18.104.22.168 What Explains the Current Spread in Models’ Climate Sensitivity Estimates?
As discussed in Chapter 10 and throughout the last three IPCC assessments, climate models exhibit a wide range of climate sensitivity estimates (Table 8.2). Webb et al. (2006), investigating a selection of the slab versions of models in Table 8.1, found that differences in feedbacks contribute almost three times more to the range in equilibrium climate sensitivity estimates than differences in the models’ radiative forcings (the spread of models’ forcing is discussed in Section 10.2).
Table 8.2. Climate sensitivity estimates from the AOGCMs assessed in this report (see Table 8.1 for model details). Transient climate response (TCR) and equilibrium climate sensitivity (ECS) were calculated by the modelling groups (using atmosphere models coupled to slab ocean for equilibrium climate sensitivity), except those in italics, which were calculated from simulations in the MMD at PCMDI. The ocean heat uptake efficiency (W m–2 °C–1), discussed in Chapter 10, may be roughly estimated as F2x x (TCR–1 – ECS–1), where F2x is the radiative forcing for doubled atmospheric CO2 concentration (see Supplementary Material, Table 8.SM.1)
|AOGCM ||Equilibrium climate sensitivity (°C) ||Transient climate response (°C) |
|1: BCC-CM1 ||n.a. ||n.a. |
|2: BCCR-BCM2.0 ||n.a. ||n.a. |
|3: CCSM3 ||2.7 ||1.5 |
|4: CGCM3.1(T47) ||3.4 ||1.9 |
|5: CGCM3.1(T63) ||3.4 ||n.a. |
|6: CNRM-CM3 ||n.a. ||1.6 |
|7: CSIRO-MK3.0 ||3.1 ||1.4 |
|8: ECHAM5/MPI-OM ||3.4 ||2.2 |
|9: ECHO-G ||3.2 ||1.7 |
|10: FGOALS-g1.0 ||2.3 ||1.2 |
|11: GFDL-CM2.0 ||2.9 ||1.6 |
|12: GFDL-CM2.1 ||3.4 ||1.5 |
|13: GISS-AOM ||n.a. ||n.a. |
|14: GISS-EH ||2.7 ||1.6 |
|15: GISS-ER ||2.7 ||1.5 |
|16: INM-CM3.0 ||2.1 ||1.6 |
|17: IPSL-CM4 ||4.4 ||2.1 |
|18: MIROC3.2(hires) ||4.3 ||2.6 |
|19: MIROC3.2(medres) ||4.0 ||2.1 |
|20: MRI-CGCM2.3.2 ||3.2 ||2.2 |
|21: PCM ||2.1 ||1.3 |
|22: UKMO-HadCM3 ||3.3 ||2.0 |
|23: UKMO-HadGEM1 ||4.4 ||1.9 |
Several methods have been used to diagnose climate feedbacks in GCMs, whose strengths and weaknesses are reviewed in Stephens (2005) and Bony et al. (2006). These methods include the ‘partial radiative perturbation’ approach and its variants (e.g., Colman, 2003a; Soden and Held, 2006), the use of radiative-convective models and the ‘cloud radiative forcing’ method (e.g., Webb et al., 2006). Since the TAR, there has been progress in comparing the feedbacks produced by climate models in doubled atmospheric CO2 equilibrium experiments (Colman, 2003a; Webb et al., 2006) and in transient climate change integrations (Soden and Held, 2006). Water vapour, lapse rate, cloud and surface albedo feedback parameters, as estimated by Colman (2003a), Soden and Held (2006) and Winton (2006a) are shown in Figure 8.14.
In AOGCMs, the water vapour feedback constitutes by far the strongest feedback, with a multi-model mean and standard deviation for the MMD at PCMDI of 1.80 ± 0.18 W m–2 °C–1, followed by the (negative) lapse rate feedback (–0.84 ± 0.26 W m–2 °C–1) and the surface albedo feedback (0.26 ± 0.08 W m–2 °C–1). The cloud feedback mean is 0.69 W m–2 °C–1 with a very large inter-model spread of ±0.38 W m–2 °C–1 (Soden and Held, 2006).
A substantial spread is apparent in the strength of water vapour feedback that is smaller in Soden and Held (2006) than in Colman (2003a). It is not known whether this smaller spread indicates a closer consensus among current AOGCMs than among older models, differences in the methodology or differences in the nature of climate change integrations between the two studies. In both studies, the lapse rate feedback also shows a substantial spread among models, which is explained by inter-model differences in the relative surface warming of low and high latitudes (Soden and Held, 2006). Because the water vapour and temperature responses are tightly coupled in the troposphere (see Section 22.214.171.124), models with a larger (negative) lapse rate feedback also have a larger (positive) water vapour feedback. These act to offset each other (see Box 8.1). As a result, it is more reasonable to consider the sum of water vapour and lapse rate feedbacks as a single quantity when analysing the causes of inter-model variability in climate sensitivity. This makes inter-model differences in the combination of water vapour and lapse rate feedbacks a substantially smaller contributor to the spread in climate sensitivity estimates than differences in cloud feedback (Figure 8.14). The source of the difference in mean lapse rate feedback between the two studies is unclear, but may relate to inappropriate inclusion of stratospheric temperature response in some feedback analyses (Soden and Held, 2006).
The three studies, using different methodologies to estimate the global surface albedo feedback associated with snow and sea ice changes, all suggest that this feedback is positive in all the models, and that its range is much smaller than that of cloud feedbacks. Winton (2006a) suggests that about three-quarters of the global surface albedo feedback arises from the NH (see Section 126.96.36.199).
Figure 8.14. Comparison of GCM climate feedback parameters for water vapour (WV), cloud (C), surface albedo (A), lapse rate (LR) and the combined water vapour plus lapse rate (WV + LR) in units of W m–2 °C–1. ‘ALL’ represents the sum of all feedbacks. Results are taken from Colman (2003a; blue, black), Soden and Held (2006; red) and Winton (2006a; green). Closed blue and open black symbols from Colman (2003a) represent calculations determined using the partial radiative perturbation (PRP) and the radiative-convective method (RCM) approaches respectively. Crosses represent the water vapour 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 feedbacks from Soden and Held (2006).
The diagnosis of global radiative feedbacks allows better understanding of the spread of equilibrium climate sensitivity estimates among current GCMs. In the idealised situation that the climate response to a doubling of atmospheric CO2 consisted of a uniform temperature change only, with no feedbacks operating (but allowing for the enhanced radiative cooling resulting from the temperature increase), the global warming from GCMs would be around 1.2°C (Hansen et al., 1984; Bony et al., 2006). The water vapour feedback, operating alone on top of this, would at least double the response. The water vapour feedback is, however, closely related to the lapse rate feedback (see above), and the two combined result in a feedback parameter of approximately 1 W m–2 °C–1, corresponding to an amplification of the basic temperature response by approximately 50%. The surface albedo feedback amplifies the basic response by about 10%, and the cloud feedback does so by 10 to 50% depending on the GCM. Note, however, that because of the inherently nonlinear nature of the response to feedbacks, the final impact on sensitivity is not simply the sum of these responses. The effect of multiple positive feedbacks is that they mutually amplify each other’s impact on climate sensitivity.
Using feedback parameters from Figure 8.14, it can be estimated that in the presence of water vapour, lapse rate and surface albedo feedbacks, but in the absence of cloud feedbacks, current GCMs would predict a climate sensitivity (±1 standard deviation) of roughly 1.9°C ± 0.15°C (ignoring spread from radiative forcing differences). The mean and standard deviation of climate sensitivity estimates derived from current GCMs are larger (3.2°C ± 0.7°C) essentially because the GCMs all predict a positive cloud feedback (Figure 8.14) but strongly disagree on its magnitude.
The large spread in cloud radiative feedbacks leads to the conclusion that differences in cloud response are the primary source of inter-model differences in climate sensitivity (see discussion in Section 188.8.131.52.2). However, the contributions of water vapour/lapse rate and surface albedo feedbacks to sensitivity spread are non-negligible, particularly since their impact is reinforced by the mean model cloud feedback being positive and quite strong.