Systematic Development of a Subgrid Scaling Framework to Improve Land Simulation

 DOE Climate Change Prediction Program (CCPP) In Response to DOE Program Notice DE-PS02-07ER07-06

 Principal Investigator: Robert E. Dickinson

Tel: 404-385-1509, Fax: 404-385-1510, Email: robted@eas.gatech.edu

 Co-Investigators: Qing Liu, Muhammad Shaikh, and Yan Huang

 Summary

             The coupling between land and atmosphere involves a wide range of vegetation, soil, and hydrological processes that occur on spatial scales small compared to that of the atmospheric model. These issues have not been satisfactorily resolved in the current DOE/NCAR sponsored Community Climate System Model (CCSM) and climate models in general. Our previous SciDAC work has made significant progresses in addressing how land-atmosphere coupling processes determine precipitation and surface air temperature, what the most important subgrid scale processes coupling land to the atmosphere are, and how they can be efficiently represented in a climate model. The objective of this proposal is to develop and test a systematic subgrid scaling framework for the land component of the CCSM based upon our past progress. It will consist of four elements: i) a complex vegetation tiling representation; ii) an orographic tiling system; iii) a tiling system to describe a distribution of water table parameters that derives a realistic statistical model of wetland; iv) extension of our current work on precipitation intensity scaling that incorporates statistical estimation of precipitation intensities based on the physics of the CAM convective parameterization. The four elements will use the same set of tile computational components. They will have an infrastructure of the highest quality data currently available including the latest satellite-based MODIS land products and high resolution digital elevation data to describe the land surface. The scaling (tiling) system is expected to provide a better base for development of dynamic vegetation carbon modeling and a more realistic description of the land hydrological and radiation environment that will improve the prediction of land surface fluxes. These improvements should improve the accuracy of climate model simulations of future climate change.     

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