RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields
RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields
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DOI：
10.1007/s00382-006-0191-z
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发表时间：
2007-02
期刊：
影响因子：
4.6
通讯作者：
A. Seth;A. Seth;S. Rauscher;S. Camargo;J. Qian;J. Pal
中科院分区：
文献类型：
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作者：
A. Seth;A. Seth;S. Rauscher;S. Camargo;J. Qian;J. Pal
为了缩小季节性预测和气候变化情景的规模，南美洲需要采用全球模型强迫的长期基线区域气候学。作为这一过程的第一步，这项工作使用嵌套在再分析数据和大气环流模型（GCM）的多重实现中的大陆尺度域来研究气候学与区域气候模型的整合。该分析对模拟大规模环流、平均年周期和年际变化的嵌套模型进行了评估，并将其与观测估计值以及南美洲东北部、亚马逊、季风和东南地区的驱动 GCM 进行了比较。结果表明，区域气候模式较好地模拟了东北地区和季风区降水年循环；它在东南部的冬季（7 月至 9 月）表现出干燥偏向，并模拟亚马逊地区夏季（12 月至 2 月）出现偏干的半年周期。 GCM 和肾分析驱动的模拟之间的年度周期几乎没有差异，但年际变化却存在显着差异。尽管年度周期存在偏差，但区域模型捕捉到了东北部、东南部和亚马逊地区观察到的大部分年际变化。在季风地区，远程影响较弱，区域模型在 GCM 的基础上进行了改进，尽管两者都没有表现出实质性的可预测性。我们的结论是，在远程影响较强且全球模式表现良好的地区，区域模式很难改善大尺度的气候特征，甚至可能会降低模拟效果。在远程强迫较弱且本地过程占主导地位的情况下，区域模型有一定的增值潜力。然而，这将需要改进高分辨率热带模拟的物理参数化。
To enable downscaling of seasonal prediction and climate change scenarios, long-term baseline regional climatologies which employ global model forcing are needed for South America. As a first step in this process, this work examines climatological integrations with a regional climate model using a continental scale domain nested in both reanalysis data and multiple realizations of an atmospheric general circulation model (GCM). The analysis presents an evaluation of the nested model simulated large scale circulation, mean annual cycle and interannual variability which is compared against observational estimates and also with the driving GCM for the Northeast, Amazon, Monsoon and Southeast regions of South America. Results indicate that the regional climate model simulates the annual cycle of precipitation well in the Northeast region and Monsoon regions; it exhibits a dry bias during winter (July–September) in the Southeast, and simulates a semi-annual cycle with a dry bias in summer (December–February) in the Amazon region. There is little difference in the annual cycle between the GCM and renalyses driven simulations, however, substantial differences are seen in the interannual variability. Despite the biases in the annual cycle, the regional model captures much of the interannual variability observed in the Northeast, Southeast and Amazon regions. In the Monsoon region, where remote influences are weak, the regional model improves upon the GCM, though neither show substantial predictability. We conclude that in regions where remote influences are strong and the global model performs well it is difficult for the regional model to improve the large scale climatological features, indeed the regional model may degrade the simulation. Where remote forcing is weak and local processes dominate, there is some potential for the regional model to add value. This, however, will require improvments in physical parameterizations for high resolution tropical simulations.