ClimTip-GML：一个用于评估气候临界事件影响的全球偏差校正与降尺度数据集

中文摘要 AI 辅助

评估未来气候情景的影响，包括亚马逊雨林（ARF）或大西洋经向翻转环流（AMOC）等主要地球系统组分的临界事件，需要准确且高分辨率的模拟。在此，我们提出ClimTip-GML，这是首个用于大规模临界情景影响评估的全球偏差校正和降尺度气候数据集，包含来自三个全球环流模型（GCMs）——CESM1-CAM5、HadGEM3-GC31-MM和MPI-ESM1-2-HR——的八个关键变量，空间分辨率为0.25°。该数据集包括100年长的气候模拟，涵盖工业化前和历史条件，以及在+2°C升温水平下有无AMOC或ARF临界转变的情景。我们应用基于再分析数据训练的生成式机器学习（GML）技术，以在空间、时间及所有八个变量上物理一致的方式对GCMs进行偏差校正和降尺度。全面验证显示，偏差显著减少，小尺度空间变率、多变量相关性得到改善，且对外部强迫和临界事件的长期气候响应保持一致。因此，这些结果使得对ARF和AMOC临界转变的影响评估得到显著改进，直接为减缓和适应政策提供信息。

英文摘要

Assessing the impacts of future climate scenarios including tipping events of major Earth system components such as the Amazon rainforest (ARF) or the Atlantic meridional overturning circulation (AMOC), requires accurate and high-resolution simulations. Here, we present ClimTip-GML, the first globally bias-corrected and downscaled climate dataset for impact assessment of large-scale tipping scenarios, comprising eight key variables at 0.25° spatial resolution from three general circulation models (GCMs): CESM1-CAM5, HadGEM3-GC31-MM, and MPI-ESM1-2-HR. The dataset includes 100-year-long climate simulations with preindustrial and historical conditions, as well as scenarios at a +2°C warming level with and without tipping transitions of the AMOC or ARF. We apply generative machine learning (GML) techniques trained on reanalysis data to bias-correct and downscale the GCMs in a manner that is physically consistent across space, time, and all eight variables. Comprehensive validation shows substantially reduced biases, improved small-scale spatial variability, multivariate correlations, and consistent long-term climate responses to the external forcing and tipping events. The results hence permit substantially improved impact assessments of tipping transitions of the ARF and AMOC, directly informing mitigation and adaptation policies.

发表机构

• Technical University of Munich（慕尼黑工业大学）
• Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）
• Ludwig-Maximilians University of Munich（慕尼黑路德维希-马克西米利安大学）
• Met Office（英国气象局）
• Utrecht University（乌得勒支大学）
• University of Exeter（埃克塞特大学）

机构由 AI 辅助整理，请以论文原文为准。