A recent paper provides further evidence of why a focus on a global average surface temperature is an ineffective climate metric if the goal is to communictate actual climate responses to scientists, policymakers and the public. The paper by M. Crucifix has appeared in Geophysical Research Letters and is entitled “Does the Last Glacial Maximum constrain climate sensitivity?” (subscription required).
The abstract reads,
“Four simulations with atmosphere-ocean climate models have been produced using identical Last Glacial Maximum ice sheets, topography and greenhouse gas concentrations. Compared to the pre-industrial, the diagnosed radiative feedback parameter ranges between -1.30 and -1.18 Wm-2K-1, the tropical ocean sea-surface temperature decreases between 1.7 and 2.7C, and Antarctic surface air temperature decreases by 7 to 11C. These values are all compatible with observational estimates, except for a tendency to underestimate the tropical ocean cooling. On the other hand, the same models have climate sensitivity to CO2 concentration doubling ranging between 2.1 and 3.9 K. It is therefore inappropriate to simply scale an observational estimate of LGM temperature to predict climate sensitivity. This is mainly a consequence of the non-linear character of the cloud (mainly shortwave) feedback at low latitudes. Changes in albedo and cloud cover at mid and high latitudes are also important, but less so.”
Excerpts from the article are
“The four models analysed here form a small sample of current state-of-the-art models used to predict the future climate, though they are fairly representative in the sense that they almost cover the commonly accepted range of likely climate sensitivity. It is therefore unexpected that these models have similar global mean radiative responses to the Last Glacial Maximum Climate. It was also shown that the ratio between LGM cooling and 2 X CO2 warming depends greatly on the model. Therefore, climate sensitivity cannot be easily estimated from the Last Glacial Maximum
“The LGM global radiative responses being similar across models may be an artifact of the small sample (four models). However, the reasons for LGM and CO2 feedback factors differing so much have been identified and they appear reasonable. The main one is that subtropical shallow convective clouds do not respond linearly to temperature change. This particular effect is probably not a direct consequence of the presence of the ice sheets on atmosphere dynamics, and therefore emphasises the fundamentally nonlinear nature of the climate response to the radiative forcing.
This result has two consequences. First, it certainly encourages a more systematic analysis of the dependency of the feedback factor on the nature and the sign of the forcing in climate models. Second, global estimates of the LGM temperature only weakly constrain climate sensitivity for two reasons: (i) the forcing is not known accurately and (ii) the ratio between LGM and CO2 feedback factors cannot be accurately estimated from current state-of-the-art coupled models. This implies that careful model-data comparisons on the details of the spatial distribution of changes in temperature and precipitation at the LGM are needed to identify the ‘‘best models’’, that is, those that reliably predict the response of climate dynamics to a given forcing. Global temperature is not sufficient.”
The message from this paper that
“…careful model-data comparisons on the details of the spatial distribution of changes in temperature and precipitation at the LGM are needed to identify the ‘‘best models’’”
“Global temperature is not sufficient.”
are two conclusions that we have emphasized on Climate Science as well as in the 2005 National Research Council Report “Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties”. Unfortunately, international assessments such as the IPCC, much of the media and most policymakers are ignoring these two scientific conclusions.