Modeling the contributions of oceanic moisture to summer precipitation in eastern China using 18O

Authors: PENG, PEIYI - Wuhan University; Zhang, Xunchang; CHEN, JIE - Wuhan University

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/29/2019
Publication Date: 11/17/2019
Citation: Peng, P., Zhang, X.J., Chen, J. 2019. Modeling the contributions of oceanic moisture to summer precipitation in eastern China using 18O. Journal of Hydrology. 581:124304. https://doi.org/10.1016/j.jhydrol.2019.124304.
DOI: https://doi.org/10.1016/j.jhydrol.2019.124304

Interpretive Summary: Ocean-continent tele-connection is an important component of the global climate systems and influences hydrological cycles at regional and local scales. Oceanic moisture advection and terrestrial evapotranspiration (ET) are the two main sources of precipitation in the monsoon region of eastern China, where the regional precipitation is dominated by oceanic moisture from the Pacific Ocean. The objectives of this study are to quantify the contribution of the oceanic moisture to precipitation in the monsoon region and to better understanding the water cycle processes in the region. Summer months when the East Asian monsoon is strongest are studied, and a modified two-component mixing model using 18O as a tracer is applied to apportion the spatiotemporal contributions of oceanic moisture in eastern China. The uncertainty of the estimated fractional contributions is also assessed. In the entire study area, oceanic moisture is the major contributor to summer precipitation, with ET playing a supplemental role. The contributions of oceanic moisture to precipitation decrease from June to August in central and northern China. Taking the central China as an example, the proportional contributions of oceanic moisture decrease from 94% in June to 92% in July to 81% in August. Spatially, the oceanic moisture contributions decrease along the transport route from south to northeast. The standard errors of the estimated fractional contributions are less than 17% for all months and sub-regions. The results should be useful to climatologists and hydrologists to validate and improve regional climate models for better water cycle prediction.

Technical Abstract: Ocean-continent interaction is an important component of the global climate systems and influences hydrological cycles at regional and local scales. Oceanic moisture advection and terrestrial evapotranspiration (ET) are the two main sources of precipitation in the monsoon region of eastern China, where the regional precipitation is dominated by oceanic moisture from the Pacific Ocean. The objectives of this study are to quantify the contribution of the oceanic moisture to precipitation in the monsoon region and to better understanding the water cycle processes in the region. Summer months when the East Asian monsoon is strongest are studied, and a modified two-component mixing model using 18O as a tracer is applied to apportion the spatiotemporal contributions of oceanic moisture. The Gaussian first-order approximation is employed to assess the uncertainty of the estimates. In the entire study area, oceanic moisture predominates summer precipitation, with ET playing a supplemental role. The contributions of oceanic moisture to precipitation decrease from June to August in central and northern China. Taking the central China as an example, the proportional contributions of oceanic moisture decrease from 94% in June to 92% in July to 81% in August. Spatially, the oceanic moisture contributions decrease along the transport route from south to northeast. The 95% confidence intervals (CIs) of estimated fractional contributions of both ET and advection sources range from ±0.07 to ±0.38, with the standard errors between 0.03 and 0.17 for all months and sub-regions. The relative uncertainty contributions of each source to the total uncertainty follow the order of precipitation vapor > advection > ET. The large variances of d18O in precipitable water vapor account for the most uncertainty of the proportional estimates for precipitation sources.