Exploring Vision Transformers for Early Detection of Climate Change Signals (Papers Track)
Sungduk Yu (Intel Labs); Brian White (UNC Chapel Hill); Anahita Bhiwandiwalla (Intel Labs); Yaniv Gurwicz (Intel Labs); Musashi Hinck (Intel Corporation); Matthew Olson (Intel Labs); Raanan Rohekar (Intel Labs); Vasudev Lal (Intel Corp)
Abstract
This study evaluates Vision Transformers (ViTs) for detecting anthropogenic climate change signals, crucial for effective policy planning and risk assessment. Compared to previously suggested models like CNN, MLP, and ridge regression, ViTs consistently detect forced climate signals earlier across three reanalysis datasets (ERA5, JRA-3Q, and MERRA-2). Interpretation with Integrated Gradients reveals consistent spatial patterns, suggesting ViTs utilize physically-grounded signals. This work highlights ViTs' potential to advance climate change detection and attribution tasks.