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Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data

机译:基于中国小时合并降水分析数据,以了解CMIP5的多模式降水预测

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摘要

Large uncertainties still exist in the simulation and projection of precipitation from current climate models. Here, the newly released state-of-the-art China Hourly Merged Precipitation Analysis (CHMPA) data has been used to evaluate the ten models from the fifth phase of the Coupled Models Intercomparison Project (CMIP5). Particularly, the precipitation predictions under the Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios in China are assessed for the period from 2008 to 2017. Interestingly, the ensemble mean precipitation under the two emission scenarios does not show systematic differences. Intercomparison analysis of precipitation between multi-model prediction and CHMPA yields a high correlation coefficient (0.85-0.95) on the annual timescale. However, most models tend to overestimate the precipitation in northern China but to underestimate that in southern China, due to the model-simulated monsoon precipitation extending to the north earlier. Relative to UKMO-HadGEM2AO model, other models overestimate precipitation at the southeastern edge of the Tibetan Plateau where the overestimation reaches up to 150%. In terms of the temporal evolution of predicted precipitation, the multi-model ensemble produces relatively small interannual variability except for more summer monsoon precipitation with biases over 0.3 mm/day, which indicates that models are not capable of reproducing the seasonal and meridional propagation of precipitation. Compared with the original model output, the precipitation corrected by quantile mapping algorithm better agrees with the observations for spatial and temporal distributions. The findings have great implications for better utilizing model-predicted precipitation in climate change studies.
机译:当前气候模型的降水模拟和预测仍然存在很大的不确定性。在这里,最新发布的最新中国小时合并降水分析(CHMPA)数据已用于评估耦合模型比对项目(CMIP5)第五阶段的十个模型。特别是,对2008年至2017年期间在中国代表性浓度路径(RCP)4.5和RCP8.5情景下的降水预测进行了评估。有趣的是,两种排放情景下的总体平均降水量没有显示出系统性差异。多模式预测和CHMPA之间的降水比对分析在年尺度上产生了很高的相关系数(0.85-0.95)。然而,由于模型模拟的季风降水更早地延伸到了北部,因此大多数模型倾向于高估中国北部的降水,而低估了中国南部的降水。相对于UKMO-HadGEM2AO模型,其他模型高估了青藏高原东南边缘的降水量,高估量高达150%。就预测降水的时间演变而言,多模式集合产生的年际变化较小,但夏季季风降水更多,且偏差超过0.3 mm / day,这表明该模型无法再现降水的季节和经向传播。 。与原始模型输出相比,通过分位数映射算法校正的降水量与时空分布的观测结果更加吻合。这些发现对于在气候变化研究中更好地利用模型预测的降水具有重大意义。

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  • 来源
    《Atmospheric research》 |2020年第1期|104671.1-104671.20|共20页
  • 作者单位

    Chinese Acad Meteorol Sci State Key Lab Severe Weather Beijing Peoples R China;

    Chinese Univ Hong Kong Dept Geog & Resource Management Sha Tin Hong Kong Peoples R China|Chinese Univ Hong Kong Inst Environm Energy & Sustainabil Sha Tin Hong Kong Peoples R China|Chinese Univ Hong Kong Ctr Environm Policy & Resource Management Sha Tin Hong Kong Peoples R China;

    China Meteorol Adm Meteorol Observat Ctr Beijing Peoples R China;

    Chinese Univ Hong Kong Inst Environm Energy & Sustainabil Sha Tin Hong Kong Peoples R China;

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