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Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation

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Abstract

Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy.

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Acknowledgments

The work was funded by the United States (US) National Science Foundation (NSF) Expeditions in Computing Grant # 1029711. The US Nuclear Regulatory Commission and Northeastern University provided partial funding. The climate model datasets were obtained from the PCMDI archive of the US Department of Energy at LLNL.

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  1. Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, 400 SN, 360 Huntington Avenue, Boston, MA, 02115, USA

    Devashish Kumar, Evan Kodra & Auroop R. Ganguly

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  1. Devashish Kumar
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  2. Evan Kodra
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  3. Auroop R. Ganguly
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Correspondence to Auroop R. Ganguly.

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Kumar, D., Kodra, E. & Ganguly, A.R. Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation. Clim Dyn 43, 2491–2518 (2014). https://doi.org/10.1007/s00382-014-2070-3

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  • DOI: https://doi.org/10.1007/s00382-014-2070-3

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