ClimDetect：用于气候变化检测和归因的基准数据集

摘要

检测和归因于气候变化导致的温度上升对于理解全球变暖并指导适应策略至关重要。区分人为引起的气候信号和自然变异的复杂性挑战了传统的检测和归因（D&A）方法，这些方法旨在识别气候响应变量中的特定“指纹”。深度学习为识别广阔空间数据集中的这些复杂模式提供了潜力。然而，缺乏标准化协议阻碍了跨研究的一致性比较。我们引入了ClimDetect，这是一个标准化数据集，包含超过816k个每日气候快照，旨在提高模型在识别气候变化信号方面的准确性。ClimDetect整合了过去研究中使用的各种输入和目标变量，确保可比性和一致性。我们还探讨了视觉Transformer（ViT）在气候数据中的应用，这是这一领域中一种新颖和现代化的方法。我们的开放获取数据和代码可作为推动气候科学发展的基准，通过改进模型评估。ClimDetect可通过Huggingface数据集库公开访问，链接为：https://huggingface.co/datasets/ClimDetect/ClimDetect。

English

Detecting and attributing temperature increases due to climate change is crucial for understanding global warming and guiding adaptation strategies. The complexity of distinguishing human-induced climate signals from natural variability has challenged traditional detection and attribution (D&A) approaches, which seek to identify specific "fingerprints" in climate response variables. Deep learning offers potential for discerning these complex patterns in expansive spatial datasets. However, lack of standard protocols has hindered consistent comparisons across studies. We introduce ClimDetect, a standardized dataset of over 816k daily climate snapshots, designed to enhance model accuracy in identifying climate change signals. ClimDetect integrates various input and target variables used in past research, ensuring comparability and consistency. We also explore the application of vision transformers (ViT) to climate data, a novel and modernizing approach in this context. Our open-access data and code serve as a benchmark for advancing climate science through improved model evaluations. ClimDetect is publicly accessible via Huggingface dataet respository at: https://huggingface.co/datasets/ClimDetect/ClimDetect.

ClimDetect：用于气候变化检测和归因的基准数据集

ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution

摘要

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