A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

R B Skeie,M Holden,M Aldrin,T Berntsen,G Myhre

doi:10.5194/esd-5-139-2014

Abstract

Abstract. Equilibrium climate sensitivity (ECS) is 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. For the net anthropogenic RF the posterior mean in 2010 is 2.0 Wm−2, with a 90% credible interval (C.I.) of 1.3 to 2.8 Wm−2, excluding present-day total aerosol effects (direct + indirect) stronger than −1.7 Wm−2. The posterior mean of the ECS is 1.8 °C, with 90% C.I. ranging from 0.9 to 3.2 °C, which is tighter than most previously published estimates. We find that using three OHC data sets simultaneously and data for global mean temperature and OHC up to 2010 substantially narrows the range in ECS compared to using less updated data and only one OHC data set. Using only one OHC set and data up to 2000 can produce comparable results as previously published estimates using observations in the 20th century, including the heavy tail in the probability function. The analyses show a significant contribution of internal variability on a multi-decadal scale to the global mean temperature change. If we do not explicitly account for long-term internal variability, the 90% C.I. is 40% narrower than in the main analysis and the mean ECS becomes slightly lower, which demonstrates that the uncertainty in ECS may be severely underestimated if the method is too simple. In addition to the uncertainties represented through the estimated probability density functions, there may be uncertainties due to limitations in the treatment of the temporal development in RF and structural uncertainties in the EBM.

Highlights

To link long-term targets of climate policy, e.g. the 2 ◦C target (UNFCCC, 2009, 2010), to a 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)
We present the results of our analysis where the parameter values in the energy balance model (EBM) are updated using observations of hemispheric temperature and ocean heat content (OHC) (0–700 m) to the year 2010 and detailed RF time series from 1750 to 2010
In this study, detailed RF time series for all well-established forcing mechanisms and observed OHC (0–700 m) and nearsurface temperature changes to the year 2010 are combined in a Bayesian framework using a simple EBM and a stochastic model

Summary

Introduction

To link long-term targets of climate policy, e.g. the 2 ◦C target (UNFCCC, 2009, 2010), to a 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. The ECS has been poorly constrained, with significant probabilities of 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) in 2007, and values substantially higher than 4.5 ◦C could not be excluded (Meehl et al, 2007). A “bottom up” approach performing Monte Carlo simulations or a multi-model experiment with general circulation models (GCMs) (Murphy et al, 2004; Piani et al, 2005; Stainforth et al, 2005; Andrews et al, 2012) and a “top down” approach constraining the ECS using RF estimates and observed data on past climate change on various timescales: 20th century warming

Methods

Results

Discussion

Conclusion