This work leverages the unique capabilities of two instruments flown in NASA's A-Train satellite constellation to examine the cloud processes that are potentially responsible for a previously documented GCM low bias in LCC frequency. The two-channel lidar aboard CALIPSO is able to identify LCCs in the Arctic, despite winter darkness and icy surfaces, while the millimeter-wavelength cloud profiling radar (CPR) aboard CloudSat is able to simultaneously detect snowfall beneath the cloud layer. The A-Train is the first and only space-borne observing system to provide these observational capabilities.
We compare A-Train derived data products (2B-CLDCLASS-LIDAR, 2B-FLXHR-LIDAR, 2C-PRECIP-COLUMN, and 2C-SNOW-PROFILE) to the Community Earth System Model Large Ensemble (CESMLE) project. CESMLE includes an array of climate realizations, all with identical microphysics and parameterizations, which together provide an envelope of outcomes - ideally, observations should fit within that envelope. We explore the observed Arctic distributions (temporal and spatial) of clouds, precipitation, radiative fluxes, and the relationships between them, and evaluate the corresponding CESMLE envelope. We find LCCs snow far more often in the model than in observations, implying that too-frequent precipitation scours the super-cooled liquid from the cloud layer. The bias in LCCs leads to an underestimation of downwelling longwave radiation reaching the surface. Implications of the biases and processes will be discussed, with particular focus on impacts to the Greenland ice sheet.