Articles | Volume 18, issue 9
https://doi.org/10.5194/essd-18-6503-2026
https://doi.org/10.5194/essd-18-6503-2026
Data description article
 | 
07 Sep 2026
Data description article |  | 07 Sep 2026

An AI-driven reconstruction of global surface temperature with emphasis on refining the Antarctic record

Chenxi Ouyang, Qingxiang Li, Zichen Li, and Sihao Wei

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Cited articles

Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., and Nahavandi, S.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges, Inform. Fusion, 76, 243–297, https://doi.org/10.1016/j.inffus.2021.05.008, 2021. 
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Bochow, N., Poltronieri, A., Rypdal, M., and Boers, N.: Reconstructing historical climate fields with deep learning, Sci. Adv., 11, eadp0558, https://doi.org/10.1126/sciadv.adp0558, 2025. 
Bromwich, D., Ensign, A., Wang, S., and Zou X.: Major Artifacts in ERA5 2 m Air Temperature Trends Over Antarctica Prior to and During the Modern Satellite Era, Geophys. Res. Lett., 51, https://doi.org/10.1029/2024GL111907, 2024. 
Bromwich, D., Wang, S.-H., Zou, X., and Ensign, A.: An updated reconstruction of Antarctic near-surface air temperatures at monthly intervals since 1958, Earth Syst. Sci. Data, 17, 2953–2962, https://doi.org/10.5194/essd-17-2953-2025, 2025a. 
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We used artificial intelligence to reconstruct a complete global temperature record since 1850, including better coverage of the poorly observed Antarctic region since 1961, by filling gaps in historical data and improving understanding of long-term climate change.
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