Preprints
https://doi.org/10.5194/essd-2021-460
https://doi.org/10.5194/essd-2021-460
 
01 Apr 2022
01 Apr 2022
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

A 148-year precipitation oxygen isoscape for China generated based on data fusion and bias correction of iGCMs simulations

Jiacheng Chen1, Jie Chen1,2, Xunchang John Zhang3, Peiyi Peng4, and Camille Risi5 Jiacheng Chen et al.
  • 1State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
  • 2Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan, 430072, China
  • 3USDA-ARS, Grazinglands Research Laboratory, 7207W. Cheyenne St., El Reno, OK 73036, USA
  • 4Chongqing Southwest Research Institute for Water Transport Engineering, Chongqing Jiaotong University, Chongqing, 400016, China
  • 5Laboratoire de Meteorologie Dynamique, IPSL, CNRS, Ecole Normale Superieure, Sorbonne Universite, PSL Research University, Paris, France

Abstract. The precipitation oxygen isotopic composition is a useful environmental tracer for climatic and hydrological studies. However, the observed precipitation oxygen is limited at both temporal and spatial scales. Isotope-equipped general circulation models (iGCMs) can compensate for the temporal and spatial discontinuity of observation networks, but they suffer from coarse spatial resolutions and systematic biases. The objective of this study is to build a high-resolution precipitation oxygen isoscape in China for a period of 148 years by integrating observed and iGCMs-simulated precipitation oxygen isotope composition (δ18Op) using data fusion and bias correction methods. The temporal and spatial resolutions are month and 50–60 km for the isoscape, respectively. Prior to building the oxygen isoscape, the performance of two bias correction methods (BCMs) and three data fusion methods (DFMs) is compared after post-processing of eight iGCM simulations. Results show that the outputs of the Convolutional Neural Networks (CNN) fusion method exhibit the strongest correlation with observations with correlation coefficient mostly larger than 0.8, and the smallest bias with root mean square error mostly smaller than 2 ‰. The other two DFMs also perform slightly better than the two BCMs, which show similar performance. Thus, precipitation oxygen isoscape is generated by using the CNN fusion method for the 1969–2007 period in which all iGCMs have output and by using the bias correction methods for the remaining years. Based on the precipitation oxygen isoscape, the spatiotemporal patterns of δ18Op across China are investigated. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ18Op is consistent with the temperature effect in northern China, and with the precipitation amount effect in southern China, and be more specific in the Qinghai-Tibet Plateau of China. The δ18Op time series mirrors a fluctuating upward trend of the temperature or precipitation in most regions of China. The temporal and spatial distribution characteristics of the generated isoscape are consistent with the characteristics of atmospheric circulation and climate change, indicating successful assimilation and extension of the observed precipitation oxygen isotopes in time and space. Overall, the built isoscape is reliable and useful for providing strong support for tracking atmospheric and hydrological processes. The dataset is available in Zenodo at https://doi.org/10.5281/zenodo.5703811 (Chen et al., 2021).

Jiacheng Chen et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-460', Liheng Wang, 12 Apr 2022
    • AC1: 'Reply on RC1', Jiacheng Chen, 18 Apr 2022
  • CC1: 'Comment on essd-2021-460', Nancy Swift, 26 Apr 2022
    • AC2: 'Reply on CC1', Jiacheng Chen, 31 May 2022
  • CC2: 'Comment on essd-2021-460', Donal Logue, 28 Apr 2022
    • AC3: 'Reply on CC2', Jiacheng Chen, 31 May 2022
  • RC2: 'Comment on essd-2021-460', Anonymous Referee #2, 14 May 2022
    • AC4: 'Reply on RC2', Jiacheng Chen, 31 May 2022
  • RC3: 'Comment on essd-2021-460', Anonymous Referee #3, 05 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-460', Liheng Wang, 12 Apr 2022
    • AC1: 'Reply on RC1', Jiacheng Chen, 18 Apr 2022
  • CC1: 'Comment on essd-2021-460', Nancy Swift, 26 Apr 2022
    • AC2: 'Reply on CC1', Jiacheng Chen, 31 May 2022
  • CC2: 'Comment on essd-2021-460', Donal Logue, 28 Apr 2022
    • AC3: 'Reply on CC2', Jiacheng Chen, 31 May 2022
  • RC2: 'Comment on essd-2021-460', Anonymous Referee #2, 14 May 2022
    • AC4: 'Reply on RC2', Jiacheng Chen, 31 May 2022
  • RC3: 'Comment on essd-2021-460', Anonymous Referee #3, 05 Sep 2022

Jiacheng Chen et al.

Data sets

Precipitation oxygen isoscape for mainland China from 1870 to 2017 generated based on data fusion and bias correction of iGCMs simulations Chen, Jiacheng; Chen, Jie; Zhang, Xunchang J.; Peng, Peiyi; Risi, Camille https://doi.org/10.5281/zenodo.5703811

Jiacheng Chen et al.

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Short summary
To make full use of the advantages of isotope observations and simulations, this study generates a new dataset by integrating multi-GCM data based on data fusion and bias correction methods. This dataset contains monthly δ18Op over mainland China for the 1870–2017 period with a spatial resolution of 50–60 km. The built isoscape shows similar spatial and temporal distribution characteristics to observations, which is reliable and useful to extend the time and space of observations in China.