By reflecting solar radiation back to space (the albedo effect of clouds) and by trapping infrared radiation emitted by the surface and the lower troposphere (the greenhouse effect of clouds), clouds exert two competing effects on the Earth’s radiation budget. These two effects are usually referred to as the SW and LW components of the cloud radiative forcing (CRF). The balance between these two components depends on many factors, including macrophysical and microphysical cloud properties. In the current climate, clouds exert a cooling effect on climate (the global mean CRF is negative). In response to global warming, the cooling effect of clouds on climate might be enhanced or weakened, thereby producing a radiative feedback to climate warming (Randall et al., 2006; NRC, 2003; Zhang, 2004; Stephens, 2005; Bony et al., 2006).
In many climate models, details in the representation of clouds can substantially affect the model estimates of cloud feedback and climate sensitivity (e.g., Senior and Mitchell, 1993; Le Treut et al., 1994; Yao and Del Genio, 2002; Zhang, 2004; Stainforth et al., 2005; Yokohata et al., 2005). Moreover, the spread of climate sensitivity estimates among current models arises primarily from inter-model differences in cloud feedbacks (Colman, 2003a; Soden and Held, 2006; Webb et al., 2006; Section 8.6.2, Figure 8.14). Therefore, cloud feedbacks remain the largest source of uncertainty in climate sensitivity estimates.
This section assesses the evolution since the TAR in the understanding of the physical processes involved in cloud feedbacks (see Section 184.108.40.206.1), in the interpretation of the range of cloud feedback estimates among current climate models (see Section 220.127.116.11.2) and in the evaluation of model cloud feedbacks using observations (see Section 18.104.22.168.3).
22.214.171.124.1 Understanding of the physical processes involved in cloud feedbacks
The Earth’s cloudiness is associated with a large spectrum of cloud types, ranging from low-level boundary-layer clouds to deep convective clouds and anvils. Understanding cloud feedbacks requires an understanding of how a change in climate may affect the spectrum and the radiative properties of these different clouds, and an estimate of the impact of these changes on the Earth’s radiation budget. Moreover, since cloudy regions are also moist regions, a change in the cloud fraction matters for both the water vapour and the cloud feedbacks (Pierrehumbert, 1995; Lindzen et al., 2001). Since the TAR, there have been some advances in the analysis of physical processes involved in cloud feedbacks, thanks to the combined analysis of observations, simple conceptual models, cloud-resolving models, mesoscale models and GCMs (reviewed in Bony et al., 2006). Major issues are presented below.
Several climate feedback mechanisms involving convective anvil clouds have been examined. Hartmann and Larson (2002) proposed that the emission temperature of tropical anvil clouds is essentially independent of the surface temperature (Fixed Anvil Temperature hypothesis), and that it will thus remain unchanged during climate change. This suggestion is consistent with cloud-resolving model simulations showing that in a warmer climate, the vertical profiles of mid- and upper-tropospheric cloud fraction, condensate and RH all tend to be displaced upward in height together with the temperature (Tompkins and Craig, 1999). However, this hypothesis has not yet been tested with observations or with cloud-resolving model simulations having a fine vertical resolution in the upper troposphere. The response of the anvil cloud fraction to a change in temperature remains a subject of debate. Assuming that an increase with temperature in the precipitation efficiency of convective clouds could decrease the amount of water detrained in the upper troposphere, Lindzen et al. (2001) speculated that the tropical area covered by anvil clouds could decrease with rising temperature, and that would lead to a negative climate feedback (iris hypothesis). Numerous objections have been raised about various aspects of the observational evidence provided so far (Chambers et al., 2002; Del Genio and Kovari, 2002; Fu et al., 2002; Harrison, 2002; Hartmann and Michelsen, 2002; Lin et al., 2002, 2004), leading to a vigorous debate with the authors of the hypothesis (Bell et al., 2002; Chou et al., 2002; Lindzen et al., 2002). Other observational studies (Del Genio and Kovari, 2002; Del Genio et al., 2005b) suggest an increase in the convective cloud cover with surface temperature.
Boundary-layer clouds have a strong impact on the net radiation budget (e.g., Harrison et al., 1990; Hartmann et al., 1992) and cover a large fraction of the global ocean (e.g., Norris, 1998a,b). Understanding how they may change in a perturbed climate is thus a vital part of the cloud feedback problem. The observed relationship between low-level cloud amount and a particular measure of lower tropospheric stability (Klein and Hartmann, 1993), which has been used in some simple climate models and in some GCMs’ parametrizations of boundary-layer cloud amount (e.g., CCSM3, FGOALS), led to the suggestion that a global climate warming might be associated with an increased low-level cloud cover, which would produce a negative cloud feedback (e.g., Miller, 1997; Zhang, 2004). However, variants of the lower-tropospheric stability measure, which may predict boundary-layer cloud amount as well as the Klein and Hartmann (1993) measure, would not necessarily predict an increase in low-level clouds in a warmer climate (e.g., Williams et al., 2006). Moreover, observations indicate that in regions covered by low-level clouds, the cloud optical depth decreases and the SW CRF weakens as temperature rises (Tselioudis and Rossow, 1994; Greenwald et al., 1995; Bony et al., 1997; Del Genio and Wolf, 2000; Bony and Dufresne, 2005), but the different factors that may explain these observations are not well established. Therefore, understanding of the physical processes that control the response of boundary-layer clouds and their radiative properties to a change in climate remains very limited.
At mid-latitudes, the atmosphere is organised in synoptic weather systems, with prevailing thick, high-top frontal clouds in regions of synoptic ascent and low-level or no clouds in regions of synoptic descent. In the NH, several climate models report a decrease in overall extratropical storm frequency and an increase in storm intensity in response to climate warming (e.g., Carnell and Senior, 1998; Geng and Sugi, 2003) and a poleward shift of the storm tracks (Yin, 2005). Using observations and reanalyses to investigate the impact that dynamical changes such as those found by Carnell and Senior (1998) would have on the NH radiation budget, Tselioudis and Rossow (2006) suggested that the increase in storm strength would have a larger radiative impact than the decrease in storm frequency, and that this would produce increased reflection of SW radiation and decreased emission of LW radiation. However, the poleward shift of the storm tracks may decrease the amount of SW radiation reflected (Tsushima et al., 2006). In addition, several studies have used observations to investigate the dependence of mid-latitude cloud radiative properties on temperature. Del Genio and Wolf (2000) showed that the physical thickness of low-level continental clouds decreases with rising temperature, resulting in a decrease in the cloud water path and optical thickness as temperature rises, and Norris and Iacobellis (2005) suggested that over the NH ocean, a uniform change in surface temperature would result in decreased cloud amount and optical thickness for a large range of dynamical conditions. The sign of the climate change radiative feedback associated with the combined effects of dynamical and temperature changes on extratropical clouds is still unknown.
The role of polar cloud feedbacks in climate sensitivity has been emphasized by Holland and Bitz (2003) and Vavrus (2004). However, these feedbacks remain poorly understood.
126.96.36.199.2 Interpretation of the range of cloud feedbacks among climate models
In doubled atmospheric CO2 equilibrium experiments performed by mixed-layer ocean-atmosphere models as well as in transient climate change integrations performed by fully coupled ocean-atmosphere models, models exhibit a large range of global cloud feedbacks, with roughly half of the climate models predicting a more negative CRF in response to global warming, and half predicting the opposite (Soden and Held, 2006; Webb et al., 2006). Several studies suggest that the sign of cloud feedbacks may not be necessarily that of CRF changes (Zhang et al., 1994; Colman, 2003a; Soden et al., 2004), due to the contribution of clear-sky radiation changes (i.e., of water vapour, temperature and surface albedo changes) to the change in CRF. The Partial Radiative Perturbation (PRP) method, that excludes clear-sky changes from the definition of cloud feedbacks, diagnoses a positive global net cloud feedback in virtually all the models (Colman, 2003a; Soden and Held, 2006). However, the cloud feedback estimates diagnosed from either the change in CRF or the PRP method are well correlated (i.e., their relative ranking is similar), and they exhibit a similar spread among GCMs.
By decomposing the GCM feedbacks into regional components or dynamical regimes, substantial progress has been made in the interpretation of the range of climate change cloud feedbacks. The comparison of coupled AOGCMs used for the climate projections presented in Chapter 10 (Bony and Dufresne, 2005), of atmospheric or slab ocean versions of current GCMs (Webb et al., 2006; Williams et al., 2006; Wyant et al., 2006), or of slightly older models (Williams et al., 2003; Bony et al., 2004; Volodin, 2004; Stowasser et al.; 2006) show that inter-model differences in cloud feedbacks are mostly attributable to the SW cloud feedback component, and that the responses to global warming of both deep convective clouds and low-level clouds differ among GCMs. Recent analyses suggest that the response of boundary-layer clouds constitutes the largest contributor to the range of climate change cloud feedbacks among current GCMs (Bony and Dufresne, 2005; Webb et al., 2006; Wyant et al., 2006). It is due both to large discrepancies in the radiative response simulated by models in regions dominated by low-level cloud cover (Figure 8.15), and to the large areas of the globe covered by these regions. However, the response of other cloud types is also important because for each model it either reinforces or partially cancels the radiative response from low-level clouds. The spread of model cloud feedbacks is substantial at all latitudes, and tends to be larger in the tropics (Bony et al., 2006; Webb et al., 2006). Differences in the representation of mixed-phase clouds and in the degree of latitudinal shift of the storm tracks predicted by the models also contribute to inter-model differences in the CRF response to climate change, particularly in the extratropics (Tsushima et al., 2006).
Figure 8.15. Sensitivity (in W m–2 °C–1) of the tropical net cloud radiative forcing (CRF) to SST changes associated with global warming (simulations in which CO2 increases by 1% yr–1). The inset shows the tropically averaged sensitivity Σ predicted by 15 AOGCMs used in this report: 7 models predict Σ < 0 and 8 models predict Σ > 0. The main panel compares the CRF sensitivity to SST predicted by the two groups of models in different regimes of the large-scale tropical circulation (the 500 hPa vertical pressure velocity is used as a proxy for large-scale motions, with negative values corresponding to large-scale ascending motions, and positive values to sinking motions).Thick lines and vertical lines represent the mean and the standard deviation of model sensitivities within each group; dotted lines represent the minimum and maximum values of model sensitivities within each dynamical regime. The discrepancy between the two groups of models is greatest in regimes of large-scale subsidence. These regimes, which have a large statistical weight in the tropics, are primarily covered by boundary-layer clouds. As a result, the spread of tropical cloud feedbacks among the models (inset) primarily arises from inter-model differences in the radiative response of low-level clouds in regimes of large-scale subsidence. Adapted from Bony and Dufresne (2005).
188.8.131.52.3 Evaluation of cloud feedbacks produced by climate models
The evaluation of clouds in climate models has long been based on comparisons of observed and simulated climatologies of TOA radiative fluxes and total cloud amount (see Section 8.3.1). However, a good agreement with these observed quantities may result from compensating errors. Since the TAR, and partly due to the use of an International Satellite Cloud Climatology Project (ISCCP) simulator (Klein and Jakob, 1999; Webb et al., 2001), the evaluation of simulated cloud fields is increasingly done in terms of cloud types and cloud optical properties (Klein and Jakob, 1999; Webb et al., 2001; Williams et al., 2003; Lin and Zhang, 2004; Weare, 2004; Zhang et al., 2005; Wyant et al., 2006). It has thus become more powerful and constrains the models more. In addition, a new class of observational tests has been applied to GCMs, using clustering or compositing techniques, to diagnose errors in the simulation of particular cloud regimes or in specific dynamical conditions (Tselioudis et al., 2000; Norris and Weaver, 2001; Jakob and Tselioudis, 2003; Williams et al., 2003; Bony et al., 2004; Lin and Zhang, 2004; Ringer and Allan, 2004; Bony and Dufresne, 2005; Del Genio et al., 2005a; Gordon et al., 2005; Bauer and Del Genio, 2006; Williams et al., 2006; Wyant et al., 2006). An observational test focused on the global response of clouds to seasonal variations has been proposed to evaluate model cloud feedbacks (Tsushima et al., 2005), but has not yet been applied to current models.
These studies highlight some common biases in the simulation of clouds by current models (e.g., Zhang et al., 2005). This includes the over-prediction of optically thick clouds and the under-prediction of optically thin low and middle-top clouds. However, uncertainties remain in the observational determination of the relative amounts of the different cloud types (Chang and Li, 2005). For mid-latitudes, these biases have been interpreted as the consequence of the coarse resolution of climate GCMs and their resulting inability to simulate the right strength of ageostrophic circulations (Bauer and Del Genio, 2006) and the right amount of sub-grid scale variability (Gordon et al., 2005). Although the errors in the simulation of the different cloud types may eventually compensate and lead to a prediction of the mean CRF in agreement with observations (see Section 8.3), they cast doubts on the reliability of the model cloud feedbacks. For instance, given the nonlinear dependence of cloud albedo on cloud optical depth, the overestimate of the cloud optical thickness implies that a change in cloud optical depth, even of the right sign and magnitude, would produce a too small radiative signature. Similarly, the under-prediction of low- and mid-level clouds presumably affects the magnitude of the radiative response to climate warming in the widespread regions of subsidence. Modelling assumptions controlling the cloud water phase (liquid, ice or mixed) are known to be critical for the prediction of climate sensitivity. However, the evaluation of these assumptions is just beginning (Doutriaux-Boucher and Quaas, 2004; Naud et al., 2006). Tsushima et al. (2006) suggested that observations of the distribution of each phase of cloud water in the current climate would provide a substantial constraint on the model cloud feedbacks at middle and high latitudes.
As an attempt to assess some components of the cloud response to a change in climate, several studies have investigated the ability of GCMs to simulate the sensitivity of clouds and CRF to interannual changes in environmental conditions. When examining atmosphere-mixed-layer ocean models, Williams et al. (2006) found for instance that by considering the CRF response to a change in large-scale vertical velocity and in lower-tropospheric stability, a component of the local mean climate change cloud response can be related to the present-day variability, and thus evaluated using observations. Bony and Dufresne (2005) and Stowasser and Hamilton (2006) examined the ability of the AOGCMs of Chapter 10 to simulate the change in tropical CRF to a change in SST, in large-scale vertical velocity and in lower-tropospheric RH. They showed that the models are most different and least realistic in regions of subsidence, and to a lesser extent in regimes of deep convective activity. This emphasizes the necessity to improve the representation and the evaluation of cloud processes in climate models, and especially those of boundary-layer clouds.
184.108.40.206.4 Conclusion on cloud feedbacks
Despite some advances in the understanding of the physical processes that control the cloud response to climate change and in the evaluation of some components of cloud feedbacks in current models, it is not yet possible to assess which of the model estimates of cloud feedback is the most reliable. However, progress has been made in the identification of the cloud types, the dynamical regimes and the regions of the globe responsible for the large spread of cloud feedback estimates among current models. This is likely to foster more specific observational analyses and model evaluations that will improve future assessments of climate change cloud feedbacks.