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The role of clouds in the climate system continues to challenge the modelling
of climate (e.g., Chapter 7, Section 7.2.2). It is generally
accepted that the net effect of clouds on the radiative balance of the planet
is negative and has an average magnitude of about 10 to 20 Wm-2. This balance
consists of a short-wave cooling (the albedo effect) of about 40 to 50 Wm-2
and a long-wave warming of about 30 Wm-2. Unfortunately, the size of the uncertainties
in this budget is large when compared to the expected anthropogenic greenhouse
forcing. Although we know that the overall net effect of clouds on the radiative
balance is slightly negative, we do not know the sign of cloud feedback with
respect to the increase of greenhouse gases, and it may vary with the region.
In fact, the basic issue of the nature of the future cloud feedback is not clear.
Will it remain negative? If the planet warms, then it is plausible that evaporation
will increase, which probably implies that liquid water content will increase
but the volume of clouds may not. What will be the effect and how will the effects
be distributed in time and space? Finally, the issue of cloud feedbacks is also
coupled to the very difficult issue of indirect aerosol forcing (see Chapter
5, Section 5.3).
The importance of clouds was summarised in the SAR: “The single largest uncertainty
in determining the climate sensitivity to either natural or anthropogenic changes
are clouds and their effects on radiation and their role in the hydrological
cycle” (Kattenberg et al., 1996, p.345). And yet, the single greatest source
of uncertainty in the estimates of the climate sensitivity continues to be clouds
(see also Chapter 7, Section 7.2). Since the SAR, there
have been a number of improvements in the simulation of both the cloud distribution
and in the radiative properties of clouds (Chapter 7, Section
7.2.2). The simulation of cloud distribution has improved as the overall
simulation of the atmospheric models has improved. In addition, the cloud sub-component
models used in the coupled models have become more realistic. Also, our understanding
of the radiative properties of clouds and their effects on climate sensitivity
have improved. And yet in Chapter 7, Section 7.2.2 we
find that, “In spite of these improvements, there has been no apparent narrowing
of the uncertainty range associated with cloud feedbacks in current climate
change simulations.”
Handling the physics and/or the parametrization of clouds in climate models
remains a central difficulty. There is a need for increased observations. J.
Mitchell highlighted the challenge in a recent paper at the World Climate Research
Programme (WCRP) Workshop on Cloud Properties and Cloud Feedbacks in Large-scale
Models where he stated that “Reducing the uncertainty in cloud-climate
feedbacks is one of the toughest challenges facing atmospheric physicists”
(Mitchell, 2000).
Cloud modelling is a particularly challenging scientific problem because it
involves processes covering a very wide range of space- and time-scales. For
example, cloud systems extending over thousands of kilometres to cloud droplets
and aerosols of microscopic size are all important components of the climate
system. The time-scales of interest can range from hundreds of years (e.g.,
future equilibrium climates) to fractions of a second (e.g., droplet collisions).
This is not to say that all cloud micro-physics must be included in modelling
cloud formation and cloud properties, but the demarcation between what must
be included and what can be parametrized remains unclear. Clarifying this demarcation
and improving both the resulting phenomenological characterisations and parametrizations
will depend critically on improved global observations of clouds (see Chapter
2, Section 2.5.5; see also Senior, 1999). Of particular importance are observations
of cloud structure and distribution against natural patterns of climate variability
(e.g., ENSO). Complementing the broad climatologies will be important observations
of cloud ice-water and liquid-water content, radiative heating and optical depth
profiles, and precipitation occurrence and cloud geometry.
The recently approved CloudSat and PICASSO missions, which will fly in formation
with the National Aeronautics and Space Administration (USA) (NASA) Earth Observing
System (EOS) PM (the Aqua Mission), will provide valuable profiles of cloud
ice and liquid content, optical depth, cloud type, and aerosol properties. These
observations, combined with wider swath radiometric data from EOS PM sensors,
will provide a rich new source of information about the properties of clouds
(Stephens et al., 2000).
And yet, this question of cloud feedback remains open, and it is not clear how it will be answered. Given that the current generation of global climate models represents the Earth in terms of grid-points spaced roughly 200 km apart, many features observed on smaller scales, such as individual cloud systems and cloud geometry, are not explicitly resolved. Without question, the strategy for attacking the feedback question will involve comparison of model simulations with appropriate observations on global or local scales. The interplay of observation and models, again, will be the key for progress. Mitchell (Mitchell, 2000) states this clearly, “Unless there are stronger links between those making observations and those using climate models, then there is little chance of a reduction in the uncertainty in cloud feedback in the next twenty years.” This is echoed in this report (see Chapter 7, Section 7.2.2), “A straightforward approach of model validation is not sufficient to constrain efficiently the models and a more dedicated approach is needed. It should be favoured by a larger availability of satellite measurements.”
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