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OCR for page 21
CLOUD, WATER VAPOR, AND LAPSE
RATE FEEDBACKS
SUMMARY
Cloud feedback and its association with water vapor feedback and lapse
rate feedback appear to be the largest contributors to uncertainty in climate
sensitivity and is therefore one of the key uncertainties in projections of
future climates. Improvements are particularly needed in the treatment of
marine boundary layer clouds and tropical convective clouds. Progress on
better understanding cloud feedback seems possible now because: (1)
current climate models have predictive cloud schemes that produce
important effects on climate sensitivity; (2) new data are becoming available
that can be used to test these new climate models; and (3) cloud-resolving
models have emerged as a new tool for understanding and testing cloud
feedback processes in climate models.
An accelerated, focused effort to test the simulation of cloud, water
vapor, and lapse rate feedbacks in climate models, and their role in climate
sensitivity should be initiated. Existing and planned observations should be
used in this new emphasis to test the simulation of clouds, water vapor, and
lapse rate in climate models and the response of these variables to known
forcings. Effective collaboration among efforts to diagnose observations, to
model cloud systems, and to model the global climate is essential. A set of
observable metrics should be used to evaluate the success of these activities.
WATER VAPOR
Water vapor feedback is the most important positive feedback in climate
models. It is important in itself, and also because it amplifies the effect of
every other feedback and uncertainty in the climate system. Most modeling
21
OCR for page 22
22
UNDERSTANDING CLIAL1 TE CHANGE FEEDBACKS
and observational studies suggest that the water vapor feedback in current
climate models has the correct sign and magnitude (Held and Soden, 2000~.
The magnitude of water vapor feedback is so large, however, that modest
uncertainty in water vapor feedback can still have a significant effect on the
magnitude of climate change.
It is known from basic physical principles that the vapor pressure in
equilibrium with a water surface increases exponentially with temperature at
a rate such that a 1 percent change in absolute temperature, a change of
about 3°C, is associated with an approximately 20 percent increase in
saturation vapor pressure. Because water vapor is the most important
greenhouse gas in Earth's atmosphere, the dependence of vapor pressure on
temperature forms the basis of one of the strongest positive feedbacks in the
climate system. If the relative humidity distribution remains approximately
constant as temperature and specific humidity increase, then water vapor
greenhouse feedback nearly doubles the sensitivity of climate above what it
would be in the absence of water vapor feedback.
On the largest spatial scales, existing data and current climate models
are basically consistent with the assumption that on interannual time scales,
relative humidity is more or less constant (Soden et al., 2002; Wentz and
Schabel, 2000~. However, local diurnal and seasonal relative humidity
variations are significant, and analysis of climate model simulations of these
features is needed. Furthermore, the relationship between temperature and
humidity on interannual and longer time scales shows substantial vertical
and regional structure, which models are only partly successful in simulating
(Bates and Jackson, 1997; Bauer et al., 2002; Ross et al., 2002~.
As shown by modeling and observational studies (Del Genio et al.,
1991; Harries, 1997; Held and Soden, 2000; Shine and Sinha, 1991; Soden
et al., 2002), water vapor variations in the tropical upper troposphere seem to
have the strongest effect on outgoing long-wave radiation. However, the
relative importance of water vapor in different regions of the atmosphere is
sensitive to the assumptions made about clouds and about the variations (or
lack thereof) of relative humidity with temperature. In fact, according to
Harries (1997), "Uncertainties of only a few percent in knowledge of the
humidity distribution in the atmosphere could produce changes to the
outgoing spectrum of similar magnitude to that caused by doubling carbon
dioxide in the atmosphere," underscoring the importance of reliable upper
tropospheric water vapor observations.
Uncertainty about water vapor feedback rests primarily on the question
of whether the relative humidity distribution might change in an altered
climate state. Several hypotheses have been put forward describing
mechanisms that could alter the relative humidity distribution in a warmed
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CLOUD, WATER VAPOR, AND LAPSE RATE FEEDBACKS
23
world. The mechanisms that seem most likely to be meaningful are those
that may govern the relationship between the area of moist and dry regions
in the upper troposphere of the tropics (Lindzen et al., 2001, Pierrehumbert,
1995~. In the tropics the greenhouse effect is strong, and large contrasts in
upper tropospheric relative humidity are sustained between regions of large-
scale ascent and descent. So far, no mechanism has been demonstrated to
operate that would provide a significantly more reliable projection than an
assumption of constant relative humidity distribution. Nonetheless, the
factors that influence water vapor distribution need further study.
One useful metric for evaluating the question of whether relative
humidity will change was put forward by Inamdar and Ramanathan (1998~.
Using Earth Radiation Budget Experiment (ERBE) data they examined the
relationship between outgoing long-wave radiation at the top of the clear
atmosphere and surface temperature. They found the slope of the regression
line between these two variables to be consistent with an assumption of
fixed relative humidity (vs. absolute humidity). Using this approach it is
possible to compute a gain factor of the clear-sky water vapor feedback.
Gain factors determined using this and other observational approaches
should be compared with the factors similarly derived from models. This
approach is discussed here for to illustrate only one of the many approaches
that can be used to assess the ability of models to faithfully represent water
vapor feedbacks.
Understanding of the water vapor distribution is being hindered by a
lack of accurate measurements of water vapor concentration with sufficient
spatial and temporal resolution and global coverage (Kley et al., 2000~.
Accurate measurements of the water vapor distribution can be used to test
understanding of the mechanisms that determine its distribution, and also
test to see if the increase of water vapor with time is consistent with models
of climate change. An integrated water vapor observing system should be
developed, which has sufficient accuracy to measure decadal trends in the
water vapor distribution and sufficient spatial resolution to test mechanisms
by which that distribution is maintained. It should include a network of in
situ sounding systems capable of measuring water vapor throughout the
troposphere and lower stratosphere, complemented by ground-based remote
sensors (such as have already been deployed at Atmospheric Radiation
Measurement Project Cloud and Radiation Testbed (ARM CART) sites).
These observations would allow quantification of temporal and vertical
water vapor variations and would allow calibration and validation of satellite
observations, which would extend the global coverage of the observing
system.
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24
UNDERSTANDING CLIMATE CHANGE FEEDBACKS
The global radiosonde network cannot be relied upon for precise water
vapor observations, unless substantial improvements are made to ensure
higher quality observations in the upper troposphere and in other cold (and
dry) regions, and to ensure the long-term continuity of the observations.
Expansion of the program for special water vapor soundings of the
troposphere and stratosphere (e.g., Oltmans and Hofmann, 1995) to more
sites (currently only Boulder is routinely observed) would be very beneficial.
These should include both oceanic and continental regions, at a variety of
latitudes. Efforts to consolidate and quality-control water vapor observations
from different sources (e.g., the NASA Water Vapor Project [NVaP], Randel
et al., 1996) should also be encouraged, so that water vapor variability can
be examined in conjunction with variations in other atmospheric variables,
particularly temperature and radiation. The water vapor observing system
should be closely linked to a global cloud, aerosol, and precipitation
observing system. Many of the issues mentioned above are discussed in
greater detail in a report by the National Research Council (NRC) on the
Global Energy and Water Cycle Experiment (GEWEX) Global Water Vapor
Project (NRC, l999c).
LAPSE RATE FEEDBACK
The strength of Earth's greenhouse effect depends on the fact that the
temperature decreases with height in the troposphere, so that emission from
water vapor and clouds in the colder upper troposphere is less than that from
the surface. A stronger lapse rate (the rate of decrease of temperature with
altitude) gives rise to a stronger greenhouse effect and a warmer surface, all
else being equal. If the lapse rate changes systematically with the surface
temperature, then a potentially strong lapse rate feedback may exist.
Radiative processes, large-scale dynamical processes, and convection
determine the lapse rate. Radiative processes generally cool the atmosphere
and heat the surface, and convection and large-scale motions in the
atmosphere generally move heat upward. In the tropics the lapse rate
generally follows the moist adiabatic lapse rate, the rate at which saturated
air parcels cool with altitude as they are raised adiabatically. The moist
adiabatic lapse rate decreases with increasing surface temperature, so by
itself lapse rate feedback is expected to be negative in the tropics (Hansen et
al., 1984; Wetherald and Manabe, 1986~.
If the assumption of fixed relative humidity is a good approximation,
then the water vapor feedback is partially cancelled by the lapse rate
feedback (Cess, 1975~. If the lapse rate is reduced, then the air at altitude is
OCR for page 25
CLOUD, WA TER VAPOR, AND LAPSE RA TE FEEDBACKS
25
warmer. The warmer air contains more water vapor. The decreased
greenhouse effect caused by a weaker lapse rate is offset by the increased
greenhouse effect from larger amounts of water vapor at higher altitudes.
Patterns of vertical temperature structure change are one of the few
parameters widely used to detect and attribute climate change to particular
forcings, or to natural variability (e.g., Tett et al., 2002~. (Surface
temperature changes are the other main detection parameter.) If climate
models correctly simulate climate feedback mechanisms, they should
correctly reproduce the change in vertical temperature structure associated
with different climate forcings. Thus, changes in lapse rate are indicators
both of the strength of lapse rate feedback and of the response to climate
~ .
torclngs.
Climate models generally reproduce the observed lapse rate in the
tropics and elsewhere through the incorporation of large-scale dynamics and
parameterized convection and radiation. Some observations suggest
relationships between surface temperature trends and temperature trends in
the free troposphere that seem inconsistent with the behavior of current
climate models (NRC, 2000a; Santer et al., 2000~. It is still unclear whether
these apparent inconsistencies are the result of a measurement problem or a
failure of our understanding of the climate system.
Unfortunately, current upper-air temperature observations are not well
suited to determining lapse rate changes. The vertical resolution of satellite
observations is too coarse for accurate lapse rate computations, although
newer instruments (e.g., the Advanced Microwave Sounding Unit) provide
better vertical resolution than older ones (e.g., the Microwave Sounding
Unit). Both satellite and radiosonde observations are hampered by time-
varying biases, which are very difficult to remove (NRC, 2000a). Lapse rate
trends are particularly sensitive to attempts to remove these biases (Lanzante
et al., in press). Similarly, trends in measures of atmospheric instability and
convection that are related to lapse rate (e.g., Convective Available Potential
Energy and Convective Inhibition) are affected by radiosonde data
inhomogeneities (Gettelman et al., in press). Thus, to improve our ability to
diagnose lapse rate feedback and to detect changes in the vertical
temperature structure of the atmosphere, improved long-term upper-a~r
temperature soundings are required. The observations must be of sufficient
precision to measure decadal trends in temperature (and water vapor)
distributions and sufficient spatial resolution to test mechanisms by which
those distributions are maintained. More information concerning upper-air
temperature monitoring requirements can be found in NRC (2000c).
Using the improved observations that are recommended here,
correlation statistics of temperature, water vapor, and clouds on various time
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26
UNDERSTANDING CLIMATE CHANGE FEEDBACKS
and space scales should be employed to rigorously diagnose the ability of
models to simulate the feedbacks that underpin interannual variability of the
lapse rate and water vapor distributions. Extending the work of, for example,
Ross et al. (2002), Sun and Held (1996), and Sun and Oort (1995), these
analyses should be focused not only on improving understanding of the
feedback processes and their representation in models but also on deriving
new, parsimonious model representations of these processes. Several
existing national and international programs (e.g., ARM and GEWEX) could
be very helpful in facilitating this work.
CLOUD FEEDBACKS
Because clouds are generally colder than the surface they overlie and
because they absorb and emit terrestrial radiation, the presence of clouds
generally reduces the energy emitted to space from Earth relative to the
emission from Earth when clouds are absent. For terrestrial radiation, clouds
thus act very much like greenhouse gases and warm the surface of Earth.
Clouds also reflect solar radiation very effectively, which reduces the
amount of solar energy reaching the surface of Earth. This tends to cool the
surface. Different cloud types have different effects on the energy balance of
Earth (Hartmann et al., 1992~. If the structure or area coverage of clouds
change with the climate, they have the potential to provide a very large
feedback and either greatly increase or decrease the response of the climate
to human-caused forcing. At this time both the magnitude and sign of cloud
feedback effects on the global mean response to human forcing are
uncertain.
It has been well documented that climate models are sensitive to the
representation of clouds and their radiative properties (e.g., Cess et al., 1990;
Paltridge, 1980; Schneider, 1972, Senior and Mitchell, 1993; Stocker et al.,
2001; Webster and Stephens, 1984~. A relatively modest change in cloud
properties can have a significant effect on Earth's energy balance. In
addition to their influence on the radiative processes that define the energy
balance of the planet, clouds processes are integral to the cycling of water
between the surface and the atmosphere.
A striking example of the contribution of cloud feedbacks to uncertainty
in climate sensitivity is exhibited by comparison of the current climate
models at the Geophysical Fluid Dynamics Laboratory (GFDL) and the
National Center for Atmospheric Research (NCAR). The GFDL model has a
rather high sensitivity (near 4°C for doubled CO2) while the NCAR model
has a rather low sensitivity (near 2°C). The primary reason for this difference
OCR for page 27
CLOUD, WATER VAPOR, AND LAPSE RATE FEEDBACKS
27
is in the response of low marine boundary layer clouds in the two models.
As the climate warms, marine boundary layer clouds decrease in the GFDL
model and increase in the NCAR model. Comparison of these two models
with observations, theory, and cloud-resolving model computations should
lead to much greater understanding of the response of marine boundary layer
clouds to changing climate, and a consequent reduction in uncertainty of
climate sensitivity.
Another key uncertainty in cloud-climate interactions is the response of
anvil clouds to surface temperature. It is unknown whether anvil clouds
expand or contract when surface temperature warms. A combination of
detailed observational studies and cloud-resolving modeling studies can shed
light on this issue. Some models incorporate a cloud optical thickness
feedback that assumes cloud water content will increase with temperature
following the saturation vapor pressure, but satellite and in situ data do not
show an obvious signal of this nature, and low clouds show an apparent
signature in the opposite sense (Tselioudis et al., 1992~.
Clouds couple many feedback processes in the climate system. Some of
the interactions of clouds with other feedback processes are illustrated
below.
Clouds and Water Vapor Feedback
The formation and evaporation of clouds are intimately tied to the
amount of water vapor in the atmosphere. The amount of water vapor and its
vertical distribution are also influenced by the amount and distribution of
clouds. For example, a number of studies have shown very clearly how the
water vapor in the middle to upper troposphere is sensitive to the presence of
ice crystals, the nature of the microphysical properties of these ice crystals,
and the way these crystals fall in the atmosphere. (e.g. Donner et al., 1997;
Stephens et al., 1998~. Vertical transport of water in both vapor and ice form
by convection in the tropics is an important source for upper tropospheric
water vapor (Pierrehumbert and Roca, 1998; Salathe and Hartmann, 1997;
Udelhofen and Hartmann, 1995~. The broad role of water vapor feedback in
climate change and the specific importance of upper tropospheric water
vapor cannot be divorced from the associated role of clouds and cloud
feedbacks.
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28
UNDERSTANDING CLIME TE CHANGE FEEDBACKS
Clouds, Lapse Rate, and Precipitation
The vertical distribution of clouds is an important factor in determining
radiative heating. In turn, radiative heating is closely coupled to the
temperature profile, convective heating, and precipitation. A number of
modeling studies have illustrated how the radiative effect of cloudiness, the
vertical profile of temperature, convection, and precipitation are tightly
coupled (e.g. Fowler and Randall, 1996; Liang and Wang, 1997; Ma et al.,
1994; and Slingo and Slingo, 1988~.
Clouds and Sea-Ice Albedo
Ice albedo feedbacks that may occur in polar regions are tightly coupled
to the surface energy balance and to clouds. Clouds can change the heat
balance of the surface and influence surface ice formation and melting, and
overlying clouds can mask the effect of surface ice on the albedo of Earth. A
complex coupling thus exists between cloud feedbacks and ice-albedo
feedback processes (see Chapter 4~.
Clouds and Soil Moisture
The feedbacks involving soil moisture and evaporation are intimately
tied to the hydrological cycle over land (see Chapter 6~. Clouds are central to
soil moisture feedbacks both through their profound influence on the surface
energy balance and through their association with precipitation. The
relationships among soil moisture, boundary layer humidity, and cloudiness
serves as a possible mechanism for a strong, coupled feedback between
clouds and the underlying land surface.
Clouds, Chemistry, and the Marine Biosphere
The effect of changing concentrations of cloud and ice condensation
nuclei (CCN and IN, respectively) on clouds and precipitation has received
much attention recently (e.g., Durkee et al., 2000~. The association between
aerosol forcing, cloud nuclei, and cloud processes provides a path that links
clouds to oceanic emissions of dimethyl sulphide (DMS) and to gas phase
chemistry (e.g., Charlson et al., 1987; Coakley et al., 1987~. The
consequences of these links are twofold. In the case of DMS emissions they
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CLOUD, WA TER VAPOR, AND LAPSE RA TE FEEDBACKS
29
provide additional feedback mechanisms if the production of nuclei depends
on temperature or solar radiation reaching the surface. In the case of
increasing aerosols the relation between aerosols and condensation nuclei
connects cloud processes to the broader problem of estimating climate
forcing through the so-called indirect aerosol forcing.
Cloud Radiation Processes
Clouds affect both the radiation balance at the top of the atmosphere and
the distribution of radiative heating between the atmosphere and surface.
The Effects of Clouds on the Top-of-the-Atmosphere Energy Budget
The radiation budget of Earth is the difference between solar radiation
absorbed by the planet and terrestrial infrared (JR) radiation emitted to
space. Clouds affect this budget by reflecting sunlight back to space (the
albedo effect of clouds), thereby decreasing the solar radiation absorbed by
the planet, and by absorbing thermal radiation emitted by the surface and
lower atmosphere (the greenhouse effect of clouds), thereby reducing the
radiation emitted to space. The balance between these negative and positive
effects on the radiation balance depends on the type and location of the
cloud in question (Hartmann et al., 1992~. The albedo effect of low clouds
over ocean, for example, tend to dominate over their greenhouse effect and
produce a negative impact on Earth's energy balance, whereas the reverse is
generally true for high, thin cirrus. Satellite experiments like ERBE and
Clouds and the Earth's Radiant Energy System (CERES) provide a
quantitative measure of the instantaneous effects of clouds on the top-of-the-
atmosphere (TOA) radiation balance and confirm our understanding of the
effect of different cloud types on this budget. Although data collected from
these satellite experiments provide an important source of information for
testing models, they do not sufficiently constrain critical assumptions about
the treatment of cloud processes in climate models.
Measurements of radiative properties and inferred column-integrated
cloud optical properties as have been made over the past 20 years are
insufficient to advance understanding and modeling of cloud feedbacks.
What is needed are measurements of those key variables prognosed from
models that describe the underlying cloud physical processes. These
variables include the mass of liquid water and ice in clouds and precipitation
and how these water masses mutate, passing from the cloud to the
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30
UNDERSTANDING CLIMATE CHANGE FEEDBACKS
precipitation state. New global observations that will be relevant to
understanding cloud feedbacks and validating global climate models are
becoming available from new satellite measurements obtained from NASA's
Earth Observing System. New global data on cloud properties, water vapor,
and aerosols instruments is expected from instruments such as the Moderate
Resolution Imaging Spectroradiometer (MODIS), the Multiangle Imaging
SpectroRadiometer (MISR), and the Advanced Infrared Sounder (AIRS)
(Aumann et al., 2003; Diner et al., 2002; King et al., 2003) (See also the
subsequent, broader description of the global-scale observations that are
required.)
The Effects of Clouds on the Partitioning of the Radiation in the
Atmosphere and at the Surface
The reflection of solar radiation by clouds causes a strong reduction in
the energy balance of the surface because most of the solar radiation that is
not reflected is absorbed at the surface. At high latitudes where insolation is
weak and the atmosphere is relatively dry, the addition of clouds can heat
the surface through increased downward IR emission by the atmosphere.
Whether cloud layers heat or cool the atmosphere relative to clear skies, and
the amount of this heating or cooling that takes place is largely determined
by the vertical location and distribution of the clouds. High clouds tend to
warm the atmosphere relative to surrounding clear skies, whereas low clouds
tend to enhance the cooling of the atmosphere. While the total incoming and
outgoing radiation at the TOA can be measured, the amount of radiative
heating that occurs within the atmosphere versus how much heating occurs
at the surface cannot be directly measured. Thus, model parameterizations of
the internal heating of the climate system cannot be tightly constrained by
observations. This shortcoming is a significant source of uncertainty in
understanding cloud feedbacks.
Clouds and the Large-Scale Circulation: The Cloud Parameter~zation
Problem
Developing and testing understanding of cloud fields and their
interactions with the larger-scale environment has proven to be difficult.
Many of the processes that control cloud feedbacks occur on scales smaller
than those resolved by large-scale models in use today. These processes are
thus parameterized, meaning that they are expressed in terms of large-scale
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CLOUD, WA TER VAPOR, AND LAPSE ~ TE FEEDBACKS
31
quantities that are resolved by models. The influence of these large-scale
properties on smaller-scale parameterized processes, and the subsequent
feedback of the latter to the large scale, is referred to as the cloud
parameterization problem. Three types of processes are critical: (1) cloud
physical processes, including processes that govern the life-cycle of cloud-
scale phenomena, (2) cloud radiative process, and (3) large-scale cloud
thermodynamical processes that determine the heating in the climate systems
and the associated atmospheric circulation.
Quantifying Processes That Govern Life Cycles of Large-Scale Cloud
Systems
Although the cloud processes that influence the radiation budget in
principle are numerous and occur over a vast range of scales, the dominant
scale of variability of cloudiness is the synoptic scale (e.g., Rossow and
Cairns, 1995~. Therefore, key first steps in understanding cloud feedbacks in
the global climate system require understanding processes that organize
clouds on this same large scale. These processes involve connections
between the general circulation of the atmosphere and the weather systems
that are a manifestation of this circulation, the formation and evolution of the
large cloud systems associated with these weather systems, and the latent
heating and radiative heating distributions organized on this larger scale.
Because the processes that govern cloud evolution are modulated by the
weather systems in which they are embedded, a fruitful strategy should
embrace the study of weather. Numerical weather prediction models, related
data assimilation activities, and synoptic data on weather and clouds should
be used to understand and model cloud and precipitation evolution over a
range of time scales from hours to weeks.
Day-to-day weather variations are carefully observed, assimilated into
models, and used to make predictions. Because day-to-day weather
variations include variations in cloud amount and type, these variations
should be used to test the ability of climate models to predict cloud
variations on these time scales (e.g., the testing of cloud simulations at the
ECMWF tHogan et al., 2001; Klein and Jacob, 19993~. This strategy has
several advantages.
.
Cloud feedbacks are currently diagnosed primarily by using coarse
resolution climate models and even simpler one-dimensional equilibrium
models. The use of Numerical Weather Prediction (NWP) models and
forecast validation will allow day-to-day weather variations and their
association with cloud variations to be used to validate models.
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32
.
UNDERSTANDING CLIMATE CHANGE FEEDBACKS
Connecting feedback diagnostic studies to NWP and data assimilation
efforts introduces a certain rigor to the exercise of model-data comparison
by tying the analysis methods more tightly to the observations and allowing
many more realizations.
· NWP and the data assimilation process offers a consistent way of
obtaining integrated datasets necessary for understanding processes deemed
important to cloud feedback.
Quantifying the Relationship Between Cloudiness and Radiative and
Latent Heating
A strategy for understanding the relationship between clouds and
precipitation in more quantitative detail requires a change in current research
practices. Research activities and observational practices for clouds and
precipitation are typically designed in isolation from each other. The
parameterization of the radiative effect of clouds is often treated separately
from the parameterization of precipitation. Advances will occur with the
adoption of a more integrated approach toward developing global cloud and
precipitation observing and projection systems.
Global precipitation, water vapor, and cloud-observing systems must be
designed in concert with one another so that the interconnectivity of these
processes can be better observed and understood. Similarly,
parameterization and projection systems must address these variables as part
of an interconnected system.
WHY HAS PROGRESS ON CLOUD, WATER VAPOR, AND LAPSE
RATE FEEDBACKS BEEN SO ELUSIVE?
The 1979 NRC report on carbon dioxide and climate contains the
following statement in reference to cloud effects on climate change:
Trustworthy answers can be obtained only through comprehensive
numerical modeling of the general circulations of the atmosphere and
oceans together with validation by comparison of the observed with the
model-produced cloud types and amounts.
This strategy remains valid today, but it has not yet been executed.
Three obstacles have heretofore limited the advancement of understanding
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CLOUD, WATER VAPOR, AND LAPSE RATE FEEDBACKS
33
of cloud, water vapor and lapse rate feedbacks: inadequate data, incomplete
theories, and untested projections.
Inadequate Data for Developing and Testing EIypotheses
Measurements of Earth's energy budget with sufficient accuracy for
climate studies began about 1985 with the Earth Radiation Budget
Experiment. Measurements of water vapor and upper tropospheric
temperature need to be improved in both accuracy and sampling. At the
present time cloud data come from two sources: surface visual observations
(Hahn and Warren, 1999) and meteorological imaging instruments on
operational satellites (Rossow and Schiffer, 1999~. The International
Satellite Cloud Climatology Project (ISCCP) data provide estimates of
cloud-top temperature and visible optical depth on spatial scales of tens of
kilometers. These data have seen only limited use in climate model
validation (e.g., Klein and Jakob 1999, Webb et al., 2001~. More of this kind
of analysis is needed. In addition, more detailed global observations of
clouds, including such things as vertical structure and cloud particle size, are
needed to test climate model parameterizations and their relationship with
precipitation, water vapor, and air temperature.
Current satellite data give only rather crude estimates of cloud particle
size and cannot readily distinguish cloud water from cloud ice. Passive
infrared sensing of cloud-top height is imprecise when the clouds are not
optically thick, which is an important constraint when studying high, thin
clouds in the tropics and elsewhere. New cloud data are becoming available
from instruments on satellites that use polarization of reflected sunlight and
active scanning with cloud radars and lidars to probe the vertical structure
and particle size of clouds. These data will provide an important new source
of data on the global distribution of clouds that should be used to further
constrain and test the simulation of clouds in climate models.
Incomplete Theories
Climate feedback hypotheses are necessarily concerned with large,
complex, and coupled systems that do not necessarily obey simple laws.
Most simplified feedback "theories" involving clouds consider only a
limited set of the critical processes, even though the neglected processes are
known to be important. For example, the rheostat hypothesis
(Ramanathan and Collins, 1991) argues that the sensitivity of tropical
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UNDERSTANDING CLIMA TE CHANGE FEEDBACKS
convective cloud albedo will constrain tropical sea surface temperature
(SST) below 303K. But the analysis implicitly assumes that spatial
variations of clouds and SST are a useful analogy for climate change, when
in fact they are not (Hardnann and Michelsen, 1993, Lau et al., 1994;
Wallace, 1992~. And, the Iris hypothesis (Lindzen et al., 2001) speculates
that the area of tropical anvil clouds will decrease with increasing SST, but
again the observational evidence uses a gradient with latitude as an analogy
for climate change, which it is probably not (Hartmann and Michelsen,
2002~. Simple theories for how clouds will respond to global warming are
difficult to test using observations, since only a small global warming has so
far been observed. It is easier to test the response to large forcings, such as
the annual and diurnal cycle, which are well observed and the response
amplitude is large.
The treatment of clouds in climate models is still highly simplified,
although current climate models are including more of the relevant physics
of cloud processes. New data to validate these models is becoming available
from measurement programs such as DOE's Atmospheric Radiation
Measurement (ARM) program and new satellite observations. In most
models, however, the key linkages between processes are often broken. For
example, the separation of parameterized convection from large-scale
cloudiness effectively decouples clouds from the model hydrological cycle.
This artificial separation creates problems when attempting to use models to
advance understanding on cloud and water vapor feedbacks. Additional
problems with cloud schemes in climate models include
.
the introduction of model resolution dependence to the
parameterizations of clouds. For example, the cloud processes represented
by the large-scale schemes are typically microphysical in nature. The
parameters that represent these processes have to be heavily tuned to the
scale resolved by the model. This introduces an unavoidable degree of
arbitrariness to the cloud feedback problem since global-scale cloud
observations of these processes needed for tuning are lacking.
· a growing confusion between those processes that are really represented
by the sub-grid-scale schemes and those processes that are represented by
the resolved scales. This in turn creates a further degree of arbitrariness as to
how to use existing observations (of precipitation, for example) to assess the
merits of different parameterizations.
cloud feedbacks currently addressed in global scale models chiefly
articulated in terms of the resolved cloudiness and thus chiefly in terms of
cloud radiation interactions. When averaged over time, the global energy
balance of the atmosphere is fundamentally between latent heating
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CLOUD, WATER VAPOR, AND LAPSE RATE FEEDBACKS
35
associated with precipitation and the radiative heating, and notably including
the contribution by clouds on the radiative heating (e.g., Stephens et al.,
1994), so that latent heat release and radiative heating must be closely
linked. The observed diurnal variation of convective precipitation is a
manifestation of the interplay between radiation and convection. It is
difficult to treat this kind of interaction in existing global models that deal
with precipitation and related processes (by sub-grid-scale convection) in
isolation from clouds and their radiative heating.
Untested Predictions
In general, quantitative tests of the role of clouds in global climate
change are difficult to devise since one cannot observe a climate change.
The best that can be done is to use a long record of climate, including cloud
information, and test the models ability to simulate the observed variability.
Such model evaluations require comparison datasets of relevant information
accumulated over extended periods of time. Developing long-term datasets
of even rudimentary parameters, let alone cloud parameters, has proven to be
difficult for a number of reasons, including the lack of dedicated global
monitoring and observing systems for this purpose (NRC, 1999a). Despite
these difficulties a number of valuable global datasets have been compiled
over the past two decades. That these datasets are underutilized is in part a
reflection of the attention that has been paid to model intercomparison, but
too little attention has been paid to testing models against data. The utility of
model-to-model intercomparison exercises for characterizing and reducing
uncertainty in climate change feedbacks is limited. Without rigorous and
multifaceted comparisons of models to as much data as possible, model
intercomparison activities tend to make the feedback processes behave
similarly to one another while generating no evidence that their consensus
behavior is any nearer to that of nature.
DEVELOPING A SCIENTIFIC STRATEGY
Despite the challenges described above, the potential for making
important strides in understanding is very high at the present time, for the
following reasons.
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UNDERSTANDING CLIMATE CHANGE FEEDBACKS
Improved Global-Scale Experimental Data
In the most sophisticated cloud-resolving models (CRMs), NWP
models, and climate models, the clouds are currently predicted in terms of
three-dimensional distributions of cloud water and ice, using conservation
equations for these quantities, so that fully prognostic cloud simulations
have become the norm. The ability of these models to simulate the three-
dimensional fields of water and ice correctly cannot be adequately tested at
the present time because of a lack of sufficiently detailed global data,
thereby thwarting model assessment and subsequent improvement. Datasets
from satellite- and ground-based observations are currently being developed
that would enable the validation of these more sophisticated models, if a
sufficient effort is made to do so. Examples of datasets include cloud and
aerosol data from Earth Observing System (EOS) instruments and cloud
vertical structure data from the Cloudsat satellite. Surface data from the
ARM provide a new capability to measure critical cloud properties at
selected locations (Masher et al., 1998; Stokes and Schwartz, 1994~. The
availability of global-scale data on precipitation, albeit confined to the global
tropics (Kummerow et al., 2000), as well as the near-future availability of
global cloud water and ice information from other planned satellite
measurements (Stephens et al., 2002), provides the much needed datasets for
evaluating cloud predictions under a variety of weather regimes.
Evaluating Model Predictions
Running models in a forecast mode is one way the link between heating
and circulation can be examined, at least in the context of testing the shorter-
time-scale feedbacks. Comparisons of the European Centre for Medium-
Range Weather Forecasts (ECMWF) NWP predictions of cloud cover and
occurrence, albeit limited in scope, show an encouraging degree of
agreement with existing data (Hogan et al., 2001; Klein and Jakob, 1999;
Miller et al., 1999~. These comparisons go beyond superficial comparisons
of areal cloud amount by examining the vitally important vertical structure.
Still missing are diagnostic studies of cloud property information such as
liquid and solid water contents with corresponding quantitative precipitation.
Information from CloudSat could help fill some of these observational gaps
(Stephens et al., 2002~.
Studies such as these highlight the utility of being able to run climate
models in an NWP mode to perform diagnostic analyses of processes that
operate on short time scales but that are critical to producing realistic
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37
projections of long-term climate change. With increasing computation power
expected in the coming years and the higher spatial resolution expected of
these global models, continued improvements in the representation of
smaller-scale cloud processes with the subsequent improvement in
predictions of cloud properties is anticipated. Thus, with improved
resolution and improved global observations noted above, more probing
testing of model parameterizations is possible, which is expected to lead to
better parameterization methods and better cloud predictions. If adequately
supported, the GEWEX Cloud Systems Study (Randall et al., 2000), which
is focused on developing improved parameterizations for a wide variety of
cloud types, is expected to contribute substantially to this effort. The
approach generally combines observations, cloud-resolving models and
global climate models.
Better cloud predictions in turn will lead to more capable assimilation
methods eventually expanding the use of existing and archived data, such as
the archived but unused cloudy-sky radiance data derived from operational
analyses. This then should feed back on model development with subsequent
improvements. Validated cloud predictions should also expand diagnostic
uses of new re-analyzed data expected from future re-analysis efforts that
could be an integral part of the cycle of model evaluation, improvement, and
data analyses.
Toward Improved Theories
Cloud-resolving models (CRMs) have evolved as one of the main tools
for studying the links between key processes pertinent to studying cloud-
related feedbacks (e.g., Browning, 1993; Grabowski, 2000~. As such, these
models may be viewed as an essential tool for articulating the underlying
theories of cloud feedbacks. These models continue to improve and are now
being adopted more widely in a variety of cloud and precipitation research
activities. CRMs are also being coupled experimentally ways into global
models to serve as an explicit form of cloud parameterization, thereby
overcoming the problematic separation between resolved cloudiness and
unresolved convection (Randall et al., in press).
CRMs embedded within GCMs should not be viewed as a panacea
because they do not actually simulate the complete cloud dynamics in a
GCM grid cell; rather, they provide a physical representation of the cloud
statistics in the cell. They are also quite computationally intensive to run in
this way. Moreover, it remains to be seen how difficult it is to develop a
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UNDERSTANDING CLIMA TE CHANGE FEEDBACKS
cloud resolving GCM with realistic sensitivity of global mean surface
temperature.
Despite improvements and increasing use of CRMs, their evaluation is
far from extensive, being limited to a few test cases from a limited number
of field campaigns. Future testing must examine the sensitivity of CRM
simulations to assumptions in their microphysics and turbulence
parameterizations and the limitations this sensitivity may impose. The cloud
evolution predicted by these models is also sensitive to initial conditions
(including the large-scale forcing that drives them). This sensitivity is
problematic given that the source of this forcing usually derives from the
analyses of large-scale operational models. Therefore, progress in CRMs has
to be intimately tied to progress in NWP global models. Mutual
improvements in turn can be expected to lead not only to better cloud
prediction schemes in global models but also can be expected to promote
new assimilation methods applied to CRMs and eventually a more
penetrating way of testing and improving models with observations. These
caveats should not overshadow the potential that CRMs present as tools to
explore the interaction between the cloud physics and the general circulation
of the atmosphere
The cloud feedback problem and the indirect effect of aerosols are
linked together. The provision of aerosols is hypothesized to affect the water
budget of clouds through the indirect effect. But this affect cannot be
understood without understanding the effects of dynamics and
thermodynamics in providing moisture for clouds. In most cases one would
expect the circulation and thermodynamics to have a much larger effect on
the cloud properties than the provision of additional aerosols. Therefore one
can argue that a good understanding of the relationship of cloud properties to
the dynamics and large-scale thermodynamic environment of the clouds is
necessary before the effect of additional aerosols can be convincingly
predicted. To resolve these issues will probably require testing when the
aerosol abundance is known as well as the dynamic and thermodynamic
conditions. The effect of the dynamic and thermodynamic environment can
then be separated from the aerosol effect and solved first. Direct
measurements of aerosols and associated cloud properties may also provide
critical information (Breon et al., 2002, Lohmann and Lesins, 2002~.
Progress in understanding cloud, water vapor, and lapse rate feedbacks
requires that an integrated effort with additional resources be developed that
cross-cuts the interests of individual agencies. We propose that this effort be
developed with the following elements:
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39
· Improving Datasets and Data Analyses. The fundamental problem is
that the scientific community's efforts to model the basic physics of cloud,
water vapor, and lapse rate feedbacks are much more advanced than the
ability to measure the nature and evaluate the accuracy of their simulation in
climate models. Therefore, a vigorous strategy should be implemented to
promote and fund research that
-maintains the important global datasets already under development but
in jeopardy due to lack of support (e.g., GEWEX-related datasets such
as the ISCCP and the Global Precipitation Climatology Project
tGPCPJ);
-uses existing datasets specifically to evaluate cloud, water vapor, and
precipitation predictions in global-scale weather and climate models, as
well as regional-scale cloud-resolving models. A focus should be placed
on developing rigorous diagnostics methods and evaluation procedures;
and
-extends these activities to embrace the new improved datasets expected
in the coming years.
· Testing predictions. A rigorous effort to test climate models against
observational metrics must be initiated and coordinated among groups
performing climate modeling, climate observation, and climate analysis.
Metrics should include comparison of observed and simulated response of
clouds, water vapor, and lapse rate to every well-observed forcing
mechanism and time scale, including the diurnal and seasonal response, the
response to ENSO and the response to volcanic eruptions. This
intercomparison should include the estimation of global feedback parameters
from seasonal variations (e.g., Tsushima and Manabe, 2001) and regional
feedbacks as understanding warrants.
Additional metrics should include cloud and water vapor variations
associated with day-to-day weather changes. Weather prediction models and
connected assimilation systems should be applied to the diagnosis of critical
links between cloudiness, water vapor, precipitation, and weather variations.
Within this effort, new methods for the assimilation of cloud, water vapor,
and precipitation data must be promoted. Therefore, ongoing attempts to
coordinate national climate modeling efforts must include an NWP
component with data assimilation as well as a data assimilation effort using
climate models. The time scales of relevance include diurnal, weekly
(characteristic of weather systems), seasonal (characteristic of natural modes
of variability; see Chapter 9), and decadal (characteristic of long-term
climate change).
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UNDERSTANDING CLIME TE CHANGE FEEDBA CKS
· Improving theory and models. A significant effort should be
undertaken that builds upon the proceeding two elements with the specific
goal of improving the representation of clouds, water vapor, and
precipitation in NWP and climate models. This activity should use an
integrated, hierarchical approach to model development connecting NWP
model and assimilation developments, climate model parameterization
developments, and cloud-resolving models. This effort must go significantly
beyond the current model intercomparison projects, which have played an
important role in identifying model errors and in developing uniform model
diagnostics, but frequently have lacked an observational underpinning.
The potential of this approach will not be realized without a more
coordinated program of research and support. Progress on atmospheric
hydrology feedbacks has been hindered by fragmented resources, which
discourages crosscutting research in modeling, observational techniques, and
diagnostic analyses. For example, research in collection and analysis of the
global datasets of cloud and water vapor information, especially those
derived from space-borne observations, are supported in large part by
NASA, the development of NWP models by the National Oceanic and
Atmospheric Administration (NOAA), and high-end climate modeling
efforts by yet other agencies, each of which have their own objectives. A
viable strategy for progress requires a thoughtful, efficient integration of
observations, diagnostic research, global model development, data
assimilation, and cloud-scale modeling. These elements have to be
connected in one program as progress on any specific element of this
strategy depends on progress on connected elements.
Representative terms from entire chapter:
lapse rate
