发表机构
UMR LOCEAN, IPSL, Sorbonne Université, IRD, CNRS, MNHN; Institute for Atmospheric and Climate Science, ETH Zurich（洛桑海洋与气候联合研究实验室，巴黎地球物理研究所，索邦大学，法国国家科研中心，国家自然历史博物馆; 苏黎世联邦理工学院大气与气候科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究采用U-Net卷积网络，利用多模式数据集分离1950-2022年的强迫与内部气候变率，其误差低于四阶多项式方法，为气候变率区分提供了更优方案。
AI 中文摘要
长期气候数据的变率由内部分量和强迫分量组成。内部变率源于稳定气候系统内部可能发生的自然过程，而强迫变率则反映了由人为温室气体和气溶胶排放等因素引发的气候变化。准确区分这两类变率，对气候波动归因、理解内部变率过程及其影响至关重要。本研究采用计算机视觉领域常用的U-Net卷积网络，基于1950年至2022年的多模式数据集，分离强迫与内部气候变率。该数据集包含五个单模式初始条件大集合的多个场，通过训练四个模式集合的数据、留取一个模式的数据进行评估的方式开展交叉验证。验证结果显示，对于局地尺度月平均地表气温的强迫变率，误差范围为0.1℃至0.4℃，不超过外强迫幅度的一半。相较于基于四阶多项式趋势估算强迫变率的简单方法，U-Net表现更优。误差主要源于采样不足及模式间一致性较差，验证期间U-Net对具有最高瞬态气候敏感度的模式的增温存在低估。对于海平面气压、降水等变量，其表现更低，原因在于这些变量的强迫变率与内部变率的比值较低。该框架或可通过在训练与验证中纳入更大的多模式数据集加以提升。
英文摘要
Long-term climate data exhibit variations composed of internal and forced components. Internal variability arises from natural processes that could occur within a stable climate. Forced variability, on the other hand, reflects climate changes induced, for example, by anthropogenic greenhouse gas and aerosol emissions. Accurately distinguishing between these types of variability is crucial for attributing climate fluctuations and understanding internal variability processes and impacts. In this study, we apply a U-Net convolutional network, a model commonly used in computer vision, to separate forced and internal climate variability from 1950 to 2022 using a multi-model dataset. The dataset includes multiple fields from five single-model initial-condition large ensembles. Cross-validation is conducted by training the U-Net using the data from the ensembles of four models, leaving out the data from one model to assess performance. Validation results yield errors ranging from 0.1°C to 0.4°C for the forced variability of local-scale monthly surface air temperature, which is no more than half the magnitude of external forcing. The U-Net achieves better performance than a simple approach based on a fourth-order polynomial trend for estimating forced variability. The error is mainly due to insufficient sampling and poor agreement among the models, as the U-Net underestimates the warming for the model with the highest transient climate sensitivity during validation. Performance is lower for variables like sea-level pressure and precipitation because of their low ratio of forced to internal variability. This framework might be enhanced by incorporating a larger multi-model dataset in the training and validation.
Comments39 pages, 10 figures, submitted to Artificial Intelligence for the Earth Systems in August 24th 2026
