Research dates: 2015-2016
Investigator(s): Steven Quiring, Trent Ford
Improved seasonal forecasting could help to mitigate the impacts of extreme heat, a prominent climate hazard in the south-central U.S. Temperature anomalies in this region exhibit temporal autocorrelation from month to month as well as longer time lags, and the magnitude of temperature persistence can provide information that is useful for seasonal climate forecasts. Using high resolution temperature data from 1900-2015, this study examine the spatiotemporal distribution of temperature persistence. Initial results suggest that temperature persistence is strongest during the summer months, and decreases as the lag time increases. Most statistically significant temporal
autocorrelations are present at month-to-month timescales, however some locations do see significant temperature persistence at longer lag times. When examining the spatiotemporal distribution of temperature persistence, this information can be utilized in order to determine the locations, seasons, and timescales where it is most appropriate to weigh heavily on persistence in constructing a seasonal temperature forecast.