Utilizing the state of ENSO as a means for season‐ahead predictor selection

Abstract

AbstractThis paper introduces the Nino Index Phase Analysis (NIPA) framework for forecasting hydroclimatic variables on a seasonal time scale. Antecedent Sea Surface Temperatures (SSTs) are commonly used in statistical predictive frameworks for seasonal forecasting, however, the typical approach of evaluating all the years on record in one bin (“phase”) 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 time scale is the El Nino Southern Oscillation (ENSO), and there are various indices used to capture the state of ENSO and provide this information. NIPA utilizes the state of ENSO to classify the years of record into four phases, operating under the hypothesis that ENSO itself is affecting the “mean state” of the atmospheric‐oceanic system, and relevant teleconnections depend on and must be selected within these mean states. A case study focused on spring precipitation over the Lower Colorado River Basin (LCRB) in Texas is chosen to illustrate NIPA's potential. Results show that correlations between wintertime SST fields and spring precipitation in the LCRB improve from 0.21 to 0.47 for the typical “one phase” and the NIPA “four‐phase” approach, respectively. Even greater improvements are seen across tercile‐based skill scores such as the Heidke Hit Skill Score and Ranked Probability Skill Score; skill is particularly strong for years exhibiting extreme wet or dry conditions. It also outperforms the North American Multi‐Model Ensemble predictions across the LCRB for the selected seasons. This is encouraging as improved predictability through NIPA may translate to better decision‐making for water managers.