Disclaimer: This material is being kept online for historical purposes. Though accurate at the time of publication, it is no longer being updated. The page may contain broken links or outdated information, and parts may not function in current web browsers. Visit https://espo.nasa.gov for information about our current projects.


Partitioning CloudSat ice water content for comparison with upper tropospheric...

Chen, W., C. P. Woods, J. F. Li, D. E. Waliser, J. Chern, W. Tao, J. H. Jiang, and A. M. Tompkins (2011), Partitioning CloudSat ice water content for comparison with upper tropospheric ice in global atmospheric models, J. Geophys. Res., 116, D19206, doi:10.1029/2010JD015179.

The ice cloud estimates in current global models exhibit significant inconsistency, resulting in a significant amount of uncertainties in climate forecasting. Vertically resolved ice water content (IWC) is recently available from new satellite products, such as CloudSat, providing important observational constraints for evaluating the global models. To account for the varied nature of the model parameterization schemes, it is valuable to develop methods to distinguish the cloud versus precipitating ice components from the remotely sensed estimates in order to carry out meaningful model‐data comparisons. The present study develops a new technique that partitions CloudSat total IWC into small and large ice hydrometeors, using the ice particle size distribution (PSD) parameters provided by the retrieval algorithm. The global statistics of CloudSat‐retrieved PSD are analyzed for the filtered subsets on the basis of convection and precipitation flags to identify appropriate particle size separation. Results are compared with previous partitioning estimates and suggest that the small particles contribute to ∼25–45% of the global mean total IWC in the upper to middle troposphere. Sensitivity measures with respect to the PSD parameters and the retrieval algorithm are presented. The current estimates are applied to evaluate the IWC estimates from the European Centre for Medium‐Range Weather Forecasts model and the finite‐volume multiscale modeling framework model, pointing to specific areas of potential model improvements. These results are discussed in terms of applications to model diagnostics, providing implications for reducing the uncertainty in the model representation of cloud feedback and precipitation.

PDF of Publication: 
Download from publisher's website.