A Modeling and Observational Framework for Diagnosing Land-Atmosphere Coupling and Impacts on Water and Energy Cycle Prediction

Details A Modeling and Observational Framework for Diagnosing Land-Atmosphere Coupling and Impacts on Water and Energy Cycle Prediction A Modeling and Observational Framework for Diagnosing Land-Atmosphere Coupling and Impacts on Water and Energy Cycle Prediction

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufm.h13h..03s&link_type=abstract

American Geophysical Union, Fall Meeting 2008, abstract #H13H-03

Physics

1807 Climate Impacts, 1818 Evapotranspiration, 1866 Soil Moisture, 3307 Boundary Layer Processes, 3322 Land/Atmosphere Interactions (1218, 1631, 1843)

Scientific paper

Land-atmosphere interactions play a critical role in determining the evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. However, the degree of coupling between the land surface and atmosphere in numerical weather prediction models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as those governing the relationship between soil moisture and precipitation. In this study, a framework for quantifying land-atmosphere interactions is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA-GSFC's Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework the land surface water and energy balance and mixed layer equilibrium established by each PBL-LSM pair are evaluated in terms of the diurnal temperature and humidity evolution. Results show how these variables are sensitive to and, in fact, integrative of the dominant processes involved in land-atmosphere interaction, and may be evaluated in terms of observable properties of the coupled system (e.g. soil moisture, PBL depth) measured by current and future remote sensing platforms. Overall, this work provides a pathway to improve water and energy cycle prediction using the LIS-WRF system, and will serve as the foundation for pilot experiments to evaluate coupled modeling efforts within the international community.

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