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Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

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Abstract

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3.9 and 6.5 °C for 2050s and 2080s relative to 1990s in Ontario, respectively; Cooling degree days and hot days will significantly increase over southern Ontario and heating degree days and cold days will significantly decrease in northern Ontario. Annual total precipitation will increase over Ontario and heavy precipitation events will increase as well. These results are consistent with conclusions in many other studies in the literature.

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Acknowledgements

The authors would like to thank the Ontario Ministry of Environment and Climate Change (MOECC) for its financial support to this study, although the content is solely the responsibility of the authors and does not necessarily represent the official views of the MOECC. We thank the two anonymous reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. Lamps, Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada

    Ziwang Deng, Xiaolan Zhou & Huaiping Zhu

  2. Department of Earth and Space Science and Engineering, York University, Toronto, ON, M3J 1P3, Canada

    Jinliang Liu

  3. NOVUS Environmental, Guelph, ON, N1G 4T2, Canada

    Xin Qiu

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  1. Ziwang Deng
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  2. Jinliang Liu
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  3. Xin Qiu
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  4. Xiaolan Zhou
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  5. Huaiping Zhu
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Correspondence to Huaiping Zhu.

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This research was funded by Ontario Ministry of the Environment and Climate Change, Canada.

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Deng, Z., Liu, J., Qiu, X. et al. Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction. Clim Dyn 51, 411–431 (2018). https://doi.org/10.1007/s00382-017-3931-3

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