Quantifying the uncertainty sources of future climate projections and narrowing uncertainties with bias correction techniques

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.