Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Abstract

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.