Reducing uncertainties in satellite estimates of aerosol-cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations
Reducing uncertainties in satellite estimates of aerosol-cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations
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DOI：
10.5194/acp-20-7167-2020
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发表时间：
2020-06-22
影响因子：
6.3
通讯作者：
Clayton, Marian
中科院分区：
文献类型：
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作者：
Painemal, David;Chang, Fu-Lung;Clayton, Marian
气溶胶对云层影响的卫星量化依赖于气溶胶光学深度（AOD）作为气溶胶浓度或云凝核（CCN）的代理。然而，卫星结果缺乏误差特征，阻碍了其用于评估和改进全球气候模型。我们表明，在亚热带广阔的海洋区域，使用 AOD 来评估气溶胶-云相互作用 (ACI) 是不够的。相反，我们假设一种更物理的方法，包括将来自云层高度的云气溶胶激光雷达和红外探路者卫星观测 (CALIPSO) 卫星的垂直分辨气溶胶数据与中分辨率成像光谱辐射计 (MODIS) Aqua 云检索相匹配，减少基于卫星的 ACI 估计的不确定性。正交偏振云气溶胶激光雷达 (CALIOP) 的云顶以下气溶胶消光系数 (sigma) (sigma(BC)) 与 MODIS Aqua 的云滴数浓度 (N-d) 相结合，在广泛的 sigma(BC ) 值范围内产生高度相关性，其中 sigma(BC ) 四分位数相关性 >= 0.78。相比之下，对于两个较低的 AOD 四分位数，基于 CALIOP 的 AOD 与 MODIS N-d 的相关性为 0.54-0.62。此外，sigma(BC)解释了MODIS N-d中41%的空间方差，而AOD仅解释了17%，这主要是由于东太平洋缺乏空间协变性造成的。与sigma(BC)相比，近地表sigma与MODIS N-d在空间上的相关性较弱，方差为16%。结论是，从 ln(N-d)-ln(sigma(BC))（量化 ACI 的标准方法）计算的线性回归比从 N-d-AOD 对得出的线性回归更具物理意义。
Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol-cloud interactions (ACIs) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (sigma) below cloud top (sigma(BC)) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (N-d) from MODIS Aqua yield high correlations across a broad range of sigma(BC )values, with sigma(BC )quartile correlations >= 0.78. In contrast, CALIOP-based AOD yields correlations with MODIS N-d of 0.54-0.62 for the two lower AOD quartiles. Moreover, sigma(BC) explains 41 % of the spatial variance in MODIS N-d, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with sigma(BC), near-surface sigma weakly correlates in space with MODIS N-d, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(N-d)-ln(sigma(BC)) (the standard method for quantifying ACIs) is more physically meaningful than that derived from the N-d-AOD pair.