|PENG, PEIYI - Wuhan University|
|CHEN, JIE - Wuhan University|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2020
Publication Date: 6/6/2020
Citation: Peng, P., Zhang, X.J., Chen, J. 2020. Bias correcting isotope-equipped GCMs outputs to build precipitation oxygen isoscape for eastern China. Journal of Hydrology. 589:125153. https://doi.org/10.1016/j.jhydrol.2020.125153.
Interpretive Summary: Water molecules consist of hydrogen and oxygen atoms. Not all oxygen atoms are identical in water. Some are heavier than others, which are called different oxygen isotopes. A specific oxygen isotope, namely oxygen-18, is rare and can be used as a tracer to track water movement in air and on the ground to provide unique insights on hydrological cycles and climate research. The oxygen-18 isotope has been incorporated into global climate models (called iGCMs) to help interpret hydrological processes. Owing to the high cost of the isotopic measurements, the measured isotopic measurements are often limited. This study was to use the limited measured oxygen-18 isotope data to calibrate the iGCMs output in order to obtain a corrected oxygen-18 database in space and time. The results showed that biases in the iGCMs output can be satisfactorily corrected. The generated new database is useful to hydrologists and meteorologists to study water cycle. This case study has successfully generated a large database for the monsoon region of eastern China, which will be used to quantify water movement and cycle in the region.
Technical Abstract: Stable oxygen isotope, as a valuable environmental tracer, can provide unique insights on hydrology and climate research. However, sparse isotopic observations hinder the utilization of interpreting hydrological processes. Eastern China is a typical region where isotopic measurements are limited. Isotope-equipped global climate models (iGCMs) are able to provide detailed isotope information to extend observations in space and time; however, biases between observations and simulations challenge the direct application of iGCMs simulations. Accordingly, this study investigates the potential of bias correcting iGCMs to create reliable and spatiotemporally continuous isotopic landscape (isoscape) for eastern China. Spatial and temporal correlations between observations and simulations are investigated at a regional scale. Performances of six iGCM simulations and two bias correction methods (BCMs) are compared in this study. Uncertainties for the precipitation oxygen isoscape (POI) are estimated by combining results from an ensemble of six iGCM simulations and two BCMs. The POI for eastern China is generated by averaging the results from an ensemble of multi-iGCM and multi-BCM. Results show that iGCM simulations are strongly correlated to observations at a given temporal and spatial scale. Model simulations generally underestimate the precipitation oxygen isotope. The two BCMs exhibit the ability of reducing biases in the simulated values. Uncertainty linked to iGCMs is larger than that related to BCMs. Results show that the POI built by a multi-iGCM and multi-BCM approach adequately preserves the spatiotemporal pattern of isotopic measurements. Overall, bias correction is imperative before applying iGCMs to build the POI and further investigation on improving iGCM simulations is recommended.