Towards quantifying uncertainty in transient climate change
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Ensembles of coupled atmosphere–ocean global circulation model simulations are required to make probabilistic predictions of future climate change. “Perturbed physics” ensembles provide a new approach in which modelling uncertainties are sampled systematically by perturbing uncertain parameters. The aim is to provide a basis for probabilistic predictions in which the impact of prior assumptions and observational constraints can be clearly distinguished. Here we report on the first perturbed physics coupled atmosphere–ocean model ensemble in which poorly constrained atmosphere, land and sea-ice component parameters are varied in the third version of the Hadley Centre model (the variation of ocean parameters will be the subject of future study). Flux adjustments are employed, both to reduce regional sea surface temperature (SST) and salinity biases and also to admit the use of combinations of model parameter values which give non-zero values for the global radiation balance. This improves the extent to which the ensemble provides a credible basis for the quantification of uncertainties in climate change, especially at a regional level. However, this particular implementation of flux-adjustments leads to a weakening of the Atlantic overturning circulation, resulting in the development of biases in SST and sea ice in the North Atlantic and Arctic Oceans. Nevertheless, model versions are produced which are of similar quality to the unperturbed and un-flux-adjusted version. The ensemble is used to simulate pre-industrial conditions and a simple scenario of a 1% per year compounded increase in CO2. The range of transient climate response (the 20 year averaged global warming at the time of CO2 doubling) is 1.5–2.6°C, similar to that found in multi-model studies. Measures of global and large scale climate change from the coupled models show simple relationships with associated measures computed from atmosphere-mixed-layer-ocean climate change experiments, suggesting that recent advances in computing the probability density function of climate change under equilibrium conditions using the perturbed physics approach may be extended to the transient case.
This work could not have been possible without the input from numerous and dedicated Hadley Centre staff, in particular Peter Good who downloaded the AR4 model data and Ian Culverwell who supplied the ocean heat transport data. Chris Brierley also helped in the analysis of ocean heat uptake efficiency. The work was supported by the UK Department of the Environment, Food and Rural Affairs under Contract PECD/7/12/37 and by the European Community ENSEMBLES (GOCE-CT-2003–505539) and DYNAMITE (GOCE-003903) projects under the Sixth Framework Programme. We acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, U.S. Department of Energy.
- Barnett DN, Brown SJ, Murphy JM, Sexton DMH, Webb MJ (2006) Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations. Clim Dyn (in press). DOI 10.1007/s00382-005-0097-1Google Scholar
- Collins M., the CMIP2 Modelling Groups (2005) El Niño- or La Niña-like climate change? Clim Dyn 24:89–104Google Scholar
- Cubasch U, Meehl GA, Boer GJ, Stouffer RJ, Dix M, Noda A, Senior CA, Raper S, Yap KS (2001) Projections of future climate change. In: Climate Change 2001: The Scientific Basis. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden P, Dai X, Maskell K, Johnson CI (eds.) Contribution of Working Group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, 525–582Google Scholar
- Doblas-Reyes FJ, Hagedorn R, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting. Part II: Calibration and combination. Tellus (in press)Google Scholar
- Hagedorn R, Doblas-Reyes FJ, Palmer TN (2004) The rationale behind the success of multimodel ensembles in seasonal forecasting. Part I: Basic concept. Tellus (in press)Google Scholar
- Levitus S, Boyer T (1994) World Ocean Atlas 1994. NOAA Atlas NESDIS, U.S. Department of Commerce, WashingtonGoogle Scholar
- Piani C, Frame DJ, Stainforth DA, Allen MR (2005) Constraints on climate change from a multi-thousand member ensemble of simulations. Geophys Res Lett 32:L23825. DOI 10.1029/2005GL024452Google Scholar
- Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of SST, sea ice and night marine air temperature since the late nineteenth century. J Geophys Res 108:10.1029/2002JD002670Google Scholar
- Senior C, Wielicki B, McAvaney B, Boer G (2004) Report on the joint WCRP CFMIP/IPCC expert meeting on climate sensitivity and feedbacks. Annex 5 of IPCC Working Group 1 report of the Workshop on Climate SensivityGoogle Scholar
- Stainforth DA, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406CrossRefGoogle Scholar
- Webb MJ, Senior CA, Williams KD, Sexton MDH, Ringer MA, McAvaney BJ, Colman R, Soden BJ, Andronova NG, Emori S, Tsushima Y, Ogura T, Musat I, Bony S, Taylor K (2006) On uncertainty in feedback mechanisms controlling climate sensitivity in two GCM ensembles. Clim Dyn (in press). DOI 10.1007/s00382-006-0111-2Google Scholar