Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023
Abstract Gridded surface air temperature (SAT) data for Antarctica is a crucial foundation for studying climate change in the region. However, significant discrepancies exist between the available Antarctic gridded temperature datasets, particularly regarding the spatial distribution characteristics...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05175-6 |
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| author | Ziqi Ma Jianbin Huang Xiangdong Zhang Yong Luo Tingfeng Dou Minghu Ding |
| author_facet | Ziqi Ma Jianbin Huang Xiangdong Zhang Yong Luo Tingfeng Dou Minghu Ding |
| author_sort | Ziqi Ma |
| collection | DOAJ |
| description | Abstract Gridded surface air temperature (SAT) data for Antarctica is a crucial foundation for studying climate change in the region. However, significant discrepancies exist between the available Antarctic gridded temperature datasets, particularly regarding the spatial distribution characteristics of long-term temperature trends. In this paper, we develop a new, regularly updated, spatio-temporally complete Antarctic monthly SAT dataset from 1979 onwards, with a spatial resolution of 1° x 1° in latitude and longitude, from multiple sources of in situ observations using deep learning method. Deep learning model was trained with daily SATs from three global reanalysis datasets. The reconstructed Antarctic SATs were successfully validated using data from staffed and automated meteorological stations, demonstrating a closer match with observations, particularly in capturing the patterns of temperature trends. This dataset represents a new advance in the development of Antarctic observational climate dataset and is an important resource that underpins research across diverse scientific disciplines, facilitating a deeper understanding of the Antarctic climate system and its global implications. |
| format | Article |
| id | doaj-art-b0ee3b4d5e5642bb877b437a341c31ee |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-b0ee3b4d5e5642bb877b437a341c31ee2025-08-20T03:48:19ZengNature PortfolioScientific Data2052-44632025-05-0112111410.1038/s41597-025-05175-6Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023Ziqi Ma0Jianbin Huang1Xiangdong Zhang2Yong Luo3Tingfeng Dou4Minghu Ding5School of Atmospheric Sciences, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)Beijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of SciencesNOAA CISESS, North Carolina State UniversityMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua UniversityCollege of Resources and Environment, University of Chinese Academy of SciencesState Key Laboratory of Severe Weather, Chinese Academy of Meteorological SciencesAbstract Gridded surface air temperature (SAT) data for Antarctica is a crucial foundation for studying climate change in the region. However, significant discrepancies exist between the available Antarctic gridded temperature datasets, particularly regarding the spatial distribution characteristics of long-term temperature trends. In this paper, we develop a new, regularly updated, spatio-temporally complete Antarctic monthly SAT dataset from 1979 onwards, with a spatial resolution of 1° x 1° in latitude and longitude, from multiple sources of in situ observations using deep learning method. Deep learning model was trained with daily SATs from three global reanalysis datasets. The reconstructed Antarctic SATs were successfully validated using data from staffed and automated meteorological stations, demonstrating a closer match with observations, particularly in capturing the patterns of temperature trends. This dataset represents a new advance in the development of Antarctic observational climate dataset and is an important resource that underpins research across diverse scientific disciplines, facilitating a deeper understanding of the Antarctic climate system and its global implications.https://doi.org/10.1038/s41597-025-05175-6 |
| spellingShingle | Ziqi Ma Jianbin Huang Xiangdong Zhang Yong Luo Tingfeng Dou Minghu Ding Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023 Scientific Data |
| title | Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023 |
| title_full | Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023 |
| title_fullStr | Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023 |
| title_full_unstemmed | Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023 |
| title_short | Deep learning-based reconstruction of monthly Antarctic surface air temperatures from 1979 to 2023 |
| title_sort | deep learning based reconstruction of monthly antarctic surface air temperatures from 1979 to 2023 |
| url | https://doi.org/10.1038/s41597-025-05175-6 |
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