A Multiscale Modeling System: Developments, Applications, and Critical Issues

Wei‐Kuo Tao, J. Chern, Robert Atlas, David A. Randall, Marat Khairoutdinov, Jui-Lin Li, Duane E. Waliser, Arthur Y. Hou, Xin Lin, C. D. Peters‐Lidard,

Bulletin of the American Meteorological Society · 2008 · 138 citations · 90 references

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TL;DR

A multiscale modeling framework replaces conventional cloud parameterizations with a cloud‑resolving model in each GCM grid column, enabling global coverage and two‑way interactions that allow explicit simulation of cloud processes with radiation and surface dynamics. The paper discusses critical issues related to the Goddard MMF’s cloud‑resolving model physical processes and configuration. The Goddard MMF, built on the NASA GSFC finite‑volume GCM and Goddard Cumulus Ensemble, improves surface precipitation, cloudiness, diurnal variability, and the Madden‑Julian oscillation relative to traditional GCMs, yet both MMFs show a large positive summer precipitation bias over the Indian Ocean and western Pacific, and the Goddard MMF produces more global rainfall from oceanic contributions while the CSU MMF yields less land rainfall and overall global rainfall.

Abstract

A multiscale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach for climate modeling. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The CRM allows for explicit simulation of cloud processes and their interactions with radiation and surface processes, and the GCM allows for global coverage. A new MMF has been developed that is based on the NASA Goddard Space Flight Center (GSFC) finite-volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using traditional cloud parameterizations. Both MMFs also produce a large and positive precipitation bias in the Indian Ocean and western Pacific during the Northern Hemisphere summer. However, there are also notable differences between the two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF simulates more global rainfall than its parent GCM because of the high contribution from the oceanic component. A number of critical issues (i.e., the CRM's physical processes and its configuration) involving the Goddard MMF are discussed in this paper.

References

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