ARM Evaluates Clouds in Climate Models
ARM data demonstrate that a new way to model clouds is needed and possible. The representation of cloud cover in numerical models has long been recognized as the key source of uncertainty in climate predictions of radiation transfer and cloud microphysics. One important contributor to this uncertainty is that clouds are often scattered over the sky rather than in uniform layers. Furthermore, scattered clouds are often at different heights and overlap. This makes it very difficult to model the clouds and how they may change as the climate changes. Atmospheric general circulation models divide the atmosphere into vertical columns (grid boxes), and each grid box has multiple vertical layers. Certain reasonable assumptions regarding cloud layer overlap have been applied in models up to now; however, these assumptions have previously not been systematically evaluated with a comprehensive data set. The long-term data collected by continuously operating instruments deployed at the Atmospheric Radiation Measurement (ARM) sites in the Tropics, middle latitudes and the Arctic now provide sufficient statistics of clouds to make this evaluation possible. ARM investigators, Drs. Gerald Mace and Sally Benson-Troth, have completed an analysis of cloud layer overlap characteristics. Their findings show that assumptions dealing with specific cloud conditions are not supported by observations. Therefore, to avoid significant biases in simulated cloud cover, the overlap properties of these layers in models will need to be modeled. The analysis also shows that the cloud layer overlap characteristics in the middle latitudes do appear to be a strong function of season, suggesting that an overlap model based on cloud system type may be possible.