ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution

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ID: 281646
2024
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Abstract
Detecting and attributing temperature increases driven by climate change is crucial for understanding global warming and informing adaptation strategies. However, distinguishing human-induced climate signals from natural variability remains challenging for traditional detection and attribution (D&A) methods, which rely on identifying specific "fingerprints" -- spatial patterns expected to emerge from external forcings such as greenhouse gas emissions. Deep learning offers promise in discerning these complex patterns within expansive spatial datasets, yet the lack of standardized protocols has hindered consistent comparisons across studies. To address this gap, we introduce ClimDetect, a standardized dataset comprising 1.17M daily climate snapshots paired with target climate change indicator variables. The dataset is curated from both CMIP6 climate model simulations and real-world observation-assimilated reanalysis datasets (ERA5, JRA-3Q, and MERRA-2), and is designed to enhance model accuracy in detecting climate change signals. ClimDetect integrates various input and target variables used in previous research, ensuring comparability and consistency across studies. We also explore the application of vision transformers (ViT) to climate data -- a novel approach that, to our knowledge, has not been attempted before for climate change detection tasks. Our open-access data serve as a benchmark for advancing climate science by enabling end-to-end model development and evaluation. ClimDetect is publicly accessible via Hugging Face dataset repository at: https://huggingface.co/datasets/ClimDetect/ClimDetect.
Reference Key
lal2024climdetect Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sungduk Yu; Brian L. White; Anahita Bhiwandiwalla; Musashi Hinck; Matthew Lyle Olson; Yaniv Gurwicz; Raanan Y. Rohekar; Tung Nguyen; Vasudev Lal
Journal arXiv
Year 2024
DOI
DOI not found
URL
http://arxiv.org/abs/2408.15993v2
Keywords
cs.lg physics.ao-ph cs.cv

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