Long-term seasonal rainfall predictions over the southeast U.S. using the FSU Global Spectral Model
Dawn C. Petraitis, Florida State University/COAPS, Tallahassee, FL; and T. E. LaRow and J. J. O'Brien
Rainfall patterns over the Southeast U.S. have been found to be connected to the El Niño-Southern Oscillation (ENSO). In coastal areas, warm ENSO events cause positive precipitation anomalies and cold ENSO events cause negative precipitation anomalies. The opposite relationship occurs in the inland areas. The purpose of this study is to evaluate the accuracy of the Florida State University Global Spectral Model (FSUGSM) in predicting seasonal rainfall over the Southeast U.S. The FSUGSM is a global spectral model with a T63 horizontal resolution (approximately 1.875°) and 27 unevenly spaced vertical levels. The experiment utilizes two runs using the Naval Research Laboratory (NRL) convection scheme and two runs using the National Centers for Environmental Prediction (NCEP) convection scheme to comprise the ensemble. The two convection schemes are slightly different in how they calculate cumulus cloud cover and convert that into precipitation, which will affect the amount of rainfall over the model domain. The simulation was done for 50 years, from 1950 to 2000. Reynolds and Smith monthly mean sea surface temperatures (SSTs) from 1950-2000 provide the lower boundary condition. Atmospheric and land conditions from January 1, 1987 and January 1, 1995 were used as the initial starting conditions. Observation data from the National Climatic Data Center's (NCDC) “Data Set 3200”, or “Surface Land Daily Cooperative Summary of the Day,” for Alabama, Florida, and Georgia will be used as the basis for comparison of the model output. Results show that the FSUGSM produces precipitation patterns consistent with the observed patterns for each phase of ENSO (warm, cold, and neutral). However, the models underestimate the amount of precipitation for each phase over the entire study area. Correlation coefficients between each model run and the observed data for each grid point in the domain are calculated to see how well the model predicts rainfall amounts. Further, cumulative distribution functions of seasonal rainfall for each location will be calculated to find any statistical significance in the data. We expect to find similar statistical findings as Cocke et al. (2005), such as forecast skill being highest in strong ENSO events, but with more confidence due to the longer simulation. We will then be able to draw firm conclusions about the skill of the model.
Poster Session 3, Climate Modeling and Diagnostic Studies
Thursday, 2 February 2006, 9:45 AM-11:00 AM, Exhibit Hall A2
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