大气所研究团队基于多套全球表面温度观测数据集，利用GMST的次季节-季节尺度信号的特征，研发了GMST统计集合预测系统，对当年年平均GMST相对于1850-1900年的异常值进行超前预测。本平台每年5~12月每月定期更新预测结果。
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1.相关成果
- Li K., Zheng F.*, Zhu J., Zeng Q., 2023: El Niño and the AMO sparked the astonishingly large margin of warming in the global mean surface temperature in 2023. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-023-3371-4.
- Zheng, F.*, et al., 2023: Will the globe encounter the warmest winter after the hottest summer in 2023? Adv. Atmos. Sci., https://doi.org/10.1007/s00376-023-3330-0.
- Li K., Zheng F.*, Cheng L., et al., 2023: Record-breaking global temperature and crises with strong El Niño in 2023-2024. The Innovation Geoscience, 1(2), 100030. https://doi.org/10.59717/j.xinn-geo.2023.100030.
- Li, K.-X., Zheng, F.*, et al., 2022: Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020. Environ. Res. Lett. 17, 094034, 10.1088/1748-9326/ac8dab.
- HadCRUT5 (https://hadleyserver.metoffice.gov.uk/hadcrut5/)
- NOAAGlobal-Temp5 (https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp)
- Berkeley Earth Global Temperature Data (https://berkeleyearth.org/data/)