A century and a half precipitation oxygen isoscape for China generated using data fusion and bias correction

Authors: CHEN, JIACHENG - Wuhan University; CHEN, JIE - Wuhan University; Zhang, Xunchang; PENG, PEIYI - Chongqing University; RISI, CAMILLE - Sorbonne Universities, Paris

Submitted to: Scientific Data
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/23/2023
Publication Date: 4/20/2023
Citation: Chen, J., Chen, J., Zhang, X.J., Peng, P., Risi, C. 2023. A century and a half precipitation oxygen isoscape for China generated using data fusion and bias correction. Scientific Data. Article 10. Article 185. https://doi.org/10.1038/s41597-023-02095-1.
DOI: https://doi.org/10.1038/s41597-023-02095-1

Interpretive Summary: Oxygen isotope composition in precipitation is a useful environmental tracer for climatic and hydrological studies. However, the observed precipitation oxygen isotope is limited at both temporal and spatial scales. Isotope-equipped general circulation models (iGCMs) can be used to fill the temporal and spatial data gaps 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 isotope dataset of China for a period of 148 years by integrating observed and iGCMs-simulated precipitation oxygen isotope composition (referred to as Delta-O18) using data fusion and bias correction methods. The built datasets are monthly values at the 50-60 km resolution. Prior to building the database, the performance of two bias correction methods (BCMs) and three data fusion methods (DFMs) is evaluated after post-processing of the simulations from eight iGCMs. Results show that the data fusion or integration using the Convolutional Neural Networks (CNN) method produced the best results. Thus, precipitation oxygen isotope dataset 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. The generated isotope dataset shows similar spatial and temporal distribution characteristics to observations, indicating successful assimilation and extension of the observed precipitation oxygen isotopes in time and space. Overall, the built database is reliable and useful for providing strong support for tracking atmospheric and hydrological processes. The dataset can be used by meteorologists and hydrologists to study water cycles at multiple temporal and spatial scales.

Technical 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 (Delta-O18) 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 Delta-O18 across China are investigated. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of Delta-O18 is consistent with the temperature effect in northern China, and with the precipitation amount effect in southern China. The Delta-O18 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).