How well do we understand and eva...
REVIEW ARTICLE How Well Do We Understand and Evaluate Climate Change Feedback Processes? SANDRINE BONY,a ROBERT COLMAN,b VLADIMIR M. KATTSOV,c RICHARD P. ALLAN,d CHRISTOPHER S. BRETHERTON,e JEAN-LOUIS DUFRESNE,a ALEX HALL,f STEPHANE HALLEGATTE,g MARIKA M. HOLLAND,h WILLIAM INGRAM,i DAVID A. RANDALL,j BRIAN J. SODEN,k GEORGE TSELIOUDIS,l AND MARK J. WEBBm a Laboratoire de M��t��orologie Dynamique, IPSL, CNRS, Paris, France b Bureau of Meteorology Research Centre, Melbourne, Australia c Voeikov Main Geophysical Observatory, St. Petersburg, Russia d Environmental Systems Science Centre, University of Reading, Reading, United Kingdom e Department of Atmospheric Sciences, University of Washington, Seattle, Washington f Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California g Centre International de Recherche sur l���Environnement et le D��veloppement, Nogent-sur-Marne, and Centre National de Recherches M��t��orologiques, M��t��o-France, Toulouse, France h National Center for Atmospheric Research, Boulder, Colorado i Atmospheric, Oceanic and Planetary Physics, Clarendon Laboratory, Oxford, and Hadley Centre for Climate Prediction and Research, Met Office, Exeter, United Kingdom j Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado k Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida l NASA GISS, and Department of Applied Physics, Columbia University, New York, New York m Hadley Centre for Climate Prediction and Research, Met Office, Exeter, United Kingdom (Manuscript received 4 July 2005, in final form 1 December 2005) ABSTRACT Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radia- tive feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs��� range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations. 1. Introduction The global mean surface air temperature change in response to a doubling of the atmospheric CO2 concen- tration, commonly referred to as the climate sensitivity, plays a central role in climate change studies. Accord- ing to the Third Assessment Report (TAR) of the In- tergovernmental Panel on Climate Change (IPCC), the equilibrium climate sensitivity1 estimates from general circulation models (GCMs) used for climate change projections range from 2�� to 5��C (Houghton et al. 2001). This range, which constitutes a major source of uncertainty for climate stabilization scenarios (Caldeira et al. 2003), and which could in fact be even larger Corresponding author address: Sandrine Bony, LMD/IPSL, Boite 99, 4 Place Jussieu, 75252 Paris CEDEX 05, France. E-mail: email@example.com 1 ���Equilibrium climate sensitivity��� refers to the global mean surface air temperature change experienced by the climate system after it has attained a new equilibrium in response to a doubling of the atmospheric carbon dioxide concentration. VOLUME 19 J O U R N A L O F C L I M A T E 1 AUGUST 2006 �� 2006 American Meteorological Society 3445
(Murphy et al. 2004 Stainforth et al. 2005), principally arises from differences in the processes internal to the climate system that either amplify or dampen the cli- mate system���s response to the external forcing [(Na- tional Research Council) NRC (2003)]. These pro- cesses are referred to as climate feedbacks (see appen- dix A for a more formal definition of climate feedbacks). Every climate variable that responds to a change in global mean surface temperature through physical or chemical processes and that directly or indirectly affects the earth���s radiation budget has the potential to consti- tute a climate change feedback. In this paper, we focus on the feedbacks associated with climate variables (i) that directly affect the top-of-the-atmosphere (TOA) radiation budget, and (ii) that respond to surface tem- perature mostly through physical (rather than chemical or biochemical) processes. We will thus focus on the radiative feedbacks associated with the interaction of the earth���s radiation budget with water vapor, clouds, temperature lapse rate, and surface albedo in snow and sea ice regions, whose role in GCM estimates of equi- librium climate sensitivity has been widely established. On the other hand, we will not consider the feedbacks associated with the response to temperature of the car- bon cycle or of aerosols and trace gases, nor those as- sociated with soil moisture changes or ocean processes, although these processes might have a substantial im- pact on the magnitude, the pattern, or the timing of climate warming (NRC 2003). Water vapor constitutes a powerful greenhouse gas, and therefore an increase of water vapor with tempera- ture will oppose the increase in radiative cooling due to increasing temperature, and so constitute a positive feedback. The earth���s cryosphere reflects part of the incoming shortwave (SW) radiation to space, and therefore the melting of snow and sea ice with rising temperature constitutes another positive feedback. The temperature lapse rate in the troposphere (i.e., the rate of decrease of atmospheric temperature with height) affects the atmospheric emission of longwave (LW) ra- diation to space, and thus the earth���s greenhouse effect (the stronger the decrease of temperature with height, the larger the greenhouse effect). Therefore, an atmo- spheric warming that is larger (smaller) in the upper troposphere than at low levels produces a negative (positive) radiative feedback compared to a uniform temperature change. Clouds strongly modulate the earth���s radiation budget, and a change in their radiative effect in response to a global temperature change may produce a substantial feedback on the earth���s tempera- ture. But the sign and the magnitude of the global mean cloud feedback depends on so many factors that it re- mains very uncertain. Several approaches have been proposed to diagnose global radiative feedbacks in GCMs (appendix B), each of these having its own strengths and weaknesses (Soden et al. 2004 Stephens 2005). Since the TAR, some of them have been applied to a wide range of GCMs, which makes it possible to compare the feed- backs produced by the different models and then to better interpret the spread of GCMs��� estimates of cli- mate sensitivity. Figure 1 compares the quantitative estimates of glob- al climate feedbacks (decomposed into water vapor, lapse rate, surface albedo, and cloud feedback compo- nents) as diagnosed by Colman (2003a), Soden and Held (2006), and Winton (2006). The water vapor feed- back constitutes by far the strongest feedback, with a multimodel mean and standard deviation of the feed- back parameter [as estimated by Soden and Held (2006) for coupled GCMs participating in the IPCC Fourth Assessment Report (AR4) of the IPCC] of 1.80 0.18 W m 2 K 1, followed by the lapse rate feedback ( 0.84 0.26 W m 2 K 1), the cloud feed- back (0.69 0.38 W m 2 K 1), and the surface albedo feedback (0.26 0.08 W m 2 K 1). These results indi- cate that in GCMs, the water vapor feedback amplifies the earth���s global mean temperature response (com- pared to a basic Planck response, see appendix A) by a FIG. 1. Comparison of GCM climate feedback parameters (in W m 1 K 1) for water vapor (WV), cloud (C), surface albedo (A), lapse rate (LR), and the combined water vapor lapse rate (WV LR). ALL represents the sum of all feedbacks. Results are taken from Colman (2003 in blue), Soden and Held (2006, in red), and Winton (2006, in green). Closed and open symbols from Col- man (2003) represent calculations determined using the PRP and the RCM approaches, respectively. Crosses represent the water vapor feedback computed for each model from Soden and Held (2006) assuming no change in relative humidity. Vertical bars depict the estimated uncertainty in the calculation of the feed- backs from Soden and Held (2006). 3446 J O U R N A L O F C L I M A T E VOLUME 19
factor of 2 or more, the lapse rate feedback reduces it by about 20% (the combined water vapor plus lapse rate feedback amplifies it by 40%���50%),2 the surface albedo feedback amplifies it by about 10%, and the cloud feedback amplifies it by 10%���50% depending on GCMs. Interestingly, these results do not substantially differ from those published in the pioneering work of Hansen et al. (1984). Although the intermodel spread of feedback strength is substantial for all the feedbacks, it is the largest for cloud feedbacks. The comparison also reveals quite a large range in the strength of water vapor and lapse rate feedbacks among GCMs. A strong anticorrelation be- tween the water vapor and lapse rate feedbacks of mod- els is also seen, consistent with long-held views on the relationships between the two feedbacks (e.g., Cess 1975). A consequence of this anticorrelation is that the spread of the combined water vapor���lapse rate feed- back is roughly half that of the individual water vapor or lapse rate feedbacks, smaller than that of cloud feed- backs, but slightly larger than that of the surface albedo feedback. As suggested by Colman (2003a), Soden and Held (2006), and Webb et al. (2005, manuscript submit- ted to Climate Dyn., hereafter WEBB) the range of climate sensitivity estimates among models thus pri- marily results from the spread of cloud feedbacks, but also with a substantial contribution of the combined water vapor���lapse rate and surface albedo feedbacks. This spread in climate feedbacks and climate sensitivity is not a new issue. It is a long-standing problem that is central to discussions about the uncertainty of climate change projections. A number of reasons for the slow progress in this area are proposed. First, climate feedback studies have long been fo- cused on the derivation of global estimates of the feed- backs using diagnostic methods that are not directly applicable to observations and so do not allow any ob- servational assessment (see Stephens 2005 for an exten- sive discussion of these aspects). Indeed, climate feed- backs are defined as partial derivatives [Eq. (A2)]. Al- though partial derivatives can be readily computed in models, it is not possible to compute them rigorously from observations because we cannot statistically ma- nipulate the observations in such a way as to insure that only one variable is changing. Nevertheless, the deriva- tion and the model-to-model comparison of feedbacks have played a key role in identifying the main sources of ���uncertainties��� (in the sense of intermodel differ- ences) in climate sensitivity estimates. Second, the evaluation of climate change feedbacks raises methodological difficulties because observed variations of the climate system may not be considered to be analogs of a global, long-term climate response to greenhouse gas forcing for example because (i) ob- served climate variations may not be in equilibrium with the forcing, (ii) the natural forcings associated with short-term insolation cycles (diurnal/seasonal) or with volcanic eruptions operate in the SW domain of the spectrum while long-term anthropogenic forcings asso- ciated with well-mixed greenhouse gases operate mostly in the LW domain, (iii) the geographical struc- tures of natural and anthropogenic forcings differ, and (iv) the fluctuations in temperature and in large-scale atmospheric circulation at short and long time scales are not comparable. In addition, in nature multiple pro- cesses are usually operating to change climate, for in- stance volcanic eruptions, the El Ni��o���Southern Oscil- lation (ENSO), and the annual cycle are often present together, and attributing an observed change to a par- ticular cause may be problematic. These limitations make relationships between temperature, water vapor, and clouds inferred from the current climate not di- rectly useful to estimate feedback processes at work under climate change (Hartmann and Michelsen 1993 Bony et al. 1995 Lau et al. 1996). Third, the complexity of the climate system and the innumerable factors potentially involved in the climate feedbacks have long been emphasized and considered as an obstacle to the assessment of feedbacks, both in nature and in models. Given these difficulties, how may we evaluate the realism of the climate change feedbacks produced by GCMs and thereby reduce the uncertainty in climate sensitivity estimates? We think that a better apprecia- tion of the physical mechanisms behind the global esti- mates of climate feedbacks would help us (i) to under- stand the reasons why climate feedbacks differ or not among models, (ii) to assess the reliability of the feed- backs produced by the different models, and (iii) to guide the development of strategies of model���data comparison relevant for observationally constraining some components of the global feedbacks. With these issues in mind, we present below some simple conceptual frameworks that may help to guide our thinking, we review our current understanding of the main physical mechanisms involved in the different radiative feedbacks, and we discuss how observations may be used to constrain them in climate models. Al- though the cloud, water vapor, lapse rate, and ice feed- backs all interact with each other (in particular the cloud���surface albedo feedbacks in snow or sea ice re- gions, the water vapor���cloud feedbacks, and the water 2 As explained by Hansen et al. (1984) and in appendix A, the feedback parameters and the feedback gains are additive but not the feedback factors. 1 AUGUST 2006 R E V I E W 3447
vapor���lapse rate feedbacks), we will consider them separately for the sake of simplicity of presentation. Ordering the feedbacks according to their contribution to the spread of climate sensitivity estimates among GCMs (Fig. 1), we will consider in turn cloud feedbacks (section 2), the combined water vapor���lapse rate feed- backs (section 3), and cryosphere feedbacks (snow and sea ice, section 4). For this discussion, we will not at- tempt an exhaustive review of the literature, but will focus on major advances that have taken place since the TAR of the IPCC (Houghton et al. 2001). 2. Cloud feedbacks Cloud feedbacks have long been identified as the largest internal source of uncertainty in climate change predictions, even without considering the interaction between clouds and aerosols3 (Cess et al. 1990 Hough- ton et al. 2001). Recent comparisons of feedbacks pro- duced by climate models under climate change show that the current generation of models still exhibits a large spread in cloud feedbacks, which is larger than for other feedbacks (Fig. 1). Moreover, for most models the climate sensitivity estimate still critically depends on the representation of clouds (e.g., Yao and Del Genio 2002 Ogura et al. 2005, manuscript submitted to J. Meteor. Soc. Japan). Defining strategies for evalua- tion of cloud feedback processes in climate models is thus of primary importance to better understand the range of model sensitivity estimates and to make cli- mate predictions from models more reliable. Progress has been made during the last few years in our under- standing of processes involved in these feedbacks, and in the way these processes may be investigated in mod- els and in observations. a. Conceptual representations of the climate system Much of our understanding of the climate system, and of climate feedbacks in particular, is due to studies using simple or conceptual models that capture the es- sential processes of the climate system in a simplified way (Pierrehumbert 1995 Miller 1997 Larson et al. 1999 Kelly et al. 1999 Lindzen et al. 2001 Kelly and Randall 2001). Drawing connections between simple climate model idealizations and the three-dimensional climate of nature or climate models would help to bet- ter understand and assess the climate feedbacks pro- duced by complex models. As a first step toward that end, we present below some simple conceptual frame- works through which climate feedbacks and cloud feed- backs in particular may be analyzed. This will serve afterward as a pedagogical basis to synthesize results from recent observational, theoretical, and modeling studies. As is already well known (and illustrated in Fig. 2), the atmospheric dynamics and thus the large-scale or- ganization of the atmosphere is a strong function of latitude. In the Tropics, large-scale overturning circula- tions prevail. These are associated with narrow cloudy convective regions and widespread regions of sinking motion in the midtroposphere (generally associated with a free troposphere void of clouds and a cloud-free or cloudy planetary boundary layer). In the extratrop- ics, the atmosphere is organized in large-scale baro- clinic disturbances. The large-scale circulation of the tropical atmosphere and its connection to cloudiness is shown as a schematic 3 In this paper, we will not discuss the microphysical feedbacks associated with the interaction between aerosols and clouds. As Lohmann and Feichter (2005) say: ���The cloud feedback problem has to be solved in order to assess the aerosol indirect forcing more reliably.��� FIG. 2. Composite of instantaneous infrared imagery from geostationary satellites (at 1200 UTC 29 Mar 2004) showing the contrast between the large-scale organization of the atmosphere and of the cloudiness in the Tropics and in the extratropics. [From SATMOS (��METEO-FRANCE and Japan Meteorological Agency).] 3448 J O U R N A L O F C L I M A T E VOLUME 19
in Fig. 3a. In idealized box models such as those devel- oped by Pierrehumbert (1995) or Larson et al. (1999), the circulation is idealized even further by partitioning the Tropics into a single moist, precipitating area cov- ered by convective clouds and a single dry, nonprecipi- tating area associated with sinking motion in the midtroposphere and a clear-free or cloudy boundary layer (Fig. 3b). These areas are coupled by the large- scale circulation and by the constraint of having a weak temperature gradient in the free troposphere. A more continuous idealization of the tropical circu- lation was proposed by Bony et al. (2004). This uses the 500-hPa large-scale vertical velocity as a proxy for large-scale vertical motions of the atmosphere and de- composes the Hadley���Walker circulation as a series of dynamical regimes defined using . In the Tropics, nearly all of the upward motion associated with en- semble-average ascent occurs within cumulus clouds, and gentle subsidence occurs in between clouds. Since the rate of subsidence in between clouds is strongly constrained by the clear-sky radiative cooling and thus nearly invariant, an increase of the large-scale mean ascent corresponds, to first order, to an increase of the mass flux in cumulus clouds (Emanuel et al. 1994). Therefore, considering dynamical regimes defined from allows us to classify the tropical regions according to their convective activity, and to segregate in particular regimes of deep convection from regimes of shallow convection. The statistical weight of the different circu- lation regimes (Fig. 4) emphasizes the large portion of the Tropics associated with moderate sinking motions in the midtroposphere (such as found over the trade wind regions), and the comparatively smaller weight of extreme circulation regimes associated with the warm pool or with the regions of strongest sinking motion and static stability such as found at the eastern side of the ocean basins. These extreme regimes correspond to the tails of the probability distribution function. The at- mospheric vertical structure (observed or modeled) can then be composited within each dynamical regime. Il- lustrations of the dependence of cloud radiative prop- erties and of precipitation on the large-scale circulation are displayed in Figs. 4b,c, showing the satellite-derived precipitation and cloud radiative forcing (CRF) as a function of ( being derived from meteorological re- analyses). These increase as the vigor of the convective mass flux increases. At midlatitudes, the atmosphere is mostly organized in synoptic weather systems (Fig. 2). An idealized baro- clinic disturbance is represented in Fig. 5a, showing the warm and cold fronts outward from the low-level pres- sure center of the disturbance, together with the occur- rence of sinking motion behind the cold front and rising motion ahead of the warm front. As discussed in Wal- lace and Hobbs (1977), the different parts of the system are associated with specific cloud types, ranging from thin low-level cumulus clouds behind the cold front, thin upper-level clouds ahead of the warm front, and thick precipitating clouds over the fronts (Fig. 5b). Given the strong connection between the large-scale atmospheric circulation and the distribution of water FIG. 3. Two conceptual representations of the relationship be- tween cloudiness and large-scale atmospheric circulation in the Tropics: (a) structure of the tropical atmosphere, showing the various regimes, approximately as a function of SST (decreasing from left to right) or mean large-scale vertical velocity in the midtroposphere (from mean ascending motions on the left to large-scale sinking motions on the right). [From Emanuel (1994).] (b) Two-box model of the Tropics used by Larson et al. (1999). The warm pool has high convective clouds and the cold pool has boundary layer clouds. Air is rising in the warm pool and sinking across the inversion in the cold pool. 1 AUGUST 2006 R E V I E W 3449