It is well known that clouds are organized into systems such as mesoscale convective complexes and squall lines. In the tropics and summertime midlatitudes, these systems are known for creating violent weather. Because these systems are characterized not only by their large horizontal extent but also for their long duration, they can also impact climate. On the other hand, subtropical boundary-layer cloud systems are the persistent weather phenomena off the west coasts of the continents and they are known to play important roles in climate and climate sensitivity. All of these cloud systems are strongly controlled by or associated with large-scale dynamical structures or circulations. Thus, weather and climate are ultimately linked through large-scale circulations.

The cloud object approach identifies a cloud object as a contiguous patch of the Earth composed of satellite footprints within a single dominant cloud-system type. The shape and size of a cloud object are determined by the satellite footprint data and by the selection criteria based upon cloud physical properties for a given cloud-system type. The selection criteria determine what types of cloud systems will be identified from satellite footprint data. For example, the selection criteria for tropical deep convective cloud objects are composed of requirements for cloud top height, cloud optical depth and footprint cloud fraction while boundary-layer cloud object types are determined by footprint cloud fraction and cloud top height.

Identified individual cloud objects are a collection of weather phenomena. Their physical properties differ greatly from one cloud object to another because of the short-term variabilities of the dynamical structures. These variations thus represent “weather noise,” rather than long-term climatic signals. In order to study climate and climate sensitivity using the cloud object data, large ensembles of cloud objects categorized by the matched atmospheric states or specific climate conditions should be combined to generate statistically robust cloud-physical characteristics. These characteristics are analyzed in terms of the summary probability density functions (pdfs) or histograms over an ensemble of cloud objects, i.e., the combined pdfs of individual cloud objects, instead of simple averages and standard deviations.

Two sets of cloud-object analyses will be presented in this study. The tropical deep convective cloud object data are analyzed to provide supporting evidence for the fixed anvil temperature hypothesis of Hartmann and Larson. It is found that the changes in macrophysical properties of cloud objects over the entire tropical Pacific were small for the large-size category of cloud objects, relative to those of the small- and medium-size categories. Another set of analyses constrasts the statistical characteristics of boundary-layer cloud object types between the tropics and the subtropics. It is found that each of the three boundary-layer cloud object types (cumulus, stratocumulus and stratus) exhibits small differences in statistical distributions of cloud optical depth, liquid water path, TOA albedo and cloud-top height, but large differences in those of cloud-top temperature and OLR between the tropics and subtropics. These results can be explained by the differences in the sea surface temperature distributions and the (local) boundary-layer dynamics.