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

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.