Sensitivity of systematic biases in South Asian summer monsoon simulations to regional climate model domain size and implications for downscaled regional process studies
Sensitivity of systematic biases in South Asian summer monsoon simulations to regional climate model domain size and implications for downscaled regional process studies
复制标题
南亚夏季风模拟系统偏差对区域气候模型域大小的敏感性及其对缩小规模的区域过程研究的影响
DOI：
10.1007/s00382-015-2565-6
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发表时间：
2015
期刊：
影响因子：
4.6
通讯作者：
Karmacharya J
中科院分区：
文献类型：
--
作者：
Karmacharya J
全球气候模型（GCM）在模拟全球范围内的气候方面具有良好的技巧，但它们在区域范围内显示出显着的系统误差。例如，许多 GCM 在南亚夏季风 (SASM) 模拟中表现出显着的偏差。这些错误不仅限制了此类 GCM 输出在驱动这些地区的区域气候模型 (RCM) 中的应用，而且还引发了对从这些 GCM 导出的 RCM 的有用性的质疑。我们专注于过程研究，其中 RCM 由大气再分析的实际横向边界条件驱动，防止远程系统误差影响区域模拟。在这种情况下，有必要研究 RCM 在其父模型显示出较大系统误差的区域上运行时是否也会遭受类似的错误。此外，RCM 模拟对域大小的一般敏感性对于理解 GCM 中系统误差的远程驱动因素以及选择最小化这些误差的合适 RCM 域提供了信息。我们通过将全球和区域模型模拟与对域进行有针对性的更改进行比较并强制进行大气重新分析来调查 SASM 中的英国气象局统一模型系统错误。我们表明，尽管父全局模型存在较大误差，但从直接感兴趣区域中排除系统误差的远程驱动因素允许将 RCM 应用到 SASM 的过程研究中。这项研究的结果也与其他模型相关，其中许多模型在 SASM 的全局模型模拟中都存在类似的系统误差模式。
Global climate models (GCMs) have good skill in simulating climate at the global scale yet they show significant systematic errors at regional scale. For example, many GCMs exhibit significant biases in South Asian summer monsoon (SASM) simulations. Those errors not only limit application of such GCM output in driving regional climate models (RCMs) over these regions but also raise questions on the usefulness of RCMs derived from those GCMs. We focus on process studies where the RCM is driven by realistic lateral boundary conditions from atmospheric re-analysis which prevents remote systematic errors from influencing the regional simulation. In this context it is pertinent to investigate whether RCMs also suffer from similar errors when run over regions where their parent models show large systematic errors. Furthermore, the general sensitivity of the RCM simulation to domain size is informative in understanding remote drivers of systematic errors in the GCM and in choosing a suitable RCM domain that minimizes those errors. We investigate Met Office Unified Model systematic errors in SASM by comparing global and regional model simulations with targeted changes to the domain and forced with atmospheric re-analysis. We show that excluding remote drivers of systematic errors from the direct area of interest allows the application of RCMs for process studies of the SASM, despite the large errors in the parent global model. The findings in this study are also relevant to other models, many of which suffer from a similar pattern of systematic errors in global model simulations of the SASM.
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影响因子：
1.9
作者：
A. Yatagai;O. Arakawa;K. Kamiguchi;H. Kawamoto;M. I. Nodzu;A. Hamada
通讯作者：
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影响因子：
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作者：
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通讯作者：
Dethloff
影响因子：
4.9
作者：
G. Martin;S. Milton;C. Senior;M. Brooks;S. Ineson;T. Reichler;J. Kim
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影响因子：
3.4
作者：
S. Pokhrel;A. Dhakate;H. Chaudhari;S. Saha
通讯作者：
S. Saha
影响因子：
3.8
作者：
Lucas-Picher, Philippe;Christensen, Jens H.;Hagemann, Stefan
通讯作者：
Hagemann, Stefan