Understanding the relationship between cloud controlling factors and the ISCCP weather states

Abstract

Abstract Two common methods used to develop a process-level understanding of global cloud cover are 1) analyzing large-scale meteorological variables (cloud controlling factors) associated with cloud variability and 2) classifying cloud types using clustering algorithms applied to satellite data, such as the International Satellite Cloud Climatology Project (ISCCP) weather states. The cloud controlling factor method is advantageous to apply to climate models, as it does not rely on cloud parameterizations or the availability of satellite simulator output. The purpose of this study is to document the relationship between cloud controlling factors and the ISCCP weather states in the observational record, providing a benchmark for the application of cloud controlling factors to study individual cloud types in future studies. Most ISCCP weather states are linked to distinct dynamical regimes characterized by unique combinations of six cloud controlling factors. These relationships are present in both the long-term mean climatology and in daily-to-monthly climate variability. For example, deep convective and midlatitude storm clouds dominate ascending regions. In descending regions, shallow cumulus is more frequent in regimes characterized by weak boundary-layer temperature inversions (EIS) and strong subsidence, and stratocumulus is more frequent in regimes with larger values of EIS, weaker subsidence, and relatively weak near-surface cold advection. Mid-level clouds are prominent in descending regions with strong cold advection. Overall, the results of this study suggest promise in using cloud controlling factors to identify dynamical regimes where individual cloud types are more or less likely and to understand the physical processes responsible for the transitions among them.