2.8.2 Spatial Patterns of Radiative Forcing
Each RF agent has a unique spatial pattern (see, e.g., Figure 6.7 in Ramaswamy et al., 2001). When combining RF agents it is not just the global mean RF that needs to be considered. For example, even with a net global mean RF of zero, significant regional RFs can be present and these can affect the global mean temperature response (see Section 2.8.5). Spatial patterns of RF also affect the pattern of climate response. However, note that, to first order, very different RF patterns can have similar patterns of surface temperature response and the location of maximum RF is rarely coincident with the location of maximum response (Boer and Yu, 2003b). Identification of different patterns of response is particularly important for attributing past climate change to particular mechanisms, and is also important for the prediction of regional patterns of future climate change. This chapter employs RF as the method for ranking the effect of a forcing agent on the equilibrium global temperature change, and only this aspect of the forcing-response relationship is discussed. However, patterns of RF are presented as a diagnostic in Section 2.9.5.
2.8.3 Alternative Methods of Calculating Radiative Forcing
RFs are increasingly being diagnosed from GCM integrations where the calculations are complex (Stuber et al., 2001b; Tett et al., 2002; Gregory et al., 2004). This chapter also discusses several mechanisms that include some response in the troposphere, such as cloud changes. These mechanisms are not initially radiative in nature, but will eventually lead to a radiative perturbation of the surface-troposphere system that could conceivably be measured at the TOA. Jacob et al. (2005) refer to these mechanisms as non-radiative forcings (see also Section 2.2). Alternatives to the standard stratospherically adjusted RF definition have been proposed that may help account for these processes. Since the TAR, several studies have employed GCMs to diagnose the zero-surface-temperature-change RF (see Figure 2.2 and Section 2.2). These studies have used a number of different methodologies. Shine et al. (2003) fixed both land and sea surface temperatures globally and calculated a radiative energy imbalance: this technique is only feasible in GCMs with relatively simple land surface parametrizations. Hansen et al. (2005) fixed sea surface temperatures and calculated an RF by adding an extra term to the radiative imbalance that took into account how much the land surface temperatures had responded. Sokolov (2006) diagnosed the zero-surface-temperature-change RF by computing surface-only and atmospheric-only components of climate feedback separately in a slab model and then modifying the stratospherically adjusted RF by the atmospheric-only feedback component. Gregory et al. (2004; see also Hansen et al., 2005; Forster and Taylor, 2006) used a regression method with a globally averaged temperature change ordinate to diagnose the zero-surface-temperature-change RF: this method had the largest uncertainties. Shine et al. (2003), Hansen et al. (2005) and Sokolov (2006) all found that that the fixed-surface-temperature RF was a better predictor of the equilibrium global mean surface temperature response than the stratospherically adjusted RF. Further, it was a particularly useful diagnostic for changes in absorbing aerosol where the stratospherically adjusted RF could fail as a predictor of the surface temperature response (see Section 18.104.22.168). Differences between the zero-surface-temperature-change RF and the stratospherically adjusted RF can be caused by semi-direct and cloud-aerosol interaction effects beyond the cloud albedo RF. For most mechanisms, aside from the case of certain aerosol changes, the difference is likely to be small (Shine et al., 2003; Hansen et al., 2005; Sokolov, 2006). These calculations also remove problems associated with defining the tropopause in the stratospherically adjusted RF definition (Shine et al., 2003; Hansen et al., 2005). However, stratospherically adjusted RF has the advantage that it does not depend on relatively uncertain components of a GCM’s response, such as cloud changes. For the LLGHGs, the stratospherically adjusted RF also has the advantage that it is readily calculated in detailed off-line radiation codes. For these reasons, the stratospherically adjusted RF is retained as the measure of comparison used in this chapter (see Section 2.2). However, to first order, all methods are comparable and all prove useful for understanding climate response.