Climate and Dynamics
Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States
Article first published online: 21 DEC 2007
Copyright 2007 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 112, Issue D24, 27 December 2007
How to Cite
2007), Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States, J. Geophys. Res., 112, D24102, doi:10.1029/2007JD008764., , , , , , and (
- Issue published online: 21 DEC 2007
- Article first published online: 21 DEC 2007
- Manuscript Accepted: 15 AUG 2007
- Manuscript Revised: 27 JUN 2007
- Manuscript Received: 9 APR 2007
- regional climate
 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.