AR4 (chapter 1 on the History of Climate Science) contains the remarkable statement:
The strong effect of cloud processes on climate model sensitivities to greenhouse gases was emphasized further through a now-classic set of General Circulation Model (GCM) experiments, carried out by Senior and Mitchell (1993). They produced global average surface temperature changes (due to doubled atmospheric CO2 concentration) ranging from 1.9°C to 5.4°C, simply by altering the way that cloud radiative properties were treated in the model. It is somewhat unsettling that the results of a complex climate model can be so drastically altered by substituting one reasonable cloud parameterization for another, thereby approximately replicating the overall intermodel range of sensitivities.
As they say, it is somewhat unsettling. On the basis that these results are “now-classic”, one would have expected them to have been prominently featured in TAR. [yeah, right.] So let’s how prominently TAR featured these results – were they as prominent as the Hockey Stick?
I did a search of Senior and Mitchell 1993 in TAR (google: grida senior mitchell 1993) and identified the following references in chapter 7 (Coordinating Lead Author – T Stocker, lead authors include Pierrehumbert), neither of which reported these “now-classic” results.
A first generation of so-called prognostic cloud schemes (Le Treut and Li, 1991; Roeckner et al., 1991; Senior and Mitchell, 1993; Del Genio et al., 1996), has used a budget equation for cloud water, defined as the sum of all liquid and solid cloud water species that have negligible vertical fall velocities.
and again in section 18.104.22.168:
Schemes predicting cloudiness as a function of relative humidity generally show an upward displacement of the higher troposphere cloud cover in response to a greenhouse warming, resulting in a positive feedback (Manabe and Wetherald, 1987). While this effect still appears in more sophisticated models, and even cloud resolving models (Wu and Moncrieff, 1999; Tompkins and Emanuel, 2000), the introduction of cloud water content as a prognostic variable, by decoupling cloud and water vapour, has added new features (Senior and Mitchell, 1993; Lee et al., 1997). As noted in the SAR, a negative feedback corresponding to an increase in cloud cover, and hence cloud albedo, at the transition between ice and liquid clouds occurs in some models, but is crucially dependent on the definition of the phase transition within models. The sign of the cloud cover feedback is still a matter of uncertainty and generally depends on other related cloud properties (Yao and Del Genio, 1999; Meleshko et al., 2000).
In the relevant AR4 chapter (chapter 8), the authors mention Senior and Mitchell 1993 in a very coy manner giving no idea of the blockbuster variations noted up in the historical review:
In many climate models, details in the representation of clouds can substantially affect the model estimates of cloud feedback and climate sensitivity (e.g., Senior and Mitchell, 1993; Le Treut et al., 1994; Yao and Del Genio, 2002; Zhang, 2004; Stainforth et al., 2005; Yokohata et al., 2005). Moreover, the spread of climate sensitivity estimates among current models arises primarily from inter-model differences in cloud feedbacks (Colman, 2003a; Soden and Held, 2006; Webb et al., 2006; Section 8.6.2, Figure 8.14). Therefore, cloud feedbacks remain the largest source of uncertainty in climate sensitivity estimates.
Senior, C.A., and J.F.B. Mitchell, 1993: Carbon dioxide and climate: The impact of cloud parameterization. J. Clim., 6, 393418.
1995 1-15, 189-228 url