ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution
Sungduk Yu, Brian L. White, Anahita Bhiwandiwalla, Musashi Hinck,, Matthew Lyle Olson, Yaniv Gurwicz, Raanan Y. Rohekar, Tung Nguyen, Vasudev, Lal

TL;DR
ClimDetect introduces a comprehensive, standardized climate dataset and explores vision transformers for improved climate change detection and attribution, facilitating consistent benchmarking and advancing research in the field.
Contribution
This paper presents ClimDetect, a large-scale, standardized dataset combining model simulations and observations, and pioneers the use of vision transformers for climate change detection.
Findings
ClimDetect enables consistent benchmarking across studies.
Vision transformers show promise in climate change detection tasks.
The dataset improves model accuracy in identifying climate signals.
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…
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Taxonomy
TopicsHydrological Forecasting Using AI
