Empirical-Statistical Downscaling in Climate Modeling

Physics

Scientific paper

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Meteorology And Atmospheric Dynamics: Climatology (1620), Meteorology And Atmospheric Dynamics: General Or Miscellaneous, Meteorology And Atmospheric Dynamics: Instruments And Techniques

Scientific paper

Research into possible impacts of a climate change requires descriptions of local and regional descriptions of climate. For instance, the local and regional aspect of a climate change is stressed in the U.S. Strategic Plan for the Climate Change Science Program (CCSP) (http://www.climatescience.gov/Library/stratplan2003/default.htm). Global climate models (GCMs) are important tools for studying climate change and making projections for the future. Although GCMs provide realistic representations of large-scale aspects of climate, they generally do not give good descriptions of the local and regional scales. It is nevertheless possible to relate large-scale climatic features to smaller spatial scales. There are two main approaches for deriving information on local or regional scales from the global climate scenarios generated by GCMs: (1) numerical downscaling (also known as ``dynamical downscaling'') involving a nested regional climate model (RCM) or (2) empirical-statistical downscaling employing statistical relationships between the large-scale climatic state and local variations derived from historical data records.

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