Reconstructing Historical Climate Fields With Deep Learning

• URL: http://arxiv.org/abs/2311.18348v2
• Date: Wed, 30 Jul 2025 09:09:40 GMT
• Title: Reconstructing Historical Climate Fields With Deep Learning
• Authors: Nils Bochow, Anna Poltronieri, Martin Rypdal, Niklas Boers,
• Abstract summary: We employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields.<n>We are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Nino and La Nina with very little given information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Ni\~no and La Ni\~na with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

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