Convective Transition Statistics over Tropical Oceans for Climate Model Diagnostics: GCM Evaluation
Convective Transition Statistics over Tropical Oceans for Climate Model Diagnostics: GCM Evaluation
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DOI：
10.1175/jas-d-19-0132.1
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发表时间：
2020-01
影响因子：
3.1
通讯作者：
Yi‐Hung Kuo;J. Neelin;Chih‐Chieh Chen;Wei-Ting Chen;L. Donner;A. Gettelman;Xianan Jiang;Kuan‐Ting Kuo;E. Maloney;C. Mechoso;Y. Ming;K. Schiro;C. Seman;Chien‐Ming Wu;Ming Zhao
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文献类型：
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作者：
Yi‐Hung Kuo;J. Neelin;Chih‐Chieh Chen;Wei-Ting Chen;L. Donner;A. Gettelman;Xianan Jiang;Kuan‐Ting Kuo;E. Maloney;C. Mechoso;Y. Ming;K. Schiro;C. Seman;Chien‐Ming Wu;Ming Zhao
为了评估各种GCM中的深对流参数化并检查快速时间尺度的对流转换，使用GFDL大气模式的两个版本的每小时输出来评估一组统计数据，这些统计数据表征了作为柱水汽（CWV）函数的降水拾取，CWV和降水的PDF和联合PDF，以及水分-降水关系对对流层温度的依赖性，版本4（AM4）、NCAR CAM 5和超参数化CAM（SPCAM）。还分析了MJO任务组（MJOTF）/GEWEX大气系统研究（GASS）项目的6小时输出。对比统计数据产生的个别模式，主要是不同的湿对流的表示表明，对流过渡统计数据可以大大区分对流表示及其与大尺度流的相互作用的差异，而不同的模式，只有在时空分辨率，微物理，或海洋-大气耦合导致类似的统计数据。大多数模型模拟的CWV超过临界值，以及对流发生在较高的CWV，但在较低的列RH温度升高时，降水急剧增加的观察到的一些版本。虽然一些模型定量捕捉这些观察到的功能和相关的概率分布，相当大的模型间的传播和偏离的降水CWV关系的各个方面的观察。例如，在许多模式中，从低CWV，非降水制度的CWV周围和以上的临界潮湿制度的过渡是不那么突然的观测。此外，有些模式在低CWV下过度产生毛毛雨，有些模式要求CWV高于强降水。对于许多模型来说，模拟高温下CWV的概率分布特别具有挑战性。
To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature.