ClimDetect：用於氣候變化檢測和歸因的基準數據集

ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution

August 28, 2024

作者: Sungduk Yu, Brian L. White, Anahita Bhiwandiwalla, Musashi Hinck, Matthew Lyle Olson, Tung Nguyen, Vasudev Lal

cs.AI

摘要

檢測和歸因於氣候變化的溫度上升對於了解全球暖化並引導適應策略至關重要。區分人為引起的氣候信號與自然變異的複雜性挑戰了傳統的檢測和歸因（D&A）方法，這些方法旨在識別氣候響應變數中的特定「指紋」。深度學習為辨識龐大空間數據集中的這些複雜模式提供了潛力。然而，缺乏標準協議阻礙了跨研究的一致比較。我們介紹了ClimDetect，這是一個標準化數據集，包含超過816,000個每日氣候快照，旨在提高識別氣候變化信號的模型準確性。ClimDetect整合了過去研究中使用的各種輸入和目標變數，確保可比較性和一致性。我們還探討了在這一背景下對氣候數據應用視覺轉換器（ViT）的可能性，這是一種新穎且現代化的方法。我們的開放數據和代碼可作為通過改進模型評估來推進氣候科學的基準。ClimDetect可通過Huggingface數據集存儲庫公開訪問，網址為：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.