Utilizing the State of ENSO as a Means for Season-Ahead Predictor Selection

Large-scale antecedent oceanic-atmospheric variables are commonly applied in statistical season-ahead prediction frameworks, however the typical approach of evaluating all years simultaneously does not often provide the level of skill required by decision makers. For many locations around the world, the most influential climate signal on the seasonal timescale is El Nino Southern Oscillation (ENSO.) This study introduces the Nino Index Phase Analysis (NIPA) framework for forecasting hydroclimatic variables on seasonal timescales utilizing the state of ENSO to classify the years of record into phases for prediction. A case study focused on spring precipitation over the Lower Colorado River Basin in Texas is presented to illustrate NIPA's potential. Results indicate significant improvements in prediction skill, particularly for years exhibiting extreme wet or dry conditions. Prediction skill for application to US climate divisions is also discussed.