Evaluating Seasonal Climate Forecasts from Dynamical Models over South America
Evaluating Seasonal Climate Forecasts from Dynamical Models over South America
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DOI：
10.1175/jhm-d-22-0156.1
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发表时间：
2023-04
影响因子：
3.8
通讯作者：
Jiaying Zhang;K. Guan;R. Fu;B. Peng;Siyu Zhao;Y. Zhuang
中科院分区：
文献类型：
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作者：
Jiaying Zhang;K. Guan;R. Fu;B. Peng;Siyu Zhao;Y. Zhuang
季节性气候预报具有社会经济价值，预报的质量对各种社会应用很重要。在这里，我们评估了三个气候变量，蒸汽压赤字（VPD），温度和降水的季节性预测，从业务动力学模型在南美洲的主要农田地区;分析其可预测性从全球和本地的环流模式，如厄尔尼诺-南方涛动（ENSO）;和属性的预测误差的来源。我们发现，欧洲中期天气预报中心（ECMWF）模型具有最高的质量模型评估。VPD和温度的预测与观测结果的一致性（平均皮尔逊相关系数分别为0.65和0.70，在ECMWF的1个月预报中的所有月份）比降水（0.40）更好。预报随着提前时间的增加而退化，退化是由于以下原因：1）未能捕捉到当地环流模式以及模式与当地气候之间的联系; 2）高估了ENSO对未受ENSO影响的地区的影响。对于受厄尔尼诺/南方涛动影响的区域，对三个气候变量及其极端情况的预报可提前6个月作出准确预测，为风险防范和管理提供了宝贵的准备时间。研究结果为进一步发展动力学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。季节性气候预报具有社会经济价值，预报的质量对其应用至关重要。该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。这些发现为那些使用季节性气候预测进行规划和管理的人提供了有用的信息。该研究还分析了气候变量的可预测性和预测误差的归因，从而为理解模式的不同性能和未来改进动力模式的季节气候预测提供了见解。
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.