Climate Dynamics

, Volume 27, Issue 2–3, pp 127–147 | Cite as

Towards quantifying uncertainty in transient climate change

  • Matthew CollinsEmail author
  • Ben B. B. Booth
  • Glen R. Harris
  • James M. Murphy
  • David M. H. Sexton
  • Mark J. Webb


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.


Ensemble Member Climate Sensitivity Flux Adjustment Fresh Water Input Perturb Physics Ensemble 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Matthew Collins
    • 1
    Email author
  • Ben B. B. Booth
    • 1
  • Glen R. Harris
    • 1
  • James M. Murphy
    • 1
  • David M. H. Sexton
    • 1
  • Mark J. Webb
    • 1
  1. 1.Hadley Centre for Climate Prediction and ResearchMet OfficeExeterUK

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