Analysis of Winter Climate in the Tennessee Valley Based on ENSO Phase and Magnitude

The large scale component of the Earth's coupled ocean-atmosphere system, commonly referred to as the El Niņo Southern Oscillation (ENSO), acts as one of the larger moderators of inter-annual variability on global and regional climate. The Tennessee Valley region in the southeastern United States lies within a spatial transition zone of associated ENSO impacts during the winter season, making prediction of temperature and precipitation trends difficult and resulting in low seasonal forecast confidence. Forecasts of impacts during La Niņa episodes, for example, indicate wetter than normal conditions are slightly favored in western portions of the Tennessee Valley, while drier than normal conditions are slightly favored in eastern portions of the region. However, these seasonal forecasts employ a simple tri-modal approach with respect to the ENSO signal (i.e., El Niņo, ENSO-neutral, La Niņa) and do not account for the magnitude of the ENSO signal. Given the potential impacts especially during strong ENSO events, or during years of preexisting drought, seasonal temperature and precipitation forecasts may play an important role in effective lake storage and winter crop management, and thus a need for higher resolution seasonal forecasts in this transitional region arises. This presentation will discuss winter precipitation and temperature trends in the Tennessee Valley given a discrete, seven-tiered sea surface temperature (SST) anomaly in the Niņo 3.4 region (170°W-120°W, 5°S-5°N). Particular emphasis is placed on the El Niņo mode of the ENSO phenomenon given current trends in the tropical Pacific. In order to maximize the number of distinct ENSO modes available for this study and to assess impacts within homogeneous climate regions, winter precipitation and temperatures are obtained from the Climate Prediction Center's Climate Division Dataset, while SST anomalies are derived from the NOAA Extended Reconstructed Sea Surface Temperature Version 4 dataset (ERSST v4).