ClimDetect: A Benchmark Dataset for Climate Change Detection and
  Attribution
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.
66d57c19b26010e571ea2ac6
Sungduk Yu
extracted_confirmed
2025-05-27T15:31:29.527Z
62449e24eb346371e056ce48
/avatars/22958483022ef2298d65be5b5899fbf3.svg
Sungduk Yu
user
sungduk
66d57c19b26010e571ea2ac7
Brian L. White
66d57c19b26010e571ea2ac8
Anahita Bhiwandiwalla
admin_assigned
2024-09-02T09:53:23.829Z
638e715049de7ae552db65f7
https://cdn-avatars.huggingface.co/v1/production/uploads/1670621324674-638e715049de7ae552db65f7.jpeg
Anahita Bhiwandiwalla
user
anahita-b
66d57c19b26010e571ea2ac9
Musashi Hinck
admin_assigned
2024-09-02T09:53:39.776Z
65de48ac04b9352580c257fb
/avatars/9fd28805926c06927cea1140e7c0440e.svg
Musashi Hinck
user
musashihinck
66d57c19b26010e571ea2aca
Matthew Lyle Olson
admin_assigned
2024-09-02T09:53:45.899Z
65dd0fe2f2687e07c2a35d6b
/avatars/bcfbb23b81cdc4854895ec383e28e852.svg
Matthew Olson
user
matthewlyleolson
66d57c19b26010e571ea2acb
Tung Nguyen
66d57c19b26010e571ea2acc
Vasudev Lal
admin_assigned
2024-09-02T09:54:13.320Z
6454021413dba495b2dfebd1
/avatars/16f019f7c321aa24000364a50a1975fe.svg
Vasudev Lal
user
vasudevlal
ClimDetect: A Benchmark Dataset for Climate Change Detection and
  Attribution
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.
ClimDetect uses vision transformers to analyze climate snapshots, offering a standardized dataset for climate change detection and attribution.
vision transformers