Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States

Details Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States

Dec 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007jgrd..11224102l&link_type=abstract

Journal of Geophysical Research, Volume 112, Issue D24, CiteID D24102

Statistics

Applications

6

Atmospheric Processes: Regional Modeling, Global Change: Regional Climate Change, Planetary Sciences: Fluid Planets: Atmospheres (0343, 1060), Planetary Sciences: Fluid Planets: Meteorology (3346), Atmospheric Composition And Structure: Pressure, Density, And Temperature

Scientific paper

Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8° lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are downscaled to a fine spatial scale of ~20 km. Dynamical and statistical downscaling methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical downscaling is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical downscaling method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. Downscaled temperatures are compared with the FSU/COAPS GSM fields and observations. Downscaled seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both downscaling techniques can be reliably used for numerous seasonal climate applications.

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