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A computationally efficient model for probabilistic local warming projections constrained by history matching and pattern scaling

Abstract. Climate projections are made using a hierarchy of models of different complexities and computational efficiencies. While the most complex climate models contain the most detailed representations of many physical processes within the climate system, both parameter space exploration and Integrated Assessment Modelling require the increased computational efficiency of reduced-complexity models. This study presents an efficient model for projecting local warming across the globe, combining observation constrained global mean projections of an efficient Earth system model with spatial pattern scaling derived from the Climate Model Intercomparison Project phase 5 (CMIP5) ensemble. First, global mean warming is projected using a 103-member ensemble of history-matched simulations with the reduced complexity Warming Acidification and Sea-level Projector (WASP) Earth system model. The ensemble-projection of global mean warming from this WASP ensemble is then converted into local warming projections using a pattern scaling analysis from the CMIP5 archive, considering both the mean and uncertainty of the Local to Global Ratio of Temperature Change (LGRTC) spatial patterns from the CMIP5 ensemble for high-end and mitigated scenarios. The LGRTC spatial pattern does not appear strongly scenario dependent in the CMIP5 ensemble, and so should be useful across a variety of arbitrary scenarios. The computational efficiency of our WASP/LGRTC model approach makes it ideal for future incorporation into an Integrated Assessment Model framework, or efficient assessment of multiple scenarios. We utilise an emergent relationship between warming and future cumulative carbon emitted in our simulations to present an approximation tool making local warming projections from total future carbon emitted.
