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Impacts of heat mitigation strategies and pollutant transport on climate and air quality from urban to global scales

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Impacts of heat mitigation strategies and pollutant transport on climate and air quality from urban to global scales

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Content 1

Impacts of Heat Mitigation Strategies and Pollutant Transport on
Climate and Air Quality from Urban to Global Scales

By

Jiachen Zhang
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
(ENVIRONMENTAL ENGINEERING)

May 2019

2

Acknowledgements
Since joining USC in 2014, I have worked with many great people who have contributed
significantly to the development of my research expertise and intellectual maturity. I would like to
take this opportunity to convey my sincere gratitude to them with my humble acknowledgement.
Above all, I would like to express my sincere thanks to my graduate advisor, Prof. George Ban-
Weiss, who gave me his wholehearted support, trust, and encouragement. His vision and
knowledge of science have inspired and promoted my growth from a student to a mature
researcher. His open-minded, enthusiastic, and collaborative personality also motivated me to be
a better person.
I would also like to gratefully acknowledge the members of my defense committee: Prof. Amy
Childress and Prof. Bistra Dilkina for taking their precious time to review this dissertation and for
their valuable suggestions. For the members of my qualifying committee: Prof. Constantinos
Sioutas, Prof. Kelly Sanders, Prof. Frank Gilliland, Prof. John Wilson, I want to thank them for
their contribution to my proposal. The faculty and staff at USC have been very helpful; I learned
a lot from taking their courses and personal communication.
Thanks to my collaborators at the Lawrence Berkeley National Laboratory: Ronnen Levinson,
Haley Gilbert, Hugo Destaillats, Sharon Chen, Xiaochen Tang, and Pablo Rosado.
Thanks to my collaborators at Peking University: Prof. Junfeng Liu, Prof. Lin Zhang, Wei Tao,
Jing Meng, and Xiao Lu.
Thanks to my collaborator Kai Zhang at the Pacific Northwest National Laboratory (PNNL).
Many thanks to South Coast Air Quality Management District (SCAQMD) and California Air
Resources Board (CARB) for providing us with valuable data.
It is my great pleasure to work with former and current colleagues in my group: Pouya Vahmani,
Arash Mohegh, Trevor Krasowsky, Mohammad Taleghani, Yun Li, Mo Chen, Joseph Ko, and
Hannah Schlaerth. There are also so many great friends at USC and outside school who have
helped me along the journey of my PhD! I really appreciate their valuable support during the past
years.
Finally, thanks to my parents their unconditional love, trust, and support in my life. Thanks to my
husband Yu for his persistent encouragement and confidence in me.
3

Table of Contents
Acknowledgements ....................................................................................................................... 2
Abstract .......................................................................................................................................... 6
Chapter 1: Introduction ............................................................................................................... 7
1.1 Background ...................................................................................................................... 7
1.2 Research objectives .......................................................................................................... 7
1.3 Rationale for completed work .......................................................................................... 8
1.3.1 Black carbon aerosols ............................................................................................... 8
1.3.2 Heat mitigation strategies ......................................................................................... 8
1.4 Overview .......................................................................................................................... 8
Chapter 2: Long-range transport of black carbon and its dependence on aging timescale 10
2.1 Introduction .................................................................................................................... 10
2.2 Method ........................................................................................................................... 12
2.2.1 Model description and configuration ...................................................................... 12
2.2.2 Dry and wet deposition schemes............................................................................. 13
2.2.3 HIAPER Pole-to-Pole Observations ....................................................................... 13
2.2.4 Tracer tagging and sensitivity simulations ............................................................. 14
2.2.5 Optimization of BC aging timescale to match HIPPO observations ...................... 15
2.3 Introduction .................................................................................................................... 16
2.4 Results ............................................................................................................................ 18
2.4.1 Optimized BC profiles over the Pacific Ocean ....................................................... 18
2.4.2 Regional contribution of source regions to BC loading.......................................... 23
2.4.3 Dependence of BC lifetime on aging timescale ...................................................... 26
2.4.4 Caveats .................................................................................................................... 29
2.5 Conclusions .................................................................................................................... 29
Chapter 3: The climate impacts of adopting cool roofs from urban to global scale ............. 31
3.1 Introduction .................................................................................................................... 31
4

3.2 Method ........................................................................................................................... 33
3.2.1 Model description ................................................................................................... 33
3.2.2 Summary of simulations ......................................................................................... 35
3.2.3 Regions of analysis ................................................................................................. 35
3.3 Results and discussion .................................................................................................... 36
3.3.1 The effects of cool roofs on urban heat islands ...................................................... 36
3.3.2 The influence of cool roofs on continental-scale energy fluxes and climate .......... 38
3.3.3 The influence of cool roofs on global energy fluxes and climate ........................... 40
3.3.4 Discussion ............................................................................................................... 41
3.4 Conclusion ...................................................................................................................... 43
Chapter 4: Systematic comparison of the influence of cool wall versus cool roof adoption on
urban climate in the Los Angeles basin .................................................................................... 44
4.1 Introduction .................................................................................................................... 44
4.2 Methodology .................................................................................................................. 46
4.2.1 Model description ................................................................................................... 46
4.2.2 Shortwave radiation calculations in the urban canopy model ................................ 47
4.2.3 Canyon air temperature ........................................................................................... 48
4.2.4 Urban land use type classification and corresponding canopy morphology........... 48
4.2.5 Simulation domain .................................................................................................. 49
4.2.6 Simulation design.................................................................................................... 50
4.2.7 Analysis on the fraction of wall-reflected sunlight that escapes the canopy .......... 51
4.2.8 Caveats .................................................................................................................... 52
4.3 Results and discussion .................................................................................................... 53
4.3.1 Diurnal cycle of grid cell albedo and reflected solar radiation ............................... 53
4.3.2 Spatial variation of grid cell albedo ........................................................................ 56
4.3.3 Spatial variation of canyon air temperatures .......................................................... 57
4.3.4 Diurnal cycle of canyon air temperatures ............................................................... 58
5

4.4 Conclusion ...................................................................................................................... 61
Chapter 5: Investigating the urban air quality effects of cool walls and cool roofs in
Southern California .................................................................................................................... 63
5.1 Introduction .................................................................................................................... 63
5.2 Method ........................................................................................................................... 64
5.2.1 Model description ................................................................................................... 64
5.2.2 Simulation domains ................................................................................................ 65
5.2.3 Emission inventories ............................................................................................... 65
5.2.4 Simulation design.................................................................................................... 66
5.2.5 Caveats .................................................................................................................... 66
5.3 Results and discussion .................................................................................................... 67
5.3.1 Meteorological conditions ...................................................................................... 67
5.3.2 Spatial distribution of ozone concentrations ........................................................... 71
5.3.3 Spatial distribution of PM2.5 species ....................................................................... 72
5.3.4 Diurnal cycles of PM2.5 species concentrations ...................................................... 75
5.3.5 Mechanisms that lead to changes in PM2.5 concentrations ..................................... 77
5.4 Conclusion ...................................................................................................................... 81
References .................................................................................................................................... 82

6

Abstract
Climate change and urban air pollution are two of the greatest contemporary global challenges.
The overarching goals of this dissertation are (1) to study how climate, air quality, and land cover
interact at spatial scales that range from local to global and (2) to inform policymaking on strategies
that can potentially mitigate both climate change and air pollution.
Black carbon (BC) particles, a component of PM2.5, can harm human health and lead to global
warming. Chapter 2 of this dissertation investigates the transport and transformation of BC in the
atmosphere. Physically-based parameterizations for BC removal are implemented to an
atmospheric chemistry model; parameters that determine BC “aging” (i.e., conversion of
hydrophobic BC to hydrophilic BC) rates are optimized to fit observations. Using the improved
model, I quantify the source-receptor relationship of BC among 13 regions across the globe. In the
former Soviet Union, middle Asia, and Canada, local emissions are found to account for no more
than 50% of the BC burden. On the other hand, BC over other regions is dominated by local
sources. Therefore, controlling local anthropogenic sources is expected to have the largest impact
on BC burdens in these regions. This information can inform collaborative effort in reducing BC
and bring climate and air quality co-benefits to our society.
Chapters 3 to 5 describe three studies that assess the climate and air quality impacts of adopting
heat mitigation strategies. These strategies can reduce temperatures and therefore mitigate the local
impacts of global warming and the urban heat island effect. In particular, solar reflective “cool
roofs” absorb less sunlight than traditional dark roofs and have been widely adopted in urban areas
to reduce temperatures. However, their impacts on regional and global climate are uncertain and
need to be studied. Thus, Chapter 3 evaluates the climate impacts of adopting cool roofs at spatial
scales larger than the local. While cool roofs are found to be an effective tool for reducing urban
heat islands and regional air temperatures, their influence on global climate is likely negligible.
Chapter 4 systematically compares the climate impacts of adopting “cool roofs” and “cool walls”
in Los Angeles County using a consistent modeling framework. This study presents the first
estimation of the climate effects of adopting cool walls. The differences in the influences of cool
roofs and walls on radiative transfer and urban temperatures are also analyzed and attributed to
different factors. Per 0.10 wall (roof) albedo increase, cool walls (roofs) can reduce summertime
daily average canyon air temperature by 0.05 K (0.06 K) in Los Angeles.
Lastly, it is important for policy makers to evaluate the effects of environmental solutions from a
systematic perspective, e.g., looking at heat mitigation impacts not just from a climate perspective
but also from an air quality perspective. Thus, Chapter 5 assesses the impacts of cool roofs and
walls on air quality (PM2.5 and ozone concentrations) in Southern California and attributes the
impacts to competing physical and chemical processes. Increasing wall (roof) albedo by 0.80, an
upper bound scenario, leads to summertime maximum daily 8-hour average ozone concentration
reductions of 0.35 (0.83) ppbv in Los Angeles County. However, cool walls (roofs) increase
summertime daily average PM2.5 concentrations by 0.62 (0.85) μg m
-3
.

7

Chapter 1: Introduction
1.1 Background
Climate change and urban air pollution are two of the greatest contemporary global challenges.
Davis et al (2013) suggest that even if future greenhouse gas emissions stay flat, global air
temperatures would still increase by 1.9 degrees Celsius by 2060, as compared to pre-industrial
temperatures. This predicted warming trend would cause disastrous problems including ice
melting, sea level rise, and habitat deterioration (IPCC, 2013). Primary air pollutants (e.g., NO,
primary particulate matter) are mostly emitted by combustion processes from transportation and
energy production. Secondary pollutants (e.g., ozone, secondary organic aerosols) are formed in
the atmosphere via chemical reactions of primary pollutants. Urban air pollution can cause adverse
health impacts and has led to severe consequences since the Industrial Revolution. For example,
the Great Smog in London and the photochemical smog in Los Angeles killed thousands of people.
Furthermore, air pollution can reduce visibility and degrade functions of cities (Hyslop, 2009).
Evidence has illustrated that climate change and urban air pollution interact with each other.
Pollutants can influence the climate by affecting radiative transfer. For example, ozone can absorb
solar radiation and warm the climate. Most aerosols (e.g. sulfate) will cause negative radiative
forcing and cool the climate, while black carbon (BC) aerosols will induce positive radiative
forcing and warm the climate (Bond et al., 2013). Similarly, future climate change is expected to
worsen air quality (Jacob and Winner, 2009). Temperature variations will influence VOC
emissions from vegetation. In addition, warmer climate will likely increase photochemical reaction
rates, and consequently increase the concentrations of secondary air pollutants (e.g. ozone) (Jacob
and Winner, 2009).
Therefore, it is important to consider the impact of pollutants on climate, and to pursue engineering
methods that can mitigate both climate change and air pollution.
1.2 Research objectives
The overarching goals of my research are (1) to study how climate, air quality, and land cover
interact at spatial scales that range from local to global and (2) to inform policymaking on strategies
that can potentially mitigate both climate change and air pollution. I hope my work can not only
advance scientific understanding but also promote policymaking on climate and air pollution
mitigation. Specifically, I investigate (a) the transport and source attribution of black carbon
aerosols and (b) the climate and air quality effects of adopting heat mitigation strategies. The
rationale for investigating these two topics is explained in Chapter 1.3.
8

1.3 Rationale for completed work
1.3.1 Black carbon aerosols
Black carbon (BC) is a component of PM2.5. Epidemiological studies have shown that BC is
associated with increased hospital admissions and premature mortalities (Janssen et al., 2011).
Thus, BC concentrations are monitored and controlled from air quality and public health
standpoints. In addition, BC is an efficient absorber of solar radiation, and therefore heats the
atmosphere and the Earth’s surface (Ramanathan and Carmichael, 2008). As an important radiative
forcing agent contributing to global warming, BC emissions should be reduced for climate
benefits.
One of my research goals is to study the transport and transformation of BC in the atmosphere.
My study will promote scientific understanding towards BC climate impacts and source-receptor
relationships, and inform policymaking regarding collaborative mitigation of BC.
1.3.2 Heat mitigation strategies
On top of global rises in temperatures due to climate change, cities are hotter than rural areas due
to the urban heat island effect (Taha, 1997b). Therefore, city dwellers are facing additional heat-
related challenges, such as increases in heat stroke and heat exhaustion rates, as well as high levels
of building energy use during the summer. Practical heat mitigation strategies include cool roofs,
cool pavements, cool walls, green roofs, and planting trees. Heat mitigation strategies can partially
reverse the local impacts of climate change and urban heat islands, but their effects on global
climate and urban air pollution are not well understood.
Solar reflective “cool roofs” absorb less sunlight than traditional dark roofs, reducing solar heat
gain, and decreasing the amount of heat transferred to the atmosphere. Cool roofs could therefore
reduce temperatures in urban areas and have been widely adopted to mitigate the urban heat island
effect. However, their impacts on regional and global climate are uncertain and need to be studied.
Thus, one of my research goals is to evaluate the climate impacts of adopting cool roofs at spatial
scales larger than local scales.
With reductions in temperatures, these heat mitigation strategies have the potential to improve or
worsen air quality. For policy makers, it is important to assess the effects of environmental
solutions from a systematic perspective, i.e., looking at heat mitigation impacts not just from a
climate perspective but also from an air quality perspective. Thus, my research also aims to
evaluate the climate and air quality impacts of cool roofs and walls in cities in Southern California.
1.4 Overview
Chapter 2 describes my effort in improving the ability of global models to predict concentrations
of black carbon (BC) over the Pacific Ocean. BC tracers from 13 source regions are tagged around
the globe in a global chemical transport model MOZART-4. Numerous sensitivity simulations are
9

carried out varying the aging timescale of BC emitted from each source region. The aging
timescale for each source region is optimized by minimizing errors in vertical profiles of BC mass
mixing ratios between simulations and HIAPER Pole-to-Pole Observations (HIPPO). The
relationship between BC lifetime and BC aging timescale for all source regions are investigated.
Chapter 3 investigates the climate impacts of solar reflective “cool roofs” at urban, continental,
and global scales, using a sophisticated Earth system model. Though past research has disagreed
on whether widespread adoption of cool roofs would cool or warm global climate, these studies
have lacked analysis on the statistical significance of global temperature changes. Chapter 3
suggests that while cool roofs are an effective tool for reducing building energy use in hot climates,
urban heat islands, and regional air temperatures, their influence on global climate is likely
negligible.
Chapter 4 compares the climate impacts of adopting solar reflective “cool roofs” and “cool walls”
in Los Angeles County, using a consistent modeling framework. It was the first time that the
climate effects of cool walls had been assessed. The differences in the influences of cool roofs and
walls on radiative transfer and urban temperatures are also analyzed and attributed to different
factors.
Chapter 5 estimates the effects of adopting solar reflective “cool roofs” and “cool walls” on air
quality (PM2.5 and ozone concentrations) in Southern California, using a regional climate and air
quality model. The competing processes driving changes in concentrations of speciated PM2.5 are
also investigated.

10

Chapter 2: Long-range transport of black
carbon and its dependence on aging timescale
This chapter is based on the following publication
Zhang, J., Liu, J., Tao, S. and Ban-Weiss, G. A.: Long-range transport of black carbon to the Pacific
Ocean and its dependence on aging timescale, Atmos. Chem. Phys., 15(20), 11521 –11535,
doi:10.5194/acp-15-11521-2015, 2015.

2.1 Introduction
Black carbon (BC) is an efficient absorber of solar radiation and therefore heats the atmosphere
and the Earth surface (Ramanathan and Carmichael, 2008). Estimates of BC’s direct radiative
forcing widely vary, ranging from 0.19 W/m
2
by Wang et al. (2014) to 0.88 W/m
2
by Bond et al.
(2013). The Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC, 2013)
assesses the direct radiative forcing of BC to be 0.40 W/m
2
with a large uncertainty range of 0.05
to 0.80 W/m
2
. Besides its direct radiative effects, BC also affects Earth’s energy budget indirectly
by influencing cloud formation (Koch and Del Genio, 2010) and snow albedo (Flanner et al., 2007;
Hansen and Nazarenko, 2004), although these processes are relatively less understood and subject
to greater uncertainties. In addition, epidemiological studies have shown that BC is associated with
increased hospital admissions and premature mortalities (Janssen et al., 2011).
Relative to greenhouse gases, BC has a shorter lifetime, and its concentration changes considerably
by location and season (Liu et al., 2011). Horizontal and vertical distributions of BC are not well
constrained with observations, contributing to the uncertainties in estimates of BC’s radiative
forcing (Bond et al., 2013). BC has higher radiative forcing efficiency (i.e. radiative forcing per
unit mass of BC) when its underlying surface is highly reflective (e.g. cloud, snow and ice). The
radiative forcing of BC also depends on its attitude, and is enhanced if located above clouds
relative to below clouds (Satheesh, 2002; Zarzycki and Bond, 2010). The direct radiative forcing
efficiency of BC may increase by a factor of 10 as altitude increases from the surface to the lower
stratosphere (Samset and Myhre, 2011), whereas forcing associated with the semi-direct effect (i.e.
changes in cloudiness due to local heating from BC) becomes more negative as altitude increases
(Samset and Myhre, 2015). On the other hand, the climate response of BC depends on altitude in
different ways than forcing. Since BC warms its surroundings, near-surface BC can warm the
surface more than BC at high altitudes, even though the high-altitude BC has higher top-of-
atmosphere direct radiative forcing efficiency. BC at high altitudes could even cause surface
cooling (Ban-Weiss et al., 2012b; Samset et al., 2013). Near-surface BC has also been found to
increase precipitation, whereas BC at higher altitudes can suppress precipitation (Ban-Weiss et al.,
2012a; Ming et al., 2010; Samset and Myhre, 2015).
BC over oceans could potentially play a significant role in changing the marine climate through
influences on the top-of-atmosphere and surface energy balance, as well as temperature and cloud
profiles. For instance, BC over the Arabian Sea has been shown to dim the surface, decrease sea
11

surface temperature, reduce monsoon circulation and vertical wind shear, and consequently
increase the intensity of cyclones (Evan et al., 2011). The Pacific Ocean is the largest ocean in the
world, extending from the Arctic to the Antarctic. Its marine climate has been shown to influence
the weather and environment in neighboring continents, for example the South Asian and East
Asian summer monsoon (Bollasina et al., 2011; Gao et al., 2013; Lau and Nath, 2012). Recently,
the HIAPER Pole-to-Pole Observation (HIPPO) campaign has enabled further research on trans-
Pacific transport of BC. HIPPO’s five deployments provide new constraints for modelling the
vertical structures of BC at a wide range of latitudes over the Pacific spanning all seasons (Kipling
et al., 2013; Wofsy, 2011). Past model inter-comparison studies have shown that a collection of
global models predict BC concentrations that are a factor of 3 and 10 higher than HIPPO
observations in the lower and upper troposphere, respectively (Schwarz et al., 2013); the vertical
profiles simulated by the 15 global models markedly differ (Samset et al., 2014).
The aforementioned inter-model differences and disagreement between models and HIPPO
observations can be attributed to the uncertainties in emissions and meteorology, along with
different treatments of convective transport and deposition (Vignati et al., 2010). Wet scavenging
processes are a major source of uncertainty in predicting BC concentrations over remote regions
(Textor et al., 2006). As emitted, BC is mostly hydrophobic (Laborde et al., 2013), but can become
coated by water-soluble components through atmospheric aging processes. The coatings transits
BC from being hydrophobic to hydrophilic, allowing the BC-containing particles to become cloud
condensation nuclei and be scavenged by precipitation (Oshima and Koike, 2013; Riemer et al.,
2010). Exponential timescale for this aging process to occur, the so-called “aging timescale”,
which is also the reciprocal of aging rate, therefore highly influences the timing of cloud formation
and wet deposition, and thus is of great research interest (Liu et al., 2011). The thickness of BC
coatings have been observed to increase with aging at the remote marine Pico Mountain
Observatory, which shows that the fraction of coated particles for plumes with an age of ~15.7
days is 87%, higher than that of ~9.5 days (57%) (China et al., 2015). Quantitatively relating the
age of BC-containing particles to its hygroscopicity using observations is very challenging
(McMeeking et al., 2011). Laboratory measurements show that BC particles are considerably
hygroscopic after being coated by condensed H2SO4 (Zhang et al., 2008), or when a sulfur-
containing compound is added to the diesel fuel itself, presumably also leading to a sulfuric coating
(Lammel and Novakov, 1995). Another study reporting observations in the United Kingdom finds
that nitrate is the primary component of the coating on BC that leads to substantial increases in
hygroscopicity (Liu et al., 2013a). In principle, the conversion of hydrophobic to hydrophilic BC
is very complicated, involving coagulation with sulfate and nitrate, condensation of nitric and
sulfuric acid, and oxidation of organic coatings (Riemer et al., 2004).
The aforementioned uncertainties in process and timescale for atmospheric aging, which converts
BC from being hydrophobic to hydrophilic, leads to significant uncertainties in the transport of
BC from source regions to remote areas. For example, previous studies that look at source regions
of Arctic BC disagree on the relative importance of contributions from North American, Asian,
and European emissions (Koch and Hansen, 2005; Shindell et al., 2008; Wang et al., 2014). BC
over the North Pacific Ocean is significantly influenced by the long-range transport of BC from
Asia (Kaneyasu and Murayama, 2000). Eastern and Central Asia is regarded as the most significant
contributor to BC burden above the oceans in the Northern Hemisphere (Ma et al., 2013).
However, the source of BC over the Pacific Ocean at different latitudes and altitudes remains
unclear.
12

The major objectives of this study are to understand the sensitivity of BC aging timescale on its
loading and source attribution using a global chemical transport model. We quantify the relative
contributions of 13 source regions to BC loadings around the globe, with a focus on BC over the
Pacific Ocean. In section 2.2, we improve the model by implementing physically-based dry and
wet deposition schemes. We also tag BC emitted from different source regions and conduct
sensitivity tests to investigate how different aging timescales affect spatial (i.e. horizontal and
vertical) distributions and source-receptor relationships for BC. In section 2.3, we optimize the
aging timescale of BC for each source region to attain the best match to HIPPO observations.
Section 2.4 identifies the most significant contributors to BC over the Pacific Ocean, and quantifies
the source-receptor relationship. We also discuss the relationship between lifetime and aging
timescales of BC in section 2.5.
2.2 Method
2.2.1 Model description and configuration
In this research, we use the Model for Ozone and Related Chemical Tracers, version 4 (MOZART-
4) (Emmons et al., 2010), a global chemical transport model developed at the National Center for
Atmospheric Research (NCAR). Built on the framework of the Model of Atmospheric Chemistry
and Transport (MATCH) (Rasch et al., 1997) with a series of updates, MOZART-4 resolves
horizontal and vertical transport, chemistry, and dry and wet deposition of 85 gas-phase and 12
bulk aerosol species. Vertical transport considers both diffusion in the boundary layer using the
Holtslag and Boville (1993) scheme, and convective mass flux using the Hack (1994) scheme for
shallow and middle convection, and the Zhang and McFarlane (1995) scheme for deep convection.
Horizontal transport is characterized by a semi-Lagrangian advection scheme (Lin and Rood,
1996). In the standard model, BC is represented by two classes of tracers: hydrophobic and
hydrophilic. Hydrophobic BC accounts for 80% of BC emissions and converts to hydrophilic BC
with an exponential aging timescale of about 1.6 days. Only hydrophilic BC can be wet scavenged.
Its first order wet scavenging rate is set to 20% of that for nitric acid, and is proportional to
precipitation rate. Precipitation is produced by stratiform clouds (i.e. large-scale precipitation) and
convective clouds (i.e. convective precipitation). Dry deposition velocity for both BC tracers is set
to 0.1 cm·s
-1
(Emmons et al., 2010).
The model is run at a horizontal resolution of approximately 1.9º×1.9º (latitude × longitude) with
28 vertical levels from surface to approximately 2 hPa, and is driven by NCEP reanalysis
meteorology. Anthropogenic emissions are based on the MACCity emission inventory
(http://www.pole-ether.fr/eccad), which is extended from the database used for IPCC Coupled
Model Intercomparison Project (Lamarque et al., 2010). Biomass burning emissions are acquired
from the Global Fire Emissions Database (GFED) version 3 (van der Werf et al., 2010). Model
simulations are for January 1, 2008 to December 31, 2011, and the first year of the simulation is
discarded as model spin-up.
13

2.2.2 Dry and wet deposition schemes
To improve model performance, we employ the dry deposition parameterization (equation 2.1)
from Gallagher et al. (2002)
𝑣 d
=
1
𝑟 a
+𝑟 s
, 𝑟 𝑠 = (𝑢 ∗
(0.001222 log (𝑧 0
) + 0.0009(
𝑧 i
𝐿 )
2
3
+ 0.003906 ))
−1
(2.1)
where vd is dry deposition rate (m·s
-1
), ra is aerodynamic resistance, rs is surface resistance, u* is
friction velocity (m·s
-1
), z0 is the roughness length (m), zi is boundary layer depth (m), and L is the
Monin-Obukhov length (m). As a result, dry deposition velocity depends on surface properties
(e.g. vegetation type).
For in-cloud wet scavenging of BC, we follow the parameterization used in Liu et al. (2011). The
first-order in-cloud scavenging rate coefficient (s
-1
) is expressed as
𝐾 in
=
𝑃 rain
𝐹 liq
+𝑃 snow
𝐹 ice
+𝑃 conv
𝐹 conv
𝐶 (2.2)
where 𝑃 rain
, 𝑃 snow
, and 𝑃 conv
are the rates of stratiform rain precipitation, stratiform snow
precipitation, and convective precipitation (kg·m
−3
·s
−1
), respectively, and C is the sum of cloud
ice and liquid water content (kg·m
−3
). For convective clouds, 𝐹 conv
is the fraction of in-cloud
hydrophilic BC that is incorporated into cloud droplets or ice crystals. For stratiform clouds, 𝐹 liq

(𝐹 ice
) is the fraction of in-cloud hydrophilic BC that is incorporated into liquid cloud droplets (ice
crystals).
As previous studies indicate, the fraction of BC that is incorporated in cloud droplets or ice crystals
decreases as temperature decreases and ice mass fraction increases in mixed-phase clouds (Croft
et al., 2010, 2012; Fan et al., 2012; Liu et al., 2011). This phenomenon is attributable to the so-
called Bergeron process, by which ice crystals grow rapidly at the expense of liquid droplets,
leaving BC-containing cloud nuclei interstitial (i.e. not activated) (Cozic et al., 2007). However,
the Bergeron process is not important in deep convective clouds where ice forms rapidly via
rimming or accretion. Thus, generally Fice < Fliq < Fconv. In this study, we set Fice=0.1, Fliq=0.5, and
Fconv=1.0 as referenced to previous studies (Hodnebrog et al., 2014; Liu et al., 2011; Wang et al.,
2011).
2.2.3 HIAPER Pole-to-Pole Observations
The HIPPO campaigns unprecedentedly provide vertical profiles from the surface to upper
troposphere for 26 species over the Pacific Ocean, spanning from approximately 90°N to 70°S, in
different seasons (Wofsy et al., 2011). BC is measured by a Single Particle Soot Photometer (SP2)
using laser-induced incandescence (Schwarz et al., 2010). The SP2 heats BC-containing particles
to its vaporization temperature and measures the resulting incandescence emitted by the BC core.
Since the intensity of incandescence responds linearly to the mass of refractory BC, SP2 measures
BC mass independent of particles morphology and mixing state (Schwarz et al., 2006;Schwarz et
al., 2008). We constrain and evaluate our model by comparing simulated vertical profiles of BC
mass mixing ratios over the central Pacific Ocean to observations from five field deployments
14

(HIPPO I on January 8
th
– January 30
th
, 2009; HIPPO II on October 31
th
– November 22
th
, 2009;
HIPPO III on March 24
th
– April 16
th
, 2010; HIPPO IV on June 14
th
– July 11
th
, 2011; HIPPO V
on August 9
th
– September 9
th
, 2011). Note that we use only the HIPPO observations taken in the
Central Pacific Ocean (130°W – 160°E) and ignore observations near source regions.
2.2.4 Tracer tagging and sensitivity simulations
In this study, we add 13 tracers to the model to explicitly track BC emissions from non-overlapping
geopolitical regions, an approach often called “tagging” (Rasch et al., 2000). Tagging is more
accurate and less computationally consuming than the widely used emission sensitivity approach
(Wang et al., 2014). We expand the ten defined continental regions in Liu et al (2009) to thirteen
source regions to better distinguish the differences in climate and emission source type between
regions. As shown in Figure 2.1, the tagged source regions are Canada (CA), North America except
Canada (NA), East Asia (EA), the former Soviet Union (SU), Europe (EU), Africa (AF), South
America (SA), the Indian subcontinent (IN), Australia (AU), Middle Asia (MA), Southeast Asia
(SE), the Middle East (ME), and the rest regions (RR). For each simulation, the tagged tracers
undergo transport and deposition processes in the same way as untagged BC. Since all the chemical
and physical processes involving BC are nearly linear in MOZART-4, the relative difference
between the sum of the 13 regional BC tracers and the untagged BC is small (i.e., in most cases
less than 1% with the largest biases less than 4%). Therefore, the sum of the 13 regional BC tracers
is approximately equal to the untagged BC.

Figure 2.1. The thirteen defined source regions: Canada (CA), North America except Canada (NA),
East Asia (EA), the former Soviet Union (SU), Europe (EU), Africa (AF), South America (SA),
India (IN), Australia (AU), Middle Asia (MA), Southeast Asia (SE), the Middle East (ME), and
the rest regions (RR).
In the model, two parameters control the hygroscopicity of BC: initial fraction of hydrophilic BC
in freshly emitted BC (20%), and a fixed e-folding aging timescale, which characterizes the
timescale for conversion of hydrophobic BC to hydrophilic BC in the atmosphere. Hygroscopicity
15

of BC-containing particle determines whether BC can be wet scavenged, and thus affects the
lifetime of BC. Therefore, constraining the aging timescale is essential for accurately simulating
long-range transport and atmospheric concentrations of BC. In global models, e-folding aging
timescale is often fixed at 1.2 or 1.6 days (27.6 or 38.4 h), even though studies find it can vary
from several hours to 2 weeks in different regions (Liu et al., 2011;Shen et al., 2014). So we
conduct 13 sensitivity simulations with different e-folding aging timescales (i.e. 4, 8, 12, 18, 24,
27.6, 38.4, 48, 60, 90, 120, 160, and 200 hours). Note that while we define aging timescale as that
for converting BC from hydrophobic to hydrophilic, some other studies use this term to describe
the change from thinly to thickly coated BC (Moteki et al., 2007).
2.2.5 Optimization of BC aging timescale to match HIPPO observations
The conversion of hydrophobic BC to hydrophilic BC in global chemistry transport models is often
expressed by a fixed exponential aging timescale of 1 to 2 days (e.g. 1.2 days in GEOS-chem, 1.6
days in MOZART-4) (Feng, 2007; Wang et al., 2011). However, previous studies have indicated
that the aging rate of BC varies spatially and temporally due to different atmospheric
photochemical conditions and co-emitted species (Huang et al., 2013; Liu et al., 2011; Shen et al.,
2014). Aging rate peaks during summer daytime and in low-latitude regions because high OH
concentrations promote the production of water-soluble condensable species. The aging rate is
slower at night and during winter because OH concentrations are low and thus coagulation, which
is slower than condensation, dominates the formation of internally mixed BC (Bian et al., 2013;
Liu et al., 2011; Riemer et al., 2004). Observations show that biomass burning emitted BC,
compared with urban BC, has larger number fraction of coated particles (70% versus 9%) and
thicker coatings (65nm versus 20nm), implying that it is more susceptible to wet deposition
(Schwarz et al., 2008). Different source regions are distinct in their source types (e.g.
anthropogenic, biomass burning) and concentrations of oxidants. Therefore, BC emitted from
different regions should undergo aging with different timescales.
In this study, we optimize the aging timescales of BC emitted from thirteen source regions to best
match the HIPPO observations. For each HIPPO deployment, we compare observations versus
simulations from 70°S to 90°N and 0 to 10 km along the HIPPO trajectory. The absolute deviation
between modeled BC (𝐵𝐶
m
) and observed BC (𝐵𝐶
o
) mass mixing ratios for each latitude and
altitude is calculated, and the average of mean normalized absolute error (MNAE) is then used as
an indicator of the model performance in each deployment:
MNAE =
1
𝑁 ∑ ∑
Abs(𝐵𝐶
m
(𝑗 ,𝑘 )−𝐵𝐶
o
(𝑗 ,𝑘 ))
Min(𝐵𝐶
m
(𝑗 ,𝑘 ) ,𝐵𝐶
o
(𝑗 ,𝑘 ))
𝑛𝑎𝑙𝑡 𝑛𝑙𝑎𝑡 (2.3)
where j indexes latitude bins, k indexes altitude bins, nlat = 16 is the total number of latitude bins
(every 10° from 70°S to 90°N), and nalt = 10 is the total number of altitude bins (every 1km from
0 to 10 km). Abs (𝐵𝐶
m
(𝑗 , 𝑘 ) − 𝐵𝐶
o
(𝑗 , 𝑘 )) represents the absolute value of modeled BC minus
observed BC averaged over the j
th
and k
th
latitude and altitude bin. N is the total number of latitude
and altitude bins with recorded HIPPO observations. The model output daily averaged BC mixing
ratios. For every record in HIPPO data (averaged in every 10s), we find modeled BC mixing ratio
at the same longitude, latitude, altitude, and on the same day correspondingly. In this way, modeled
and observed BC mixing ratios are paired, and then are averaged respectively over latitude and
altitude bins. We normalize the absolute errors by the minimum of observed and modeled BC so
16

that MNAE weights both high bias and low bias equally. Unlike the root mean square error, the
MNAE does not amplify the importance of the outliers.
Aging timescale affects atmospheric concentrations of BC through its influence on hygroscopicity
and wet deposition of the particle. Thus, BCm(j,k) and MNAE are functions of aging timescale. We
perform 13 simulations, each with different constant aging timescales (i.e. 4, 8, 12, 18, 24, 27.6,
38.4, 48, 60, 90, 120, 160 or 200 hours). Every simulation tags BC from each of 13 regions (i.e.,
North America, East Asia, Canada, …); as mentioned in Section 2.2.3, BCm(j,k)=∑ 𝐵𝐶
m
(j, k, r )
r
,
where r denotes each region. We construct BCm(j,k) using all possible combinations of 𝐵𝐶
m
(j, k, r )
from the 13 simulations. Then we check which combination of 𝐵𝐶
m
(j, k, r ) best matches BC
observations. Note that we constrain the aging rates of BC emitted from Africa, South America,
and Australia to be the same since these three regions are all biomass burning dominated sources
in the Southern Hemisphere, which effectively reduces the total number of tagged tracers from 13
to 11. Thus, we determine the best-fit BC aging timescale for each source region (out of 13
11

combinations in total) that minimizes MNAE.

2.3 Introduction
Black carbon (BC) is an efficient absorber of solar radiation and therefore heats the atmosphere
and the Earth surface (Ramanathan and Carmichael, 2008). Estimates of BC’s direct radiative
forcing widely vary, ranging from 0.19 W/m
2
by Wang et al. (2014) to 0.88 W/m
2
by Bond et al.
(2013). The Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC, 2013)
assesses the direct radiative forcing of BC to be 0.40 W/m
2
with a large uncertainty range of 0.05
to 0.80 W/m
2
. Besides its direct radiative effects, BC also affects Earth’s energy budget indirectly
by influencing cloud formation (Koch and Del Genio, 2010) and snow albedo (Flanner et al., 2007;
Hansen and Nazarenko, 2004), although these processes are relatively less understood and subject
to greater uncertainties. In addition, epidemiological studies have shown that BC is associated with
increased hospital admissions and premature mortalities (Janssen et al., 2011).
Relative to greenhouse gases, BC has a shorter lifetime, and its concentration changes considerably
by location and season (Liu et al., 2011). Horizontal and vertical distributions of BC are not well
constrained with observations, contributing to the uncertainties in estimates of BC’s radiative
forcing (Bond et al., 2013). BC has higher radiative forcing efficiency (i.e. radiative forcing per
unit mass of BC) when its underlying surface is highly reflective (e.g. cloud, snow and ice). The
radiative forcing of BC also depends on its attitude, and is enhanced if located above clouds
relative to below clouds (Satheesh, 2002; Zarzycki and Bond, 2010). The direct radiative forcing
efficiency of BC may increase by a factor of 10 as altitude increases from the surface to the lower
stratosphere (Samset and Myhre, 2011), whereas forcing associated with the semi-direct effect (i.e.
changes in cloudiness due to local heating from BC) becomes more negative as altitude increases
(Samset and Myhre, 2015). On the other hand, the climate response of BC depends on altitude in
different ways than forcing. Since BC warms its surroundings, near-surface BC can warm the
surface more than BC at high altitudes, even though the high-altitude BC has higher top-of-
atmosphere direct radiative forcing efficiency. BC at high altitudes could even cause surface
cooling (Ban-Weiss et al., 2012b; Samset et al., 2013). Near-surface BC has also been found to
17

increase precipitation, whereas BC at higher altitudes can suppress precipitation (Ban-Weiss et al.,
2012a; Ming et al., 2010; Samset and Myhre, 2015).
BC over oceans could potentially play a significant role in changing the marine climate through
influences on the top-of-atmosphere and surface energy balance, as well as temperature and cloud
profiles. For instance, BC over the Arabian Sea has been shown to dim the surface, decrease sea
surface temperature, reduce monsoon circulation and vertical wind shear, and consequently
increase the intensity of cyclones (Evan et al., 2011). The Pacific Ocean is the largest ocean in the
world, extending from the Arctic to the Antarctic. Its marine climate has been shown to influence
the weather and environment in neighboring continents, for example the South Asian and East
Asian summer monsoon (Bollasina et al., 2011; Gao et al., 2013; Lau and Nath, 2012). Recently,
the HIAPER Pole-to-Pole Observation (HIPPO) campaign has enabled further research on trans-
Pacific transport of BC. HIPPO’s five deployments provide new constraints for modelling the
vertical structures of BC at a wide range of latitudes over the Pacific spanning all seasons (Kipling
et al., 2013; Wofsy, 2011). Past model inter-comparison studies have shown that a collection of
global models predict BC concentrations that are a factor of 3 and 10 higher than HIPPO
observations in the lower and upper troposphere, respectively (Schwarz et al., 2013); the vertical
profiles simulated by the 15 global models markedly differ (Samset et al., 2014).
The aforementioned inter-model differences and disagreement between models and HIPPO
observations can be attributed to the uncertainties in emissions and meteorology, along with
different treatments of convective transport and deposition (Vignati et al., 2010). Wet scavenging
processes are a major source of uncertainty in predicting BC concentrations over remote regions
(Textor et al., 2006). As emitted, BC is mostly hydrophobic (Laborde et al., 2013), but can become
coated by water-soluble components through atmospheric aging processes. The coatings transits
BC from being hydrophobic to hydrophilic, allowing the BC-containing particles to become cloud
condensation nuclei and be scavenged by precipitation (Oshima and Koike, 2013; Riemer et al.,
2010). Exponential timescale for this aging process to occur, the so-called “aging timescale”,
which is also the reciprocal of aging rate, therefore highly influences the timing of cloud formation
and wet deposition, and thus is of great research interest (Liu et al., 2011). The thickness of BC
coatings have been observed to increase with aging at the remote marine Pico Mountain
Observatory, which shows that the fraction of coated particles for plumes with an age of ~15.7
days is 87%, higher than that of ~9.5 days (57%) (China et al., 2015). Quantitatively relating the
age of BC-containing particles to its hygroscopicity using observations is very challenging
(McMeeking et al., 2011). Laboratory measurements show that BC particles are considerably
hygroscopic after being coated by condensed H2SO4 (Zhang et al., 2008), or when a sulfur-
containing compound is added to the diesel fuel itself, presumably also leading to a sulfuric coating
(Lammel and Novakov, 1995). Another study reporting observations in the United Kingdom finds
that nitrate is the primary component of the coating on BC that leads to substantial increases in
hygroscopicity (Liu et al., 2013a). In principle, the conversion of hydrophobic to hydrophilic BC
is very complicated, involving coagulation with sulfate and nitrate, condensation of nitric and
sulfuric acid, and oxidation of organic coatings (Riemer et al., 2004).
The aforementioned uncertainties in process and timescale for atmospheric aging, which converts
BC from being hydrophobic to hydrophilic, leads to significant uncertainties in the transport of
BC from source regions to remote areas. For example, previous studies that look at source regions
of Arctic BC disagree on the relative importance of contributions from North American, Asian,
18

and European emissions (Koch and Hansen, 2005; Shindell et al., 2008; Wang et al., 2014). BC
over the North Pacific Ocean is significantly influenced by the long-range transport of BC from
Asia (Kaneyasu and Murayama, 2000). Eastern and Central Asia is regarded as the most significant
contributor to BC burden above the oceans in the Northern Hemisphere (Ma et al., 2013).
However, the source of BC over the Pacific Ocean at different latitudes and altitudes remains
unclear.
The major objectives of this study are to understand the sensitivity of BC aging timescale on its
loading and source attribution using a global chemical transport model. We quantify the relative
contributions of 13 source regions to BC loadings around the globe, with a focus on BC over the
Pacific Ocean. In section 2.2, we improve the model by implementing physically-based dry and
wet deposition schemes. We also tag BC emitted from different source regions and conduct
sensitivity tests to investigate how different aging timescales affect spatial (i.e. horizontal and
vertical) distributions and source-receptor relationships for BC. In section 2.3, we optimize the
aging timescale of BC for each source region to attain the best match to HIPPO observations.
Section 2.4 identifies the most significant contributors to BC over the Pacific Ocean, and quantifies
the source-receptor relationship. We also discuss the relationship between lifetime and aging
timescales of BC in section 2.5.
2.4 Results
2.4.1 Optimized BC profiles over the Pacific Ocean
To give a sense of the influence of aging timescale on BC, global BC burdens for the minimum
and maximum aging timescales considered here (i.e. 4 and 200 hours) are shown in Figure 2.2. BC
burden increases with aging timescale in both the lower (Figure 2.2d,e) and mid and upper
troposphere (Figure 2.2a,b). For most regions, BC burden in remote areas and in the mid and upper
troposphere is more sensitive to aging timescale than that in source regions and in the lower
troposphere (Figure 2.2c,f). BC over the Pacific Ocean increases by a factor of 5-100 as the aging
timescale increases from 4 to 200h (Figure 2.2c,f).
19

Figure 2.2. The annual averaged (2009-2011) column burden of BC at 200-800hPa (top) and 800-
1000hPa (bottom) when e-folding aging time is 4hr (left) or 200hr (center), and the ratio of BC
burden between 200h and 4h (right).

The dominant regional contributors to annual averaged BC burden for aging timescales of 4 and
200 hours are shown in Figure 2.3. Longer aging timescales increase the footprint areas dominated
by the highest emitting source regions. For example, the area over the Pacific Ocean for which
East Asian emissions dominate the burden is larger when aging timescale is 200 versus 4 h. Over
source regions, BC in the lower troposphere is dominated by local emissions for both aging
timescales. However, the dominant source of BC in the mid and upper troposphere over source
regions can switch from local source emissions to long-range transport from other source regions
when increasing aging timescale. For example, BC in the mid and upper troposphere over the
United States is dominated by local emissions when aging timescale is 4 hours, but dominated by
East Asian emissions when aging timescale is 200 hours. Thus, varying aging timescale can lead
to substantial differences in BC simulation over the Pacific Ocean, supporting the need to constrain
the aging timescale by observations.
20

Figure 2.3. The most significant regional contributors to the annual averaged (2009-2011) column
burden of BC at 200-800hPa (top) and at 800-1000hPa (bottom) when e-folding aging time is 4h
(left) and 200h (right). Dotted area is where the most significant contributor accounts for more
than 50% of the total BC burden.

Optimized aging timescales for each source region and season are shown in Table 2.1. As shown
in Table 2.1, values differ significantly by source region and season. The aging timescale of BC
from East Asia, North America, India, and Southeast Asia is in most cases relatively short (i.e.,
less than half a day). The optimized BC timescales reported here for East Asia and North America
are consistent with observations in these regions, which show that BC is quickly mixed with
hydrophilic species. For instance, observations over an urban region of Japan find that the
timescale for BC to become internally mixed is 12 hours, with coatings made of primarily sulfate
and soluble organic carbon (Moteki et al., 2007). In Beijing and Mexico City, urban BC is observed
to become internally mixed with sulfate in a few hours (Johnson et al., 2005;Cheng et al., 2012).
Over Southeast Asia, BC emissions are mainly anthropogenic in origin (with a fast aging rate),
except during spring when large-scale biomass burning activities generate tremendous amounts of
BC. The optimized springtime BC aging timescale for Southeast Asia is around 2 days, consistent
with the findings of Shen et al. (2014). On the contrary, the optimized aging rate is relatively slow
in the high-latitude regions (Canada, the former Soviet Union and in particular Europe) in all
seasons except summer (June-July-August, JJA), which can be explained by slower
photochemistry in high latitudes under low sunlight in non-summer months. Since measurements
on BC aging timescale are scarce and limited to few places, more observations are needed to
21

measure the hygroscopicity of BC-containing particles in different continents covering both source
and downwind areas.

Table 2.1. Best-fit aging timescales for 13 regional BC tracers (units = hours), the mean normalized
absolute error (MNAE) for the improved model (imp) and the original model (ori), and the mean
normalized bias (MNB) for the improved model compared to the vertical profiles measured by
HIPPO.
CA SU EU MA EA ME NA SE IN AF SA AU RR
MNAE
(imp)
MNAE
(ori)
*MNB
(imp)
HIPPO1 Jan 200 90 120 120 4 12 160 4 4 4 4 4 90 3.8 26.2 0.04
HIPPO2 Nov 200 160 160 90 4 4 4 4 4 90 90 90 200 2.0 13.1 0.17
HIPPO3 Apr 200 200 200 200 38 200 4 38 27 24 24 24 200 1.5 6.1 -0.05
HIPPO4 Jun 60 4 160 12 4 160 4 4 4 4 4 4 200 1.1 10.6 0.11
HIPPO5 Aug 120 4 18 4 4 4 4 4 4 60 60 60 4 2.4 18.4 0.23
* MNB =
1
𝑁 ∑ ∑
𝐵𝐶
m
(j,k)−𝐵𝐶
o
(j,k)
(𝐵𝐶
m
(j,k)+𝐵𝐶
o
(j,k))/2
𝑛𝑎𝑙𝑡 𝑛𝑙 at

The seasonality of aging timescale reported here is largely consistent with Liu et al. (2011), who
develop a parameterization for BC aging rate as a function of OH radical concentration. In this
study, we further improve the parameterization of Liu et al. (2011) by finding best-fit values for
constants that best match HIPPO observations with reference to our BC aging timescales. After
conducting additional sensitivity simulations, we find that a set of parameters (i.e., β=2.4 × 10
−11
,
and γ=1 × 10
−6
in Equation (4) in Liu et al. (2011)) when employed in MOZART-4 can fit well
the HIPPO observations as well as ground observations (see Figure S1 and S2 in supplementary
material).
Figure 2.4 compares vertical profiles of BC simulated by the “improved model” and the “original
model” with HIPPO observations in different latitude bands. Here, BC from the improved model
is computed as the sum of tagged tracers corresponding to the optimized timescale for each region,
whereas that from the original model uses the default configuration with aging timescale of 1.6
days. The vertical profiles of BC simulated by the improved model are much closer to the
observations than the original model, which overestimates BC mass mixing ratios in nearly every
campaign. In particular, values simulated by the improved model are near those for HIPPO2, 3
and 4 in both pattern and magnitude. NMAE is reduced significantly for each latitude band and
HIPPO campaign, with reductions ranging from a factor of 2 to 25 (Figure 2.4). Campaign-
averaged NMAE is also reduced by a factor of 4-10 (Table 2.1). For comparison, we also derive
the mean normalized bias (MNB) used in Samset et al. (2014). Values for the improved model are
below 25% for every campaign (Table 2.1), lower than their reported MNB for most AeroCom
models.
22

Figure 2.4. Vertical profiles of the simulated and observed BC concentrations over 0.5 km altitude
bins along the flight tracks of HIPPO 1-5 over the central Pacific Ocean (130ºW-160ºE). The data
are averaged over 70ºS-20°S, 20°S-20°N,20°N-60°N, and 60°N-90°N. The black, red, and green
lines are mean values of BC concentrations from observations, original and improved models,
respectively. The gray dots represent measured BC concentrations. The green (red) number
indicates the averaged NMAE for improved (original) model (see Equation 2.3).
23

In a few cases, relatively large differences between the improved model and observations remain.
These differences could be attributed to any number of factors (e.g., emissions, transport,
cloud/precipitation, aging process, wet removal efficiency, etc.). For example, models could
misrepresent BC wet deposition, originating from biases in precipitation. As another example, the
model uses a monthly biomass burning emission inventory. This means that modeled emissions
lack daily variation in biomass burning activities that could be important where biomass burning
emissions dominate BC loading. Underestimates in BC mixing ratio may be partially due to abrupt
emissions events that are not captured by the model. Lastly, since this study assumes that BC aging
timescale in all the southern hemispheric continents is the same, we do not account for variability
in BC aging rates from these regions that may exist in reality.
2.4.2 Regional contribution of source regions to BC loading
Seasonally varying optimized aging timescales for each source region are used to investigate the
dominant source regions contributing to zonal mean mass mixing ratio of BC over the Pacific
Ocean (130 W-160E) (Figure 2.5a), and column burden of BC around the globe (Figure 2.5b).
We assume that optimized aging timescales for HIPPO1,2,3 and 4 are representative for DJF,
SON, MAM, and JJA, respectively (see Table 2.1). BC in the lower troposphere over the Pacific
Ocean is mostly controlled by either emissions from RR (“rest regions”, i.e. ships), or the closest
upwind source regions like Australia, South America and East Asia (Figure 2.5a). On the other
hand, BC in the mid and upper troposphere is influenced mostly by BC emissions from major
source regions: East Asia, Australia, South America, Africa, and North America. East Asian BC
emissions, which are mainly of anthropogenic origin, dominate BC loading over the Northern
Pacific Ocean even though its aging is fast. Also, as shown in Figure 2.5b, the Arctic BC is
dominated by European emissions, while BC in the Antarctica is dominated by South American
emissions.

24

Figure 2.5. The most significant regional contributors to (a) zonal mean BC concentration over the
Pacific (130°W-160°E), and (b) the column burden of BC in the troposphere (2009-2011 average).
Dotted area represents where the most significant contributor accounts for more than 50% of the
total BC.
The relative contribution of emissions from each source region to BC burden over each receptor
region is presented in Table 2.2. We add an extra receptor region, the central Pacific Ocean, defined
as 60°S-58°N, 160°E-130°W. In the central Pacific Ocean, the dominant contributor is East Asia,
accounting for 26% of the burden. In the former Soviet Union, middle Asia, and Canada, local
emissions account for no more than 50% of the BC burden, whereas Europe contributes 44%, 43%,
25

and 14% to their burdens, respectively. BC over other regions is dominated by local sources. For
example, local sources are responsible for 89%, 77%, and 73% of the BC burden in India, East
Asia and North America. Thus, controlling local anthropogenic sources is expected to have the
largest impact on BC burdens in these regions.
Table 2.2. Relative contribution (%) of emissions from thirteen source regions to BC burden in the
troposphere (200-1000 hPa) over local and non-local receptors, and to over the central Pacific
Ocean (PO, 160°E-130°W and 60°S-58°N). Relative contributions that are highlighted with gray
shading.
Receptor
CA SU EU MA EA ME NA SE IN AF SA AU RR PO
Source
CA 48.5 1.6 1.3 0.9 0.1 0.3 5.2 0.0 0.0 0.1 0.0 0.0 1.4 1.5
SU 5.4 30.3 1.2 7.2 1.9 0.4 1.0 0.1 0.1 0.0 0.0 0.0 1.8 5.9
EU 13.7 43.6 82.4 42.8 3.8 15.2 3.2 0.3 1.1 4.5 0.3 0.0 7.0 7.9
MA 0.8 4.2 0.6 18.1 1.0 1.3 0.3 0.1 0.3 0.0 0.0 0.0 0.3 0.9
EA 6.2 9.5 0.5 1.8 76.9 0.5 4.6 6.2 0.4 0.1 0.0 0.1 9.6 26.3
ME 2.4 4.1 3.7 20.3 2.3 53.3 1.5 1.1 4.7 3.2 0.2 0.1 2.5 3.8
NA 10.6 1.9 2.4 1.8 0.4 1.4 72.9 0.2 0.3 0.6 0.4 0.0 4.8 4.2
SE 0.6 0.2 0.1 0.1 5.0 0.2 1.3 60.1 0.9 0.1 0.1 3.3 6.0 8.6
IN 0.8 0.3 0.2 0.9 6.8 4.9 1.2 22.1 89.1 0.8 0.0 0.1 9.8 5.4
AF 0.8 1.2 5.2 4.4 1.1 20.8 2.3 1.6 2.7 88.5 9.8 9.8 35.9 7.4
SA 0.1 0.1 0.0 0.0 0.0 0.1 1.5 0.2 0.0 1.2 88.2 5.7 11.6 6.5
AU 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.3 0.0 0.0 0.2 79.1 3.4 7.0
RR 9.9 3.1 2.4 1.7 0.7 1.5 5.0 2.8 0.5 0.8 0.8 1.8 6.0 14.7

Table 2.3 compares the annual mean (2009-2011 average) dry deposition flux, wet deposition flux,
burden, and lifetime for BC emitted from different source regions. The lifetime of BC estimated
by the improved model is 4.9 days. This lifetime is quite similar to that from recent studies (Wang
et al. (2014) and Hodnebrog et al. (2014)). They modify model scavenging processes to better
reproduce HIPPO observations, and find that BC lifetimes are shortened from 5.9 to 4.2 days, and
from 6.3 to 3.9 days, respectively. In addition, Samset et al. (2014) find that while the lifetime of
BC is 6.8 + 1.8 days averaged over 13 AeroCom models, the models with lifetime less than 5 days
26

best match HIPPO observations. Our result is in accordance with their conclusions, and is lower
than the BC lifetime of 6.1 days estimated by Bond et al. (2013).
Table 2.3. Global budget for BC emitted from thirteen source regions (2009-2011 average) using
the optimized aging timescale for each region.
CA SU EU MA EA ME NA SE IN AF SA AU RR All
Emission (Tg/yr) 0.07 0.14 0.46 0.03 1.93 0.17 0.35 0.55 0.75 1.62 0.64 0.15 0.12 6.98
Dry Dep (Tg/yr) 0.01 0.02 0.11 0.01 0.23 0.03 0.05 0.04 0.14 0.24 0.09 0.02 0.02 1.00
Wet Dep (Tg/yr) 0.06 0.12 0.35 0.02 1.70 0.14 0.30 0.51 0.61 1.38 0.55 0.13 0.10 5.98
Burden (Gg) 1.2 1.7 9.0 0.6 11.4 4.3 3.6 4.7 10.7 31.6 9.1 2.2 2.8 92.8
Lifetime (d) 6.3 4.4 7.1 7.1 2.2 9.3 3.7 3.1 5.2 7.1 5.2 5.3 8.4 4.9

Table 2.3 also shows that the lifetime of BC varies significantly by source region, ranging from 2
to 10 days. Regional variation in the lifetime of BC is likely caused by differences in wet
scavenging, which depends on precipitation patterns and the hygroscopicity of BC-containing
particles. The lifetimes of BC emitted from the former Soviet Union (4.4 days), East Asia (2.2
days), North America (3.7 days) and Southeast Asia (3.1 days) are shorter than the corresponding
global average. Given the wide range of BC lifetime by source region, the relative contribution of
different regions to burdens is not well characterized by the relative rates of emissions. For
example, although East Asia emits the largest amount of BC, its lifetime is the shortest (~2 days).
This means that the contribution of East Asian emissions to the global BC burden is only 1/3 of
that of the second-leading source region (Africa). Using a different model and a rough division of
source regions, Wang et al. (2014) also find that the lifetimes of East Asian (2.8 days), Southeast
Asian (2.1 days), and American BC emissions (3.0 days) are shorter than the global average
lifetime (4.7 days).
2.4.3 Dependence of BC lifetime on aging timescale
In this section, we further investigate the dependence of lifetime (derived by the annual mean
burden and removal flux) on aging timescale for BC emitted from different source regions. As
shown in Figure 2.6, the lifetime of BC originating from different regions increases approximately
linearly with aging timescale. Although there is variation in the y-intercepts for curves of lifetime
versus aging timescale, slopes are quite similar. In an effort to understand the drivers of the
relationship between lifetime T (hr) and aging timescale 𝜏 (hr), we derive a theoretical description
here. Taking the global atmosphere as a box, the mass balance for 𝐵 1
(annual mean hydrophobic
BC burden, units of kg) and 𝐵 2
(annual mean hydrophilic BC burden, units of kg) are
d𝐵 1
dt
= (1 − 𝛼 )𝐸 −
𝐵 1
𝜏 − 𝐾 D
𝐵 1

(2.4)
d𝐵 2
dt
= 𝛼𝐸 +
𝐵 1
𝜏 − (𝐾 D
+ 𝐾 W
)𝐵 2
(2.5)
27

where α is the fraction of BC emitted that is hydrophilic, 𝐸 (kg hr
-1
) is the annual mean emission
rate, and 𝐾 𝐷 and 𝐾 𝑊 are the first-order dry and wet deposition coefficients (hr
-1
), respectively. 𝐾 𝑊
accounts for both precipitation intensity and scavenging efficiency.
Assuming that both hydrophilic and hydrophobic BC is in steady state, we derive the lifetime of
BC as:
𝑇 =
𝐵 1
+𝐵 2
𝐸 =
((1−𝛼 )𝐾 W
+𝐾 D
)𝜏 +1
(1+𝐾 D
𝜏 )(𝐾 D
+𝐾 W
)
(2.6)
If further assuming that 𝐾 D
and 𝐾 W
are not dependent on 𝜏 , we then derive the slope S (Equation
2.7) and intercept (Equation 2.8) of the T- 𝜏 curve:
𝑠 =
𝑑𝑇
𝑑𝜏
=
(1−𝛼 )𝐾 w
(𝐾 D
+𝐾 W
)(1+𝐾 D
𝜏 )
2
(2.7)
T(𝜏 =0)=
1
𝐾 w
+𝐾 D
(2.8)
The slope 𝑠 represents the sensitivity of BC lifetime to aging timescale, which is a function of wet
and dry deposition coefficients of BC, and the fraction of BC emitted that is hydrophilic. Given
Equation 2.7, if 𝛼 = 1 then s = 0, implying that all BC is aged and therefore hydrophilic as
emitted. If 𝐾 D
= 0 thens= 1 - 𝛼 . Therefore, if 𝐾 𝐷 is negligible, the lifetime of BC will be linearly
related to aging timescale. In addition, lower fractions of hydrophilic BC in emissions (𝛼 ) will lead
to larger sensitivities of BC lifetime to aging timescale (s ). In MOZART-4, 𝛼 is assumed to be 0.2
for all emission sources. So if KD is negligible, the theoretical slope of the T- 𝜏 curve is 1-0.2=0.8,
which is very close to the curve for untagged BC (black line) in Figure 2.6.
28

Figure 2.6. The lifetime of global BC (black) and tagged BC emitted from 13 source regions (colors)
as a function of aging timescale. The opposite sides of the grey parallelogram indicate lines with
a slope of 0.8.

The intercepts of T- 𝜏 curves represent the lifetime of BC when the aging process is extremely fast
(i.e. low values of aging timescale) such that all emitted BC can be regarded to be hydrophilic. As
shown by equation 2.8, the intercept is a function of local wet deposition coefficient and dry
deposition coefficient. Intercepts of the T-τ curves vary by source, ranging from 40 to 170 hours.
BC emitted in the Middle East, Africa, Canada, Australia, and South America has a larger intercept
than the untagged BC because the climate in these regions lacks precipitation. The Middle East is
dry and lacks precipitation in general, and emissions from Africa, Canada, and Australia are mainly
from biomass burning activities that usually occur during their dry seasons.
It should be noted that in our derivation of Eqs. (2.7) and (2.8), we assume that 𝐾 D
and 𝐾 𝑊 are
independent of 𝜏 . In reality, however, 𝐾 D
and 𝐾 W
can depend on 𝜏 . For example, as aging
timescale increases, BC has a longer lifetime and is more likely to encounter precipitation in
regions farther away from the source. Nonetheless, the discussion above helps elucidate that the
dependence of lifetime on aging timescale is determined by the fraction of emitted BC that is
hydrophilic, and the factors that influence dry and wet deposition (e.g. precipitation).
Since the aging timescale varies by region and season, the common practice in modeling of setting
a fixed global uniform aging rate may lead to significant misrepresentation of BC lifetime and
burden. Employing realistic aging timescales is especially important for regions shown in Figure
2.6 with the highest slopes and lowest intercepts; changes in aging timescale would lead to the
largest relative changes in BC lifetime in these regions. For instance, for Southeast Asia, increasing
the aging timescale of BC from 0 to 60 hours nearly doubles its lifetime.
29

Policies that control SO2 and other soluble compounds may slow BC aging, increase the lifetime
of BC, and partially offset efforts made on BC mitigation. For example, as indicated by a chamber
study, employing after-treatment technologies such as oxidation catalysts in combustion systems
can reduce emissions of volatile organic compounds and formation of secondary organic aerosols
(SOA) that could internally mix with BC, ultimately slowing the aging of BC (Tritscher et al.,
2011). Thus, policies for protecting human health that target reductions in emissions of only fine
soluble particulate matter (i.e., sulfate, nitrate and SOA) could increase BC burden through
increases in aging timescale, and potentially enhances its positive radiative forcing.
2.4.4 Caveats
We note that there are multiple limitations to our approach. Firstly, we assume that model
parameterizations of wet and dry deposition, precipitation, transport, and emissions are realistic,
even though these processes also affect BC distributions and have uncertainties (Vignati et al.,
2010;Fan et al., 2012). Consequently, the optimized aging timescales may partially counter biases
in these processes (i.e. other than aging), and may vary according to the model used. For example,
as model resolution increases, aerosol-cloud interactions in climate models can be better resolved,
which can improve the simulation of BC transport (Ma et al., 2013). Therefore, the optimized
aging timescales might change if models with different cloud schemes or spatial resolutions are
used. Secondly, due to limitations in computing resources, we carry out simulations assuming 13
discrete values for aging timescale Optimized aging timescale could have been more precisely
determined with more simulations. Lastly, as new observations become available, this study could
be repeated to more accurately optimize the aging timescale for source regions with lower relative
contributions to BC over the Pacific (e.g. Middle Asia). The goal of the optimization presented
here is not to provide precise aging timescales that can be directly used in models, since models
differ significantly in their parameterizations of physical and chemical processes, particularly the
wet scavenging. Also, BC aging includes complicated chemistry and physics, but is simplified in
our modeling as a first-order conversion from hydrophobic to hydrophilic BC. Nevertheless, this
study proposes a useful method to utilize all HIPPO observations and explore the spatiotemporal
pattern of BC aging timescales globally.

2.5 Conclusions
In this study, we tag BC emitted from thirteen regions around the globe, and conduct a set of
sensitivity simulations to investigate how different aging timescales affect spatial distributions and
source-receptor relationships for BC in a global chemical transport model, MOZART-4. We find
that BC burden and source-receptor relationships are remarkably sensitive to the assumed aging
timescale in the model; this motivates our use of HIPPO observations to optimize BC aging
timescale by minimizing model-measurement differences. Physically-based dry and wet
deposition schemes and optimized aging timescales for different regions are employed in
MOZART-4, which significantly improves the model’s performance over the Pacific Ocean
relative to the default model; the campaign-averaged mean normalized absolute error is reduced
by a factor of 4-10. The optimized aging timescales vary greatly by source region and season. In
the Northern Hemisphere, we find that the aging timescale for BC emitted in mid- and low-latitude
30

locations is in general less than half a day, whereas that for BC emitted from high-latitude locations
in most seasons (i.e. Spring, Fall, and Winter) is 4-8 days.
Using the improved model, we find that the dominant contributors to BC in the lower troposphere
over the central Pacific Ocean are local sources (i.e. ship emissions), Australia, South America
and East Asia. For the mid and upper troposphere over the Pacific Ocean, the dominant sources
are East Asia, Australia, South America, Africa, and North America. East Asian emissions
contribute the most (26%) to the total burden of tropospheric BC over the Pacific Ocean. We also
find that BC emitted from different source regions has distinct atmospheric lifetimes, suggesting
that comparing only emissions of different regions does not directly predict their contribution to
burden and therefore climate consequences. The lifetimes of BC emitted from East Asia, Southeast
Asia, North America, and the former Soviet Union are 2.2, 3.2, 3.8, and 4.4 days respectively,
shorter than 4.9 days, the global average lifetime.
Using model sensitivity simulations we determine the sensitivity of BC lifetime to aging timescale
for emissions from each source region. The lifetime-aging timescale relationship is for most
regions nearly linear. The sensitivity is influenced by wet and dry deposition rates, and more
importantly by a parameter that describes the fraction of BC emissions that are emitted directly as
hydrophilic.
Future observations that speciate coatings on BC and measure hygroscopicity of both freshly
emitted and aged BC in different regions and seasons are needed to further constrain aging
timescales and understand the physics and chemistry of the aging process. The lifetime of BC
determines its global reach, and consequently its radiative forcing on the climate system. BC with
slow aging timescales and long lifetimes can influence the climate in remote areas substantially
(e.g. over the oceans and the Arctic). In principle, the estimated climate impacts of BC emitted
from different regions rely on the representation of particles’ hygroscopicity and the assumptions
on aging timescales. Our study highlights the importance of accurately representing aging
processes in models and parameterizing aging timescales differently in different regions and
seasons.
We recommend that future inter-model comparisons like AeroCom use tagging techniques to
compare model estimates of the lifetimes of BC emitted from different regions. The tracer tagging
technique utilized here can also be used to estimate regional source contributions to BC observed
in future aircraft campaigns, to help choose locations for future campaigns, and to attribute
discrepancies in inter-model comparisons to specific source regions.

31

Chapter 3: The climate impacts of adopting cool
roofs from urban to global scale
This chapter is based on the following publication
Zhang, J., Zhang, K., Liu, J. and Ban-Weiss, G.: Revisiting the climate impacts of cool roofs around the
globe using an Earth system model, Environ. Res. Lett., 11(8), doi:10.1088/1748-9326/11/8/084014,
2016.

3.1 Introduction
Solar reflective “cool” roofs (e.g. white roofs) absorb less sunlight than traditional dark roofs, and
consequently stay cooler in the sun. Cool roofs therefore transmit less heat to the building below
and the atmosphere above, and can thus cool the atmosphere. Widespread adoption of cool roofs
is thought to be an effective strategy for mitigating “urban heat islands” (Lynn et al., 2009; Oleson
et al., 2010; Synnefa et al., 2008b), a phenomenon in which urban areas are warmer than their
surroundings due to the ubiquitous use of absorptive surfaces, less vegetation cover, and abundant
anthropogenic heating in cities (Oke, 1973a; Tao et al., 2015). Cool roofs have also been proposed
as a geoengineering strategy to partially and temporarily counter warming associated with
anthropogenic climate change (Akbari et al., 2012) by increasing solar radiation reflected by Earth.
The albedo of other surfaces on Earth could analogously be intentionally increased including that
of agricultural land (Campra et al., 2008), forest (Luyssaert et al., 2014), and urban pavements
(Santamouris, 2014).
Past research has suggested that cool roofs could effectively mitigate urban heat islands. For
example, Oleson et al (2010) estimate that adopting cool roofs in cities around the globe could
decrease the difference between urban and rural air temperature by 0.4 K on average. Synnefa et
al (2008b) show that in Athens, increasing roof albedo by 0.45 and 0.67 could lead to a decrease
in urban air temperature at noon as high as 1.5 K and 2.2 K, respectively. Georgescu et al (2014)
estimate that under a scenario assuming maximum urban expansion by 2100, the adoption of cool
roofs could reduce summertime urban surface air temperatures by 1.2 K in Florida to 3.2 K in the
Mid-Atlantic United States of America. Taha (2008a) and Taha (2008b) show that large-scale
increase in urban albedo can reduce summertime 2-pm air temperatures in various cities in the
United States, and moreover decrease building energy use and mitigate photochemical air
pollution.
The influence of cool roofs on regions larger than urban scale is more complex due to possible
atmospheric feedbacks that may behave differently on mesoscale versus larger scale meteorology.
In addition, as the spatial extent of the region of analysis increases beyond the urban scale, the
magnitude of climate response from the urban albedo modification decreases and can approach the
magnitude of natural climate variety, making it difficult to tease out the influence of the initial
forcing. Millstein and Menon (2011) perform simulations suggesting that employing cool roofs in
cities can lead to statistically significant decreases in summertime surface air temperature over
32

Northern California of 0.011 K, and statistically insignificant decreases over the United States of
0.004 K. They also find insignificant increases in summertime surface air temperatures in Texas
and Florida, which they attribute to an atmospheric feedback causing fewer clouds, less
precipitation, and lower soil moisture.
Investigating the climate impacts of cool roofs at the global scale is challenging due to difficulties
in characterizing urban physical properties and physics processes in global models with coarse
spatial resolution. Instead, some research has simplified the study of the global impacts of cool
roofs by estimating its associated radiative forcing and then inferring climate impacts by
computing the CO2 reduction that would cause equivalent forcing. Akbari et al (2009) estimate the
global radiative forcing of installing high-albedo roofs worldwide (i.e. increasing urban albedo by
0.06) to be −2.6×10
−2
W m
−2
, which is equivalent to 25 billion tonnes (Gt) of CO2 emission
reduction. Menon et al (2010) employ a detailed land surface model to estimate radiative forcing
from cool roofs, and estimate the equivalent CO2 emission reduction as 31 Gt.
A few past studies use climate models to directly estimate the influence of cool roofs on global
scale temperature. Using an earth system model without treatment for urban physics, Akbari et al
(2012) simulate global average surface air temperature changes after increasing the albedo of land
in 20°S -20°N and 45°S -45°N, respectively, and scale the results by the fraction of urban area to
land area. They report a global temperature decrease of 0.01-0.07 K from increasing urban albedo
by 0.1, offsetting 25-150 Gt CO2 emissions. Using the same model and similar methodology,
Akbari and Matthews (2012) estimate that increasing urban albedo by 0.1 can offset CO2 emissions
by 160 Gt. However, the atmospheric component in the earth system model used in these studies
is a simplified two-dimensional energy and moisture balance model, lacking cloud and aerosol
schemes, as well as feedback processes in the atmosphere. In contrast, Jacobson and Ten Hoeve
(2012) adopt a sophisticated earth system model that includes cloud feedback processes and
subgrid urban parameterizations. They estimate that installing white roofs would actually increase
global average air temperature by 0.07 K. They attribute this increase in air temperature to (1)
decreased surface air temperature causing lower sensible and latent heat fluxes, contributing to a
decrease in cloudiness, (2) increased sunlight reflected by newly adopted cool roofs being
absorbed by aerosols in the atmosphere, and (3) feedbacks of local changes to global scale causing
cloud reductions and snow and sea ice melting in Antarctica. Given that they counter-intuitively
conclude that cool roofs can increase global temperatures, these results should be verified by
additional research. While Oleson et al (2010) implement urban parameterizations in a global land
model, and couple it with an advanced global atmospheric model to investigate the influence of
cool roofs on urban climate, they do not report global average temperature change caused by cool
roofs. Note that one additional distinction between the two studies is that the global model used in
Jacobson and Ten Hoeve includes a fully dynamic ocean model, while that of Oleson et al uses
fixed sea surface temperatures, which would tend to inhibit climate feedbacks.
The radiative benefits and climate impacts of cool roofs can be influenced by aerosol loadings and
clouds. Millstein and Fischer (2014) show that in five Indian cities, where aerosol optical depth is
high, aerosols reduce the radiative benefits of cool roofs (i.e. the additional outgoing solar radiation
at the top of the atmosphere due to installation of cool roofs) by 70%. Past studies also highlight
cloud feedbacks as an important factor when evaluating the influences of cool roofs on climate
(Jacobson and Ten Hoeve, 2012; Millstein and Menon, 2011). Nevertheless, Jacobson and Ten
Hoeve is the only past study that investigates the global impacts of cool roofs using a model that
33

accounts for atmospheric feedbacks including cloud changes. There is also a lack of research that
quantifies and compares these influences in different regions.
To help overcome the aforementioned gaps in research on the influence of cool roofs on climate,
we investigate in this research the potential climate impacts of cool roofs on different spatial scales
– urban-scale (i.e. urban heat islands), continental-scale, and global-scale. We use an Earth System
Model that includes a latest generation three-dimensional atmosphere model, a land model that
resolves urban physics, and a slab ocean model. Changes in energy fluxes, temperature, cloud
fraction, snow, and precipitation, after increasing roof albedo in cities around the globe, are
predicted. We also investigate to what extent the radiative benefits of cool roofs are influenced by
aerosols and clouds in several countries (i.e., United States, China, India, and Europe).

3.2 Method
3.2.1 Model description
We use the Community Earth System Model (CESM) version 1.2.0 developed by National Center
for Atmospheric Research (NCAR), which couples the Community Atmosphere Model version
5.0 (CAM5), the Community Land Model version 4.0 (CLM4) (Lawrence et al., 2011), and the
slab ocean model. CAM5 uses a two-moment stratiform cloud microphysics parameterization
(Morrison and Gettelman, 2008). Deep convection is parameterized by Zhang and McFarlane
(1995). Moist turbulence and shallow convection are parameterized by Park and Bretherton
(2009). The radiative transfer calculations are performed by RRTMG (Rapid Radiative Transfer
Model for General circulation model applications). The aerosol life cycle in the model is
represented by the modal aerosol module MAM3 (Liu et al., 2012). Aerosols can affect cloud
formation by acting as cloud condensation nuclei (CCN) and ice nucleating species (solution
droplets or ice nuclei). Emissions of anthropogenic aerosols and their precursors are from CMIP5
and representative of the year 2000 (Lamarque et al., 2010). Dust and sea salt emissions are
calculated online as a function of wind speed, vegetative cover, soil properties, and sea surface
temperatures. The model is run at a horizontal resolution of 1.9º×2.5º (latitude × longitude), with
30 layers in the vertical from the surface to 2 hPa.
Figure 3.1a shows the global distribution of “urban fraction” (i.e. the fraction of land area within
each model grid cell that is urban) in CLM4. The urban portion of a grid cell is treated as a canyon
system consisting of five facets: roof, sunlit wall, shaded wall, pervious floor, and impervious
floor. Temperatures, sensible and latent heat fluxes, incident and reflected radiation, and heat
storage are calculated for each individual surface in the urban parameterization. The geometry of
an urban canyon is characterized by building height, and the ratio of building height to canyon
floor, which allows for computing multiple reflections and trapping of radiation by canyon
surfaces. Waste heat fluxes due to space heating and air conditioning are also considered by
comparing the interior building temperature to prescribed maximum and minimum comfort
temperatures. Model evaluation in Mexico City and Vancouver shows that the urban
parameterization in the land model in general captures the diurnal cycles of surface temperatures
and energy fluxes. (Oleson et al., 2008b, 2008a, 2008c, 2010)
34

Figure 3.1. Global maps showing (a) distributions of urban fraction (i.e. the fraction of land area
within each grid cell that is urban), (b) the four continental-scale regions of analysis: the United
States (US), Europe (EU), China (CN), India (IN), and (c) albedo change (∆𝛼 gridcell
) in each grid
cell due to increasing roof albedo from 0.15 to 0.9, calculated using equation 3.1. Note that
∆𝛼 gridcell
represents only albedo change from cool roofs, and not from feedbacks that lead to
changes in snow and ice.

35

3.2.2 Summary of simulations
We investigate the differences in equilibrium climate states of two cases. In the DARK case, roof
albedo is set to 0.15 (traditional dark roof albedo), and in the COOL case, roof albedo is set to 0.9
(cool roof albedo). Currently available cool roof products attain albedos of near 0.9 even after
considering albedo decreases associated with aging and soiling (see the Rated Products Directory
at Cool Roofs Rating Council http://coolroofs.org/). Oleson et al (2010) choose a similar roof
albedo of 0.91 in their cool roof simulation. The difference in dark and cool roof albedo of 0.75 in
our study is chosen as an upper bound in order to maximize the climate forcing and subsequent
response in the climate model. Note that we intentionally choose an idealized spatially uniform
roof albedo for DARK since we aim to investigate how atmospheric responses vary spatially, and
having spatially varying albedo differences would complicate this analysis.
The model is run assuming dark roof albedo from 1985 to 1999 as a spin-up, and then branches
into six ensemble simulations: three for the COOL case with high roof albedo (0.9) and three for
the DARK case with low roof albedo (0.15). Each ensemble simulation is run for 40 years, and the
first 10 years are discarded as spin-up; only the results from 2010 to 2039 after the model has
attained equilibrium are used for analysis. For each case, the first ensemble member is a
continuation of the initial spin-up simulation (i.e. from 1985-1999). The second and third ensemble
members are started from November 1, 1999, and December 1, 1999 respectively and are
initialized using model output from the initial spin-up run. Averaging among the three ensemble
members reduces the influence of natural climate variability (i.e. climate variability due to natural
processes in atmosphere, ocean, and land) (Kay et al., 2015), helping single out the climate
response due to the introduced forcing (i.e. albedo increase from cool roofs). The Student T-test
with 3×30 = 90 annual values for each case is used to assess whether the climate response from
implementing cool roofs is statistically distinguishable from the natural climate variability.

3.2.3 Regions of analysis
Besides investigating changes in climate in cities around the globe, we also focus on four
continental-scale regions: United States, China, India, and Europe (see Figure 3.1b). We increase
roof albedo in all grid cells with nonzero urban fraction. Figure 3.1c shows the grid cell-averaged
albedo change induced by cool roofs (∆𝛼 gridcell
), estimated using equation (3.1),
∆𝛼 gridcell
= ∆𝛼 roof
× 𝐹 land
× 𝐹 urban
× 𝐹 roof
(3.1)
where ∆𝛼 roof
is the change in roof albedo (0.9-0.15=0.75), 𝐹 land
is the fraction of a grid cell that
is land, 𝐹 urban
is the fraction of land area that is urban, and 𝐹 roof
is the fraction of urban area that
is roof. Note that while variations in snow and ice can influence simulated surface albedo, we
intentionally compute ∆𝛼 gridcell
using model input data so that it includes only contributions from
roof albedo, and thus represents the forcing without the climate response.
In this study, all analyses in the four regions defined above use only grid cells that are conditionally
sampled for ∆𝛼 gridcell
>0.004. This value is chosen to maximize the contribution of the forcing (i.e.
36

roof albedo increase) to the computed total climate response while decreasing the influence of
natural variability (i.e. in areas without roof albedo increases). Thus, continental-scale results are
representative of regions with urban areas, and are not averages over the entire country.

3.3 Results and discussion
3.3.1 The effects of cool roofs on urban heat islands
Figure 3.2 shows the impacts of adopting cool roofs on urban heat islands around the globe.
Following Oleson et al (2010), the urban heat island is defined as the surface air temperature (i.e.
air temperature at 2 m) difference between urban and rural areas in each grid cell. Urban heat
islands are shown to decrease around the globe in summer, winter, and annually. Decreases are
statistically significant in most regions, except in some high-latitude areas in winter, and in some
parts of Africa and Mexico that have low urban fraction. The annual- and global-mean urban heat
island is reduced from 1.6 K to 1.2 K. Our results slightly differ from Oleson et al (2010), which
suggests that wintertime urban heat islands can increase in high latitudes due to an increase in
anthropogenic heating, and estimates that the annual- and global-mean urban heat island is reduced
from 1.2 K to 0.8 K. Differences between our study and Oleson et al (2010) could be partially
attributed to different assumed dark roof albedo; they assume a spatially varying albedo
distribution with an average of 0.4. In addition, although we run earth system models with similar
atmosphere and land components, our model is also coupled to ocean and ice modules whereas
Oleson et al (2010) use only prescribed sea surface temperatures. Moreover, we use the updated
version of the community atmospheric model (version 5), while Oleson et al use the previous
version (CAM4). Nonetheless, both Oleson et al. and our study suggest a global mean reduction
in the urban heat island of 0.4 K.

37

Figure 3.2. Changes in urban heat islands (urban minus rural surface air temperature) from
increasing roof albedo, averaged annually (ANN), in summer (JJA), and winter (DJF). Dotted
areas are where differences are not statistically significant at 95% confidence interval. Each panel
shows differences between COOL and DARK cases.

38

3.3.2 The influence of cool roofs on continental-scale energy fluxes and climate
Figure 3.3 depicts the impacts of cool roofs on energy fluxes averaged over our four continental-
scale regions of analysis (see Figure 3.1b). Recall that only grid cells with ∆𝛼 gridcell
greater than
0.004 are included in the analyses presented here.

Figure 3.3. Scatter plots showing changes (COOL minus DARK) in (a) all-sky (i.e. including
clouds) upward shortwave flux (W m
-2
) at the surface versus (∆𝛼 gridcell
), (b) see below, (c) all-sky
upward shortwave flux at the surface versus the product of insolation at the surface and (∆𝛼 gridcell
)
in DARK case, (d) clear-sky (i.e. no cloud) upward shortwave flux at the top of the atmosphere
versus changes in clear-sky upward shortwave flux at the surface, (e) all-sky upward shortwave
flux at the top of the atmosphere versus changes in all-sky upward shortwave flux at the surface,
and (f) shortwave aerosol forcing at the top of the atmosphere versus changes in all-sky upward
39

shortwave flux at the surface. Each dot represents a different model grid cell. Colors denote
different continental-scale regions: the United States, China, India, and Europe, which are
represented by 20, 66, 65, and 81 grid cells, respectively. Dashed lines denote best fit linear
regressions derived using least squares for each region. Aerosol forcing refers to the difference in
outgoing solar radiation at the top of the atmosphere in the presence and absence of aerosols. A
scatter plot for the slopes of regressions in Figure 3.3a versus average downward shortwave flux
at the surface in the four continental-scale regions in DARK case is shown in (b). Each point
represents a different region in this panel, and 1-sigma uncertainties in the slopes are included.

For each region, increasing 𝛼 gridcell
proportionally enhances the solar radiation reflected by the
surface (Figure 3.3a). As shown by the slopes of the best fit lines in Figure 3.3a, reflected solar
radiation appears more sensitive to 𝛼 gridcell
for regions at low-latitudes and with lower baseline
cloudiness, both of which tend to increase incoming solar radiation. Under clear-sky (i.e. no cloud)
conditions, the increase in solar radiation reflected by the surface from cool roofs leads to
proportional increases in outgoing solar radiation at the top of the atmosphere (Figure 3.3b) in each
of the four regions. The sensitivity of outgoing solar radiation at the top of atmosphere versus
upward radiation at the surface, as diagnosed using the slope of the best fit lines in Figure 3.3b, is
similar among the regions. Considering all-sky (i.e. with clouds) conditions (Figure 3.3c) rather
than clear-sky conditions (Figure 3.3b) leads to more variation among regions in the
aforementioned sensitivity. This is likely caused by differences among regions in baseline cloud
cover, which affects the fraction of upward solar radiation at the surface that escapes to space.
Similarly, there is more variability within each region in the all-sky case, a consequence of natural
variability in simulated cloud cover between each ensemble member for the COOL and DARK
cases. Variations in clouds strongly influence energy transfer from the surface to the top of the
atmosphere, and in some cases can outweigh the influence of increasing roof albedo.
Aerosols are expected to decrease the radiative benefits of cool roofs by absorbing upwelling solar
radiation, and reflecting upwelling radiation back to the surface (Jacobson and Ten Hoeve, 2012;
Millstein and Fischer, 2014). However, this effect has never been quantified using a three-
dimensional atmosphere model, and has not been computed either at larger than urban scales or in
different countries. Aerosol forcing (i.e. the difference in outgoing solar radiation at the top of the
atmosphere in the presence versus absence of aerosols) can quantify the influence of aerosols on
energy transmitted through the atmosphere. As shown in Figure 3.3d, aerosol forcing becomes
more negative as reflected solar radiation at the surface from cool roofs increases. This suggests
that in the presence of aerosols, less energy reflected by the surface escapes to space. The slope of
the best fit line for △aerosol forcing versus △reflected solar radiation varies from -0.04 in the
United States to -0.18 in more polluted China (Figure 3.3d), suggesting that aerosols offset 4% to
18% of the radiative benefits of cool roofs.
Table 3.1 summarizes temperature changes from cool roof adoption in the four continental-scale
regions. There are statistically significant decreases in annual mean surface air temperatures in the
United States (0.11±0.10 K) and China (0.14±0.12 K). Temperature changes in India and Europe
are statistically indistinguishable from zero (-0.08±0.12 K and 0.07±0.15 K, respectively). Thus,
adoption of cool roofs is likely to reduce surface air temperatures in China and the United States,
40

while its impacts in Europe and India appear smaller than natural climate variability when changes
are computed at continental-scale.
Table 3.1. 30-year mean surface air temperatures averaged over four continental-scale regions for
the DARK case, and differences between the COOL and DARK cases. Uncertainties are quantified
using the Student’s t-test at 95% confidence level. Only grid cells with ∆αgridcell>0.004 are included
in these continental-scale averages. Values represent grid cell averages including both the urban
and non-urban portions of the cell.
Region DARK COOL-DARK
2-m air
temperature
(K)
U.S. 287.7 -0.11±0.10
China 287.8 -0.14±0.12
India 299.4 -0.08±0.12
Europe 284.8 0.07±0.15

3.3.3 The influence of cool roofs on global energy fluxes and climate
Table 3.2 summarizes the impacts of cool roofs (COOL minus DARK) on global mean
temperatures, energy fluxes, and hydrological variables. Note that global averages include all grid
cells, not just those with ∆𝛼 gridcell
> 0.004 as in the previous section. The difference in global-
and annual- mean surface air temperature between COOL versus DARK is -0.0021 K, with a 95%
confidence interval of -0.028 to +0.024 K. Previous studies disagree on the global climate impacts
of cool roofs. Akbari et al (2012) suggest that increasing urban albedo by 0.1 globally would reduce
global average temperatures by 0.01 or 0.07 K, based on two different datasets of urban area
respectively. On the other hand, Jacobson and Ten Hoeve (2012) suggest that cool roofs would
warm the globe by 0.07 K. Uncertainty estimates for global average temperature changes are not
provided by these studies. Based on a suite of ensemble simulations to estimate uncertainty, our
results suggest that although cool roofs might exert a cooling effect (0.002 K) on global climate,
the cooling is smaller than the uncertainty from natural variability.
Table 3.2. 30-year globally averaged temperatures, albedo, energy fluxes, and hydrologic variables
for the DARK case, and differences between the DARK and COOL cases. Uncertainties are
quantified using the Student’s t-test at 95% confidence level. Unlike the continental-scale means
(Table 3.1), all grid cells are included in these averages.

Variables DARK COOL-DARK
Ground temperature (K) 289.6 -0.0013±0.026
41

Surface air temperature (K) 288.8 -0.0021±0.026
Albedo (shortwave, direct)
a
0.57 0.00029±0.00009
Albedo (shortwave, diffuse)
a
0.55 0.00028±0.00009
Net solar flux at surface (W m
-2
)
b
160.8 -0.042±0.084
Net solar flux at top of model (W m
-2
)
b
237.3 -0.053±0.079
Net longwave flux at surface (W m
-2
)
c
52.3 0.053±0.049
Net longwave flux at top of model (W m
-2
)
c
237.4 -0.041±0.073
Surface latent heat flux (W m
-2
)
c
90.9 -0.075±0.085
Surface sensible heat flux (W m
-2
)
c
17.1 -0.005±0.031
Precipitation rate (m s
-1
) 1.0×10
-8

-3.0×10
-11
±4.7×10
-11

Water equivalent snow depth (m) 3.8×10
-2
9.9×10
-5
±6.9×10
-5

2m humidity (kg kg
-1
) 0.011 -1.4×10
-5
±1.7×10
-5

Column precipitable water (kg m
-2
) 28.13 -0.046±0.050
Low cloud fraction 0.42 -7.2×10
-4
±5.4×10
-4

Mid-level cloud fraction 0.26 2.0×10
-4
±3.1×10
-4

High cloud fraction 0.38 7.0×10
-4
±5.0×10
-4

a
Albedo is output by the simulations including feedbacks, as opposed to ∆𝛼 gridcell
, which is the change we imposed
in the input file.
b
Fluxes are positive downward.
c
Fluxes are positive upward.

Our simulations suggest that global adoption of cool roofs cause statistically insignificant
decreases in surface ground and air temperatures, and precipitation (Table 3.2). As discussed in
previous research (Jacobson and Ten Hoeve (2012), Georgescu et al (2012) and Georgescu et al
(2014)), decreasing surface air temperature can in some cases suppress convection to the point that
precipitation is reduced. Increases in global average surface albedo, including both adoption of
cool roofs and feedbacks in snow and ice, are statistically significant (0.0003±0.00009), while
decreases in the downward positive net solar flux at the surface (-0.042±0.084) and at the top of
the model (-0.053±0.079) are statistically insignificant. Adoption of cool roofs leads to global
mean low cloud fraction (>700 hPa) decreases of 0.02%±0.13%, mid-level (400-700 hPa) cloud
fraction increases of 0.08%±0.12%, and high (<400 hPa) cloud fraction increases of
0.02%±0.13%. Increasing snow depth (0.26%±0.18%) may be a feedback from lower surface air
temperatures. It should be noted that all the global changes listed in Table 3.2 except changes in
surface albedo and snow depth are statistically insignificant, meaning most changes induced by
cool roofs are smaller than the natural variability on global scale.

3.3.4 Discussion
Previous studies have quantified the equivalent CO2 offset from cool roof adoption by comparing
changes in the TOA radiation balance from cool roofs to changes in radiative forcing from CO 2
increases (Akbari et al., 2009, Menon et al., 2010, Millstein and Menon, 2011, Millstein and
42

Fischer, 2014). Radiative forcing is the change in downward positive radiative flux at the top of
the atmosphere after introducing a forcing agent, and was originally developed as an effective
framework to evaluate and compare the climate impacts of various greenhouse gases. Note that
there are many different methods for calculating radiative forcings of climate change agents
(Hansen et al., 1997, 2005).
Greenhouse gases are mostly well-mixed in the atmosphere and warm the Earth by reducing the
amount of longwave radiation that escapes the Earth system to space. Thus, radiative forcing from
different greenhouse gases (e.g. carbon dioxide, methane, nitrous oxide) lead to climate responses
via similar physical mechanisms, and likewise have similar climate sensitivity parameters (i.e. the
ratio of global mean surface air temperature response to radiative forcing). Though radiative
forcing can be an effective framework for comparing the effects of greenhouse gases on global
temperatures, other radiative forcing agents that are not globally well mixed, or alter the shortwave
rather than longwave energy budget, may not fit into the same framework as well due to differences
in climate sensitivity parameter (Ban-Weiss et al., 2012a; Hansen et al., 2005). Black carbon
particles, which are globally heterogeneous, and absorb shortwave rather than longwave radiation,
are a prime example. The climate sensitivity of black carbon particles at different altitudes can
range from 61% lower to 25% higher than the climate sensitivity of CO2 (Ban-Weiss et al., 2012a).
This suggests that equivalent radiative forcings from CO2 and black carbon are expected to cause
vastly different global temperature impacts. The same logic applies to cool roofs as the climate
sensitivity parameters of CO2 and cool roofs are likely to be different given that the latter changes
the planetary shortwave radiation budget while the former changes the longwave radiation budget.
Additionally, cool roofs are concentrated in urban regions and are therefore highly globally
heterogeneous. Therefore, we suggest that making the equivalence between CO2 and cool roofs in
terms of radiative forcing may be less meaningful than desired, especially if comparing their
impacts on global temperature is sought after. Rather than reporting this equivalence in the present
research, we investigate the potential global climate impacts of cool roofs using simulations from
an Earth system model.
Although the impacts of cool roofs on radiative forcing can be suggestive of their climate impacts,
the climate response (i.e. including feedbacks) to the radiative forcing determines their climate
consequences. Employing a coupled Earth system model can simulate the atmospheric feedback
processes and therefore is important for furthering understanding of the climate impacts of cool
roofs on large scales. Using such a model, we find that cool roofs significantly reduce temperatures
in most cities around the globe. The impacts of cool roofs at the continental-scale are more
uncertain given that urban regions make up a smaller fraction of the total area. Surface air
temperature decreases are statistically significant in the United States and China, but insignificant
in India; there is a statistically insignificant surface air temperature increase in Europe. Cool roofs
are found to reduce global average surface air temperatures, but the temperature change is not
statistically significant since its effect is smaller than natural variability.
It should be noted that we have only assessed the impacts of cool roofs as an approach to reduce
absorbed solar radiation by the Earth system. Cool roofs can also indirectly influence the climate
by reducing heat transferred into buildings, decreasing air conditioning energy use, and reducing
greenhouse gas emissions associated with energy production (Levinson and Akbari, 2010). In
addition, as with any research using models, the results presented here may be model dependent
and should be further corroborated using other Earth system models.
43

3.4 Conclusion
In this study, we use the Community Earth System Model to investigate the impacts of deploying
cool roofs in urban regions around the globe on urban, continental-scale, and global climate. We
find that increasing roof albedo from 0.15 to 0.90 reduces urban heat islands (i.e. urban minus rural
air temperature) everywhere. The decreases are statistically significant, except in some areas in
Africa and Mexico where urban fraction is low, and some high-latitude areas during wintertime.
The annual- and global-mean urban heat island decreases from 1.6 K to 1.2 K.
We analyze the impacts of cool roofs at the continental-scale in four areas: United States, China,
India, and Europe. For each region, the solar radiation reflected by the Earth surface increases
proportionally to the estimated surface albedo increase, as expected. For clear-sky (i.e., cloud free)
conditions, the increase in reflected solar radiation at the surface proportionally enhances outgoing
shortwave radiation at the top of atmosphere, suggesting a radiative cooling effect of cool roofs.
However, for all-sky (i.e. with clouds) conditions, the influence of natural climate variability
especially from changes in cloud cover on the energy balance at the top of the atmosphere in some
cases outweighs the influence of cool roofs. Aerosols have been hypothesized to partially offset
the effects of cool roofs by directly absorbing upwelling solar radiation, and reflecting upwelling
radiation back to the surface. However, the magnitude of this effect has never been quantified
using an Earth system model. We find that the additional aerosol forcing induced by cool roofs is
4-18% of the increase in reflected solar radiation at the surface; regions with higher AOD are
shown to absorb more reflected upward radiation than those with lower AOD. With cool roofs,
annual mean surface air temperatures in the United States and China show statistically significant
decreases of 0.11±0.10 K and 0.14±0.12 K, respectively, while in Europe and India the mean
surface air temperature changes are statistically insignificant.
Past studies disagree on the influence of cool roofs on global climate. Some studies suggest that
implementing cool roofs in cities around the globe would lead to global cooling (Akbari et al.,
2009, 2012), while another study Jacobson and Ten Hoeve (2012) suggest that cool roofs could
cause the globe to warm. The research presented here indicates that adoption of cool roofs around
the globe would lead to statistically insignificant reductions in global mean air temperature
( −0.0021 ± 0.026 K). Thus, we suggest that while cool roofs are an effective tool for reducing
building energy use in hot climates, urban heat islands, and regional air temperatures, their
influence on global climate is likely negligible.

44

Chapter 4: Systematic comparison of the
influence of cool wall versus cool roof adoption
on urban climate in the Los Angeles basin
This chapter is based on the following publication
Zhang, J., Mohegh, A., Li, Y., Levinson, R. and Ban-Weiss, G.: Systematic Comparison of the Influence
of Cool Wall versus Cool Roof Adoption on Urban Climate in the Los Angeles Basin, Environ. Sci.
Technol., 52, 11188 –11197, doi:10.1021/acs.est.8b00732, 2018.

4.1 Introduction
The urban heat island effect (UHIE) is a phenomenon in which urban areas are warmer than
surrounding rural areas, a result of urban-rural differences in land cover and population density.
The UHIE can exacerbate challenges associated with high temperatures in urban areas including
(a) human health impacts from extreme heat (Palecki et al., 2001), such as heat stroke, heat
exhaustion rates, and premature deaths; (b) daily total and peak air conditioning energy use during
summer (Kolokotroni et al., 2006); and (c) increases in urban ozone concentrations and potential
influences on other air pollutants (Tao et al., 2015, 2017). Several important environmental
processes, which are driven by the effects of urban land expansion on surface-atmosphere
coupling, can aggravate or mitigate the UHIE. First, widespread application of materials with high
solar absorptance (e.g., asphalt concrete and many roofing materials) in urban areas increases
absorption of solar energy. Second, extensive use of materials with high heat capacity increases
retention of solar energy throughout the day. Third, the geometry of urban canyons (i.e., the space
between buildings and above streets) can trap both shortwave (solar) and longwave (thermal
infrared) radiation (Theeuwes et al., 2014). Fourth, lack of vegetation cover in cities reduces
evaporative cooling and shading of the ground surface, thereby increasing urban temperatures
(Taha, 1997b). Fifth, increases in soil moisture from irrigation in urban areas can increase
evaporative fluxes and cool cities during the day, while at night causing increases in upward
ground heat fluxes (due to the increase in soil moisture and thermal conductivity) and therefore
nocturnal warming (Vahmani and Ban-Weiss, 2016a). Sixth, changes in surface roughness from
urbanization can also alter wind flows and vertical mixing (Vahmani et al., 2016), with subsequent
effects on temperatures that can vary by location. Finally, human activities and industrial processes
contribute to releasing waste heat in cities (Fan and Sailor, 2005; Oke et al., 1991).
To lessen the UHIE, heat mitigation strategies have been proposed and employed in some locations
to alter the energy balance in cities and decrease temperatures. For example, planting trees and/or
adopting vegetative roofs could increase evaporative cooling and reduce urban temperatures (Li et
al., 2014a). Increasing the albedo, also referred to as solar reflectance (ratio of reflected to incident
sunlight) of roofs, walls, and pavements could reduce solar heat gain, lower surface temperatures,
decrease heat transfer from the surface to the atmosphere, and consequently cool the outside air.
45

Heat mitigation strategies can also influence urban climate by changing the hydrological cycle
(Georgescu et al., 2014).
The effects of solar-reflective “cool” roofs on urban climate have been well studied in previous
research. Large-scale implementation of cool roofs has been predicted to effectively reduce city-
wide air temperatures in Athens, Greece (Synnefa et al., 2008a); Sacramento (Taha, 2008a);
Baltimore-Washington (Li et al., 2014a); and other cities (Zhang et al., 2016). In Los Angeles, the
reduction of peak air temperature induced by increasing both roof and pavement albedo was
estimated to reach 1.5 K (Rosenfeld et al., 1998), while a more recent study (Millstein and Menon,
2011) estimated the reduction at 13:00 local standard time (LST) in summer to be 0.5 K. In
addition, Vahmani et al. (2016) concluded that widespread adoption of cool roofs could reduce
Southern California summer urban air temperatures by 0.9 K at 14:00 LST and by 0.5 K at 22:00
LST. Santamouris (2014) summarized previous literature and concluded that daily average
ambient temperatures are expected to decrease linearly with average grid cell albedo increase in
cities, declining 0.3 K per 0.1 albedo increase. (We note here that grid cell albedo represents a
“bird’s eye view” of both impervious and pervious surfaces within modeled urban regions.)
Cool pavements have been studied less than cool roofs. While they are both horizontal surfaces,
temperature reductions per unit facet albedo increase can differ between them in part because cool
pavements are at the bottom of the urban canyon while roofs are at the top. Mohegh et al. (2017)
simulated the influence of employing cool pavements on near-surface air temperatures in
Californian cities. They found that increasing pavement albedo by 0.40 could lead to annual
average air temperature reductions at 14:00 LST ranging by city from 0.19 K to 0.87 K.
Temperatures at 14:00 LST declined by 0.32 K per 0.10 increase in grid cell average albedo.
Despite previous studies that have examined the effects of raising albedo of horizontal surfaces in
different cities, the influence of increasing the albedo of vertical surfaces (e.g., walls) on
temperatures has not yet been systematically investigated. The climate effects of increasing wall
albedo are expected to differ from those for cool roofs. First, increasing wall albedo and roof
albedo by the same amount will influence the energy budget of the urban canopy (i.e., the urban
canyon plus roof surfaces) differently for four reasons:
Diurnal cycles of solar irradiance (incident radiative power per unit area) and daily solar irradiation
(incident radiative energy per unit area) received by vertical walls differ from those received by
(nearly) horizontal roofs. For example, in July, the north, east, south and west walls of a building
in Burbank and Riverside, CA collectively typically receive about 40% as much daily solar
irradiation as horizontal roofs.
Walls make up a different fraction of urban areas than do roofs.
Walls can be shaded when sun is low, so the fraction of wall area that is illuminated varies by time
of day. In our study, we assume that roofs are not shaded. (In the real-world, non-uniform building
heights and trees can lead to roof shading, but we ignore these effects in this study.)
A portion of the solar radiation that is reflected by walls is absorbed by opposing walls and
pavements, and is thus trapped in the canyon. Solar radiation reflected by roofs, on the other hand,
46

mostly escapes the canopy without being absorbed by other urban facets. Unlike cool roofs, the
effect of cool walls depends on the height to width ratio of the urban canopy.
Hence, solar reflections from cool walls differ in timing and magnitude from those from cool roofs.
Second, atmospheric temperature changes induced by cool surfaces are determined not only by the
change in the canyon energy budget but also by the diurnal cycle in surface-atmosphere
interactions. Diurnal variations in wind speeds, planetary boundary layer (PBL) heights, and
atmospheric stability can influence the relationship between changes in the surface energy budget
and resulting atmospheric temperature reductions (Bonan, 2010). Cool walls and roofs induce
different diurnal cycles in reflected solar radiation. Thus, the diurnal cycles in surface-atmosphere
coupling contribute to differences in air temperature change. In other words, even if increases in
daily reflected solar radiation were the same for cool walls and roofs, their different diurnal cycles
would be expected to lead to different daily average air temperature changes.
Lastly, walls are in the urban canyon whereas roofs are at the top of the canopy. This means that
walls may more directly influence in-canyon air temperatures than roofs, while roofs may more
directly influence above-canopy air temperatures.
In this study, we use a regional climate model, coupled to an urban canopy model, to investigate
how adopting cool walls would influence albedo, reflection of sunlight, and near-surface air
temperature in the Los Angeles basin. We adopt a new parameterization that diagnoses near-
surface air temperature within the urban canyon, which is likely more relevant to pedestrian
thermal comfort and building energy use than default “2 m air temperature” diagnosed by the
model. A suite of additional cool roof simulations systematically compares the climate effects of
cool walls to those of cool roofs within a consistent modeling framework.
4.2 Methodology
4.2.1 Model description
We use the Weather Research and Forecasting model (WRF) version 3.7 (Skamarock et al., 2008)
to investigate the effects of raising wall albedo on near-surface canyon air temperatures in the Los
Angeles basin. WRF is developed collaboratively by the National Center for Atmospheric
Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA) and other
institutes, and is widely used to study regional-scale meteorology and climate.
WRF provides several parameterizations that can be used to represent processes that occur at
resolutions finer than model grid cells. We summarize here the parameterizations chosen for our
simulations. Physics schemes include the Rapid Radiative Transfer Model (RRTM) scheme for
long-wave radiation (Mlawer et al., 1997), the Dudhia shortwave scheme (Dudhia, 1989) for
shortwave radiation, and the Yonsei University scheme (Hong et al., 2006) for the planetary
boundary layer, and the Lin et al. scheme (Lin et al., 1983a) for cloud microphysics. To simulate
cumulus clouds in the middle and outer domains, the Kain-Fritsch convective parameterization is
used (Kain, 2004). The Noah Land Surface Model (Chen and Dudhia, 2001) couples the land
surface and atmosphere to compute exchanges in energy (e.g., latent and sensible heat fluxes),
47

momentum, and water. A single-layer urban canopy model (UCM) simulates the influence of
urban surface-atmosphere coupling (Chen et al., 2011). Parameterizations for physics are chosen
to be consistent with our previous modeling studies for Southern California (Vahmani and Ban-
Weiss, 2016b, 2016a), which were extensively evaluated by comparing to observations.
The National Land Cover Database for 2006 is used for land cover type classification in the model
(Fry et al., 2011). For urban grid cells (i.e., cells dominated by urban land cover), we use
impervious surface data from the National Land Cover Database (NLCD) (Wickham et al., 2013)
to compute grid cell specific impervious fractions (i.e., the fraction of each grid cell covered by
impervious surfaces). The urban canopy model resolves surface-atmosphere exchange for the
impervious part of the grid cell, while the Noah model is used for the pervious portion of urban
grid cells. Note that the Noah Land Surface model also handles non-urban grid cells. Urban land
use classification and urban morphology will be discussed in section 4.2.4.
Following Vahmani and Ban-Weiss (2016b), we have improved the default version of the WRF
model by utilizing MODIS satellite observations to determine grid cell specific green vegetation
fraction and leaf area index for pervious areas (i.e., for both the pervious portion of urban grid cells
and for non-urban cells). Previous research has found that accounting for high resolution
heterogeneity in land surface properties in urban areas can improve model simulations when
comparing to observations of meteorology in Los Angeles (Vahmani et al., 2016).
4.2.2 Shortwave radiation calculations in the urban canopy model
In the single-layer urban canopy model (UCM) employed in WRF (Kusaka et al., 2001), the urban
canopy is represented as an infinitely long street (a.k.a. ground, or canyon floor) bounded by two
infinitely long buildings of identical width. That is, there is no separation between adjacent
buildings on the same side of the street. Recall that we refer to the urban canopy as the canyon
plus roof surfaces.
Direct (beam) and diffuse solar radiation are tracked separately in our model. At solar noon, most
beam sunlight strikes horizontal surfaces (roofs and ground), rather than vertical surfaces (walls).
In early morning and late afternoon, the ratio of beam vertical radiation to beam horizontal
radiation is higher than that at solar noon. Buildings shade the ground when the sun is not at zenith,
reducing the ground’s solar irradiance and solar heat gain. Since in the canopy model all sunlight
not incident on roofs strikes walls or the ground, the beam solar radiation (power) intercepted by
the sun-facing wall equals beam horizontal irradiance (power/area) times the length of the ground
shadow. Canyon orientation (i.e., the angle between canyon centerline and north) and solar
position are considered in the calculations of ground shadow length throughout the day. Eight
canyon orientations (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°) are considered, and the shadow
length is averaged among these eight orientations. Ground shadows are longest when the sun is
low and shortest when the sun is high. Thus, the fraction of global horizontal irradiance that is
incident on the ground peaks at solar noon, while that incident on walls reaches its minimum at
solar noon and peaks in the early morning and late afternoon.
The diffuse part of solar radiation can strike all impervious facets (roofs, walls, and ground).
Downwelling diffuse solar irradiances on wall and ground surfaces are proportional to the view
factors from wall to sky and from ground to sky, respectively. Each facet is assumed to reflect
48

sunlight diffusely. The view factors from ground to wall, from wall to ground, and from sun-facing
wall to sun-opposing wall influence the reception of reflected radiation by walls and ground. Note
that radiation reflected twice by facets is assumed to fully escape from the urban canopy. For
example, absorption within the canopy of light reflected from wall to wall to ground is ignored in
the model.
Note that even though WRF-UCM includes a “shadow model” that treats direct and diffuse
radiation separately, the default code of WRF-UCM has the “shadow model” turned off. Thus, all
solar radiation is treated as diffuse and shadows casted by buildings is not considered. (See Line
853 of module_sf_urban.F in WRF3.7 where SHADOW = .false.) We turn on the shadow
calculations by setting SHADOW = .true. We also add to the default shadow model wall-to-wall
reflection effects following Kusaka et al. (2001).
Downward solar radiation that is not absorbed by roofs, walls, and ground is reflected out of the
canopy as upwelling solar radiation. The UCM calculates canopy albedo as the ratio of upwelling
sunlight to downwelling sunlight at the horizontal plane bounding the top of the canopy. The
changes to canopy albedo upon increasing wall albedo are computed here using the single-layer
urban canopy model. Canopy albedo represents the aggregated “above-canopy” albedo of all facets
in the urban portion of grid cells, not including contributions from the non-urban portion of the
grid cell. Since only the urban portion of the grid cell is modified, changes in grid cell albedo are
then computed as change in canopy albedo multiplied by urban fraction (Figure 4.1c). Note that
canopy and grid cell albedo are diagnostic variables and are not used in other model calculations.
4.2.3 Canyon air temperature
For urban grid cells (i.e., grid cells dominated by urban land cover; Figure 1b), the standard WRF
diagnoses and outputs canyon temperature and 2-m grid cell air temperature. Canyon temperature
is effectively an aggregated skin temperature for walls and ground. This temperature is used in
calculations of sensible heat flux from the canyon to the atmosphere. The calculation for the default
grid cell 2-m air temperature diagnosed by WRF uses the roughness length of grass, leading to
unphysical results in urban grid cells (Li and Bou-Zeid, 2014).
To better simulate the temperature near the ground level in cities, we implement the
parameterization proposed by Theeuwes et al. (2014) to calculate near-surface air temperature
within the urban canyon, which we refer to as canyon air temperature.
4.2.4 Urban land use type classification and corresponding canopy morphology
Urban morphology in the UCM is described by roof width (𝑅 ), building height (𝐻 ), and ground
width (𝑊 ). The UCM uses the urban morphology to compute (a) solar irradiance (power/area)
incident onto each facet; (b) shortwave radiation (power/area) reflection and longwave radiation
(power/area) transfer from each facet to other canyon facets and to the sky; and (c) area weighting
factors for averaging solar absorption (power/area) and sensible heat fluxes (power/area) among
facets.
49

Data describing spatially resolved urban morphology from the National Urban Database and
Access Portal Tool (NUDAPT) are used where available. NUDAPT data (Ching et al., 2009) cover
only a small portion of our domain, but include downtown Los Angeles, where unusually tall
buildings are found. For grid cells where NUDAPT data are not available, we derive urban
morphology used in this study (Table 4.1) using two real-world datasets for Los Angeles County
: LARIAC (2014) and LARIAC (2016). LARIAC (2016) provides information for every building
in Los Angeles County, including ZIP Code, roof area, building height, and shape. LARIAC
(2014) provides the geographical centerlines for each street in Los Angeles County. Urban
morphologies in the UCM are derived satisfying (a) 𝐻 /𝑊 in the UCM as the ratio between
building height from LARIAC (2016) and ground width from LARIAC (2014), and (b) 2 × 𝐻 /𝑅
in the UCM as the ratio of wall area (excluding windows) to roof area in LARIAC (2016).
Table 4.1. Two-dimensional urban morphology used in our urban canopy model (UCM), derived
from LARIAC (2014) and LARIAC (2016) descriptions of the three-dimensional morphology of
Los Angeles County.
Urban land use type Building
height 𝐻
(m)
Roof
width 𝑅
(m)
Ground width 𝑊
(m)
Low-intensity residential 5.8 6.0 26.6
High-intensity residential 5.3 5.4 24.3
Commercial/industrial 6.5 6.2 27.1

4.2.5 Simulation domain
Three nested domains are simulated at resolutions of 18 km, 6 km, and 2 km, respectively (Figure
4.1a). The outermost domain (d01) covers California; the middle domain (d02) simulates southern
California; and the innermost domain (d03) encompasses the Los Angeles basin and San Diego.
The domain is the same as that used in our previous modeling work (Vahmani et al., 2016;
Vahmani and Ban-Weiss, 2016a, 2016b). Each inner domain uses values from the adjacent outer
domain as boundary conditions. The outermost domain (d1 in Figure 4.1a) uses the North
American Regional Reanalysis (NARR) dataset (Mesinger et al., 2006) as boundary conditions.
The NARR dataset has a spatial resolution of 32 km and temporal resolution of 3 hours. The
atmosphere is simulated using 30 layers in the vertical. Urban areas in Los Angeles County (see
Figure 4.1b) are included in our analysis on diurnal cycles.

50

Figure 4.1. Maps showing (a) the three nested simulation domains d01 (Western United States),
d02 (Central and Southern California), and d03 (Southern California), (b) dominant urban land
use types for domain d03, where the red outline bounds Los Angeles County considered in our
analysis of diurnal air temperatures cycles, and (c) urban fractions in domain d03.
4.2.6 Simulation design
Our analysis of the influence of cool walls on the climate of Southern California considers three
scenarios: CONTROL, in which roof, ground, and wall albedos are all set to 0.10;
COOL_WALL_LOW, in which wall albedo is raised to 0.50; and COOL_WALL_HIGH, in which
wall albedo is raised to 0.90. To compare the effect of increasing wall albedo to that of raising roof
albedo, we add two more scenarios: COOL_ROOF_LOW, in which roof albedo is raised to 0.50;
and COOL_ROOF_HIGH, in which roof albedo is raised to 0.90 (Table 4.2). In this way, the
modified-facet albedo increases are 0.40 for COOL_WALL_LOW and COOL_ROOF_LOW
scenarios, and 0.80 for COOL_WALL_HIGH and COOL_ROOF_HIGH scenarios. Note that cool
surface albedos in cases COOL_WALL_HIGH (wall albedo 0.90) and COOL_ROOF_HIGH (roof
albedo 0.90) are higher than those of actual cool walls and roofs, especially after weathering and
soiling. For example, the albedo of an initially bright-white roof might fall to about 0.60 - 0.70
from about 0.80 - 0.90 after several years of exposure (Berdahl et al., 2008; Sleiman et al., 2011).
The initial albedo of a non-white cool surface, such as a "cool colored" roof, typically ranges from
about 0.25 to about 0.50 (Levinson et al., 2007), and its albedo loss upon exposure tends to be
smaller than that experienced by a bright-white roof (Sleiman et al., 2011). The albedo values in
our simulations are chosen to gauge the upper bound effect of applying cool walls and roofs, and
to test the linearity of canyon air temperatures with increasing wall and roof albedos. We also run
three additional ensemble members for another scenario that we refer to as
COOL_ROOF_WALL_HIGH where the albedos of walls and roofs are both raised to 0.90; this
scenario is added to test the linearity of adopting cool walls and roofs simultaneously or separately.
We perform three ensemble simulations per scenario to reduce the influence of model internal
variability on results. The ensemble simulations are carried out by initiating the model simulations
51

at different times (14:00 LST on 28 June 2012, 14:00 LST on 29 June 2012, and 14:00 LST on 30
June 2012). Ensemble means are reported for each scenario.
Simulations are performed for 12-14 days (varying by ensemble member) from 28 June 2012 to
11 July 2012. Due to intrinsic uncertainties in initial conditions, modeled results at the start of the
simulations are unreliable (Warner, 2011). A previous study with the same model configuration
discarded the first 12 simulated hours as model “spin-up” (Vahmani et al., 2016). In this study, the
first three to five days (i.e. varying by ensemble member) are discarded as “spin-up,” and only the
results from 3 July 2012 to 11 July 2012 are used in our analysis of changes in canyon air
temperatures and albedo.
Table 4.2. Wall and roof albedos, and the fraction of sunlight reflected by walls that escapes the
urban canopy, for the CONTROL and four perturbation scenarios. Ground albedo is 0.10 in each
scenario.

a
The fraction is constant during daytime for each scenario.
4.2.7 Analysis on the fraction of wall-reflected sunlight that escapes the canopy
We define variables as follows.
𝐹 W→S
is view factor from wall to sky.
𝐹 G→S
is view factor from ground to sky.
𝐹 W→W
is view factor from wall to wall.
𝐹 W→G
is view factor from wall to ground.
Scenario Wall albedo Roof albedo Fraction of sunlight reflected
by walls that escapes urban
canopy (%)
a

CONTROL 0.10 0.10 50
COOL_WALL_LOW 0.50 0.10 54
COOL_WALL_HIGH 0.90 0.10 59
COOL_ROOF_LOW 0.10 0.50 50
COOL_ROOF_HIGH 0.10 0.90 50
52

∝
G
is ground albedo (∝
𝐺 = 0.10).
∝
W
is wall albedo.
∝
W
= {
0.10 (CONTROL )
0.50 (COOL _WALL _LOW )
0.90 (COOL _WALL _HIGH)

f is the fraction of solar radiation reflected by walls that escapes the canopy.
Table 4.1 shows urban morphology configuration for grid cells where NUDAPT urban
morphologies are not used. View factors are calculated based on the morphology for each land use
type.
The WRF model approximates 𝑓 by
𝑓 WRF
= 𝐹 W→S
+ 𝐹 W→W
× ∝
W
+ 𝐹 W→G
× ∝
G
(4.1)
The percentage of sunlight reflected by walls that escapes the urban canopy is calculated using
equation 4.1 and is shown in Table 4.2.
4.2.8 Caveats
Note that the results later presented in this report rely on the ability of the model to accurately
simulate atmospheric processes and surface-atmosphere interactions. Results may vary depending
on the modeling systems employed. Also, note that we focus our analysis on Los Angeles County,
and accordingly set the urban canopy morphology and impervious fraction based on region-
specific GIS datasets. The climate effects of cool walls and roofs are expected to vary depending
on urban morphology and impervious fraction, as well as the baseline climate of the city under
investigation (Millstein and Menon, 2011; Mohegh et al., 2017).
We focus on reductions in air temperatures in this study, but the adoption of cool walls may also
bring co-benefits and unintended penalties. Regarding penalties, solar reflective cool walls may
lead to increased reflection onto pedestrians, causing glare and reducing their thermal comfort.
Levinson et al. (2018) examined the colors of cool wall products and their effects on pedestrian
thermal comfort. They found that wall albedos of about 0.60 to 0.70 could be attained using readily
available off-white or dull-white exterior wall paints with CIELAB lightness (L*) values around
85 (scale 0 – 100). Simulations with the Temperature of Urban Facets Indoor-Outdoor Building
Energy Simulator (TUF-IOBES) model predicted that in Los Angeles, raising wall albedo to 0.60
(cool wall) from 0.25 (conventional wall) would increase the annual average daytime mean radiant
temperature of a near-wall pedestrian by about 1 K, and increase her annual average daytime
standard equivalent temperature (SET*) by about 0.5 K. Cool roofs, on the other hand, are not
very likely to lead to these unintended consequences. Policymakers should consider all the
aforementioned factors when they plan to adopt cool surfaces.
53

4.3 Results and discussion
4.3.1 Diurnal cycle of grid cell albedo and reflected solar radiation
Figure 4.2a shows the diurnal cycle of albedo changes induced by cool walls averaged over urban
grid cells in Los Angeles County. Figure 4.2a shows that during the daytime, the urban grid cell
albedo rise induced by increasing wall albedo to 0.90 from 0.10 (COOL_WALL_HIGH –
CONTROL) is smallest (0.02) at solar noon, and greatest (~0.10) in the early morning (06:00 LST)
and late afternoon (18:00 LST) in Los Angeles County. (The average sunrise and sunset times for
our analysis period are 04:48 LST and 19:07 LST, respectively.) Grid cell albedo increases at
18:00 LST are observed to be similar to the increases at 06:00 LST. This diurnal cycle occurs
because wall albedo has its maximum influence on grid cell albedo in the early morning and late
afternoon as the ratio of solar irradiance on vertical surfaces versus horizontal surfaces reaches its
maximum. On the other hand, increasing roof albedo by 0.80 (COOL_ROOF_HIGH –
CONTROL) will result in a constant urban grid cell albedo rise of 0.07 because the modeled roof
is horizontal. Increasing roof albedo, as compared to increasing wall albedo by the same amount,
can lead to a greater increase in average urban grid cell albedo in Los Angeles County from 07:00
to 17:00 LST.
Figure 4.2b shows the diurnal cycle of changes in grid cell upflux (upwelling sunlight) reflected
through the horizontal plane bounding the urban canopy. The upflux is calculated as the product
of downflux (global horizontal irradiance, or GHI) and grid cell albedo. GHI peaks at noon (Figure
4.2c). The increase in upflux induced by cool walls reaches its two greatest values at 10:00 and
15:00 LST, a result of diurnal variations of both GHI and grid cell albedo increase. Therefore, cool
walls can reject more sunlight from the urban canopy in the late morning and early afternoon than
during other daylight hours. The diurnal cycles of increases in reflected radiation induced by cool
roofs are concave down with larger diurnal variations relative to cool walls, following the trend of
horizontal irradiance (Figure 4.2c). The increase in solar reflection reaches the maximum at solar
noon, the time associated with peak horizontal irradiance.
Relative to CONTROL, the daily average increase in grid cell upflux for COOL_WALL_HIGH
(9.1 W m
-2
) is 43% of that for COOL_ROOF_HIGH (21.3 W m
-2
) (Table 4.3). Three factors
contribute to the difference in increased reflected solar radiation induced from cool walls versus
roofs: (a) wall area (excluding windows) is a factor of ~1.7 larger than roof area in Los Angeles
County; (b) solar irradiance (W m
-2
) onto walls and roofs differ, and daily cumulative solar
irradiation (J m
-2
) onto walls (2,857 J m
-2
) is 38% of that onto roofs (7,575 J m
-2
) over the analysis
period; and (c) in our model the solar radiation reflected by walls is partially (50-59%) absorbed
by walls and pavements, while all solar radiation reflected by roofs escapes the canopy (Table 4.2).
In our simulations, the increase in wall albedo for COOL_WALL_HIGH – CONTROL (0.80) is
twice that for COOL_WALL_LOW – CONTROL (0.40) (Table 4.2). These wall albedo increases
lead to grid cell albedo increases at 06:00 LST and 12:00 LST that differ by a factor of ~2 (Table
4.3). This means that the urban increases in grid cell albedo are proportional to wall albedo rises.
54

Similarly, increase in daily cumulative reflected solar radiation scale approximately linearly with
wall albedo rise. These linear relationships also apply to cool roofs.

Figure 4.2. Diurnal cycles of differences between the four perturbation simulations and
CONTROL for (a) grid cell albedo and (b) upwelling sunlight; and (c) absolute values for all five
scenarios of diurnal cycles of downward solar radiation (global horizontal irradiance). Values
represent spatial averages in Los Angeles County (shown in Figure 4.1b) for urban grid cells
averaged over July 3 to 12. Only hours of the day when downward solar flux is greater than 5 W
m
-2
are shown.
55

Table 4.3. Grid cell albedo, grid cell solar upflux (reflected solar radiation)
a
, and canyon air temperature at different times of day for
urban grid cells in Los Angeles County, including absolute values for CONTROL, and changes relative to CONTROL for the five
perturbation scenarios.
b

Scenario
Albedo at
06:00 LST
Albedo at
12:00 LST
Daily average
solar upflux (W m
-
2
)
Daily (24-hour)
average canyon air
temperature (K)
Canyon air
temperature (K) at
14:00 LST
Canyon air
temperature (K) at
20:00 LST
CONTROL 0.143 0.148 50.2 295.2 302 294.5
COOL_WALL_LOW
minus CONTROL
0.045 0.008 4.3 -0.19 -0.19 -0.18
COOL_WALL_HIGH
minus CONTROL
0.097 0.017 9.1 -0.43 -0.41 -0.40
COOL_ROOF_LOW minus
CONTROL
0.033 0.033 10.7 -0.23 -0.34 -0.18
COOL_ROOF_HIGH
minus CONTROL
0.065 0.065 21.3 -0.48 -0.72 -0.36
a
Daily average solar upflux (W m
-2
) multiplied by 86,400 seconds (24 hours) is equal to daily cumulative solar upflux (J m
-2
).
b
All values are averages for July 3 to 12.

56

4.3.2 Spatial variation of grid cell albedo
Figure 4.3 shows spatial variations in grid cell albedo for CONTROL (Figure 4.3a), as well as
albedo changes due to raising wall and roof albedos by 0.80 (Figure 4.3b,c). The spatially resolved
albedo increases for 06:00 and 12:00 LST are shown because these times represent when the
minimum and maximum albedo increases occur, respectively. Spatial variability in grid cell albedo
increase (Figure 4.3b,c) is caused by spatial variation in urban fraction (Figure 4.1c) and urban
canyon morphologies. Urban grid cells with higher urban fraction (Figure 4.1c) tend to have larger
albedo increases after implementing cool walls or roofs. For example, the albedo increases for
COOL_WALL_HIGH – CONTROL in downtown Los Angeles can reach as high as 0.24 at 06:00
LST, which is larger than the spatial average over urban grid cells (0.10). Consistent with Figure
4.2, the grid cell albedo increase from adopting cool walls is larger at 06:00 than at 12:00 LST. At
06:00 LST (12:00 LST), grid cell albedo increase induced by adopting cool walls is larger (smaller)
than that induced by cool roofs with the same facet albedo rise.

57

Figure 4.3. Simulated grid cell albedo at 06:00 LST (top) and 12:00 LST (bottom) for (a)
CONTROL, and differences for (b) COOL_WALL_HIGH – CONTROL and (c)
COOL_ROOF_HIGH – CONTROL. Values represent averages for July 3 to 12.
4.3.3 Spatial variation of canyon air temperatures
Figure 4.4 shows spatial variation in canyon air temperatures for the control, cool wall, and cool
roof simulations at 14:00 LST (daytime) and 20:00 LST (nighttime). In CONTROL, desert regions
and the eastern portion of the Los Angeles basin are hotter than the coastal regions, as expected.
Employing cool walls and cool roofs reduces temperatures in the urban portions of the domain.
Air temperature decreases in inland urban areas are larger than those in coastal areas. This is likely
because the effects of cool walls and roofs accumulate as winds (which in Los Angeles are
primarily due to sea breeze) advect air from west to east. Cool walls lead to similar canyon air
temperature reductions at 14:00 LST and 20:00 LST, while cool roofs cause larger temperature
reductions at 14:00 LST than at 20:00 LST. Adopting cool walls shows a greater cooling effect
than cool roofs with the same albedo rise relative to CONTROL at 20:00 LST, but a smaller

58

cooling effect at 14:00 LST (Figure 4.4 and Table 4.3). We discuss the temporal variation of
temperature reductions in section 4.3.4.

Figure 4.4. Simulated canyon air temperature (K) at 14:00 LST (top) and 20:00 LST (bottom) for
(a) CONTROL, and differences for (b) COOL_WALL_HIGH – CONTROL and (c)
COOL_ROOF_HIGH – CONTROL. Values represent averages for July 3 to 12.
4.3.4 Diurnal cycle of canyon air temperatures
Figure 4.5 shows the diurnal cycle of canyon air temperatures for each simulation, and changes in
temperatures upon raising wall or roof albedo, spatially averaged over the urban regions of Los
Angeles County. Figure 4.5a shows that in each scenario, canyon air temperature reaches its
maximum at 13:00 LST. Peak (greatest) air temperature reduction for cool walls (i.e., 0.65 K for
COOL_WALL_HIGH – CONTROL and 0.28 K for COOL_WALL_LOW – CONTROL) occurs
at 09:00 LST (Figure 4.5b). There is a second (smaller) peak in air temperature reduction observed
at 18:00 LST. We hypothesize three factors contributing to the shape of the simulated diurnal
cycles for canyon air temperature changes due to cool wall adoption. First, increases in reflected
solar radiation and reductions in solar heat gain induced by cool walls are greatest at 10:00 and
15:00 LST (Figure 4.2b). Second, increasing albedo leads to solar heat gain reductions that

59

accumulate throughout the day. Reductions in the surface temperature of thermally massive
structures are related to decreases in accumulated, rather than instantaneous, solar heat gain. Third,
the height of the planetary boundary layer (PBL) has a diurnal cycle that is concave down, with a
maximum occurring at ~13:00 LST. Shallower PBL heights reduce the volume of air heated by
sensible heat fluxes. This means that a given reduction in sensible heat flux caused by surface
temperature decreases would lead to larger reductions in atmospheric heating rate
(temperature/time) in the boundary layer when PBL heights are shallow versus deep. Thus,
sensible heat flux decreases from cool wall adoption are expected to have larger air temperature
effects when the PBL is shallow. (Previous research has highlighted the importance of the diurnal
cycle in PBL height in determining urban air temperatures; for example, higher PBL heights in
urban areas relative to rural areas can contribute to a morning urban cool island (Theeuwes et al.,
2015). While this study is not directly related to our research, it shows how PBL height can
influence atmospheric heating and air temperature in urban areas.)
PBL height, increase in upflux, and the accumulation of solar heat gain affect diurnal cycles of
canyon air temperature reduction from adopting cool walls and cool roofs. All three factors
contribute to the fact that the greatest reduction in canyon air temperature induced by cool walls
occurs at 09:00 LST, which is one hour before wall irradiance peaks and a time at which the PBL
height is relatively low. The second peak occurs at 18:00 LST due to the accumulation effect of
reductions in solar heat gain and relatively low PBL height.
For cool roofs, the peak temperature reduction of 0.88 K (COOL_ROOF_HIGH – CONTROL)
occurs at 10:00 LST. This peak temperature reduction occurs later in the morning than for cool
walls because of the difference in diurnal cycle of increased reflected solar radiation (Figure 4.2a),
which reaches its maximum at solar noon for roofs rather than in the morning and afternoon for
walls. A previous study on cool pavements (Mohegh et al., 2017) also found that near-surface air
temperature reductions peaked in the morning and the evening. They hypothesized that it was due
to the combined effects of diurnal cycles in solar irradiance, accumulated solar heat gain, and PBL
height.
From 09:00 to 17:00 LST, the canyon air temperature reduction induced by cool roofs is greater
than that from cool walls (Figure 4.5b). This can be attributed to the higher increase in reflected
solar radiation that escapes the urban canopy from cool roofs versus walls. However, cool walls
(relative to cool roofs) create higher canyon air temperature reductions per increase in reflected
solar radiation from the canopy at most times of day. This is likely because walls are in the urban
canyon, so they can more directly cool canyon air than roofs. In addition, cool walls lead to a
greater cooling at night relative to cool roofs. The atmosphere is stable at night, meaning that there
is little vertical mixing. This means that above-canopy air temperature reductions from cool roofs
would undergo less mixing into the canyon, and thus have less effect on canyon air temperatures
relative to cool walls at night.
As shown in Table 4.3, canyon air temperature reductions for COOL_WALL_HIGH relative to
CONTROL at 14:00 and 22:00 LST are about the same (0.41 K and 0.40 K, respectively). The
reduction at 14:00 LST is lower than that induced by cool roofs (0.72 K), while the reduction at
22:00 LST is higher than that induced by cool roofs (0.36 K). Increasing wall albedo by 0.40 and
0.80 reduces daily average canyon air temperatures by 0.19 K and 0.43 K, respectively. In Los

60

Angeles County, the daily average temperature reductions induced by cool walls are slightly less
than those induced by cool roofs with the same facet albedo increase. On the other hand, for a daily
cumulative grid cell upflux increase of 1 J m
−2
, the daily canyon temperature reduction induced
by cool walls would be 0.55 µK, higher than by cool roofs (0.26 µK).
The ratio of the daily average temperature reduction for COOL_WALL_HIGH – CONTROL to
that for COOL_WALL_LOW – CONTROL (0.43 K / 0.19 K = 2.3) is close to the ratio of the wall
albedo rises for the two scenarios (0.80 / 0.40 = 2) (Table 4.1), indicating that the average
temperature reduction induced by cool walls is approximately proportional to increase in wall
albedo. A similar linear relationship between facet albedo increase and temperature reduction is
also observed for cool roofs (0.48 K / 0.23 K = 2.1). Adopting cool walls (roofs) leads to 0.05 K
(0.06 K) daily canyon air temperature reduction per 0.10 facet albedo increase. The reduction in
canyon air temperature in COOL_ROOF_WALL_HIGH is approximately the sum of the
reductions in COOL_WALL_HIGH and COOL_ROOF_HIGH relative to CONTROL (not shown
in figure). This suggests that the effects of adopting cool walls and roofs are linear. Thus, results
reported here can be interpolated to estimate the effects of increasing wall and/or roof albedo by
other amounts.
Canyon air temperature reductions from adopting cool walls or roofs in the Los Angeles basin
reported in this study can be used to inform policymaking for urban heat island mitigation or
climate change adaptation.

61

Figure 4.5. The diurnal cycle of (a) spatially averaged canyon air temperature (K) for CONTROL,
COOL_WALL_LOW, COOL_WALL_HIGH, COOL_ROOF_LOW, and COOL_ROOF_HIGH;
and (b) differences in canyon air temperatures for COOL_WALL_LOW – CONTROL,
COOL_WALL_HIGH – CONTROL, COOL_ROOF_LOW – CONTROL, and
COOL_ROOF_HIGH – CONTROL. Values represent spatial averages in Los Angeles County
(i.e., shown in Figure 4.1b) for urban grid cells between July 3 and 12.
4.4 Conclusion
This study for the first time assesses the influence of employing solar reflective “cool” walls on
the urban energy budget and summertime climate of the Los Angeles basin. We systematically
compare the effects of cool walls to cool roofs, a heat mitigation strategy that has been widely
studied and employed, using a consistent modeling framework (Weather Research and Forecasting
model). Adoption of cool walls leads to increases in urban grid cell albedo that peak in the early

62

morning and late afternoon, when the ratio of solar radiation onto vertical walls versus horizontal
surfaces is at a maximum. In Los Angeles County, daily average increase in grid cell reflected
solar radiation from increasing wall albedo by 0.80 is 9.1 W m
-2
, 43% of that for increasing roof
albedo. Cool walls reduce canyon air temperatures in Los Angeles by 0.43 K (daily average), with
the peak reduction (0.64 K) occurring at 09:00 LST and a secondary peak (0.53 K) at 18:00 LST.
Per 0.10 wall (roof) albedo increase, cool walls (roofs) can reduce summertime daily average
canyon air temperature by 0.05 K (0.06 K). Results reported here can be used to inform policies
on urban heat island mitigation or climate change adaptation.

63

Chapter 5: Investigating the urban air quality
effects of cool walls and cool roofs in Southern
California
5.1 Introduction
Urbanization is occurring around the world; global urban land area in 2030 is projected to be up
to triple that in 2000 (Seto et al., 2012). Compared to rural areas with natural land cover, urban
areas contain more impervious surfaces made of solar absorptive and thermally massive materials
such as asphalt concrete, and less vegetation that can provide shade and evaporative cooling. These
differences in urban and natural land cover contribute to the urban heat island (UHI) effect (i.e.,
cities being hotter than their surrounding rural areas) (Oke, 1973b), which can in turn affect air
pollutant concentrations. The air quality effects of urban land expansion have been studied in
previous research (Civerolo et al., 2007; Li et al., 2014b; Stone, 2008; Wang et al., 2007; Zhong
et al., 2018), although only a few studies clearly explained the mechanisms driving these effects
(Sarrat et al., 2006; Tao et al., 2015, 2017). Tao et al. (2015) suggested that with emissions held
constant, urbanization in eastern China would increase ozone concentrations from the surface to 4
km. However, it would also enhance turbulent mixing and vertical advection, therefore reducing
the concentrations of primary pollutants.
While many studies have explored the air quality effects of urban land expansion, fewer studies
have investigated how strategies that mitigate the UHI effect would influence urban air quality
(Rosenfeld et al., 1998; Taha, 2008a, 2015; Taha et al., 2000). For example, adopting solar
reflective cool surfaces (roofs, walls, and pavements) can increase city albedo and the solar
radiation reflected by cities, and therefore reduce urban surface temperatures and near-surface air
temperatures (Georgescu et al., 2014; Li et al., 2014a; Mohegh et al., 2017; Taha et al., 1999;
Vahmani and Ban-Weiss, 2016a; Zhang et al., 2016). However, adopting cool surfaces might
change air quality in unexpected ways. For primary pollutants (i.e., pollutants directly emitted to
the atmosphere) such as black carbon and carbon monoxide, lower surface temperatures in cities
may suppress convection and therefore reduce atmospheric mixing heights and vertical dispersion
of pollutants, leading to increases in pollutant concentrations. Changes in horizontal temperature
distributions can also influence wind speed and direction, affecting the horizontal transport and
distribution of pollutants. For secondary pollutants (i.e., pollutants formed in the atmosphere from
primary pollutants), in addition to the previously mentioned changes in transport and dispersion,
pollutant concentrations can also be influenced by temperature dependent emissions and reactions.
Ozone is the product of nitrogen oxides (NOx) and volatile organic compounds (VOCs). Lowering
air temperature decreases biogenic VOC emissions from vegetation, reducing ozone
concentrations in urban areas where VOC availability limits ozone formation (Nowak et al., 1998).
Meanwhile, reactions that produce ozone are slowed by the air temperature reduction. Therefore,
ozone concentrations are expected to decrease with lower temperatures (Taha, 1997a). Secondary
particulate matter includes sulfate, nitrate, ammonium, and secondary organic aerosols (SOA).

64

While temperature-dependent reactions that form secondary particulate matter should be slower
due to reduced temperatures, gas-particle partitioning for semi-volatile species (ammonium nitrate
and semi-volatile SOA) favors the particle phase (Moya et al., 2001; Pun et al., 2002). The
competing physical and chemical processes lead to uncertainties in changes to air pollution
concentrations induced by heat island mitigation strategies.
The complexity of the aforementioned processes requires the use of sophisticated models that
resolve atmospheric physics and chemistry to predict how cool surface adoption would influence
city-level air quality. Taha et al. (Taha, 2008b, 2008a) estimated that increasing city surface albedo
would effectively reduce ozone concentrations in Southern California and Central California.
Epstein et al. (2017) predicted that 8-hour daily maximum ozone concentrations would decrease
if cool roofs do not reflect more solar ultraviolet (UV) that do dark roofs; if solar UV reflection is
increased, ozone concentrations could rise.
Despite previous literature on the influence of cool roofs on ozone concentrations, there is a lack
of research on their influence on particulate matter. Epstein et al. (2017) is the only study that has
investigated the influence of cool roofs on particulate matter. They found that increasing roof
albedo would increase the annual mean concentrations of PM2.5, because reduced ventilation
would suppress dispersion of pollutants. However, they did not investigate (1) the various
physicochemical processes driving cool roof impacts on concentrations and (2) the varying
responses of different species to cool roof adoption.
Cool walls are less studied than cool roofs. Zhang et al. (Zhang et al., 2018) for the first time
estimated the influence of cool walls on urban climate, and systematically compared the effects of
cool walls to cool roofs. They found that adopting cool walls in Los Angeles would lead to daily
average canyon air temperature reductions of up to 0.40 K, slightly lower than that induced by
adopting cool roofs (0.43 K). However, the influence of cool walls on air quality has never been
studied.
To address the aforementioned science knowledge gaps and inform policymaking on heat
mitigation strategies, we seek to (1) estimate the air quality effects of adopting cool walls and
roofs; (2) investigate the physicochemical processes leading to changes in particulate matter
concentrations; and (3) systematically compare the influences of cool walls and cool roofs on
urban air quality.

5.2 Method
5.2.1 Model description
We use the Weather Research and Forecasting model coupled with Chemistry Version 3.7 (WRF-
Chem V3.7), a state-of-the-science climate and air quality model, to estimate the impacts of
employing cool walls and roofs on air quality (Grell et al., 2005). The following schemes are

65

chosen for WRF physics: the Rapid Radiative Transfer Model (RRTM) for long-wave radiation
(Mlawer et al., 1997), the Goddard shortwave radiation scheme (Chou and Suarez, 1999), the Lin
et al. scheme (Lin et al., 1983b) for cloud microphysics, the Grell 3D ensemble cumulus cloud
scheme (Grell and Dévényi, 2002), and the Yonsei University scheme for the planetary boundary
layer (Hong et al., 2006).
Land use classification (Figure 4.1b) and impervious fraction in urban grid cells (Figure 4.1c) are
obtained from the National Land Cover Database (NLCD) (Fry et al., 2011; Wickham et al., 2013).
The Noah land surface model (Chen et al., 2001) simulates land-atmosphere interactions in non-
urban grid cells and for the pervious portion of urban grid cells. The single-layer urban canopy
model resolves urban physics and simulates land-atmosphere interactions for the impervious
portion of urban grid cells (Kusaka et al., 2001). Urban grid cells are classified as low-intensity
residential, high-intensity residential, or commercial/industrial in NLCD. Urban morphology (i.e.,
roof width, canyon floor width, and building height) is determined for each urban land use type
based on real-world building and street datasets for Los Angeles County, following (Zhang et al.,
2018); the datasets include National Urban Database and Access Portal (NUDAPT) (Ching et al.,
2009) and the Los Angeles Region Imagery Acquisition Consortium (LARIAC) (LARIAC, 2014,
2016). Since the default WRF-Chem is not compatible with the NLCD land use classification
system, we modify the model code to allow for use of the three NLCD urban land use types,
following Fallmann et al. (2016). We also implement satellite-based green vegetation fraction into
the model following Vahmani and Ban-Weiss (Vahmani and Ban-Weiss, 2016a).
Gas phase chemistry is simulated using the Regional Atmospheric Chemistry Mechanism
(RACM) (Stockwell et al., 1997) scheme further updated by National Oceanic and Atmospheric
Administration (NOAA) Earth System Research Laboratory (ESRL) (Kim et al., 2009). The
RACM-ESRL scheme covers organic and inorganic chemistry simulating 23 photolysis and 221
other chemical reactions (Ahmadov et al., 2015). The Modal Aerosol Dynamics Model for Europe
(MADE) simulates aerosol chemistry (Ackermann et al., 1998). The volatility basis set (VBS) is
used for simulating secondary organic aerosols (Ahmadov et al., 2012). We evaluate modeled
ozone and PM2.5 concentrations against observations from Air Quality System.
5.2.2 Simulation domains
We simulate three nested domains (d1, d2, and d3, as shown in Figure 4.1a) with 30 layers in the
vertical at horizontal resolutions of 18 km, 6 km, and 2 km, respectively. The three domains each
cover the Western United States (d1); Central and Southern California (d2); and Southern
California, including Los Angeles and San Diego (d3). Each outer domain provides boundary
conditions for the adjacent inner domain. In this paper we report results in the innermost domain.
5.2.3 Emission inventories
WRF-Chem requires gridded emissions inputs for each simulation. We use state-of-the-science
emission inventories from the South Coast Air Quality Management District (SCAQMD) and
California Air Resources Board (CARB) for year 2012. For the outer two domains (d01 and d02),
hourly emissions for the entire year at 4-km resolution are provided by CARB for California

66

(California Air Resources Board, 2017). Emissions outside California but within the simulation
domain are from National Emissions Inventory (NEI) by the Environmental Protection Agency for
year 2011 (USEPA, 2014). For the innermost domain (d03), we use hourly emissions for the entire
year at 4-km resolution provided by SCAQMD (South Coast Air Quality Management District,
2017). These emissions represent all anthropogenic sources including motor vehicles; point
sources such as refineries; and off-road sources, such as construction. Emission inventories are
regridded to match the grid for the modeled domains and chemical speciation for RACM-ESRL
and MADE/VBS mechanisms used in this study. The Model of Emissions of Gases and Aerosols
from Nature (MEGAN) is used to generate temperature-dependent biogenic organic emissions
(Guenther et al., 2006).
5.2.4 Simulation design
To investigate the air quality effects of cool walls and roofs in Southern California, we simulate
three scenarios: CONTROL, where wall, roof, and pavement albedos are each set to 0.10;
COOL_WALL, where wall albedo is increased to 0.90; and COOL_ROOF, where roof albedo is
increased to 0.90. These cool surface albedos are intentionally chosen to quantify the upper bound
effects of adopting cool surfaces. Note that cool surface albedos of actual cool walls and roofs are
usually lower than 0.90. For example, the albedo of a bright-white cool roof may decrease to 0.60–
0.70 from an initial value of albedo of 0.80–0.90 after several years of soiling and weathering
(Berdahl et al., 2008; Sleiman et al., 2011).
Simulations are performed for 28 June 2012 to 11 July 2012, with the first five days discarded as
“spin-up.” We analyze the results from 00:00 local standard time (LST) on July 3 to 00:00 LST
on July 12.
5.2.5 Caveats
In this study, we assume that adopting cool surfaces would not change solar reflectance in
reflectance in the solar UV spectrum (280–400 nm). However, based on spectral reflectance
measurements, UV reflectance could increase from adopting cool roofs (Epstein et al., 2017).
Increases in UV reflectance could enhance ozone production and atmospheric oxidation capacity,
which influences the formation of other secondary pollutants. Therefore, changes in ozone are a
result of competing effects among (a) ozone increases induced by enhanced UV reflection and (b)
ozone decreases induced by decreased temperatures, and (c) ozone changes induced by reduced
ventilation, which could affect the dispersion of ozone and its precursors.
Also, the influence of widespread adoption of cool surfaces is likely to vary by city due to
differences in baseline climate and land cover (e.g., vegetation distributions, building distributions,
urban canyon morphology). Also note that results might be different if simulated using another
model.

67

5.3 Results and discussion
5.3.1 Meteorological conditions
Figure 5.1 shows spatial distributions of near-surface air temperatures in the afternoon and
evening. For the CONTROL scenario (Figure 5.1a), temperatures in inland areas are hotter than
coastal areas, as expected. Temperature reductions induced by adopting cool surfaces are higher
in inland areas than in coastal areas (Figure 5.1b,c). This is due to an accumulation effect in air
temperature reduction as the sea breeze advects air from the coast to inland. Cool roofs are
simulated to induce higher temperature reductions than cool walls at both 14:00 and 20:00 LST.
Daily average near-surface air temperature reductions induced by adopting cool walls (roofs) is
0.24 K (0.45 K) over urban areas in Los Angeles County (Table 5.1). Temperature reductions
induced by cool roofs are larger than those from cool walls. Although total wall area in Los
Angeles County is larger than roof area by a factor of 1.7, daily average solar irradiance (W m
-2
)
on walls is 38% that on roofs (Zhang et al., 2018). In addition, 50-59% of the solar radiation
reflected by cool walls is absorbed by opposing walls or pavements, while all the radiation
reflected by cool roofs escapes the urban canopy (Zhang et al., 2018). Therefore, daily average
temperature reductions induced by cool roofs are larger than cool walls.
Note that past studies investigating how air temperatures influence atmospheric chemistry often
report 2-meter air temperatures (“T2”) (Fallmann et al., 2016; Tao et al., 2015). However, 2-meter
air temperature is a diagnostic variable that is not used in model calculations of atmospheric
chemistry. The chemistry model actually uses the four-dimensional (x, y, z, t) atmospheric
temperature. Therefore, we present temperatures in the lowest atmospheric layer as “near-surface
air temperature” rather than “T2.”
Figure 5.2 shows horizontal wind vectors. For the CONTROL scenario, winds are southwesterly
from coast to inland and wind speed is higher during daytime than nighttime. As shown in Table
5.1, spatially averaged wind speed in urban areas is 4.2 m s
-1
and 2.3 m s
-1
at 14:00 LST and 20:00
LST, respectively. Simulations predict that adopting cool roofs or walls decreases onshore wind
speeds. This can be explained by the reduced temperature difference between urban land and
ocean, which is a driver for the sea breeze.
Figure 5.3 show the diurnal cycle of planetary boundary layer (PBL) height. PBL height reaches
its maximum at 12:00 LST. Adopting cool walls reduces PBL height by about 5% at most times
of day. Adopting cool roofs reduces PBL height by about 5% at night and about 10% during the
day. The reduction in PBL height can be attributed to decreases in surface temperatures and
consequent reductions in convection. Decreases in wind speeds and PBL height tend to reduce
ventilation for pollutants. The influence of changes in ventilation on particulate matter is discussed
in Section 5.5.1.

68

Figure 5.1. Spatially resolved near-surface air temperatures (K) at 14:00 LST and 20:00 LST for
(a) the CONTROL scenario, and the difference relative to CONTROL for (b) COOL_WALL and
(c) COOL_ROOF.

69

Figure 5.2. 10-meter wind vectors at 14:00 LST and 20:00 LST for (a) CONTROL, as well as the
differences for (b) COOL_WALL – CONTROL and (c) COOL_ROOF – CONTROL. Values
are temporally averaged over the period of 00:00 LST on July 3 to 00:00 LST on July 12.

70

Figure 5.3. Diurnal cycles of planetary boundary layer height (PBLH) for (a) CONTROL,
COOL_WALL, and COOL_ROOF; and (b) the change in PBLH for COOL_WALL and
COOL_ROOF relative to CONTROL. Values represent spatial averages in Los Angeles County
for urban grid cells from 00:00 LST on July 3 to 00:00 LST on July 12.

Table 5.1. Spatially averaged meteorological variables and pollutant concentrations for the
CONTROL scenario, and the change relative to CONTROL for COOL_WALL and
COOL_ROOF. Values represent spatial averages in Los Angeles County for urban grid cells
from 00:00 LST on July 3 to 00:00 LST on July 12.

CONTROL
COOL_WALL
minus CONTROL
COOL_ROOF
minus CONTROL
Daily average near-surface air temperature
a
(K) 292.85 -0.24 -0.45
10-meter wind speed at 14:00 LST (m s
-1
) 4.15 -0.06 -0.21

71

10-meter wind speed at 20:00 LST (m s
-1
) 2.28 -0.08 -0.09
Daily maximum 8-hour average ozone concentration
(ppbv)
38.47 -0.35 -0.83
Daily average PM 2.5 concentration (μg m
-3
) 12.25 0.62 0.85
Daily average nitrate concentration
b
(μg m
-3
) 0.89 0.11 0.18
Daily average ammonium concentration
b
(μg m
-3
) 0.98 0.07 0.10
Daily average sulfate concentration
b
(μg m
-3
) 1.91 0.11 0.13
Daily average EC concentration
b
(μg m
-3
) 0.87 0.05 0.06
Daily average anthropogenic SOA concentration
b
(μg m
-3
) 1.22 0.01 0.04
Daily average biogenic SOA concentration
b
(μg m
-3
) 0.51 0.01 0.01
Daily average POA concentration
b
(μg m
-3
) 1.90 0.12 0.14
a
Near-surface air temperature refers to the temperature in the lowest atmospheric layer.

b
Mass concentrations for particles with diameter less than 2.5 μm (i.e., nuclei and accumulation mode)
are included for each species.

5.3.2 Spatial distribution of ozone concentrations
Figure 5.4 shows the spatial distribution of daily maximum 8-hour average (MDA8) ozone
concentrations. MDA8 ozone is regulated by the National Ambient Air Quality Standards of the
Environmental Protection Agency. For the CONTROL scenario, the ozone concentration over
urban areas is lower than rural areas because (a) southwesterly winds transport ozone and its
precursors from the coast to the inland areas, creating an accumulation effect as this secondary
pollutant is generated in the atmosphere; and (b) nitric oxide emissions in urban areas can titrate
ozone. Adopting cool walls can decrease the spatially averaged MDA8 ozone concentration by
0.35 ppbv in the urban areas of Los Angeles County (Table 5.1). Adopting cool roofs can lead to
a greater reduction in MDA8 ozone concentration (0.83 ppbv) than cool walls. This is likely
because the near-surface air temperature reductions induced by cool roofs are larger than that
induced by cool walls during daytime and thus the decreases in reaction rates for ozone production
are larger for COOL_ROOF than COOL_WALL relative to CONTROL. Note that we present the
changes in ozone here without determining driving mechanisms since PM (rather than ozone) is
the focus of this study.

72

Figure 5.4. Spatially resolved daily maximum 8-hour average (MDA8) ozone concentrations
(ppbv) for (a) the CONTROL scenario, and changes relative to CONTROL for (b)
COOL_WALL and (c) COOL_ROOF.

5.3.3 Spatial distribution of PM2.5 species
Figure 5.5 shows the spatial distribution of daily average PM2.5 species concentrations and changes
due to adopting cool surfaces. PM2.5 concentrations reported here represent dry particle mass.
Spatial distributions of PM2.5 species concentrations in the CONTROL scenario are mainly
attributable to spatial patterns in emissions and meteorology. For example, when the sea breeze
advects air from the coast to inland, EC (a primary pollutant) accumulates, leading to higher
concentrations in locations further east. For spatial distributions of sulfate concentrations, there
are higher concentrations near the ports of Los Angeles and Long Beach that are likely due to hot
spots in SO2 (the precursor of secondary sulfate) and primary sulfate emissions from ships and
power plants. Meanwhile, southwesterly winds transport these emissions to downtown Los
Angeles, making concentrations downtown greater than those further east. The spatial variability
of anthropogenic and biogenic SOA is relatively small compared to other species. While SOA
includes a variety of species and its chemistry is complicated, our model does a good job at
capturing the contribution of SOA to total organic aerosols (TOA). In this study, SOA constitutes
48% of organic aerosols on daily average (computed using Table 5.1); recent observations in
downtown Los Angeles suggest that SOA constitutes about 35% of OA on annual average
(Mousavi et al., 2018) and about 50% during summer (Saffari et al., 2015).
The concentrations of total PM2.5 and each individual species increase due to cool surface adoption
(Figure 5.5). The increase in each PM2.5 species induced by adopting cool roofs is larger than that
induced by cool walls, though their spatial patterns are similar. Spatial distributions of increases
in total PM2.5 and individual species (except nitrate) are consistent with the spatial patterns of

73

absolute concentrations in the CONTROL scenario. In other words, the regions with the highest
baseline concentrations show the largest changes in PM2.5 due to meteorological shifts from cool
surface adoption. The exception is for nitrate, which shows larger increases in urban residential
areas northeast of downtown where baseline concentrations are low, rather than downtown where
baseline concentrations are the highest in CONTROL. This is likely due to the greater temperature
reductions in regions northeast of downtown Los Angeles relative to downtown, especially at night
(Figure 5.1b,c). The processes leading to nitrate increases will be discussed in Section 5.3.5. The
increase in SOA is relatively smaller than other species, which will also be explained in Section
5.3.5.

74

Figure 5.5. Daily average PM2.5 concentrations (μg m
-3
) by species for CONTROL (left column),
as well as the differences for COOL_WALL – CONTROL (middle column) and COOL_ROOF
– CONTROL (right column). Values are temporally averaged over the period of 00:00 LST on
July 3 to 00:00 LST on July 12.

75

5.3.4 Diurnal cycles of PM2.5 species concentrations
Figure 5.6 shows the diurnal cycles of spatially averaged PM2.5 species concentrations and their
changes in the urban areas of Los Angeles County. For the CONTROL scenario, PM 2.5, nitrate,
ammonium, sulfate, EC, and primary organic aerosol (POA) concentrations reach their maximum
between 08:00 and 09:00 LST and their minimum at 16:00 LST, while biogenic and anthropogenic
SOA reach their maximum near 14:00 LST and their minimum at night.
Raising roof or wall albedo leads to increases in concentrations of total PM2.5 and most individual
species (except for biogenic SOA) throughout the day (Figure 5.6). Increases in nitrate
concentrations are the largest among all PM2.5 species, followed by increases in POA, sulfate, and
ammonium, while the increases in concentrations of other PM2.5 species are relatively small. The
changes in speciated PM2.5 concentrations due to adopting cool walls or roofs vary by time of day,
and the mechanisms contributing to the changes will be discussed in Section 5.3.5. For all PM2.5
species except biogenic SOA, increases in PM2.5 concentrations induced by adopting cool roofs
are larger than those induced by adopting cool walls during most daytime hours (07:00-19:00
LST). On daily average, cool roof adoption contributes to greater increases in particulate matter
than cool wall adoption (Table 5.1) for total PM2.5 and each species. Daily average increases in
total PM2.5 concentrations are simulated to be 0.62 (0.85) μg m
-3
upon increasing wall (roof) albedo
by 0.80 in Los Angeles County.

76

77

Figure 5.6. Diurnal cycles of spatially averaged PM2.5 concentrations by species. The left column
shows the diurnal cycle of spatially averaged PM2.5 (μg m
-3
) for CONTROL, COOL_WALL, and
COOL_ROOF. The right column shows the differences in PM2.5 species for COOL_WALL –
CONTROL and COOL_ROOF – CONTROL and the differences if ventilation effect is
excluded. Values represent spatial averages in Los Angeles County (i.e., shown in Figure 4.1) for
urban grid cells from 00:00 LST on July 3 to 00:00 LST on July 12. Note that vertical axis
ranges vary for each species.

5.3.5 Mechanisms that lead to changes in PM2.5 concentrations
As mentioned in the introduction, adopting cool surfaces can influence PM2.5 concentrations
mainly via (1) reducing ventilation, (2) slowing temperature dependent reactions and emissions,
and (3) increasing the likelihood that semi-volatile species will partition to particle phase. In the
following sections we report on the relative importance of these pathways.
5. 3. 5. 1 Ventilation
For primary pollutants such as elemental carbon (EC), mass concentrations depend highly on
ventilation and are insensitive to atmospheric chemistry in the model. (Note that strictly speaking,
hydrophilic species can coat EC and increase its hygroscopicity, enabling the in-cloud wet
scavenging of EC (Zhang et al., 2015a). This so-called aging process depends on temperature-
dependent atmospheric photochemical reactions that form hydrophilic species such as sulfate.
However, the aging of EC should not be a very important process during summer when there is
little precipitation in the Los Angeles Basin.) Decreases in ventilation impede the dilution and
transport of pollutants in source regions and may also reduce dry deposition, leading to increases
in near-surface pollutant concentrations. This ventilation effect is driven by vertical and horizontal
mixing of pollutants in the planetary boundary layer, which can be investigated using PBL height

78

and surface wind speeds, respectively.

Figure 5.7 shows that fractional increase in EC is positively correlated with the fractional
reductions in PBL height and 10-meter wind speed. Fractional reduction in PBL height can explain
42% of the variability in the fractional increase in EC concentrations for both COOL_WALL –
CONTROL and COOL_ROOF – CONTROL, respectively. Fractional reduction in horizontal

79

wind speed explains 17% (79%) of the variability in fractional increase of EC concentrations due
to adopting cool walls (roofs).

80

Figure 5.7. Scatter plots showing fractional increase in EC concentrations induced by cool walls
and cool roofs versus (a) fractional reduction in PBL height and (b) fractional reduction in 10-
meter wind speed. The value on each dot represents the hour of day (e.g., 9 = 09:00 LST). Least-
squares linear regressions and corresponding coefficients of determination (R
2
) are also shown.
Values represent spatial averages in Los Angeles County (i.e., shown in Figure S1b) for urban
grid cells from 00:00 LST on July 3 to 00:00 LST on July 12.

5. 3. 5. 2 Quantifying the relative importance of ventilation versus other factors for
driving changes in PM
Carbon monoxide (CO) is considered a chemically inert pollutant at urban scale, with
concentrations controlled by meteorological conditions. Therefore, past studies have used CO as
a tracer for transport and dispersion of pollutants (Tao et al., 2017; Zhang et al., 2015b). Similarly,
in our study, we use the increase in CO concentration relative to CONTROL to quantify the
increase in PM2.5 that is attributable to ventilation (∆𝐶 𝑃𝑀 (vent )
), as
∆𝐶 𝑃𝑀 (vent )
=
∆𝐶 CO
𝐶 CO
× 𝐶 𝑃𝑀
(5.1)
where ∆𝐶 CO
is the change in CO mixing ratio (ppbv) relative to CONTROL, 𝐶 CO
is the mixing
ratio (ppbv) of CO for CONTROL, 𝐶 𝑃𝑀
is the concentration (μg m
-3
) of a PM species (i.e., total
PM2.5, sulfate, nitrate, EC, POA, anthropogenic SOA, or biogenic SOA) for CONTROL, and all
variables are spatial averages over urban areas in Los Angeles County.
The change in concentration of a PM2.5 species that is not attributable to ventilation ∆𝐶 𝑃𝑀 (no vent)

(μg m
-3
)

is then calculated as
∆𝐶 𝑃𝑀 (no vent )
= ∆𝐶 𝑃𝑀
−
∆𝐶 CO
𝐶 CO
× 𝐶 𝑃𝑀
(5.2)
In this way, we attribute increases in PM2.5 species to reductions in ventilation and changes in all
other processes. Note that while sea salt aerosols contribute to total PM2.5 concentrations, we omit
this species from the discussion because they are naturally produced and are not a public health
concern. Reductions in ventilation may also contribute to less vertical mixing and consequent
reductions in dry deposition of pollutants.

81

As indicated in Figure 5.6, after removing the effects of ventilation, the change in spatially
averaged EC and primary organic aerosol (POA) is close to zero. Therefore, increases in primary
pollutant (EC and POA) concentrations are attributable to suppressed ventilation.
A large fraction of the increase in sulfate from cool surface adoption can be attributed to suppressed
ventilation. Other driving processes can affect sulfate concentrations: (a) reductions in
temperature-dependent reaction rates would decrease sulfate production; and (b) changes in cloud
cover can also influence in-cloud SO2 oxidation, which occurs faster than gas-phase oxidation of
SO2 if clouds are present. When the ventilation effect is excluded, sulfate concentrations slightly
increase from 04:00 to 14:00 LST but decrease at most other hours, due to adopting cool surfaces.
Nevertheless, ventilation is the dominant process leading to sulfate increases, contributing to 76
% (91%) of the daily average increase for COOL_WALL – CONTROL (COOL_ROOF –
CONTROL).
On the other hand, the ventilation effect accounts for a small portion of the increase in semi-volatile
species such as nitrate and ammonium (in the form of ammonium nitrate). Concentrations of these
particulate species rise drastically even when the ventilation effect is excluded. This is because the
reaction between gas-phase ammonia and nitric acid that forms particulate nitrate is reversible, and
the equilibrium constant for the reaction is highly temperature dependent. Temperature reductions
would cause gas to particle conversion and increase the concentrations of ammonium nitrate(Moya
et al., 2001). Note that the amount of nitrate at equilibrium has a non-linear relationship with
temperature. Thus, the relationship between increase in nitrate concentration due to gas-to-particle
conversion (Figure 5.6) and temperature reduction is not linear; the increase in nitrate depends not
only on the magnitude of temperature reduction but also the baseline temperature. In contrast to
shifting equilibrium of the reaction between nitric acid and ammonia, which would increase nitrate,
cool surfaces adoption may also reduce photochemistry and impede the formation of nitric acid
precursors (i.e., OH and NO2) during the day, leading to a reduction in nitrate. Increased gas-to-
particle conversion and suppressed ventilation outweigh reductions in photochemistry, leading to
overall increases in nitrate concentrations (Figure 5.6).
For secondary organic aerosols (SOA), reductions in ventilation should lead to increases in SOA,
while temperature decreases would be expected to cause (a) increases in gas-to-particle conversion
for semi-volatile species, which would lead to SOA increases, and (b) reduced rates of
temperature-dependent reactions, which would lead to SOA decreases. Biogenic SOA may also
be influenced by reductions in temperature dependent VOC emissions (e.g. isoprene) from
vegetation. As shown in Figure 5.6, both anthropogenic and biogenic SOA increase when
including the influence of changes in ventilation, but decrease when ventilation changes are
excluded. Daily average SOA concentrations increase by 0.018 (0.046) μg m
-3
for COOL_WALL
(COOL_ROOF) relative to CONTROL. After removing the ventilation effect, daily average SOA
concentrations decrease by 0.057 (0.071) μg m
-3
for COOL_WALL (COOL_ROOF) relative to
CONTROL. This means that SOA reductions induced by slowed temperature dependent reactions
and biogenic emissions outweigh the expected increases in semi-volatile SOA species due to phase
partitioning. On the other hand, increases in SOA due to suppressed ventilation and increased gas-
to-particle conversion outweigh decreases in SOA due to reduced reaction and emission rates.

82

These competing effects lead to an overall increase in SOA concentrations, although fractional
increases are small relative to other species.
In this paper, we discuss the climate and air quality implications of cool roofs and cool walls,
which have been used in cities to reduce temperatures and thus combat global warming and urban
heat islands. Our results show that reductions in urban surface temperatures lead to both co-
benefits of reducing ozone concentrations and penalties of slightly increasing PM2.5 concentrations
in the Los Angeles Basin. We suggest further studies to assess the air quality effects of other heat
strategies and the effects in other cities. For policy makers, it is important to assess the effects of
environmental solutions from a systematic perspective, i.e., looking at heat mitigation impacts not
just from a climate perspective but also from an air quality perspective.
5.4 Conclusion
Solar reflective cool roofs and walls can be used to mitigate the urban heat island effect. While
many past studies have investigated the climate impacts of adopting cool surfaces, few studies
have investigated their effects on air pollution, especially on particulate matter (PM). This
research for the first time investigates the influence of widespread deployment of cool walls on
urban air pollutant concentrations, and systematically compares cool wall to cool roof effects.
Simulations using a coupled meteorology-chemistry model (WRF-Chem) show that cool walls
and roofs can reduce urban air temperatures, wind speeds, and planetary boundary heights in the
Los Angeles Basin. Consequently, increasing wall (roof) albedo by 0.80, an upper bound
scenario, leads to summertime maximum daily 8-hour average ozone concentration reductions of
0.35 (0.83) ppbv in Los Angeles County. However, cool walls (roofs) increase summertime daily
average PM 2.5 concentrations by 0.62 (0.85) μg m
-3
. We investigate the competing processes
driving changes in concentrations of speciated PM 2.5. Increases in primary PM (elemental carbon
and primary organic aerosols) concentrations can be attributed to reductions in ventilation of the
Los Angeles Basin. Increases in concentrations of semi-volatile species (e.g., nitrate) are mainly
driven by increases in gas-to-particle conversion due to reduced atmospheric temperatures.

83

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Abstract (if available)

Abstract Climate change and urban air pollution are two of the greatest contemporary global challenges. The overarching goals of this dissertation are (1) to study how climate, air quality, and land cover interact at spatial scales that range from local to global and (2) to inform policymaking on strategies that can potentially mitigate both climate change and air pollution. ❧ Black carbon (BC) particles, a component of PM₂.₅, can harm human health and lead to global warming. Chapter 2 of this dissertation investigates the transport and transformation of BC in the atmosphere. Physically-based parameterizations for BC removal are implemented to an atmospheric chemistry model

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Asset Metadata

Creator Zhang, Jiachen (author)

Core Title Impacts of heat mitigation strategies and pollutant transport on climate and air quality from urban to global scales

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Engineering (Environmental Engineering)

Publication Date 05/03/2019

Defense Date 11/30/2018

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

Tag Air pollution,air quality,black carbon,CESM,Climate,cool roofs,cool walls,global climate,heat mitigation strategies,MOZART-4,OAI-PMH Harvest,ozone,PM2.5,policymaking,pollution transport,source-receptor relationship,urban heat island effect,WRF-Chem

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ban-Weiss, George (committee chair), Childress, Amy (committee member), Dilkina, Bistra (committee member)

Creator Email jiachen.zhang.usc@gmail.com,jiachen.zhang@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-166443

Unique identifier UC11662487

Identifier etd-ZhangJiach-7403.pdf (filename),usctheses-c89-166443 (legacy record id)

Legacy Identifier etd-ZhangJiach-7403.pdf

Dmrecord 166443

Document Type Dissertation

Format application/pdf (imt)

Rights Zhang, Jiachen

Type texts

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

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA