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Ranking of CMIP5 GCM Skills in Simulating Observed Precipitation over the Lower Mekong Basin, Using an Improved Score-Based Method

机译:CMIP5 GCM技能在基于改进分数法的湄公河下游地区观测到的降水中的排名

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This study assessed the performances of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in reproducing observed precipitation over the Lower Mekong Basin (LMB). Observations from gauge-based data of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) precipitation data were obtained from 1975 to 2004. An improved score-based method was used to rank the performance of the GCMs in reproducing the observed precipitation over the LMB. The results revealed that most GCMs effectively reproduced precipitation patterns for the mean annual cycle, but they generally overestimated the observed precipitation. The GCMs showed good ability in reproducing the time series characteristics of precipitation for the annual period compared to those for the wet and dry seasons. Meanwhile, the GCMs obviously reproduced the spatial characteristics of precipitation for the dry season better than those for annual time and the wet season. More than 50% of the GCMs failed to reproduce the positive trend of the observed precipitation for the wet season and the dry season (approximately 52.9% and 64.7%, respectively), and approximately 44.1% of the GCMs failed to reproduce positive trend for annual time over the LMB. Furthermore, it was also revealed that there existed different robust criteria for assessing the GCMs’ performances at a seasonal scale, and using multiple criteria was superior to a single criterion in assessing the GCMs’ performances. Overall, the better-performed GCMs were obtained, which can provide useful information for future precipitation projection and policy-making over the LMB.
机译:这项研究评估了34个耦合模型比较项目阶段5(CMIP5)的一般循环模型(GCMs)在再现湄公河下游盆地(LMB)上观测到的降水方面的性能。从1975年至2004年从亚洲降水-高度分辨的观测数据集成向水资源评估(APHRODITE)降水数据的基于量规的数据中获得的观测值。采用了一种改进的基于得分的方法对GCM在繁殖过程中的性能进行排名在LMB上观测到的降水。结果表明,大多数GCM有效地再现了平均年周期的降水模式,但它们通常高估了观测到的降水。与湿季和干季相比,GCM在年度期间显示出良好的再现降水时间序列特征的能力。同时,GCM明显再现了旱季降水的空间特征,好于年间和雨季。超过50%的GCM无法重现雨季和干旱季节观测到的降水的正趋势(分别约为52.9%和64.7%),大约44.1%的GCM无法重现年度的正趋势。 LMB上的时间。此外,还发现存在不同的可靠标准来评估GCM在季节性范围内的绩效,并且在评估GCM绩效时,使用多个标准要优于单个标准。总体而言,获得了性能更好的GCM,可以为LMB的未来降水预测和政策制定提供有用的信息。

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