Kowal Katherine M, Slater Louise J, García López Alan, Van Loon Anne F
Department of Geography and the Environment University of Oxford Oxford UK.
Sección de Aplicaciones Climáticas en el Departamento de Investigación y Servicios Meteorológicos Instituto Nacional de Sismología, Vulcanología, Meteorología e Hidrología (INSIVUMEH) Guatemala City Guatemala.
Int J Climatol. 2023 Apr;43(5):2175-2199. doi: 10.1002/joc.7969. Epub 2023 Jan 6.
Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting.
季节性降雨预报提前数月提供信息以支持决策。这些预报可能采用动力学、统计或混合方法，但在中美洲，它们的相对价值尚未得到充分理解。本研究对季节性降雨预报进行了区域评估，重点关注两个主要的动力气候集合：哥白尼气候变化服务季节性预报系统（C3S）和北美多模式集合（NMME）。我们比较了中美洲关键雨季期间季节性降雨的多模式集合平均值和单个模式预测，以更好地了解它们在季节尺度上的相对预报技能。比较了三种类型的降雨预报：C3S和NMME集合的直接动力降雨预测、使用滞后观测海表面温度（SST）的统计方法以及一种间接混合方法，即利用动力集合SST预测驱动统计模型。结果表明，C3S和NMME表现出相似的区域变异性，在中美洲北部太平洋地区表现强劲，而在尼加拉瓜东部技能较弱。在该地区的北部太平洋部分，这些模式在整个雨季都具有较高的技能。在直接预报预测能力较低时（例如，雨季早期的尼加拉瓜东部），间接预报在特定情况下可能优于直接降雨预报。间接技能通常反映了SST与降雨的关联强度。基于热带北大西洋SST的间接预报在雨季早期最佳，基于Niño3.4 SST的间接预报在雨季后期最佳，此时每个SST区域与降雨的关联更强。统计预测在多种情况下与间接和直接预报具有竞争力，尤其是在雨季后期，这表明多种预报方法如何能够增强季节性预报。
