Ice age constraints on climate sensitivity

Note then, that the SEA definition of sensitivity includes feedbacks associated with vegetation, which was considered a forcing in the standard Charney definition. Thus for the sensitivity determined by SEA to be comparable to the others, one would need to know the forcing due to the modelled vegetation change. KEA estimated that LGM vegetation forcing was around -1.1+/-0.6 W/m2 (because of the loss of trees in polar latitudes, replacement of forests by savannah etc.), and if that was similar to the SEA modelled impact, their Charney sensitivity would be closer to 2ºC (down from 2.3ºC).

Other studies have also expanded the scope of the sensitivity definition to include even more factors, a definition different enough to have its own name: the Earth System Sensitivity. Notably, both the Pliocene warm climate (Lunt et al., 2010), and the Paleocene-Eocene Thermal Maximum (Pagani et al., 2006), tend to support Earth System sensitivities higher than the Charney sensitivity.

Is sensitivity symmetric?

The first thing that must be recognized regarding all studies of this type is that it is unclear to what extent behavior in the LGM is a reliable guide to how much it will warm when CO2 is increased from its pre-industrial value. The LGM was a very different world than the present, involving considerable expansions of sea ice, massive Northern Hemisphere land ice sheets, geographically inhomogeneous dust radiative forcing, and a different ocean circulation. The relative contributions of the various feedbacks that make up climate sensitivity need not be the same going back to the LGM as in a world warming relative to the pre-industrial climate. The analysis in Crucifix (2006) indicates that there is not a good correlation between sensitivity on the LGM side and sensitivity to 2XCO2 in the selection of models he looked at.

There has been some other work to suggest that overall sensitivity to a cooling is a little less (80-90%) than sensitivity to a warming, for instance Hargreaves and Annan (2007), so the numbers of Schmittner et al. are less different from the “3ºC” number than they might at first appear. The factors that determine this asymmetry are various, involving ice albedo feedbacks, cloud feedbacks and other atmospheric processes, e.g., water vapor content increases approximately exponentially with temperature (Clausius-Clapeyron equation) so that the water vapor feedback gets stronger the warmer it is. In reality, the strength of feedbacks changes with temperature. Thus the complexity of the model being used needs to be assessed to see whether it is capable of addressing this.

Does the model used adequately represent key climate feedbacks?

Typically, LGM constraints on climate sensitivity are obtained by producing a large ensemble of climate model versions where uncertain parameters are systematically varied, and then comparing the LGM simulations of all these models with “observed” LGM data, i.e. proxy data, by applying statistical approach of one sort or another. It is noteworthy that very different models have been used for this: Annan et al. (2005) used an atmospheric GCM with a simple slab ocean, Schneider et al. (2006) the intermediate-complexity model CLIMBER-2 (with both ocean and atmosphere of intermediate complexity), while the new Schmittner et al. study uses an oceanic GCM coupled to a simple energy-balance atmosphere (UVic).

These models all suggest potentially serious limitations for this kind of study: UVic does not simulate the atmospheric feedbacks that determine climate sensitivity in more realistic models, but rather fixes the atmospheric part of the climate sensitivity as a prescribed model parameter (surface albedo, however, is internally computed). Hence, the dominant part of climate sensitivity remains the same, whether looking at 3ºC cooling or 3ºC warming. Slab oceans on the other hand, do not allow for variations in ocean circulation, which was certainly important for the LGM, and other intermediate models have all made key assumptions that may impact these feedbacks. However, in view of the fact that cloud feedbacks are the dominant contribution to uncertainty in climate sensitivity, the fact that the energy balance model used by Schmittner et al cannot compute changes in cloud radiative forcing is particularly serious.

Uncertainties in LGM proxy data

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References

  1. D.J. Lunt, A.M. Haywood, G.A. Schmidt, U. Salzmann, P.J. Valdes, and H.J. Dowsett, "Earth system sensitivity inferred from Pliocene modelling and data", Nature Geosci, vol. 3, pp. 60-64, 2009. http://dx.doi.org/10.1038/NGEO706
  2. M. Pagani, K. Caldeira, D. Archer, and J.C. Zachos, "ATMOSPHERE: An Ancient Carbon Mystery", Science, vol. 314, pp. 1556-1557, 2006. http://dx.doi.org/10.1126/science.1136110
  3. M. Crucifix, "Does the Last Glacial Maximum constrain climate sensitivity?", Geophysical Research Letters, vol. 33, 2006. http://dx.doi.org/10.1029/2006GL027137
  4. J.C. Hargreaves, A. Abe-Ouchi, and J.D. Annan, "Linking glacial and future climates through an ensemble of GCM simulations", Climate of the Past, vol. 3, pp. 77-87, 2007. http://dx.doi.org/10.5194/cp-3-77-2007
  5. J.D. Annan, J.C. Hargreaves, R. Ohgaito, A. Abe-Ouchi, and S. Emori, "Efficiently Constraining Climate Sensitivity with Ensembles of Paleoclimate Simulations", SOLA, vol. 1, pp. 181-184, 2005. http://dx.doi.org/10.2151/sola.2005-047