An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer
- 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
- 2NASA Goddard Space Flight Center, Greenbelt, MD, USA
- 3Department of Meteorology and Geophysics, Instituto Português do Mar e da Atmosfera, Lisbon, Portugal
- 4Division of Climate System Research, Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan
Abstract. The global-scale patterns and covariances of subtropical marine boundary layer (MBL) cloud fraction and spatial variability with atmospheric thermodynamic and dynamic fields remain poorly understood. We describe an approach that leverages coincident NASA A-train and the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data to quantify the relationships in the subtropical MBL derived at the native pixel and grid resolution. A new method for observing four subtropical oceanic regions that capture transitions from stratocumulus to trade cumulus is demonstrated, where stratocumulus and cumulus regimes are determined from infrared-based thermodynamic phase. Visible radiances are normally distributed within stratocumulus and are increasingly skewed away from the coast, where trade cumulus dominates. Increases in MBL depth, wind speed, and effective radius (re), and reductions in 700–1000 hPa moist static energy differences and 700 and 850 hPa vertical velocity correspond with increases in visible radiance skewness. We posit that a more robust representation of the cloudy MBL is obtained using visible radiance rather than retrievals of optical thickness that are limited to a smaller subset of cumulus. The method using the combined A-train and MERRA data set has demonstrated that an increase in re within shallow cumulus is strongly related to higher MBL wind speeds that further correspond to increased precipitation occurrence according to CloudSat, previously demonstrated with surface observations. Hence, the combined data sets have the potential of adding global context to process-level understanding of the MBL.