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Assessment of GCM performances for the Arabian Peninsula, Brazil, and Ukraine and indications of regional climate change

机译:评估阿拉伯半岛,巴西和乌克兰的GCM绩效以及区域气候变化的迹象

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摘要

This study presents an assessment of global climate model (GCM) performances for the regions Arabian Peninsula, Brazil and Ukraine, all facing problems related to water availability as well as quality and so are the focus regions for investigations within the Integrated Water Resources Management Project 'International Water Research Alliance Saxony'. Results from the model performance assessment have been applied to derive estimates of the future climate in the regions by means of ensemble averaging. The study uses GCM data from the third Climate Model Intercomparison Project. Model performances were assessed by the Model Performance Index (MPI) as well as Taylor diagrams applying different reference data sets like reanalysis data as well as observational data sets. The model performances were analysed for different domain sizes. Climate variables of different tropospheric levels were classified by applying projections of the overall model ensemble as well as projections of a subset of models (selected according to the MPI) in variables with different levels of certainty of change. The MPI as well as the reliability ensemble averaging method were employed to calculate ensemble averages for variables with high certainties of change. Results are presented for mean values within the time periods 2040-2060 an 2080-2100. Applying different data sets as reference data shows that model performance crucially depends on the reference data set. The size of the model domain is of minor importance. Model rankings vary between regions. Certain changes are found for temperatures and geopotential heights in all levels but, e.g., not for precipitation.
机译:这项研究提出了对阿拉伯半岛,巴西和乌克兰地区的全球气候模式(GCM)绩效的评估,这些地区都面临着与水资源可用性和水质有关的问题,因此,综合水资源管理项目中的研究重点是“国际水研究联盟萨克森州。来自模型性能评估的结果已被用于通过集合平均来得出该地区未来气候的估计值。该研究使用了第三个气候模型比对项目的GCM数据。模型性能通过模型​​性能指数(MPI)以及应用了不同参考数据集(例如重新分析数据和观察数据集)的泰勒图进行评估。针对不同的域大小分析了模型性能。对流层不同水平的气候变量通过应用整体模型集合的预测以及具有不同确定性水平的变量中的模型子集(根据MPI选择)的预测进行分类。 MPI以及可靠性集成平均法用于计算具有高确定性的变量的集成平均。呈现的结果是2040-2060和2080-2100时间段内的平均值。将不同的数据集用作参考数据表明,模型性能至关重要地取决于参考数据集。模型域的大小并不重要。模型排名因地区而异。发现所有水平的温度和地势高度都有某些变化,但没有降水。

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