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Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?

机译:气候变化对极端降水的局部影响分析:是否需要高分辨率的气候模型才能进行逼真的模拟?

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摘要

This study explores whether climate models with higher spatial resolutions provide higher accuracy for precipitation simulations and/or different climate change signals. The outputs from two convection-permitting climate models (ALARO and CCLM) with a spatial resolution of 3-4 km are compared with those from the coarse-scale driving models or reanalysis data for simulating/projecting daily and sub-daily precipitation quantiles. Validation of historical design precipitation statistics derived from intensityduration-frequency (IDF) curves shows a better match of the convection-permitting model results with the observations-based IDF statistics compared to the driving GCMs and reanalysis data. This is the case for simulation of local subdaily precipitation extremes during the summer season, while the convection-permitting models do not appear to bring added value to simulation of daily precipitation extremes. Results moreover indicate that one has to be careful in assuming spatial-scale independency of climate change signals for the delta change downscaling method, as high-resolution models may show larger changes in extreme precipitation. These larger changes appear to be dependent on the timescale, since such intensification is not observed for daily timescales for both the ALARO and CCLM models.
机译:这项研究探索了具有更高空间分辨率的气候模型是否为降水模拟和/或不同的气候变化信号提供了更高的精度。将两个空间分辨率为3-4 km的对流允许气候模型(ALARO和CCLM)的输出与用于模拟/预测日和次日降水量的粗略驱动模型或再分析数据的输出进行比较。对强度持续时间-频率(IDF)曲线得出的历史设计降水统计数据的验证表明,与驱动GCM和再分析数据相比,对流允许模型结果与基于观测值的IDF统计数据具有更好的匹配性。夏季模拟本地次日极端降水的情况就是这种情况,而对流允许模型似乎并未为每日极端降水的模拟带来附加值。此外,结果表明,对于三角洲变化缩减方法,必须假设气候变化信号的空间尺度独立性,因为高分辨率模型可能显示极端降水的较大变化。这些较大的变化似乎取决于时间尺度,因为对于ALARO和CCLM模型,在每日时间尺度上都没有观察到这种加剧。

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