Articles | Volume 18, issue 9
https://doi.org/10.5194/essd-18-6503-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-6503-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An AI-driven reconstruction of global surface temperature with emphasis on refining the Antarctic record
Chenxi Ouyang
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Key Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education, Zhuhai, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Key Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education, Zhuhai, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Zichen Li
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Key Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education, Zhuhai, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Sihao Wei
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Key Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education, Zhuhai, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
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EGUsphere, https://doi.org/10.5194/egusphere-2026-2198, https://doi.org/10.5194/egusphere-2026-2198, 2026
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Observational records indicate that austral summer rainfall over southern Africa experienced substantial wetting during the late 20th century, followed by pronounced drying over the past two decades. We show that East Asian sulphate aerosol emissions played a key role in shaping these rainfall trends. These findings have important implications for reducing uncertainties in regional rainfall projections and for understanding their links to ecosystem changes.
Peter W. Thorne, John M. Nicklas, John J. Kennedy, Bruce Calvert, Baylor Fox-Kemper, Mark T. Richardson, Adrian Simmons, Ed Hawkins, Robert Rhode, Kathryn Cowtan, Nerilie J. Abram, Axel Andersson, Simon Noone, Phillipe Marbaix, Nathan Lenssen, Dirk Olonscheck, Tristram Walsh, Stephen Outten, Ingo Bethke, Bjorn H. Samset, Chris Smith, Anna Pirani, Jan Fuglestvedt, Lavanya Rajamani, Richard A. Betts, Elizabeth C. Kent, Blair Trewin, Colin Morice, Tim Osborn, Samantha N. Burgess, Oliver Geden, Andrew Parnell, Piers M. Forster, Chris Hewitt, Zeke Hausfather, Valerie Masson-Delmotte, Jochem Marotzke, Nathan Gillett, Sonia I. Seneviratne, Gavin A. Schmidt, Duo Chan, Stefan Brönnimann, Andy Reisinger, Matthew Menne, Maisa Rojas Corradi, Christopher Kadow, Peter Huybers, David B. Stephenson, Emily Wallis, Joeri Rogelj, Andrew Schurer, Karen McKinnon, Panmao Zhai, Fatima Driouech, Wilfran Moufouma Okia, Saeed Vazifehkhah, Sophie Szopa, Christopher J. Merchant, Shoji Hirahara, Masayoshi Ishii, Francois A. Engelbrecht, Qingxiang Li, June-Yi Lee, Alex J. Cannon, Christophe Cassou, Karina von Schuckmann, Amir H. Delju, and Ellie Murtagh
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-825, https://doi.org/10.5194/essd-2025-825, 2026
Preprint under review for ESSD
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We reassess the basis for determining the present level of long-term global warming. Unbiased estimates of both realised warming and anthropogenic warming are possible that approximate a 20-year retrospective mean. Our resulting estimates of 1.40 [1.23–1.58] °C (realised) and 1.34 [1.18–1.50] °C (anthropogenic) as at end of 2024 highlight the urgency of immediate, far-reaching and sustained climate mitigation actions if we are to meet the long term temperature goal of the Paris Agreement.
Sihao Wei, Qingxiang Li, Qiya Xu, Zichen Li, Hanyu Zhang, and Jiaxue Lin
Earth Syst. Sci. Data, 17, 4985–5005, https://doi.org/10.5194/essd-17-4985-2025, https://doi.org/10.5194/essd-17-4985-2025, 2025
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This study introduces the update to the C-LSAT 2.1 station data and its gridded dataset (5° × 5°) for 1850–2024, into which nearly 3000 additional stations were merged. Building on this, high‑resolution (0.5° × 0.5°) land surface air temperature (C‑LSAT HRv1) and diurnal temperature range (C‑LDTR HRv1) datasets for 1901–2023 were produced via thin-plate spline interpolation of the climatology fields and adjusted inverse distance weighted interpolation of the anomaly fields.
Zengyun Hu, Xi Chen, Deliang Chen, Zhuo Zhang, Qiming Zhou, and Qingxiang Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-82, https://doi.org/10.5194/gmd-2024-82, 2024
Preprint withdrawn
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ERC firstly unified the evaluating, ranking, and clustering by a simple mathematic equation based on Euclidean Distance. It provides new system to solve the evaluating, ranking, and clustering tasks in SDGs. In fact, ERC system can be applied in any scientific domain.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Wenbin Sun, Yang Yang, Liya Chao, Wenjie Dong, Boyin Huang, Phil Jones, and Qingxiang Li
Earth Syst. Sci. Data, 14, 1677–1693, https://doi.org/10.5194/essd-14-1677-2022, https://doi.org/10.5194/essd-14-1677-2022, 2022
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The new China global Merged Surface Temperature CMST 2.0 is the updated version of CMST-Interim used in the IPCC's AR6. The updated dataset is described in this study, containing three versions: CMST2.0 – Nrec, CMST2.0 – Imax, and CMST2.0 – Imin. The reconstructed datasets significantly improve data coverage, especially in the high latitudes in the Northern Hemisphere, thus increasing the long-term trends at global, hemispheric, and regional scales since 1850.
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Short summary
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.
We used artificial intelligence to reconstruct a complete global temperature record since 1850,...
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