Articles | Volume 17, issue 9
https://doi.org/10.5194/essd-17-4985-2025
© Author(s) 2025. 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-17-4985-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Updates to C-LSAT 2.1 and the development of high-resolution land surface air temperature and diurnal temperature range datasets
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 Laboratory of Ocean Science and Engineering (Guangdong 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 Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China
Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Ürümqi, China
Qiya Xu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Key Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education, Zhuhai, China
Southern Laboratory of Ocean Science and Engineering (Guangdong 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 Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China
Hanyu Zhang
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Key Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education, Zhuhai, China
Southern Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China
Jiaxue Lin
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Key Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education, Zhuhai, China
Southern Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-82, https://doi.org/10.5194/gmd-2024-82, 2024
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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.
Peng Si, Qingxiang Li, and Phil Jones
Earth Syst. Sci. Data, 13, 2211–2226, https://doi.org/10.5194/essd-13-2211-2021, https://doi.org/10.5194/essd-13-2211-2021, 2021
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This paper documents the various procedures necessary to construct a homogenized daily maximum and minimum temperature series starting in 1887 for Tianjin. The newly constructed temperature series provides a set of new baseline data for the field of extreme climate change at the century-long scale and a reference for construction of other long-term reliable daily time series in the region.
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Short summary
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.
This study introduces the update to the C-LSAT 2.1 station data and its gridded dataset (5° ×...
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