Evaluation of CMIP5 upper troposphere and lower stratosphere geopotential height with GPS radio occultation observations
Evaluation of CMIP5 upper troposphere and lower stratosphere geopotential height with GPS radio occultation observations
复制标题
利用GPS无线电掩星观测评估CMIP5对流层上层和平流层下层位势高度
DOI：
10.1002/2014jd022239
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Yukimoto
中科院分区：
文献类型：
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作者：
C. Ao;J. Jiang;A. Mannucci;H. Su;O. Verkhoglyadova;C. Zhai;J. Cole;L. Donner;T. Iversen;C. Morcrette;L. Rotstayn;M. Watanabe;S. Yukimoto
我们详细比较了耦合模式相互比较项目第5阶段（CMIP 5）模式和GPS无线电掩星（RO）卫星观测之间的位势高度场。我们的比较集中在2002-2008年的200 hPa高度场的年平均，季节循环和年际变化。使用CMIP 5档案中的大量纯大气模型运行（AMIP）样本，我们发现大多数模型在年平均值和年际变化方面与热带地区的观测和天气再分析结果一致。然而，协议是穷人的热带外最大的模型传播在高纬度地区和最大的偏见，在南部中高纬度地区，坚持所有季节。模型还显示，在北方中纬度陆地地区以及南大洋的季节变化过大，但在热带和南极洲的变化不足。虽然模型差异的根本原因需要进一步分析，但这项研究表明，GPS RO的全球观测提供了对流层上部和平流层下部的准确基准质量测量，通过这些测量可以识别气候模型和天气再分析的偏差。
We present a detailed comparison of geopotential height fields between the Coupled Model Inter‐Comparison Project phase 5 (CMIP5) models and satellite observations from GPS radio occultation (RO). Our comparison focuses on the annual mean, seasonal cycle, and interannual variability of 200 hPa geopotential height in the years 2002–2008. Using a wide sample of atmosphere‐only model runs (AMIP) from the CMIP5 archive, we find that most models agree well with the observations and weather reanalyses in the tropics in both the annual means and interannual variabilities. However, the agreement is poor over the extratropics with the largest model spreads in the high latitudes and the largest bias in the southern middle to high latitudes that persist all seasons. The models also show excessive seasonal variability over the Northern midlatitude land areas as well as the Southern Ocean but insufficient variability over the tropics and Antarctica. While the underlying causes for the model discrepancies require further analyses, this study demonstrates that global observations from GPS RO provide accurate benchmark‐quality measurements in the upper troposphere and lower stratosphere through which biases in climate models as well as weather reanalyses can be identified.