From the Strüngmann Forum Report, Clouds in the Perturbed Climate System:
Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation
Edited by Jost Heintzenberg and Robert J. Charlson. 2009.
© MIT Press ISBN 978-0-262-01287-4

6
Temporal and Spatial Variabilityof Clouds and Related Aerosols
Theodore L. Anderson, Rapporteur
Andrew Ackerman, Dennis L. Hartmann, George A. Isaac,
Stefan Kinne, Hirohiko Masunaga, Joel R. Norris, Ulrich Pöschl,
K. Sebastian Schmidt, Anthony Slingo, and Yukari N. Takayabu
Hygroscopic Properties of Atmospheric Aerosols
Appear to be Better Constrained than Other Key
Parameters of Warm Cloud Formation
Aerosol–cloud interactions may have played an important role in the observed
climate change of the 20th century, and, if so, this may have partly been causedby changes in cloud condensation nuclei (CCN) as a result of industrialization. Sea salt, sulphate, and organics are key components of CCN; the atmospheric sulphate load is known to have changed significantly because of industrialization. The relative change in CCN caused by industrialization depends
critically on (a) preindustrial CCN levels, (b) anthropogenic emissions ofparticles and aerosol precursor gases, (c) atmospheric processing which generates, transforms, and removes aerosol particles, and (d) the ability of particles to act as CCN; that is, their hygroscopicity. Hygroscopicity, as a functionof organic mass fraction, has attracted much attention in recent years (e.g.,
Bates et al. 2006). However, the first three aspects of the problem pose more
important uncertainties.
Briefly, preindustrial CCN levels have been estimated almost exclusively
using models that remove anthropogenic sources (e.g., Schulz et al. 2006).
Andreae (2007), however, has argued that preindustrial CCN levels may havebeen fundamentally different. Transport models require detailed emission data
(i.e., fluxes for all major particle constituents and precursor gases as a function of particle size, injection altitude, and with high temporal and geographical resolution), but such data are presently sparse and uncertain. Atmospherictransformations of aerosols are frequently dominated by in-cloud processing,128

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which can generate particulate mass from precursor gases, remove particles via
precipitation, and alter radically the particle size distribution. Currently, models represent all these processes, but observational constraints are again very
sparse relative to the full range of atmospheric conditions. As a result, major
uncertainties exist in terms of particle concentrations (number and mass), size
distribution, and aerosol sources and sinks (primary emissions, secondary formation, dry and wet deposition). These continue to pose obstacles to our understanding of aerosol effects on warm cloud formation in the atmosphere.
Relative to the above issues, the warm-cloud hygroscopic properties of atmospheric aerosols (i.e., the effects of chemical composition on equilibrium
water uptake) appear to be fairly well constrained. The effective hygroscopicity
of aerosol particles can be efficiently approximated by a single hygroscopicity
parameter (κ, Petters and Kreidenweis 2007). With simplifying assumptions,this parameter is easy to calculate from the chemical composition of the particles, and recent laboratory and field measurements suggest that on average it
is constrained to fairly narrow value ranges for continental and marine boundary layer aerosols (0.3±0.1 and 0.7±0.2, respectively) (Andreae and Rosenfeld
2008; Kreidenweis et al., this volume; Kinne et al., this volume).
This result is significant because, at present, general circulation models(GCMs) employ a wide range of hygroscopic coefficients for organic particles,
and simulations of the 20th-century temperature record are sensitive to thisparameter. Thus, it would appear that current understanding of CCN needs
to be incorporated as a much tighter constraint (rather than a tuning parameter) in GCMs. At this point, the hygroscopic properties of aerosols do notappear to be a limiting factor for advances in large-scale atmospheric andclimate modeling.
This is not to imply that aerosol hygroscopicity is fully understood. For
detailed mechanistic studies, CCN closure experiments, and the like, more sophisticated approaches are essential to progress. Improved understanding of
the hygroscopic properties of organic substances and organic–inorganic mixtures is needed as well as an improved understanding of kinetic limitations.

Ice Nuclei and Ice Microphysics Still Pose Many Open Questions
Our understanding of ice nucleation (the seemingly tiny fraction of aerosol
particles that constitute ice nuclei; the roles of homogeneous and heterogeneous nucleation processes) is currently very poor, as is our knowledge of the“ice-multiplication” processes that generate observed concentrations of small
ice crystals. These processes are critical not only for high-latitude precipitation
but also for tropical and midlatitude rainfall. Heavy rainfall events generated
by mesoscale convective systems, tropical cyclones, and extratropical storms
all involve cold rain processes. If we wish to improve our ability to forecast
extreme weather and to quantify the global climatology of precipitation, aTemporal and Spatial Variability of Clouds and Related Aerosols 129
better understanding of ice microphysics is imperative. Meteorologists havebeen stymied by these issues for many decades. There are severe limitations incurrent methods for measuring microphysics of mixed-phase and ice clouds,
both in terms of in-situ approaches and remote sensing. Even the new satellite
techniques (CloudSat and CALIPSO) provide only limited phase information,and only in the vicinity of cloud top.
GCMs are developing parameterization strategies that enable composition to be linked to droplet activation in clouds. In principle, the approach canalso be used for ice activation. Unlike droplet activation, however, the links
between atmospheric composition and ice formation (nucleation, multiplication) are not well known. Representing interactions between aerosols and iceor mixed-phase clouds in GCMs is problematic without adequate knowledgeof ice particle formation.
Little to nothing is known about the collision efficiencies of ice particles,
and thus most cloud-resolving bin microphysics models tend to assume thatthose efficiencies (ice–ice and ice–liquid) are the same as those between water
drops, which is certainly a crude simplification that limits the reliability ofsimulations of mixed-phase and cold clouds with bin microphysics models.
Remote-sensing Limitations
Ice water clouds are far more difficult to characterize from space than liquidwater clouds. One level of complexity is added by the shape of ice crystals. In
absence of that information, the lookup tables for visible/near-infrared retrievals are usually based on some mixture of crystal shapes. Since the compositionof the actual cloud differs from the assumed shape, large errors in both effective radius and optical thickness may be introduced, which then propagate into
the ice water path retrieval as well. There is currently no technique available
to retrieve crystal shape from satellites. Less problematic is phase discrimination, though this is often merely qualitative and not always consistent between
different techniques (e.g., MODIS and POLDER), especially for mixed-phase
clouds. With the launch of CloudSat and CALIPSO, it has become possible tovalidate the cloud-top height products from infrared imagery, and first biases
have been identified. However, algorithms for retrievals of liquid water path,
crystal size, phase, and the vertical cloud structure remain to be validated within-situ aircraft measurements.
In-situ Measurement Limitations
In-situ probes have persistent problems in ice clouds. This is reflected in theongoing debate about the existence of small crystals, which have been detected through combinations of (bulk) measurements of ice water content, crystal
number, and extinction (e.g., Garrett et al. 2003; Ström et al. 1994). Such small
crystals change the warming/cooling properties of cirrus clouds substantially

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(Heintzenberg et al. 1995). For crystal-size spectrometers such as the Cloud
Aerosol and Precipitation Spectrometer (CAPS), it has been shown in windtunnel tests that at least part of the small crystals may be attributed to particle
breakup (“shattering”) on the tips of the probe. A related problem is a major
jump in the CAPS-size distributions, which occurs at the transition from thesmall-size range (forward scattering probe 2–50 μm diameter) to the mediumsize range (cloud imaging probe, 50–1500 μm). This jump makes it impossible
to derive meaningful properties, such as effective crystal radius from the size
distributions, and can most likely be explained not only by shattering alone
but through an association with multiple forward scattering peaks from multifaceted ice crystals, which are counted separately by the forward scattering
probe but not by the imaging probe. A further practical problem is that there isno agreed upon standard on how to calibrate the probes and process the dataand correct for various effects. Often, the probes are not calibrated at all during
field experiments. Therefore, it is not unusual that wing-by-wing comparisonsof size distributions differ by orders of magnitude in certain size ranges. Bulk
probes (e.g., CVI and Nevzorov probes for ice water content and nephelometers and extinctiometers for extinction measurements) help constrain the size
distribution obtained from size spectrometers and, when combined, provide an
independent measurement for crystal effective radius. However, they may also
suffer from shattering.
New techniques, such as Gayet’s cloud nephelometer (Gayet et al. 1997) or
polarized measurements, enable cloud phase discrimination and can therefore
be used in mixed-phase clouds.
Verification of Remote Sensing Using In-situ Aircraft
It is essential to verify remote-sensing techniques, both ground- and satellitebased, with in-situ aircraft measurements and a clear specification of errors
and limitations. For example, satellite retrievals of cloud phase, effective crystal radius, cloud ice water content, and precipitation must be validated taking
into account horizontal and vertical inhomogeneities. In the absence of thesecomparisons, it is rarely possible to attach robust error bars to remotely sensed
cloud parameters.

The Confounding Influence of Meteorology
Description of the Problem
Although it is crucial for our understanding of climate change to identify the
influences of anthropogenic aerosol and global warming on cloudiness, this is achallenging problem since natural meteorological processes exert such a dominating control over cloud properties and precipitation. For example, in regionsTemporal and Spatial Variability of Clouds and Related Aerosols 131
of persistent marine stratocumulus, over 80% of the seasonal/geographicalvariance in cloud cover can be explained by variability in lower troposphericstability (LTS) (Klein and Hartmann 1993; Wood and Bretherton 2006). Despite the potentially strong susceptibility of marine stratocumulus clouds toaerosol perturbations that result from their low natural droplet concentrationand intermediate optical thickness (Platnick and Twomey 1994), on scales
larger than ship tracks, it is difficult to identify clearly aerosol effects amid theimpacts of variations in LTS, temperature advection, and divergence. Moreover, because changes in aerosol concentration are associated with changes
in the large-scale flow (i.e., the meteorology), even perfect observations ofcoincident clouds and aerosols are insufficient to determine the response of
the clouds to aerosols alone (e.g., Mauger and Norris 2007). The extent of thiscovariance between meteorology and aerosols is poorly understood.
Another reason why large-scale aerosol effects on clouds have not beenclearly discerned is that competing mechanisms, each of which is inadequately
understood, may be involved. Straightforward physical theory and extensive
observations indicate that increased particle number concentration will lead toa higher droplet number concentration at cloud base (e.g., Warner and Twomey
1967; Coakley et al. 1987; Breon et al. 2002), but beyond this, little is knownwith confidence. For example, hypotheses exist by which aerosols may increase cloud water through suppression of precipitation or decrease cloud water through enhancement of evaporation. These effects will, in turn, change
the cloud dynamics (Feingold and Siebert, this volume). Currently, the important research questions are whether (and if so, how) aerosol-induced changes
in droplet number affect cloud albedo, cloud cover, and precipitation. For acoupled system, it is both important and difficult to isolate the effects of asingle variable.
Strategies for Separating Aerosol and Meteorological Effects on Clouds
Inadequate Methods
Constant liquid water path (LWP). One common approach to “control” for meteorology in efforts to observe the effects of aerosols on cloud albedo in complex systems (i.e., those other than plume studies as typified by ship tracks) isthrough binning cloud observations by LWP. However, LWP is an emergent
property that results from the interplay between meteorological conditions andcloud microphysics, on scales down to that of a cloud element (e.g., a stratocumulus cell). In fact, changing LWP may in some cases be the primary mechanism for aerosol-induced albedo changes. Thus, it is impossible to measure
how clouds are being changed by an aerosol perturbation if one eliminates themost important response by design.
Common source region. Another way to distinguish meteorological andaerosol effects on clouds and precipitation in small regions is to segregate the132

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data according to air mass back trajectories from different geographical regions.
For example, in eastern Canada, when trajectories are from the south, the air
masses come from heavily polluted areas, whereas with northerly trajectories,
the air masses pass over areas with fewer aerosol and anthropogenic gas phasesource regions. The limitation of this method is that air masses from different
regions are usually associated with different meteorological conditions. For
instance, air parcels over the ocean with higher aerosol concentration, which
are the result of recent contact with a continental region, are likely to be drier
and thus have fewer clouds on account of meteorology.
Useful Methods
Small-scale aerosol perturbations. For small-scale effluent plumes (e.g., ship
tracks), the assumption of constant meteorology between impacted and unimpacted regions of the cloud appears to be robust in at least some cases (e.g.,
Radke et al. 1989). Extending the results of such studies to larger scales, however, is not straightforward because the diffusion of plumes and corresponding
aerosol effects over larger areas occur at the same timescale as do changes
in meteorology.
Meteorological proxies. In addition to identifying source regions by backtrajectory, the large-scale meteorological conditions experienced by clouds
prior to the time of observation can be obtained from reanalyses and measurements. It is essential to use the history of meteorological forcing rather thandata obtained solely from the time of the cloud observation because cloudiness
responds to changes in forcing with a time lag. The potential impact of aerosols
on cloud properties can be estimated from a large sample of aerosol and cloud
observations by comparing average cloud properties between subsets withsimilar meteorological history but differing aerosol amounts. For example, thestudy of Mauger and Norris (2007) found that satellite-reported large aerosol optical depth (AOD) was associated with both large low-level cloud fraction and large LTS during the previous three days. Moreover, the difference incloud fraction between subsamples with large and small AOD, but equivalent
LTS, was less than the difference in cloud fraction for subsamples with large
and small AOD and unconstrained LTS, thus illustrating the confounding roleof LTS in controlling cloud amount. Note that compositing on parameters like
LTS only establishes a minimum value for the magnitude of meteorologicalinfluence (and an upper limit for aerosol influence), since other unidentified
processes may also affect cloudiness. Failure to account adequately for all meteorological effects will lead to an overestimate of the influence of aerosols
on cloudiness.
Cloud properties surveyed in diverse aerosol environments. The observation of similar cloud properties across many regions of the world, where diverse aerosol environments are expected, can be used to reject the hypothesisthat aerosols are a major influence. For example, the statistics of cloud crystal

Temporal and Spatial Variability of Clouds and Related Aerosols 133
number concentration were found to be similar in experiments over Canada,
the United Kingdom, and the southern hemisphere. Specifically, Korolev etal. (2000), Gultepe et al. (2001), Field et al. (2005), and Gayet et al. (2006)
all showed similar ice particle (effective diameter > 100 μm) concentrations
around the world for clouds with temperatures warmer than about –35°C. Presumably, these experiments involve very different aerosol environments. Thus,
one could infer that the concentration of large ice particles in clouds warmer
than about –35°C is not sensitive to aerosol variations. The observation of different cloud properties across regions with diverse aerosol environments is nota sufficient condition for demonstrating an aerosol influence, however, sincedifferent regions have different meteorology.
Discussion
What is the appropriate null hypothesis? Many observational studies ofaerosol–cloud relationships have mistakenly inferred causation from correlation; that is, if a change in aerosol amount is seen to occur with a change incloudiness, then aerosols necessarily produced the cloud change. For example,
Nakajima and Schulz (this volume) provide an extensive literature review of
the apparent sensitivity of cloud properties to aerosols calculated via linear
regression with aerosol loading as the sole independent variable. Considering
the dominating impact of meteorology on cloudiness, a superior “null” hypothesis would be that aerosols and clouds do not interact, and that observed correlations only arise from their common origins and trajectories in the generalcirculation. This, however, is too strict a criterion since no predictors of clouds
from either observation, forecast, or reanalysis are currently accurate enoughto rule out such a “null” hypothesis. Nevertheless, the effects of meteorologicalfactors known to control strongly cloudiness as well as satellite retrieval andother measurement biases must be eliminated before attributing cloud changes
to aerosols.
Other confounding aspects of the problem exist. It is possible for clearsky radiative effects of aerosols to alter directly the meteorology of a region
by changing surface and atmospheric radiative absorption (Wendisch et al.
2008). This, in turn, could weaken or strengthen circulations on various scales
and consequently change cloudiness. Observational analyses that controlled
for meteorology would fail to reveal an indirect effect of this nature. To test
such a hypothesis, examination of the sensitivity of atmospheric circulation toan imposed radiative forcing in a global or regional climate model seems tobe the best option. One must be careful in interpreting the results, however,since inadequate boundary layer, convection, and cloud parameterizations or
inappropriate boundary conditions (e.g., fixed sea surface temperature in longsimulations) may cause the model to behave in an unrealistic manner.

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Evidence for Large-scale Aerosol Impacts on Cloud Albedo,
Cloud Amount, and Precipitation Remain Ambiguous
Evidence for an aerosol influence on clouds at large scales has recently emerged
from satellite studies (see Table 17.2 in Nakajima and Schulz, this volume), butthese studies have significant limitations. First, there is inadequate consideration of meteorology as an alternative explanation for the reported aerosol–
cloud correlation; another concerns the difficulty in distinguishing aerosols
from clouds by remote sensing (Martins et al. 2002; Kaufman et al. 2005;
Charlson et al. 2007), which could lead to artificial positive correlations between retrieved AOD and cloud fraction in satellite scenes. Studies of aerosol–
precipitation relationships suffer from similar shortcomings, and Ayers andLevin (this volume) describe how meteorology is a more plausible explanation
for precipitation changes than an aerosol microphysical mechanism is. Moreover, despite decades of research, there is no robust statistical evidence for theefficacy of intentional weather modification through aerosol impacts on cloud
microphysics (i.e., “cloud seeding”; see Cotton, this volume). Recent evidence
from satellites of cloud perturbations over the remote oceans downwind of
fuming volcanoes (Gassó 2008) is suggestive of large-scale aerosol impacts on
cloud albedo, yet the absence of data on aerosol concentration means that anaerosol mechanism cannot be conclusively established or quantified.
The potential influence of aerosols on cloud properties can also be assessedby looking for long-term trends in cloudiness in regions that have experienced
long-term trends in aerosols. This type of analysis has the benefit of mostly (butnot completely) averaging out the confounding effects of synoptic meteorological variability. If aerosols influence cloudiness significantly, and aerosols havestrongly increased or decreased in a particular area over several decades, then
one would expect to see a physically consistent change in cloudiness over thesame time period. For example, the presence of absorbing aerosols in a cloudlayer has been hypothesized to decrease cloud fraction because of the absorption of solar radiation and commensurate reduction of humidity (Ackerman
et al. 2000). The observation that no long-term reduction of cloud cover has
occurred over the Northern Indian Ocean, despite the large increase in absorbing aerosols, indicates that this aerosol mechanism has not actually had ameasurable influence on cloud cover in this region (Norris 2001). As anotherexample, an increase in the number concentration of hygroscopic aerosols has
been hypothesized to enhance cloud fraction by reducing precipitation loss ofcloud water (Albrecht 1989). The observation that cloud cover decreased over
Europe during the time period of increasing sulfate emission and increased
over Europe during the time period of decreasing sulfate emissions indicatesthat this aerosol mechanism has not had a measureable influence (Norris andWild 2007). One observational study by Krüger and Graßl (2002) reported adecrease in cloud albedo over Europe during a time of decreasing sulfate aerosol, but they did not investigate whether this might have instead been the resultTemporal and Spatial Variability of Clouds and Related Aerosols 135
of meteorology. The North Atlantic Oscillation exhibited a substantial trend
during this time period, which could have affected cloudiness over Europe.

A Major Motivation to Study Aerosols and Climate Is
to Understand 20th-century Temperature Change
Global mean climate forcing from anthropogenic aerosols is in the process ofleveling off and, in strong contrast to forcing from greenhouse gases (GHGs),
aerosol forcing is not expected to increase dramatically during the 21st century(Penner et al. 2001). If these projections are correct, then most of the aerosol
perturbation to climate that humans will ever impose has already happened.
In global climate simulations of the 20th century, aerosol forcing appears
to compensate for differences in climate sensitivity (Kiehl et al. 2007). Better
understanding of aerosol forcing has the prospect of improving our knowledgeof climate sensitivity and thus our forecasts of future climate change, where theanthropogenic forcing will be increasingly dominated by GHGs.
Anthropogenic aerosols exert regional perturbations with large temporalvariations (both increases and decreases). Anthropogenic GHGs, in contrast,
exert global-scale perturbations that continuously increase over time. Given
these very distinct patterns, it should be possible to separate these two typesof perturbation to the climate system. In particular, focusing on regional variations would seem to be a plausible approach for investigating aerosol effects.
Although the aerosol is unlikely to exhibit substantial growth in the future, interms of global mean loading, it will undoubtedly continue to exhibit strong regional signals in areas where it is either growing or diminishing. Nevertheless,the difficulty of separating aerosol from meteorological effects, as previously
discussed, must not be overlooked.

Since Some Global Warming Signals Are Now Becoming
Evident, We Should Search for Associated Cloud Changes
As radiative forcing by anthropogenic GHGs becomes increasingly strong, weshould expect to see specific changes in the Earth’s climate system in addition
to the rise in global mean temperature. Recent studies have accordingly reported a decline in the summertime Arctic sea ice area (Nghiem et al. 2007), a poleward shift of the Hadley circulation (Fu et al. 2006), a rise in tropopause height(Thomson et al. 2000), and trends toward more precipitation in wet places
as well as less precipitation in dry places (Trenberth et al. 2007). Theoreticalconsiderations and global climate models predict that all of these phenomena
will occur with global warming, and they illustrate some of the changes wewill experience over the coming decades. Considering the great importance ofcloud feedbacks to climate sensitivity and our ignorance concerning them, it

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is essential to investigate the observational record for cloud changes that arelikely to be associated with global warming. Although this has been previously
difficult because of the large uncertainties in data and substantial natural variability, we expect that cloud signals will become more distinct as greenhouseradiative warming becomes increasingly globally dominant over aerosol radiative cooling. Moreover, distinct regional trend patterns will be more robust
than the global average cloud changes. For example, one likely cloud change isa shift of frontal clouds accompanying the poleward retreat of the storm tracks.
Cloud simulations in global climate models are unlikely to provide reliableguidance since current models represent cloud processes poorly and inconsistently. Nevertheless, global climate models can provide robust information
on particular changes in atmospheric structure and circulation associated with
global warming. Reasoning from theory, we can then hypothesize which cloudchanges would be physically consistent with these. The identification of suchcloud changes in the observed record would be useful for assessing which
global climate models are likely to have the most realistic cloud parameterizations for global warming simulations. This, in turn, would help constrain therange of uncertainty in our estimates of cloud feedbacks and climate sensitivity. Although we know many meteorological factors that govern cloudiness(e.g., low-level cloud fraction and lower tropospheric static stability), it is notnecessarily clear which factors will be the most influential under global warming. Thus, improved theories are needed.
Clouds interact strongly with mean thermodynamic properties and withorganized circulations from the scale of individual convective cells, throughmesoscale organization, to synoptic and large scales. These circulations arepotentially sensitive to direct effects of GHG and aerosol changes as well as
to the changes in temperature and humidity that are expected in association
with global warming. Some circulations, such as the mesoscale circulations
within convective cloud complexes in the tropics are strongly coupled to cloud
microphysical processes (Houze 1982). Changes in the location or intensity of
extratropical storms and associated storm tracks, which may occur in response
to global warming, could have significant effects upon mean cloud properties
and their effect upon climate.
One effect that is widely regarded to be important is the change in dry static
stability that will accompany the decrease in temperature lapse rate in response
to warming of the tropical sea surface (Knutson and Manabe 1995). The enhanced stability, coupled with radiative cooling rates which are not expected tochange much, may decrease the rate of overturning associated with the Hadley
and Walker circulations of the tropics. At the same time, the increase in surface
saturation humidity associated with the warming will increase the amount oflatent energy available in surface air. It is unclear how these two influences
will change the structure, frequency, and intensity of tropical convective cloudsystems. In addition, the changed large-scale circulation could influence theproperties of trade cumulus and stratocumulus clouds, which are known toTemporal and Spatial Variability of Clouds and Related Aerosols 137
have a significant influence on the energy balance of Earth. None of thesecloud effects are well understood, and it is likely that global climate models
do not incorporate them accurately. It is unclear how these macroscale changes
will interact with cloud changes associated with changes in aerosol loading,but they are likely to be as important and are not any better understood (cf.
Bretherton and Hartmann, this volume).
What data and approaches are available to address cloud macroscale effects? The primary means to address these issues is to gain a complete understanding of the relevant processes, to develop models of these processes,
and to test these models rigorously against data. Field programs to addressthese issues have been mounted in the recent and distant past (e.g., for tropicalclouds GATE, TOGA-COARE, KWAJEX). Satellite data are providing a richdataset which can be used to validate certain aspects of the cloud simulation.
For example, MODIS cloud data provide a good representation of the opticalproperties of convective clouds that are important for the Earth’s energy balance, such as the area covered by clouds with particular optical depths andcloud-top temperature pairings. These data can be provided on scales (~5 km)that can be used to test these properties, as they are simulated by a hierarchyof models, including models with high spatial resolution, often called cloudresolving models. CloudSat and CALIPSO are producing information on theinternal vertical structure of these clouds. AIRS is providing global analyses
of water vapor and temperature profiles that are coincident with MODIS andCloudSat/CALIPSO data. Reanalysis datasets are providing estimates of global
six-hourly analyses of large-scale vertical and horizontal winds that are much
more accurate and reliable than previous estimates, even over remote ocean
areas. Vigorous testing of emerging models with emerging datasets should leadto progress in understanding and simulating macroscale cloud processes thatare critical to climate change.

Existing Cloud, Aerosol, and Radiation Records
Need to be Reprocessed, and the Continuity of theCurrent Observing System Must be Guaranteed
It is very challenging to extract decadal trends from the current observational
record for aerosols, clouds, and broadband radiative flux (energy balance).
Prospects for improved understanding include (a) reanalysis of existing data,
where the possibility exists of correcting known problems; (b) regional-scale
analysis, where large secular changes in aerosol emissions and climate change
signals are known; and (c) maintaining and expanding high-quality observations into the future.
Since several decades may be needed for human-induced trends in cloudiness and radiation flux to rise above natural variability, it is essential that we
improve the satellite record as well as sustain and develop further our present

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observing system. Currently, the available satellite cloud and radiation datasetswith lengthy records, sometimes back to around 1980, are generally not usefulin analyzing long-term changes that result from the presence of many inhomogeneities and artifacts. These arise from changes in satellite instrumentation, degradation of sensors, drifts of satellite orbits, and shifts in satellite view
angle. Most data originated from weather satellites, which were never intended
to be an accurate and stable observing system. Careful processing must therefore be applied to produce a homogeneous record (as is the case for nearly allother climate measurements). Although the amount of resources needed for thistask is very small compared to the cost of a satellite, the provision thus far has
been inadequate. Most previous corrections and adjustments to satellite records
have had an empirical and statistical origin, whereas it would be preferable todevelop better physical models for the algorithms. New information gained
from recent satellites (e.g., CERES, MODIS, CloudSat, and CALIPSO) will
be useful for interpreting the more limited measurements of the older record,
and multiple satellite datasets can be brought together to provide a “best guess”
of trends and variability. Even if global trends in the reconstructed satellite
record suffer from large uncertainties and unknown systematic biases, regional
trends are likely to be more robust. The importance of clouds and aerosols in
our understanding of the climate provides a strong motivation to overcome thedifficulties in getting the best use out of the satellite radiance record.
It is clearly important to make the best use of the existing archive of satellite data on clouds. Reprocessing this data with new or improved algorithms
is one approach. Another idea with great potential is to assimilate the satellite radiances into numerical weather prediction (NWP) models to produce reanalyses that include clouds. The existing reanalyses from NWP centers, suchas the European Centre for Medium-Range Weather Forecasts (ECMWF) andNational Centers for Environmental Protection (NCEP), have created consistent long-term datasets for dynamic and thermodynamic variables. Until recently, NWP centers have avoided the assimilation of cloudy radiances, andthe representation of the hydrological cycle has been only partially successful. However, methods for addressing cloud parameters (e.g., radiances) arenow being introduced, and this suggests the possibility of using the reanalysis
methodology to add clouds to the list of variables and to perform a complete
reanalysis of the hydrological cycle. Among other things, this would require
knowledge of the error characteristics of the data and a reasonably good simulation of clouds in the model. Although challenging, this objective could deliver datasets with a consistent representation of both clouds and the meteorology, which has thus far been lacking. Currently, data assimilation systems
are also being used to provide analyses of the global distribution of aerosols(work in progress at ECMWF). Adding clouds would enable the relationships
between the meteorology, clouds, and aerosols to be studied within a consistent framework—a necessary step toward understanding the global impact ofaerosols on clouds and climate.

Temporal and Spatial Variability of Clouds and Related Aerosols 139
Top-of-atmosphere (TOA) radiation budget for detection of cloud feedback
requires the ability to detect global trends on the order of 0.3 W m–2 per decade(Loeb et al. 2007). This is extremely challenging and yet critical if we are to
improve our knowledge of climate sensitivity. In addition to continuing thesatellite programs dedicated to this objective (ERBE/CERES), it makes sense
to pursue independent approaches:
1.

2.

3.

Monitoring ocean heat content provides a similar capability to detect
interannual variations in global energy budget through an entirely independent method.
The Earthshine method appears promising, even though initial implementation at a single site may have been flawed. This is a low-cost
method such that maintenance of a consistent record over the next three
decades (or longer) would not be difficult. Expansion to additional sites(to provide more nearly global coverage) and analysis by other groups
would have potentially high value.
Whole-earth monitoring from Lagrange point L1 (DSCOVR mission)
would provide a continuous, well-calibrated albedo proxy with potential diagnostic value.

Surface sites provide direct measurements of many variables relevant to theaerosol–cloud problem. Variables currently measured include the surface radiation budget (e.g., BSRN, GEBA) and remote sensing of AOD and Ångström
coefficient (e.g., AERONET). There are a few sites with a comprehensive range
of instruments (e.g., ARM, Cloudnet) that extend the list of variables to include
active sensing of the vertical structure of clouds and aerosol layers. Although
the distribution of these sites is extremely heterogeneous and is limited to land,
they provide a wealth of direct measurements that are used in process studies
to detect trends in surface radiation, to support field studies that evaluate thesimulation of clouds and aerosols in operational and climate models, and as“ground truth” for satellite retrievals. Compared with new satellites, these sites
are relatively inexpensive to establish and maintain. Continuation and indeed
expansion of these sites for the coming decades should therefore be a high priority in planning a well-balanced observation system for monitoring aerosols
and clouds.

New Strategies Are Required to Observe Key
Variables More Accurately for Understanding
Perturbed Clouds in the Climate System
Because of the complex and coupled nature of the Earth system, scientificprogress requires tight integration of modeling and observational efforts. Bothmodels and observations exist across an extremely wide range of scales, andintegration across these different scales is probably the most challenging aspect

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of developing a coherent scientific strategy in climate research. In addition,
there are the usual challenges of developing effective collaborations acrossvarious subdisciplinary divides (e.g., in-situ/remote sensing, aerosol/cloud, or
laboratory/field).
A general strategy for fostering a fully integrated approach would involve(a) modelers framing specific (but realistic) data needs, (b) designing measurement and data archiving protocols that are tailored to modeling needs, (c) getting experimentalists from different groups and communities to combine andsynthesize their results intelligently, and (d) getting modelers to actually use
the data products. These goals can be facilitated by focusing research efforts on
broadly defined domains in a way that attracts the involvement of experimentalists and modelers from many subdisciplines.
In terms of detecting aerosol effects on the global cloud system, some strategic principles include (a) looking for regional/temporal signatures wherelarge aerosol variations are known to exist, (b) developing testable hypotheses,(c) including model validation in experimental design, and (d) combining
chemical/physical approaches into one integrated strategy. Aerosols are veryvariable in time, so it is inherently challenging to compute their integrated effect on clouds and climate. This challenge constitutes a wonderful opportunity
for integrating modeling and observational approaches at many scales.
Another strategic challenge is to achieve “critical mass” in field experiments. For example, if we do not get good observations of vertical profile ofcloud microphysics, we really have nothing with which to build or constrain amodel; that is, there is no way to understand the controlling processes. Putting
this in context of large-scale observables and bringing in detailed modelingcapability requires a major, coordinated effort.
Strategies for Integrating Modeling/Measurement Activities
One successful strategy is to have modelers in the field during field campaigns.
This builds relationships, gives modelers a chance to learn what types of data
are realistic, and gives measurement people a chance to become familiar withmodeling capabilities and needs. This strategy works well for detailed processmodels, but a more challenging problem consists of engaging the global climate modeling community. As fully coupled models, GCMs are difficult to
run for case studies. Recent projects like VOCALS and AMMA are attempting to integrate modelers working across a full range of temporal and spatial scales. For example, large-scale climate links associated with low clouds
affecting ENSO.
Improving GCMs Is Not Straightforward
It is very easy to identify deficiencies in GCMs and yet very hard to correct
them. One of the sad aspects of GCMs is that an improved parameterization

Temporal and Spatial Variability of Clouds and Related Aerosols 141
based on successful field measurements may result in a worse simulation.
Why? Implementing any change in a GCM will effect multiple aspects of themodel, often degrading model performance in unexpected ways. Every GCM
group has wonderful counterintuitive examples of such effects. Thus, implementing a significant change requires that the model be re-tuned to ensurethat it continues to simulate successfully known aspects of the climate system.
Tuning a GCM, in turn, is a complex, multidimensional optimization problem.
Given the importance of GCMs to climate science (not to mention society), it
is rather shocking how few people are working on developing and implementing model improvements.
Need for Consistent Global Data
Measurement protocols must be globally consistent to be able to identify geographical differences, trends over time, and to allow valid interpretation ofsatellite data. Such consistency is facilitated by coordinated monitoring networks (Kinne et al., this volume) that include uniform and rigorously enforced
quality control and data analysis procedures. The cost and effort required toestablish and maintain such programs is not trivial, although they generally
amount to a small fraction of the cost of launching a satellite. There is thus aneed for improved financial support for ground-based networks and integrated
data analysis.
A major gap in current scientific strategies involves extrapolating from detailed local measurements to consistent global datasets. Effective new strategies will need to include satellites, models spanning the entire range of scales,and mathematical methods to account for varying measurement/retrieval uncertainties as well as spatiotemporal variability. One example is the need forconsistent, global aerosol datasets. “The complexity of the aerosol–climate
problem implies that no single type of observation or model is sufficient to characterize the current system or to provide the means to predict aerosol impacts
in the future with high confidence. Consequently, information must be drawn
from multiple observational and theoretical techniques, platforms, and vantage
points, and strategies that explicitly plan for the integration and interpretationof the various components need to be designed” (Diner et. al. 2004, p. 1492).
Scaling up the Results of Field Studies to Large Models
Using measurements to impose constraints on models that exist at the same
scale is difficult enough. For example, constraining large eddy simulation(LES) models with in-situ measurements runs into the difficulty that data areneeded simultaneously throughout the domain. It is even more challenging todevise ways of using measurements obtained at one scale to constrain a model
at a larger scale. However, this is essential in the development of regional- andglobal-scale models. At present, there is some effective collaboration between

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the global climate modeling and measurement communities, a leading example
being the GEWEX Cloud System Study (GCSS). However, for the perturbedcloud problem, there is no framework at present. An obvious suggestion, therefore, would be to establish a GCSS working group on perturbed clouds.
Specific Examples and Proposals
Weekly cycle of aerosols. Look for cloud changes associated with the weekly
cycle of industrial aerosol emissions (the “weekend” effect). A weekly cycle ofdiurnal temperature range has been observed in many of the world’s industrialized regions, suggestive of aerosol effects and possibly involving aerosol–cloudinteractions (Forster and Solomon 2003; Gong et al. 2006). Research aimed atbetter elucidating the physical mechanisms would be useful. Advantages ofthis technique are that it involves short timescales (no need to observe longterm trends) and that it can make use of current observational capabilities.
Regional aerosol studies. Detection of aerosol effects becomes less daunting if we go to the regional scale, where the perturbation is large. Coherent
regional patterns should be easier to detect and attribute to causes.
Amazon smoke investigation. The aerosol perturbation associated with dryseason biomass burning over Amazonia is massive, yet highly localized insome cases. The forest provides a uniform environmental context such that
there should be minimal meteorological differences between impacted andunimpacted locations. In addition, there are year-to-year variations, since theamount of burning varies with socio-economic factors like the price of soybeans. A problem that must be confronted (in this and other regions) is sortingout the direct thermal effect of aerosol from microphysical effect on clouds.
Recent intensification of industrial emissions in China. Currently, industrial emissions over China constitute a massive aerosol perturbation that has
been growing over the past several decades. Several datasets exist for assessing changes in surface radiation and cloudiness in conjunction with the aerosol
increases. One suggestion is that aerosol particles are so absorbing that theychange deep convection. Investigating this poses an interesting methodological problem. One would want to compare model simulations of the convection
with and without the observed aerosol (or with the varying aerosol concentrations observed in the different cases). However, mixed-phase convection is sopoorly understood that it is questionable whether model sensitivities to evenlarge aerosol perturbations could be trusted. Investigating aerosol effects on
cloud cover would have to take care that cloud cover observations were notdirectly affected by high aerosol concentrations (Charlson et al. 2007). A moregeneral problem is that the direct thermal effects of the aerosol could alter themeteorological context (e.g., affect the strength of the monsoon circulation oralter convective instability by changing the height of latent heat release). This
could lead to cloudiness changes, which need to be separated from aerosolinduced microphysical changes.

Temporal and Spatial Variability of Clouds and Related Aerosols 143
Ice formation problem. A sustained laboratory program is needed to identify precisely which particles act as ice nuclei and by which mechanisms.
Thereafter it should be possible to design a new generation of in-situ airborne
devices to identify them. Extensive field programs with airborne in-situ sensors for ice nuclei and ice particle size spectra, coupled with ground-based orairborne multi-parameter Doppler cloud radars and lidars sensing the same
parameters, should provide a more complete description of the developmentof frozen precipitation.
Ice formation problem. It is known from field experiments and cloud modeling studies that secondary ice multiplication (e.g., splintering of existing ice
crystals or the so-called Hallett–Mossop effect) are important factors in clouddevelopment and for the initiation and intensity distribution of precipitation.
Only a few laboratory investigations exist on secondary ice formation. Morelaboratory experiments are needed to investigate and quantify secondary ice
formation, if possible for relevant ice crystal shapes and sizes, under conditions (temperatures, saturations, and turbulence) resembling those encounteredin real clouds. Such experiments may be conducted with single droplets and ice
crystals or in cloud simulation chambers.
Precipitation. Integrate observed changes in precipitation (Takayabu and
Masunaga, this volume) with observed changes in aerosol and other potentiallycontrolling factors.
LES validation for convective clouds. Cumulus updrafts play a central rolein the vertical transport of water, momentum, trace chemicals, and heat. Still,there are inadequate observations of the statistical distribution of buoyancy,
vertical velocity, and condensate in cumulus updrafts, as a function of cloud
depth and aerosol profiles, and surface forcing environment. Such measurements are key constraints on LES and cumulus parameterizations. They arechallenging to make from aircraft because of the associated sampling challenges and wetting issues, but shortwave-length radar-based remote-sensing
techniques may provide a new opportunity to gather such statistics.
Cloud overlap representation in GCMs. Currently, GCMs predict cloud
fraction in each vertical level. Uncertainty as to how to distribute this fractional cloud, both as a function of horizontal resolution and as a function of
the distribution of clouds in the other layers of the model, gives model developers considerable freedom in distributing the modeled cloud amount so as
to satisfy the radiative constraints at the top of the atmosphere. Experience
with different GCMs shows that although they may have large layer-by-layer
differences in cloud fraction and cloud amount, suitable choices for the cloud
overlap assumption enables each to match the top of the atmosphere radiative
constraints. Therefore, providing measurements of cloud overlap would provide further and valuable constraints on the model.
Observational study of cloud lifetime. Satellite measurements with highspatial resolution typically lack temporal resolution. Being able to measure
the temporal evolution of clouds as well would provide a valuable constraint

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on process models of clouds and their environmental interactions. In this regard, for instance, a geostationary observatory that focused a large telescopeon particular cloud scenes (to complement field measurements or in-situ studies) would be invaluable.
Spectrally resolved radiation budget measurements from space. Stable andaccurate observations of changes in the Earth’s radiation field over several decades are needed to quantify the forcing and radiative response of the climate
system. For the purpose of quantifying trends in the Earth’s radiation field,
spectrally resolved measurements are especially useful for separation and attribution of the radiative effects from climate forcing and climate response(Goody et al. 1998). The small magnitude of the effects and long integration
times required for detection together imply that very stable, absolutely calibrated satellite instruments are required. Several groups are now exploringsatellite radiometers designed to detect the radiative forcing, thermal response,
and radiative feedbacks in the Earth’s climate system. Spectral radiometers
can be developed with absolute calibration against traceable standards to insure that trends in the observations are as free as possible of instrumental artifacts (Keith et al 2001; Anderson et al. 2004). Potentially both the infrared andultraviolet/visible/near-infrared radiation fields could be measured with such
instruments. Although the feasibility for detection of infrared GHG forcing
has been amply illustrated in modeling and satellite studies (e.g., Haskins etal. 1997), it is important to recognize that the utility of the infrared measurements or detection of longwave cloud feedbacks remains unproven (Leroy etal. 2008). The advantages of the ultraviolet/visible/near-infrared radiation datafor detection and estimation of shortwave forcings and feedbacks have notbeen demonstrated in detail.
Earth observations from Lagrange point L1. Satellites deployed at Lagrange
point L1, which are designed to measure the radiation emitted by the sunlightside of the Earth (e.g., the Deep Space Climate Observatory, DSCOVR; Valero
et al. 1999), could provide valuable long-term measurements. DSCOVR would
include several single-pixel NISTARs with a ground-based calibration chain
tied directly to primary national standards. These instruments would measure
the total solar, near-infrared, and infrared radiance field emitted by the Earth in
the direction of Lagrange point L1. The inherent stability and traceable calibration of these instruments are ideally suited for the detection of secular trends inthe Earth’s short- and longwave radiation.

Acknowledgments
T. L. Anderson acknowledges support from the U.S. National Science Foundation(grants ATM-0601177 and ATM-0205198).

Temporal and Spatial Variability of Clouds and Related Aerosols 145

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