Preprints
https://doi.org/10.5194/essd-2022-45
https://doi.org/10.5194/essd-2022-45
 
01 Mar 2022
01 Mar 2022
Status: this preprint is currently under review for the journal ESSD.

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

Haibo Gong1,2,3,4,5, Huiyu Liu1,2,3,4,5, Xueqiao Xiang1,2,3,4,5, Fusheng Jiao1,2,3,4,5, Li Cao1,2,3,4,5, and Xiaojuan Xu1,2,3,4,5 Haibo Gong et al.
  • 1Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
  • 2Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China
  • 3State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, 210023, China
  • 4College of Geography Science, Nanjing Normal University, Nanjing 210023, China
  • 5Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing Normal University, Nanjing 210023, China

Abstract. Long-term climate data and high-quality baseline climatology surface with high resolution are essential to multiple fields including climatological, ecological, and environmental sciences. Here, we created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1km monthly precipitation and temperatures dataset in China during 1952–2019 (ChinaClim_time-series). Thin plate spline (TPS) algorithm in each month with different model formulations by accounting for satellite-driven products and climatic research unit (CRU) datasets, was used to generate ChinaClim_baseline and monthly climate anomaly surface. Climatologically aided interpolation (CAI) was used to superimpose monthly anomaly surface with ChinaClim_baseline to generate ChinaClim_time-series. Our results showed that ChinaClim_baseline exhibited very high performance in four climatic regions with the RMSEs of precipitation and temperature elements estimation being 1.276 ~28.439 mm and 0.310 ~ 2.040 °C, respectively. The correlations among ChinaClim_baseline and WorldClim2 and CHELSA were high, but our results also captured clearly spatial differences among them. WorldClm2 and CHELSA might overestimated (or underestimated) climate events such as warming and drought in temperate continental region and high cold Tibetan plateau where weather stations were sparse. For ChinaClim_time-series, precipitation and temperature elements had average RMSEs between 7.502 mm ~ 52.307 mm, and 0.461 °C ~ 0.939 °C for all months, respectively. Compared with Peng’s climate surface and CHELSAcruts, R2 increased by ~ 7 %, RMSE and MAE decreased by ~ 17 % for precipitation; for temperature elements, R2 hardly increased, but RMSE and MAE decreased by ~50 %. Our results showed ChinaClim_baseline obviously improved the accuracy of time-series climatic elements estimation, and the satellite-driven data can greatly improve the accuracy of time-series precipitation estimation, but not the accuracy of time-series temperatures estimation. Overall, ChinaClim_baseline, an excellent baseline climatology surface, can be used for obtaining high-quality and long-term climate datasets from past to future. In the meantime, ChinaClim_time-series of 1km spatial resolution based on ChinaClim_baseline, is suitable for investigating the spatial-temporal patterns of climate changes and their impacts on eco-environmental systems in China.

Here, ChinaClim_baseline is available at 10.5281/zenodo.5900743 (Gong, 2020a), ChinaClim_time-series of precipitation is available at 10.5281/zenodo.5919442 (Gong, 2020b), ChinaClim_time-series of maximum temperature is available at 10.5281/zenodo.5919448 (Gong, 2020c), ChinaClim_time-series of minimum temperature is available at 10.5281/zenodo.5919423 (Gong, 2020d) and ChinaClim_time-series of average temperature is available at 10.5281/zenodo.5919450 (Gong, 2020e).

Haibo Gong et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-45', Anonymous Referee #1, 18 Mar 2022 reply
    • AC1: 'Reply on RC1', Gong Haibo, 30 Apr 2022 reply

Haibo Gong et al.

Data sets

1 km Monthly Minimum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series) Gonghaibo https://doi.org/10.5281/zenodo.5919423

1 km Monthly Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series) Gonghaibo https://doi.org/10.5281/zenodo.5919448

1 km Monthly Average Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series) Gonghaibo https://doi.org/10.5281/zenodo.5919450

1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_time-series) Gonghaibo https://doi.org/10.5281/zenodo.5919442

A Brand-New and High-Quality Baseline Climatology Surface for China (ChinaClim_baseline) Gonghaibo https://doi.org/10.5281/zenodo.5900743

Haibo Gong et al.

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Short summary
We created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1km monthly precipitation and temperatures dataset in China during 1952–2019 (ChinaClim_time-series). Our results showed that ChinaClim_baseline and ChinaClim_time-series exhibited very high performance over China. ChinaClim_baseline and ChinaClim_time-series can play huge role in investigating the patterns of climate changes and their impacts on eco-environmental systems in China.