Assessing Impact of the Sulfate
Aerosol First Indirect Effect on
Tropical Cyclone Activity
MASSACHUSETTS INSTITUTE
OF TECHNOLOLGY
JUN 08 2015
by
LIBRARIES
Hao-yu Derek Chang
B.S., Civil and Environmental Engineering
Massachusetts Institute of Technology, 2014
Submitted to the Department of
Earth, Atmospheric, and Planetary Science
in Partial Fulfillment of the Requirements
for the Degree of
Master of Science
at the
Massachusetts Institute of Technology
June 2015
@2015 Massachusetts Institute of Technology. All rights
reserved.
The author hereby grants to MIT permission to reproduce
and to distribute publicly paper and electronic copies of
this thesis document in whole or in part in any medium
now known or hereafter created.
Signature redacted
Signature of Author
Departm' nt of Earth, Atmospheric, and,,,Petary Science
Signature redacted
(
May 25, 2015
Certified by
Kerry A. Emanuel
Cecil(;:) Ida Green Professor of Atmospheric Science
Thesis Supervisor
Signature redacted
Accepted by
Robert van der Hilst
of Earth Sciences
Professor
Schlumberger
Sciences
Planetary
and
Head, Department of Earth, Atmospheric,
Assessing Impact of the Sulfate Aerosol First
Indirect Effect on Tropical Cyclone Activity
by
Hao-yu Derek Chang
Submitted to the Department of Earth, Atmospheric, and
Planetary Sciences in Partial Fulfillment of the Requirements for
the Degree of Master of Science in Climate Physics and Chemistry
May 2015
Abstract
Tropical cyclones (TCs) are among the most expensive and lethal geophysical
hazards. Studies suggest that the intensity of TCs will increase due to the thermodynamic effects of anthropogenic greenhouse gas input. In contrast, while
aerosols are shown to have an overall cooling effect on global climate, their impact on TCs is not yet well-understood. This paper explores the influence of
the sulfate aerosol first indirect effect (AIE) on Atlantic hurricane intensity and
genesis.
I use a single-column radiative convective model that incorporates the first AIE
(aerosol enhancement of cloud reflectivity) through parameterization of cloud
droplet number, radius, and optical depth. Cloud droplet number is parameterized using an empirical scheme, while the radius is determined from cloud
liquid water content and number concentration moments, and the optical depth
scheme is embedded in the original single-column model. The model is run with
both the IGAC/SPARC Chemistry Climate Model Initiative (CCMI) historical simulations of sulfate concentrations over the hurricane main development
region during hurricane peak season (August-October) and a self-generated inventory of sulfate concentrations based on realistic vertical variability in sulfate
levels.
The model was run to radiative-convective equilibrium (RCE), then rerun under weak temperature gradient mode (WTG). Runs successfully produce the
Twomey or first indirect effect, which states that increased aerosols will increase
cloud droplet number concentration, decrease the effective cloud droplet radius,
and increase the cloud optical depth. The net effect is increased reflection of
radiation from the atmosphere, which theoretically cools the Earth, decreasing
the potential intensity and genesis potential of TCs. While model runs produce
the expected changes in cloud properties, cloud cover is not sufficient for sulfate
concentrations to have a substantial impact on hurricane activity via the AIE
3
when the model is run to RCE. The WTG mode is then implemented with the
goal of producing low-lying stratocumulus clouds to increase total cloud cover,
but the single-column WTG scheme was not able to produce stratocumulus that
did not also produce an overly strong negative feedback.
Using the single-column model, one can demonstrate the indirect effect of sulfate
aerosols on cloud reflectivity and that sufficient cloud cover is needed to produce
a noticeable cooling and change in expected hurricane behavior. A further
study of the subject could include parameterization of the poorly-understood
cold or mixed-phase clouds, which can include characterization of additional
aerosol types. In addition, a two-dimensional model has greater capacity to
model phenomena such as low-lying stratocumulus, which could produce a more
substantial ambient effect.
Thesis Supervisor: Kerry A. Emanuel
Title: Cecil and Ida Green Professor of Atmospheric Science
Department of Earth, Atmospheric, and Planetary Science
4
Acknowledgments
I would like to thank my adviser, Professor Kerry A. Emanuel, for providing
research guidance throughout the year and the opportunity to do a Master's
degree at MIT. In particular, I wish to thank Professor Emanuel for being
receptive of my research topic suggestions and for helping construct a thesis
topic that is meaningful and relevant.
Prof. Dan Cziczo and Dr. Chien Wang have provided guidance on aerosolcloud parameterization selection and considerations. I also wish to thank Prof.
Colette Heald and Dr. David Ridley for answering questions about aerosol and
cloud physics. In particular, special acknowledgments go out to Justin Bandoro
and Dr. Alex Avramov for their extensive suggestions and guidance
I would also like to thank my fellow EAPS office mates and classmates, as well
as my flatmates for making the year highly memorable. Most importantly, I
wish to thank my family for their endless love and support.
5
6
Contents
1
Introduction to Aerosol Impacts on Hur13
ricanes
i.i
1.2
1.3
1.4
1.5
2
.14
.16
Tropical Cyclone Dynamics ...............
Aerosol Properties and Effects .............
Aerosol Cloud Nucleation . . . . . . . . . . . . . . . .
Cloud Convective Processes . . . . . . . . . . . . . . .
Thesis Outline and Contributions . . . . . . . . . . .
Setup of Study
2.1
2.2
2.4
3
4
20
21
23
Aerosol Concentrations ...................
Radiative-Convective Model Description......
Parameterization of Sulfate Aerosol First Indirect E ffect .. . ... . . . . . . . . . . . . . . . . . . . . . .
Modeling of Stratocumulus Cloud Effects .....
.
2.3
18
24
27
29
31
2.5 Sum m ary .............................
32
Model Results and Analysis
35
3.1
Changes to environmental conditions ........
35
3.2
Weak Temperature Gradient (WTG) Mode . .
38
3.2.1
Modification of Ocean Heat Flux ............
40
3.2.2
Initialization of Sea Surface Temperature
.....
42
3.3 Impact on TC intensity and cyclogenesis .....
45
3.4 Sum m ary .............................
50
Application to IGAC/SPARC Simulation
53
53
4.2
Changes to Environmental Conditions .......
Impact on TC Intensity and Cyclogenesis .....
4.3
Error Bars for Aerosol Impact .............
57
4.1
7
55
5
Conclusions and Future Work
5.1
Heterogeneous Nucleation Schemes . . . . . . . . . .
5.2
The Ammonia-Nitric Acid-Sulfuric Acid-Water
63
63
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Parameterization of Aerosol Impact on Cold and
Mixed-Phase Clouds .....................
66
5.4 Two-Column Study of Radiative Convective Model 67
5.3
8
List of Figures
I
2
3
Self-generated vertical sulfate aerosol concentration profiles. Each
curve represents one of 24 self-generated profiles. . . . . . . . . .
26
Sulfate concentration over the hurricane main development region
(5-20 N, 30-70 W), averaged over each decade from 1850-2000.
Each curve represents the concentration at one of 46 elevations
represented in the RCE model, with the highest concentration
curve representing 1000 hPa and the lowest curve representing 5
.................................
hPa. .........
26
Cloud droplet number concentration (Nd) as a function of pressure for each self-generated vertical sulfate concentration profile.
Each curve represents one of 24 concentration profiles. . . . . . .
36
4
Average cloud droplet radius (re,i) plotted against the self-generated
sulfate concentration profiles for a surface wind speed of 5-7 m/s,
for model runs to RCE. . . . . . . . . . . . . . . . . . . . . . . . 37
5
Total (vertically-summed) cloud optical thickness (,r) plotted against
self-generated sulfate concentration profiles for surface wind speed
of 5-7 m/s, for model runs to RCE. . . . . . . . . . . . . . . . . . 37
6
Environmental conditions for the indicated variables using the
varying self-generated sulfate aerosol profiles. . . . . . . . . . . .
39
Equilibrium surface temperature as a function of the constant
cooling term C, which ranges from 2.4 x 10-5 to 2.5 x 10-5. The
model is run with 0.9 pg/m 3 sulfate concentration at the surface
and surface wind speed of 7 m/s. Note the drastic temperature
drop at around C = 2.44 x 10-5, which corresponds with a cloud
fraction of 1.00 at altitudes near the surface. . . . . . . . . . . .
41
Cloud effective droplet radius (re,i) and total optical depth (r)
plotted as a function of sulfate concentration at the lowest elevation (1000 hPa). Trends are plotted for surface wind speeds of
5-7 m/s and equilibrium is achieved under WTG mode with SST
set constant at 26 C. . . . . . . . . . . . . . . . . . . . . . . . .
44
7
8
9
10
2
Difference between longwave and shortwave flux (W/m ) as a
function of 1000 hPa sulfate concentration (self-generated profiles) for 6 and 7 m/s, in both RCE and WTG conditions. Note
that longwave flux is about 5 W/m 2 greater than shortwave flux
for the runs in WTG mode. . . . . . . . . . . . . . . . . . . . . .
45
SST trend for 100-day WTG run to equilibrium with a surface
wind speed of 5 m/s; note the two distinct equilibrium temperatures reached by the runs. Displayed are curves for the 24 different self-generated concentration profiles. SST was initialized at
. . . . .
26 'C and surface temperature is set to be interactive.
46
9
11
12
13
14
15
16
Environmental variables as a function of self-generated aerosol
vertical concentration profiles (0-2.3 ug/m3 at lowest vertical
level), WTG mode, initialized SST at 26 0C, interactive surface
tem perature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
Potential intensity (top), numerator and denominator of X (middle left and right), saturation deficity (bottom left), thermodynamic component of GPI (bottom right) as a function of selfgenerated aerosol vertical concentration profiles (0-2.3 ug/m 3 at
lowest vertical level), WTG mode, initialized SST at 26 'C, interactive surface temperature. . . . . . . . . . . . . . . . . . . . .
51
Average cloud effective liquid droplet radius (rei) and verticallysummed cloud optical thickness (r) as a function of vertical sulfate concentration profiles decadally averaged over the hurricane
MDR and years 1850-2000. . . . . . . . . . . . . . . . . . . . . .
54
2
Difference between longwave and shortwave flux (W/m ) as a
function of surface sulfate concentration (SPARC simulation) for
6 and 7 m/s, in both RCE and WTG conditions. Note that
longwave flux is about 5 W/m 2 greater than shortwave flux, for
the runs in WTG mode. . . . . . . . . . . . . . . . . . . . . . . .
55
Environmental variables as a function of year (IGAC/SPARC
simulation data for sulfates), WTG mode, SST initialized at 26
0C, interactive surface temperature.
. . . . . . . . . . . . . . . .
56
Potential intensity (top), numerator and denominator of X (middle left and right), saturation deficitX (bottom left), thermodynamic component of GPI (bottom right) as a function of year
(IGAC/SPARC simulation data for sulfates), WTG mode, SST
initialized at 26 'C, interactive surface temperature. . . . . . . .
58
17
Average in-cloud condensate mixing ratio (kg/kg) as a function of
altitude, averaged over the sixteen decades of the IGAC/SPARC
simulation and results at 6-7 m/s surface wind speed reaching RCE. 61
18
Total rlq (liquid water contribution) as a function of year. The
vertical sulfate concentration profiles used are mean concentrations using the IGAC/SPARC simulations, and profiles one standard deviation above or below the mean. The cloud condensate
mixing ratio (for each layer and year) used is the same as from
Figure 17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
62
Standard deviation in total r as a function of year, with variance
originating from the vertical sulfate concentrations. . . . . . . . . 62
10
List of Tables
1
2
3
4
5
6
7
Sulfate concentrations (pg/m 3) for the 24 self-generated vertical concentration profiles at nine selected pressure levels (hPa).
The self-generated profiles were created by relating concentration
trends from the IGAC/SPARC historical simulations. . . . . . .
25
Common parameters used in calculations of radiative-convective
equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
Surface temperature ('C) as a function of constant cooling term
C in K/s. As C approaches values around 2.4 - 2.5 x 10-5, the
surface temperature drops rapidly to unrealistically low SSTs due
to too strong a cloud feedback near the surface (cloud fraction
around 1.00 at several near-surface altitudes). Surface wind speed
is set at 7 m /s. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41
Equilibrium temperature as in Table 3, except that the surface
wind speed is set to 15 m/s. . . . . . . . . . . . . . . . . . . . .
42
Parameters used in calculations of single-column model using
WTG mode and initialized SST (setup outlined in 3.2.2). . . . .
42
Standard deviation of aerosol concentrations (pg/m 3) for each
decade and selected pressure levels (hPa) . . . . . . . . . . . . .
59
Percentage (%) of standard deviation as value of average sulfate
concentration at a given pressure (hPa), displayed for selected
pressure levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
11
12
1
Introduction to Aerosol Impacts on Hur-
ricanes
Tropical cyclones (TCs), also known as hurricanes or typhoons depending on
their geographic location, are among the most deadly geophysical hazards.
Within the U.S., the most lethal and expensive natural disasters were TCs: the
Galveston Hurricane of 1900 that killed about 8,000 people, and Hurricane Andrew of 1992 that produced over $35 billion in damage (Emanuel 2003). Studies
suggest that the intensity of TCs will increase as the planet warms due to the
thermodynamic effects of increasing greenhouse gases (Emanuel, 1987; Webster
et al., 2005; Bender et al., 2010). Because of the destructive potential - physical, human, and financial - of TCs to select coastal regions, understanding how
anthropogenic atmospheric input influences TCs is important in understanding
the risks of TC-induced destruction. While there is a reasonably developed consensus on the impact of greenhouse gases on TC behavior, our understanding
of anthropogenic aerosol impact is lacking. Mann and Emanuel (2005) indicate
that aerosols, which have a net cooling effect on the atmosphere, have offset a
substantial fraction of anthropogenic warming due to greenhouse gases in the
North Atlantic region. The increased temperatures due to anthropogenic greenhouse gas input theoretically increases TC intensity, while the cooling of aerosols
suppresses some of the potential for TC intensity by greenhouse gases.
Aerosol cooling effects are divided by convention into (1) direct effects, the
capacity for aerosols to reflect radiation and directly cool the Earth; and (2)
indirect effects, the impact of aerosols on cloud properties that affect radiative
forcings. Aerosol indirect effects (AIEs) may be broken into the first and second
AIEs. In the first effect, aerosols increase droplet concentration and decrease
the droplet size, thereby increasing the optical thickness of a cloud and thus
the cloud's albedo. The second effect occurs because aerosols increase the cloud
cover and lifetime of low-level clouds by a reduction in drizzle and increase in
moisture content within the clouds.
Justin Bandoro, a graduate student in MIT's Program for Atmospheres, Oceans,
and Climate (PAOC), studied the relationship between sulfate aerosol direct
effects and Atlantic hurricane activity. The study found that volcanic sulfate
13
aerosols in the stratosphere following major volcanic eruptions resulted in a
weakening of theoretical maximum storm intensity and genesis potential, due
to reductions in air-sea enthalpy disequilibrium. This disequilibrium was driven
by a warming of the lower stratosphere and subsequent cooling of sea surface
temperatures. The aim of this study is to extend the sulfate aerosol examination
by studying the impact of the sulfate aerosol first indirect effect on hurricane
intensity and genesis potential.
The ECHAM5 general circulation model developed at the Max Planck Institute produced simulations that predicted a -1.9 W - m-
2
change in top-of-the-
atmosphere net radiation between present day and industrial times (Lohmann
et al., 2007). The contribution of the cloud albedo (first indirect) effect amounts
to -0.45 W - m-
2
with a 90% uncertainty range of -1.2 to 0 W -m-2, suggesting
lack of certainty in first AIE predictions (Boucher et al., 2013). Quantification of
second indirect effects on radiative forcing has been poorly understood. While
Albrecht posited that aerosols at least initially suppress precipitation, they may
not continuously decrease precipitation in an evolving cloud field (Boucher et al.,
2013). The 5th IPCC Report grouped all aerosol-cloud effects into the variable
'effective radiative forcing due to aerosol-cloud interactions' (ERFaci) because
of the difficulty in isolating each aerosol indirect effect in isolation.
Because
consensus is lacking on the magnitude of impact of the second effect, we will
not examine the second AIE in this paper.
In the following sections, key concepts regarding TCs, aerosol properties, aerosolcloud interactions, and cloud convective processes are reviewed to provide context for the study.
1.1
Tropical Cyclone Dynamics
TCs originate over tropical oceans by definition and are driven principally by
heat transfer from the ocean. Generally, TCs develop over ocean water whose
surface temperature exceeds 26 'C in the current climate. In the Atlantic Ocean,
development is most likely to occur in the 5-20 N latitude band and the months
of June through November, reaching peak activity in September. However,
hurricanes often move up to and make landfall at higher latitudes, i.e. latitudes
corresponding to the U.S. Atlantic seaboard.
14
Tropical cyclones are driven by enthalpy fluxes from the sea and limited mostly
by surface drag. The energy cycle of a mature tropical storm may be idealized
as a Carnot engine, in which a series of isothermal and isentropic processes
results in a net work output as energy is transferred from a hot pool to a cool
pool. The first leg, isothermal expansion, occurs as air spirals in from far away
to the storm center, accompanied by increased entropy due to enthalpy transfer
from the sea surface. The flow reaches the eyewall and undergoes a stage of
adiabatic expansion, by following surfaces of constant entropy as it turns upward
to regions of lower pressure.
In the distant environment, the flow undergoes
isothermal compression as it descends and loses entropy by electromagnetic
radiation to space. An adiabatic compression stage concludes the energy cycle.
Work, usually an output of this Carnot heat transfer from a hot to cold source,
is used up in the turbulent dissipation of the storm's atmospheric boundary
layer.
There is lack of consensus regarding the genesis conditions for hurricanes. Broadly,
genesis requires transforming an existing disturbance into a feedback cycle between surface enthalpy fluxes and surface wind. A necessary condition for genesis is the establishment of a 100-km-wide column of nearly saturated air, so
that cumulus convection rising into this air cannot produce low entropy downdrafts driven by evaporating rain.
The creation of such a column has been
described by several proposed mechanisms.
These proposed mechanisms in-
clude nearly classic baroclinic development, interaction of easterly waves with
tropical upper tropospheric disturbances, and accumulation of wave energy in
diffluent large-scale flow (Emanuel 2003). Theory has also developed regarding
the ability of mesoscale convective systems, which with sufficient vorticity, can
merge to intensify the cyclone. The hurricane intensifies as increasing surface
wind speeds produce increasing surface enthalpy flux via a feedback mechanism,
with the heat transfer increasing the storm winds. Once energy production and
dissipation roughly balance, the storm has achieved a quasi-steady state.
Interannual variabilility in the frequency of TCs is partially driven by internal
variability of coupled atmosphere-ocean phenomena, such as the phases of the El
Nifno Southern Oscillation (ENSO) and the equatorial Quasi-Biennial Oscillation
(QBO) (Gray, 1984; Chan, 1995). These phenomena affect the environmental
conditions that play an important role in TC genesis.
Because TC genesis
potential decreases with greater vertical wind shear, there tend to be less TCs
during El Nifno and easterly QBO years when the wind shear is greater. TCs are
15
also sensitive to low-level vorticity, sea surface temperatures (SST), and relative
humidity of the free troposphere (Frank and Roundy, 2006). Natural climate
fluctuations of SST due to the Atlantic multi-decadal oscillation (Goldenberg et
al, 2001; Klotzbach and Gray, 2008; Nigam and Guan 2011) create multi-decadal
variability in TC genesis.
1.2
Aerosol Properties and Effects
Aerosols are typically defined as suspensions of fine solid or liquid particles
in a gas - and therefore consist of both the gas component and the liquid or
solid component (Seinfeld and Pandis, 2006) - but common usage refers to the
aerosol as the non-gas component only. In this study, aerosols refer to the particles that serve as surfaces on which suspensions are formed.
Aerosols are a
diverse group of particles that have wide ranges in size (from the nanometer
to micrometer scale) and composition. In terms of size range, they occur both
in accumulation or coarse modes, and have the ability to coagulate and form
larger aerosols. They may be emitted from natural sources, which include sulfate
and soot from volcanic eruptions; desert dust; sea salt; and organic materials
such as smoke, pollen, spores, and bacteria that results in organic carbon (OC)
aerosols. However, aerosols have become a research focus of environmental and
climate scientists in recent decades due to increases in anthropogenic aerosol
emissions, which have damaging and poorly understood effects on the environment. Combustion - from factories, vehicles, and other sources - is a key source
of anthropogenic aerosols in the form of sulfates and black carbon (BC). The
final aerosol product may be created either from direct emission, or formed after
atmospheric gas-to-particle conversion processes.
Sulfate aerosols tend to originate from two types of natural sources:
(1) re-
lease by volcanoes and (2) dimethyl sulfide (DMS) from biogenic sources such
as plankton. Perhaps the most publicly well-known volcanic phenomenon that
affected aerosol concentrations is the Mt. Pinatubo eruption in 1991 - which
released 20 million tonnes of sulfur dioxide (SO 2 ) into the stratosphere and
led to the creation of an unusually high concentration of atmospheric sulfate
aerosols. The result was an increase in tropical aerosol optical depth by over
2 orders of magnitude (McCormick and Veiga, 1992) and a roughly 0.5 *C de-
16
crease in global surface temperatures. Unlike release from volcanic eruptions,
DMS has a more significant potential effect on cloud optical properties because
the sulfates released enter into the troposphere. DMS is usually produced by
phytoplankton and released into the marine atmosphere, where it is oxidized
into compounds such as sulfur dioxide, dimethyl sulfoxide, and sulfuric acid,
among others. Sulfuric acid has the potential to create new aerosols to serve as
cloud condensation nuclei (CCN). In addition, SO 2 has the potential to create
sulfuric acid via secondary reactions. However, sulfate aerosols have become of
environmental concern due to increases in atmospheric sulfate concentrations
from anthropogenic activity, primarily industrial combustion. The aerosols play
a major role in urban air pollution, while in a global atmosphere they have an
overall effect of offsetting global warming or cooling the Earth.
Chang et al. (2010) conclude that anthropogenic sulfate aerosol emissions, originating mainly from the Northern Hemisphere, may have significantly altered
tropical Atlantic rainfall climate over the twentieth century. Sulfate emissions
are projected to decrease over North America and Europe, but will increase in
the tropics and Southern Hemisphere, which may impact the sulfate entering
the hurricane main development region. There is overwhelming evidence that
anthropogenic processes have a significant impact on sulfate content over the
North Atlantic (Van Dingenen et al., 1995), the region of concern regarding hurricane development. The non-sea-salt (nss) fraction of the sulfate concentrations
measured shows high correlation with black carbon, a byproduct of incomplete
combustion and thus a strong indicator of industrial sources. Nss-sulfate is a
strong indicator of anthropogenic sources as sulfate tends to arise from either
anthropogenic emissions or DMS/sea salt. This indicates that anthropogenic
sulfate advected from the continents contributes substantially to particulate
sulfate over the North Atlantic. In addition, both black carbon and nss-sulfate
are shown to be directly correlated to CCN number concentrations, suggesting
that anthropogenic sources have significant potential to affect cloud properties
and that sulfate represents a viable parameter to predict CCN concentrations
in both clean and polluted sites (this paper needs to be reviewed and the coverage reorganized).
High concentrations of sulfate are found in continentally
derived air masses, in which aerosols are cloud-activated at high supersaturations enhancing the number of cloud droplets or particles in accumulation mode.
17
1.3
Aerosol Cloud Nucleation
By definition, clouds are masses of liquid droplets or ice crystals made of water or
activated by atmospheric aerosols. A cloud forms when an air parcel is cooled
sufficiently and condenses when the supersaturation of air exceeds a critical
value according to K6hler theory. This critical value is determined by curvature
of the liquid-vapor interface (greater curvature increases vapor pressure) and
amount of solute in the vapor solution (solute decreases vapor pressure). Because supersaturations of several hundred percent are necessary for formation
of water droplets in particle-free air, particles are necessary for cloud formation
by decreasing the vapor pressure of the solvent and thus serving as a surface
upon which cloud water droplets form. Condensation nuclei (CN) are usually
defined in literature as those particles that form droplets at supersaturations
>400%. For all intents and purposes, CN include all available aerosol particles.
Cloud condensation nuclei (CCN) are the particles that can initiate cloud drop
formation at a given supersaturation.
The process by which aerosol particles are activated and serve as surfaces upon
which water molecules accumulate to form cloud droplets is known as nucleation scavenging. This process determines the initial composition of the cloud
droplets. Activation can occur once water supersaturation is achieved, and
aerosols become activated if they have sufficient size, degree of supersaturation
of cloud water, and content of soluble material (Seinfeld and Pandis, 2006). The
cloud droplet distribution can then be further altered by additional processes
such as aqueous chemical reactions involving the aerosols, collisions between
non-activated aerosols and cloud drops, and coalescence (amalgamation) among
cloud drops. When cloud droplets accumulate sufficient mass, they can be rained
out of the cloud. Thus the speed at which droplets grow in size directly affects
a cloud's precipitation efficiency.
Given constant water content, the cloud droplet number concentration increases
with increased aerosol concentration, because more aerosols can serve as particles for cloud formation, resulting in partitioning of water across more particles.
The relationship, however, is not one-on-one because additional particles depress maximum supersaturation, and thus the rate at which CCN increases
slows down. A reasonable ballpark range for cloud droplet radii is 10-15 microns, though it is quite common for clouds over marine regions to have somewhat larger radii. Marine cumulus clouds - the clouds most important in this
18
study - have a median droplet concentration of about 45 particles cm- 3. Not
all aerosols present initially nucleate - some aerosols remain unactivated and
are known as interstitial aerosols. Studies done by Ten Brink et al. (1987) and
Daum et al. (1984) showed that most of the aerosol sulfate mass is incorporated
into cloud droplets, i.e. sulfate has a high mass nucleation scavenging efficiency.
Clouds can exist in the ice phase as well, or in a mixed phase consisting of both
liquid water and ice.
OC does not necessarily indicate presence of ice clouds
because water readily supercools in the atmosphere. Cloud ice particle formation can occur at higher temperatures in the presence of ice nuclei (IN) that
A temperature below
provide nucleation sites for water molecule crystallization.
Transformation of
droplets to ice can occur via several methods: deposition mode, water vapor
adsorption onto the IN surface and transformation to ice; contact mode, collision of supercooled droplet with an IN that leads to formation of an ice cloud
particle; immersion mode, water droplet freezing triggered by immersion of IN
in a supercooled water droplet; and freezing mode, ice droplet transformation
into a supercooled droplet without the presence of an IN. Deposition, contact,
and immersion methods are classified as heterogeneous freezing modes because
particles other than water vapor are required. Freezing mode is also known
as homogeneous nucleation. Aerosols that serve as IN tend to be insoluble in
water and have chemical bonding and crystallographic structures similar to ice,
and include certain mineral dusts, biological aerosols, carbonaceous combustion
aerosols, and volcanic ash (Murray et al., 2012). In particular, soot is important for immersion nucleation while mineral dust is important for contact and
deposition nucleation (Liu and Ghan 2007). Sulfate aerosols tend not to play a
major role in ice nucleation, though water droplets formed on a sulfate surface
could be transformed into ice crystals via homogeneous freezing. As temperature decreases, the likelihood of ice crystals predictably increases. Many models
with a simplified liquid-ice partitioning scheme determine the proportions of
liquid and ice water in clouds by empirically relating them to the temperature.
19
1.4
Cloud Convective Processes
Van den Heever et al. (2010) studied the impacts of aerosol indirect forcings
using a radiative-convective framework, specifically the effects of CCN on dynamical and microphysical properties of tropical convective clouds, the relevant
cloud classification in this study. The AIEs were shown not to have significant
impact on the large-scale organization of convection, in comparison to the impact of large-scale dynamics. However, aerosols have significant impact on the
dynamics of three key tropical cloud modes (shallow cumulus, cumulus congestus, and deep convection), at a local scale. The weaker domain-wide response
may also be due to the differing signs of the aerosol forcings on the three convective cloud modes. Enhanced CCN concentrations from increased aerosols were
shown to decrease low cloud frequency (shallow cumulus) but increase middle
and high-level cloud frequency (cumulus congestus and deep convection).
Precipitation occurs when cloud droplets grow to a critical raindrop size (typically defined as drops with radii or diameters greater than 100 pm) and can thus
fall out of a cloud (Wang et al., 2012). Droplets reach the necessary size primarily by collision and coalescence, but the initial size of the droplets is important in
determining the precipitation efficiency, as it dictates how much larger droplets
must become before fallout. Increased aerosol concentrations, which decrease
cloud droplet size due to increased competition for water vapor among nuclei,
should theoretically decrease precipitation. This phenomenon is known as the
suppression of warm rain. Indeed, van den Heever et al. (2010) demonstrated
that precipitation rates decreased for shallow cumulus clouds with increased
aerosols. The effect of aerosols on precipitation becomes more complex in ice
or mixed phase clouds such as deep cumulus. Generally, increased aerosols
enhances the Wegener-Bergeron-Findeisen (WBF) process by which cloud ice
crystals grow by vapor derived from evaporated cloud drops, and inhibits riming by which cloud ice crystals grow via cloud water accretion. This relationship
makes the effect of aerosols on the formation of cold rain that is derived from
melting of snow, graupel, and hail in mixed clouds more complicated. As a
result, one may expect deep convective clouds to show a more mixed precipitation response to aerosols. The simulations by van den Heever et al. (2010)
revealed higher precipitation from cumulus congestus and deep cumulus clouds.
The suppression of warm rain results in increased cloud water, which is available to be transported upward and possibly transformed into ice. The higher
20
precipitation in deep convective clouds shown in the simulations may be a result
of this increased ice-cloud ratio and a sufficient fallout of cold rain from melted
ice.
Past studies have indicated the effect of aerosols in invigorating convective
clouds (Andreae et al., 2004; Khain et al., 2008). An increase in aerosols results
in larger concentrations of cloud condensation nuclei (CCN) and smaller cloud
droplets. Because small cloud droplets have lower fall velocities, they are less
likely to be precipitated and more likely to be uplifted in the cloud due to updrafts, resulting in taller and longer-lasting clouds (Koren et al., 2005; Fan et
al., 2009). The moisture freezes at higher elevations when the temperature is
sufficiently low, releasing latent heat that enhances buoyancy and propels the
cloud top even higher.
1.5
Thesis Outline and Contributions
Chapter 2 presents the setup for the study that will be conducted. First, a set
of self-generated sulfate vertical profiles is presented to assess hurricane activity
sensitivity with incrementally increased concentrations.
The results from the
profiles will be used to back the results for aerosol vertical profiles generated
using the IGAC/SPARC simulation output, which is conducted in Chapter 4.
Next, a scheme for the effect of sulfate aerosols on cloud liquid droplets and
optical depth (i.e. an approximation for the first AIE) is presented. This scheme
is discussed in the context of the single-column radiative-convective model used
to study hurricanes in our study. The model is also run under weak temperature
gradient (WTG) mode to assess the impact of stratocumulus clouds in the study,
and the motivation for WTG runs is discussed.
Chapter 3 begins by assessing the impact of aerosols on environmental variables
relevant to radiative-convective equilibrium (RCE) under the self-generated
aerosol vertical profile.
Next, a scheme for evaluating the theoretical hurri-
cane intensity and genesis potential is detailed. Results are presented for the
model run under both non-WTG and WTG mode. In particular, several different WTG setups, used in an attempt to produce sufficient stratocumulus, are
discussed.
21
Chapter 4 then applies the IGAC/SPARC historical aerosol vertical profile to
assess the effect of anthropogenic input on hurricanes over the Atlantic ocean.
Error bar measurements are laid out and derived for the historical profile.
Chapter 5 summarizes the results of the study and presents potential future
directions on which study of AIEs on hurricanes can be elaborated.
22
2 Setup of Study
Studying the impact of aerosol indirect effects on hurricanes presents unique
problems because two sciences of different length scales must be integrated:
atmospheric dynamics and cloud microphysics. A simple model that predicts
theoretical properties of hurricanes must model dynamics - especially convection - on a large scale, but such a setup makes it difficult to account for cloud
processes that occur at microscopic scales. There are significant discrepancies
in current understanding of cloud and aerosol behavior, which affects the confidence in the parameterizations that may be used.
The dynamics model used is the one-dimensional radiative-convective model
described by Bony and Emanuel (2001) with a representation of convective
clouds and their optical properties. These representations of cloud formation
dynamics are used in place of coupling the radiative convective model with
an external cloud resolving model. The averaged vertical sulfate concentration
profiles (constant with time) used are tailored to such a scheme. The model does
not physically generate hurricane events, but the theoretical hurricane potential
intensity (V1) and genesis potential (GPI) can be derived from the model's
radiative-convective equilibrium (RCE) properties, for which the setup will be
laid out in Chapter 3.
One area of uncertainty regarding understanding of clouds is cold phase clouds
and heterogeneous ice nucleation schemes. Ice nucleation schemes tend to be
based on two differing hypotheses regarding the conversion of IN into cloud
droplets: one based on stochastics in which nucleation depends on particle properties and environmental conditions, and one in which nucleation is controlled
by IN surface active sites or impurities (Tao et al., 2012). This discrepancy
makes studying ice nucleation in the context of hurricanes more difficult. The
most common IN include mineral dust or soot particles - not sulfates - but sulfates can still play an indirect role by nucleating liquid droplets that then freeze.
As will be discussed in Section 2.3, the ice nuclei effect will be eliminated in this
study, and the liquid droplet effect will serve as a first-order analysis.
Chapter 2 will outline the vertical sulfate aerosol concentration profiles used as
23
well as the aerosol-cloud interaction parameterization that is based on a set of
ambient sulfate concentration measurements.
2.1
Aerosol Concentrations
I use aerosol concentrations for sulfate (SO2-) derived from model simulation
output, specifically data from the IGAC/SPARC Chemistry Climate Mode Initiative (CCMI) historical simulations of sulfuric acid concentrations. The results
are given as decadally averaged concentrations averaged at the 5th year of each
decade. The simulation for IGAC/SPARC data was performed using the Community Atmosphere Model Version 3.5 (CAM3.5) with a bulk aerosol model
driven by the Community Climate System Model (CCSM) SSTs and 1850-2000
IPCC emissions data. The IGAC/SPARC output that is used as input into the
radiative-convective model consists of sulfate concentrations at each decade and
vertical level averaged over the entire horizontal space in question. Aerosol concentrations are averaged over the hurricane Main Development Region (MDR),
i.e. 5-20 N and 20-70 W, and the peak hurricane season months of August,
September, and October.
In addition to the IGAC/SPARC data, a second set of self-generated vertical
sulfate concentration profiles is used in this study, in order to assess the sensitivity of aerosol optical depth and hurricane behavior to changes in sulfate
concentrations. The vertical profiles are generated for vertical concentrations
of 0 to 2.3 pg m- 3 at the lowest vertical level of 1000 hPa (which presumably
has the highest sulfate concentrations) with a 0.1 Lg m- 3 concentration step,
with 2.3 ug m- 3 chosen because this concentration roughly represents the highest concentration reached in the CCMI data at the lowest vertical level during
the 1850-2006 period, while the 0 ug m- 3 concentration is used to estimate an
expected hurricane response without sulfate forcing. The concentrations at the
vertical levels above 1000 hPa are obtained by extrapolating how concentrations
at each vertical level vary in relation to concentrations at the lowest level, using
the CCMI data, with a check to ensure that the concentration at each level is 0
ug m- 3 for the lowest concentration profile.
The Clean Air Act was passed in 1963 to combat pollution from human emissions, with subsequent amendments targeted at stratospheric ozone and acid
rain. Air pollution standards are set for six criteria pollutants, one of which is
sulfur dioxide, a precursor to sulfate aerosols. The 1850-2000 period allows us
24
Pressure
1000
850
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
1.1000
1.2000
1.3000
1.4000
1.5000
1.6000
1.7000
1.8000
1.9000
2.0000
2.1000
2.2000
2.3000
0.0000
0.0738
0.1477
0.2215
0.2954
0.3692
0.4431
0.5169
0.5907
0.6646
0.7448
0.8206
0.8942
0.9978
1.1127
1.1734
1.2476
1.3398
1.4321
1.5243
1.5930
1.6892
1.7854
1.8793
[
700
550
400
250
100
0.0000
0.0535
0.1071
0.1606
0.2142
0.2677
0.3213
0.37483
0.4284
0.4819
0.5353
0.5713
0.6117
0.6651
0.8002
0.8787
0.9549
1.0281
1.1014
1.1746
1.2309
1.3013
1.3717
1.3818
0.0000
0.0146
0.0292
0.0438
0.0584
0.0730
0.0876
0.1022
0.1168
0.1314
0.1443
0.1563
0.1659
0.1797
0.2110
0.2408
0.2657
0.2842
0.3027
0.3212
0.3780
0.3884
0.3988
0.3411
0.0000
0.0028
0.0057
0.0085
0.0114
0.0142
0.0170
0.0199
0.0227
0.0256
0.0285
0.0309
0.0324
0.0350
0.0381
0.0430
0.0469
0.0497
0.0525
0.0552
0.0673
0.0676
0.0679
0.0652
0.0000
6.73e-4
0.0013
0.0020
0.0027
0.0034
0.0040
0.0047
0.0054
0.0061
0.0065
0.0070
0.0078
0.0084
0.0093
0.0103
0.0112
0.0119
0.0126
0.0133
0.0152
0.0156
0.0160
0.0147
0.0000
2.62e-4
5.25e-4
7.87e-4
0.0010
0.0013
0.0016
0.0018
0.0021
0.0024
0.0024
0.0025
0.0027
0.0028
0.0033
0.0037
0.0041
0.0044
0.0046
0.0048
0.0093
0.0085
0.0077
0.0050
Table 1: Sulfate concentrations (pg/m3 ) for the 24 self-generated vertical concentration profiles at nine selected pressure levels (hPa). The self-generated
profiles were created by relating concentration trends from the IGAC/SPARC
historical simulations.
to examine sulfate concentrations during pre-industrial times, as well as before
and after the enactment of the Clean Air Act. As is evident in Figure 2, a decrease in sulfate concentrations occurs around the same time as the enactment
of the Clean Air Act.
25
0
100
200t
300
C. 400
S500
600
700
800
\
900
1
1.5
Sulfate Concentration (pg/m 3
2
)
0.5
Figure 1: Self-generated vertical sulfate aerosol concentration profiles.
curve represents one of 24 self-generated profiles.
Each
2.5
=L2
! 1.5
8
0
lii
01.
0.5
(II
1iBSO
1950
1900
2000
Year
Figure 2: Sulfate concentration over the hurricane main development region
(5-20 N, 30-70 W), averaged over each decade from 1850-2000. Each curve
represents the concentration at one of 46 elevations represented in the RCE
model, with the highest concentration curve representing 1000 hPa and the
lowest curve representing 5 hPa.
26
2.2
Radiative-Convective Model Description
We use the MIT single-column radiative convective model described by Bony
and Emanuel (2001), based on the earlier model of Renn6 et al. (1994). The
convection scheme in the model is an updated and modified version of Emanuel
(1991), as presented in Emanuel and Zivkovic-Rothman (1999) and uses a buoyancy sorting algorithm whereby buoyant parcels ascend through the cloud, mix,
and detrain while negative buoyant particles descend, mix and detrain. The
scheme allows parcels to move between the boundary layer and any layer within
this model, mix with the environmental air in that layer, and then ascend or
descend, according to whether the parcel is positively or negatively buoyant.
The model has representation of an entire spectrum of convective clouds, from
shallow, non-precipitating cumulus to deep precipitating cumulonimbus.
Re-
evaporation of cloud water, resulting from entrainment of dry air, drives penetrative downdrafts within the clouds that imports enthalpy and moisture into
the subcloud layer. The cloud base mass flux is continuously relaxed to produce
near neutrality of a parcel lifted dry adiabatically, and then moist adiabatically,
to the first level above the lifted condensation level. This maintains a boundary
layer quasi-equilibrium whereby convection acts to maintain neutral stability.
A large-scale supersaturation adjustment scheme is applied to each layer, in
which water that exceeds saturation is condensed and a fraction of this condensate is precipitated out of the layer. The final water content in each layer
is then used as input for the cloud parameterization scheme.
The cloud pa-
rameterization predicts the cloud amount and water content that is associated
with convection. The predicted cloudiness is dependent on the condensate produced by both the large-scale supersaturation and subgrid-scale cumulus convection. Cloud optical properties (optical thickness, longwave emissivity) are
calculated for each layer, and are dependent on the cloud fraction and in-cloud
condensate mixing ratio for each layer. As will be elaborated in Section 2.3, the
aerosol-cloud interaction parameterization used for this study is integrated in
the single-column model's cloud optical properties scheme.
27
Turbulent fluxes of sea-air sensible and latent heat are parameterized using
standard bulk aerodynamic flux formulae (differences in temperature and water vapor mixing ratio between the surface and air immediately above it). A
background surface wind speed is specified, given the absence of large-scale atmospheric circulation, in order to have turbulent fluxes of heat from the ocean
mixed layer. The model was run with the surface represented entirely by an
ocean and with interactive surface temperatures calculated through surface energy balance: if more energy leaves the surface then entered, that slab of water
cools down. The configuration used for this study has 46 vertical atmospheric
levels spaced 25 hPa from 1000 to 100 hPa and then 9 more levels at smaller intervals to the top at 5 hPa. Using the calculated vertical fluxes of enthalpy and
moisture by the radiative, convective, cloud, and surface schemes, the model calculates time tendencies of temperature and specific humidity marching forward
in time-steps of 5 minutes.
Radiative transfer is computed interactively using the two-stream shortwave solar parameterization of Fouquart and Bonnel (1980) and the longwave terrestrial
radiation parameterization of Morcrette (1991). The solar energy impinging on
the Earth is parameterized in terms of a solar constant So, latitude (zenith
angle), and a value of surface albedo. Radiative fluxes are calculated at each
vertical level every 2 hours using instantaneous profiles of temperature, humidity, cloud fraction, cloud water path, and climatological distribution of ozone
with specified concentrations of important greenhouse gases such as carbon dioxide, methane, and chlorofluorocarbons. In this study the diurnal cycle of solar
radiation is accounted for and cycled over the same day of the year with a fixed
fractional cloudiness profile in the column until radiative-convective equilibrium
is reached.
28
Parameter
Solar constant, Wm-
Value
1360
Latitude
26.750
Date, (month-day)
Run Length, days
Surface albedo
Time step, mins
Frequency of radiation calls, hours
Surface wind speed, ms-
03-01
1000
0.10
5
3
5-7
CO 2 concentration, ppm
360.0
Table 2: Common parameters used in calculations of radiative-convective equilibrium
2.3
Parameterization of Sulfate Aerosol First Indirect Effect
Nd =
-
5
1 0
2.21+0.41log(m
)
The parameterization of the sulfate aerosol first indirect effect is adopted from
Quaas et al. (2004), and tuned to the conditions set by the radiative-convective
model. The cloud droplet number concentration (Nd, in cm- 3) is diagnosed from
the sulfate aerosol mass concentration m 0 using the following empirical formula:
The 5j coefficient was not in the original parameterization but was added as a
tuning for the produced cloud droplet radius results in the RCE model. Ballpark
average cloud droplet effective radius values should fall between 10-15 microns,
and droplets can often be larger over oceans. The coefficient was included so
that most of the produced average radius distribution falls within or slightly
above this range. In addition, a minimum or background cloud droplet number
concentration of 20 particles/cm 3 is applied to avoid unrealistically small droplet
number concentrations at the higher altitude levels. Note that because the
empirical formula applies specifically to cloud droplets (not ice crystals or total
water content), the number concentration is applied to the specified liquid water
content in the cloud. Cloud droplet sizes are not uniform but rather come in
29
the form of a distribution, often lognormal. This study uses a simplified scheme
in which radius is set a single average value for a given layer and time.
Cloud droplet radius schemes require two moments: number concentration and
volume of condensate available. The number of droplets is partitioned among
the available condensate, with the condensate being determined as the fraction
of a unit volume of cloud that is water (i.e. volume of cloud droplet = condensate
fraction / Nd):
4
Volumed =
3
rrd
condensate fraction
Nd
LWC/pwater
Nd
(qipair)/pwater
Nd
where qj is the liquid water mixing ratio, and pairand Pwater are the densities of
air and water, respectively. The condensate fraction is determined by dividing
the liquid water content of the cloud by the density of water (LWC = qipair).
Rearranging the equation, we arrive at the following relationship for rd:
4qipair
rd =
S7PwaterNd
Note that qj is determined by the radiative-convective model using a temperaturebased liquid-ice partitioning function:
1.0
T >0 C
T-T= _-
-150C < T < O C
0.0
0
T < -15 0 C
where fi is the fraction of water in the cloud in liquid state, T is the temperature
at the current altitude level, To is 273.15 K and Tice is 258.15 K. qi can be
determined from the following formula:
q = q fi
where q is the total water content (liquid and ice). Finally, the effective liquid
droplet radius re,iis given as
re,i =
1.1rd
Another caveat in the parameterization is that while sulfate aerosols theoreotically do not serve as ice nuclei, ice crystal formation may occur from contact
or immersion modes, in which freezing occurs on existing supercooled liquid
droplets that may have been nucleated by sulfates. Therefore, an ideal scheme
30
includes modeling of sulfate effect on the ice phase. Due to the difficulty in determining the ice radius, we keep the model parameterization for the ice radius
consistent, and use just a scheme for liquid droplet formation as a first-order
analysis:
re
= 0.71T + 61.29
where temperature (T) is in Celsius. Of course, ice crystals formed from deposition mode were initially in water vapor and therefore are not affected in a
similar fashion. Finally, the first indirect effect is approximated via modification
of optical depth (r), which is determined using the RCE parameterization
LWP
-73+IWP
=
2
a
b/re,i
re,
where LWP is the liquid water path, IWP is the ice water path, a = 3.448 x
10-3and b = 2.431.
2.4
Modeling of Stratocumulus Cloud Effects
Stratocumulus clouds are puffy, low-lying clouds with most of the mass lying
below 2,400 m (8,000 ft). They are usually the product of weak convective currents that create only shallow cloud layers because the currents are inhibited
by drier, stable air above due to a sharp inversion.
Generally, stratocumu-
lus do not produce precipitation, but if they do, it is in the form of drizzle.
Stratocumulus-topped mixed layers are common over cold ocean waters such as
the eastern subtropical North Atlantic, where large-scale subsidence in the atmosphere is coupled with upwelling of cold water in the ocean. In warmer water,
the stratocumulus layer tends to break up and reform as a trade-cumulus boundary layer. The stratocumuli cover large areas of eastern ocean basins, have high
albedos, and reflect much of the incoming solar radiation when present. Thus,
they sometimes play a role in cooling the Tropics and subtropics.
The albedo feedback of stratocumulus is complex but might play an important role in changing longwave-shortwave balance over the ocean. Longwave
radiative cooling in the cloud top can drive turbulent eddies in the atmospheric
boundary layer that pick up moisture from the sea surface.
The eddies can
also entrain warm, dry air from above the inversion that lifts the cloud top and
31
creates feedback betwen cloud geometry and entrainment rate. These feedbacks
result in coupling with changing SST, subsidence rate, and the daily cycle of
absorption of sunlight, which can alter the necessary conditions for hurricane
genesis (Lilly 1968). In addition, the feedback maintains the cloud top against
large-scale subsidence. Usually, the presence of stratocumulus is coupled with
cooler SSTs. A study of stratocumulus cover over the southeast Pacific showed a
strong diurnal cycle, with thicker clouds and substantial drizzle (mainly evaporating from the sea surface) during the late night and early morning. The EPIC
2001 Stratocumulus Study (Bretherton et al., 2004) study also captured the
expected strong inversion. Finally, the study captured decreased drizzle during high cloud droplet concentration, providing evidence of the second indirect
effect.
Because stratocumuli are only a few hundred meters thick and lie under a sharp
temperature inversion, they are difficult to represent in many climate models
(Bretherton et al., 2004). However, one can roughly model stratocumulus in
the radiative-convective model by running the model under weak temperature
gradient (WTG) mode. This is accomplished by fixing temperature between
a user-specified pressure level (850 hPa) and the tropopause. The formation
of a temperature inversion at the top of the boundary layer traps moisture,
which leads to the creation of stratocumulus clouds. Equilibrium is reached
and temperature gradient preserved above the boundary layer to represent the
thinness of stratocumulus.
Both the WTG and non-WTG mode will be applied to the sulfate concentration
datasets used in this study. As is outlined in Chapter 3, SST and hurricane potential intensity are directly related. If the WTG mode sufficiently approximates
stratocumulus representation, then stratocumulus should have a significant effect on hurricanes. The impact, of course, is highly dependent on the properties
of the stratocumulus lying over the ocean, which could be better represented
using a multi-dimensional model.
2.5
Summary
In this chapter, the difficulties in modeling aerosol indirect effects on largescale convective processes and hurricane activity are discussed, and a first-order
method for connecting these processes is laid out. A liquid water droplet param32
eterization scheme is described for the cumulus clouds within the single-column
radiative-convective model for which input parameters used in this study are
defined. The sulfate aerosol data to be applied - IGAC/SPARC historical simulations and a self-generated profile to assess model sensitivity - are discussed.
Finally, the chapter concludes with a discussion of assessing stratocumulus cloud
impacts by utilizing the model's weak temperature gradient (WTG) mode.
33
34
3
Model Results and Analysis
This section presents an analysis of the change in cloud properties, large-scale
environmental variables, and expected hurricane behavior when the indirect effects of sulfate aerosols on clouds are incorporated into a radiative-convective
model. In this section, I present an analysis of the impact of incremental changes
in sulfate vertical concentration profiles, i.e. run the single-column model using the self-generated sulfate concentration set. Given more time, suggested
extension methods of examining impacts on hurricanes are to do an analysis of
historical hurricane data or to simulate the genesis and tracks of synthesized
hurricanes with aerosol input in cumulus clouds.
3.1
Changes to environmental conditions
In Section 3.1, results for cloud properties and large-scale environmental conditions are discussed for the single-column model run to radiative-convective
equilibrium for surface wind speeds of 5-7 m/s. Based on theory of the first
AIE, increased sulfate concentrations should increase number concentration
(Nd), decrease cloud droplet radius (re,i), and increase cloud optical depth (r).
The cloud droplet number concentration (Nd) is only dependent on the sulfate
aerosol concentrations in our applied parameterizations, as noted in Chapter 2.
Therefore, Nd does not depend on several of the other variables that have been
varied in the study, such as the cloud liquid water content or the surface wind
speed. As Figure 3 indicates, the theoretical
Nd
at each layer (assuming that
there are clouds) decreases with increasing altitude because sulfate concentrations decrease as one goes up the atmosphere.
The model produces decreasing average cloud droplet radius (re,i) with increasing sulfate concentrations for all the surface wind speeds. The average re,I is in
the range of 12.5-13.5 pg/m 3 at the lowest sulfate concentration (surface concentration of 0 pg/m 3) and decreases with increasing sulfate amounts, reaching an
average of around 10 pg/rn3 at the highest sulfate concentration. Note that the
35
0
100200
300
400
800
900-
1000
20
40
30
50
60
particles/cm
70
80
90
100
3
Figure 3: Cloud droplet number concentration (Nd) as a function of pressure for
each self-generated vertical sulfate concentration profile. Each curve represents
one of 24 concentration profiles.
rate at which re,I decreases becomes slower with incremental increases in sulfate.
13
This makes sense; as noted in Section 2.3, the radius is related to N-1 , which
means that incremental increases in Nd (a function of sulfate concentration)
results in smaller decreases in re, as Nd gets large.
The model produces increasing cloud optical depth (r) with increasing sulfate
concentrations for all the surface wind speeds. At low sulfate concentrations,
r is in the 130-150 range. At the high end of sulfate concentrations, r reaches
around the 180-200 range.
The results for re, and
r are not strictly monotonic, despite the noticeable
trends in relation to changes in sulfate concentrations. This result can again be
explained by the relationship of re,I and r on both in-cloud liquid water content
and number droplet concentration, not just on number concentration alone.
The large-scale environmental variables are within the expected ranges of realistic ambient conditions and change noticeably with respect to the surface wind
speed, but not with respect to the vertical sulfate concentration profile used.
2
The TOA shortwave flux is mainly in the 273-274 W/m range for surface wind
36
14
-- 5 nV&
13.5
13
12.5
12
I.2 11.51
C
.2
I1I
C-,
10.5 }
10
0
0.5
1.5
1
Surface Sulfate Concentration in jg/m
2
3
Figure 4: Average cloud droplet radius (reI) plotted against the self-generated
sulfate concentration profiles for a surface wind speed of 5-7 m/s, for model runs
to RCE.
7 In
-
200
--
6 aft
190
3
f-
180
170
160
150
.2
0 140
130
120
1 in
0
1.5
1
0.5
3
Surface Sulfate Concentration In pg/m
2
Figure 5: Total (vertically-summed) cloud optical thickness (r) plotted against
self-generated sulfate concentration profiles for surface wind speed of 5-7 m/s,
for model runs to RCE.
37
speeds of 5-6 m/s and the 276-277 W/m 2 range for surface wind speed of 7 m/s.
The longwave flux has similar values as the shortwave flux for all velocites. Precipitation is in the 4.75-4.7 mm/day range for 5 m/s, 4.8-4.9 mm/day range for
6 m/s, and 5-5.05 mm/day range for 7 m/s. Finally, the temperature at the
lowest vertical level (1000 hPa) is in mainly in the 28.5-28.7 *C range for 5 m/s,
the 28.3-28.5
0C
range for 6 m/s, and the 28.6-28.9 'C range for 7 m/s. The
increased LW/SW fluxes and precipitation, and the decreased temperatures are
expected from ambient conditions.
The model was modified somewhat in order to produce the desired results for
The original RCE code set
T.
r for a specific layer to be 0 when the fraction of the
layer covered by clouds was below a specific threshold, in order to denote that
cloud cover was insufficient to have a noticeable effect on radiation, even if the
cloud's theoretical r was high. This condition was omitted in order to observe
the expected behavior of
r to increased sulfate, regardless of the layer's cloud
cover.
3.2
Weak Temperature Gradient (WTG) Mode
Section 3.2 is motivated by the lack of aerosol indirect effect if the single-column
model is run to RCE, due to insufficient cloud cover as discussed above. Running
the model in weak temperature gradient (WTG) mode in an attempt to produce
sufficient stratocumulus may potentially provide a solution to this challenge, as
discussed in Section 2.4. The single-column model is first run to equilibrium
for a given sulfate concentration vertical profile and surface wind speed. Then,
the model is run in WTG mode with the temperature of the free troposphere
fixed between 850 hPa and the tropopause. Two WTG setups were executed in
an attempt to produce more stratocumulus: (1) altering the ocean heat flux by
adding a cooling term and (2) initializing the sea surface temperature a couple
degrees lower than the RCE result. The setup and results for these two methods
will be discussed independently.
38
T"A Lmiuae Flux
EZ!t
277
276
27
I Ob
4276
274
274
273l
0
05
Suiface Sufite
1
Concenrinion
15
2
Surface
51
5.06
7M%
LEI1~J
297
WA
K
20L6
28.5
496
489
2&4
20
4.6
0
05
Surfam
1
Suraft Cacantrabon
1.6
in
2
at Lom..t VerdtilLeel
28's
-
E5
15
1
SulOa COnAtrlion In pg
TudMi
284.
475
05
0
in pglm3
2
3
0
0.5
1.fi
Suam Sutfjl Concentraion in
pgfr13
pgIm"
Figure 6: Environmental conditions for the indicated variables using the varying
self-generated sulfate aerosol profiles.
39
3.2.1
Modification of Ocean Heat Flux
In the WTG run, a cooling term is added to the ocean heat budget (FTS) that
would mimic an upward flux of heat from the ocean:
FTS = Jrad - Jsea d p1 w Cp,,w
c
where jrad is the radiative flux, jea is the sea flux, d is the mixed layer depth
(set at 1m), p1w is the density of liquid water, Cp,w is the constant pressure
heat capacity of liquid water, and C is the cooling term. The FTS units are in
K/s. Therefore, a cooling term of 10-6 K/s translates to a roughly 0.6 K ocean
surface cooling in a week. Because equilibrium is achieved more quickly using
this setting, I decrease the run length from 1000 days to 100 days.
Applying WTG mode with a modified FTS unfortunately did not produce the
desired results. To test the appropriate conditions for WTG runs, several values
of C were tested for the vertical sulfate concentrate profile with a surface sulfate
concentration of 0.9 pig/m 3 and for the original model with no sulfate forcing.
Both runs were done with a surface wind speed of 7 m/s. The values of C
are displayed below in Table 3. At a point after C was greater than 2.0 x
l0-5, there was a noticeable drop in the sea surface temperature but insufficient
clouds around the desired altitude (~900 mb) were formed. Then, after C was
increased slightly again, the sea surface temperature dropped well below the
freezing point and substantial clouds formed around ~900 mb. However, the
cloud cover at around 900 mb was often 1.00, which was unrealistic and led to
the unrealistically low SSTs.
A suggested alternative study for the problem would be to create a two-column
model in which low, warm clouds form in the first column and colder clouds
are formed in the second column. Advection will occur between warm clouds in
first column and colder clouds in the second column to prevent the unrealistic
temperature profile we produced. A different latitude is specified for each of the
two columns.
Next, we attempt to a similar setup with the only change being an increase of
the surface wind speed to 15
with sulfate forcing is much
sufficiently high values of C.
makes it possible for sulfate
m/s. It is worth noting that in Figure 4, the SST
lower than the SST with no sulfate forcing given
This run provides proof that sufficient cloud cover
forcing to have a noticeable effect on large-scale
40
C
No sulfate forcing
0.9 jug/M 3 surface sulfate concentration
0
1.0 X 10-6
2.0 x 105.0 x 10-6
1.0 X 10-5
1.5 x
2.0 x 10- 5
2.25 x 10~
2.5 x 10- 5
3.0 x 10-5
27.40
27.39
27.28
27.23
23.12
21.47
20.55
19.84
-22.19
-19.75
27.52
27.65
27.64
27.03
23.04
21.79
21.03
20.32
19.55
-19.70
Table 3: Surface temperature (0C) as a function of constant cooling term C in
K/s. As C approaches values around 2.4 - 2.5 x 10-5, the surface temperature
drops rapidly to unrealistically low SSTs due to too strong a cloud feedback
near the surface (cloud fraction around 1.00 at several near-surface altitudes).
Surface wind speed is set at 7 m/s.
7%
20
15
10
5
0
-5
-10
-15
-20
-251
-30'
24
24.1
242
24.3
24.4
24.5
24.6
24.7
24.8
24.9
25
Constant Cooling Term (1/10 ls)
Figure 7: Equilibrium surface temperature as a function of the constant cooling
term C, which ranges from 2.4 x 10-1 to 2.5 x 10-5. The model is run with
0.9 pg/m3 sulfate concentration at the surface and surface wind speed of 7
m/s. Note the drastic temperature drop at around C = 2.44 x 10-1, which
corresponds with a cloud fraction of 1.00 at altitudes near the surface.
41
C
No sulfate forcing
0.9 pg/m 3 surface sulfate concentration
0
1.0 x 10-6
29.35
29.35
27.77
27.75
2.0 x 10-6
5.0 x 10-6
29.34
29.34
24.60
23.10
22.75
21.62
3.74
24.30
27.62
24.30
21.92
18.72
20.10
-10.17
1.0 X 10~
1.5
2.0
2.5
3.0
x
x
x
x
10-5
10~
10- 5
10-
Table 4: Equilibrium temperature as in Table 3, except that the surface wind
speed is set to 15 m/s.
Parameter
Value
Solar constant, W m-'
Latitude
Date, (month-day)
Run Length, days
Surface albedo
Time step, mins
Frequency of radiation calls, hours
Surface wind speed, m s-1
Initial sea surface temperature
1360
26.750
03-01
100
0.10
5
2
5-7
26 0 C
Table 5: Parameters used in calculations of single-column model using WTG
mode and initialized SST (setup outlined in 3.2.2).
environmental conditions, even if the actual ambient conditions are not realistic
in tropical regions.
3.2.2
Initialization of Sea Surface Temperature
In the second study, I first run each vertical sulfate profile to RCE, then run
the model in WTG mode with an initialized SST about two degrees lower than
the RCE SST. Surface temperature remains interactive. Runs are executed
at surface wind speeds of 5-7 m/s. Such a setup should produce a radiative
imbalance, with longwave radiation having greater magnitude than shortwave
radiation. This imbalance may possibly be noticed in changes in LW and SW
as a response to differing sulfate forcings.
42
The cloud droplet radius (re,i) decreases from around 16 pm to 9.5 prm for 5 m/s,
and around 14 pm to 9 pm for 6-7 m/s, with increasing sulfate concentrations.
The cloud optical depth (-r) increases from around 20 to 35 for an increase in
sulfate concentrations, for all velocities tested. The cloud droplet radius drops
over time with an increase in sulfate concentrations, while the optical depth
increases with higher sulfate levels, as is expected in theory and in agreement
with the RCE results.
The values of environmental variables do not change significantly with increases
in sulfate concentrations. The TOA shortwave flux is within the 253-254 W/m 2
range and the longwave flux is within the 258-260 W/m 2 range for both 6 m/s
and 7 m/s surface wind speeds. The precipitation is around 2.35 mm/day for 6
m/s surface wind speed and in the 2.40-2.45 mm/day range for 7 m/s surface
wind speed. The temperature at 1000 hPa is around 24.95 'C for 6 m/s and
around 24.84 'C for 7 m/s surface wind speed.
While the study does not produce clouds at sufficiently low altitudes, it does
produce clouds at lower altitudes than the clouds for runs to RCE. Because
stratocumulus cloud production is still not sufficient, there is not a noticeable
change in environmental variables induced by sulfate forcing.
However, the
weak temperature gradient conditions can explain the lowered precipitation in
comparison to RCE, with the precipitation likely to come in occasional light
drizzles. Temperature has decreased noticeably, as is expected for WTG or the
presence of stratocumulus.
Results for 5 m/s surface wind speed were omitted from the figures because
the environmental variables settled at different equilibria depending on the run,
suggesting that two equilibria exist in WTG for the conditions of 5 m/s and
initialized SST at 26 'C (see Figure 10).
Using the WTG mode did not produce appropriate environmental conditions in
the case of changing the ocean flux. In addition, running the WTG mode with
an initialized SST produced realistic environmental conditions but not sufficient
clouds to simulate a more significant effect from the aerosols, though this set of
runs did display a resulting radiative imblanace that is worth noting. A twodimensional WTG simulation may be necessary to produce desired results with
enough clouds.
43
17
LII I~IL
16
15
(A
14
13
0
2
11
10
9
0
0.5
1
1.5
Surface Sulfate Concentration in pgtm
3
2
40
5 Mis
MIS
6
35
.230
25
0
0
20
0
0.5
1.5
1
Surfam Sulfate Concentration in pg/M
3
2
Figure 8: Cloud effective droplet radius (re,I) and total optical depth (r) plotted
as a function of sulfate concentration at the lowest elevation (1000 hPa). Trends
are plotted for surface wind speeds of 5-7 m/s and equilibrium is achieved under
0
WTG mode with SST set constant at 26 C.
44
15
6 Mn
WTG
7 M, WTG
6 Mn, RCE
7 nVS. RCE
10-
0
-5
-10
0
0.5
1
1.5
Surface Sulfate Concentration in pg/m
2
3
Figure 9: Difference between longwave and shortwave flux (W/m 2 ) as a function
of 1000 hPa sulfate concentration (self-generated profiles) for 6 and 7 m/s, in
both RCE and WTG conditions. Note that longwave flux is about 5 W/m 2
greater than shortwave flux for the runs in WTG mode.
3.3
Impact on TC intensity and cyclogenesis
Using the single-column model as a method to determine predicted TC behavior,
we assess the impact of aerosols on TCs by using theoretical formulations for
a cyclone's maximum potential intensity and genesis potential. Because of the
one-dimensional nature of the model and the simplified formulation of aerosol
parameterizations, it is not appropriate in this case to simulate actual hurricane
events and tracks, and follow the evolution of their properties over time. We
discuss results for potential intensity and genesis potential based on the WTG
runs with interactive surface temperature, using the surface wind speeds of 5-7
m/s.
As discussed in Chapter 1, TCs are driven by enthalpy flux between the sea and
the land. The flux of momentum into the sea and the flux of enthalpy from the
sea are usually quantified in the following forms:
F, = -CDP
45
V
IV
27
26.5
26
25.5
25
24
0
10
20
30
40
50
60
70
80
90
100
Day of Run
Figure 10: SST trend for 100-day WTG run to equilibrium with a surface wind
speed of 5 m/s; note the two distinct equilibrium temperatures reached by
the runs. Displayed are curves for the 24 different self-generated concentration
profiles. SST was initialized at 26 *C and surface temperature is set to be
interactive.
46
TOA Shorho.w Fkix
25dB
TOA Lmnguav. Flux
263.
257
256
261
255
250
254
6
259
253
252
f-~.
I
P.
256
251
257
250
256
249
05
S
fx0
SurfaD@
1
15
0.5
2
Slft Concentration in pg m'
Poeiftion
250
-- u]
2.55
1
Temparareat
2
15
Surfam SuLte Concentraion
in
pgfm5
Laa t Vuiwca Lveil
zzI~.
25
2496
25
S24
24
94
24
92
24.9
2 4
235
2484
24 82
23
225
2 2'
5
05
SrIfmM
SWU9
1
I5
1
Concentrabon inpkOm
24
84
24
82
24 0'
2
2
Surface Suifff Conoentraton inpgm3
Figure 11: Environmental variables as a function of self-generated aerosol vertical concentration profiles (0-2.3 ug/m3 at lowest vertical level), WTG mode,
initialized SST at 26 'C, interactive surface temperature.
47
Fk = Ck p |V | (k* - k)
where V is some near-surface wind speed, p is the air density, k is the specific
enthalpy of air near the surface, and k* is the enthalpy of air in contact with the
ocean, assumed to be saturated with water vapor at ocean temperature.
The
vertically integrated dissipative heating of the atmospheric boundary layer can
be modeled as
D = CDP
3
Then, using the Carnot theorem definition, we obtain an equation for the net
production of mechanical energy in the cycle:
P
27r T=
T,
[CkP IV |(k* - k)+CDP
o
V
3
]rdr
where the integral is taken over the first leg of the Carnot cycle (reversible
isothermal expansion), T, is the sea surface temperature and To is the temperature of the cold source. The net energy dissipation then is
D = 27r
CDP
V 3 rdr
By making the assumption that the integrals of the mechanical energy production and energy dissipation equations are dominated by the values of their
integrands near the radius of maximum wind speed, we can equate the equations
as a conservation of energy statement to derive an approximate expression for
the maximum wind speed:
-
0k
V
2
O
Ts
TO
CD
(k*
k
One can deduce that hurricane strength is a function of the ratio between the
enthalpy and momentum transfer coefficients, where an increased ocean enthalpy transfer coefficient intensifies hurricanes; and of the difference between
sea surface temperature and cold source temperature, with greater difference
intensifying hurricanes. The leftmost term has outflow temperature rather than
inflow temperature in the denominator, to reflect the added contribution from
dissipative heating. The rightmost term, which is the difference between the
specifric enthalpy of air in the surface boundary and the saturated enthalpy of
air in contact with the ocean, represents a measure of thermodynamic equilibrium between the tropical ocean and atmosphere. The single-column model is
programmed to output V.
48
As discussed in Chapter 1, there has been disagreement regarding the variables
that impact hurricane genesis. However, the frequency of TCs has been elucidated by Gray (1984) to be dependent on sea surface temperatures, mid-level
tropospheric relative humidity, vertical wind shear, and low-level vorticity. Recent evidence from Emanuel (2008) has suggested that the dependence is more
appropriately based on saturation deficit than on RH. Emanuel (2010) presents
a formulation known as the genesis potential index (GPI):
GPI =1 r
- I MAX(V
- 35ms- 1 , 0) 2 (25ms- 1 + Vshear) 4
where qj is the absolute low-level vorticity and Vhear is the magnitude of the
wind shear between the lower and upper troposphere. The nondimensional
parameter
y represents the moist entropy deficit of the middle troposphere and
is given by
sb -sm
where sb, sm, and s* are the moist entropies of the boundary layer, middle
troposphere, and the saturation moist entropy of the sea surface. The moist
entropy is defined (Emanuel, 1994) as
s =c lnT RT- np+
Lug
T - Rqln H
where L, is the latent heat of vaporization, Rd and
R,
are the specific gas
constants for dry air and water vapor, and H is the relative humidity. In regions
susceptible to TCs, the atmosphere is approximately neutral to moist convection
and the lapse rate of the troposphere is nearly moist adiabatic so sb ~ s* where
s* is the saturation entropy of the troposphere above the boundary layer and
is approximately constant with height so it can be evaluated at any level in the
free/middle troposphere. As such
8~s* - sm
8b- s
and
The variability in the numerator of
s*
=
sm
*
x
X is controlled by changes in relative humid-
ity, while the denominator is proportional to surface evaporation at fixed surface
wind speed (Emanuel et al., 2008). In the single-column model, we can neglect
49
large-scale dynamics and thus the low level vorticity (r1) and the horizontal wind
shear. We now define the thermodynamic GPI as
GPITD = XAMAX(V, - 35ms- 1 , 0)2
Potential intensity (vp) decreases with increasing surface wind speed, with values
around 58-59 m/s for 6 m/s surface wind speed and 55-56 m/s for 7 m/s surface
wind speed . The saturation deficit (x) is fairly constant at slightly above 0.08
for 6 m/s surface wind speed and decreases slightly over time from 0.1 to 0.07
for 7 m/s surface wind speed. The thermodynamic component of the genesis
potential index (GPITD) increases with increasing surface wind speed.
At 6
2
range,
m/s surface wind speed, GPITD has values in the 15,000-15,700 m 2 /S
and at 7 m/s GPITD is in the 16,200-16,600 m 2 /S 2 range.
None of the relevant hurricane variables (vp,
X, GPITD) showed significant
variation with sulfate concentration. This is not surprising, given that the sea
surface temperature and entropy-weighted mean outflow temperature (T,, TO,
respectively) do not vary significantly with respect to sulfate concentrations in
the model runs, and difference between these temperatures has a major effect
on vp. As noted above, the numerator of
X (s * -sm) is controlled by changes
in relative humidity while the denominator (so * -s*) is proportional to surface
evaporation at fixed surface wind speed. Therefore the denominator changes
noticeably when the model is run with different surface wind speeds, but because
relative humidity does not change significantly with the aerosol profile used, the
numerator does not change as much. GPITD thus does not change significantly
with aerosol concentration, as it is a function of vp and
x,
which also do not
change much with the sulfates.
3.4
Summary
In this chapter, the single-column model is run to equilibrium, and environmental variables and hurricane characterization metrics are recorded. A scheme
for determining potential intensity (vp) and genesis potential index (GPITD) is
laid out. An expected cloud droplet radius and optical depth profile (decreasing
and increasing with increased aerosol concentration, respectively) is produced.
However, increased aerosol concentrations do not have a significant impact on
the large-scale environmental variables such as SST, precipitation, or LW and
50
PolInmi
int
Wo
57
56
0
0,5
Surfe
1
15
SAlMe Concnration inp
m3
Mos Enkro W-)
360
40
37S
35
370-
366
25
355
20
10
0e
Surfam
1
Sulft
1.5
2
CwnwrAroin In
0
1
05
m3
Surface
Gakwasfl Ddkf
15
SifAe Corotnuawn
2
in Pg m3
ON TO Compwont
1
O 1C4
7 /n
1.64
162
-
0 12
01
I's
004
002
152
0
S
05
1 C0n
is
Surface Sudabs Concenilration
inPgWMa
2
0
05
Sumface
I
Sulfa
Is
Cancentratn In
2
Pgim
Figure 12: Potential intensity (top), numerator and denominator of x (middle
left and right), saturation deficitX (bottom left), thermodynamic component of
GPI (bottom right) as a function of self-generated aerosol vertical concentration
profiles (0-2.3 ug/m 3 at lowest vertical level), WTG mode, initialized SST at
26 'C, interactive surface temperature.
51
SW fluxes. This result occurred because although the aerosols impact clouds
locally, there are not enough clouds produced by the model to have a large-scale
effect.
The WTG mode with a modified ocean flux was investigated in an effort to
produce more low-lying clouds and thus the impact of aerosols on large-scale
environmental variables. However, as indicated by temperature and cloud cover
change for alterations in ocean fluxes, the single-column model could not simultaneously produce accurate cloud cover at low altitudes and realistic SSTs.
A two-column model is recommended for such a goal in future studies. The
WTG mode was then examined for a predetermined, lowered SST. This study
also did not produce enough clouds for sulfate to have a significant impact, but
a predicted LW-SW imbalance with greater LW was produced.
In addition,
certain characteristics of WTG conditions (such as lowered precipitation and
SSTs) were observed.
52
4
Application to IGAC/SPARC Simulation
The objective of Chapter 4 is to apply the findings from Chapter 3 to determine
how historical variations in sulfate aerosol concentrations impact hurricane activity. The setup used is the same - the changes on cloud properties and key
environmental parameters are assessed, followed by the effect on TC parameters. Discussion in the context of sulfate sources follows. Finally, an assessment
of the variability due to sulfate forcing in the results is discussed.
4.1
Changes to Environmental Conditions
re,I decreases for the time period 1850-2000 for both surface wind speeds studied.
At 6 m/s, re,, decreases from 10.4 pum to 9.2pm. At 7 m/s, re,, decreases from
10.6gm to around 9.5pm. At both speeds, minima occur around 1960-70 and
the re,i increases following that time period. At a surface wind speed of 6 m/s,
the cloud optical depth (r) is around 26-27 pre-1900, then increases to around
37 in 1960 and levels off thereafter. At surface wind speed of 7 m/s, T has values
around 24-25 up until the mid-1900s, peaks around 35 in 1960, then decreases
sharply thereafter.
As indicated by Figure 2, sulface concentrations show a general upward trend
with time as predicted.
The concentration increases are particularly notice-
able for the 1940-70 time period, and concentrations decrease again post-1970
corresponding closely with the trends following the 1963 Clean Air Act. The
behavior of re, correlates reasonably well with this trajectory. The behavior
of -r correlates strongly with the sulfate time trends for surface wind speed of
7 m/s though not for the surface wind speed of 6 m/s. There is a decrease in
T post-1960 for 6 m/s, but this decrease is insignificant. In general, however,
cloud properties are noticeably altered by changes in sulfate concentrations over
time.
The TOA SW flux hovers around 253-254 W/m 2 at both 6 m/s and 7 m/s
surface wind speeds. The TOA LW flux is around the 258-260 range for both
surface wind speeds. Sea surface temperature is around 24.95 'C at 6 m/s and
around 24.84
0C
at 7 m/s. Precipitation is around 2.34-2.37 mm/day at 6 m/s
53
10.8
10.6
10.4
3
10.2
10
*
9.8
9.6
9.4
9.2
9
'
0.0
1850
20
1950
1900
00
Year
L---40
E,
35
25
20L1850
1950
1900
2000
Year
Figure 13: Average cloud effective liquid droplet radius (re,I) and verticallysummed cloud optical thickness (r) as a function of vertical sulfate concentration
profiles decadally averaged over the hurricane MDR and years 1850-2000.
54
15
6 mevs, WTG
7 M/3, WTG
6 at&, RCE
7 Wus, WrG-
10 -
E-50.-
-10
1850
1900
1950
2000
Year
Figure 14: Difference between longwave and shortwave flux (W/m 2) as a function of surface sulfate concentration (SPARC simulation) for 6 and 7 m/s, in
2
both RCE and WTG conditions. Note that longwave flux is about 5 W/m
greater than shortwave flux, for the runs in WTG mode.
and 2.4-2.43 mm/day at 7 m/s. In general, the environmental variables vary
insignificantly over time, or in relation to the changes in sulfate concentration
over time. As we observed in Chapter for the self-generated sulfate concentration
profiles, a noticeable flux imbalance of about 5 W/m 2 between the TOA LW
and SW flux develops for WTG runs using the SPARC simulations in which the
LW flux is greater.
4.2
Impact on TC Intensity and Cyclogenesis
Potential intensity (vp) is within the rough range of 58-59 m/s for surface wind
speed of 6 m/s and 55-56 m/s for surface wind speed of 7 m/s. There is no
noticeable trend over time, but vp decreases with increased surface wind speed.
For a surface wind speed of 6 m/s, the saturation deficit X is within the 0.07 range
until around 1950, then increases and peaks around 0.09. For a surface wind
speed of 7 m/s,
x
is around 0.1 then decreases over time, with a particularly
sharp drop post-1950 to around 0.04 in 2000. Because GPITD is related to a
55
TA Sherwev. Fux
" Lmwave Flux
263
256
262
-
2555
255
261
254.5
256
2525
25.
5
1850
256
190
190
Year
00
250
5650
20
950
25M
Yer
Pn~~o
2,5
100
250
To
erure at Lowest Vwftm Level
25
-
245
-
251
24
235
249.
2.3
24.85
229
1850
1900
1950
2000
1950
1900
1V0
20M2
Year
Year
Figure 15: Environmental variables as a function of year (IGAC/SPARC simulation data for sulfates), WTG mode, SST initialized at 26 0C, interactive
surface temperature.
56
factor of
x- 4 /3
it shows a reciprocal trend compared to
x
for both surface wind
speeds.
Overall, vP does not change significantly with changes in sulfate concentrations.
However, there is a noticeable increasing trend in GPITD for both surface wind
speeds studied.
4.3
Error Bars for Aerosol Impact
This study presents an approximate representation of the first AIE of sulfate
aerosols. The RCE model is one-dimensional and does not have the capacity
to execute cloud-resolving or directly simulate hurricanes (rather, theoretical
hurricane metrics are inferred from the equilibrium conditions). Therefore, the
model is capable of generally representing theoretical behavior but does not
physically simulate events. In evaluating potential sources of error, I eliminate
the uncertainties or lack of resolution that characterize the RCE model and
focus instead on the uncertainties in the sulfate first AIE scheme used. Potential
margins in the error may originate from a couple sources:
- According to Twomey (1977), aerosols can increase both the optical thickness
and the absorption coefficient. The latter effect actually decreases the albedo of
clouds and dominates the optical thickness effect for thicker clouds. However,
the understanding of absorption coefficient effect is unknown and most literature
suggests the effect on optical thickness is dominant, which is the cloud optical
property addressed in the Quaas parameterization.
- The sulfate aerosol concentration input has been averaged over the hurricane
main development region. Instead, a range of concentrations could be possible
depending on the location at which the hurricane is initiated, so the variability
of sulfate concentrations at each vertical level and time is worth investigation.
- The final sources of uncertainty lie in the parameterization of number concentration from sulfate aerosol concentrations used by Quaas et al. (2004). The
number concentration scheme was derived from Boucher and Lohmann (1995)
and determined using best fits of the relationship between logarithms of number
concentration and sulfate concentration in three different cloud environments.
No explicit values for the potential error in this relationship were listed in the
study, but the relationship is not perfectly correlated and thus the Nd scheme
contains variability as well.
57
0
S5
55
105
85
1850
Yew
MVA4 E5oIy 11-$)
MtoW Elropy Se-)
366.
38
315
42
352
26
24
1850
350
1850
201 0r
100
lo
100
190
2M
Yew
Yew
2
Sawnfian DdOAd
GPO
TD Component
0 11
0.09
007
7'
N
\
14
12
065
041
1850
190
10
1850
2000
1950
1585
20W
Year
Yew
Figure 16: Potential intensity (top), numerator and denominator of x (middle
left and right), saturation deficitX (bottom left), thermodynamic component of
GPI (bottom right) as a function of year (IGAC/SPARC simulation data for
sulfates), WTG mode, SST initialized at 26 0C, interactive surface temperature.
58
Centered Year
1000
800
600
400
200
60
1855
1865
1875
1885
1895
1905
1915
1925
1935
1945
1955
1965
1975
1985
1995
2005
0.2184
0.1989
0.1796
0.1795
0.3565
0.5296
0.4687
0.3874
0.3610
0.4286
0.7078
0.7820
0.9756
1.210
0.9783
1.0715
0.0257
0.0423
0.0374
0.0856
0.0972
0.0442
0.0925
0.1950
0.0462
0.1238
0.0451
0.0197
0.0611
0.1474
0.1578
0.1713
0.1095
0.0951
0.0968
0.0811
0.0856
0.1126
0.1084
0.1256
0.1238
0.1431
0.1419
0.1741
0.2121
0.2260
0.2035
0.1611
0.0037
0.0026
0.0031
0.0026
0.0052
0.0041
0.0044
0.0096
0.0061
0.0049
0.0090
0.0138
0.0156
0.0195
0.0106
0.0170
0.0012
0.0010
0.0010
0.0012
0.0011
0.0008
0.0017
0.00194
0.0010
0.0015
0.0027
0.0039
0.0043
0.0041
0.0019
0.0027
6.32e-5
7.44e-5
7.28e-5
6.72e-5
5.55e-5
7.27e-5
5.08e-5
4.08e-5
2.86e-5
1.58e-5
5.75e-5
1.13e-4
1.65e-4
3.20e-4
7.84e-4
8.43e-4
Table 6: Standard deviation of aerosol concentrations (ptg/m 3 ) for each decade
and selected pressure levels (hPa)
In this study, I focus on the potential error due to the sulfate aerosol concentration input. I first calculate the standard deviations in the sulfate concentrations
over the MDR. As noted in Chapter 2, aerosol concentrations are averaged over
5-20 N, 7-20 W, and August-October. The standard deviation is calculated per
time-altitude pairing and for the values over this zonal, meridional, and seasonal
range. Then, I calculate the standard deviation as a percentage of the mean to
assess the proportional variability over each decade and height.
We can observe that not only are sulfate concentrations higher at lower altitudes, but the standard deviation as a percentage of the mean is also higher
at lower altitudes. In addition, standard deviation as a proportion of the mean
increases over time. A large variance in the sulfate concentrations at a given altitude and time only significantly impacts cloud properties given sufficient cloud
liquid water content (refer to Section 2.3).
Therefore it is necessary to carry
out the variance study from sulfate concentrations to cloud properties (Nd, re,,
-r). Therefore we then convert sulfate concentrations to expected number con-
59
Centered Year_[1000
800
1 600
1 400
1 200
60
1855
1865
1875
1885
1895
25.51
23.63
20.97
20.69
38.91
4.82
8.01
6.65
14.80
15.82
69.33
56.55
56.41
45.44
45.44
16.84
11.49
13.71
11.88
21.18
28.89
23.37
23.04
25.69
24.53
4.27
5.17
4.59
4.36
3.55
1905
1915
49.15
41.98
6.02
12.75
51.66
48.77
14.57
15.14
15.27
31.99
4.43
3.11
1925
1935
31.26
28.29
23.38
5.48
52.23
51.03
31.97
19.40
31.55
17.00
2.97
1.72
1945
32.30
12.73
52.55
15.21
24.30
0.88
1955
1965
47.38
43.45
4.07
1.37
39.38
38.09
21.54
27.67
34.18
38.51
2.86
4.65
1975
1985
1995
2005
44.90
57.05
52.82
58.48
3.56
8.82
10.96
11.94
41.37
40.11
39.93
30.75
25.91
31.92
17.55
27.40
40.44
35.66
17.61
24.02
6.10
9.45
18.82
17.71
Table 7: Percentage (%) of standard deviation as value of average sulfate concentration at a given pressure (hPa), displayed for selected pressure levels.
centrations (Nd) using the scheme laid out in Section 2.3. Then we determine
the averaged in-cloud condensate mixing ratio averaged over runs for all sixteen
decades after equilibrium is achieved for surface wind speeds of 6 and 7 m/s
(model runs to RCE).
Finally, we calculate total vertically-summed
Tliq
, the liquid water cloud con-
tribution to the optical depth, for each run. By rearranging the equations from
Section 2.3, we obtain the following equation:
7-11
-
3 1000 [(4/3)irpH2 01/3 /Xp 2/3
q, N
2 1.1
g
Pair
1/3
where PH2 0 is the density of water, Pair is the density of air, g is the gravitational
constant,
AP is the pressure difference between the current and above vertical
level, qj is the mixing ratio, and
Nd
is the number concentration. We can observe
that qj has double the impact of Nd. Because the variability of the mixing ratio
over time has been eliminated, our resulting trend for
r mimics more closely the
trend for sulfate concentrations over time, as evidenced by Figure 18.
Also note that the peak cloud liquid water content tends to occur at pressure
60
U
100
200
300
CL 400
I500
600
700
800900
1000
132
134
136
138
140
142
144
146
In-doud condensate mixing ratio (kg/kg)
148
150
Figure 17: Average in-cloud condensate mixing ratio (kg/kg) as a function of
altitude, averaged over the sixteen decades of the IGAC/SPARC simulation and
results at 6-7 m/s surface wind speed reaching RCE.
levels of around 100 hPa (see Figure 17) while the highest sulfate concentrations tend to occur near the surface (900-1000 hPa). Nevertheless, there is a
substantial level of cloud liquid water content near the surface as well, so that
the greater sulfate concentration changes near the surface can have an effect
on
Tiq.
The impact of the variability in sulfate concentrations is evident in
Figure 18 and Figure 19, though the impact of the in-cloud mixing ratio will
add a further degree of variability if examined in more depth. However, without
sufficient cloud cover, the increase in r will not lead to substantial change in
ambient conditions.
61
120
mom
115
110.0
0)
105
32
100
95
90
1850
1950
1900
2000
Year
Figure 18: Total Tr jq (liquid water contribution) as a function of year. The
vertical sulfate concentration profiles used are mean concentrations using the
IGAC/SPARC simulations, and profiles one standard deviation above or below
the mean. The cloud condensate mixing ratio (for each layer and year) used is
the same as from Figure 17.
5.5
5
4.5
4
o 3.5
S3
2.5F
2
1.51
18E i
1950
1900
2000
Year
Figure 19: Standard deviation in total r as a function of year, with variance
originating from the vertical sulfate concentrations.
62
5
Conclusions and Future Work
The study used a single-column radiative convective model and an aerosol-cloud
parameterization scheme to demonstrate that sulfate aerosols have a significant
effect on cloud properties. Specifically, the aerosols increase cloud droplet number concentration, decrease the effective cloud droplet radius, and increase total
optical depth. However, the single-column model was unable to simultaneously
produce both sufficient cloud cover and appropriate ambient conditions using
several RCE and WTG setups, so the global effect of the sulfates is insignificant. Several proposed suggestions for further study of the subject matter are
outlined below:
5.1
Heterogeneous Nucleation Schemes
A study of spatiotemporal trends and size distributions of aerosols over the
marine boundary layer at Barbados in the Caribbean (therefore part of the
MDR) suggests that African dust may impact the size distribution of non-seasalt sulfate aerosol in the marine boundary layer (Li-Jones and Prospero, 1998).
SO 2 from European pollutants can heterogeneously react with the suspended
dust over North Africa, resulting in larger sulfate particles during dust events.
On days when dust and pollution concentrations were low, the primary sulfate
source was ascribed to DMS. This finding implies that sulfate aerosol might
be a weaker radiative forcing agent than otherwise implied when coupled with
dust events, due to the removal of sulfate from the radiatively more effective
submicrometer size range. While the study was conducted during a single month
(April-May 1994) that included four dust events and the timeframe therefore
limits the strength of the evidence, previous studies of chemical and physical
characteristics of African dust events suggest that the dust properties vary little
from event to event. This study may also have implications for other regions
where dust can play an important role in aerosol chemistry, such as much of
Asia and the Indian subcontinent.
63
The impact of dust on sulfates is dependent on dust composition and thus
the origins of the dust storms, which were detailed in the Li-Jones and Prospero
(1998) study. In early April, a dust stormed resulted from a convergence formed
between a strong high-pressure system from the northeast North Atlantic to the
northwest African coast and a low-pressure system from the Mediterreanean
to Central Europe.
The convergence resulted in transport of European air
and associated pollutants into north Africa. This process generated large dust
storms, and this mixture was carried across the North Atlantic to the Caribbean
region. Later in April, a dust storm was formed out of the convergence of airflow
associated a high-pressure system off the northwest coast of Africa and a lowpressure system formed in central Africa. During low-dust days, transport to
Barbados is usually instead influenced by a high-pressure system off the east
coast of the United States.
Li-Jones and Prospero (1998) suggest that a high proportion of coarse sulfate
particles could be the result of SO 2 reaction with ozone and calcium-rich dust
particles:
SO 2 +03
SO-+O2
4
H2 S04 + CaCO3 -+ CaSO 4 + H2 0 + CO 2
In addition, Manktelow et al. (2010) demonstrated that dust enhanced the mass
concentration of coarse sulfate (DP > 1.0ptm) by more than an order of magnitude but total sulfate concentrations increase by less than 2% due to decreases
in fine sulfate.
The decrease in fine sulfate could occur through coagulation
scavenging of small particles or removal of H 2 SO4 vapor leading to reduced
condensation on existing aerosol and a reduction in new particle formation.
The Goddard Institute for Space Studies also studied heterogeneous sulfate
formation at mineral dust surfaces using the institute's climate model (Bauer
and Koch, 2005). Approximately 40-45% of mineral particles mixes internally
with sulfate during their transport in the troposphere. The study found a large
loss of SO 2 (about 32%) through dust surface reactions, which provides further
evidence that dust-sulfate interactions may decrease sulfate's impact on the first
AIE. However, Liao et al. (2003) produced only a 5% loss of SO 2 , suggesting
that the mechanisms concerning sulfate-dust surface reactions may be poorly
understood.
Indeed, heterogeneous sulfate formation is highly dependent on
dust uptake rate of sulfate and mineral dust size distributions, properties that
64
are not very well-known. Surface saturation and solubility are other factors that
may have a potential impact on results. Finally, nitrate coatings also influence
dust solubility and may have a larger impact on Saharan dust than sulfate
coatings depending on the origins of the particles (nitrate precursors tend to
be released from both industrial and agricultural areas, while sulfate precursors
are predominantly released in industrial regions).
With possible removal of sulfate from the sub-micrometer size range due to
dust-sulfate reactions, accurate modeling of sulfate impacts on clouds must include schemes for sulfate-dust interactions for regions such as the tropical Atlantic north of the equator. Such a scheme also necessitates inclusion of sulfate
size distributions (usually lognormal). The heterogeneous chemistry of sulfate
and dust (along with potentially nitrate) could significantly impact the cloud
droplet number concentration parameterization, but an accurate update on a
parameterization would not be feasible until uncertainties such as the discrepancies between the Bauer and Koch (2005) and Liao et al. (2003) studies are
smoothed. Dust-sulfate interactions will also affect the parameterizations for
deposition and condensation ice nucleation.
In addition, dust has a larger impact on gas phase H 2 SO 4 than predicted for
H 2 SO 4 formation from SO 2 oxidation by OH is guided by the following
equations:
SO 2 ..
SO 2 + OH +-* HOSO 2 + M
HOSO
SO 3
2
+ 02 -4 HO2 + SO 3
+ 2H20
-* H 2 S04-H2 0
where M is an appropriate catalyst that could be dust.
The finding is that
sulfate associated with dust during the dust storm originated primarily from
uptake of H 2 SO 4 and is sufficient to explain observed coarse sulfate.
Dust
severely depletes gas phase H 2 SO 4 concentrations in dusty regions (Lee et al.,
2009).
65
5.2
The Ammonia-Nitric Acid-Sulfuric Acid-Water
System
Thermodynamics are different for atmospheric aerosol systems (consisting of
a mixture of chemical subcomponents) than for the aerosol components individually. Because nitric acid and ammonia may also likely be components
of anthropogenic emissions, studying the effect of their interactions would be
more accurate than studying sulfate alone.
The system of interest (Seinfeld
and Pandis, 2006) consists of components in the gas phase (NH 3 HNO 3 H 2 SO4
H20), aqueous phase (NH 4+ H+ HS0 4 2 - S0 42 N0 3 ~ H20) and solid phase
(ionic compounds formed from aqueous phase components). In such a system,
(NH 4 ) 2 SO 4 is the preferred form of sulfate.
Therefore, in an ammonia-poor
environment, there is insufficient NH 3 to neutralize the available sulfate and
the aerosol phase will be acidic, with sufficient sulfate remaining in aerosol or
aqueous phase. The sulfate will tend to drive the nitrate to gas phase. In an
ammonia-rich environment, the sulfate aerosol phase (sulfate in aqueous phase)
will be neutralized to a large extent. Parameterization this system would be particularly relevant in more holistically examining the impact of the 1963 Clean
Air Act.
5.3
Parameterization of Aerosol Impact on Cold
and Mixed-Phase Clouds
As mentioned in Section 2.3, an ideal cloud parameterization model includes
a scheme for ice crystal-aerosol interactions, though sulfates are generally considered to be poor ice nuclei. While understanding of cold cloud processes is
relatively limited compared to understanding of warm clouds, a couple ice parameterization schemes exist that could be coupled to the single-column model
with some modifications (Liu and Penner, 2005; Lohmann et al., 2007; DeMott
et al., 2010). Sulfates generally do not have significant effect on nucleation of
cold clouds, but they could have an indirect effect on ice crystal formation because they could nucleate water droplets that then freeze under the appropriate
conditions. An inference about sulfate effects on ice crystals could be made
based on this information, that can guide the formation of a cold cloud parameterization that does not involve other aerosols types. If aerosols that are strong
ice nuclei (i.e. dust, soot) are also included in the model, this would make study
66
of ice nucleation more meaningful. More importantly, because ice nucleation is
poorly understood in comparison to liquid droplet nucleation, these extended
parameterizations will ideally be coupled with research on ice nucleation processes.
5.4
Two-Column Study of Radiative Convective
Model
As mentioned in Section 3.2, a two-column study of the single-column model
might allow us to reproduce low-lying stratocumulus without an unrealistically
high negative feedback, which might produce enough clouds to produce a noticeable aerosol indirect effect.
In the two-column model, both columns are
interactive and low-lying clouds will form in one column. Advection between
the cold clouds and warm, low-lying clouds would prevent the unrealistic negative feedback formed in the single-column model under WTG (cloud fraction of
1.00 at several of the lower altitudes).
67
68
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