AUTHOR=Guan Tiesheng , Liu Yanli , Sun Zhouliang , Zhang Jianyun , Chen Hua , Wang Guoqing , Jin Junliang , Bao Zhenxin , Qi Wei 

TITLE=A Framework to Identify the Uncertainty and Credibility of GCMs for Projected Future Precipitation: A Case Study in the Yellow River Basin, China

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.863575

DOI=10.3389/fenvs.2022.863575

ISSN=2296-665X

ABSTRACT=General circulation models (GCMs) could simulate precipitation under climate change and have been recognized as a major tool to project future water resources, but huge inherent uncertainties mean that their credibility is widely questioned. Current analysis mainly focuses on some aspects of uncertainty and few on the whole chain process to yield a more reliable projection. This study proposes a framework to identify the uncertainty and credibility of GCMs, consisting of downscaling, uncertainty analysis (model spread and Taylor diagram), ensemble analysis (a Grid-based BMA), credibility analysis (Signal to noise ratio) and probability projection. Based on five selected climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the uncertainties and credibility of simulated precipitation in the Yellow River of China were analysed. By comparing models’ output with observation in the historical period of 1986-2005, we can see that large uncertainty exists among models’ annual precipitation. For different-class precipitation, the uncertainties of five models are small in relatively weak rain, but large in heavy rainfall, which indicates more risk in future projections and the necessity to explore their credibility. Moreover, in such a large-span basin, GCMs show big spatial differences in space and even opposite trends in some regions, demonstrating the limits of Bayesian model averaging (BMA) on multi-model ensemble due to one weight group overall whole basin. Thus, a Grid-based weighted Bayesian Model Averaging (GBMA) method was proposed to cope with the spatial inconsistencies of models. Given the multi-model ensemble results, the future precipitation of the period of 2021-2050 and 2061-2090 were projected, and the probability and credibility of future precipitation change in terms of spatial distribution identified. Model credibility identification allowed for more reliable projections of precipitation change trends especially for different spatial regions, which will be very valuable for decision-making related to water resources management and security.