the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A daily highest air temperature estimation method and spatial-temporal changes analysis of high temperature in China from 1979 to 2018
- 1School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China
- 2School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, China
- 3Institute of agricultural resources and regional planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- 4School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
- 5Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
- 6Centre of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- 7Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK
- These authors contributed equally to this work.
- 1School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China
- 2School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, China
- 3Institute of agricultural resources and regional planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- 4School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
- 5Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
- 6Centre of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- 7Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK
- These authors contributed equally to this work.
Abstract. The daily highest air temperature (Tmax) is a key parameter for global and regional high temperature analysis, which is very difficult to be obtained in areas where there are no meteorological observation stations. This study proposes an estimation framework for obtaining high-precision Tmax. Firstly, we build a near surface air temperature diurnal variation model to estimate Tmax for China from 1979 to 2018 based on multi-source data. Then in order to further improve the estimation accuracy, we divided China into six regions according to climate conditions and topography, and established calibration models for different region. The analysis shows that the mean absolute error (MAE) of the dataset (https://doi.org/10.5281/zenodo.5602897) is about 1.07 °C and RMSE is 1.52 °C, which improves the accuracy of the traditional method by nearly 1 °C. The spatial-temporal variations analysis of Tmax in China indicated that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. In summer and autumn, the Tmax in northeast China increased the fastest among the six regions, which were 0.4 °C/10a and 0.39 °C/10a, respectively. The number of summer days and warm days showed an increasing trend in all regions, while the number of icing days and cold days showed a decreasing trend. The abnormal temperature changes mainly occurred in El Niño years or La Niña years. We found that the influence of the Indian Ocean Basin Warming (IOBW) on air temperature in China were generally greater than those of the North Atlantic Oscillation and the NINO3.4 area sea surface temperature after making analysis of ocean climate modal indices with air temperature. In general, this Tmax dataset and analysis are of great significance to the study of climate change in China, especially for environmental protection.
Ping Wang et al.
Status: final response (author comments only)
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CEC1: 'Comment on gmd-2021-435', Juan Antonio Añel, 01 Mar 2022
Dear authors,
I see that you have included the Zenodo repository corresponding to the materials in your paper under the 'Assets' label. However, you have not included the information in the 'Code and Data Availability' section of the manuscript, and you must include the information in both parts. Therefore, please have a modified 'Code and Data Availability' section with the link to the repository and its DOI in a potential reviewed version of your manuscript.
Regards,
Juan A. Añel
Geosci. Model Dev. Exec. Editor-
AC1: 'Reply on CEC1', kebiao mao, 03 Mar 2022
Dear Editor,
Thanks for your guidance. We have supplemented 'Code and Data Availability' section in the manuscript, and send the revised manuscript to the editor by email. Thanks.
Sincerely,
Kebiao Mao et al.
Modify as follows:
Code and Data availability. CMFD is available from the National Qinghai-Tibet Plateau Science Data Center (https://data.tpdc.ac.cn/). ERA5 data can be obtained from the ECMWF ERA5 data website (https://cds.climate.copernicus.eu/). Meteorological station data is available by CMA National Meteorological Information Center (http://data.cma.cn/). IOBW index can be accessed at the National Climate Center of CMA (http://cmdp.ncc-cma.net/cn/index.htm), and NAO index and NINO3.4 index are from the National Oceanic and Atmospheric Administration of the United States (https://psl.noaa.gov/data/climateindices/list/). The daily highest air temperature dataset from 1979 to 2018 can be downloaded at https://doi.org/10.5281/zenodo.6322881 (Wang et al., 2021).
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AC1: 'Reply on CEC1', kebiao mao, 03 Mar 2022
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RC1: 'Comment on gmd-2021-435', Anonymous Referee #1, 02 Apr 2022
General comment: The daily highest air temperature (Tmax) is a key parameter for climate change analysis. The authors proposed an estimation model to obtain high-precision Tmax, and built a dataset of Tmax in China from 1979 to 2018. The authors analyzed the spatial-temporal variation characteristics of high temperature in China using the estimated Tmax data, and found that the frequency of high temperature events in most areas of China exhibited an increasing trend. The authors also showed that the temperature abrupt changes mostly occurred in El Niño years or La Niña years, indicating that China was highly vulnerable to global climate change. By analyzing the impact of ocean climate modes on temperature in China, the authors found that the warming of the Indian Ocean had a strong positive impact on warm events in most regions of China. This study is novel and significant for estimating Tmax by using multi-source data and predicting extreme weather. I suggest to accept this manuscript after minor revisions. Follows are some specific comments.
Major comments:
- Section 2: How are the boundaries of regional divisions determined?
- Section 4.1: How to solve the problem of different spatial resolutions of ERA5 data and CMFD data?
- Section 4.4: Are the 90th percentile in TX90p and the 10th percentile in TX10p determined based on all the data in a year?
- Section 5.2: What is the significance level represented by "Significant upward" and "Significant downward" in Table 2?
Minor comments:
- L26: “region” to “regions”.
- L50: “model” should be “models”.
- L55: “spatialize” should be “spatialized”.
- L77: “use” should be “used”.
- L93: “use” to “used”.
- L94: “constructs” to “constructed”.
- L178: “considers” to “consider”.
- L196: I think “the missing point” should be “the missing values”.
- L200: Should “used it” be “used sine function”?
- L205: No need to express here: “Use the least square method to solve the unknowns A and B.”
- L284: “may be” to “can be”.
- L306: delete “the correlation coefficient”.
- L384: “opposing” to “opposite”.
- L386: “the regions with the fastest Tmax rise in spring, summer, autumn and winter are III, I, I and VI respectively” to “in spring, summer, autumn and winter, the regions with the fastest Tmax rise are III, I, I and VI respectively”.
- L443: “built data” to “the dataset”.
- L460: “first decreasing” to “decreasing first”.
- L482: The tense of “The regions with no significant change in SU are mainly distributed in region VI” and the following sentence are not consistent.
- L605: delete “daily”.
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AC2: 'Reply on RC1', kebiao mao, 13 Apr 2022
Dear referee,
Thank you for your valuable comments on our manuscript and help us to improve the quality of our paper. We have carefully studied all the comments and have made revisions for manuscript. Please find our detailed responses in PDF file. Thanks again.
Sincerely,
Wang Ping and co-authors
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RC2: 'Comment on gmd-2021-435', Anonymous Referee #2, 18 May 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-435/gmd-2021-435-RC2-supplement.pdf
Ping Wang et al.
Data sets
A Daily Maximum Air Temperature dataset in China from 1979 to 2018 Ping Wang; Kebiao Mao; Fei Meng; Zhihao Qin; Shu Fang; Sayed M. Bateni; Mansour Almazroui https://doi.org/10.5281/zenodo.5602897
Ping Wang et al.
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