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- W2528017898 abstract "Climate models are our major source of knowledge about climate change. The impacts of climate change are often quantified by impact models. Whereas impact models typically require high resolution unbiased input data, global and regional climate models are in general biased, their resolution is often lower than desired. Thus, many users of climate model data apply some form of bias correction and downscaling. A fundamental assumption of bias correction is that the considered climate model produces skillful input for a bias correction, including a plausible representation of climate change. Current bias correction methods cannot plausibly correct climate change trends, and have limited ability to downscale. Cross validation of marginal aspects is not sufficient to evaluate bias correction and needs to be complemented by further analyses. Future research should address the development of stochastic models for downscaling and approaches to explicitly incorporate process understanding." @default.
- W2528017898 created "2016-10-14" @default.
- W2528017898 creator A5053857204 @default.
- W2528017898 date "2016-10-10" @default.
- W2528017898 modified "2023-10-14" @default.
- W2528017898 title "Bias Correcting Climate Change Simulations - a Critical Review" @default.
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- W2528017898 doi "https://doi.org/10.1007/s40641-016-0050-x" @default.
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