001.0 6.9 0.2 32.5
39604.6 24b 2001.0 7.2 0.2 32.4
39604.6 24b 2000.0 7.8 0.2 30.6
66 MAXBERKELHAMMER
39604.7 24b 1999.0 6.7 0.2 29.6
39604.7 24b 1998.0 7.4 0.2 30.7
39604.7 24b 1998.0 6.0 0.2 31.6
39604.7 IAEA 5.0 7.0 0.0 32.4
39604.7 IAEA 6.0 7.9 0.0 32.7
39604.7 IAEA 7.0 6.9 0.0 33.2
39604.8 24b 1997.0 7.0 0.2 28.9
39604.8 24b 1996.0 8.6 0.2 31.5
39604.8 24b 1996.0 7.6 0.2 31.7
39604.8 24b 1995.0 8.2 0.2 29.9
39604.8 24b 1994.0 8.7 0.2 31.1
39604.9 24b 1993.0 8.5 0.2 31.2
39604.9 24b 1993.0 7.5 0.2 31.2
39604.9 24b 1992.0 6.5 0.2 32.3
39604.9 24b 2005.0 9.2 0.3 31.2
39604.9 24b 2005.0 7.9 0.2 32.8
39605.0 24b 1991.0 9.4 0.3 30.6
39605.0 24b 1990.0 8.0 0.2 30.9
39605.0 IAEA 8.0 7.7 0.2 32.0
39605.0 IAEA 9.0 8.0 0.2 33.5
39605.0 IAEA 10.0 8.7 0.2 31.7
39605.0 24b 1989.0 8.7 0.3 30.9
39605.1 24b 1988.0 7.5 0.2 31.2
39605.1 24b 1987.0 8.8 0.2 30.2
39605.1 24b 1987.0 8.8 0.2 29.5
39605.1 24b 1986.0 8.5 0.2 29.7
39605.1 IAEA 11.0 8.9 0.2 32.6
39605.2 IAEA 12.0 8.5 0.2 31.5
39605.2 IAEA 13.0 8.1 0.2 31.3
39608.7 IAEA 1.0 17.0 - 34.8
39608.7 IAEA 2.0 9.0 - 33.5
39608.7 IAEA 3.0 8.0 - 32.4
39608.7 IAEA 4.0 8.4 - 32.5
39608.8 IAEA 5.0 10.0 - 32.7
39608.8 IAEA 6.0 8.4 0.2 31.8
39608.8 IAEA 7.0 8.7 0.2 30.4
39608.8 IAEA 8.0 9.6 0.2 32.9
39608.8 IAEA 9.0 9.2 0.2 32.3
39608.9 IAEA 10.0 9.8 0.2 33.1
39608.9 IAEA 11.0 10.0 0.2 31.4
39608.9 Alm1 1986.0 10.4 0.2 31.5
39608.9 Alm1 1985.0 9.9 0.2 30.5
39608.9 Alm1 1984.0 8.6 0.2 31.8
SUPPLEMENTARY DATA TABLE 67
39608.9 Alm1 1983.0 10.6 0.2 28.8
39609.0 Alm1 1982.0 11.3 0.2 33.2
39609.0 Alm1 1982.0 9.8 0.2 31.7
39609.0 Alm1 1981.0 10.6 0.2 34.3
39609.0 Alm1 1980.0 9.3 0.2 28.9
39609.0 Alm1 1980.0 10.8 0.2 33.1
39609.1 Alm1 1979.0 8.3 0.2 29.7
39609.1 Alm1 1978.0 8.2 0.2 34.2
39609.1 Alm1 1977.0 11.3 0.3 34.0
39609.1 IAEA 12.0 10.6 0.2 33.9
39609.1 IAEA 13.0 9.1 0.2 34.1
39609.2 IAEA 14.0 10.4 0.2 33.9
39609.2 Alm1 1976.0 11.6 0.3 32.5
39609.2 Alm1 1976.0 10.8 0.2 32.1
39609.2 Alm1 1975.0 9.4 0.2 30.7
39609.2 Alm1 1974.0 9.9 0.2 31.3
39609.3 Alm1 1973.0 11.3 0.3 32.9
39609.3 Alm1 1973.0 8.5 0.2 34.0
39609.3 Alm1 1972.0 11.4 0.3 32.3
39609.3 Alm1 1971.0 7.0 0.2 32.0
39609.3 Alm1 1971.0 8.1 0.2 29.6
39609.3 Alm1 1970.0 8.3 0.2 30.1
39609.4 Alm1 1969.0 10.9 0.3 30.1
39609.4 IAEA 15.0 10.1 0.2 31.1
39609.4 IAEA 16.0 8.4 0.2 28.1
39609.4 IAEA 17.0 9.7 0.2 31.4
39609.5 Sucrose 1.0 9.0 0.2 34.5
39624.6 IAEA 2.0 8.1 0.2 32.4
39624.6 IAEA 3.0 8.1 0.2 32.3
39624.6 BenE 1.0 8.0 0.3 72.1
39624.6 BenE 2.0 7.9 0.3 70.5
39624.6 BenD 1.0 8.5 0.3 22.1
39624.7 BenD 2.0 8.0 0.3 22.3
39624.7 BenD 3.0 8.5 0.4 23.1
39624.7 IAEA 4.0 7.2 0.2 32.1
39624.7 IAEA 5.0 7.6 0.2 32.2
39624.7 IAEA 6.0 8.2 0.2 33.7
39624.7 Alm1 1957.0 8.3 0.2 32.5
39624.7 Alm1 1952.0 9.0 0.2 33.8
39624.8 Alm1 1938.0 9.5 0.2 32.8
39624.8 Alm1 1936.0 7.2 0.2 33.3
39624.8 Alm1 1933.0 7.4 0.2 34.8
39624.8 Alm1 1930.0 9.6 0.2 31.9
68 MAXBERKELHAMMER
39624.8 Alm1 1926.0 7.2 0.2 33.6
39624.8 Alm1 1924.0 9.2 0.2 33.8
39624.8 Alm1 2004.0 8.2 0.2 32.2
39624.9 Alm1 2005.0 7.0 0.2 31.7
39624.9 Alm1 1987.0 7.3 0.2 29.8
39624.9 Alm1 1998.0 8.3 0.2 30.6
39624.9 IAEA 7.0 8.4 0.2 32.6
39624.9 IAEA 8.0 7.3 0.2 31.3
39624.9 IAEA 9.0 9.2 0.2 33.2
39624.9 Alm1 2007.0 8.1 0.2 30.0
39624.9 Alm1 2002.0 8.9 0.2 31.9
39625.0 Alm1 1986.0 7.8 0.2 29.4
39625.0 Alm1 1983.0 8.8 0.2 30.6
39625.0 Alm1 1982.0 8.6 0.2 29.1
39625.0 Alm1 1981.0 7.6 0.2 29.7
39625.0 Alm1 1979.0 7.4 0.2 31.9
39625.0 Alm1 1977.0 7.2 0.2 32.4
39625.0 Alm1 1973.0 8.0 0.2 32.0
39625.1 Alm1 1971.0 9.0 0.2 32.2
39625.1 Alm1 1971.0 9.1 0.2 32.4
39625.1 Alm1 1968.0 8.7 0.2 31.5
39625.1 IAEA 10.0 9.2 0.2 32.6
39625.1 BenD 4.0 9.1 0.4 20.2
39625.1 BenD 5.0 9.0 0.4 23.5
39625.2 BenD 6.0 8.5 0.3 23.3
39629.4 IAEA 1.0 7.9 0.2 31.9
39629.4 IAEA 2.0 8.6 0.2 31.3
39629.5 IAEA 3.0 8.1 0.2 32.9
39629.5 IAEA 4.0 6.1 0.1 30.6
39629.5 IAEA 5.0 7.4 0.2 31.2
39629.5 IAEA 6.0 8.2 0.2 31.4
39629.5 Sucrose 1.0 6.6 0.2 34.8
39629.5 IAEA 7.0 7.7 0.2 30.6
39629.5 Sucrose 2.0 7.6 0.2 35.9
39629.6 Sucrose 3.0 7.8 0.2 36.2
39629.6 Sucrose 4.0 6.4 0.1 35.1
39629.6 IAEA 8.0 7.7 0.2 31.1
39629.6 IAEA 9.0 7.7 0.2 31.7
39629.6 IAEA 10.0 7.8 0.2 31.0
39629.6 Alm1 1982.0 6.7 0.2 29.2
39629.6 Alm1 1981.0 6.6 0.2 30.1
39629.6 Alm1 1980.0 7.9 0.2 31.8
39629.7 Alm1 1979.0 6.9 0.2 31.9
SUPPLEMENTARY DATA TABLE 69
39629.7 Alm1 1978.0 7.6 0.2 31.9
39629.7 Alm1 1918.0 7.3 0.2 32.3
39629.7 Alm1 1918.0 6.8 0.2 32.7
39629.7 Alm1 1917.0 7.2 0.2 33.5
39629.7 Alm1 1916.0 7.4 0.2 33.7
39629.7 Alm1 1916.0 8.1 0.2 34.1
39629.8 Alm1 1915.0 7.7 0.2 32.7
39629.8 Alm1 1914.0 8.2 0.2 33.3
39629.8 IAEA 11.0 6.6 0.2 31.1
39629.8 IAEA 12.0 6.7 0.2 32.9
39629.8 IAEA 13.0 7.1 0.2 32.3
39629.8 IAEA 14.0 8.4 0.2 31.8
39629.8 Alm1 1913.0 8.0 0.2 34.9
39629.9 Alm1 1912.0 7.2 0.2 31.9
39629.9 Alm1 1911.0 8.1 0.2 32.1
39629.9 Alm1 1910.0 8.0 0.2 33.6
39629.9 Alm1 1910.0 8.1 0.2 33.0
39629.9 Alm1 1909.0 8.0 0.2 32.4
39629.9 Alm1 1908.0 7.9 0.2 32.4
39629.9 Alm1 1908.0 7.8 0.2 32.2
39630.0 Alm1 1907.0 6.7 0.2 32.4
39630.0 Alm1 1906.0 6.4 0.2 31.3
39630.0 Alm1 1905.0 6.5 0.2 33.0
39630.0 Alm1 1904.0 8.3 0.2 33.3
39630.0 IAEA 15.0 8.2 0.2 32.8
39630.0 IAEA 16.0 7.5 0.2 33.5
39630.1 IAEA 17.0 7.7 0.2 31.9
39630.1 BenD 1.0 5.5 0.3 20.8
39630.1 BenD 2.0 6.8 0.3 23.7
39630.1 BenD 3.0 7.3 0.3 23.3
39630.2 Alm1 1980.0 6.7 0.2 30.9
39630.2 Alm1 1982.0 7.0 0.2 29.6
39630.2 Alm1 1914.0 6.8 0.2 32.4
39630.2 Alm1 1914.0 6.1 0.2 31.0
39630.2 Alm1 1913.0 6.7 0.2 34.0
39630.2 Alm1 1912.0 6.0 0.2 31.3
39630.2 Alm1 1912.0 7.0 0.2 31.0
39630.3 Alm1 1911.0 7.4 0.2 31.7
39630.3 Alm1 1910.0 7.1 0.2 32.5
39630.3 Alm1 1910.0 6.8 0.2 32.6
39630.3 IAEA 18.0 6.5 0.2 31.6
39630.3 IAEA 19.0 6.3 0.2 32.1
39630.3 IAEA 20.0 6.5 0.2 31.9
70 MAXBERKELHAMMER
39630.4 Alm1 1909.0 7.5 0.2 32.1
39630.4 Alm1 1908.0 7.0 0.2 31.9
39630.4 Alm1 1908.0 7.1 0.2 32.1
39630.4 Alm1 1907.0 7.8 0.2 34.3
39630.4 Alm1 1906.0 7.7 0.2 32.1
39630.4 Alm1 1906.0 7.9 0.2 33.0
39630.4 Alm1 1905.0 7.5 0.2 34.0
39630.5 Alm1 1904.0 7.0 0.2 33.2
39630.5 Alm1 1899.0 6.8 0.2 34.1
39630.5 Alm1 1899.0 7.3 0.2 35.0
39630.5 Alm1 1898.0 8.2 0.2 33.1
39630.5 Alm1 1897.0 6.4 0.2 33.9
39630.5 IAEA 21.0 6.6 0.2 31.7
39630.5 IAEA 22.0 6.4 0.2 30.9
39630.6 IAEA 23.0 6.4 0.2 32.5
39630.6 Sucrose 5.0 8.4 0.2 38.0
39630.6 Sucrose 6.0 8.6 0.2 36.9
39630.6 Sucrose 7.0 9.3 0.2 37.5
39630.6 Alm1 1906.0 6.5 0.2 32.1
39630.7 Alm1 1906.0 7.2 0.2 32.6
39630.7 Alm1 1914.0 6.7 0.2 34.1
39630.7 Alm1 1905.0 8.4 0.2 31.3
39630.8 Alm1 1913.0 7.1 0.2 33.6
39630.8 Alm1 1913.0 7.1 0.2 34.8
39630.8 Alm1 1903.0 6.8 0.2 34.9
39630.8 Alm1 1903.0 6.1 0.2 33.9
39630.8 Alm1 1902.0 6.9 0.2 35.7
39630.9 Alm1 1899.0 7.3 0.2 35.1
39630.9 IAEA 24.0 5.8 0.2 33.5
39630.9 IAEA 25.0 6.2 0.2 33.4
39630.9 IAEA 26.0 7.6 0.2 32.7
39630.9 IAEA 27.0 7.3 0.2 33.4
39630.9 BenD 4.0 6.8 0.4 24.3
39631.0 BenD 5.0 9.7 0.5 25.2
39631.0 BenD 6.0 6.8 0.3 22.4
39631.0 BenE 1.0 7.4 0.2 65.0
39631.0 Sucrose 8.0 8.6 0.2 38.1
39631.6 IAEA 1.0 8.0 0.2 34.3
39631.7 IAEA 2.0 6.7 0.2 33.8
39631.7 IAEA 3.0 7.5 0.2 32.5
39631.7 IAEA 4.0 7.4 0.2 32.5
39631.7 IAEA 5.0 6.3 0.2 33.0
39631.7 IAEA 6.0 6.9 0.2 32.1
SUPPLEMENTARY DATA TABLE 71
39631.7 IAEA 7.0 6.6 0.2 32.3
39631.8 IAEA 8.0 6.9 0.2 32.5
39631.8 IAEA 9.0 5.8 0.2 32.6
39631.8 IAEA 10.0 6.6 0.2 31.4
39631.8 Alm1 1903.0 7.0 0.2 33.6
39631.8 Alm1 1902.0 7.7 0.2 34.4
39631.8 Alm1 1901.0 6.4 0.2 32.8
39631.9 Alm1 1901.0 6.1 0.2 33.7
39631.9 Alm1 1900.0 7.1 0.2 34.7
39631.9 Alm1 1897.0 7.1 0.2 34.5
39632.0 IAEA 11.0 7.4 0.2 32.0
39632.0 IAEA 12.0 6.6 0.2 31.3
39632.0 IAEA 13.0 6.8 0.2 32.1
39632.0 Sucrose 1.0 8.2 0.2 36.3
39632.1 Sucrose 2.0 7.2 0.2 36.6
39632.1 Sucrose 3.0 8.2 0.2 36.3
39632.1 Alm1 1896.0 6.8 0.2 33.9
39632.1 Alm1 1896.0 7.2 0.2 34.4
39632.2 Alm1 1895.0 7.4 0.2 31.8
39632.2 Alm1 1894.0 6.9 0.2 33.3
39632.2 Alm1 1894.0 6.8 0.2 34.7
39632.2 Alm1 1893.0 7.5 0.2 35.0
39632.3 Alm1 1893.0 7.1 0.2 35.2
39632.3 Alm1 1892.0 7.2 0.2 33.9
39632.3 Alm1 1891.0 7.5 0.2 33.0
39632.3 Alm1 1890.0 8.1 0.2 33.5
39632.4 Alm1 1890.0 8.3 0.2 33.6
39632.4 Alm1 1889.0 6.9 0.2 33.4
39632.4 IAEA 14.0 6.7 0.2 34.7
39632.4 IAEA 15.0 7.2 0.2 32.0
39632.5 IAEA 16.0 7.6 0.2 34.9
39632.5 BenD 1.0 10.5 0.5 22.1
39632.5 BenD 2.0 7.0 0.3 23.4
39632.6 IAEA 1.0 6.4 0.3 30.9
39632.6 IAEA 2.0 8.1 0.3 32.7
39632.6 Alm1 1888.0 7.5 0.2 33.6
39632.6 Alm1 1888.0 6.8 0.2 33.7
39632.6 BenD 3.0 8.6 0.2 23.2
39632.6 BenD 4.0 8.7 0.2 23.5
39632.7 BenD 5.0 8.0 0.2 22.7
39632.7 Alm1 1887.0 7.1 0.2 32.5
39632.7 Alm1 1886.0 7.4 0.2 33.9
39632.7 Alm1 1886.0 7.1 0.2 34.7
72 MAXBERKELHAMMER
39632.7 Alm1 1885.0 7.2 0.2 33.5
39632.7 Alm1 1884.0 6.4 0.2 32.0
39632.8 Alm1 1884.0 6.2 0.2 34.0
39632.8 Alm1 1883.0 7.0 0.2 33.4
39632.8 Alm1 1882.0 7.0 0.2 33.1
39632.8 Alm1 1881.0 8.3 0.2 33.4
39632.8 Alm1 1881.0 8.1 0.2 34.2
39632.8 IAEA 3.0 6.7 0.2 31.4
39632.9 IAEA 4.0 6.2 0.2 31.3
39632.9 IAEA 5.0 7.1 0.2 32.7
39632.9 Alm1 1880.0 6.2 0.2 33.7
39632.9 Alm1 1879.0 6.1 0.2 32.6
39632.9 Alm1 1879.0 6.7 0.2 31.2
39632.9 Alm1 1878.0 6.7 0.2 33.8
39633.0 Alm1 1877.0 5.6 0.2 34.6
39633.0 Alm1 1877.0 5.9 0.2 34.2
39633.0 Alm1 1876.0 6.1 0.2 32.7
39633.0 Alm1 1875.0 7.2 0.2 32.9
39633.0 Alm1 1875.0 6.2 0.2 31.6
39633.0 Alm1 1874.0 6.2 0.2 32.6
39633.1 Alm1 1873.0 7.3 0.2 32.9
39633.1 IAEA 6.0 6.7 0.2 31.0
39633.1 IAEA 7.0 7.8 0.2 31.4
39633.1 IAEA 8.0 6.7 0.2 32.2
39633.1 IAEA 9.0 7.5 0.2 32.8
39645.6 BenD 1.0 7.9 0.3 22.7
39645.6 BenD 2.0 9.4 0.3 23.9
39645.6 BenD 4.0 8.5 0.3 23.6
39645.7 BenD 5.0 9.2 0.4 23.5
39645.7 BenD 6.0 8.5 0.3 22.6
39645.7 BenD 7.0 8.0 0.3 22.8
39645.7 Sucrose 1.0 8.9 0.2 36.3
39645.7 Sucrose 2.0 8.4 0.2 36.4
39645.7 Sucrose 3.0 7.0 0.2 35.7
39645.7 Sucrose 4.0 9.0 0.2 36.8
39645.8 Sucrose 5.0 8.3 0.2 36.4
39645.8 Sucrose 6.0 7.7 0.2 34.0
39645.8 IAEA 1.0 8.8 0.2 33.0
39645.8 IAEA 2.0 9.1 0.2 32.8
39645.8 IAEA 3.0 7.1 0.2 32.5
39645.8 IAEA 4.0 8.3 0.2 33.0
39645.9 IAEA 5.0 9.1 0.2 32.1
39645.9 IAEA 6.0 8.5 0.2 31.9
SUPPLEMENTARY DATA TABLE 73
39645.9 IAEA 7.0 8.0 0.2 33.3
39645.9 IAEA 8.0 7.6 0.2 32.0
39645.9 IAEA 9.0 7.8 0.2 32.4
39645.9 IAEA 10.0 7.9 0.2 32.4
39646.0 IAEA 11.0 7.6 0.2 33.2
39646.0 IAEA 12.0 7.6 0.2 32.4
39646.0 IAEA 13.0 8.0 0.2 32.2
39646.0 IAEA 14.0 8.9 0.2 31.9
39646.0 IAEA 15.0 7.9 0.2 31.8
39646.0 IAEA 16.0 8.7 0.2 31.9
39646.1 IAEA 17.0 8.0 0.2 32.0
39646.1 IAEA 18.0 9.1 0.2 31.3
39646.1 IAEA 19.0 8.9 0.2 31.9
39646.1 IAEA 20.0 7.6 0.2 31.7
39646.1 Alm1 1872.0 9.0 0.2 31.8
39646.1 Alm1 1871.0 8.8 0.2 34.7
39646.2 Alm1 1871.0 6.9 0.2 35.6
39646.2 Alm1 1870.0 7.7 0.2 32.7
39646.2 Alm1 1870.0 7.3 0.2 32.7
39646.2 Alm1 1869.0 7.6 0.2 31.2
39646.2 Alm1 1868.0 7.7 0.2 28.8
39646.2 Alm1 1868.0 8.2 0.2 29.8
39646.3 Alm1 1867.0 7.6 0.2 31.3
39646.3 Alm1 1866.0 7.6 0.2 28.8
39646.3 Alm1 1866.0 8.3 0.2 30.5
39646.3 Alm1 1865.0 8.1 0.2 30.4
39646.5 IAEA 8.0 8.3 0.2 32.1
39646.5 IAEA 9.0 7.6 0.2 32.4
39646.5 IAEA 10.0 7.3 0.2 32.4
39646.5 IAEA 11.0 8.3 0.2 32.2
39646.6 IAEA 12.0 7.4 0.2 32.1
39646.6 IAEA 13.0 7.5 0.2 31.9
39646.6 BenD 1.0 7.7 0.4 23.9
39646.6 BenD 2.0 8.7 0.3 22.5
39646.6 BenD 3.0 8.7 0.3 23.5
39646.6 BenD 4.0 8.0 0.3 19.7
39646.7 IAEA 2.0 7.8 0.2 32.1
39646.7 IAEA 3.0 7.5 0.2 31.9
39646.7 IAEA 4.0 7.9 0.2 32.0
39646.7 Alm1 1863.0 7.0 0.2 33.8
39646.7 Alm1 1863.0 8.0 0.2 34.3
39646.7 Alm1 1862.0 7.2 0.2 33.3
39646.8 Alm1 1860.0 7.3 0.2 30.6
74 MAXBERKELHAMMER
39646.8 Alm1 1860.0 8.5 0.2 33.9
39646.8 Alm1 1859.0 7.7 0.2 33.7
39646.8 Alm1 1858.0 7.2 0.2 31.3
39646.8 Alm2 2007.0 7.4 0.2 28.7
39646.9 Alm2 2006.0 6.8 0.2 30.3
39646.9 Alm2 2005.0 7.5 0.2 30.2
39646.9 IAEA 14.0 7.5 0.2 29.8
39646.9 Alm2 2005.0 7.4 0.2 29.8
39647.0 Alm2 2001.0 6.8 0.2 30.2
39647.0 Alm2 2000.0 8.1 0.2 31.5
39647.1 Alm2 1999.0 8.3 0.2 30.9
39647.1 Alm2 1999.0 7.9 0.2 30.5
39647.1 Alm2 1998.0 8.2 0.2 29.8
39647.1 Alm2 1998.0 6.5 0.2 29.8
39647.1 Alm2 1997.0 6.9 0.2 30.9
39647.1 IAEA 15.0 7.6 0.2 32.0
39647.2 IAEA 16.0 7.0 0.2 31.3
39647.2 IAEA 17.0 8.0 0.2 32.4
39647.2 Alm2 1996.0 7.2 0.2 30.7
39647.2 Alm2 1995.0 7.7 0.2 31.3
39647.2 Alm2 1995.0 7.7 0.2 31.5
39647.3 Alm1 1965.0 6.9 0.2 31.8
39647.3 Alm1 1962.0 7.0 0.2 31.7
39647.3 Alm1 1959.0 7.8 0.2 32.7
39647.3 Alm1 1957.0 7.1 0.2 32.2
39647.3 Alm1 1954.0 8.3 0.2 33.7
39647.3 Alm1 1951.0 8.1 0.2 32.2
39647.4 Alm1 1948.0 8.0 0.2 32.4
39647.4 Alm1 1945.0 7.8 0.2 31.2
39647.4 Alm1 1942.0 8.2 0.2 27.9
39647.4 Sucrose 1.0 8.1 0.2 31.9
39647.4 Sucrose 2.0 8.2 0.2 34.6
39647.5 Sucrose 3.0 7.7 0.2 36.3
39647.5 IAEA 18.0 7.4 0.2 31.9
39647.5 IAEA 19.0 8.1 0.2 32.3
39647.5 Alm1 1939.0 8.0 0.2 32.3
39647.5 Alm1 1936.0 6.9 0.2 33.6
39647.5 Alm2 1994.0 7.0 0.2 31.2
39647.6 Alm2 1993.0 7.4 0.2 30.9
39647.6 IAEA 20.0 8.0 0.2 33.1
39647.6 IAEA 21.0 7.1 0.2 32.9
39647.6 Sucrose 4.0 8.4 0.2 36.9
39647.6 Sucrose 5.0 8.2 0.2 36.2
SUPPLEMENTARY DATA TABLE 75
39647.7 BenD 4.0 8.6 0.4 23.0
39647.7 BenD 5.0 8.5 0.4 23.2
39647.7 IAEA 22.0 7.4 0.2 31.0
39647.7 IAEA 23.0 6.9 0.2 31.3
39647.8 IAEA 1.0 7.0 0.2 31.7
39647.8 IAEA 2.0 7.8 0.2 28.3
39647.8 IAEA 3.0 7.7 0.2 33.1
39647.8 IAEA 4.0 8.2 0.2 32.3
39647.8 IAEA 5.0 7.1 0.2 32.5
39647.8 IAEA 6.0 6.8 0.2 33.1
39647.9 IAEA 7.0 8.4 0.2 32.2
39647.9 IAEA 8.0 6.0 0.2 31.9
39647.9 Alm2 1993.0 6.8 0.2 32.2
39647.9 Alm2 1992.0 7.5 0.2 29.7
39647.9 Alm2 1991.0 7.2 0.2 29.2
39648.0 Alm2 1991.0 7.1 0.2 32.5
39648.0 Alm2 1990.0 7.1 0.2 28.0
39648.0 Alm2 1989.0 8.2 0.2 31.1
39648.0 Alm2 1989.0 7.2 0.2 31.0
39648.0 Alm2 1988.0 7.0 0.2 30.0
39648.0 Alm2 1987.0 6.8 0.2 30.8
39648.1 Alm2 1987.0 6.9 0.2 30.7
39648.1 Alm2 1986.0 8.2 0.2 30.4
39648.1 Alm2 1985.0 7.0 0.2 32.2
39648.1 IAEA 9.0 6.8 0.2 33.5
39648.1 IAEA 10.0 7.5 0.2 31.6
39648.1 IAEA 11.0 7.1 0.2 29.3
39648.2 Alm2 1984.0 6.9 0.2 31.2
39648.2 Alm2 1983.0 7.3 0.2 30.3
39648.2 Alm2 1983.0 7.0 0.2 29.8
39648.2 Alm2 1982.0 6.4 0.2 30.6
39648.2 Alm2 1981.0 6.7 0.2 31.0
39648.2 Alm2 1980.0 6.3 0.2 30.7
39648.3 Alm2 1980.0 7.7 0.2 29.2
39648.3 Alm2 1979.0 6.5 0.2 30.1
39648.3 Alm2 1979.0 7.1 0.2 32.0
39648.3 Alm2 1978.0 7.9 0.2 33.7
39648.3 Alm2 1977.0 7.4 0.2 30.8
39648.3 Alm2 1977.0 8.0 0.2 32.7
39648.4 IAEA 12.0 7.7 0.2 31.3
39648.4 IAEA 13.0 8.6 0.2 22.6
39648.4 BenD 1.0 8.3 0.4 23.1
39648.4 BenD 2.0 8.8 0.3 23.4
76 MAXBERKELHAMMER
39648.5 Alm2 1976.0 6.9 0.2 29.7
39723.5 IAEA 1.0 8.6 0.2 31.9
39723.5 IAEA 2.0 8.4 0.2 32.4
39723.5 IAEA 3.0 9.0 0.2 32.5
39723.5 IAEA 4.0 9.2 0.2 32.2
39723.5 IAEA 5.0 9.2 0.2 32.3
39723.5 24b 1927.0 8.7 0.2 32.1
39723.6 24b 1928.0 8.6 0.2 31.4
39723.6 24b 1928.0 8.9 0.2 31.3
39723.6 24b 1929.0 8.6 0.2 32.2
39723.6 24b 1930.0 8.7 0.2 32.2
39723.6 24b 1930.0 8.7 0.2 31.4
39723.6 24b 1931.0 8.8 0.2 33.1
39723.7 24b 1932.0 8.9 0.2 34.3
39723.7 24b 1933.0 9.1 0.2 34.2
39723.7 24b 1933.0 8.7 0.2 34.4
39723.7 24b 1934.0 9.0 0.2 35.7
39723.7 24b 1935.0 8.6 0.2 33.6
39723.7 24b 1935.0 8.5 0.2 33.1
39723.8 24b 1936.0 8.7 0.2 33.3
39723.8 IAEA 6.0 8.3 0.2 31.6
39723.8 IAEA 7.0 8.9 0.2 32.2
39723.8 IAEA 8.0 8.8 0.2 32.0
39723.8 24b 1937.0 8.6 0.2 33.2
39723.9 24b 1938.0 8.3 0.2 32.9
39723.9 24b 1938.0 8.6 0.2 32.2
39723.9 24b 1939.0 8.5 0.2 32.9
39723.9 24b 1940.0 8.9 0.2 33.0
39723.9 24b 1941.0 8.6 0.2 31.9
39723.9 24b 1941.0 8.6 0.2 31.9
39724.0 24b 1942.0 8.7 0.2 31.4
39724.0 24b 1942.0 8.8 0.2 31.6
39724.0 24b 1943.0 8.3 0.2 33.7
39724.0 24b 1944.0 8.9 0.2 35.5
39724.0 24b 1945.0 8.9 0.2 34.1
39724.0 24b 1945.0 8.8 0.2 33.1
39724.1 IAEA 9.0 8.7 0.2 32.2
39724.1 IAEA 10.0 8.5 0.2 32.5
39724.1 IAEA 11.0 8.2 0.2 32.6
39724.1 24b 1946.0 8.3 0.2 33.6
39724.1 24b 1947.0 8.4 0.2 32.7
39724.1 24b 1947.0 8.4 0.2 32.3
39724.2 24b 1948.0 8.5 0.2 32.3
SUPPLEMENTARY DATA TABLE 77
39724.2 24b 1949.0 9.2 0.2 32.0
39724.2 24b 1949.0 8.3 0.2 32.3
39724.2 24b 1950.0 8.5 0.2 32.6
39724.2 24b 1951.0 8.4 0.2 32.5
39724.2 24b 1952.0 8.6 0.2 33.9
39724.3 24b 1953.0 8.2 0.2 32.8
39724.3 24b 1953.0 8.8 0.2 32.4
39724.3 24b 1954.0 8.7 0.2 34.5
39724.3 24b 1955.0 9.1 0.2 32.5
39724.3 24b 1956.0 9.1 0.2 34.4
39724.4 24b 1957.0 9.0 0.2 31.4
39724.4 24b 1959.0 9.0 0.2 33.1
39724.4 24b 1959.0 8.7 0.2 32.9
39724.4 IAEA 12.0 8.2 0.2 32.1
39724.4 IAEA 13.0 8.3 0.2 31.3
39724.4 IAEA 14.0 8.5 0.2 31.8
39724.5 Sucrose 1.0 8.8 0.2 35.5
39724.5 Sucrose 2.0 10.3 0.2 37.1
39724.5 Sucrose 3.0 10.3 0.2 37.4
39724.5 Sucrose 4.0 9.7 0.0 37.2
39724.5 IAEA 15.0 8.7 0.0 32.4
39724.5 24b 1960.0 6.8 0.2 28.2
39724.6 24b 1961.0 9.2 0.2 33.1
39724.6 24b 1962.0 9.1 0.2 33.0
39724.6 24b 1963.0 9.3 0.2 34.0
39724.6 24b 1964.0 8.8 0.2 32.2
39724.6 24b 1965.0 9.7 0.2 32.8
39724.6 24b 1966.0 8.9 0.2 33.6
39724.7 24b 1967.0 9.0 0.2 31.9
39724.7 24b 1967.0 9.0 0.2 32.0
39724.7 24b 1969.0 9.0 0.2 31.7
39724.7 24b 1969.0 9.3 0.2 31.3
39724.7 24b 1970.0 8.9 0.2 32.1
39724.7 24b 1970.0 8.9 0.2 32.6
39724.8 24b 1971.0 9.0 0.2 33.3
39724.8 IAEA 16.0 8.5 0.2 32.5
39724.8 IAEA 17.0 8.4 0.2 32.4
39724.8 IAEA 18.0 8.5 0.2 32.4
39724.8 24b 1972.0 8.8 0.2 32.2
39724.8 24b 1972.0 9.0 0.2 32.2
39724.9 24b 1973.0 8.9 0.2 30.8
39724.9 24b 1975.0 9.0 0.2 31.6
39724.9 24b 1975.0 8.4 0.2 31.1
78 MAXBERKELHAMMER
39724.9 24b 1976.0 9.1 0.2 32.8
39724.9 24b 1858.0 9.0 0.2 32.9
39725.0 24b 1859.0 8.7 0.2 33.6
39725.0 24b 1859.0 8.5 0.2 33.6
39725.0 24b 1860.0 8.6 0.2 34.0
39725.0 24b 1864.0 9.3 0.2 33.6
39725.0 24b 1870.0 8.7 0.2 34.2
39725.0 24b 1870.0 8.8 0.2 33.7
39725.1 24b 1923.0 9.0 0.2 32.1
39725.1 IAEA 19.0 9.4 0.2 32.9
39725.1 IAEA 20.0 8.8 0.2 32.2
39725.1 IAEA 21.0 9.0 0.2 32.6
39725.1 Sucrose 5.0 9.0 0.2 36.3
39725.1 Sucrose 6.0 8.6 0.2 36.2
39725.2 Intra1 90 7 9.8 0.2 33.2
39725.2 Intra1 90 7 9.3 0.2 33.4
39725.2 Intra1 90 6 9.1 0.2 31.7
39725.2 Intra1 90 5 8.9 0.2 33.0
39725.2 Intra1 90 4 9.4 0.2 34.9
39725.2 Intra1 90 4 10.0 0.2 35.0
39725.3 Intra1 90 3 8.9 0.3 36.2
39725.3 24b 1960.0 8.7 0.2 32.6
39725.3 24b 1960.0 8.9 0.2 32.8
39725.3 Intra1 90 2 8.7 0.2 35.1
39725.3 Intra1 90 1 9.1 0.2 32.6
39725.3 Intra1 90 1 9.0 0.2 34.3
39725.4 Intra1 89 7 9.1 0.2 33.4
39725.4 Intra1 89 6 9.2 0.2 32.6
39725.4 Intra1 89 6 9.0 0.2 31.9
39725.4 IAEA 22.0 9.0 0.2 32.6
39725.4 IAEA 23.0 9.1 0.2 32.0
39725.5 IAEA 24.0 8.6 0.2 32.2
39725.5 IAEA 1.0 8.6 0.2 31.8
39725.5 IAEA 2.0 9.3 0.2 32.2
39725.5 IAEA 3.0 9.1 0.2 32.3
39725.5 IAEA 4.0 8.6 0.2 32.2
39725.5 Intra1 89 5 8.6 0.2 32.8
39725.6 Intra1 89 4 7.9 0.2 32.5
39725.6 Intra1 89 3 9.4 0.2 30.9
39725.6 Intra1 89 3 9.2 0.2 33.0
39725.6 Intra1 89 2 8.5 0.2 32.7
39725.6 Intra1 89 1 8.8 0.2 33.4
39725.6 Intra1 89 6 8.7 0.2 33.0
SUPPLEMENTARY DATA TABLE 79
39725.7 Intra1 89 5 9.0 0.2 32.1
39725.7 Intra1 89 5 9.0 0.2 32.9
39725.7 Intra1 89 4 9.3 0.2 30.7
39725.7 Intra1 89 3 8.6 0.2 30.3
39725.7 Intra1 89 3 8.8 0.2 30.3
39725.7 IAEA 5.0 8.5 0.2 32.2
39725.8 IAEA 6.0 9.4 0.2 32.4
39725.8 IAEA 7.0 9.5 0.2 31.3
39725.8 Intra1 89 2 8.9 0.2 31.0
39725.8 Intra1 89 1 9.0 0.2 31.8
39725.8 Intra1 89 1 9.1 0.2 32.4
39725.8 Intra1 88 7 9.2 0.2 33.6
39725.9 Intra1 88 6 9.0 0.2 32.7
39725.9 Intra1 88 6 8.8 0.2 32.4
39725.9 Intra1 88 5 8.8 0.2 32.0
39725.9 Intra1 88 4 8.7 0.2 33.1
39725.9 Intra1 88 4 9.0 0.2 33.8
39726.0 Intra1 88 3 9.3 0.2 34.1
39726.0 Intra1 88 2 9.0 0.2 33.7
39726.0 Intra1 87 5 9.0 0.2 31.9
39726.0 Intra1 87 5 8.8 0.2 32.8
39726.0 Intra1 87 4 8.9 0.2 31.5
39726.0 IAEA 8.0 9.0 0.2 32.4
39726.1 IAEA 9.0 9.1 0.2 32.1
39726.1 IAEA 10.0 9.3 0.2 32.3
39726.1 Intra1 87 3 9.0 0.2 31.1
39726.1 Intra1 87 3 9.2 0.2 31.4
39726.1 Intra1 87 2 8.8 0.2 31.9
39726.1 Intra1 87 1 8.7 0.2 33.2
39726.2 24b 1930.0 9.1 0.2 32.8
39726.2 24b 1945.0 9.6 0.2 33.3
39726.2 IAEA 9.5 0.2 32.4
39726.2 IAEA 9.0 0.2 31.9
39729.5 IAEA 1.0 7.8 - 33.1
39729.6 IAEA 2.0 7.8 - 32.6
39729.6 IAEA 3.0 8.2 - 32.4
39729.6 IAEA 4.0 8.3 0.2 32.6
39729.6 IAEA 5.0 9.0 0.2 31.7
39729.6 IAEA 6.0 8.8 0.2 32.6
39729.6 IAEA 7.0 9.0 0.2 32.5
39729.7 IAEA 8.0 8.7 0.2 32.8
39729.7 IAEA 9.0 8.6 0.2 32.9
39729.7 Intra1 85 6 8.9 0.2 33.7
80 MAXBERKELHAMMER
39729.7 Intra1 85 6 8.5 0.2 34.6
39729.7 Intra1 85 5 8.9 0.2 32.0
39729.7 Intra1 85 4 9.0 0.2 30.9
39729.8 Intra1 85 3 8.8 0.2 31.3
39729.8 Intra1 85 3 9.3 0.2 31.5
39729.8 Intra1 85 2 8.8 0.2 33.1
39729.8 Intra1 85 1 8.7 0.2 35.2
39729.8 Intra1 85 1 8.5 0.2 34.7
39729.8 Intra1 84 6 9.3 0.2 32.4
39729.9 Intra1 84 6 9.1 0.2 33.2
39729.9 Intra1 84 5 8.9 0.2 31.3
39729.9 IAEA 10.0 8.1 0.2 31.8
39729.9 IAEA 11.0 8.3 0.2 31.8
39729.9 IAEA 12.0 8.5 0.2 31.9
39729.9 Intra1 84 4 8.8 0.2 30.9
39730.0 Intra1 84 4 8.6 0.2 31.2
39730.0 Intra1 84 3 8.7 0.2 31.0
39730.0 Intra1 84 2 8.9 0.2 32.2
39730.0 Intra1 84 2 8.6 0.2 32.4
39730.0 Intra1 84 1 9.0 0.2 33.2
39730.0 Intra1 82 6 8.8 0.2 33.5
39730.1 Intra1 82 5 8.5 0.2 33.7
39730.1 Intra1 82 4 8.9 0.2 33.4
39730.1 Intra1 82 3 8.8 0.2 31.3
39730.1 Intra1 82 2 8.9 0.2 32.2
39730.1 Intra1 82 1 8.4 0.0 31.7
39730.2 IAEA 13.0 8.5 0.2 33.4
39730.2 IAEA 14.0 8.9 0.2 32.9
39730.2 IAEA 15.0 9.5 0.2 31.5
39736.4 IAEA 2.0 8.6 0.2 32.6
39736.4 IAEA 3.0 8.4 0.2 32.3
39736.4 IAEA 4.0 8.9 0.2 32.3
39736.4 IAEA 5.0 8.6 0.2 32.1
39736.4 IAEA 6.0 8.8 0.2 32.2
39736.5 IAEA 7.0 8.7 0.2 31.9
39736.5 IAEA 8.0 9.2 0.2 32.1
39736.5 IAEA 9.0 8.4 0.2 31.9
39736.5 IAEA 10.0 8.6 0.2 32.1
39736.6 IAEA 11.0 9.3 0.0 31.7
39736.6 IAEA 12.0 9.9 0.0 32.7
39736.6 Intra1 82 123 8.3 0.2 30.8
39736.6 Intra1 82 5 8.4 0.0 32.8
39736.6 Intra1 82 345 5.9 0.0 30.8
SUPPLEMENTARY DATA TABLE 81
39736.6 Intra1 82 6 9.5 0.2 32.3
39736.7 Intra1 81 5 7.2 0.2 33.2
39736.7 Intra1 81 4 9.7 0.2 34.2
39736.7 Intra1 81 4 9.1 0.2 34.3
39736.7 Intra1 81 3 8.4 0.2 33.9
39736.7 Intra1 81 1 9.2 0.2 34.4
39736.7 IAEA 13.0 9.3 0.2 32.2
39736.8 IAEA 14.0 9.2 0.2 32.3
39736.8 IAEA 15.0 9.8 0.2 32.5
39736.8 Intra1 89 2 9.3 0.2 33.1
39736.8 Intra1 89 5 9.8 0.2 34.0
39736.8 Intra1 87 1 9.6 0.2 33.1
39736.8 Intra1 90 3 9.3 0.2 36.9
39736.9 IAEA 16.0 9.6 0.2 32.5
39736.9 IAEA 17.0 9.3 0.2 32.0
39736.9 IAEA 18.0 9.3 0.2 32.2
39736.9 Intra1 80 6 8.7 0.2 34.3
39736.9 Intra1 80 6 9.3 0.2 33.7
39736.9 Intra1 80 5 9.8 0.2 33.8
39737.0 Intra1 80 4 9.6 0.2 33.3
39737.0 Intra1 80 4 9.0 0.2 32.6
39737.0 Intra1 80 3 8.9 0.2 33.5
39737.0 Intra1 80 2 9.8 0.2 34.5
39737.0 Intra1 80 2 8.8 0.2 34.2
39737.0 Intra1 80 1 9.2 0.2 35.1
39737.1 IAEA 19.0 8.4 0.2 32.2
39737.1 IAEA 20.0 8.8 0.2 32.2
39737.1 IAEA 21.0 9.6 0.2 32.1
39737.1 24b 2003.0 9.2 0.2 32.7
39737.1 24b 1981.0 9.2 0.2 31.8
39737.2 24b 1866.0 9.8 0.2 31.7
39737.2 24b 1865.0 9.1 0.2 32.1
39737.2 24b 1918.0 9.6 0.2 33.8
39737.2 24b 1918.0 9.5 0.2 34.1
39737.2 24b 1919.0 9.4 0.2 33.7
39737.2 24b 1920.0 9.3 0.2 32.7
39737.3 24b 1921.0 9.2 0.2 31.9
39737.3 IAEA 22.0 8.8 0.2 32.2
39737.3 IAEA 23.0 9.5 0.2 32.9
39737.3 IAEA 24.0 9.0 0.2 32.9
39769.6 IAEA 1.0 8.1 0.1 33.3
39769.7 IAEA 2.0 7.3 0.1 32.8
39769.7 IAEA 3.0 8.6 0.1 34.0
82 MAXBERKELHAMMER
39769.7 IAEA 4.0 7.9 0.1 33.0
39769.7 IAEA 5.0 8.2 0.1 33.1
39769.8 IAEA 6.0 9.5 0.2 32.7
39769.8 IAEA 7.0 9.0 0.1 33.2
39769.8 IAEA 8.0 9.4 0.1 32.7
39769.8 IAEA 9.0 8.9 0.1 32.7
39769.8 IAEA 10.0 9.5 0.1 32.5
39769.8 Intra2 2002 4 7.9 0.1 34.3
39769.9 Intra2 2002 3 8.8 0.1 35.9
39769.9 Intra2 2002 2 8.0 0.1 34.5
39769.9 Intra2 2002 2 8.4 0.1 34.5
39769.9 Intra2 2002 1 7.8 0.1 33.0
39769.9 Intra2 2001 4 8.6 0.1 32.5
39769.9 Intra2 2001 3 8.7 0.1 32.2
39770.0 Intra2 2001 3 9.0 0.0 32.5
39770.0 Intra2 2001 2 9.7 0.1 32.0
39770.0 Intra2 2001 1 8.9 0.1 32.9
39770.0 Intra2 2000 6 8.3 0.1 32.9
39770.0 Intra2 2000 5 8.8 0.1 31.4
39770.1 Intra2 2000 5 8.4 0.1 31.9
39770.1 Intra2 2000 4 8.8 0.1 32.0
39770.1 Intra2 2000 3 8.5 0.1 32.5
39770.1 IAEA 11.0 9.2 0.1 31.9
39770.1 IAEA 12.0 9.5 0.1 31.6
39770.1 IAEA 13.0 9.6 0.1 31.9
39770.2 Intra2 2000 2 7.9 0.1 31.9
39770.2 Intra2 2000 2 9.0 0.1 32.3
39770.2 Intra2 2000 1 9.2 0.1 31.4
39770.2 Intra2 1999 5 8.8 0.1 30.2
39770.2 Intra2 1999 5 8.0 0.1 30.3
39770.2 Intra2 1999 6 8.3 0.1 29.6
39770.3 Intra2 1999 4 9.0 0.1 31.9
39770.3 Intra2 1999 3 8.3 0.1 32.7
39770.3 Intra2 1999 2 7.9 0.1 31.9
39770.3 Intra2 1999 2 9.4 0.1 32.2
39770.3 Intra2 1999 1 7.8 0.1 32.1
39770.3 Intra2 1998 6 8.8 0.1 31.4
39770.4 Intra2 1998 6 8.6 0.1 31.7
39770.4 Intra2 1998 5 9.1 0.1 33.2
39770.4 Intra2 1998 4 8.3 0.1 33.8
39770.4 IAEA 14.0 8.8 0.1 32.2
39770.4 IAEA 15.0 10.0 0.2 31.3
39770.4 IAEA 16.0 10.1 0.2 31.6
SUPPLEMENTARY DATA TABLE 83
39770.5 Sucrose 1.0 8.4 0.1 36.8
39770.5 Sucrose 2.0 9.9 0.1 36.8
39770.5 Sucrose 3.0 9.9 0.1 35.6
39770.5 Intra2 1998 3 8.9 0.1 32.9
39770.5 Intra2 1998 2 9.2 0.1 32.8
39770.5 Intra2 1998 2 8.2 0.1 33.2
39770.6 Intra2 1998 1 8.8 0.1 32.4
39770.6 Intra2 1997 5 9.2 0.1 32.0
39770.6 Intra2 1997 5 8.0 0.1 32.3
39770.6 Intra2 1997 4 9.2 0.1 33.3
39770.6 Intra2 1997 3 9.5 0.1 33.5
39770.7 Intra2 1997 2 9.0 0.1 33.8
39770.7 Intra2 1997 1 8.1 0.1 33.9
39770.7 Intra2 1996 5 8.7 0.1 34.2
39770.7 Intra2 1996 4 8.6 0.1 35.8
39770.7 Intra2 1996 4 8.9 0.1 35.9
39770.7 Intra2 1996 3 9.2 0.1 35.2
39770.8 Intra2 1996 2 8.2 0.1 34.4
39770.8 Intra2 1996 1 8.8 0.1 33.3
39770.8 IAEA 17.0 9.4 0.1 32.3
39770.8 IAEA 18.0 9.0 0.1 31.9
39770.8 IAEA 19.0 8.4 0.1 32.3
39770.8 Blanc 1.0 7.9 0.1 33.6
39770.9 Blanc 2.0 8.4 0.1 35.3
39770.9 Blanc 3.0 8.1 0.1 35.0
39770.9 Blanc 3.0 8.6 0.1 34.6
39770.9 Patr 1.0 9.0 0.1 33.1
39770.9 Patr 2.0 9.3 0.1 31.8
39770.9 Patr 2.0 7.7 0.1 32.2
39771.0 Patr 3.0 8.3 0.1 32.4
39771.0 Meth 1.0 9.5 0.1 33.3
39771.0 Meth 1.0 8.0 0.1 35.1
39771.0 Meth 2.0 9.0 0.1 32.3
39771.0 Meth 3.0 8.2 0.1 30.8
39771.0 Intra2 1995 5 7.9 0.1 34.5
39771.1 Intra2 1995 4 7.8 0.1 35.8
39771.1 Intra2 1995 3 9.1 0.1 35.3
39771.1 Intra2 1995 2 9.0 0.1 34.6
39771.1 Intra2 1995 2 8.0 0.1 32.9
39771.1 Intra2 1995 1 9.0 0.1 32.2
39771.2 IAEA 20.0 8.5 0.1 33.3
39771.2 IAEA 21.0 8.6 0.1 32.6
39771.2 IAEA 22.0 9.3 0.1 32.7
84 MAXBERKELHAMMER
39771.2 Intra2 1994 4 8.9 0.1 32.9
39771.2 Intra2 1994 3 8.6 0.1 33.1
39771.2 Intra2 1994 2 8.7 0.1 30.9
39771.3 Intra2 1994 2 7.7 0.1 33.8
39771.3 Intra2 1994 1 8.8 0.1 32.5
39771.3 Intra2 1993 5 8.8 0.1 33.6
39771.3 Intra2 1993 4 8.3 0.1 35.4
39771.3 Intra2 1993 3 8.6 0.1 35.0
39771.3 Intra2 1993 2 8.8 0.1 35.3
39771.4 Intra2 1993 1 8.0 0.1 34.2
39771.4 Intra2 1993 1 8.7 0.1 34.1
39771.4 IAEA 23.0 8.0 0.1 32.5
39771.4 IAEA 24.0 8.6 0.1 31.6
39771.4 IAEA 25.0 8.3 0.1 31.8
39771.6 Patr 2.0 6.5 0.1 36.1
39771.6 IAEA 6.0 7.0 0.1 33.2
39771.7 IAEA 7.0 8.4 0.1 32.7
39771.7 IAEA 8.0 7.5 0.1 32.3
39771.7 24b 1919.0 8.7 0.1 30.8
39771.7 24b 1920.0 9.0 0.1 31.6
39771.7 24b 1921.0 8.1 0.1 30.7
39771.7 24b 1923.0 8.4 0.1 33.8
39771.8 24b 1922.0 9.1 0.1 34.1
39771.8 24b 1924.0 8.6 0.1 34.9
39771.8 24b 1924.0 8.5 0.1 34.5
39771.8 24b 1925.0 8.1 0.1 33.2
39771.8 24b 1926.0 9.1 0.1 33.7
39771.9 24b 1927.0 9.1 0.1 32.2
39771.9 24b 1983.0 9.3 0.1 29.0
39771.9 24b 1992.0 8.9 0.1 29.0
39771.9 24b 2004.0 8.8 0.1 32.4
39771.9 IAEA 9.0 8.5 0.1 30.6
39771.9 IAEA 10.0 8.9 0.1 32.8
39772.0 IAEA 11.0 8.7 0.1 31.6
39772.0 33b 2001.0 8.7 0.1 30.4
39772.0 33b 1998.0 9.1 0.1 28.2
39772.0 33b 1996.0 9.6 0.1 30.3
39772.0 33b 2005.0 8.7 0.1 30.6
39772.0 33b 1993.0 8.7 0.1 31.8
39772.1 33b 1989.0 8.6 0.1 30.6
39772.1 33b 1989.0 9.4 0.1 30.0
39772.1 33b 1984.0 8.6 0.1 31.1
39772.1 33b 1979.0 8.1 0.1 30.1
SUPPLEMENTARY DATA TABLE 85
39772.1 33b 1974.0 9.0 0.1 32.4
39772.1 33b 1971.0 8.7 0.1 32.3
39772.2 33b 1967.0 9.1 0.1 31.2
39772.2 33b 1963.0 9.3 0.1 32.1
39772.2 IAEA 12.0 8.3 0.1 33.1
39772.2 IAEA 13.0 9.4 0.1 31.6
39772.2 IAEA 14.0 8.5 0.1 32.3
39772.2 33b 1960.0 8.7 0.1 33.0
39772.3 33b 1957.0 8.9 0.1 31.3
39772.3 33b 1954.0 8.8 0.1 33.9
39772.3 33b 1950.0 8.3 0.1 31.6
39772.3 33b 1946.0 8.9 0.1 33.0
39772.3 33b 1941.0 8.6 0.1 30.2
39772.3 33b 1938.0 8.1 0.1 30.8
39772.4 33b 1933.0 9.3 0.1 33.4
39772.4 33b 1929.0 8.8 0.1 29.2
39772.4 33b 1926.0 8.7 0.1 28.8
39772.4 33b 1921.0 8.3 0.1 30.1
39772.4 IAEA 15.0 8.4 0.1 32.8
39772.5 IAEA 16.0 9.4 0.1 32.5
39772.5 IAEA 17.0 9.0 0.1 31.9
39772.5 IAEA 18.0 8.9 0.1 32.1
39772.5 IAEA 19.0 8.7 0.1 32.1
39772.5 33b 1917.0 8.3 0.1 30.2
39772.5 33b 1917.0 8.5 0.1 30.1
39772.6 33b 1913.0 8.7 0.1 29.9
39772.6 33b 1909.0 7.7 0.1 29.9
39772.6 33b 1905.0 9.1 0.1 30.1
39772.6 33b 1902.0 9.4 0.1 31.3
39772.6 33b 1902.0 8.7 0.1 30.5
39772.6 33b 1900.0 9.3 0.1 31.2
39772.7 33b 1922.0 8.4 0.1 31.3
39772.7 33b 1925.0 8.7 0.1 28.9
39772.7 33b 1930.0 8.6 0.1 29.2
39772.7 33b 1939.0 9.0 0.1 29.2
39772.7 33b 1939.0 9.0 0.1 30.4
39772.7 33b 1943.0 8.4 0.1 30.8
39772.8 33b 1949.0 8.1 0.1 31.4
39772.8 IAEA 20.0 9.1 0.1 32.0
39772.8 Sucrose 1.0 9.2 0.1 36.8
39772.8 Sucrose 2.0 7.8 0.1 36.2
39772.8 Sucrose 3.0 8.6 0.1 36.1
39772.8 IAEA 21.0 9.1 0.1 31.1
86 MAXBERKELHAMMER
39772.9 33b 1898.0 9.1 0.1 30.7
39772.9 33b 1895.0 8.6 0.1 30.4
39772.9 33b 1890.0 7.7 0.1 31.1
39772.9 33b 1886.0 8.0 0.1 32.7
39772.9 33b 1886.0 8.8 0.1 32.4
39773.0 33b 1882.0 8.1 0.1 31.3
39773.0 33b 1878.0 8.4 0.1 30.0
39773.0 33b 1874.0 7.9 0.1 32.2
39773.0 33b 1871.0 8.2 0.1 32.7
39773.0 33b 1866.0 9.0 0.1 28.4
39773.0 33b 1866.0 7.9 0.1 28.7
39773.1 33b 1864.0 8.2 0.1 32.1
39773.1 33b 2005.0 7.9 0.1 30.1
39773.1 33b 1999.0 8.6 0.1 30.0
39773.1 33b 1995.0 8.6 0.1 32.3
39773.1 IAEA 21.0 8.0 0.1 32.4
39773.1 IAEA 22.0 9.5 0.1 32.5
39773.2 IAEA 23.0 8.1 0.1 32.3
39773.2 33b 1963.0 9.3 0.1 32.5
39773.2 33b 1966.0 8.2 0.1 31.6
39773.2 33b 1966.0 8.0 0.1 31.1
39773.2 33b 1971.0 8.2 0.1 32.2
39773.2 33b 1974.0 8.6 0.1 31.4
39773.3 33b 2002.0 9.3 0.1 31.7
39773.3 33b 1955.0 8.5 0.1 31.7
39773.3 Intra2 95 1 8.7 0.1 32.0
39773.3 Intra2 95 2 8.2 0.1 34.0
39773.3 Intra2 90 1 8.3 0.1 32.6
39773.3 Intra2 90 2 8.1 0.1 32.3
39773.4 Intra2 90 3 8.0 0.1 32.0
39773.4 Intra2 90 3 8.3 0.1 31.2
39773.4 Intra2 90 4 9.0 0.1 30.5
39773.4 Intra2 90 5 8.8 0.1 30.3
39773.4 Intra2 90 5 8.4 0.1 29.9
39773.4 IAEA 24.0 8.7 0.1 31.5
39773.5 IAEA 25.0 8.6 0.1 31.6
39773.5 IAEA 26.0 8.1 0.1 32.2
40053.7 Std 1.0 4.1 36.7
40053.7 Std 2.0 4.8 36.6
40053.7 Std 3.0 4.7 36.6
40053.7 Std 4.0 5.4 36.0
40053.7 33b 2005.0 4.7 30.2
40053.7 33b 2004.0 4.5 31.3
SUPPLEMENTARY DATA TABLE 87
40053.7 33b 2001.0 4.5 30.6
40053.7 33b 2000.0 4.5 30.3
40053.7 33b 1999.0 4.9 29.6
40053.7 33b 1998.0 4.4 30.0
40053.7 33b 1997.0 4.6 30.3
40053.7 33b 1996.0 4.4 31.2
40053.7 33b 1984.0 4.4 30.6
40053.8 33b 1985.0 4.4 30.3
40053.8 33b 1986.0 4.7 30.9
40053.8 33b 1987.0 4.9 30.2
40053.8 33b 1988.0 4.4 30.7
40053.8 33b 1989.0 4.4 30.1
40053.8 Std 5.0 5.5 36.1
40053.8 Std 6.0 5.7 36.1
40053.8 Std 7.0 5.3 36.3
40053.8 33b 1990.0 4.6 30.2
40053.8 33b 1991.0 5.0 30.4
40053.8 33b 1992.0 4.9 31.2
40053.8 33b 1993.0 4.4 32.1
40053.8 33b 1994.0 4.7 31.9
40053.8 33b 1995.0 4.4 31.6
40053.8 33b 1983.0 5.1 29.6
40053.8 33b 1982.0 4.4 29.5
40053.8 33b 1981.0 4.6 29.9
40053.8 33b 1980.0 4.3 30.1
40053.9 33b 1979.0 4.6 29.8
40053.9 33b 1978.0 5.3 31.2
40053.9 Std 8.0 4.4 36.9
40053.9 Std 9.0 5.5 36.1
40053.9 Std 10.0 5.4 36.2
40053.9 33b 1977.0 5.3 31.1
40053.9 33b 1976.0 4.6 31.7
40053.9 33b 1972.0 4.4 31.9
40053.9 33b 1964.0 5.2 30.9
40053.9 33b 1960.0 5.1 30.6
40053.9 33b 1957.0 4.7 31.9
40053.9 33b 1951.0 4.9 31.0
40053.9 33b 1948.0 4.5 31.9
40053.9 33b 1944.0 5.9 29.7
40053.9 33b 1941.0 5.5 29.2
40053.9 33b 1940.0 4.5 29.6
40053.9 Std 11.0 5.0 29.2
40054.0 Std 12.0 4.6 36.5
88 MAXBERKELHAMMER
40054.0 Std 13.0 5.4 36.0
40054.0 33b 1938.0 5.3 35.8
40054.0 33b 1931.0 4.5 30.9
40054.0 33b 1928.0 5.4 30.6
40054.0 33b 192x 4.9 30.2
40054.0 33b 1925.0 4.6 29.4
40054.0 33b 1921.0 4.8 29.8
40054.5 Std 14.0 5.0 35.7
40054.5 Std 15.0 5.6 35.0
40054.5 33b 1921.0 5.0 29.1
40054.5 33b 1918.0 4.5 29.3
40054.5 Std 16.0 5.1 35.8
40054.5 Std 17.0 5.4 35.7
40054.5 33b 1915.0 4.8 29.3
40054.5 33b 1914.0 5.2 28.1
40054.5 33b 1913.0 4.9 30.7
40054.5 33b 1912.0 4.4 30.1
40054.5 33b 1946.0 4.9 31.4
40054.5 33b 1936.0 4.4 29.5
40054.5 33b 1956.0 4.5 30.0
40054.5 33b 1959.0 4.4 31.2
40054.6 33b 1962.0 4.6 31.5
40054.6 33b 1967.0 4.6 30.1
40054.6 33b 1970.0 4.6 31.4
40054.6 33b 1920.0 4.4 29.6
40054.6 33b 1930.0 4.4 30.2
40054.6 33b 1932.0 4.7 31.9
40054.6 33b 1958.0 5.2 30.5
40054.6 33b 1947.0 5.3 31.1
40054.6 Std 18.0 5.3 35.8
40054.6 Std 19.0 5.2 36.1
40054.6 Std 20.0 4.9 36.3
40054.6 33b 1911.0 4.7 29.8
40054.6 33b 1910.0 4.5 30.6
40054.6 33b 1909.0 4.4 30.8
40054.6 33b 1908.0 4.2 30.1
40054.6 33b 1907.0 5.2 29.2
40054.6 33b 1906.0 4.7 30.0
40054.7 33b 1905.0 5.3 29.1
40054.7 33b 1904.0 4.8 29.8
40054.7 33b 1903.0 5.0 29.8
40054.7 33b 1902.0 5.3 30.8
40054.7 33b 1901.0 4.4 31.2
SUPPLEMENTARY DATA TABLE 89
40054.7 33b 1900.0 4.3 32.1
40054.7 Std 21.0 4.5 36.5
40054.7 Std 22.0 4.7 36.1
40054.7 Std 23.0 5.1 35.9
40054.7 33b 1888.0 4.6 32.6
40054.7 33b 1889.0 4.8 30.3
40054.7 33b 1890.0 5.1 30.6
40054.7 33b 1891.0 4.8 29.3
40054.7 33b 1892.0 5.1 32.1
40054.7 33b 1893.0 4.4 31.4
40054.7 33b 1894.0 4.6 30.9
40054.7 33b 1865.0 4.9 29.3
40054.7 Std 1.0 3.9 32.7
40054.8 Std 2.0 4.4 32.2
40054.8 Std 3.0 4.0 32.4
40057.3 Sucrose 1.0 5.1 36.2
40057.3 Sucrose 2.0 5.5 36.1
40057.3 Sucrose 3.0 4.7 36.3
40057.3 Sucrose 4.0 4.9 36.3
40057.3 Sucrose 5.0 5.2 36.5
40057.3 Sucrose 6.0 5.3 36.2
40057.4 Sucrose 7.0 5.5 36.2
40057.4 IAEA 1.0 4.5 31.9
40057.4 IAEA 2.0 4.6 32.7
40057.4 IAEA 3.0 4.5 32.4
40057.4 IAEA 4.0 4.5 32.2
40057.4 IAEA 5.0 4.3 31.8
40057.4 IAEA 6.0 4.3 32.7
40057.4 Vitex 1.0 5.2 29.6
40057.4 Vitex 2.0 4.6 31.0
40057.4 Vitex 3.0 5.3 29.8
40057.4 Vitex 4.0 5.1 29.1
40057.4 Vitex 5.0 4.5 28.8
40057.5 Vitex 6.0 4.4 30.2
40057.5 Vitex 7.0 5.0 28.1
40057.5 33b 1876.0 4.2 33.2
40057.5 33b 1876.0 4.1 33.9
40057.5 33b 1876.0 4.3 34.0
40057.5 33b 1876.0 4.6 33.5
40057.5 33b 1897.0 4.4 30.5
40057.5 33b 1898.0 4.3 31.4
40057.5 33b 1899.0 4.7 31.6
40057.5 33b 1876.0 5.0 31.3
90 MAXBERKELHAMMER
40057.5 33b 1877.0 4.9 31.9
40057.5 33b 1878.0 4.3 32.0
40057.5 33b 1879.0 4.7 32.7
40057.5 33b 1879.0 4.6 31.4
40057.5 Sucrose 8.0 5.4 36.4
40057.6 Sucrose 9.0 4.9 36.7
40057.6 Sucrose 10.0 5.4 36.4
40057.6 33b 1880.0 4.1 33.3
40057.6 33b 1881.0 4.3 33.1
40057.6 33b 1882.0 4.6 32.1
40057.6 33b 1883.0 4.2 32.3
40057.6 33b 1884.0 4.6 31.8
40057.6 33b 1884.0 5.2 31.5
40057.6 33b 1885.0 4.3 31.5
40057.6 33b 1886.0 4.6 32.2
40057.6 33b 1887.0 4.8 31.6
40057.6 33b 1875.0 4.6 32.2
40057.6 33b 1874.0 4.2 32.6
40057.6 33b 1874.0 4.4 31.5
40057.6 Sucrose 11.0 5.0 36.7
40057.6 Sucrose 12.0 5.0 36.7
40057.6 33b 1873.0 4.4 32.6
40057.7 33b 1872.0 4.3 31.9
40057.7 33b 1874.0 4.4 31.6
40057.7 33b 1897.0 4.7 31.2
40057.7 33b 1871.0 4.2 30.6
40057.7 33b 1870.0 4.3 31.2
40057.7 33b x 4.4 32.2
40057.7 33b 1869.0 4.4 30.4
40057.7 33b 1868.0 4.8 28.8
40057.7 33b 1867.0 4.9 28.3
40057.7 33b 1866.0 4.4 28.5
40057.7 33b 1865.0 4.4 29.3
40057.7 Sucrose 13.0 5.0 36.2
40057.7 Sucrose 14.0 4.6 36.7
40057.7 33b 1864.0 4.7 31.0
40057.7 33b 1851.0 5.0 30.7
40057.7 33b 1853.0 4.4 31.3
40057.7 33b 1855.0 5.1 31.4
40057.7 33b 18x 5.1 31.7
40057.8 33b 1856.0 4.5 32.3
40057.8 33b 1857.0 4.6 31.4
40057.8 33b 1858.0 4.6 30.8
SUPPLEMENTARY DATA TABLE 91
40057.8 33b 1859.0 4.8 32.6
40057.8 33b 1860.0 9.0 30.7
40057.8 33b 1861.0 5.0 30.6
40057.8 Baker 5.1 27.5
40057.8 Baker 4.9 27.7
40057.8 Baker 4.5 28.6
40057.8 Baker 4.3 28.2
40057.8 Sucrose 4.9 38.3
40057.8 33b 1863.0 5.1 30.9
40057.8 33b 1833.0 4.4 30.6
40057.8 33b 1834.0 4.8 31.0
40057.8 33b 1835.0 4.9 30.9
40057.8 33b 1836.0 4.8 30.6
40057.8 33b 1837.0 4.4 30.9
40057.8 33b 1838.0 4.5 31.1
40057.9 33b 1839.0 5.1 30.4
40057.9 33b 1840.0 4.3 30.6
40057.9 33b 1842.0 4.7 30.6
40057.9 33b 1843.0 4.8 30.2
40057.9 33b 1847.0 4.5 29.8
40057.9 33b 1850.0 5.0 32.8
40057.9 Sucrose 4.8 37.0
40057.9 33b 1821.0 4.5 31.6
40057.9 33b 1822.0 4.8 30.8
40057.9 33b 1823.0 5.1 31.5
40057.9 33b 1824.0 4.7 32.1
40057.9 33b 1825.0 5.0 31.5
40058.3 33b 1826.0 4.8 32.1
40058.3 33b 1867.0 4.6 28.3
40058.3 33b 1868.0 4.5 28.2
40058.3 Sucrose 4.8 36.2
40126.0 33b 1783.0 30.3 178.2
40126.0 33b 1784.0 32.8 176.6
40126.0 33b 1785.0 31.4 178.7
40126.0 33b 1786.0 31.2 213.9
40126.0 33b 1787.0 30.5 212.4
40126.0 33b 1788.0 31.7 213.2
40126.0 33b 1789.0 31.9 161.7
40126.0 33b 1790.0 33.8 158.9
40126.0 33b 1791.0 31.9 170.1
40126.0 33b 1793.0 32.6 165.8
40126.0 33b 1794.0 31.2 165.9
40126.0 33b 1795.0 31.5 175.6
92 MAXBERKELHAMMER
40126.0 33b 1796.0 31.4 165.9
40126.0 33b 1797.0 31.3 159.7
40126.0 33b 1798.0 33.1 160.1
40126.0 33b 1799.0 31.7 161.6
40126.0 33b 1800.0 31.4 164.6
40126.0 33b 1801.0 31.6 162.0
40126.0 33b 1802.0 31.9 164.0
40126.0 33b 1803.0 32.1 161.7
40126.0 33b 1804.0 33.5 179.4
40126.0 33b 1805.0 33.0 177.3
40126.0 33b 1807.0 32.8 178.2
40126.0 33b 1808.0 31.8 180.0
40126.0 33b 1809.0 30.6 189.4
40126.0 33b 1810.0 32.4 176.3
40126.0 33b 1811.0 31.3 168.3
40126.0 33b 1812.0 31.0 163.1
40126.0 33b 1813.0 31.7 173.9
40126.0 33b 1814.0 32.7 162.5
40126.0 33b 1815.0 32.3 167.5
40126.0 33b 1816.0 31.9 168.1
40126.0 33b 1817.0 30.9 167.0
40126.0 33b 1818.0 32.2 169.7
40126.0 33b 1819.0 30.9 176.7
40126.0 33b 1820.0 31.2 167.8
40126.0 Std IAEA 32.2 213.5
40126.0 Std IAEA 32.2 214.1
40126.0 Std IAEA 32.1 214.3
40126.0 Std IAEA 32.4 171.3
40126.0 Std IAEA 32.0 169.4
40126.0 Std IAEA 32.4 159.3
40126.0 Std Sucrose 36.2 156.6
40126.0 Std Sucrose 36.2 149.4
40126.0 33b Sucrose 36.3 149.9
40126.0 Std Sucrose 36.5 157.7
40126.0 Std Sucrose 36.6 163.7
40126.0 Std Sucrose 36.6 151.6
40127.0 Std IAEA 32.3 177.4
40127.0 Std IAEA 32.3 175.7
40127.0 Std IAEA 32.0 172.3
40127.0 Std Sucrose 36.3 211.3
40127.0 Std Sucrose 36.3 158.1
40127.0 33b 1782.0 32.1 149.8
40127.0 33b 1781.0 30.2 159.3
SUPPLEMENTARY DATA TABLE 93
40127.0 33b 1780.0 32.1 159.3
40127.0 33b 1779.0 31.5 171.3
40127.0 33b 1778.0 33.4 166.2
40127.0 33b 1777.0 32.8 159.0
40127.0 33b 1776.0 32.3 155.5
40127.0 33b 1775.0 31.3 168.8
40127.0 33b 1774.0 31.0 169.9
40127.0 33b 1773.0 32.1 169.7
40127.0 33b 1772.0 31.9 167.0
40127.0 33b 1771.0 34.5 162.7
40127.0 33b 1769.0 30.2 164.6
40127.0 33b 1768.0 30.3 173.6
40127.0 Std IAEA 32.1 175.6
40127.0 Std IAEA 32.1 161.0
40127.0 33b 1767.0 30.6 155.5
40127.0 33b 1765.0 29.9 165.6
40127.0 33b 1764.0 31.0 168.2
40127.0 33b 1763.0 31.3 161.9
40127.0 33b 1762.0 30.8 158.6
40127.0 33b 1761.0 29.3 162.5
40127.0 33b 1760.0 29.9 170.8
40127.0 33b 1759.0 31.0 181.7
40127.0 33b 1758.0 32.1 177.3
40127.0 33b 1757.0 33.6 175.0
40127.0 33b 1756.0 35.5 180.7
40127.0 33b 1755.0 34.5 211.5
40127.0 Std Sucrose 36.6 212.5
40127.0 Std Sucrose 36.5 167.2
40127.0 33b 1754.0 32.6 165.8
40127.0 33b 1753.0 32.8 160.9
40127.0 33b 1752.0 29.5 165.1
40127.0 33b 1749.0 30.9 178.6
40127.0 33b 1748.0 29.7 176.6
40127.0 33b 1747.0 34.5 170.0
40127.0 33b 1746.0 30.9 174.2
40127.0 33b 1745.0 31.5 168.5
40127.0 33b 1743.0 31.5 164.9
40127.0 33b 1744.0 31.6 213.1
40127.0 Std IAEA 36.5 212.8
40127.0 Std IAEA 36.6 179.2
40127.0 Std Sucrose 32.0 27.0
40140.8 std IAEA 31.9
40140.8 std IAEA 31.5
94 MAXBERKELHAMMER
40140.8 std IAEA 31.9
40140.9 33b 1742.0 32.9
40140.9 33b 1741.0 32.7
40140.9 33b 1740.0 32.2
40140.9 33b 1739.0 33.2
40140.9 33b 1738.0 31.6
40140.9 33b 1737.0 32.3
40140.9 33b 1736.0 31.8
40140.9 33b 1735.0 32.1
40140.9 33b 1712.0 30.2
40140.9 33b 1711.0 31.5
40140.9 33b 1710.0 32.3
40140.9 33b 1709.0 31.1
40140.9 std Sucrose 36.3
40140.9 std Sucrose 36.4
40140.9 std Sucrose 36.4
40141.0 33b 1708.0 32.0
40141.0 33b 1707.0 31.6
40141.0 33b 1734.0 31.0
40141.0 33b 1733.0 30.6
40141.0 33b 1732.0 30.6
40141.0 33b 1731.0 31.1
40141.0 33b 1680.0 30.9
40141.0 33b 1706.0 32.5
40141.0 33b 1704.0 31.1
40141.0 33b 1703.0 30.9
40141.0 33b 1702.0 29.9
40141.0 std Sucrose 36.3
40141.0 std Sucrose 36.5
40141.0 std IAEA 32.4
40141.0 33b 1700.0 32.0
40141.0 33b 1699.0 33.3
40141.1 33b 1698.0 31.6
40141.1 33b 1697.0 32.3
40141.1 33b 1696.0 32.6
40141.1 33b 1695.0 32.2
40141.1 33b 1694.0 31.7
40141.1 33b 1772.0 32.6
40141.1 33b 1796.0 31.5
40141.1 33b 1802.0 32.1
40141.1 33b 1807.0 33.0
40141.1 std IAEA 32.2
40141.4 Std Sucrose 36.4
SUPPLEMENTARY DATA TABLE 95
40141.4 Std Sucrose 36.5
40141.5 Std Sucrose 36.4
40141.5 Std IAEA 32.2
40141.5 Std IAEA 32.2
40141.5 33b 1730.0 30.8
40141.5 33b 1729.0 32.3
40141.5 33b 1728.0 32.5
40141.5 33b 1727.0 31.1
40141.5 33b 1726.0 30.3
40141.5 33b 1725.0 29.8
40141.5 33b 1724.0 30.1
40141.5 33b 1723.0 31.6
40141.5 33b 1722.0 32.0
40141.5 33b 1721.0 31.9
40141.5 33b 1720.0 30.5
40141.5 33b 1719.0 32.9
40141.6 Std Sucrose 36.4
40141.6 Std Sucrose 36.5
40141.6 33b 1716.0 32.0
40141.6 33b 1715.0 31.1
40141.6 33b 1714.0 29.8
40141.6 33b 1713.0 29.7
40141.6 33b 1673.0 31.8
40141.6 33b 1672.0 31.4
40141.6 33b 1671.0 31.0
40141.6 33b 1670.0 31.2
40141.6 33b 1669.0 30.8
40141.6 33b 1668.0 30.9
40141.6 Std IAEA 32.1
40141.6 Std IAEA 32.4
40141.7 33b 1666.0 31.3
40141.7 33b 1664.0 30.9
40141.7 33b 1663.0 31.3
40141.7 33b 1662.0 30.3
40141.7 33b 1661.0 31.7
40141.7 33b 1660.0 31.5
40141.7 33b 1659.0 31.9
40141.7 33b 1658.0 31.6
40141.7 33b 1657.0 31.5
40141.7 33b 1656.0 31.5
40141.7 33b 1655.0 31.4
40141.7 Std IAEA 32.1
40141.7 Std IAEA 32.0
96 MAXBERKELHAMMER
40141.7 Std Sucrose 36.3
40142.9 std Sucrose 36.6
40142.9 std Sucrose 36.4
40142.9 std IAEA 31.9
40142.9 std IAEA 32.0
40142.9 33b 1693.0 32.3
40142.9 33b 1665.0 31.2
40142.9 33b 1691.0 31.1
40142.9 33b 1690.0 32.0
40142.9 33b 1689.0 31.3
40142.9 33b 1688.0 30.5
40142.9 33b 1687.0 31.5
40142.9 33b 1686.0 32.5
40142.9 33b 1685.0 31.5
40142.9 33b 1683.0 31.5
40142.9 33b 1681.0 31.4
40143.0 33b 1679.0 31.2
40143.0 std Sucrose 36.5
40143.0 std Sucrose 36.4
40143.0 33b 1678.0 32.2
40143.0 33b 1677.0 31.8
40143.0 33b 1676.0 32.1
40143.0 33b 1675.0 32.4
40143.0 33b 1674.0 32.2
40143.0 33b 1654.0 33.4
40143.0 33b 1653.0 33.1
40143.0 33b 1652.0 30.5
40143.0 33b 1651.0 32.6
40143.0 33b 1650.0 32.3
40143.0 33b 1648.0 31.5
40143.0 33b 1649.0 31.4
40143.0 std IAEA 32.2
40143.1 std IAEA 32.2
40143.1 33b 1647.0 31.4
40143.1 33b 1646.0 32.5
40143.1 33b 1645.0 32.2
40143.1 33b 1644.0 32.1
40143.1 33b 1642.0 31.2
40143.1 33b 1643.0 31.0
40143.1 33b 1641.0 31.4
40143.1 33b 1640.0 31.6
40143.1 33b 1637.0 31.4
40143.1 33b 1636.0 31.0
SUPPLEMENTARY DATA TABLE 97
40143.1 33b 1635.0 32.3
40143.1 33b 1634.0 32.8
40143.1 std IAEA 32.3
40143.1 std IAEA 32.1
40143.2 std Sucrose 36.4
40143.2 std Sucrose 36.7
40144.0 Std Sucrose 36.2 210.3
40144.0 24.0 2000-1 31.1 145.6
40144.0 24.0 2000-2 31.8 130.6
40144.0 24.0 2000-4 31.8 125.4
40144.0 24.0 2000-5 31.7 140.0
40144.0 24.0 2000-6 31.9 152.4
40144.0 24.0 1999-1 31.5 156.1
40144.0 24.0 1999-3 31.9 140.7
40144.0 24.0 1999-4 31.3 135.0
40144.0 24.0 1999-5 29.6 136.7
40144.0 24.0 1999-6 29.6 144.0
40144.0 24.0 1998-1 31.1 148.2
40144.0 24.0 1998-2 31.9 146.3
40144.0 std Sucrose 36.6 210.6
40144.0 std Sucrose 36.5 212.7
40144.0 24.0 1998-3 32.7 142.4
40144.0 24.0 1998-4 33.1 139.9
40144.0 24.0 1998-5 32.4 140.9
40144.0 24.0 1998-6 31.6 149.3
40144.0 24.0 1997-1 33.0 150.8
40144.0 24.0 1997-2 33.3 145.6
40144.0 24.0 1997-3 33.4 138.1
40144.0 24.0 1997-4 32.7 138.4
40144.0 24.0 1997-5 31.6 145.7
40144.0 24.0 1996-1 33.2 154.7
40144.0 24.0 1996-2 33.3 151.3
40144.0 24.0 1996-3 34.4 141.1
40144.0 24.0 1996-4 34.8 136.7
40144.0 24.0 1996-5 33.6 140.4
40144.0 24.0 1990-1 31.6 146.5
40144.0 24.0 1990-2 31.6 140.7
40144.0 24.0 1990-3 35.0 142.5
40144.0 24.0 1990-4 30.5 132.3
40144.0 24.0 1990-6 31.2 146.0
40144.0 24.0 1990-7 32.5 156.6
40144.0 24.0 1987-1 31.5 142.9
40144.0 24.0 1987-2 32.6 145.4
98 MAXBERKELHAMMER
40144.0 24.0 1987-3 33.6 148.6
40144.0 24.0 1987-4 33.9 147.5
40144.0 24.0 1987-5 33.3 146.3
40144.0 24.0 1987-6 31.8 155.4
40144.0 std IAEA 31.8 172.4
40144.0 std IAEA 32.2 174.2
40144.0 std IAEA 32.5 176.0
40144.0 std Sucrose 36.5 211.6
40146.0 std Sucrose 36.5
40146.0 std Sucrose 36.2
40146.0 std Sucrose 36.5
40146.0 std IAEA 32.3
40146.0 std IAEA 32.1
40146.0 33b 1633.0 32.8
40146.0 33b 1630.0 32.5
40146.0 33b 1628.0 31.9
40146.0 33b 1627.0 31.5
40146.0 33b 1626.0 32.3
40146.0 33b 1625.0 32.3
40146.0 33b 1624.0 31.8
40146.1 33b 1621.0 31.7
40146.1 std Sucrose 36.5
40146.1 std Sucrose 36.5
40146.1 33b 1620.0 32.2
40146.1 33b 1619.0 31.7
40146.1 33b 1618.0 31.5
40146.1 33b 1617.0 31.0
40146.1 33b 1616.0 30.1
40146.1 33b 1615.0 30.3
40146.1 33b 1614.0 31.6
40146.1 33b 1613.0 31.1
40146.1 33b 1612.0 30.5
40146.1 33b 1611.0 30.9
40146.1 33b 1610.0 31.2
40146.1 33b 1609.0 31.9
40146.2 std IAEA 32.4
40146.2 std IAEA 31.9
40146.2 33b 1608.0 31.6
40146.2 33b 1608.0 31.5
40146.2 33b 1607.0 29.1
40146.2 33b 1606.0 30.2
40146.2 33b 1605.0 32.0
40146.2 33b 1604.0 31.9
SUPPLEMENTARY DATA TABLE 99
40146.2 33b 1601.0 31.6
40146.2 33b 1600.0 32.4
40146.2 33b 1599.0 33.1
40146.2 33b 1598.0 33.0
40146.2 std IAEA 32.2
40146.3 std IAEA 31.9
40146.3 std Sucrose 36.5
40146.0 std IAEA 32.1 168.8
40146.0 24.0 1993-2 34.1 142.7
40146.0 24.0 1993-3 34.4 130.5
40146.0 24.0 1993-4 33.3 138.0
40146.0 24.0 1993-5 32.1 141.3
40146.0 24.0 1988-2 30.8 146.4
40146.0 24.0 1988-3 31.4 138.6
40146.0 24.0 1988-6 31.6 144.2
40146.0 24.0 1989-2 33.0 139.3
40146.0 24.0 1989-3 34.2 136.9
40146.0 24.0 1989-4 33.6 143.5
40146.0 24.0 1989-5 32.2 141.2
40146.0 24.0 1989-7 32.0 151.0
40146.0 std Sucrose 36.2 209.8
40146.0 std Sucrose 36.3 216.7
40146.0 24.0 1984-1 31.7 149.8
40146.0 24.0 1984-2 33.3 133.4
40146.0 24.0 1984-3 33.5 130.3
40146.0 24.0 1984-4 33.3 132.0
40146.0 24.0 1984-5 32.5 138.2
40146.0 24.0 1984-6 31.5 143.0
40146.0 24.0 1983-1 30.6 144.7
40146.0 24.0 1983-2 32.6 141.6
40146.0 24.0 1983-3 33.8 134.7
40146.0 24.0 1981-1 33.2 140.4
40146.0 24.0 1981-2 32.8 135.5
40146.0 24.0 1981-3 32.2 130.3
40146.0 std IAEA 32.4 168.8
40146.0 std IAEA 32.2 170.5
40146.0 24.0 1981-4 31.9 123.5
40146.0 24.0 1981-5 32.0 117.3
40146.0 24.0 1981-6 33.1 128.5
40146.0 24.0 1981-7 32.7 139.1
40146.0 24.0 2003-1 33.5 156.7
40146.0 24.0 2003-2 33.5 155.2
40146.0 24.0 2003-3 33.7 150.0
100 MAXBERKELHAMMER
40146.0 24.0 2003-4 33.6 159.5
40146.0 24.0 2003-5 33.6 157.9
40146.0 24.0 1979-1 33.6 152.7
40146.0 24.0 1979-2 34.0 146.8
40146.0 24.0 1979-3 34.6 141.0
40146.0 24.0 1979-4 32.6 144.5
40146.0 24.0 1979-5 32.4 144.3
40146.0 std IAEA 32.4 168.3
40146.0 std IAEA 32.2 171.3
40146.0 std Sucrose 36.4 211.8
40146.5 std Sucrose 35.8
40146.6 std IAEA 32.3
40146.6 std IAEA 31.8
40146.6 33b 1632.0 32.8
40146.6 33b 1631.0 32.5
40146.6 33b 1603.0 31.5
40146.6 33b 1602.0 33.0
40146.6 33b 1623.0 31.0
40146.6 33b 1622.0 31.8
40146.6 33b 1597.0 32.4
40146.6 33b 1596.0 32.7
40146.6 33b 1595.0 32.1
40146.6 33b 1594.0 31.1
40146.6 33b 1593.0 31.1
40146.6 33b 1592.0 31.8
40146.6 std Sucrose 36.4
40146.6 std Sucrose 36.5
40146.7 33b 1591.0 31.6
40146.7 33b 1590.0 31.2
40146.7 33b 1589.0 31.0
40146.7 33b 1588.0 30.6
40146.7 33b 1587.0 32.4
40146.7 33b 1586.0 31.9
40146.7 33b 1585.0 32.1
40146.7 33b 1584.0 32.9
40146.7 33b 1583.0 32.8
40146.7 33b 1582.0 32.0
40146.7 33b 1581.0 31.5
40146.7 33b 1580.0 32.0
40146.7 std IAEA 32.4
40146.7 std IAEA 32.2
40146.7 33b 1579.0 32.8
40146.8 33b 1578.0 32.4
SUPPLEMENTARY DATA TABLE 101
40146.8 33b 1577.0 32.9
40146.8 33b 1576.0 33.1
40146.8 33b 1575.0 33.5
40146.8 33b 1572.0 32.4
40146.8 33b 1571.0 31.8
40146.8 33b 1570.0 31.6
40146.8 33b 1569.0 31.7
40146.8 33b 1568.0 32.4
40146.8 33b 1567.0 32.7
40146.8 std IAEA 32.3
40146.8 std IAEA 32.2
40146.8 std Sucrose 36.3
40153.0 Std SUCROSE 36.4 216.3
40153.0 Std SUCROSE 36.5 216.5
40153.0 Std IAEA 32.2 183.5
40153.0 24.0 1977-1 33.8 156.4
40153.0 24.0 1977-2 33.8 149.4
40153.0 24.0 1977-3 34.0 140.7
40153.0 24.0 1977-4 33.7 148.7
40153.0 24.0 1977-5 32.9 153.5
40153.0 24.0 1978-1 32.8 157.9
40153.0 24.0 1978-2 33.4 149.4
40153.0 24.0 1978-3 34.3 143.1
40153.0 24.0 1978-4 34.2 146.8
40153.0 24.0 1978-5 33.4 158.2
40153.0 24.0 1972-1 33.3 156.1
40153.0 24.0 1972-2 33.2 156.2
40153.0 Std SUCROSE 36.5 211.6
40153.0 Std SUCROSE 36.5 214.7
40153.0 24.0 1972-3 34.1 158.0
40153.0 24.0 1972-4 34.7 152.7
40153.0 24.0 1972-5 34.7 152.4
40153.0 24.0 1972-6 34.1 155.3
40153.0 24.0 1973-1 33.7 151.0
40153.0 24.0 1973-2 33.2 154.8
40153.0 24.0 1973-3 32.9 154.6
40153.0 24.0 1973-4 32.7 152.6
40153.0 24.0 1973-5 32.5 144.0
40153.0 24.0 1973-6 32.5 144.5
40153.0 24.0 1971-1 35.0 163.7
40153.0 24.0 1971-2 35.5 165.8
40153.0 Std IAEA 32.0 180.0
40153.0 Std IAEA 32.1 181.4
102 MAXBERKELHAMMER
40153.0 24.0 1971-3 35.9 165.2
40153.0 24.0 1971-4 36.0 156.9
40153.0 24.0 1971-5 34.4 152.6
40153.0 24.0 1970-1 32.7 162.2
40153.0 24.0 1970-2 33.3 163.3
40153.0 24.0 1970-3 33.7 162.7
40153.0 24.0 1970-4 33.8 158.1
40153.0 24.0 1970-5 33.7 154.4
40153.0 24.0 1970-6 33.6 150.1
40153.0 24.0 1970-7 33.4 154.6
40153.0 24.0 1970-8 34.1 162.6
40153.0 24.0 1975-23 33.4 156.2
40153.0 24.0 1975-4 33.3 150.9
40153.0 24.0 1975-6 33.3 149.2
40153.0 24.0 1975-7 33.9 156.1
40153.0 Std IAEA 32.2 178.9
40153.0 Std Sucrose 36.6 214.8
40154.0 Std SUCROSE 36.2 216.3
40154.0 Std SUCROSE 36.2 215.6
40154.0 Std IAEA 32.1 186.3
40154.0 33b 1566 30.3 165.2
40154.0 33b 1565 29.0 168.6
40154.0 33b 1564 31.1 163.6
40154.0 33b 1563 31.5 169.4
40154.0 33b 1550 32.2 171.1
40154.0 33b 1549 31.3 168.6
40154.0 33b 1548 29.9 156.8
40154.0 33b 1547 30.7 157.6
40154.0 33b 1545 32.1 154.0
40154.0 33b 1546 31.0 166.8
40154.0 33b 1551 32.0 166.8
40154.0 33b 1552 31.2 166.1
40154.0 Std SUCROSE 36.4 212.8
40154.0 Std SUCROSE 36.5 214.1
40154.0 33b 1554 31.9 178.2
40154.0 33b 1555 30.2 169.0
40154.0 33b 1556 30.8 163.2
40154.0 33b 1562 31.7 169.6
40154.0 33b 1561 32.1 167.2
40154.0 33b 1560 31.7 177.1
40154.0 33b 1559 31.6 178.4
40154.0 33b 1558 31.8 168.4
40154.0 33b 1557 30.4 170.8
SUPPLEMENTARY DATA TABLE 103
40154.0 33b 2005 30.2 153.3
40154.0 33b 1998 29.7 156.4
40154.0 33b 1995 31.8 161.4
40154.0 Std IAEA 32.5 180.1
40154.0 33b 1992 30.7 148.3
40154.0 33b 1991 30.5 146.7
40154.0 33b 1989 30.4 153.5
40154.0 33b 1984 30.6 154.2
40154.0 33b 1979 29.9 157.5
40154.0 33b 1968 30.6 159.2
40154.0 33b 1969 30.5 160.6
40154.0 33b 1967 30.5 156.4
40154.0 33b 1965 31.8 155.8
40154.0 24b 1984 30.7 163.0
40154.0 24b 2006 31.8 173.7
40154.0 24b 1991 30.5 160.9
40154.0 24b 1989 30.9 167.6
40167.0 Std SUCROSE 36.0 238.8
40167.0 Std SUCROSE 36.3 239.6
40167.0 Std SUCROSE 36.2 241.0
40167.0 33b 1730.0 30.3 169.3
40167.0 33b 1659.0 31.9 174.2
40167.0 33b 1688.0 30.3 174.7
40167.0 33b 1647.0 31.1 190.4
40167.0 33b 1632.0 32.3 187.5
40167.0 33b 1611.0 30.9 179.1
40167.0 33b 1545.0 32.0 178.1
40167.0 33b 1588.0 30.1 175.1
40167.0 33b 1599.0 32.8 195.4
40167.0 33b 1781.0 30.3 165.7
40167.0 33b 1797.0 30.8 181.1
40167.0 Std SUCROSE 36.5 237.8
40167.0 Std SUCROSE 36.5 238.8
40167.0 33b 1788.0 31.6 177.1
40167.0 33b 1777.0 33.0 184.0
40167.0 33b 1760.0 29.8 185.3
40167.0 33b 1739.0 33.2 190.3
40167.0 33b 1734.0 30.9 192.6
40167.0 1963.0 31.3 175.9
40167.0 1959.0 31.0 177.9
40167.0 1937.0 29.3 178.1
40167.0 1921.0 29.9 178.1
40167.0 1916.0 29.8 174.9
104 MAXBERKELHAMMER
40167.0 1955.0 33.1 173.0
40167.0 1942.0 30.5 174.9
40167.0 Std IAEA 32.2 193.3
40167.0 Std IAEA 32.4 200.5
40167.0 1948.0 31.5 168.7
40167.0 1892.0 33.3 183.1
40167.0 1909.0 32.4 183.1
40167.0 1873.0 33.7 175.2
40167.0 1924.0 34.0 170.5
40167.0 1860.0 33.8 178.6
40167.0 1866.0 30.4 164.5
40167.0 Std IAEA 32.0 194.3
40168.0 Std IAEA 31.8 190.5
40168.0 Std SUCROSE 36.3 238.2
40168.0 Std SUCROSE 36.3 239.3
40168.0 Std SUCROSE 36.4 239.9
40168.0 33b 1526.0 30.8 180.1
40168.0 33b 1525.0 30.9 180.3
40168.0 33b 1524.0 31.6 178.8
40168.0 33b 1523.0 31.8 185.4
40168.0 33b 1522.0 32.7 191.4
40168.0 33b 1521.0 32.0 185.4
40168.0 33b 1520.0 31.7 184.6
40168.0 33b 1519.0 31.8 187.7
40168.0 33b 1518.0 31.8 183.6
40168.0 33b 1517.0 31.3 176.7
40168.0 33b 1516.0 31.8 180.3
40168.0 33b 1515.0 31.2 177.2
40168.0 Std IAEA 32.0 195.1
40168.0 Std IAEA 32.2 196.1
40168.0 33b 1514.0 31.2 180.8
40168.0 33b 1513.0 31.8 182.8
40168.0 33b 1510.0 31.2 181.7
40168.0 33b 1509.0 31.4 179.9
40168.0 33b 1508.0 31.8 176.1
40168.0 33b 1507.0 30.6 175.8
40168.0 33b 1544.0 31.5 173.1
40168.0 33b 1543.0 32.8 172.6
40168.0 33b 1542.0 33.3 179.5
40168.0 33b 1541.0 32.5 175.9
40168.0 33b 1540.0 32.0 179.3
40168.0 33b 1539.0 31.1 180.1
40168.0 Std SUCROSE 36.6 238.9
SUPPLEMENTARY DATA TABLE 105
40168.0 Std SUCROSE 36.6 237.3
40168.0 33b 1538.0 31.8 171.8
40168.0 33b 1537.0 32.4 185.4
40168.0 33b 1536.0 31.1 171.6
40168.0 33b 1535.0 31.7 175.4
40168.0 33b 1534.0 32.1 172.8
40168.0 33b 1533.0 31.7 177.9
40168.0 33b 1532.0 31.9 175.4
40168.0 33b 1531.0 31.9 181.2
40168.0 33b 1529.0 31.9 190.0
40168.0 33b 1530.0 31.7 182.5
40168.0 33b 1528.0 31.6 173.8
40168.0 33b 1527.0 31.0 172.1
40168.0 Std IAEA 32.6 192.2
40168.0 Std IAEA 32.2 195.1
40168.0 Std SUCROSE 36.6 235.9
40173.0 Std Sucrose 36.4 241.5
40173.0 Std Sucrose 36.3 235.4
40173.0 Std Sucrose 36.5 240.3
40173.0 33b 1506.0 30.6 190.6
40173.0 33b 1505.0 30.7 191.9
40173.0 33b 1504.0 30.4 192.8
40173.0 33b 1503.0 30.6 196.4
40173.0 33b 1502.0 31.8 195.4
40173.0 33b 1499.0 31.6 196.4
40173.0 33b 1501.0 31.9 195.5
40173.0 33b 1498.0 30.7 195.3
40173.0 33b 1497.0 30.5 192.6
40173.0 33b 1496.0 31.0 191.6
40173.0 33b 1495.0 31.4 190.4
40173.0 33b 1494.0 30.6 188.5
40173.0 Std IAEA 32.2 198.3
40173.0 Std IAEA 32.2 197.5
40173.0 33b 1493.0 30.7 188.4
40173.0 33b 1492.0 31.3 194.3
40173.0 33b 1491.0 30.7 191.3
40173.0 33b 1490.0 31.5 185.3
40173.0 33b 1489.0 32.6 197.3
40173.0 33b 1500.0 31.5 189.1
40173.0 33b 1488.0 31.0 194.3
40173.0 33b 1487.0 30.6 181.4
40173.0 33b 1486.0 31.0 183.3
40173.0 33b 1485.0 31.8 178.0
106 MAXBERKELHAMMER
40173.0 7a 1854.0 31.8 202.0
40173.0 7a 1855.0 32.3 183.3
40173.0 Std SUCROSE 36.4 240.1
40173.0 Std SUCROSE 36.4 241.5
40173.0 7a 1853.0 31.8 190.7
40173.0 7a 1852.0 32.6 185.0
40173.0 7a 1851.0 32.6 188.0
40173.0 7a 1850.0 30.3 180.0
40173.0 7a 1849.0 31.5 184.9
40173.0 7a 1848.0 31.4 183.7
40173.0 7a 1847.0 30.0 184.1
40173.0 7a 1846.0 29.8 194.2
40173.0 7a 1844.0 31.1 204.0
40173.0 7a 1842.0 30.9 207.2
40173.0 7a 1840.0 30.7 196.0
40173.0 7a 1839.0 30.3 183.7
40173.0 Std IAEA 32.3 200.7
40173.0 Std IAEA 32.0 198.6
40173.0 Std IAEA 32.2 200.0
40173.0 Std SUCROSE 36.4 238.7
40174.0 Std IAEA 31.9 195.4
40174.0 Std Sucrose 36.1 241.2
40174.0 Std Sucrose 36.2 243.2
40174.0 7b 1837.0 31.6 185.8
40174.0 7b 1836.0 31.3 179.7
40174.0 7b 1835.0 31.9 174.7
40174.0 7b 1834.0 30.3 172.9
40174.0 7b 1833.0 31.0 171.3
40174.0 7b 1833.0 31.6 169.5
40174.0 7b 1832.0 30.2 173.9
40174.0 7b 1831.0 30.4 183.3
40174.0 7b 1830.0 31.1 188.8
40174.0 7b 1829.0 31.9 185.3
40174.0 7b 1827.0 30.3 171.3
40174.0 7b 1828.0 31.6 184.1
40174.0 Std IAEA 32.0 195.8
40174.0 Std IAEA 32.1 196.6
40174.0 7b 1826.0 31.6 184.2
40174.0 7b 1825.0 31.4 188.3
40174.0 7b 1824.0 30.7 189.7
40174.0 7b 1823.0 31.4 177.9
40174.0 7b 1822.0 31.0 171.7
40174.0 7b 1821.0 30.8 176.2
SUPPLEMENTARY DATA TABLE 107
40174.0 7b 1820.0 32.0 180.3
40174.0 7b 1819.0 31.2 185.1
40174.0 7b 1818.0 31.9 182.2
40174.0 7b 1817.0 32.7 185.8
40174.0 7b 1816.0 32.1 176.8
40174.0 7b 1815.0 31.3 178.3
40174.0 Std SUCROSE 36.2 241.3
40174.0 Std SUCROSE 36.3 240.3
40174.0 7b 1814.0 31.4 182.6
40174.0 7b 1813.0 31.8 181.9
40174.0 7b 1812.0 32.4 179.4
40174.0 7b 1811.0 31.8 180.0
40174.0 7b 1810.0 30.9 176.8
40174.0 7b 1809.0 32.2 180.2
40174.0 7b 1808.0 33.4 179.2
40174.0 7b 1807.0 33.7 200.7
40174.0 7b 1806.0 33.5 193.1
40174.0 7b 1805.0 32.2 190.3
40174.0 7b 1804.0 31.9 183.0
40174.0 7b 1803.0 32.7 180.0
40174.0 Std IAEA 32.4 198.8
40174.0 Std IAEA 32.1 198.2
40174.0 Std SUCROSE 36.4 240.9
40174.0 Std SUCROSE 36.5 241.7
40175.0 Std Sucrose 36.1 237.3
40175.0 Std Sucrose 36.2 235.7
40175.0 Std Sucrose 36.1 234.7
40175.0 Std IAEA 31.8 202.0
40175.0 7b 1790.0 33.5 174.1
40175.0 7b 1789.0 31.9 169.8
40175.0 7b 1788.0 31.5 172.4
40175.0 7b 1787.0 31.3 165.9
40175.0 7b 1786.0 31.5 166.4
40175.0 7b 1785.0 33.6 172.7
40175.0 7b 1796.0 31.6 178.7
40175.0 7b 1795.0 31.2 180.8
40175.0 7b 1794.0 33.0 178.5
40175.0 7b 1793.0 33.1 173.0
40175.0 7b 1792.0 32.4 179.5
40175.0 7b 1791.0 33.3 177.2
40175.0 Std IAEA 32.3 200.6
40175.0 Std IAEA 32.4 199.5
40175.0 7b 1802.0 31.3 174.1
108 MAXBERKELHAMMER
40175.0 7b 1801.0 31.4 186.8
40175.0 7b 1800.0 32.2 178.0
40175.0 7b 1799.0 32.8 187.1
40175.0 7b 1798.0 31.5 184.3
40175.0 7b 1797.0 31.6 176.6
40175.0 7b 1784.0 30.0 163.5
40175.0 7b 1783.0 32.8 163.8
40175.0 7b 1782.0 31.0 155.3
40175.0 7b 1781.0 32.7 168.3
40175.0 7b 1780.0 31.4 158.3
40175.0 7b 1779.0 33.4 179.5
40175.0 Std SUCROSE 36.3 235.7
40175.0 7b 1778.0 33.8 180.3
40175.0 7b 1777.0 33.4 178.7
40175.0 7b 1776.0 32.6 175.0
40175.0 7b 1775.0 32.2 179.3
40175.0 7b 1774.0 33.1 178.6
40175.0 7b 1773.0 32.2 186.3
40175.0 7b 1772.0 33.7 182.4
40175.0 7b 1771.0 32.4 177.7
40175.0 7b 1770.0 31.1 172.0
40175.0 7b 1769.0 30.9 172.7
40175.0 7b 1768.0 31.5 174.4
40175.0 7b 1767.0 30.9 164.6
40175.0 33b 1493.0 30.7 181.1
40175.0 33b 1489.0 32.6 192.1
40175.0 33b 1487.0 30.5 178.0
40175.0 Std SUCROSE 36.3 236.0
40175.0 Std IAEA 32.2 200.0
40176.0 IAEA 32.0 198.7
40176.0 Sucrose 36.3 238.7
40176.0 Sucrose 36.3 238.8
40176.0 Sucrose 36.1 235.9
40176.0 IAEA 32.1 198.4
40176.0 1766.0 31.2 174.5
40176.0 1765.0 31.9 175.4
40176.0 1764.0 32.4 176.4
40176.0 1763.0 30.8 176.5
40176.0 1762.0 30.5 183.7
40176.0 1761.0 32.1 196.5
40176.0 1760.0 32.1 192.7
40176.0 1759.0 33.8 198.7
40176.0 1758.0 35.7 194.7
SUPPLEMENTARY DATA TABLE 109
40176.0 1757.0 35.4 198.3
40176.0 1756.0 34.1 189.7
40176.0 1755.0 33.3 184.2
40176.0 SUCROSE 36.5 237.8
40176.0 SUCROSE 36.6 238.0
40176.0 1754.0 31.7 184.6
40176.0 1753.0 30.9 185.9
40176.0 1752.0 30.9 190.6
40176.0 1751.0 31.4 187.8
40176.0 1750.0 31.9 187.5
40176.0 1749.0 30.9 192.5
40176.0 1748.0 30.4 196.8
40176.0 1747.0 31.5 191.3
40176.0 1746.0 31.6 195.7
40176.0 1745.0 32.1 190.6
40176.0 1744.0 32.1 188.3
40176.0 1743.0 33.6 179.8
40176.0 IAEA 32.3 200.6
40176.0 IAEA 32.1 198.9
40176.0 1742.0 32.6 179.4
40176.0 1741.0 33.5 191.3
40176.0 1740.0 32.9 183.4
40176.0 1739.0 32.3 177.5
40176.0 1738.0 32.5 176.1
40176.0 1737.0 32.9 189.7
40176.0 1736.0 31.4 186.7
40176.0 1735.0 31.1 180.0
40176.0 1734.0 31.2 177.9
40176.0 1733.0 31.0 182.9
40176.0 1732.0 31.4 179.5
40176.0 1731.0 33.5 187.5
40176.0 SUCROSE 36.4 235.5
40176.0 IAEA 32.7 201.4
40177.0 Std Sucrose 36.3 236.0
40177.0 Std Sucrose 36.3 238.0
40177.0 Std Sucrose 36.3 235.7
40177.0 Std IAEA 32.0 199.0
40177.0 Std IAEA 32.3 199.4
40177.0 7b 1711.0 32.3 191.3
40177.0 7b 1710.0 33.0 194.9
40177.0 7b 1709.0 32.1 189.9
40177.0 7b 1708.0 30.8 185.3
40177.0 7b 1707.0 31.4 189.9
110 MAXBERKELHAMMER
40177.0 7b 1706.0 33.5 202.5
40177.0 7b 1705.0 31.5 181.8
40177.0 7b 1704.0 31.3 184.6
40177.0 7b 1703.0 31.3 195.1
40177.0 7b 1702.0 32.1 191.2
40177.0 7b 1701.0 33.3 198.5
40177.0 7b 1700.0 31.8 187.9
40177.0 Std SUCROSE 36.5 237.0
40177.0 Std SUCROSE 36.5 239.5
40177.0 7b 1699.0 32.4 189.7
40177.0 7b 1698.0 33.4 180.1
40177.0 7b 1697.0 32.6 184.6
40177.0 7b 1696.0 32.6 185.4
40177.0 7b 1695.0 33.1 193.3
40177.0 7b 1694.0 30.6 188.5
40177.0 7b 1730.0 32.9 195.4
40177.0 7b 1717.0 32.3 183.3
40177.0 7b 1728.0 30.5 185.3
40177.0 7b 1727.0 30.0 184.1
40177.0 7b 1726.0 30.7 193.4
40177.0 7b 1725.0 32.0 197.8
40177.0 Std IAEA 32.0 196.8
40177.0 Std IAEA 32.2 197.8
40177.0 7b 1724.0 32.2 184.3
40177.0 7b 1723.0 32.5 189.6
40177.0 7b 1722.0 31.6 183.0
40177.0 7b 1721.0 33.5 191.9
40177.0 7b 1720.0 32.2 195.2
40177.0 7b 1719.0 32.0 182.3
40177.0 7b 1718.0 33.2 174.2
40177.0 7b 1729.0 32.1 187.5
40177.0 7b 1713.0 30.7 167.3
40177.0 7b 1715.0 30.6 182.5
40177.0 7b 1714.0 31.2 186.1
40177.0 7b 1712.0 31.4 185.6
40177.0 Std SUCROSE 36.6 236.5
40177.0 Std IAEA 32.3 201.0
40178.0 Std Sucrose 35.7 241.3
40178.0 Std Sucrose 35.8 241.3
40178.0 Std Sucrose 35.9 238.4
40178.0 Std IAEA 32.0 197.3
40178.0 Std IAEA 31.9 199.8
40178.0 7b 1675.0 30.8 191.2
SUPPLEMENTARY DATA TABLE 111
40178.0 7b 1674.0 31.3 196.0
40178.0 7b 1673.0 30.7 209.1
40178.0 7b 1672.0 32.1 191.6
40178.0 7b 1671.0 31.2 184.9
40178.0 7b 1670.0 31.5 193.7
40178.0 7b 1669.0 31.4 190.6
40178.0 7b 1668.0 31.3 183.4
40178.0 7b 1666.0 31.4 197.7
40178.0 7b 1665.0 30.7 182.8
40178.0 7b 1664.0 32.2 201.6
40178.0 7b 1667.0 31.2 191.4
40178.0 Std SUCROSE 36.5 236.5
40178.0 Std SUCROSE 36.4 238.1
40178.0 7b 1661.0 31.9 186.8
40178.0 7b 1660.0 31.5 188.1
40178.0 7b 1659.0 31.7 200.1
40178.0 7b 1658.0 31.3 192.8
40178.0 7b 1657.0 33.0 190.7
40178.0 7b 1656.0 32.9 196.6
40178.0 7b 1693.0 31.9 178.7
40178.0 7b 1692.0 32.8 184.9
40178.0 7b 1691.0 31.3 184.9
40178.0 7b 1690.0 30.8 172.4
40178.0 7b 1689.0 32.7 181.1
40178.0 7b 1688.0 32.0 190.5
40178.0 Std IAEA 32.1 194.5
40178.0 Std IAEA 32.1 197.0
40178.0 7b 1687.0 32.7 193.4
40178.0 7b 1686.0 32.4 184.6
40178.0 7b 1685.0 31.1 181.2
40178.0 7b 1684.0 32.7 192.4
40178.0 7b 1683.0 30.5 174.7
40178.0 7b 1682.0 31.4 189.9
40178.0 7b 1681.0 31.7 183.9
40178.0 7b 1680.0 33.2 185.3
40178.0 7b 1679.0 31.6 183.0
40178.0 7b 1678.0 33.1 193.4
40178.0 7b 1677.0 32.0 181.6
40178.0 7b 1676.0 31.8 183.0
40178.0 Std SUCROSE 36.4 234.9
40178.0 Std IAEA 32.5 200.9
40188.0 Std IAEA 31.9 179.2
40188.0 Std SUCROSE 36.4 235.1
112 MAXBERKELHAMMER
40188.0 Std SUCROSE 36.5 232.2
40188.0 Std SUCROSE 36.3 234.5
40188.0 Std IAEA 32.2 186.1
40188.0 Alm7 1637.0 32.1 176.7
40188.0 Alm7 1636.0 32.5 176.3
40188.0 Alm7 1635.0 32.7 176.5
40188.0 Alm7 1634.0 33.0 174.3
40188.0 Alm7 1633.0 32.6 167.0
40188.0 Alm7 1632.0 32.0 167.2
40188.0 Alm7 1631.0 31.6 172.4
40188.0 Alm7 1630.0 31.6 163.0
40188.0 Alm7 1629.0 32.6 170.3
40188.0 Alm7 1628.0 32.2 169.8
40188.0 Alm7 1627.0 31.9 168.2
40188.0 Alm7 1626.0 31.7 169.2
40188.0 Std SUCROSE 36.7 236.5
40188.0 Std SUCROSE 36.6 234.5
40188.0 Alm7 1625.0 31.6 164.1
40188.0 Alm7 1624.0 32.2 162.8
40188.0 Alm7 1623.0 32.2 177.8
40188.0 Alm7 1622.0 31.7 185.0
40188.0 Alm7 1621.0 31.3 170.8
40188.0 Alm7 1620.0 30.6 166.5
40188.0 Alm7 1643.0 32.2 175.3
40188.0 Alm7 1642.0 32.5 174.3
40188.0 Alm7 1641.0 32.5 180.6
40188.0 Alm7 1640.0 32.1 183.4
40188.0 Alm7 1639.0 31.7 173.3
40188.0 Alm7 1638.0 31.2 168.7
40188.0 Std IAEA 32.0 189.8
40188.0 Std IAEA 32.5 189.7
40188.0 Alm7 1649.0 30.9 188.1
40188.0 Alm7 1648.0 32.8 195.3
40188.0 Alm7 1647.0 33.5 178.9
40188.0 Alm7 1646.0 32.3 178.8
40188.0 Alm7 1645.0 32.6 192.4
40188.0 Alm7 1644.0 32.0 180.3
40188.0 Alm7 1655.0 31.8 173.7
40188.0 Alm7 1654.0 31.0 166.9
40188.0 Alm7 1653.0 31.2 185.8
40188.0 Alm7 1652.0 33.1 182.8
40188.0 Alm7 1651.0 32.1 172.2
40188.0 Alm7 1650.0 31.9 179.6
SUPPLEMENTARY DATA TABLE 113
40188.0 Std SUCROSE 32.1 189.2
40188.0 Std SUCROSE 32.2 189.5
40188.0 Std IAEA 36.3 229.2
40189.0 Std SUCROSE 36.3 236.3
40189.0 Std SUCROSE 36.4 233.3
40189.0 Std SUCROSE 36.5 237.5
40189.0 Std IAEA 32.0 195.6
40189.0 Alm7 1602.0 32.2 167.7
40189.0 Alm7 1603.0 32.6 167.9
40189.0 Alm7 1604.0 31.8 174.3
40189.0 Alm7 1605.0 31.6 170.9
40189.0 Alm7 1606.0 32.3 171.6
40189.0 Alm7 1607.0 32.5 173.9
40189.0 Alm7 1608.0 29.9 163.9
40189.0 Alm7 1609.0 29.9 159.7
40189.0 Alm7 1610.0 31.5 166.8
40189.0 Alm7 1611.0 31.9 179.5
40189.0 Alm7 1612.0 31.7 176.0
40189.0 Alm7 1613.0 31.2 162.7
40189.0 Std SUCROSE 36.6 234.5
40189.0 Std SUCROSE 36.6 234.9
40189.0 Alm7 1614.0 31.9 169.2
40189.0 Alm7 1615.0 31.8 167.8
40189.0 Alm7 1616.0 30.6 168.7
40189.0 Alm7 1617.0 30.9 173.6
40189.0 Alm7 1618.0 30.3 163.1
40189.0 Alm7 1619.0 30.2 162.7
40189.0 Alm7 1584.0 32.5 179.4
40189.0 Alm7 1585.0 33.0 184.8
40189.0 Alm7 1586.0 33.1 177.2
40189.0 Alm7 1587.0 32.7 165.8
40189.0 Alm7 1588.0 32.4 166.0
40189.0 Alm7 1589.0 32.5 172.7
40189.0 Std IAEA 32.1 192.5
40189.0 Std IAEA 32.2 194.4
40189.0 Alm7 1590.0 31.6 172.2
40189.0 Alm7 1591.0 32.5 180.4
40189.0 Alm7 1592.0 33.0 176.4
40189.0 Alm7 1594.0 33.1 173.4
40189.0 Alm7 1593.0 33.0 170.7
40189.0 Alm7 1595.0 31.7 167.4
40189.0 Alm7 1596.0 31.6 164.7
40189.0 Alm7 1597.0 32.6 168.5
114 MAXBERKELHAMMER
40189.0 Alm7 1598.0 33.4 174.5
40189.0 Alm7 1599.0 33.4 184.2
40189.0 Alm7 1600.0 33.7 183.8
40189.0 Alm7 1601.0 32.8 184.7
40189.0 Std SUCROSE 36.4 236.2
40189.0 Std IAEA 32.2 197.9
40189.0 Std IAEA 32.1 195.4
40190.0 Std SUCROSE 36.4 233.6
40190.0 Std SUCROSE 36.4 233.6
40190.0 Std SUCROSE 36.5 235.2
40190.0 Std IAEA 32.4 192.7
40190.0 Std IAEA 32.0 191.0
40190.0 7b 1583.0 32.3 179.9
40190.0 7b 1582.0 32.7 189.1
40190.0 7b 1581.0 33.8 190.9
40190.0 7b 1580.0 33.6 180.6
40190.0 7b 1579.0 34.1 190.4
40190.0 7b 1578.0 34.0 190.7
40190.0 7b 1577.0 34.7 189.3
40190.0 7b 1576.0 33.0 179.7
40190.0 7b 1575.0 31.9 173.2
40190.0 7b 1574.0 33.8 176.6
40190.0 7b 1573.0 33.1 174.9
40190.0 7b 1572.0 33.0 184.4
40190.0 Std SUCROSE 36.6 232.2
40190.0 Std SUCROSE 36.7 233.9
40190.0 7b 1571.0 32.7 185.3
40190.0 7b 1570.0 32.4 190.0
40190.0 7b 1569.0 33.3 193.7
40190.0 7b 1568.0 31.6 181.3
40190.0 7b 1567.0 29.6 175.8
40190.0 7b 1566.0 31.7 178.1
40190.0 7b 1565.0 32.1 171.5
40190.0 7b 1564.0 32.8 179.7
40190.0 7b 1563.0 31.8 173.8
40190.0 7b 1561.0 32.0 182.7
40190.0 7b 1560.0 31.8 180.3
40190.0 7b 1559.0 32.4 182.4
40190.0 Std IAEA 32.1 193.1
40190.0 Std IAEA 32.3 195.0
40190.0 7b 1558.0 30.9 178.9
40190.0 7b 1557.0 30.5 174.3
40190.0 7b 1556.0 31.8 183.4
SUPPLEMENTARY DATA TABLE 115
40190.0 7b 1553.0 32.9 190.8
40190.0 7b 1552.0 32.4 193.6
40190.0 7b 1551.0 30.4 182.5
40190.0 7b 1550.0 31.3 189.3
40190.0 7b 1549.0 31.2 174.6
40190.0 7b 1548.0 31.9 181.0
40190.0 7b 1547.0 33.6 182.8
40190.0 7b 1546.0 32.4 179.4
40190.0 7b 1545.0 33.2 180.7
40190.0 Std SUCROSE 36.9 234.8
40190.0 Std IAEA 32.2 193.5
40190.0 Std IAEA 32.2 194.8
40190.0 Std IAEA 32.3 186.8
40190.0 Std SUCROSE 36.1 233.5
40190.0 Std SUCROSE 36.3 236.8
40190.0 Std IAEA 32.2 188.9
40190.0 7b 1544.0 34.0 180.8
40190.0 7b 1543.0 33.3 172.7
40190.0 7b 1542.0 33.3 174.4
40190.0 7b 1541.0 32.6 185.4
40190.0 7b 1540.0 32.3 174.1
40190.0 7b 1539.0 32.7 175.0
40190.0 7b 1523.0 33.5 188.2
40190.0 7b 1538.0 32.6 185.0
40190.0 7b 1537.0 32.2 176.2
40190.0 7b 1536.0 33.0 168.7
40190.0 7b 1535.0 32.7 171.2
40190.0 7b 1522.0 32.4 173.8
40190.0 Std SUCROSE 36.3 235.1
40190.0 Std SUCROSE 36.4 235.6
40190.0 7b 1521.0 32.1 175.5
40190.0 7b 1534.0 33.0 178.8
40190.0 7b 1533.0 32.9 176.3
40190.0 7b 1532.0 32.0 181.5
40190.0 7b 1530.0 31.9 170.8
40190.0 7b 1529.0 32.0 169.4
40190.0 7b 1531.0 32.6 188.3
40190.0 7b 1527.0 32.2 177.1
40190.0 7b 1526.0 32.5 168.4
40190.0 7b 1525.0 32.1 176.2
40190.0 7b 1524.0 33.8 175.2
40190.0 7b 1520.0 33.3 184.3
40190.0 Std IAEA 32.4 188.1
116 MAXBERKELHAMMER
40190.0 Std IAEA 32.0 192.0
40190.0 7b 1519.0 32.1 170.9
40190.0 7b 1518.0 32.3 172.5
40190.0 7b 1528.0 31.6 170.5
40190.0 7b 1856.0 33.1 165.4
40190.0 7b 1857.0 31.7 165.8
40190.0 7b 1858.0 32.0 168.8
40190.0 7b 1859.0 32.9 168.3
40190.0 7b 1860.0 33.4 177.4
40190.0 7b 1861.0 32.1 166.2
40190.0 7b 1862.0 32.4 162.7
40190.0 7b 1863.0 32.9 173.9
40190.0 7b 1864.0 31.9 170.9
40190.0 Std SUCROSE 36.5 233.6
40190.0 Std SUCROSE 36.5 235.9
40190.0 Std IAEA 32.1 192.3
40190.0 Std IAEA 32.6 190.4
40192.0 Std IAEA 32.1 192.0
40192.0 Std SUCROSE 36.4 235.4
40192.0 Std SUCROSE 36.4 232.2
40192.0 Std SUCROSE 36.4 233.0
40192.0 Std IAEA 32.0 190.5
40192.0 7b 1880.0 33.0 187.2
40192.0 7b 1881.0 33.1 193.1
40192.0 7b 1882.0 32.5 188.7
40192.0 7b 1883.0 32.4 174.7
40192.0 7b 1884.0 31.5 179.8
40192.0 7b 1885.0 32.7 186.7
40192.0 7b 1886.0 32.7 186.4
40192.0 7b 1887.0 33.0 191.1
40192.0 7b 1888.0 33.0 183.9
40192.0 7b 1889.0 32.4 183.8
40192.0 7b 1890.0 32.1 186.5
40192.0 7b 1891.0 30.4 170.7
40192.0 Std SUCROSE 36.6 234.7
40192.0 Std SUCROSE 36.6 235.8
40192.0 7b 1892.0 32.6 188.2
40192.0 7b 1893.0 33.1 169.8
40192.0 7b 1894.0 33.6 187.9
40192.0 7b 1895.0 30.9 172.8
40192.0 7b 1896.0 31.5 177.3
40192.0 7b 1897.0 31.2 178.4
40192.0 7b 1865.0 29.7 149.0
SUPPLEMENTARY DATA TABLE 117
40192.0 7b 1866.0 29.2 167.1
40192.0 7b 1867.0 28.7 183.2
40192.0 7b 1868.0 28.2 169.3
40192.0 7b 1869.0 29.2 176.8
40192.0 7b 1870.0 31.2 179.5
40192.0 Std IAEA 32.3 194.9
40192.0 Std IAEA 32.3 194.3
40192.0 7b 1871.0 32.2 183.5
40192.0 7b 1872.0 31.7 176.7
40192.0 7b 1873.0 33.2 181.7
40192.0 7b 1874.0 31.3 178.1
40192.0 7b 1875.0 31.2 176.0
40192.0 7b 1876.0 32.4 183.4
40192.0 7b 1877.0 32.3 179.5
40192.0 7b 1878.0 31.9 183.7
40192.0 7b 1879.0 32.0 169.7
40192.0 7b 2007.0 29.6 166.9
40192.0 7b 2005.0 32.6 166.4
40192.0 7b 2006.0 31.4 164.6
40192.0 Std SUCROSE 36.4 233.4
40192.0 Std IAEA 31.9 193.2
40192.0 Std IAEA 32.3 193.4
40192.0 Std SUCROSE 36.4 236.2
40192.0 Std IAEA 32.0 188.8
40192.0 Std SUCROSE 36.4 232.9
40192.0 7b 1916.0 31.5 168.8
40192.0 7b 1917.0 31.5 173.3
40192.0 7b 1918.0 31.7 174.2
40192.0 7b 1919.0 31.9 181.8
40192.0 7b 1920.0 30.6 181.2
40192.0 7b 1921.0 31.1 167.7
40192.0 7b 1922.0 31.3 177.5
40192.0 7b 1923.0 31.9 176.5
40192.0 7b 1924.0 32.7 171.4
40192.0 7b 1925.0 31.3 170.4
40192.0 7b 1927.0 30.8 170.5
40192.0 7b 1928.0 30.5 167.3
40192.0 Std SUCROSE 36.4 231.2
40192.0 Std SUCROSE 36.5 232.8
40192.0 7b 1929.0 31.0 167.7
40192.0 7b 1930.0 30.9 168.6
40192.0 7b 1931.0 31.4 171.4
40192.0 7b 1932.0 32.0 169.4
118 MAXBERKELHAMMER
40192.0 7b 1933.0 32.3 176.1
40192.0 7b 1934.0 33.0 175.7
40192.0 7b 1908.0 31.0 183.8
40192.0 7b 1907.0 31.0 175.2
40192.0 7b 1906.0 30.8 174.0
40192.0 7b 1905.0 31.0 172.8
40192.0 7b 1904.0 31.2 178.6
40192.0 7b 1903.0 31.2 177.1
40192.0 Std IAEA 32.1 189.2
40192.0 Std IAEA 32.4 190.5
40192.0 7b 1902.0 33.5 175.9
40192.0 7b 1901.0 31.6 191.1
40192.0 7b 1900.0 31.9 187.9
40192.0 7b 1899.0 31.9 172.9
40192.0 7b 1898.0 30.8 172.7
40192.0 7b 1909.0 30.5 186.6
40192.0 7b 1910.0 33.2 171.5
40192.0 7b 1911.0 31.4 170.2
40192.0 7b 1912.0 31.7 169.5
40192.0 7b 1913.0 33.4 185.2
40192.0 7b 1914.0 30.7 186.8
40192.0 7b 1915.0 31.0 167.1
40192.0 Std IAEA 32.1 185.3
40192.0 Std IAEA 32.3 190.8
40192.0 Std SUCROSE 36.5 234.7
40192.0 Std SUCROSE 36.4 237.6
40194.0 Std IAEA 32.2 193.8
40194.0 Std SUCROSE 36.0 233.5
40194.0 Std SUCROSE 36.2 233.5
40194.0 Std SUCROSE 36.2 233.6
40194.0 Std IAEA 32.2 195.2
40194.0 7b 1990.0 31.4 165.8
40194.0 7b 1989.0 30.4 164.6
40194.0 7b 1987.0 31.4 163.5
40194.0 7b 1900.0 33.3 198.5
40194.0 7b 1902.0 34.0 181.2
40194.0 7b 1903.0 31.5 173.4
40194.0 7b 1904.0 32.5 184.8
40194.0 7b 1905.0 33.1 182.2
40194.0 7b 1906.0 31.9 174.2
40194.0 7b 1907.0 32.2 175.0
40194.0 7b 1908.0 33.2 183.4
40194.0 7b 1909.0 33.1 182.7
SUPPLEMENTARY DATA TABLE 119
40194.0 Std SUCROSE 36.4 231.1
40194.0 Std SUCROSE 36.4 234.1
40194.0 7b 1911.0 31.9 167.5
40194.0 7b 1913.0 34.7 182.2
40194.0 7b 1914.0 31.7 176.5
40194.0 7b 1916.0 32.6 168.7
40194.0 7b 1918.0 32.5 179.5
40194.0 7b 1920.0 32.6 180.1
40194.0 7b 1976.0 32.6 166.4
40194.0 7b 1975.0 31.8 161.8
40194.0 7b 1974.0 29.8 149.4
40194.0 7b 1972.0 33.1 169.7
40194.0 7b 1971.0 31.5 163.6
40194.0 7b 1970.0 31.1 177.9
40194.0 Std IAEA 32.3 194.0
40194.0 Std IAEA 32.3 191.3
40194.0 7b 1969.0 30.7 167.3
40194.0 7b 2004.0 32.1 167.6
40194.0 7b 2003.0 32.0 163.4
40194.0 7b 2002.0 31.1 168.2
40194.0 7b 2001.0 30.6 151.4
40194.0 7b 1998.0 32.2 163.2
40194.0 7b 1997.0 32.6 165.7
40194.0 7b 1996.0 30.8 174.1
40194.0 7b 1995.0 31.6 154.3
40194.0 7b 1994.0 31.3 148.8
40194.0 7b 1993.0 30.3 154.4
40194.0 7b 1992.0 30.5 162.3
40194.0 Std SUCROSE 36.5 231.5
40194.0 Std IAEA 32.3 195.9
40194.0 Std IAEA 31.9 195.9
40194.0 Std SUCROSE 36.3 234.9
40194.0 Std SUCROSE 36.4 234.7
40194.0 Std SUCROSE 36.5 233.1
40194.0 Std IAEA 32.1 199.3
40194.0 7b 1944.0 32.1 192.4
40194.0 7b 1943.0 31.5 174.3
40194.0 7b 1942.0 30.0 177.8
40194.0 7b 1941.0 29.8 179.1
40194.0 7b 1940.0 31.0 177.8
40194.0 7b 1939.0 31.9 174.3
40194.0 7b 1938.0 30.5 175.8
40194.0 7b 1937.0 31.5 180.1
120 MAXBERKELHAMMER
40194.0 7b 1936.0 32.2 180.6
40194.0 7b 1935.0 31.9 192.3
40194.0 7b 1986.0 30.7 164.9
40194.0 7b 1985.0 31.2 168.0
40194.0 Std SUCROSE 36.4 231.0
40194.0 Std SUCROSE 36.5 235.6
40194.0 7b 1984.0 30.7 175.6
40194.0 7b 1983.0 29.4 174.7
40194.0 7b 1982.0 30.3 175.0
40194.0 7b 1980.0 31.3 163.1
40194.0 7b 1978.0 31.8 170.3
40194.0 7b 1977.0 32.5 168.9
40194.0 7b 1968.0 30.7 169.9
40194.0 7b 1967.0 30.5 185.6
40194.0 7b 1965.0 31.5 169.1
40194.0 7b 1964.0 31.5 170.2
40194.0 7b 1962.0 32.9 167.3
40194.0 7b 1961.0 32.1 171.0
40194.0 Std IAEA 32.4 194.6
40194.0 Std IAEA 32.1 194.5
40194.0 7b 1959.0 32.0 174.5
40194.0 7b 1958.0 31.9 166.1
40194.0 7b 1957.0 30.1 169.2
40194.0 7b 1956.0 32.2 173.6
40194.0 7b 1954.0 32.7 172.1
40194.0 7b 1953.0 31.2 172.5
40194.0 7b 1952.0 31.9 168.3
40194.0 7b 1951.0 31.7 162.6
40194.0 7b 1950.0 31.3 183.2
40194.0 7b 1948.0 31.8 181.8
40194.0 7b 1947.0 33.7 177.3
40194.0 7b 1946.0 32.4 163.5
40194.0 Std SUCROSE 36.5 228.8
40194.0 Std IAEA 32.1 197.0
40194.0 Std IAEA 32.3 197.6

Abstract (if available)

Abstract Late Holocene paleoclimatology of the southwestern United States has been reconstructed largely through the analysis of ring width variability from a network of gridded tree chronologies. Trees commonly respond to moisture stress in this semi-arid environment providing a spatially coherent annually-resolved record of PDSI variability. At a handful of high altitude sites, trees are thermally stressed, providing a record of temperature variability. This thesis addresses two prominent questions that arise from the tree ring network

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Berkelhammer, Max Benjamin (author)

Core Title Perspectives on drought and temperature variability for the southwestern United States from a new hydro-isotopic network

School College of Letters, Arts and Sciences

Degree Juris Doctor / Doctor of Philosophy

Degree Program Geological Sciences

Publication Date 08/20/2010

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag climate change,dendroclimatology,Drought,Hydrology,isotope geochemistry,OAI-PMH Harvest,paleoclimate

Place Name Southwest (region), USA (countries)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Stott, Lowell D. (committee chair), Emile-Geay, Julien (committee member), Manhan, Donal (committee member)

Creator Email berkelha@usc.edu,mberkelhammer@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3406

Unique identifier UC1340929

Identifier etd-Berkelhammer-3430_with_suppl_data.pdf (filename),usctheses-m40 (legacy collection record id),usctheses-c127-681462 (legacy record id),usctheses-m3406 (legacy record id)

Legacy Identifier etd-Berkelhammer-3430_with_suppl_data.pdf

Dmrecord 681462

Document Type Dissertation

Rights Berkelhammer, Max Benjamin

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu