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https://doi.org/10.5194/essd-2020-361
https://doi.org/10.5194/essd-2020-361
08 Jan 2021
 | 08 Jan 2021
Status: this preprint has been withdrawn by the authors.

1 km Monthly Precipitation and Temperatures Dataset for China from 1952 to 2019 based on a Brand-New and High-Quality Baseline Climatology Surface

Haibo Gong, Xueqiao Xiang, Huiyu Liu, Xiaojuan Xu, Fusheng Jiao, and Zhenshan Lin

Abstract. Long-term climate data and high-quality baseline climatology surface with high resolution are highly essential to multiple fields in climatological, ecological, hydrological, and environmental sciences. Here, we created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1 km monthly precipitation and temperatures dataset in China during 1952–2019 (ChinaClim_timeseries). Thin plate spline (TPS) algorithm in each month with different model formulations by accounting for satellite-driven products, was used to generate ChinaClim_baseline and monthly climate anomaly surface. Meanwhile, climatologically aided interpolation (CAI) was used to superimpose monthly anomaly surface with ChinaClim_baseline to generate ChinaClim_timeseries. Our results showed that ChinaClim_baseline exhibited very high performance. For precipitation estimation, the value of all R2 was over 0.860, and the values of RMSEs and MAEs were 8.149 mm~21.959 mm and 2.787~14.125 mm, respectively. Temperature elements had an average R2 of 0.967~0.992, an average MAEs of 0.321~0.785 °C, and an average RMSEs between 0.485 and 1.233 °C for all months. ChinaClim_baseline performed much better than WorldClim2 and CHELSA and there were many spatial discrepancies captured among those surfaces, especially in summer months and the regions with low-density weather stations in temperate continental and high cold Tibetan Plateau. For ChinaClim_timeseries, precipitation had an average R2 of 0.699~0.923, an average RMSE between 7.449 mm and 56.756 mm, and an average of MAE of 4.263~40.271 mm for all months. Temperature elements had an average R2 of 0.936~0.985, an average RMSE between 0.807 °C and 1.766 °C, and an average MAE of 0.548~1.236 °C for all months. Compared with Peng's climate surface and CHELSAcruts, R2 increased by approximately 6 %, RMSE and MAE decreased by approximately 15 % for precipitation; R2 of temperatures had no obviously changes, but RMSE and MAE decreased by 8.37~34.02 %. The results showed that the interannual variations of ChinaClim_timeseries performed much better than other datasets, thanks to the help of ChinaClim_baseline and satellite-driven products. However, ChinaClim_baseline did not significantly improve the accuracy of precipitation estimation, but it greatly improved the accuracy of temperature estimation; the satellite-driven TRMM3B43 anomaly greatly improve the accuracy of precipitation estimation after 1998, while the LST anomaly did not effectively improve the accuracy of temperature estimation. ChinaClim_baseline can be used as an excellent baseline climatology surface for obtaining high-quality and long-term climate datasets from past to future. In the meantime, ChinaClim_timeseries of 1 km spatial resolution based on ChinaClim_baseline, is very suitable for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China. Here, ChinaClim_baseline is available at https://doi.org/10.5281/zenodo.4287824 (Gong, 2020a), ChinaClim_timeseries of precipitation is available at https://doi.org/10.5281/zenodo.4288388 (Gong, 2020b), ChinaClim_timeseries of maximum temperature is available at https://doi.org/10.5281/zenodo.4288390 (Gong, 2020c) and ChinaClim_timeseries of minimum temperature is available at https://doi.org/10.5281/zenodo.4288392 (Gong, 2020d).

This preprint has been withdrawn.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Haibo Gong, Xueqiao Xiang, Huiyu Liu, Xiaojuan Xu, Fusheng Jiao, and Zhenshan Lin

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2020-361', Anonymous Referee #1, 07 Feb 2021
  • RC2: 'Comment on essd-2020-361', Anonymous Referee #2, 31 Jul 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2020-361', Anonymous Referee #1, 07 Feb 2021
  • RC2: 'Comment on essd-2020-361', Anonymous Referee #2, 31 Jul 2021
Haibo Gong, Xueqiao Xiang, Huiyu Liu, Xiaojuan Xu, Fusheng Jiao, and Zhenshan Lin

Data sets

A Brand-New and High-Quality Baseline Climatology Surface for China (ChinaClim_baseline) H. Gong https://doi.org/10.5281/zenodo.4287824

1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_timeseries) H. Gong https://doi.org/10.5281/zenodo.4288388

1 km Monthly Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_timeseries) H. Gong https://doi.org/10.5281/zenodo.4288390

1 km Monthly Minimum Temperature Dataset for China from 1952 to 2019 (ChinaClim_timeseries) H. Gong https://doi.org/10.5281/zenodo.4288392

Haibo Gong, Xueqiao Xiang, Huiyu Liu, Xiaojuan Xu, Fusheng Jiao, and Zhenshan Lin

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Short summary
We created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1 km monthly precipitation and temperatures dataset during 1952–2019 (ChinaClim_timeseries). ChinaClim_baseline can be used as an excellent baseline climatology surface for obtaining long-term climate datasets.ChinaClim_timeseries based on ChinaClim_baseline, is suitable for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.
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