Guest Commentary by Brian Soden (RSMAS, Miami)
Current model estimates of the climate sensitivity, defined as the equilibrated change in global-mean surface temperature resulting from a doubling of CO2, range from 2.6 to 4.1 K, consistent with observational constraints (see previous article). This range in climate sensitivity is attributable to differences in the strength of ‘radiative feedbacks’ between models and is one of the reasons why projections of future climate change are less certain than policy makers would like.
Although radiative forcings and radiative feedbacks both influence the climate by altering the radiative fluxes at the top of the atmosphere, it is important to distinguish between the two. A radiative forcing results from changes that are external to the climate system and may be either natural or anthropogenic in origin. For example, anthropogenic emissions of CO2, changes in solar flux, and the reflection of sunlight from volcanic aerosols are all examples of radiative forcings. A radiative forcing initiates a change in climate that is distinct from the system’s internal variability. A radiative feedback, on the other hand, arises from the response of the climate to either external forcing or internal variability. These responses can either amplify (a positive feedback) or dampen (a negative feedback) the initial perturbation. The exact boundary between a feedback and a forcing depends on what is considered to be part of the ‘system’ and can sometimes be a little fuzzy. This discussion addresses just the feedbacks associated with the atmospheric physical system (see this earlier article for why that is), but other, less well understood, feedbacks (changes in land vegetation, biogeochemical processes, and atmospheric chemical feedbacks – see the NRC 2003 report), while potentially important, are not part of the generally understood definition of ‘climate sensitivity’.
In the absence of radiative forcings, the amount of sunlight absorbed by the earth roughly balances its thermal emission to space; i.e., the earth is in a quasi-steady radiative equilibrium. Doubling the concentration of CO2 decreases the emission of thermal radiation by ~4 W/m2. Because the earth is now emitting less radiation than it absorbs, there is a surplus of energy going into the system and its surface must warm. Because the thermal emission of energy increases as an object warms, the increasing temperature acts to restore radiative equilibrium. In the absence of any feedbacks, a doubling of CO2 would result in an increase in global surface temperature of ~1 K. However, as the climate warms in an attempt to restore radiative equilibrium, other changes occur. These changes can also influence the top-of-atmosphere radiative fluxes and thus act to either decrease (a negative feedback) or to increase (a positive feedback) the radiative surplus. For example, as the climate warms the amount of snow and ice cover decreases which leads to more sunlight being absorbed, thus enhancing the initial radiative surplus and requiring greater warming to restore equilibrium.
There are a number of different radiative feedbacks in the climate system, some more complex than others. Those which are most commonly represented in climate models are feedbacks from water vapor, snow/ice cover, clouds and lapse rate (the change in temperature with height).
Despite the importance of these feedbacks in determining projections of future climate change, there has never been a coordinated intercomparison of their values in GCMs. In a recent issue of the Journal of Climate, Isaac Held and I estimated the range of feedback strengths in current models using an archive of 21st century climate change experiments performed for the upcoming IPCC AR4. The results of this analysis are presented in the figure which expresses the strength of the global mean feedback for each model in terms of their impact on TOA radiative fluxes per degree global warming (units are W/m2/K).
Figure 1 from Soden and Held (2006) showing ranges for each model for each of the key atmospheric feedbacks for the IPCC AR4 models and a comparison with an earlier survey (Colman, 2003).
All models predict the concentrations of water vapor to increase as the climate warms due to the rapid increase in saturation vapor pressure with temperature. Because water vapor is the dominant greenhouse gas, this provides a strong positive feedback in the climate system. In current models, water vapor was found to provide the largest positive feedback in all models and its strength was shown to be consistent with that expected from a roughly constant relative humidity change in water vapor mixing ratio. It should be noted that models are not constrained to conserve relative humidity and significant regional changes in relative humidity are simulated by models in response to atmospheric warming. On the global scale, however, changes in relative humidity are small.
Models do exhibit a range of values for water vapor feedback. This range is not due to departures from constant relative humidity behavior, but rather from intermodel differences in the response of the atmospheric lapse rate to surface warming. All models suggest that the troposphere warms more than the surface (at equilibrium at least – responses are more varied for a short transient period – see the CCSP report). This amplified warming of the troposphere represents a key negative feedback in models because it further increases the thermal emission of energy to space. Models with more surface warming in low latitudes tend to have larger atmospheric warming (and more negative lapse rate feedback) because the surface and free troposphere are more strongly coupled in the tropics than at higher latitudes. Because the water vapor and temperature responses are tightly coupled in the troposphere, models with a larger (negative) lapse-rate feedback also have a larger (positive) water vapor feedback. These act to offset each other. As a result, it is more reasonable to consider the sum of water vapor and lapse-rate feedbacks as a single quantity when analyzing the causes of intermodel variability in climate sensitivity. As shown in the figure, the range for the sum of these two feedbacks is considerably smaller than the range of either the water vapor or lapse rate feedbacks individually.
Not surprisingly, the surface albedo feedback due to changes in snow and ice cover was also found to be positive in all models, although its magnitude is only about 25% of that from the combined “water vapor plus lapse rate” feedback.
Consistent with previous studies, clouds were found to provide the largest source of uncertainty in current models. For the most sensitive models, cloud feedback is positive and comparable in strength to the combined “water vapor plus lapse rate” feedback. For the least sensitive models, cloud feedback is close to neutral. Many specialists and non-specialists alike are sometimes surprised to see that the model-predicted values for cloud feedback ranges from neutral to strongly positive; often believing that cloud feedback is more uniformly distributed between negative and positive values. This confusion may stem, in part, from misinterpretations of the change in the easily-diagnosed “cloud radiative forcing” in model simulations of climate change. This diagnostic, based on the comparison of clear sky and cloudy sky radiation differences (Cess et al, 1996) is related to the more-difficult-to-calculate cloud feedback, but can be negatively biased by correlated changes in water vapor and temperature (Soden et al., 2004). Thus studies that use the “cloud radiative forcing” calculation have reported a more negatively skewed ‘cloud feedback’ then seen here. However, based on these estimates and on a survey of published values of feedback calculations (Colman 2003), there do not appear to be any models for which clouds provide a substantial negative feedback on the climate.
Observational studies do have the potential to help narrow the uncertainties in these individual feedbacks – for instance from studies of the response to Mt. Pinatubo and from long time series of satellite measurements – but observational constraints of cloud feedback remain elusive.