Abstract

The equilibrium climate sensitivity (ECS, in K) to hbox {CO}_{2} doubling is a large source of uncertainty in projections of future anthropogenic climate change. Estimates of ECS made from non-equilibrium states or in response to radiative forcings other than hbox {2}times hbox {CO}_{2} are called “effective climate sensitivity” (EffCS, in K). Taking a “perfect-model” approach, using coupled atmosphere–ocean general circulation model (AOGCM) experiments, we evaluate the accuracy with which hbox {CO}_{2} EffCS can be estimated from climate change in the “historical” period (since about 1860). We find that (1) for statistical reasons, unforced variability makes the estimate of historical EffCS both uncertain and biased; it is overestimated by about 10% if the energy balance is applied to the entire historical period, 20% for 30-year periods, and larger factors for interannual variability, (2) systematic uncertainty in historical radiative forcing translates into an uncertainty of {pm },30, {rm to} ,45% (standard deviation) in historical EffCS, (3) the response to the changing relative importance of the forcing agents, principally hbox {CO}_{2} and volcanic aerosol, causes historical EffCS to vary over multidecadal timescales by a factor of two. In recent decades it reached its maximum in the AOGCM historical experiment (similar to the multimodel-mean hbox {CO}_{2} EffCS of 3.6 K from idealised experiments), but its minimum in the real world (1.6 K for an observational estimate for 1985–2011, similar to the multimodel-mean value for volcanic forcing). The real-world variations mean that historical EffCS underestimates hbox {CO}_{2} EffCS by 30% when considering the entire historical period. The difference for recent decades implies that either unforced variability or the response to volcanic forcing causes a much stronger regional pattern of sea surface temperature change in the real world than in AOGCMs. We speculate that this could be explained by a deficiency in simulated coupled atmosphere–ocean feedbacks which reinforce the pattern (resembling the Interdecadal Pacific Oscillation in some respects) that causes the low EffCS. We conclude that energy-balance estimates of hbox {CO}_{2} EffCS are most accurate from periods unaffected by volcanic forcing. Atmosphere GCMs provided with observed sea surface temperature for the 1920s to the 1950s, which was such a period, give a range of about 2.0–4.5 K, agreeing with idealised hbox {CO}_{2} AOGCM experiments; the consistency is a reason for confidence in this range as an estimate of hbox {CO}_{2} EffCS. Unless another explosive volcanic eruption occurs, the first 30 years of the present century may give a more accurate energy-balance historical estimate of this quantity.

Highlights

  • The equilibrium climate sensitivity (ECS), defined as the steady-state global-mean surface air temperature change due to a doubling of the atmospheric carbon dioxide concentration, has been used for decades as a benchmark for the magnitude of climate change predicted by general circulation models (GCMs) in response to CO2 increase

  • The atmosphere–ocean general circulation model (AOGCM) indicate that the most recent decades should have closest to its CO2 value, but in Sect. 6 we present evidence that the historical time-variation of in the AOGCMs may be unrealistic in that regard, by comparison with atmosphere GCM (AGCM) amip-piForcing experiments

  • The step-model mean shows more warming during the historical period than the AOGCM mean (Fig. 2a). We suggest that this is because the AR5 F is larger than the AOGCM mean F, due to the negative anthropogenic aerosol forcing being stronger in AOGCMs than in reality, consistent with the expert judgement of Myhre et al (2013)

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Summary

Introduction

The equilibrium climate sensitivity (ECS), defined as the steady-state global-mean surface air temperature change due to a doubling of the atmospheric carbon dioxide concentration, has been used for decades as a benchmark for the magnitude of climate change predicted by general circulation models (GCMs) in response to CO2 increase. Recent work shows that historical climate change tends to give a larger median estimate of , and a smaller EffCS, than GCMs do under idealised high-CO2 scenarios, such as abrupt4xCO2, which have ERF of the magnitude typically projected for the 21st century (Forster 2016). The amip-piForcing experiment gives a larger (smaller EffCS) for historical climate change than experiments using the same AGCMs, incorporated in coupled atmosphere–ocean GCMs (AOGCMs), to simulate the response to 4 × CO2. We make use of AOGCM experiments that simulate change due to unforced variability alone and to subsets of historical forcings, whereas we cannot control these influences in the real world. 4 we show that, if the AOGCMs are realistic, dR∕dT evaluated from historical climate change by Eq (3) may be an imprecise and biased estimate of the historical , owing to the statistical effects of unforced variability.

AOGCM historical experiments
Historical radiative forcing
Diagnosis using AGCMs
Forcing due to tropospheric and volcanic aerosol
Estimate of CMIP5 historical forcing
Using regression to estimate historical climate feedback
Time‐variation of historical climate feedback related to forcing agents
Time‐variation of climate feedback in the historical experiment
Greenhouse‐gas forcing
Comparison of historicalGHG and abrupt4xCO2 climate feedback
Volcanic and anthropogenic aerosol forcings
Comparison of historical and abrupt4xCO2 climate feedback
Comparison of unforced and abrupt4xCO2 climate feedback
Time‐variation of historical climate feedback related to SST patterns
Time‐variation of climate feedback in the amip‐piForcing experiment
Effect of patterns of SST change on radiative response
Differences between simulated and observed responses to volcanic forcing
How accurately can CO2 EffCS be estimated from historical EffCS?
SST and EffCS since 1975
Prospects for estimating the climate response to CO2
A The step model
B Choice of independent variable for regression
C Error in estimating climate feedback from a single ensemble member
D Statistical issues in regression
The difference method is a special case of regression
No bias in mdue to uncorrelated noise in y
Bias in mdue to uncorrelated noise in x
Correct choice of independent variable
Uncorrelated noise in both x and y
Findings
Correlated noise in x and y
Full Text
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