Modeling aerosol influence on clouds in
CAM-Oslo
By Trude Storelvmo
Department of Geosciences
Section of Meteorology and Oceanography
University of Oslo
2006
From Both Sides
I’ve looked at clouds from both sides now
From up and down and still somehow
It’s cloud illusions I recall
I really don’t know clouds at all
Joni Mitchell
Contents
Acknowledgements……………………………………………………..
1. General introduction
2. On atmospheric aerosols
2.1 Aerosol types…………………………………………………
2.2 Aerosol direct effect………………………………………….
3. Aerosol indirect effect – aerosol influence on clouds
3.1 Cloud microphysical and optical properties………………….
3.2 Twomey effect………………………………………………..
3.3 Albrecht effect………………………………………………..
3.4 Semi-direct effect……………………………………………..
3.5 Effect on ice clouds…………………………………………..
4. Modeling aerosol influence on warm clouds
5. CAM-Oslo – the modeling tool in this study
6. Summary of papers
6.1 Paper I………………………………………………………. ..
6.2 Paper II………………………………………………………..
6.3 Paper III……………………………………………………….
7. Future work – aerosol influence on cold clouds
References………………………………………………………………
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Acknowledgements
The work presented has been carried out at the Department of Geosciences, Section of
Meteorology and Oceanography, University of Oslo. Funding was provided by the Norwegian
Research Council through the COMBINE project (grant no. 155968/S30). Furthermore, this work
has received support of the Norwegian Research Council’s program for Supercomputing through
a grant of computer time.
I am deeply grateful to my supervisor Jón Egill Kristjánsson for being my mentor since I showed
up at the University of Oslo in 1999. His enthusiasm for climate research has inspired me since
then. I am also thankful to Jón Egill for introducing me to some of the most brilliant scientists
within our field. I would especially like to mention Professor Steve Ghan at the Pacific Northwest
National Laboratory (PNNL), Professor Joyce Penner at University of Michigan and Professor
Ulrike Lohmann at ETH-Zürich. I’m also thankful to the whole atmospheric physics group at
ETH-Zürich for hosting me for three months during winter 2006. The visit was truly inspiring to
me.
Special thanks go to my sister and office mate, Line Gulstad, who has always been there for me.
To the rest of my colleagues at the department of Geosciences, section for meteorology and
oceanography (MetOs), I am grateful for a stimulating work environment and for good company
at countless lunches and coffee-breaks.
I’m obliged to all my co-authors of the three papers presented in this thesis, in particular Alf
Kirkevåg and Øyvind Seland at MetOs.
I am forever thankful to my parents, Edel and Viggo Storelvmo; mostly for raising me to believe
that everything can be accomplished if one tries hard enough, but also for introducing me to the
amazing world of physics. Mom and dad, thanks for always being there when I need you the
most!
Last, but certainly not least, thanks to Thomas Leirvik, for being an amazing boyfriend, soulmate and source of strength.
2
Chapter 1
General Introduction
Human activities now occur on a scale that is starting to interfere with natural systems such as
the global climate. This is the opening sentence in the introduction to the Intergovernmental
Panel of Climate Change (IPCC) and a statement that fewer and fewer would object to.
However, the hypotheses on how mankind is influencing the climate system are numerous and
often controversial. Some are well-known and largely accepted by the scientific community.
An example is the climatic effect of anthropogenic emissions of greenhouse gases. Atmospheric
concentrations of greenhouse gases like CO2 and CH4 have been increasing significantly over the
last two decades, resulting in an increase in atmospheric absorption of longwave radiation.
Consequently, more energy is trapped in the earth-atmosphere system and a temperature rise at
the earth’s surface and in the lower troposphere should be expected. Rising temperatures have
indeed been observed at surface meteorological stations around the globe, the global average
temperature rise since the late 19th century amounting to approximately 0.6oC according to the
Third Assessment Report of IPCC (Folland et al., 2001, Figure 1). Since the report was
published, temperatures have continued to increase around the globe
(http://data.giss.nasa.gov/gistemp/graphs/) and the corresponding number in the Fourth
Assessment Report of IPCC is therefore expected to be somewhat higher. Over the last decades
monitoring atmospheric temperatures from space has also been possible. The significantly
weaker temperature trends observed from satellite compared to surface observations were
apparently a paradox until recently, when improved retrieval algorithms merged measurements
from surface and from space (Fu et al., 2004). Currently, there is a consensus in the scientific
community on the qualitative effect of increased GHG levels in the atmosphere. However,
quantifications of future warming are difficult to make, partly because of the uncertainty
connected to tropospheric aerosols (Hansen et al., 2002). The global average aerosol burden has
increased substantially since preindustrial times due to human activities like fossil fuel and
biomass burning and changes in land use (Charlson, 1998). This increase is assumed to have both
direct and indirect effects on the earth’s energy balance; direct effects through particle scattering
and absorption of solar and thermal radiation (Section 2.2) and indirect effects through their
interactions with clouds (Chapter 3). Most studies of these effects conclude that both represent a
net cooling of the earth-atmosphere system, although estimates vary by more than an order of
magnitude. It has been suggested that the temporary global cooling observed from 1945 to 1975
can be partly explained by human activities increasing global aerosol burdens in this period. In
the 1970s aerosol emissions were gradually reduced as the connection between air quality and
human health became evident (mainly in Europe and North America). Due to this awareness and
the significantly longer lifetime of greenhouse gases compared to aerosols, aerosol cooling is
expected to decline relative to greenhouse gas warming in the future (Andreae et al., 2005)
Different aerosol types exert different effects on climate, depending on their chemical, physical
and optical properties. Section 2.1 provides a short description of these properties for different
aerosol species.
3
The importance of understanding aerosol influence on climate, both directly and through their
interaction with clouds, was recently pointed out by Kerr (2005). The author singled out aerosols
and clouds as the biggest source of uncertainty in model predictions of future climate. This topic
was addressed in a special issue of Science, as one of 125 scientific puzzles that are driving
scientific research. This is the challenge that today’s aerosol and cloud scientists face - one can
hardly imagine a better motivation.
Figure 1: Variations of the Earth’s temperature over the last 140 years and the last millennium.
Upper panel: Earth’s surface temperature is shown year by year (bars) and approximately decade
by decade (red line). Lower panel: Year by year (blue curve) and 50-year average (red curve)
variations in surface temperature of the northern hemisphere for the past 1000 years. The grey
region corresponds to the 95% confidence range (Folland et al. 2001).
4
Chapter 2
On atmospheric aerosols
2.1 Aerosols types
In the following subsection the main aerosol species found in the atmosphere are described
briefly. Typically, atmospheric aerosols consist of two or more aerosol species (i.e. internal
mixtures), but “pure” aerosols are also found, especially when newly formed or emitted. While
emission/formation pathways differ significantly for the different species, their atmospheric
removal processes are more or less similar: gravitational settling, dry deposition and wet
deposition. Additionally, water soluble aerosols may be lost from the interstitial aerosol mass by
acting as cloud condensation nuclei (CCN) for cloud droplet activation.
Sea salt: Sea salt aerosols are generated in maritime environments through evaporation of sea
spray produced by bursting bubbles or wind-induced wave-breaking (Blanchard and Woodcock,
1980). Hence, sea salt production is highly dependent on the surface wind speed. Typical
atmospheric particle concentrations are normally in the range of 100 – 300 cm-3 (Seinfeld and
Pandis, 1997), and the typical variation of number concentration with particle size is best
described by three lognormal modes. The smallest mode, the Aitken mode (diameter, D < 0.1µm),
dominates the sea salt number concentration, while the contribution to the total sea salt mass is
relatively small. The accumulation mode particles are of intermediate sizes (0.1 < D < 0.6µm),
while the coarse mode particles typically have diameters larger than approximately 0.6 µm and
represent most of the sea salt mass although number concentrations are low.
Sea salt aerosols are highly water soluble and as such represent excellent cloud condensation
nuclei (CCN) in marine environments. Sea salt aerosols also have a direct effect on earth’s
radiative balance through scattering of solar radiation. Hence, particularly in areas associated
with high wind speeds, sea salt aerosols may cause local cooling either directly or through their
influence on clouds.
Mineral dust: Dust aerosols are generated from weathering in source regions when the wind
speed exceeds a certain threshold, which is dependent on surface roughness, soil moisture and
grain size. The main source regions are the earth’s desert areas, Sahara in particular. However,
semi-arid regions with sparse vegetation are also potential dust sources. Dust emissions may be
influenced by human activity through vegetation reduction or soil surface disturbances. As for
sea salt aerosols, the size distribution is typically tri-modal. Dust aerosols are mostly insoluble,
although their ability to act as CCN depends on the mineralogy as discussed in Paper I. However,
dust aerosols are efficient ice nuclei (IN) in the atmosphere (Diehl et al., 2006).
In contrast to e.g. sea salt particles, dust aerosols can not only scatter but also absorb solar
radiation. However, the mineral dust net solar forcing at the TOA is mainly negative, depending
on surface albedo and cloudiness. Dust aerosols are strong absorbers in the infrared part of the
spectrum, leading to a positive forcing at the top of the atmosphere. Although the globally
averaged solar and infrared forcings tend to cancel each other at the TOA, local and regional
forcings may be significant. Tegen et al. (1996) estimated that up to 50% of the atmospheric dust
loading originates from human activity through disturbances in surface vegetation. Unfortunately,
5
large uncertainties in key dust properties make quantifications of the radiative forcing associated
with this anthropogenic dust loading problematic (Sokolik et al., 2001).
Black carbon: Black carbon (BC), often also called elemental carbon, is emitted to the
atmosphere through incomplete combustion of fossil fuel and biomass. Consequently, a major
fraction of the black carbon burden is of anthropogenic origin. The ambient BC size distribution
in polluted areas is typically bimodal with peaks roughly for particle diameters of 0.l and 1.0µm
(Seinfeld and Pandis, 1997). Black carbon aerosols are efficient light absorbers, and as such have
a significant impact on the vertical temperature profile in fossil fuel or biomass burning areas.
However, BC aerosols are water insoluble and hence not suitable as CCN. BC may still affect
clouds through their effect on the temperature profile. This effect, known as the “semi-direct
effect” of aerosols on clouds, will be discussed more closely in section 3.4. Additionally, BC
aerosols are found to be efficient ice nuclei (IN) (Gorbunov et al., 2001) and may therefore
contribute in heterogeneous freezing processes of cloud droplets. These processes will be
described in Section 3.5 and Chapter 7.
Organic aerosols: Organic aerosols are usually divided into primary and secondary organic
aerosols. Primary organic aerosols are typically emitted to the atmosphere through pyrogenic
processes (e.g. fossil fuel burning, biomass burning, domestic burning and agricultural waste
burning). Secondary organic aerosols (SOA) are produced chemically in the atmosphere. SOA
gaseous precursors are hydrocarbons of both natural and anthropogenic origins, which undergo
gas-to-particle conversions to form sub-micron particles in the atmosphere. Organic aerosol size
distributions are not well understood, as observations are limited and often only report total mass
of particles smaller than a certain size (Kanakidou et al, 2004). Organic carbon aerosols tend to
be mainly scattering. However, quantifiying the direct radiative effect of organic aerosols is
challenging due to their complex chemical composition. The ability of organic aerosols to act as
CCN is difficult to quantify, as they are typically complex mixtures of hundreds of different
organic compounds with different hygroscopic properties (Kanakidou et al., 2004). Several
laboratory studies have investigated the ability of various organic compounds to act as CCN.
Bilde and Svenningson (2004) studied adipic acid (AA), a moderately soluble organic frequently
found in the atmosphere. They found that low concentrations of either a soluble species or a
surface active species dramatically increased the ability of AA to become activated. Lohmann et
al. (2004) found AA coated with ammonium sulfate to be a much more efficient CCN than an
insoluble dust particle with the same coating.
Sulfate: The main source of atmospheric sulfate aerosol particles is through oxidation of sulfurbearing precursor gases. Naturally occurring precursor gases are dimethyl sulfide (DMS)
produced by the marine flora and volcanic emissions of sulfur dioxide (SO2). Anthropogenic
sources of precursor gases are sulfur dioxide from fossil fuel burning and to a lesser extent from
biomass burning. The major oxidizing agents are OH, H2O2 and O3.
Sulfate particles are highly water soluble and serve as efficient CCN in the atmosphere, whereas
their ability to act as IN is poor. Sulfate aerosols are purely scattering aersosols (except from the
infrared part of the spectrum) with a relatively high backward relative to forward scattering.
Hence, model studies find that sulfate aerosols represent a cooling effect on the Earth-atmosphere
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system by increasing the albedo both directly and through their influence on liquid clouds (e.g.
Chuang et al., 1997 and Ghan et al., 2001)
Nitrate aerosols: Nitrate is the end product of a variety of reactions involving trace gases like
nitrogen oxides, volatile nitrogen-bearing acids and gaseous nitrates (d`Almeida et al., 1991).
Until recently, nitrate has not been considered in assessments of the radiative effect of aerosols.
Model studies carried out in recent years have resulted in a large spread in the direct radiative
effect of nitrate at the TOA. As nitrate aerosols are essentially scattering aerosols, all the studies
have concluded that they represent a cooling of the Earth-atmosphere system. However, estimates
vary by more than an order of magnitude. Jacobson (2001) suggested a radiative forcing of -0.02
W/m2, while Adams et al. (2001) found a significantly stronger forcing of -0.22 W/m2. More
recent estimates include Liao and Seinfeld (2005) and Myhre and Grini (2006), who found
radiative forcings of -0.16 W/m2 and -0.02 W/m2, respectively. It seems clear that more model
estimates are required to narrow down the uncertainty connected to the direct radiative effect of
nitrate. Kulmala et al. (1998) suggested that condensation of nitric acid onto aerosol particles
potentially plays an important role for the aerosol indirect effect, as it may add water soluble
material to the particle surface and enhance hygroscopic growth. Hence, nitrate may represent
similar direct and indirect radiative forcings to that of sulfate, and might become relatively more
important in the coming decades due to the decline of sulfur emissions in Europe and North
America (Adams et al., 2001).
Biogenic aerosols: According to Jaenicke (2005), particles injected directly from the biosphere
constitute a major portion of atmospheric aerosols, while such particles have previously been
assumed to occur in insignificant concentrations (Penner et al., 2001). Primary biological aerosol
particles (PBAPs) include fur fibers, dandruff, skin fragments, plant fragments, pollen, spores,
bacteria, algae, fungi, viruses etc. PBAPs may potentially affect climate through their abilities to
act as cloud condensation nuclei or ice nuclei. While e.g. pollen grains are assumed to be
effective CCN, certain bacteria and marine plankton are suitable for ice nucleation.
Additionally, they may affect radiation balance directly through absorption, especially in the UVB region according to Hayers et al. (1998). Human activities may alter PBAP loadings through
e.g. land-use changes and population growth.
2.2 Aerosol direct effect on climate
Aerosols influence the Earth’s radiative balance through scattering and absorption of solar and
thermal infrared radiation. Although the aerosol direct effect is better understood than the indirect
effect, model estimates still vary and uncertainties are high. In the 2001 IPCC report, the level of
understanding was considered to be low for the direct radiative forcing from sulfate aerosols and
very low for other aerosol species. Since then, tremendous effort has been invested into this
problem by different scientific communities, an example being the Aerosol Model
Intercomparison Initiative (AeroCom, http://nansen.ipsl.jussieu.fr/AEROCOM/). Various
observations and results from a large number of models have been assembled to compare and
document the state of the art modeling of the global aerosol (Textor et al., 2005). In models, the
7
aerosol optical properties depend on the aerosol species, and are described by three parameters:
the extinction coefficient, the single scattering albedo and the asymmetry factor. The first
parameter determines the degree of interaction between the aerosol particle and the radiation, the
second determines the degree of absorption vs. scattering, and the third determines the degree of
forward vs. backward scattering. The three parameters are functions of wavelength, aerosol size
and refractive index. Schultz et al. (2006) present a model intercomparison on aerosol radiative
forcing including 9 different global models. For the model simulations, all models used the same
aerosol emissions for both present-day and preindustrial runs. The aerosol direct effect is
calculated in the models as the difference in net all-sky radiative forcing at the top of the
atmosphere (TOA) between present-day and preindustrial model runs. The average direct aerosol
forcing for the 9 models is -0.2 W/m2, with a standard deviation of +/- 0.2 W/m2. Uncertainties
are introduced by model differences in aerosol residence time, extinction coefficients and forcing
efficiencies. The latter is defined as the forcing per unit optical depth. This parameter introduces
uncertainties mainly because all-sky forcing estimates require proper representations of the cloud
field and the relative altitude placement of clouds and absorbing aerosols. Hence, a better
understanding of clouds is necessary to quantify both direct and indirect aerosol effects on
climate.
Chapter 3
Aerosol indirect effect – aerosol influence on clouds
3.1 Clouds – microphysical and optical properties
According to more than 20 years of satellite measurements by the International Satellite Cloud
Climatology Project (ISCCP), clouds cover about 2/3 of earth’s surface on average (Rossow and
Schiffer, 1999). Clouds have a cooling effect on the earth atmosphere system through reflection
of incoming solar radiation, and a warming effect contributing to the natural greenhouse effect.
Clouds represent extremely important regulators of the earth’s climate, their net effect being a
cooling due to a negative radiative signal of approximately 20 W/m2. However, not only their
direct radiative effects at the top of the atmosphere (TOA) are of interest from a climate point of
view. Clouds are central for various heterogeneous chemical processes, and strongly influence
the hydrological cycle and the dynamics of the atmosphere. Additionally, it is generally accepted
(e.g., Stocker et al., 2001) that clouds and cloud processes represent large sources of uncertainty
in future climate predictions.
As clouds play such an important role in the earth-atmosphere system, it is important to be able to
understand and describe them accurately. Throughout the history of cloud research, various
classifications of clouds have been used. It is common to differentiate between so called
convective clouds and stratiform clouds. One can also classify clouds according to the dominant
phase of the cloud condensate (ice or water). Other classifications are based on cloud height:
Convective clouds are formed by local ascent of warm buoyant air parcels in a
conditionally unstable environment. Typical updraft velocities are on the order of a few meters
per second, although significantly higher updraft speeds can occur.
8
Convective cloud lifetimes are typically short (from minutes to hours) and they are usually not
very extensive horizontally. The high vertical velocities associated with convective clouds
produce high supersaturations, and hence high water contents on the order of 1 g/m3. The strong
updrafts (and corresponding downdrafts) are also efficient transport mechanisms in the
atmosphere. Convective clouds are also called cumulus clouds, and are often vertically extensive.
Stratiform clouds are often referred to as layered clouds, produced by forced lifting of
stable air. Vertical velocities in stratiform clouds are much lower than those of convective clouds,
typically a few centimeters per second. As a consequence of the lower updraft velocities, lower
supersaturations and water contents are found in stratiform clouds compared to convective
clouds. However, they are horizontally more extensive and persist longer than convective clouds
(Wallace and Hobbs, 1977).
There are significant differences between clouds containing liquid water and those consisting
partly or entirely of ice. In particular, precipitation release mechanisms differ substantially, as ice
crystals are typically much larger than cloud droplets. Reasons for this are discussed further in
Section 3.5.
Clouds with cloud base below 3 km are often classified as low clouds, while clouds with cloud
bases above ~6km are termed high clouds. Clouds at these heights often contain ice and are
referred to as cirrus clouds. In between high and low clouds are a group called middle clouds.
Clouds affect the longwave radiation budget by absorbing incident radiation and re-emitting it at
a wavelength determined by their temperature, according to the Stefan-Boltzmann law. In
discussions of the longwave radiation transfer in the atmosphere it is commonly assumed that, in
localized portions, the atmosphere is in thermodynamic equilibrium, as well as being planeparallel. If we consider a cloud layer in such an atmosphere absorbing radiation emitted from the
surface, the reduction in longwave radiation emerging to space depends on the temperature of the
given cloud layer and its optical depth. The cloud optical depth indicates the depletion that a
beam of radiation experiences as a result of passing through the cloud layer, and increases with
increasing cloud water content (and secondarily with decreasing cloud particle size). Generally,
clouds absorb practically all incoming longwave radiation, even for relatively low water contents.
The larger the difference between the surface temperature and the cloud top temperature, the
stronger is the decrease in longwave radiation emerging to space. Consequently, high (i.e. cold)
clouds will tend to trap more energy in the earth-atmosphere system than low (i.e. warm) clouds.
However, their net effect is determined by the sum of their effect on the shortwave and longwave
radiation budget. Incoming solar radiation is efficiently reflected back to space by clouds. The
amount of solar radiation penetrating a cloud and reaching the surface is determined by the
optical depth of the given cloud for short wavelengths. The optical depth at short wavelengths is
determined by the water content and the size and the shape of the cloud particles. Clouds with
high water content and small particles scatter solar radiation more efficiently than clouds with
low water content and large particles. In addition, the amount of radiation reflected back to space
also depends on the degree of forward versus backward scattering (often given by a so called
asymmetry factor) for the various cloud particles. The asymmetry factor is determined by the size
and shape of the particles (Liou, 2002).
While low and middle clouds often have higher cloud albedos (i.e. reflect more solar radiation
back to space) than high clouds, one can conclude that clouds represent two competing effects on
the radiative balance of the earth-atmosphere system; high clouds tend to have a net warming
effect, while low clouds cool the system. Hence, if the amount of high versus low clouds were to
change in response to a warming climate, this could potentially represent an extremely strong
feedback mechanism (Hartmann, 1994).
9
3.2 Twomey effect
Twomey (1989) found that for clouds with comparable water contents, an increase in cloud
droplet number concentration (CDNC) due to increased CCN concentrations corresponds to a
decrease in cloud droplet effective sizes.
For a given LWP, smaller cloud droplets will lead to increased scattering by the cloud, and hence
an increase in the cloud albedo. This effect is known as the Twomey effect, the first indirect effect
or the radius effect. As shown by several satellite observation data sets (e.g. Han (1994), Han
(1998)) and in situ measurements (Wallace and Hobbs, 1977), continental CDNC are typically
higher than the maritime CDNC whereas cloud droplet effective radii are typically smaller. As
CCN concentrations are substantially higher over land than over oceans this is supporting the
mechanism behind the Twomey hypothesis (i.e. increased CCN concentrations yield increased
CDNC) but not necessarily confirming it. Since meteorological conditions are different over land
and ocean, this will influence the two parameters. So called “ship tracks” (Figure 2) represent
more convincing evidence for the hypothesis being real. In marine stratocumulus cloud decks,
more reflecting clouds have been observed along ship tracks due to aerosols in the ship exhaust
acting as CCN (Durkee et al., 2000). Since number concentrations of hydrophilic aerosol species
like sulfate and organic carbon are assumed to have increased substantially since preindustrial
times, it is likely that over all, present-day clouds contain more cloud droplets with smaller sizes.
According to the reasoning above, this corresponds to a negative radiative forcing at the TOA
and acts to cool the current climate. This is the essence of the first aerosol indirect effect (AIE),
and has been the subject of numerous studies over the last decades. The reason for this attention
from the scientific community is the large uncertainty connected to the magnitude of the negative
AIE forcing. Attempts to measure the Twomey effect have been carried out using satellite data,
for example by looking for statistical significant relationships between aerosol optical depth
(AOD) and cloud droplet effective radius (CER). Whereas some studies find strong negative
relationships between the two parameters, others find weaker relationships or no relationships at
all (Nakajima et al. (2001), Sekiguchi et al. (2003) and Paper III). However, as changes in CDNC
can also lead to changes in the cloud water content (see following section), it is difficult to isolate
the Twomey effect in such studies. Several modeling studies of the Twomey effect have been
carried out recently, some of which will be discussed in Section 4.
10
Figure 2: Ship tracks over the Atlantic Ocean off the east coast of the United States, observed by
the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on NASA’s Terra
satellite on May 11, 2005 (http://modis.gsfc.nasa.gov/).
3.3 Albrecht effect
As described in the previous section, pollution may have caused an increase in CDNC and a
corresponding decrease in cloud droplet sizes since preindustrial times. Albrecht (1989)
hypothesized that a possible effect of the reduced cloud droplet sizes is an increase in cloud
lifetime due to less efficient precipitation release. This effect is referred to as the Albrecht effect,
the lifetime effect or the second indirect effect. The reasoning behind this effect is that as cloud
droplets become smaller, e.g. due to an increase in CCN, cloud droplets will less frequently grow
beyond the threshold for efficient autoconversion, which is required to generate precipitation in
warm clouds. Autoconversion simply refers to the growth of droplets through collision processes
until their fall-speed exceeds the cloud updraft. Hence, if the large droplets (diameter > ~ 15 µm
(Rosenfeld et al., 2002)) required in order for this process to be efficient are lacking, the clouds
will typically live longer, contain more water and be more extensive both vertically and
horizontally. Several studies have investigated the relationships between aerosol parameters and
cloud liquid water path (LWP) from satellite observations to find evidence of the cloud lifetime
effect. Quaas et al. (2004) found positive correlations between the so called aerosol index (AI)
and LWP from the POLDER-1 instrument, while Paper III of this thesis found overall positive
correlations between aerosol optical depth (AOD) and LWP from the MODIS instrument.
11
Although these results support the Albrecht hypothesis, they do not necessarily confirm it as
these correlations may also be influenced by other factors (Myhre et al., 2006). The Albrecht
effect is only relevant for liquid clouds, as precipitation formation mechanisms are different in
ice-containing clouds. This issue will be discussed further in Section 3.5
3.4 Semi-direct effect
As mentioned in Section 2.1, BC aerosols absorb solar radiation and thereby alter the
atmospheric temperature profiles. The semi-direct effect refers to local heating due to absorbing
aerosols leading to evaporation of cloud droplets (Hansen et al., 1997). As a result of cloud
droplet evaporation, cloud water content and/or cloud cover may decrease. Cloud cover may also
decrease if an absorbing aerosol layer prevents cloud from forming at all. The reasoning behind
this effect is that absorbing aerosols heat the atmosphere, cool the surface and consequently
stabilizes the boundary layer (Koren et al., 2004). For most clouds, the mechanisms mentioned
above would lead to a warming of the Earth-atmosphere system. Observational evidence for this
effect was found by Ackerman et al. (2000) during the Indian Ocean Experiment (INDOEX).
However, many model studies have found the effect to be relatively insignificant on a global
scale, although local effects may be noticeable (Lohmann and Feichter, 2001, Penner et al.,
2003). The latter study even suggested that the semi-direct effect could be cooling the earthatmosphere system, depending on the injection height of the BC aerosols.
In contrast, Jacobson (2002) proposed that a reduction of BC emissions might be the most
efficient method of slowing global warming, partly due to the semi-direct effect. As a multitude
of BC aerosol effects on climate were considered in this study and only the sum of the effects
was quantified, it is not clear to what extent the semi-direct effect contributed to the simulated
warming due to BC aerosols.
Finally, a study by Johnson et al. (2004) pointed out that the semi-direct effect does not
necessarily represent a warming to the earth-atmosphere system. Large-eddy Simulations (LES)
showed that if an absorbing aerosol layer is located above a cloud layer, it increases LWP due to
a lower entrainment rate and a shallower and moister PBL, leading to a negative semi-direct
effect.
3.5 Indirect effect on ice clouds
While the three previous sections have described different aerosol effects on clouds containing
liquid water, this section will discuss aerosol influences on ice clouds and why they are different
from the influences on warm clouds. Although unresolved problems still exist, the understanding
of the aerosol indirect effect on clouds containing liquid water has improved as more effort has
been put into investigating this topic. Unfortunately, the general understanding of the aerosol
indirect effect on cold clouds is still fairly poor. The current ability to describe cold clouds in
numerical models used for future climate prediction is very limited. This is likely to be a result of
the even higher degree of complexity introduced as soon as ice phase occurs in a cloud, but
possibly also because availability of high quality observational data has so far been limited.
First of all, determining the ice fraction of a cloud is fundamental for calculations of its radiative
properties, but not at all straightforward. Secondly, determining the ice water content and the
sizes and shapes of the ice crystals correctly is extremely important and equally challenging. All
12
the parameters mentioned above are to some extent influenced by aerosols through various
processes, many of which are not well understood. (Lohmann and Feichter, 2005).
A complicating factor associated with ice-containing clouds is the number of different freezing
processes that can initiate cloud ice formation or cloud glaciation. While warm clouds always
form by water vapor condensing onto CCN, the story is far more complex in the case of ice
clouds. Ice crystals can form through freezing of cloud droplets, without the aid of freezing nuclei
facilitating the phase-transition. This so called homogenous freezing typically occurs at
temperatures below -40oC. For warmer temperatures, ice phase clouds are typically formed
through various heterogeneous freezing processes, meaning that IN facilitate the phase transitions
from vapor or liquid water to ice. Aerosols can act as IN by coming into contact with supercooled
cloud droplets (contact freezing), by initiating freezing from within the cloud droplet (immersion
or condensation freezing) or by acting as deposition nuclei. The last process is the least efficient
one due to the energy barrier that must be overcome for the phase change from vapor to ice.
Natural IN are typically insoluble dust aerosols and certain biogenic aerosols (Diehl et al., 2006).
Aerosols with crystalline structures seem to be particularly suitable for ice nucleation. Soot
particles in the atmosphere are considered to be almost entirely of anthropogenic origin, and are
found to be efficient IN in laboratory studies (Gorbunov et al., 2001).
In most state of the art GCMs, the cloud ice fraction (fice) is determined using a highly simplified
approach. In the models, ice fraction is simply a function of one parameter only, namely the
temperature of the given model grid box. In the real atmosphere, the cloud ice fraction is
determined by a number of parameters, important ones being supersaturation, atmospheric
stability, ambient aerosol concentrations, cloud age and temperature. An attempt to take more of
these parameters into account when determining fice has been developed by Lohmann(2002) for
the ECMWF-Hamburg (ECHAM4) GCM. This study was one of the first addressing aerosol
indirect effects associated with cold clouds. It has been shown experimentally (Pruppacher and
Klett, 1997) that when cloud droplets become smaller as a result of increased CDNC, they require
lower temperatures in order to freeze. However, an increase in CDNC would be expected to
increase the probability of freezing. Hence, the two effects counteract each other, and the net
radiative effect was found to be small in Lohmann et al. (2000). In addition to the two effects
mentioned, aerosols may also influence cold clouds by acting as IN in heterogeneous freezing
processes. In Lohmann (2002) a prognostic treatment of ice crystal number concentration was
employed to study the anthropogenic aerosol effect on contact freezing. A potentially high
sensitivity was found, although other heterogeneous freezing processes than contact freezing
were not parameterized as a function of IN concentration. Additionally, aerosols were not
allowed to influence freezing processes at temperatures below -35oC. At these temperatures, the
rate of ice crystal formation was determined based on saturation adjustment with respect to ice
and the parameterization of ice crystal size developed by Ou and Liou (1995). The latter
expresses ice crystal effective radius as a function of temperature. Similar relationships have been
developed by Kristjánsson et al. (2000), Ivanonva et al. (2001) and Boudala et al. (2002).
Unfortunately, these relationships deviate quite substantially from each other for certain
temperature intervals. As these empirical relationships stem from field campaigns in different
environments, this is not surprising and points out the need for more observations.
Ideally, ice crystal sizes could be determined as long as ice crystal number concentration (ICNC)
and ice water content (IWC) are known. With prognostic model equations for ice crystal number
concentrations, this can be achieved. However, this approach is not as straightforward for ice
clouds as it is for liquid clouds. Whereas cloud droplets are always spherical, a wide range of ice
crystal shapes have been observed. Figure 3 gives 5 examples of typical ice crystal shapes found
13
in the atmosphere. To obtain an ice crystal effective radius for radiation calculations from ICNC
and IWC, the crystal shape must be known. However, knowledge on ice crystal shape is crucial
not only to determine the effective size of the ice crystals; the five different ice crystal shapes
displayed in Figure 3 all represent different optical properties, disregarding size, and no exact
theory for calculations of their optical properties exists due to their non-sphericity (Kristjánsson
et al., 2000).
Figure 3: Some typical ice crystal shapes in ice clouds: (a) polycrystal, (b) column, (c) plate, (d)
bullet rosette, (e) aggregate (Baran et al., 1999).
In a more recent study by Lohmann and Diehl (2006), the effectiveness of dust and black carbon
aerosols as contact and immersion freezing IN is parameterized based on laboratory studies. This
is the first study taking the chemical composition of the IN into account, and a significant
sensitivity to this parameter in the net radiation at the TOA is found. This study was also carried
out for mixed-phase clouds, i.e. for temperatures lower than approximately -35oC. A study of
cirrus cloud formation (i.e. cloud formation at temperatures below 235K) was carried out by
Kärcher and Lohmann (2002). They found that homogeneous freezing processes are controlled
primarily by vertical velocity and temperature, while details of the aerosol size distributions were
less important. Aircraft data suggests that freezing in mid-latitude cirrus could be initiated at ice
saturation ratios lower than those required for homogeneous freezing (Heymsfield et al., 1998).
Consequently, a model study of the importance of heterogeneous freezing processes for cloud
formation at temperatures below -35oC was carried out by Kärcher and Lohmann (2003). They
reported that the potential anthropogenic aerosol influence is highly dependent on the vertical
velocity driving the ice nucleation processes.
In a recent paper, Kärcher et al. (2006) takes into account the competition between homogeneous
and heterogeneous freezing processes in a parameterization suitable for GCM simulations. If one
allows for this competition, they find that much stronger indirect aerosol effects on cirrus clouds
are possible.
Bailey and Hallet (2002) carried out laboratory studies revealing something truly
intriguing from a climate change point of view; namely that different IN tend to favor different
ice crystal shapes. This brings two of the most challenging questions in the study of cold clouds
together: “What determines the ice crystal shape in an ice cloud?” and “What is the
anthropogenic aerosol influence on ice clouds?”
14
Chapter 4
Modeling aerosol influence on liquid clouds
The aerosol influence on clouds can be calculated in GCMs by running two simulations: one with
aerosol emissions corresponding to present day (PD) conditions, and one with preindustrial (PI)
emissions representing conditions approximately 250 years ago. The aerosol indirect effect (AIE)
equals the difference in net cloud radiative forcing at the top of the atmosphere (TOA) between
the two simulations. As differences in longwave forcing (LWF) are typically small, many studies
use the change in shortwave forcing (SWF) as a surrogate for the AIE. The first attempts to
calculate the Twomey effect in numerical models were carried out by Kaufman and Chou (1993)
and Jones et al. (1994), in a 2D and 3D model respectively.
In both studies, empirical relationships between cloud droplet number concentration and aerosol
concentration or CCN were employed, and only the indirect effect introduced by anthropogenic
sulfate was considered. Kaufman and Chou (1993) found a change in radiative forcing at the
TOA of -0.45 W/m2 caused by anthropogenic sulphate emissions between preindustrial times and
1990. However, this pioneering study included some significant simplifications, an important one
being that the study was carried out with a zonally averaged (2D) model. Jones et al. (1994) used
the 3D Hadley Centre GCM in their study, resulting in a Twomey effect of -1.3W/m2 due to a
reduction in the global average cloud effective radius of -2.2 µm. In Boucher and Lohmann
(1995), the Twomey effect was investigated by implementing an empirical relationship between
CDNC and sulfate aerosol mass concentration in two (3D) GCMs. Sulfate aerosol mass
concentrations were obtained from a chemical transport model (CTM), providing monthly mean
data for input to the LMD (Laboratoire de Météorologie Dynamique) and the ECHAM (European
Centre for medium range weather forecast model, HAMburg version) GCMs. The framework
resulted in approximately the same AIE (-1 W/m2) for both models. Changes in global average
effective radius were -0.87 and -0.68µm, respectively, which is significantly smaller than in Jones
et al. (1994). Chuang et al. (1997) obtained a global average AIE comparable to Jones et al.
(1994) and Boucher and Lohmann (1995), but the geographical distribution was somewhat
different with AIE maxima shifted off the coasts of polluted regions. They parameterized CDNC
as a function of aerosol number, aerosol size distribution and updraft velocity based on a
microphysical lagrangian model. A lower AIE was found when sulfate was internally mixed with
the background aerosols rather than being externally mixed. The model used in this study was a
modified version of the National Center for Atmospheric Research (NCAR) CCM1 coupled with
the GRANTOUR tropospheric chemistry model.
The first GCM study taking both the first and second aerosol indirect effects into account was
presented in Lohmann and Feichter (1997). They found a change in SWF at the TOA of 1.4W/m2, 40% of which was due to the radius effect and 60% due to the cloud lifetime effect.
They also estimated the change in LWF at TOA to be +0.1W/m2, resulting in an AIE of 1.3W/m2. For previous studies which did not include changes in cloud water content and lifetime,
no changes in LWF should be expected as longwave radiation is typically independent of cloud
droplet sizes in most radiation schemes. Rotstayn (1999) also included estimates of the first and
second indirect effect, based on an empirical relationship between sulfate mass and CDNC. They
also presented explicit estimates of the two effects, obtaining a lifetime effect of -1.0W/m2 and a
radius effect of -1.2W/m2. Their estimated change in LWF was similar to that of Lohmann and
15
Feichter (1997). Kiehl et al. (2000) compared the first AIE (i.e. the radius effect only) resulting
from four different methods to relate CDNC to sulfate mass, and reported significant differences.
The need for a less empirical and more physical approach was pointed out.
With Ghan et al. (1997) and Lohmann et al. (1999) this new approach was introduced; rather than
diagnosing CDNC from aerosol or sulfate mass using empirical relationships, a continuity
equation for CDNC was introduced. The prognostic equation consists of an advection term, a
droplet nucleation term and sink terms representing collection and precipitation processes,
freezing and evaporation. No estimates of the AIE were given in these papers. However, in
Lohmann et al. (2000) the new continuity equation was used to give an estimate of the AIE from
both sulfate and carbonaceous aerosols. An AIE of -1.1W/m2 was obtained for internally mixed
aerosols, while for external mixtures a slightly stronger AIE of -1.5W/m2 was found. In both
cases the contribution from the carbonaceous aerosols dominated that from sulfate aerosols.
In Ghan et al. (2001) the continuity equation presented in Ghan et al. (1997) was used to calculate
the AIE from anthropogenic sulfate aerosols, amounting to -1.7W/m2 for the combined radius
and lifetime effects. The significantly higher values found in this study compared to Lohmann et
al. (2000) (-0.3W/m2 for sulfate aerosols only) is explained by the lower bounds for aerosol
number concentration and CDNC used in that study.
A more physical approach was also taken in Kristjánsson (2002), although the CDNC was
calculated in a diagnostic rather than prognostic manner. By making assumptions on
supersaturations, the CDNC was determined using look-up tables based on the Köhler equation
(Köhler, 1936). An AIE due to anthropogenic sulfate and black carbon aerosols of -1.8W/m2 was
obtained, approximately 75% of which was due to the radius effect.
In Menon et al. (2002), a more sophisticated empirical relationship between CDNC and aerosol
mass was presented. They parameterized CDNC as a function of not only sulfate mass, but also
the mass of organic carbon and sea salt. An AIE ranging from -1.55W/m2 to -4.36W/m2 was
found in this study. An interesting sensitivity study was also carried out, revealing the AIE
sensitivity to the ratio of present-day to preindustrial sulfate burdens.
Takemura et al. (2005) calculated CDNC diagnostically, employing the sophisticated cloud
droplet activation scheme of Abdul-Razzak and Ghan (2000). They found an AIE of -0.94W/m2,
contributions from the first and second indirect effects being practically equal.
Yet another new and interesting approach was taken by Quaas et al. (2005), where satellite data
was used to constrain the total aerosol indirect effect in two GCMs (ECHAM4 and LMDZ).
When fitting the model parameterizations to satellite data, the AIE was significantly reduced in
both models, to -0.5 and -0.3 W/m2 for LMDZ and ECHAM4, respectively.
These AIE estimates are similar to those presented in Paper I in this study.
It should be pointed out that although all the AIE estimates above are based on changes in aerosol
loadings between PD and PI conditions, emission scenarios differ and will add to the spread of
the estimates.
Figure 4 shows the model estimates discussed above (and some additional estimates) as a
function of publication year of the papers they were presented in. Although the spread in the
estimates in Figure 4 is wide, they are all negative, the difference between the lowest and highest
estimates being approximately 2W/m2. Inverse model calculations estimate the combined direct
and indirect aerosol effect to be between 0 and approximately -1W/m2, with uncertainties
extending down to -1.9W/m2 (Anderson et al., 2003). Although one cannot regard these
calculations as the ground truth, they indicate that most of the studies presented in Figure 4
overestimate the AIE. The 10th order polynomial fit to the estimates in Figure 4 (solid line) is
included to indicate the growth of the estimates in the 1990s and the decline in recent years. The
16
first estimates were probably low because they only included aerosol influence on cloud droplet
size (i.e. first indirect effect), but disregarded their effect on cloud lifetime (second indirect
effect). When both effects were included, estimates generally became higher. Understanding the
importance of background aerosol loadings (Menon et al., 2002) and the so called competition
effect (Ghan et al., 1998) for AIE may have contributed to the low estimates in recent years.
Figure 4: Model estimates of aerosol indirect effect on liquid clouds from the last 15 years. Solid
lines represent a polynomial fit to the data points. Squares represent estimates of the first indirect
effect only, while circles represent estimates of both the first and second indirect effect. If a study
resulted in more than one estimate and none of the estimates were characterized as a best
estimate, the average of all estimates in the study is given
17
Chapter 5
CAM-Oslo – The modeling tool in this study
CAM-Oslo is in this thesis referring to an extended version of the National Center for
Atmospheric Research (NCAR) Community Atmosphere Model Version 2.0.1 (CAM2.0.1).
CAM-Oslo is based on the primitive equations solved for 26 vertical levels ranging from sea
level to approximately 3.5hPa. The horizontal and temporal resolutions depend on which
dynamical core we choose for our model runs. In this study we have run the model with both
Eulerian dynamical core (PaperI and Paper III) and a Finite-Volume dynamical core (Paper II).
The former corresponds to a horizontal resolution of 2.8ox2.8o (T42) and a dynamical time step of
20 minutes, while the latter one corresponds to a horizontal resolution of 2.0ox2.5o and a
dynamical time step of 30 minutes.
The process of deep convection is treated with a parameterization scheme developed by Zhang
and McFarlane (1995). The scheme is based on a convective plume ensemble approach, i.e. it is
assumed that whenever the lower troposphere is conditionally unstable an ensemble of
convective scale updrafts may exist. Shallow and middle-level convection are treated separately
In the host model, the parameterization of non-convective cloud processes follows Rasch and
Kristjánsson (1998). Prognostic variables in the cloud microphysics scheme are water vapor and
cloud water (i.e. the sum of liquid and ice phase condensate). The latter is assumed to be
sufficiently short-lived that resolved processes have little influence upon it, while convective and
turbulent processes can affect it. In addition to the advective term, the prognostic equation for
cloud water consists of a condensation term, an evaporation term and loss terms due to
precipitation processes. Condensation occurs when the relative humidity is above a critical value.
Ice saturation is not treated explicitly, but rather a weighted average of saturation mixing ratios
over ice and water is assumed between 0oC and -20oC. An improved treatment of the saturation
mixing ratio is discussed in Section 7. The fractional cloud cover is determined based on the
relative humidity and a variable describing vertical stability. Shortwave radiation calculations are
carried out using the δ-Eddington method. It allows for absorption by O2, CO2, O3 and H2O,
molecular scattering and scattering/absorption by aerosols and clouds. A maximum/random cloud
overlap approach (Collins, 2001) is taken for radiation interactions with clouds, which is
otherwise described by Slingo (1989) for water clouds and by Ebert and Curry (1992) for ice
clouds. Longwave radiation calculations are carried out using an absorptivity/emissivity
formulation and a broad band model approach for atmospheric transmission calculations.
For the simulations presented in Papers I-III in this thesis, NCAR CAM2.0.1 was run with
several model extensions, which together form the CAM-Oslo modeling tool. The extensions
include an aerosol life-cycle module, an aerosol size distribution module, a cloud droplet
activation module and a prognostic equation for cloud droplet number concentration (CDNC).
All these extensions are described in more detail in Paper I of this thesis, in Iversen and Seland
(2002), in Kirkevåg et al. (2002) and in Kirkevåg et al. (2005).
18
Chapter 6
Summary of papers
6.1 Paper I
Storelvmo, T., Kristjánsson, J.E, Ghan, S. J., Kirkevåg, A., Seland, Ø. and T. Iversen: Predicting
cloud droplet number concentration in CAM-Oslo, J. Geophys. Res., Accepted, May 2006.
In this paper, a new framework for calculations of the aerosol indirect effect is presented. Central
in this new framework is a prognostic equation for calculations of cloud droplet number
concentration. This continuity equation for CDNC consists of a droplet activation term and
several microphysical sink terms. The activation term is calculated using the activation scheme
presented in Abdul Razzak and Ghan (2000). The aerosol number fraction activated is
determined based on a sub-grid distribution of vertical velocity and hence supersaturation. The
supersaturation is also dependent on the so called competition effect. It takes into account the fact
that a CCN must compete with all other CCN present for the available water vapor. This
competition limits the maximum supersaturation reached in an updraft. The predicted CDNC and
the resulting effective radius, liquid water path and cloud radiative forcing compares well with
observations, indicating the soundness of the new framework. The AIE calculated with this
framework is somewhat lower than most other comparable studies, partly due to the introduction
of microphysical sink terms and the competition effect. Both factors reduce the difference in
CDNC between clean and polluted cases. Some sensitivity studies are also presented in this
paper, to give an indication of the robustness of our results. We find that the AIE is highly
sensitive to variations in the soluble fraction of mineral dust. This parameter is dependent on the
mineralogy of the dust aerosols, and estimates found in the literature vary greatly.
6.2 Paper II
Penner, J., Quaas, J., Storelvmo, T., Takemura, T., Boucher, O., Guo, H., Kirkevåg, A.,
Kristjánsson, J. E. and Ø. Seland: Model intercomparison of aerosol indirect effect, Atmos.
Chem. Phys., 6, 3391-3405, SRef-ID: 1680-7324/acp/2006-6-3391, 2006.
In this paper, the aerosol indirect effect is investigated by comparing the results for three different
GCM for 6 experiments. Each experiment was set up to investigate a certain aspect of the AIE.
The three models taking part in the model intercomparison were: CAM-Oslo, the Laboratoire de
Météorolgie Dynamique-Zoom (LMD-Z) and the Center for Climate System Research
(CCSR)/University of Tokyo, National Institute for Evironmental Studies (NIES), and Frontier
Research Center for Global Change (FRCGC) general circulation model. All experiments were
carried out with both preindustrial and present-day emission scenarios. The first experiment is
designed to examine the influence of cloud variability on the aerosol indirect effect for each of
the models, while the purpose of the second one is to investigate the influence of the different
CDNC parameterizations used in the models. Both experiments include only the Twomey effect,
19
while in the third experiment a common method for aerosol influence on precipitation efficiency
is introduced in the models. The fourth experiment differs from the third one only in the use of
individual autoconversion parameterizations rather than a common one.
In the fifth experiment each model calculates the aerosol size distribution individually, whereas it
was prescribed in the first four experiments. This led to pronounced differences in the AIE
among the models. In the sixth and final experiment, aerosol scattering and absorption were
included in order to investigate the direct and semi-direct effects of aerosols.
6.3 Paper III
Storelvmo, T., Kristjánsson, J. E., Myhre, G., Johnsrud, M. and F. Stordal: Combined
observational and modeling based study of the aerosol indirect effect, Atmos. Chem. Phys., 6,
3583-3601, SRef-ID: 1680-7324/acp/2006-6-3583, 2006.
This paper presents an extensive comparison of aerosol and cloud parameters from CAM-Oslo
and the MODIS instrument onboard the TERRA and AQUA satellites. Globally, the following
parameters are compared: aerosol optical depth (AOD), cloud optical depth (COD), cloud
fraction (CFR), cloud liquid water path (LWP) and cloud droplet effective radius (CER).
Regionally, the relationship between AOD and crucial cloud parameters (COD, CER, LWP) from
the model and observations are compared. The purpose of investigating these three sets of
parameters was to identify correlations that could be interpreted as a result of aerosol-cloud
interactions, and determine if these correlations were similar in the model and the observations.
We calculated correlations (and the associated statistical significances) for 15 selected regions for
each calendar month, for both the model and the satellite data. Both MODIS and CAM-Oslo
show a positive correlation between AOD and COD in more than 90 % of the cases investigated.
Chapter 7
Future work – Aerosol influence on cold clouds
This thesis is focused entirely on aerosol influences on warm clouds and the liquid fraction of
mixed phase clouds. Consequently, a natural extension to this work would be investigating
aerosol influence on ice clouds. A preliminary framework for simulations of the aerosol effect on
cold clouds has already been developed and implemented in CAM-Oslo. In the new framework,
cloud formation is treated in a more physical manner than in the host model (NCAR CAM2). The
host model does not treat ice saturation separately, but rather it is approximated as a weighted
average of saturation ratio with respect to ice and water between 0oC and -20oC. At temperatures
below -20oC saturation with respect to ice is assumed. In the new framework, a newly formed
cloud at temperatures above -35oC is assumed to be saturated with respect to water. This is in
20
better agreement with the observations reported by Korolev and Isaac (2006). However, as
heterogeneous freezing processes form sufficient ice crystals to allow the Bergeron-Findeisen
process to become efficient, supersaturation with respect to ice is assumed.
The calculation of cloud ice fraction is also improved in the new framework. In the host model,
ice fraction is increasing linearly from 0 to 1 over the temperature range from 0oC to -20oC.
In the new framework, the ice fraction is prognostic rather than diagnostic. It is calculated as:
qi
qi + q w
f ice =
(1)
where qi and qw are cloud ice water mixing ratio and cloud liquid water mixing ratio, respectively.
Both parameters are prognostic variables in the new framework, whereas they are merged into
one prognostic variable in the host model. In addition, a prognostic equation for ice crystal
number concentration is part of the new framework:
N
dN i
= A N − i ( PSAUT + PSACI − frz imm − frz con − frz hom − subl − melt ) − selfc + mult
dt
qi
i
(2)
Ni: ice crystal number concentration in m-3
ANi: transport (convection and turbulence)
PSAUT: autoconversion of ice condensate
PSACI: snow collecting ice
frzimm: immersion freezing
frzcon: contact freezing
frzhom: homogeneous freezing
subl: sublimation of cloud ice
melt: melting of cloud ice
selfc: selfcollection of ice crystals
mult: Ice multiplication (Hallet-Mossop process)
The resulting zonal and annual average ice crystal number concentration from a one year
simulation with the new framework is presented in Figure 5 as a function of latitude and pressure.
The contact ice nuclei concentration in this simulation follows Young (1974):
N a , cnt = N a 0 (270.15 − T ) 1.3
(3)
where Na0 =2·105m-3 is the number of active ice nuclei at 269.15 K and T is temperature.
One would expect the results in Figure 5 to be similar to those presented in Figure 3 in Lohmann
(2002). Although the concentrations are in fact similar, the vertical distribution is somewhat
different. This should not be surprising, as the results stem from two different GCMs using
different parameterizations to describe physical processes. To understand the differences between
the results entirely requires further investigations, as one of many interesting continuations to the
work already carried out.
21
Other extensions are as follows:
- Introduce new expressions for immersion freezing and secondary production, where IN
concentrations, droplet size and droplet concentration come into play.
- Further investigations of the hypothesis presented in Bailey and Hallet (2002) . In
particular, attempt to find an answer to the following questions: Do different IN nucleate
ice crystals with different ice crystal phases? Do anthropogenic IN differ significantly
from natural IN in this sense? If so, what is the climatic effect of this anthropogenic ice
crystal phase change?
- Take the variations in mineralogy into account in our model, to improve description of the
CCN and IN capability of dust aerosols.
Figure 5: Zonal and annual mean ice crystal number concentration from simulation with CAMOslo including the new framework for calculations of aerosol influence on ice clouds.
22
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