Efficiently Constraining Climate Sensitivity with Ensembles of Paleoclimate Simulations

Abstract

We use a recently-developed efficient probabilistic estimation technique to estimate the sensitivity of the Earth's temperature to a doubling of atmospheric carbon dioxide. The method is based on the ensemble Kalman filter which we apply to the CCSR/NIES/ FRCGC AGCM (the atmospheric component of MIROC 3.2) at T21L20 resolution coupled to a slab ocean. The method combines prior beliefs about the model, with observational data, to simultaneously estimate 25 model parameters in an efficient and objective manner. We perform a sensitivity analysis to investigate the effect of different assumptions regarding model error, since this is a necessarily subjective input which has not yet been well characterised. We attempt to validate the resulting ensembles against out-of-sample data by comparing their hindcasts of the Last Glacial Maximum (LGM) to paleoclimate proxy data, and demonstrate through this that our ensembles of simulations are probably biased towards too high a sensitivity. Within the framework of our single-model ensemble experiment, we show that climate sensitivity of much greater than 6°C is hard to reconcile with the paleoclimate record, and that of greater than 8°C seems virtually impossible. Our estimate for the most likely climate sensitivity is in the region of 4.5°C. Although these results are reasonably consistent with the most widely accepted estimates of climate sensitivity, they disagree with some recent research which has suggested a significant probability of sensitivities well in excess of these values. These results suggest that paleoclimatic evidence could provide a useful, albeit imprecise, constraint on ensemble forecasts of future climate change.

Recent Progress in Constraining Climate Sensitivity With Model Ensembles

“…For example, some studies used potentially observable information (such as the mean climate state [26] or the amplitude of the present-day seasonal cycle [19]) within these ensembles to attempt to find ECs on ECS, which could then be used without a first-order dependency on the sampling prior. Annan et al [1] used an ensemble Kalman filter approach together with PPE simulations of the last glacial maximum using the MIROC model, using LGM temperatures as an EC.…”

Perturbed physics ensemble using the MIROC5 coupled atmosphere–ocean GCM without flux corrections: experimental design and results

“…Although the CS of MIROC5 PPE is low (2.2-3.2°C), the CS within the PPE of MIROC3 ASGCM (an old version of MIROC) is high ([4°C) (Annan et al 2005). Because many of the physical schemes in MIROC3 and MIROC5 differ (Watanabe et al 2010), it is not clear which schemes are largely responsible for determining the structural dependency.…”

“…However, these wide ranges in CS are not universal. The PPEs used versions of the CAM3 GCM (Jackson et al 2008;Sanderson 2011) and the EGMAM GCM (Niehörster and Collins 2009) consistently yielded a CS of less than 3°C, whereas the PPEs of MIROC3 produced a CS greater than 4°C (Annan et al 2005;Yokohata et al 2010;Yoshimori et al 2011). The distributions of CS in PPEs are contingent upon the model structures as well as the experimental design.…”

“…Most of the previous PPE studies used atmosphere/slab-ocean (mixed layer ocean) GCMs (ASGCMs) rather than coupled atmosphere/full-ocean GCMs (CGCMs) (Murphy et al 2004;Stainforth et al 2005;Annan et al 2005;Sanderson 2011;Klocke et al 2011). One of the reasons for this is that the computational costs required to reach equilibrium for the ASGCMs are lower than those for the CGCMs.…”

Optimal tuning of a GCM using modern and glacial constraints

“…We define a comprehensive cost function which takes into account seasonality and incorporates variables representing different aspects of the climate system. As well as using present day observations in our cost function, we choose to include a proxy for the climate of the LGM, in part because our model is particularly useful for paleoclimate studies and also because the LGM has been shown to provide additional constraint for the climate sensitivity of the models (Edwards et al 2007;Annan et al 2005c). After briefly describing the model in Sect.…”