title: ClimDetect: A Benchmark Dataset for Climate Change Detection and
    Attribution
summary: 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.
paper_authors._id: 66d57c19b26010e571ea2ac6
paper_authors.name: Sungduk Yu
paper_authors.status: extracted_pending
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paper_authors.avatarUrl: /avatars/b0536050e5a46ccb5ad0fabfc5339630.svg
paper_authors.fullname: Sungduk Yu
paper_authors.type: user
paper_authors.user: sungduk
paper_authors._id: 66d57c19b26010e571ea2ac7
paper_authors.name: Brian L. White
paper_authors._id: 66d57c19b26010e571ea2ac8
paper_authors.name: Anahita Bhiwandiwalla
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1670621324674-638e715049de7ae552db65f7.jpeg
paper_authors.fullname: Anahita Bhiwandiwalla
paper_authors.type: user
paper_authors.user: anahita-b
paper_authors._id: 66d57c19b26010e571ea2ac9
paper_authors.name: Musashi Hinck
paper_authors.status: admin_assigned
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paper_authors._id: 65de48ac04b9352580c257fb
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paper_authors.fullname: Musashi Hinck
paper_authors.type: user
paper_authors.user: musashihinck
paper_authors._id: 66d57c19b26010e571ea2aca
paper_authors.name: Matthew Lyle Olson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-02T09:53:45.899Z
paper_authors._id: 65dd0fe2f2687e07c2a35d6b
paper_authors.avatarUrl: /avatars/bcfbb23b81cdc4854895ec383e28e852.svg
paper_authors.fullname: Matthew Olson
paper_authors.type: user
paper_authors.user: matthewlyleolson
paper_authors._id: 66d57c19b26010e571ea2acb
paper_authors.name: Tung Nguyen
paper_authors._id: 66d57c19b26010e571ea2acc
paper_authors.name: Vasudev Lal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-02T09:54:13.320Z
paper_authors._id: 6454021413dba495b2dfebd1
paper_authors.avatarUrl: /avatars/16f019f7c321aa24000364a50a1975fe.svg
paper_authors.fullname: Vasudev Lal
paper_authors.type: user
paper_authors.user: vasudevlal
paper_title: ClimDetect: A Benchmark Dataset for Climate Change Detection and
    Attribution
paper_summary: 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.
paper_ai_keywords: vision transformers (Vi) deep learning climate change signals fingerprints climate response variables standardized data protocols neural protocols ClimDetect open-access data Huggingface dataset repository**