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.

中文翻译：

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2002-2020 年 ENSO 业务预报模型的基于事件的评估：对季节性水资源管理的影响

鉴于厄尔尼诺-南方涛动（ENSO）预报对开发决策支持系统的实际影响及其在季节到年际尺度全球气候预测中的重要性，人们为加强 ENSO 预报做出了重大努力。本研究提出了基于事件的 ENSO 预报评估，并讨论了此类预报对水资源管理的影响。本研究利用ENSO对2002-2020年期间的预测，评估和比较了两种类型的预测模型（即统计模型和动力模型）在预测海面温度（SST）方面的结果。通过斯皮尔曼相关性和均方误差这两个指标来评估不同目标季节的预测技能。此外，还评估了两种模型在不同交付周期下的性能。结果表明，这些模型的预测能力相当，两者对北方秋冬季节的预测能力较高，而对北方春季的预测能力较低。基于事件的分析表明，动力和统计模型低估了厄尔尼诺事件爆发月份的海温异常，尽管在大多数情况下，预测误差随着预测领先时间的减小而减小。对于拉尼娜事件，海温异常预报误差可能是正值，也可能是负值。模型也很难准确预测从一个 ENSO 阶段到另一个阶段的快速转变。讨论了造成这些挑战的因素。还讨论了 ENSO 预报对美国东南部地区供水公司水资源管理（主要是水流预报）的影响。这项研究的结果提供了对 ENSO 预报技能及其实际影响的见解。