83rd Annual

Tuesday, 11 February 2003: 11:28 AM
Global land surface radiation budget and impact on water and energy cycles
C. Jesse Meng, University of Maryland Baltimore county, Baltimore, MD; and P. R. Houser, K. Mitchell, M. Rodell, U. Jambor, J. Gottschalk, K. Arsenault, J. Entin, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin, J. P. Walker, H. L. Pan, and G. Gayno
Poster PDF (137.6 kB)
A 1/4 degree resolution, near-real time Global Land Data Assimilation System (GLDAS) has been developed, which makes use of various new satellite- and ground-based observation systems within a land data assimilation framework, in order to produce optimal output fields of land surface states and fluxes. The development of GLDAS will improve land surface, weather, and climate predictions. Furthermore, the high-quality, assimilated, global land surface fields that GLDAS provides will be useful for subsequent research and applications.

Surface radiation budget is the primary forcing to land surface processes. Within the GLDAS framework, surface radiation budget from the following three global model and data assimilation systems will be continuously evaluated: (1) the Air Force Weather Agency AGRMET, (2) the NASA GSFC Data Assimilation Office GEOS, and (3) the NOAA NCEP GDAS. On regional and global scales, analysis will be focused on large-scale climate features represented in these models. On local scale, the performance of these models will be evaluated against surface observations from various networks and individual sites.

Surface radiation budget has strong impacts on energy and water cycles. Altering surface radiation forcing will lead to a significant adjustment in surface temperature, moisture, and fluxes during the consequent complex land surface thermodynamic and hydrological processes. The response of land surface to different radiation forcing, as generated from different models, will be studied. This analysis provides an opportunity to identify the sources and feedbacks of land water and energy budgets. It will contribute to the decision making in future developments of land and atmosphere modeling and data assimilation system.

Supplementary URL: