Understanding scale-dependence and subgrid variability for climate model parameterizations

Representation of clouds remains as one of the largest sources of uncertainty in climate models. A major obstacle to further development of cloud parameterizations is our inadequate knowledge and understanding of subgrid variability of cloud properties, and their dependence on averaging scales. Information on the scale dependence is particularly needed to develop scale-aware parameterization for future climate models. This study focuses on the issue of subgrid variability and scale dependence, exploring their representation in climate models. We will show the dependence of cloud bulk parameters (e.g., liquid water path and optical thickness) on sampling scales and weather regimes using remote sensing retrievals from selected observations by the Atmospheric Radiation Measurement (ARM) Program, starting with the 1-minute microwave-radiometer retrievals of liquid water path gathered during 2010 at the ARM Barrow site in Alaska. We will also examine the probability density function, autocorrelation function, and key statistical parameters as a function of the sampling scales. The probability density function (and its moments) of these cloud bulk parameters are obtained using two independent methods: the traditional discrete bin method and the modern kernel density estimator method.