410 Regional climate modeling in the Lower Mississippi River Valley for water resources assessments

Thursday, 27 January 2011
Washington State Convention Center
Valentine G. Anantharaj, Oak Ridge National Laboratory, Oak Ridge, TN; and G. V. Mostovoy, Y. Lau, C. A. Peterson, C. D. Armstrong, and X. Fan

The agricultural economy in the Lower Mississippi River Valley (LMRV) is highly susceptible to influences of weather and climate, which determine the availability of water and its demand for irrigation. The agricultural practices in the LMRV is water-intensive and sensitive to the changes in the availability of water. Studying the impacts of climate change on natural resources is a key step to the adaptive management of available resources. Climate models and global reanalysis datasets have provided long-term climate simulations and reanalysis of past and present climates and projections of future climate change. However, decision-making activities for resource management, such as policy development and risk assessment, need to be at relevant regional scales. Hence, the coarse resolution IPCC Global Climate Model (GCM) simulations need to be downscaled to appropriate regional scales for decision support activities.

We have implemented a regional climate model (RCM) covering the Lower Mississippi River Valley (LMRV) and the northern coastal zone of the Gulf of Mexico. The RCM configuration is based on the Weather Research and Forecast (WRF) model, used to downscale the IPCC AR4 global climate projections from the NASA GISS ModelE. The RCM downscaling efforts have been targetted to support impacts assessment and decision-making in water resources management, since water availability and quality are fundamental for the economic activity in the LMRV region. We are also incorporating the new regional climate projections into a data server, which will serve the needs of economic and risk management frameworks in order to facilitate climate impacts assessments under future regional climate change scenarios at relevant spatial scales. We will present initial results from a set of decade long simulations and the lessons learned.

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