Ice age constraints on climate sensitivity

Perhaps the key difference of Schmittner et al. to some previous studies is their use of all available proxy data for the LGM, whilst other studies have selected a subset of proxy data that they deemed particularly reliable (e.g., in Schneider et al. SST data from the tropical Atlantic, Greenland and Antarctic ice cores and some tropical land temperatures). Uncertainties of the proxy data (and the question of knowing what these uncertainties are) are crucial in this kind of study. A well-known issue with LGM proxies is that the most abundant type of proxy data, using the species composition of tiny marine organisms called foraminifera, probably underestimates sea surface cooling over vast stretches of the tropical oceans; other methods like alkenone and Mg/Ca ratios give colder temperatures (but aren’t all coherent either). It is clear that this data issue makes a large difference in the sensitivity obtained.

The Schneider et al. ensemble constrained by their selection of LGM data gives a global-mean cooling range during the LGM of 5.8 +/- 1.4ºC (Schnieder Von Deimling et al, 2006), while the best fit from the UVic model used in the new paper has 3.5ºC cooling, well outside this range (weighted average calculated from the online data, a slightly different number is stated in Nathan Urban’s interview – not sure why).

Curiously, the mean SEA estimate (2.4ºC) is identical to the mean KEA number, but there is a big difference in what they concluded the mean temperature at the LGM was, and a small difference in how they defined sensitivity. Thus the estimates of the forcings must be proportionately less as well. The differences are that the UVic model has a smaller forcing from the ice sheets, possibly because of an insufficiently steep lapse rate (5ºC/km instead of a steeper value that would be more typical of dryer polar regions), and also a smaller change from increased dust.

Model-data comparisons

So there is a significant difference in the headline results from SEA compared to previous results. As we mentioned above though, there are reasons to think that their result is biased low. There are two main issues here. First, the constraint to a lower sensitivity is dominated by the ocean data – if the fit is made to the land data alone, the sensitivity would be substantially higher (though with higher uncertainty). The best fit for all the data underpredicts the land temperatures significantly.

Match of model results against proxy data (Fig 2 of SEA). Black line is zonal mean of the proxy data, colored lines are means over the location from different models. Note that the values are the mean changes of SAT over land, and SST over ocean.

However, even in the ocean the fit to the data is not that good in many regions – particular the southern oceans and Antarctica, but also in the Northern mid-latitudes. This occurs because the tropical ocean data are weighing more heavily in the assessment than the sparser and possibly less accurate polar and mid-latitude data. Thus there is a mismatch between the pattern of cooling produced by the model, and the pattern inferred from the real world. This could be because of the structural deficiency of the model, or because of errors in the data, but the (hard to characterise) uncertainty in the former is not being carried into final uncertainty estimate. None of the different model versions here seem to get the large polar amplification of change seen in the data for instance.

Response and media coverage

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  1. T. Schneider von Deimling, A. Ganopolski, H. Held, and S. Rahmstorf, "How cold was the Last Glacial Maximum?", Geophysical Research Letters, vol. 33, 2006.