GCMagicc v1：面向多变量气候影响集合的快速生成式模拟器

中文摘要 AI 辅助

预估气候变化的影响需要大规模的气候变量集合，这些集合需与历史观测匹配、与IPCC评估的升温范围一致，并能高效运行新的未来排放情景，包括最新一代气候模型情景（CMIP7）以及符合各国《巴黎协定》承诺的路径。生成影响研究所需规模的气候集合通常在计算上是不可行的。我们通过GCMagicc弥补了这一差距，它是一种混合模型，将简单的物理气候模型与机器学习相结合，以全尺度地球系统模型的分辨率生成10个气候变量的集合，且无需依赖GPU资源或针对新情景重新训练。GCMagicc在32个CMIP6地球系统模型和观测/再分析数据上训练，是对地球系统模型的补充而非替代。我们将其应用于一系列未来路径：最新IPCC报告的典型SSP情景（升温1.2-6.1°C，各情景5-95百分位范围的最小-最大值）、当前政策（2.3-4.0°C）、《巴黎协定》下的国家承诺（1.5-3.3°C）以及CMIP7从'VL'到'H'情景的范围（1.2-4.2°C），并发布了一个大型公共数据集。作为示例，我们使用GCMagicc集合对2025年伊朗严重干旱进行了归因分析，并与三个CMIP6大型集合进行了比较，分别考虑有和没有人为强迫的情况。结果表明强烈的人为信号：在人为强迫下，出现至少与观测同等严重干旱的中位概率为29%，而在仅自然强迫的模拟中该概率为零。未来，干旱状况预计将显著恶化，加剧农业和粮食安全影响的潜力，以及将水资源短缺作为武器的地缘政治冲突。GCMagicc数据可在以下网址获取：此https URL。

英文摘要

Projecting the impacts of climate change requires large ensembles of climate variables that match historical observations, align with the warming ranges assessed by the IPCC, and can efficiently run new future emissions scenarios, including the newest generation of climate model scenarios (CMIP7) and pathways consistent with countries' Paris Agreement pledges. Generating such ensembles at the scale needed for impact studies is normally computationally prohibitive. We close this gap with GCMagicc, a hybrid model that pairs a simple physical climate model with machine learning to generate ensembles of 10 climate variables at the resolution of full-scale Earth system models, without relying on GPU resources or retraining for new scenarios. Trained on 32 CMIP6 Earth system models and observational/reanalysis data, GCMagicc complements rather than replaces Earth system models. We apply it to a range of future pathways: the canonical SSP scenarios of the latest IPCC report (1.2-6.1°C warming, min-max across scenarios of 5-95 percentile ranges), current policies (2.3-4.0°C), national pledges under the Paris Agreement (1.5-3.3°C) and the CMIP7 range from the 'VL' to 'H' scenarios (1.2-4.2°C), releasing a large public dataset. As an illustration, we perform an attribution analysis of the severe 2025 Iranian drought using GCMagicc ensembles, with three CMIP6 large ensembles for comparison, with and without anthropogenic forcings. The results suggest a strong anthropogenic signal: a median probability of drought at least as severe as observed of 29% with anthropogenic forcing, and zero under natural-forcing-only simulations. In the future, drought conditions are projected to materially worsen, amplifying the potential for agricultural and food security impacts and geopolitical conflicts that use water scarcity as a weapon. GCMagicc data is available at https://gcmagicc.org.

发表机构

• The University of Melbourne（墨尔本大学）
• Climate Resource（气候资源公司）
• University of Washington（华盛顿大学）
• International Institute for Applied Systems Analysis（国际应用系统分析研究所）
• ETH Zürich（苏黎世联邦理工学院）

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