Cloud Feedbacks in a Changing Climate

Cloud and radiative observations are only available for a short period (at best for about 25 years, more generally for just a few years), and no climate variation occurring at this timescale may be considered as an analog of long-term climate change. Until long time series (three decades or more) of cloud and radiation data become available, it is hopeless to assess directly the response of clouds to global climate changes using observations and to compare it with model simulations. This is even more true since establishing long-term trends based on satellite or surface-based measurements is made very difficult by problems such as changes in instrument calibration, or satellite drift in altitude, etc. Once reliable and long time series of observations (of clouds but not only) become available, it might become easier to assess cloud feedback processes directly from observations and then to evaluate cloud feedbacks in climate models. In the meantime, available observational records can be useful in assessing the natural climate variability on various time scales, and also in investigating the physics that controls cloud changes and variability. For this purpose, some approaches have been developed over the last few years that take advantage of the available observations to assess climate model simulations in a way that may be relevant for assessing cloud-climate feedbacks.

Cloud feedbacks are related to the response of clouds to changing climate conditions. To have confidence in the feedbacks produced by climate models, it is therefore not sufficient to evaluate mean cloud properties. Assessing the sensitivity of clouds to changing environmental conditions is more likely to be relevant for assessing the realism of the simulated feedbacks.

For this purpose, one approach is to use compositing techniques to assess, in models and in observations, how clouds change in association with dynamic or thermodynamic conditions (e.g., with changes in lower tropospheric stability, in the intensity of large-scale rising or sinking motions in the free troposphere, in humidity and temperature). For this purpose, observations and model simulations are not only compared in terms of geographical distributions but also in terms of covariations between several variables. For instance, recognizing that many cloud properties (in particular, the prominent cloud type) are controlled to a large degree by the large-scale atmospheric circulation, several studies have stratified cloud observations as a function of dynamic regimes (cf. Bretherton and Hartmann, this volume) and then investigated how, for speci-fi ed dynamic conditions, these cloud properties varied with other environmental conditions, such as surface temperature, static stability, or horizontal advections (Bony et al. 1997; Williams et al. 2003, Bony et al. 2004, Norris and Iacobellis 2005). Other studies have decomposed global cloudiness into a small number of prominent cloud regimes and used this decomposition to understand and assess the response of clouds to long-term climate changes (e.g., Williams and Tselioudis 2007).

Such an approach makes it possible to evaluate simulations from idealized simulations having different geographical distributions of the dynamic features (e.g., aquaplanets) by using observations. Decomposing the large-scale feedback mechanisms or cloud changes in terms of a series of composites also makes it possible to bridge more easily climate studies with process studies. Once a cloud process or a sensitivity is identified as a key component of cloud-climate feedbacks, more detailed investigations using uni-dimensional models, cloud resolving models, or more detailed observations such as those collected during campaigns such as RICO or BOMEX may be performed to explore more deeply the underlying physics.

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