Considerations in the Selection of Global Climate Models for Regional Climate Projections: The Arctic as a Case Study

James E. Overland NOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

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Muyin Wang Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington

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Nicholas A. Bond Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington

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John E. Walsh University of Alaska Fairbanks, Fairbanks, Alaska

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Vladimir M. Kattsov Voeikov Main Geophysical Observatory, St. Petersburg, Russia

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William L. Chapman University of Illinois at Urbana–Champaign, Urbana, Illinois

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Abstract

Climate projections at regional scales are in increased demand from management agencies and other stakeholders. While global atmosphere–ocean climate models provide credible quantitative estimates of future climate at continental scales and above, individual model performance varies for different regions, variables, and evaluation metrics—a less than satisfying situation. Using the high-latitude Northern Hemisphere as a focus, the authors assess strategies for providing regional projections based on global climate models. Starting with a set of model results obtained from an “ensemble of opportunity,” the core of this procedure is to retain a subset of models through comparisons of model simulations with observations at both continental and regional scales. The exercise is more one of model culling than model selection. The continental-scale evaluation is a check on the large-scale climate physics of the models, and the regional-scale evaluation emphasizes variables of ecological or societal relevance. An additional consideration is given to the comprehensiveness of processes included in the models. In many but not all applications, different results are obtained from a reduced set of models compared to relying on the simple mean of all available models. For example, in the Arctic the top-performing models tend to be more sensitive to greenhouse forcing than the poorer-performing models. Because of the mostly unexplained inconsistencies in model performance under different selection criteria, simple and transparent evaluation methods are favored. The use of a single model is not recommended. For some applications, no model may be able to provide a suitable regional projection. The use of model evaluation strategies, as opposed to relying on simple averages of ensembles of opportunity, should be part of future synthesis activities such as the upcoming Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

Corresponding author address: J. E. Overland, NOAA/PMEL, 7600 Sand Point Way NE, Seattle, WA 91885. Email: james.e.overland@noaa.gov

* National Oceanic and Atmospheric Administration Contribution Number 1760 and Pacific Marine Environmental Laboratory Contribution Number 3458.

Abstract

Climate projections at regional scales are in increased demand from management agencies and other stakeholders. While global atmosphere–ocean climate models provide credible quantitative estimates of future climate at continental scales and above, individual model performance varies for different regions, variables, and evaluation metrics—a less than satisfying situation. Using the high-latitude Northern Hemisphere as a focus, the authors assess strategies for providing regional projections based on global climate models. Starting with a set of model results obtained from an “ensemble of opportunity,” the core of this procedure is to retain a subset of models through comparisons of model simulations with observations at both continental and regional scales. The exercise is more one of model culling than model selection. The continental-scale evaluation is a check on the large-scale climate physics of the models, and the regional-scale evaluation emphasizes variables of ecological or societal relevance. An additional consideration is given to the comprehensiveness of processes included in the models. In many but not all applications, different results are obtained from a reduced set of models compared to relying on the simple mean of all available models. For example, in the Arctic the top-performing models tend to be more sensitive to greenhouse forcing than the poorer-performing models. Because of the mostly unexplained inconsistencies in model performance under different selection criteria, simple and transparent evaluation methods are favored. The use of a single model is not recommended. For some applications, no model may be able to provide a suitable regional projection. The use of model evaluation strategies, as opposed to relying on simple averages of ensembles of opportunity, should be part of future synthesis activities such as the upcoming Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

Corresponding author address: J. E. Overland, NOAA/PMEL, 7600 Sand Point Way NE, Seattle, WA 91885. Email: james.e.overland@noaa.gov

* National Oceanic and Atmospheric Administration Contribution Number 1760 and Pacific Marine Environmental Laboratory Contribution Number 3458.

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