Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature

4224 JOURNAL OF CLIMATE VOLUME 19 Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature RETO KNUTTI AND GERALD A. MEEHL National Center for Atmospheric Research,* Boulder, Colorado MYLES R. ALLEN AND DAVID A. STAINFORTH Atmospheric and Oceanic Physics, Oxford University, Oxford, United Kingdom (Manuscript received 16 June 2005, in final form 29 November 2005) ABSTRACT The estimated range of climate sensitivity has remained unchanged for decades, resulting in large uncertainties in long-term projections of future climate under increased greenhouse gas concentrations. Here the multi-thousand-member ensemble of climate model simulations from the climateprediction.net project and a neural network are used to establish a relation between climate sensitivity and the amplitude of the seasonal cycle in regional temperature. Most models with high sensitivities are found to overestimate the seasonal cycle compared to observations. A probability density function for climate sensitivity is then calculated from the present-day seasonal cycle in reanalysis and instrumental datasets. Subject to a number of assumptions on the models and datasets used, it is found that climate sensitivity is very unlikely (5% probability) to be either below 1.5–2 K or above about 5–6.5 K, with the best agreement found for sensitivities between 3 and 3.5 K. This range is narrower than most probabilistic estimates derived from the observed twentieth-century warming. The current generation of general circulation models are within that range but do not sample the highest values. 1. Introduction spheric CO2 concentration, equivalent to a radiative forcing of about 3.7 W mϪ2 (Myhre et al. 1998). It is Projections of future increases in global temperature used to quantify all important feedbacks, including al- for a given emission scenario are uncertain, with recent bedo, cloud, and water vapor feedbacks, which poten- quantitative methods estimating a spread of the models tially amplify the warming. Both global temperature of about 30% around the best-guess projection (Knutti increase and its uncertainty on time scales of centuries et al. 2002; Stott and Kettleborough 2002). This spread scale almost linearly with sensitivity. arises mainly from uncertainties in the carbon cycle, The almost canonical range of 1.5–4.5 K for climate radiative forcing for given atmospheric concentrations, sensitivity was derived from the range covered by dif- climate sensitivity, and how the ocean mixing affects ferent atmosphere–ocean general circulation models the transient ocean heat uptake. The current range of (AOGCMs) and has remained virtually unchanged for climate sensitivity is the dominant contribution to the decades (Charney 1979; Houghton et al. 1996, 2001). total uncertainty in future warming projections after No probability was ever formally attached to this range, about the year 2050 (Knutti et al. 2002). Climate sen- but sometimes assumed ad hoc due to the lack of better sitivity is usually referred to as the global mean equi- knowledge (Wigley and Raper 2001). However, if we librium near-surface warming for doubling the atmo- are interested for example in the likelihood of exceed- ing a certain temperature threshold for a given forcing, a probability density function (PDF) for sensitivity is * The National Center for Atmospheric Research is sponsored inevitably needed. by the National Science Foundation. A number of methods were proposed to constrain climate sensitivity from observations in a probabilistic Corresponding author address: Reto Knutti, NCAR, P.O. Box way. Studies based on a combination of the recon- 3000, Boulder, CO 80307. structed radiative forcing and the observed surface E-mail: [email protected] warming and ocean heat uptake (Andronova and © 2006 American Meteorological Society Unauthenticated | Downloaded 09/25/21 01:42 AM UTC JCLI3865 1SEPTEMBER 2006 KNUTTI ET AL. 4225 Schlesinger 2001; Forest et al. 2002; Gregory et al. 2002; effects are related. Patterns of temperature differences Knutti et al. 2002, 2003; Forest et al. 2006; Frame et al. like the land–ocean contrast, Northern Hemisphere 2005) find ranges of sensitivity consistent with observa- meridional temperature contrast, interhemispheric tions that are much larger than the range of 1.5–4.5 K temperature contrast, the seasonal cycle, and the diur- given by the Intergovernmental Panel on Climate nal temperature range have been found to correlate Change (IPCC) Third Assessment Report (TAR; strongly with global temperature (Braganza et al. 2003), Houghton et al. 2001). In particular, the upper end is suggesting also that the amplitude of those patterns is difficult to constrain, and several studies have high- determined at least partly by the same feedbacks as lighted the possibility of sensitivity being8Kormore. those that control global temperature. A full quantifi- Ensembles of climate models with perturbed physics cation of all individual relevant feedbacks is impossible evaluated against present-day climatology also yield at this stage, but also not needed. We simply argue that wide sensitivity ranges (Murphy et al. 2004; Stainforth there is (at least in some regions) a relation between the et al. 2005). Paleoclimate data seems to provide some amplitude of the seasonal temperature cycle and cli- constraints on climate sensitivity (Hoffert and Covey mate sensitivity if climate model parameters are varied, 1992; Lea 2004) but attempts are hampered by an often and that such a relation can be approximated by a sta- poor quantification of the uncertainties associated with tistical method. Using that relation and observations on paleoclimate data, and more recent estimates based on the amplitude of the seasonal cycle allows us to derive climate models (Annan et al. 2005; Hegerl et al. 2006; a PDF of climate sensitivity. Schneider von Deimling et al. 2006) are not conclusive. The fact that only proxies of temperature are available and that those can be found only in some locations, 2. Model and statistical procedure makes expert judgement inevitable. Thus, it seems that recent quantitative attempts to constrain climate sensi- Climateprediction.net (CPDN) is a distributed com- tivity have rather increased than decreased the likely puting project that runs thousands of climate model range compared to IPCC TAR (Houghton et al. 2001). simulations on standard public or home computers In particular, the low probability but high impact case of very large sensitivities is a reason for concern in (Stainforth et al. 2005; more information available on- adaptation or mitigation planning of future climate line at http://climateprediction.net/). Parameters and change. initial conditions in a version of the Third Hadley Cen- In this paper we propose that the amplitude of the tre atmosphere-slab ocean model (HadSM3; Pope et al. seasonal cycle in temperature and its geographical dis- 2000; Murphy et al. 2004) are perturbed to explore the tribution provides a constraint on climate sensitivity widest possible range of model responses to doubling and can thus be used for the evaluation of model per- atmospheric CO2. First results in this simplified setup formance. The idea is based on the fact that seasonal yielded a wide range of responses, with climate sensi- sensitivity (i.e., the local temperature change for the tivity values from less than 2 to more than 11 K (Stain- solar insolation difference between summer and winter forth et al. 2005). Even model versions with sensitivities at a given location) is at least partly determined by the larger than 10 K could not be excluded based on annual same processes as climate sensitivity, the equilibrium mean climatology. global temperature response to a global change in forc- For the analysis presented here, 2500 CPDN simula- ing. For some feedbacks, it is obvious that their strength tions were used. Quality control and the calculation of on a seasonal scale will be relevant for global warming climate sensitivity were done as described by Stainforth (e.g., how fast the land warms or cools compared to the et al. (2005). Seasonal amplitude (S) in temperature ocean). Hall and Qu (2006) also found a strong rela- was calculated as boreal summer June–August (JJA) tionship of the surface albedo feedback on seasonal and minus winter December–February (DJF) temperature on decadal time scales for a series of AOGCMs. In this in 26 regions, the same as used in previous studies case, a strong dependence of the surface albedo and (Giorgi and Francisco 2000; Houghton et al. 2001) plus snow cover on temperature results in both a strong sea- hemispheric and extratropical hemispheric averages sonal cycle and a strong global warming signal, in par- (see Fig. 1). Mean values were calculated from years ϫ ticular in the high-latitude regions. This is in line with 7–15 for both the control and 2 CO2 phase and were the fact that we find high-latitude Northern Hemi- taken over the full latitude–longitude box for each re- sphere regions to provide the strongest constraint on gion, and thus can include some ocean area (see Stain- climate sensitivity. For other feedbacks like water va- forth et al. 2005 for details about the experimental por and clouds, it is not clear how seasonal and decadal setup and available output from the CPDN project). Unauthenticated | Downloaded 09/25/21 01:42 AM UTC 4226 JOURNAL OF CLIMATE VOLUME 19 FIG. 1. The 5%–95% ranges (lines) and medians (crosses) of the boreal summer Ϫ winter (JJA Ϫ DJF) temperature covered by all ϫ CPDN simulations, for the control (gray solid) and 2 CO2 simulations (black dashed) and for each region considered. Observed mean values for the ERA-40, NCEP–NCAR, and HadCruT2v datasets are given as black dots for comparison. The 5%–95% range for Sco in the control phase across the seasonal cycle in temperature Spr over four regions the ensemble is shown in Fig. 1 (gray solid lines) for as a function of climate sensitivity from 2500 simula- each region. It is important to note that seasonality is tions of the CPDN dataset is shown in Fig.

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