A CloudSat cloud object partitioning technique and assessment and integration of deep convective anvil sensitivities to sea surface temperature

A cloud object partitioning algorithm is developed to provide a widely useful database of deep convective clouds. It takes contiguous CloudSat cloudy regions and identifies various length scales of clouds from a tropical, oceanic subset of data. The methodology identifies a level above which anvil characteristics become important by analyzing the cloud object shape. Below this level in what is termed the pedestal region, convective cores are identified based on reflectivity maxima. Identifying these regions allows for the assessment of length scales of the anvil and pedestal of deep convective clouds. Cloud objects are also appended with certain environmental quantities from European Centre for Medium-Range Weather Forecasts. Simple geospatial and temporal assessments show that the cloud object technique agrees with standard observations of local frequency of deep convective cloudiness. Deep convective clouds over tropical oceans play important roles in Earth’s climate system. The newly developed data set is used to evaluate the response of tropical, deep convective clouds to sea surface temperature (SST). Several previously proposed responses are examined: the Fixed Anvil Temperature Hypothesis, the Iris Hypothesis, and the Thermostat Hypothesis. When the data are analyzed per cloud object, increasing SST is found to be associated with increased anvil thickness, decreased anvil width, and cooler cloud top temperatures. Implications for the corresponding hypotheses are discussed. A new response suggesting that the base temperature of deep convective anvils remains approximately constant with increasing SSTs is introduced. These cloud dependencies on SST are integrated to form a more comprehensive theory for deep convective anvil responses to SST.