87th AMS Annual Meeting

Tuesday, 16 January 2007
Studying Cloud Processes with the Cloud-resolving Modeling Approach: Professor Arakawa's Vision
Exhibit Hall C (Henry B. Gonzalez Convention Center)
Kuan-Man Xu, LRC, Hampton, VA
The cloud-resolving modeling approach was in its infancy when I began my graduate research at UCLA in 1987, under the guidance of Professor Akio Arakawa. Almost twenty years later, this approach has been greatly expanded into a premier tool for global climate modeling, by either resolving clouds in a global model or coupling a two-dimensional (2-D) version of a cloud-resolving model (CRM) with the dynamic core of a global model. In the latter, the 2-D CRM replaces all cloud parameterizations in the global model.

Back in the late 1980s, Professor Arakawa envisioned that cloud-resolving models (CRMs) were used to study cloud processes that could not be directly observed. The UCLA CRM developed by Steve Krueger and Akio Arakawa was based upon the anelastic system of cloud-scale dynamics. The most unique feature of this model was its third-order closure scheme for turbulence parameterization. With this model, idealized simulations driven by observed mean forcing profiles were performed to address fundamental issues in cumulus parameterization, for example, whether or not cumulus convection is parameterizable. The deterministic and diagnostic aspects of the cumulus parameterization problem were addressed by evaluating the Arakawa-Schubert cumulus parameterization scheme as a function of the grid size of large-scale models. Diagnostic cloudiness parameterizations, which relate cloud fraction to large-scale predictors such as relative humidity, were evaluated while I was at UCLA.

Today, using observed time-varying advective tendencies from field experiments, CRM simulations are routinely performed to study cloud processes and compare with observations. State-of-the-art models include interactive radiative transfer and advanced cloud microphysics parameterizations. Observations of cloud parameters are now more readily available than they were twenty years ago. Statistics of these observations can be compared with those of CRM simulations in order to further improve the realism of the model. In this study, a few examples will be given to illustrate the diverse applications of the cloud-resolving modeling approach. Specifically, results from CRM intercomparison projects will be shown to illustrate the current capability of this type of models. Comparisons of CRM simulations with satellite measurements will be also presented to draw further attention to the importance of using observational data to evaluate models and guide the development of improved models.

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