Utilizing the State of ENSO to Improve Probabilistic Seasonal Forecasts for Temperature and Precipitation

Abstract

This thesis illustrates a novel methodology developed around the ENSO phenomenon - however, instead of using the ENSO signal itself as a predictor, ENSO is used to bin the years on record, somewhat analogous to using the seasonal cycle to bin months within a year. Predictive sea surface temperature anomalies are then elucidated within these ‘phases’ of ENSO, and a marked increase in skill is shown for seasonal precipitation and temperature forecasts over a wide range of the continental US in nearly every season as compared to traditional forecasting methods. Skill varies depending on the location, phase, and season, with temperature forecasts generally showing better skill than precipitation forecasts. The most interesting results is observed for both temperature and precipitation during the El Nino phase for most seasons - within El Nino events, the strength of the El Nino seems to predict temperature/precipitation anomalies in particular locations with impressive accuracy. Some potential reasons for this are explored.