14.2.3.1 Clouds

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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