| 摘 要： |
Quantifying future precipitation changes in central Asia (CA), one of the largest semiarid-to-arid regions in the world, is increasingly attracting attention. Extreme precipitation changes in CA have been studied based on global climate models (GCMs) in the Coupled Model Intercomparison Project (CMIP). However, quantitative information regarding projected changes in extreme precipitation in CA under the 1.5-4 degrees C global warming scenario is lacking; the potential advantages of the latest-generation GCMs (i.e., CMIP6) in characterizing precipitation in CA have not been investigated. Thus, in this study, we evaluated the models' overall performance in reproducing the spatiotemporal pattern of four extreme precipitation indices and further performed an observationally constrained projection of changes in extreme precipitation indices for 1.5-4 degrees C global warming based on a weighted multimodel ensemble. Regarding homologous models, CMIP6 models exhibited limited improvement relative to their earlier versions in CMIP5. We project that extreme precipitation indices in CA will increase approximately linearly as global warming increases, except for the consecutive dry days (CDD) index. The changes in precipitation intensity and accumulation exhibit robust consistency between models, whereas the signal of CDD changes is masked by the noise produced by intermodel uncertainties. The changes in the average annual accumulated and maximum 1-day precipitation relative to the reference period (1981-2010) in CA are 12.0 and 14.2% at 3 degrees C global warming (similar to late-century warming projected based on current mitigation policies), respectively. Moreover, we demonstrate the advantage of the weighted scheme over the traditional unweighted scheme for multimodel ensemble projection. |
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