Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models
Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models
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评估 37 个 CMIP5 模式模拟的亚洲-澳大利亚季风区矢量风
DOI：
10.1007/s00382-018-4599-z
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发表时间：
2019-07-01
期刊：
影响因子：
4.6
通讯作者：
Guo, Weidong
中科院分区：
文献类型：
--
作者：
Huang, Fang;Xu, Zhongfeng;Guo, Weidong
矢量风通过传递能量和水分，在塑造区域气候中起着至关重要的作用。在这项研究中，我们评估了37个耦合模式相互比较项目第5阶段（CMIP 5）模式和多模式集成（MME）的气候平均状态，年周期，和年际变化的矢量风在亚洲-澳大利亚季风（A-AM）地区。与以往大多数研究不同的是，我们将矢量风作为一个整体，采用最近开发的矢量场评估方法来评估纬向和纬向风。结果表明：（1）MME模式在模拟矢量风场的气候学平均值方面表现最好，其次是CESM 1-CAM 5和3个MPI-ESM模式。然而，模式仍然显示出显着的偏差，其特点是高估了低层矢量风及其空间变化。这种偏差主要来源于矢量风场的异常分量，并在地形复杂的地区观测到。(2)CMIP 5模式能很好地模拟对流层上层矢量风的年循环，特别是在对流层外区域，但在对流层下层复杂地形上显示出较大的偏差和分散。(3)尽管大多数CMIP 5模式高估了对流层低层矢量风场的强度，但MME模式在模拟矢量风场年际变化方面仍优于单个模式。(4)在A-AM区域，模式在模拟气候平均值、年周期和年际变率方面的技能在一定程度上彼此正相关，这表明气候平均值的改善可能会导致更好地模拟矢量风的年周期或年际变率。
Vector wind plays a crucial role in shaping regional climate through transferring energy and moisture. In this study, we evaluate 37 Coupled Model Intercomparison Project Phase 5 (CMIP5) models and multi-model ensembles (MME) in terms of the climatological mean state, annual cycle, and interannual variability of vector winds in the Asian-Australian monsoon (A-AM) region. Unlike most previous studies those assessed meridional and zonal wind separately, we treat vector wind as a whole by employing a recently developed vector field evaluation method. The results are summarized as follows: (1) MME exhibits the best performance in reproducing the climatological mean of vector winds, followed by CESM1-CAM5 and three MPI-ESM models. However, models still show significant biases characterized by overestimated lower level vector winds and its spatial variation. The biases are mainly rooted in the anomaly components of vector winds and are observed in the regions with complex topography. (2) CMIP5 models can well simulate the annual cycle of upper-tropospheric vector winds, especially in the extratropical regions, but show large biases and dispersion over complex terrains in the lower troposphere. (3) MME still outperforms individual model for the simulation of interannual variance of vector winds, although most CMIP5 models overestimate the strength of vector wind variability in the lower troposphere. (4) Model skills in simulating climatological means, annual cycle, and interannual variability are positively correlated with each other to a certain degree over the A-AM region, suggesting an improvement in climatological mean may lead to a better simulation in the annual cycle or interannual variability of vector winds.