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A coupled dynamical-copula downscaling approach for temperature projections over the Canadian Prairies

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Abstract

In this study, a coupled dynamical-copula downscaling approach was developed through integrating the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and the copula method. This approach helps to reflect detailed features at local scales based on dynamical downscaling, while also effectively simulating the interactions between large-scale atmospheric variables (predictors) and local surface variables (predictands). The performance of the proposed approach in reproducing historical climatology of the Canadian Prairies was evaluated through comparison with observations. Future climate projections generated by the developed approach were analyzed over three time slices (i.e., the 2030s, 2050s, and 2080s) to help understand the plausible changes in temperature over the Canadian Prairies in response to global warming. The results showed that there would be an apparent increasing pattern over the Canadian Prairies. The projections of future temperature over three time slices can provide decision makers with valuable information for climate change impacts assessment over the Canadian Prairies.

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Acknowledgements

This research was supported by the National Key Research and Development Plan (2016YFA0601502, 2016YFC0502800), the Natural Sciences Foundation (51520105013, 51679087), the 111 Program (B14008) and the Natural Science and Engineering Research Council of Canada.

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Authors and Affiliations

  1. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, SK, S4S 0A2, Canada

    Xiong Zhou, Guohe Huang, Yurui Fan & Guanhui Cheng

  2. School of Geosciences, University of Louisiana at Lafayette, Lafayette, LA, 70503, USA

    Xiuquan Wang

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  1. Xiong Zhou
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  2. Guohe Huang
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  4. Yurui Fan
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Correspondence to Guohe Huang.

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Zhou, X., Huang, G., Wang, X. et al. A coupled dynamical-copula downscaling approach for temperature projections over the Canadian Prairies. Clim Dyn 51, 2413–2431 (2018). https://doi.org/10.1007/s00382-017-4020-3

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