Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for South Korea |
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| Authors: | Email author" target="_blank">Hyung-Il?EumEmail author Alex?J?Cannon Trevor?Q?Murdock |
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| Institution: | 1.APEC Climate Center (APCC),Busan,Republic of Korea;2.Canadian Centre for Climate Modeling and Analysis, Climate Research Division,Environment and Climate Change Canada,Victoria,Canada;3.Pacific Climate Impacts Consortium,University of Victoria,Victoria,Canada |
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| Abstract: | A number of statistical downscaling methodologies have been introduced to bridge the gap in scale between outputs of climate models and climate information needed to assess potential impacts at local and regional scales. Four statistical downscaling methods bias-correction/spatial disaggregation (BCSD), bias-correction/constructed analogue (BCCA), multivariate adaptive constructed analogs (MACA), and bias-correction/climate imprint (BCCI)] are applied to downscale the latest climate forecast system reanalysis (CFSR) data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. All methods are calibrated with observational station data for 19 years from 1973 to 1991 and validated for the more recent 19-year period from 1992 to 2010. We construct a comprehensive suite of performance metrics to inter-compare methods, which is comprised of five criteria related to time-series, distribution, multi-day persistence, extremes, and spatial structure. Based on the performance metrics, we employ technique for order of preference by similarity to ideal solution (TOPSIS) and apply 10,000 different weighting combinations to the criteria of performance metrics to identify a robust statistical downscaling method and important criteria. The results show that MACA and BCSD have comparable skill in the time-series related criterion and BCSD outperforms other methods in distribution and extremes related criteria. In addition, MACA and BCCA, which incorporate spatial patterns, show higher skill in the multi-day persistence criterion for temperature, while BCSD shows the highest skill for precipitation. For the spatial structure related criterion, BCCA and MACA outperformed BCSD and BCCI. From the TOPSIS analysis, we found that MACA is the most robust method for all variables in South Korea, and BCCA and BCSD are the second for temperature and precipitation, respectively. We also found that the contribution of the multi-day persistence and spatial structure related criteria are crucial to ranking the skill of statistical downscaling methods. |
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