The Influence of the El Nino-Southern Oscillation and North Atlantic Oscillation on Wintertime Weather in the Ohio and Tennessee Valleys

While the general winter effects of the El Nino-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) in the Ohio and Tennessee Valleys are known, this study digs deeper into the analysis of how the two phenomena work in synergy to modulate winter weather conditions in the area. In an effort to develop a climatology-based forecasting tool, historical temperature, precipitation, and snowfall data since 1950 were analyzed for different scenarios of positive and negative phases of ENSO and NAO. This analysis was performed for 123 Local Climatological Data (LCD) and Cooperative Observer Program (COOP) sites within the area of study. The study utilized many resources, including xmACIS2, the Local Climate Analysis Tool (LCAT), Climate Prediction Center (CPC) database, and ESRI Geographic Information System (GIS) programs.

Standard deviations and averages were calculated for each station and weather variable in the study region. Preliminary results for NWS Louisville's area of responsibility (central Kentucky and southern Indiana) indicate that a combined La Nina/Positive NAO pattern has the greatest impact, with warmer than normal temperatures, above average precipitation, and below average snowfall for the December-January-February period. Results also show that the most significant cold, dry winters occur during an El Nino/ Negative NAO pattern. Preliminary findings for all NWS offices in the Ohio and Tennessee Valleys will be discussed. Near future work will concentrate on other types of statistical analyses and using similar procedures on other weather phenomena.

This multi-faceted study will allow forecasters to identify upcoming seasonal trends to facilitate a Weather-Ready Nation by answering customer questions as they prepare for unique cyclical weather challenges. In addition, forecasters will be better equipped to anticipate day-to-day weather patterns and storm behavior based on ENSO and NAO phase combinations to enhance user decision support.