Bias correcting isotope-equipped GCMs outputs to build precipitation oxygen isoscape for eastern China

Abstract

Stable oxygen isotope, as a valuable environmental tracer, can provide unique insights on hydrology and climate research. However, sparse isotopic observations hinder its 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 time at a large spatial scale; however, biases between observations and simulations challenge the direct application of iGCMs simulations. Accordingly, this study investigates the potential of bias correcting iGCMs to generate 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 comparing results from an ensemble mean of six iGCM simulations and two BCMs, as well as using a variance decomposition method. The POI for eastern China is generated by averaging the results from the six iGCM simulations corrected by the two BCMs. 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. The iGCMs seem to contribute more uncertainty to mean δ18Op estimates than the two BCMs used in this study, followed by the interaction between iGCMs and BCMs, with the least in BCMs. The POI built by a multi-iGCM and multi-BCM approach adequately preserves the spatiotemporal pattern of isotopic measurements. Overall, bias correction is imperative and effective in building high resolution POI by making of spatiotemporally coherent iGCMs simulations.