Cloud properties and radiative forcing over the maritime storm tracks of the Southern Ocean and North Atlantic derived from ATrain

[1] Annually averaged cloud properties, cloud radiative effects, and cloud radiative heating from 20° × 20° latitude‐longitude regions in the Southern Ocean (50°S, 135°W) and the North Atlantic (55°N, 25°W) are compared using quantities derived from measurements collected by active and passive remote sensors in the NASA A‐Train. The algorithm suite used to infer cloud properties along the nadir track of the CloudSat and CALIPSO satellites takes input from the cloud boundaries from the merged active remote sensors, radar reflectivity from CloudSat, liquid water path derived from the Advanced Microwave Scanning Radiometer on Aqua, optical depth derived from the Moderate Resolution Imaging Spectroradiometer on Aqua, and top‐of‐atmosphere (TOA) fluxes measured by the Clouds and the Earth’s Radiant Energy System. Errors in annually averaged cloud radiative effect are estimated to range from approximately 5 to 10 W m and heating rate uncertainties range from 0.5 to 2 K day. The study regions demonstrate a high degree of similarity in cloud occurrence statistics, in cloud properties, and in the radiative effects of the clouds. Both regions are dominated by a background state of boundary layer clouds (mean liquid water path ∼150 g m). Boundary layer clouds and cirrus (mean ice water path ∼100 g m) occurring either alone or together amount to approximately 75% of all clouds. Deeper frontal clouds amount to 10%–12% of the coverage. A strong net TOA cooling effect is partitioned between solar cooling of the surface and IR cooling of the atmosphere that is dominated by the ubiquitous boundary layer clouds. It is shown that regimes inferred according to their cloud top pressure and optical depth are often dominated by multiple hydrometeor layers and therefore defy simple classification. Because of this vertical distribution, hydrometeor‐induced heating is distributed within the atmosphere in a different way than would be inferred from passive remote‐sensing data considered alone.