Assessing Cloud Feedbacks Over the Atlantic With Bias‐Corrected Downscaling

Abstract

Clouds exert a significant impact on global temperatures and climate change. Cloud‐radiativefeedback (CRF) is one of the major sources of climate change uncertainty. Understanding CRF is thereforecrucial for accurate climate projections. Biases like the double‐ITCZ problem in Global Climate Models(GCMs) hamper precise climate projections. Here, we explore a bias‐corrected downscaling method toconstrain the cloud feedback uncertainties in the tropical and sub‐tropical Atlantic region. We use regionalclimate model (RCM) simulations with convection permitting resolution, driven by debiased driving fields fromthree different global climate models (GCMs). Bias‐corrected downscaling significantly reduces biases in ITCZintensity and position, eliminating the double‐ITCZ bias across all six experiments (three GCMs for historicaland future periods). We explore the new methodology's potential to investigate the CRF in comparison to that ofthe driving GCMs. Results indicate that additional GCMs and RCMs are necessary for a more comprehensiveuncertainty estimation and more conclusive results, while our simulations suggest a potentially narrower rangeof CRF over the tropical and subtropical Atlantic, primarily due to an improved representation of stratocumulusclouds. Our study highlights the potential of bias‐corrected downscaling in constraining the uncertainty ofsimulations and estimates of cloud feedback and equilibrium climate sensitivity. The results advocate for furthersimulations with additional RCMs and domains for a more comprehensive analysis.