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Using MODIS cloud regimes to sort diagnostic signals of...

Oreopoulos, L., N. Cho, and D. Lee (2017), Using MODIS cloud regimes to sort diagnostic signals of aerosol-cloud-precipitation interactions, J. Geophys. Res., 122, doi:10.1002/2016JD026120.
Abstract: 

Coincident multiyear measurements of aerosol, cloud, precipitation, and radiation at near-global scales are analyzed to diagnose their apparent relationships as suggestive of interactions previously proposed based on theoretical, observational, and model constructs. Specifically, we examine whether differences in aerosol loading in separate observations go along with consistently different precipitation, cloud properties, and cloud radiative effects. Our analysis uses a cloud regime (CR) framework to dissect and sort the results. The CRs come from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are defined as distinct groups of cloud systems with similar covariations of cloud top pressure and cloud optical thickness. Aerosol optical depth used as proxy for aerosol loading comes from two sources, MODIS observations and the MERRA-2 reanalysis, and its variability is defined with respect to local seasonal climatologies. The choice of aerosol data set impacts our results substantially. We also find that the responses of the marine and continental component of a CR are frequently quite disparate. Overall, CRs dominated by warm clouds tend to exhibit less ambiguous signals but also have more uncertainty with regard to precipitation changes. Finally, we find weak, but occasionally systematic covariations of select meteorological indicators and aerosol, which serve as a sober reminder that ascribing changes in cloud and cloud-affected variables solely to aerosol variations is precarious. Plain Language Summary Aerosols are known to affect clouds and rainfall. This study examines whether satellite observations sampled and organized under a new framework can be used to detect the interactions and whether the results are consistent with expectations. The study is more extensive than previous similar efforts and highlights what is feasible and what is still challenging when attempting to find and evaluate signals of the interactions and providing interpretations of the underlying processes.

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Research Program: 
Modeling Analysis and Prediction Program (MAP)
Radiation Science Program (RSP)