Abstract
Temperature changes have widespread impacts on the environment, economy, and municipal planning. Generating accurate climate prediction at finer spatial resolution through downscaling could help better assess the future effects of climate change on a local scale. Ensembles of multiple climate models have been proven to improve the accuracy of temperature prediction. Meanwhile, machine learning techniques have shown high performance in solving various predictive modeling problems, which make them a promising tool for temperature downscaling. This study investigated the performance of machine learning (long short-term memory (LSTM) networks and support vector machine (SVM)) and statistical (arithmetic ensemble mean (EM) and multiple linear regression (MLR)) methods in developing multi-model ensembles for downscaling long-term daily temperature. A case study of twelve meteorological stations across Ontario, Canada, was conducted to evaluate the performance of the proposed ensembles. The results showed that both machine learning and statistical techniques performed well at downscaling daily temperature with multi-model ensembles and had similar performance with relatively high accuracy. The R2 of 12 stations ranged between 0.756 and 0.820 and RMSE ranged between 4.318 and 7.063 °C. Both machine learning and statistical ensembles for downscaling had difficulty in predicting extreme values for temperature below − 10 °C and above 20 °C. The results provided technical support for using statistical and machine learning methods to generate high-resolution daily temperature prediction.
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Acknowledgments
This study is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the McMaster Engineering Big Ideas Initiative of McMaster University. We thank Environment Canada for providing observed temperature data. We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modeling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 2 of this paper) for producing and making available their model output. We also acknowledge the U.S. Department of Defense ESTCP for its support of the NA-CORDEX data archive.
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Li, X., Li, Z., Huang, W. et al. Performance of statistical and machine learning ensembles for daily temperature downscaling. Theor Appl Climatol 140, 571–588 (2020). https://doi.org/10.1007/s00704-020-03098-3
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DOI: https://doi.org/10.1007/s00704-020-03098-3