Plésiat Étienne, Dunn Robert J H, Donat Markus G, Kadow Christopher
German Climate Computing Center (DKRZ), Hamburg, Germany.
Met Office Hadley Centre, Exeter, United Kingdom.
Nat Commun. 2024 Oct 24;15(1):9191. doi: 10.1038/s41467-024-53464-2.
The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.
对近期极端气候的理解以及气候风险的特征描述需要在历史背景下审视这些极端情况。然而，由于空间外推不足，现有的极端气候观测数据集普遍存在空间空白和不准确的问题。这个问题源于用于解决观测数据不足的传统统计方法，在20世纪中叶之前尤为普遍。在这项工作中，我们利用来自CMIP6的地球系统模型数据，通过迁移学习，使用人工智能重建欧洲极端气候（暖日、暖夜、冷日和冷夜）的观测数据。我们的方法超越了传统统计技术和扩散模型，展示了其重建过去极端事件的能力，并揭示了大多数再分析数据集未涵盖的广泛时间跨度（1901 - 2018年）内的空间趋势。将我们的数据集提供给气候学界将改善对极端气候的特征描述，从而实现更好的风险管理和政策制定。
