IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis 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.


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, 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

Figure 8.14

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).




  1. ^  Under these simplifying assumptions the amplification of the global warming from a feedback parameter λ (in W m-2 °C–1) with no other feedbacks operating is , where λp is the ‘uniform temperature’ radiative cooling response (of value approximately –3.2 W m–2 °C–1; Bony et al., 2006). If n independent feedbacks operate, λ is replaced by (λ1 + λ2 +...λn ).