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Two Decades of Temperature and Specific Humidity Variance Scaling With the Atmospheric Infrared Sounder

大气红外探测仪 先进的微波电测深单位 测深 缩放比例 环境科学 气象学 红外线的 大气科学 湿度 对流 微波部件探测器 对流层 大气探测 气候学 偏斜 物理 数学 地质学 统计 光学 海洋学 几何学
作者
Brian H. Kahn,Eric J. Fetzer,J. Teixeira,Qing Yue
出处
期刊:Journal Of Geophysical Research: Atmospheres [American Geophysical Union]
卷期号:128 (19)
标识
DOI:10.1029/2023jd039244
摘要

Abstract A two‐decade climatology of height‐resolved horizontal variance scaling exponents ( 𝛼 ) for temperature ( T ) and specific humidity ( q ) is described using Aqua Atmospheric Infrared Sounder (AIRS) sounding profiles. The AIRS Team Version 6 (V6), Version 7 (V7), and Community Long‐term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieval algorithms are compared to European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) reanalyses. Large‐scale T exponents derived between 600 and 1,200 km ( 𝛼 L ) show close agreement between V6, V7, and CLIMCAPS algorithms. However, small‐scale q exponents derived between 150 and 400 km ( 𝛼 S ) are in poor agreement, including an unrealistically steep 𝛼 S in the planetary boundary layer (PBL) in the V7 and CLIMCAPS algorithms that is caused by a combination of algorithm damping and overconstraint by the first guess fields. ERA5 and MERRA‐2 reanalyses have large values of 𝛼 L and 𝛼 S for both T and q that indicate reduced small‐scale variability in the reanalysis fields. Differences in 𝛼 S between free‐running MERRA‐2 AMIP and MERRA‐2 are negligible, implying that suppressed small‐scale variability in reanalyses is imposed by the background model and not caused by the data assimilation process. AIRS has positively skewed T distributions in the tropical‐free troposphere that is consistent with positively buoyant air parcels in convection, and negative skewness in the PBL that is related to the existence of cold pools, behavior that is mostly absent in ERA5 and MERRA‐2. AIRS provides a global view of scale‐dependent variance and skewness that is useful for subgrid parameterization development and validation of weather and climate prediction models.

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