Evaluating Seasonal Climate Forecasts from Dynamical Models over South America

IF 3.1 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Hydrometeorology Pub Date : 2023-04-01 DOI:10.1175/jhm-d-22-0156.1
Jiaying Zhang, K. Guan, R. Fu, B. Peng, Siyu Zhao, Y. Zhuang
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引用次数: 1

Abstract

Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.
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评估南美洲动力模式的季节气候预报
季节气候预报具有社会经济价值,预报质量对各种社会应用都很重要。在此,我们评估了南美洲主要农田地区运行动力模式对三个气候变量(蒸汽压差、温度和降水)的季节预报。从全球和局部环流模式,如厄尔Niño-Southern涛动(ENSO)分析其可预测性;并指出预测误差的来源。我们发现欧洲中期天气预报中心(ECMWF)模式在所有评估模式中具有最高的质量。对VPD和温度的预测与观测结果的一致性较好(在ECMWF的1个月预测中,所有月份的平均Pearson相关性分别为0.65和0.70),而降水的平均Pearson相关性为0.40)。预报随着提前时间的增加而退化,其原因如下:1)未能捕捉到局地环流模式以及这些模式与局地气候之间的联系;2)高估了ENSO对未受ENSO影响地区的影响。对于受ENSO影响的地区,可以提前6个月准确预测这三种气候变量及其极端情况,为风险准备和管理提供宝贵的提前时间。这些结果为进一步发展动力模式和利用季节气候预报进行规划和管理提供了有用的信息。季节气候预报具有社会经济价值,预报质量对其应用至关重要。本研究评估了对农业管理、风险评估和自然灾害预警至关重要的三个重要气候变量的月度预报质量。这些发现为那些利用季节气候预报进行规划和管理的人提供了有用的信息。本研究还分析了气候变量的可预测性和预测误差的归因,从而为理解模式的变化性能和未来改进动力模式的季节气候预报提供了见解。
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来源期刊
Journal of Hydrometeorology
Journal of Hydrometeorology 地学-气象与大气科学
CiteScore
7.40
自引率
5.30%
发文量
116
审稿时长
4-8 weeks
期刊介绍: The Journal of Hydrometeorology (JHM) (ISSN: 1525-755X; eISSN: 1525-7541) publishes research on modeling, observing, and forecasting processes related to fluxes and storage of water and energy, including interactions with the boundary layer and lower atmosphere, and processes related to precipitation, radiation, and other meteorological inputs.
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