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。
May Jun Jul Aug Seq Oct Nov Dec
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
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.
2.全球表面温度观测数据集来源:
  • 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/)