Article

Climate Dynamics

, Volume 36, Issue 9, pp 1737-1766

Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles

  • Matthew CollinsAffiliated withMet Office Hadley Centre Email author 
  • , Ben B. B. BoothAffiliated withMet Office Hadley Centre
  • , B. BhaskaranAffiliated withMet Office Hadley Centre
  • , Glen R. HarrisAffiliated withMet Office Hadley Centre
  • , James M. MurphyAffiliated withMet Office Hadley Centre
  • , David M. H. SextonAffiliated withMet Office Hadley Centre
  • , Mark J. WebbAffiliated withMet Office Hadley Centre

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Abstract

Ensembles of climate model simulations are required for input into probabilistic assessments of the risk of future climate change in which uncertainties are quantified. Here we document and compare aspects of climate model ensembles from the multi-model archive and from perturbed physics ensembles generated using the third version of the Hadley Centre climate model (HadCM3). Model-error characteristics derived from time-averaged two-dimensional fields of observed climate variables indicate that the perturbed physics approach is capable of sampling a relatively wide range of different mean climate states, consistent with simple estimates of observational uncertainty and comparable to the range of mean states sampled by the multi-model ensemble. The perturbed physics approach is also capable of sampling a relatively wide range of climate forcings and climate feedbacks under enhanced levels of greenhouse gases, again comparable with the multi-model ensemble. By examining correlations between global time-averaged measures of model error and global measures of climate change feedback strengths, we conclude that there are no simple emergent relationships between climate model errors and the magnitude of future global temperature change. Algorithms for quantifying uncertainty require the use of complex multivariate metrics for constraining projections.

Keywords

Ensembles Uncertainty Model errors Climate feedbacks Observational constraints