Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties With Bias Correction Techniques. Issue 11 (26th October 2022)

• Record Type: Journal Article
• Authors: Wu, Yi; Miao, Chiyuan; Fan, Xuewei; Gou, Jiaojiao; Zhang, Qi; Zheng, Haiyan
• Is Part Of: Earth's future. Volume 10:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-26
• Subjects: climate projection -- uncertainty -- bias correction -- temperature -- precipitation; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2022EF002963
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: Decomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures. Plain Language Summary: Global climate models (GCMs) have a powerful ability to reproduce past climate characteristics and project future climate evolution, and they are currently one of the most effective tools for climate change research. However, quantitative climate projections from GCMs are subject to high uncertainty due to our incomplete knowledge of climate, insufficient representation of climate system, and limited computer resources. Clarifying uncertainty sources can provide important scientific support for enhancing the credibility of future projection results and resolve relevant scientific questions for subsequent modeling applications. In this study, we tried to quantify the uncertainty in temperature and precipitation projections from three sources‐model uncertainty, scenario uncertainty, and internal variability‐arising from model outputs of 21 phase 5 of the Coupled Model Intercomparison Project and 26 phase 6 of the Coupled Model Intercomparison Project GCMs. We also investigated the potential of four bias correction methods for narrowing uncertainty in temperature and precipitation projections over the globe and continents. Key Points: For uncertainties in phase 6 of the Coupled Model Intercomparison Project temperatures, the contribution of model uncertainty decreases with time, while the contribution of scenario uncertainty increases with time For precipitation projections, the model uncertainty predominates (98%) the total uncertainty Four bias correction (BC) methods have exhibited excellent ability to reduce uncertainty, and BC and spatial disaggregation has the best performance