ArticleScientific data2023
Newly reconstructed Arctic surface air temperatures for 1979-2021 with deep learning method.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Reconstruction of Antarctic sea ice thickness from sparse satellite laser altimetry data via deep learning.Scientific data · 2025Article
- Article
- Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023.Scientific data · 2025Article
- Daily station-level records of air temperature, snow depth, and ground temperature in the Northern Hemisphere.Scientific data · 2024Article
- Newly reconstructed Arctic surface air temperatures for 1979-2021 with deep learning method.Scientific data · 2023Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.
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