Evaluating Seasonal Climate Forecasts from Dynamical Models over South America

Jiaying Zhang aAgroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana–Champaign, Urbana, Illinois

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Kaiyu Guan aAgroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana–Champaign, Urbana, Illinois
bDepartment of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois Urbana–Champaign, Urbana, Illinois
cNational Center of Supercomputing Applications, University of Illinois Urbana–Champaign, Urbana, Illinois

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Rong Fu dDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Bin Peng aAgroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana–Champaign, Urbana, Illinois
bDepartment of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois Urbana–Champaign, Urbana, Illinois
cNational Center of Supercomputing Applications, University of Illinois Urbana–Champaign, Urbana, Illinois

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Siyu Zhao dDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Yizhou Zhuang dDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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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.

Significance Statement

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.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Kaiyu Guan, kaiyug@illinois.edu; Jiaying Zhang, zhjiay5@illinois.edu

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.

Significance Statement

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.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Kaiyu Guan, kaiyug@illinois.edu; Jiaying Zhang, zhjiay5@illinois.edu

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