CR 41:1-14 (2010)  -  doi:10.3354/cr00836

Use of multi-model ensembles from global climate models for assessment of climate change impacts

Mikhail A. Semenov*, Pierre Stratonovitch

Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK

ABSTRACT: Multi-model ensembles of climate predictions constructed by running several global climate models for a common set of experiments are available for impact assessment of climate change. Multi-model ensembles emphasize the uncertainty in climate predictions resulting from structural differences in the global climate models as well as uncertainty due to variations in initial conditions or model parameterisations. This paper describes a methodology of using multi-model ensembles from global climate models for impact assessments which require local-scale climate scenarios. The approach is based on the use of a weather generator capable of generating the local-scale daily climate scenarios used as an input by many process-based impact models. A new version of the LARS-WG weather generator, described in the paper, incorporates climate predictions from 15 climate models from the multi-model ensemble used in the IPCC Fourth Assessment Report (AR4). The use of the AR4 multi-model ensemble allows assessment of the range of uncertainty in the impacts of climate change resulting from the uncertainty in predications of climate. As an example, the impact of climate change on the probability of heat stress during flowering of wheat, which can result in significant yield losses, was assessed using local-scale climate scenarios in conjunction with a wheat simulation model at 4 European locations. The exploitation of much larger perturbed physics ensembles is also discussed.

KEY WORDS: Weather generator · Probabilistic prediction · Uncertainty · IPCC AR4 · LARS-WG

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Cite this article as: Semenov MA, Stratonovitch P (2010) Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res 41:1-14

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