To link long-term targets of climate policy, e.g. the 2˚C target (UNFCCC, 2009, 2010), to more specific emission mitigation policy, a key question in climate science is to quantify the sensitivity of the climate system to perturbation in the radiative forcing (RF). The equilibrium climate sensitivity (ECS) is defined as the global mean surface temperature change following a doubling of the CO2 concentration when the system has reached a new equilibrium. However, the ECS has been poorly constrained, with significant probabilities for high values. The ECS was given a likely (> 66% probability) range of 2 to 4.5˚C with a best estimate of 3˚C by the Intergovernmental Panel on Climate Change (IPCC, 2007) and values substantially higher than 4.5˚C could not be excluded. For constraining the ECS there are two main approaches. A “bottom up” approach performing Monte Carlo simulations or multi-model experiment with General Circulation Models (GCMs) (Murphy et al., 2004; Piani et al., 2005; Stainforth et al., 2005; Andrews et al., 2011) and a “top down” approach constraining the ECS using RF estimates and observed data on past climate change on various timescales.
In my talk I will present some of our recent estimates of the equilibrium climate sensitivity (ECS) constrained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model (EBM) and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data.
Our posterior mean of the ECS is 1.8˚C with 90% confidence interval ranging from 0.9 to 3.2˚C which is lower and tighter than most previously published estimates. I will discuss the causes and implications of this estimate.