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- W96702978 abstract "Climatic extremes strongly affect society and environment at the local scale, and their behavior under a potentially changing climate needs to be taken into account for any planning purposes. As current global climate models are too coarse to provide sufficient local detail, empirical-statistical downscaling is one way to bridge the gap between these scales. Empirical downscaling starts with a statistical analysis of present climatic conditions for an area, as represented by large-scale (e. g. analyses) and local observations (e. g. station data). From this analysis a statistical ʼmodelʼ is derived that transforms the large-scale information (predictors) to the local scales (predictands). The model is comprised of a set of numerical tools (such as quantile mapping or linear regression) whose parameters (such as fitting coefficients, means, variances) represent the climate of the location in question. Under the assumption that climate is changing only moderately, the predictor-predictand relation remains valid so that future projections of local climate can be derived from the large scale climate fields.Given the multitude of techniques and results, downscaling intercomparison projects provide guidance for choosing the best method for a purpose in question. Ideally, there should be just one big intercomparison including all possible methods, possibly stratified according to region. Here we study the performance of the three methods" @default.
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- W96702978 date "2010-12-01" @default.
- W96702978 modified "2023-09-23" @default.
- W96702978 title "Downscaling extremes with EDS, TreeGen, and BCSD" @default.
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