Assessing Cloud Feedbacks Over the Atlantic With Bias-Corrected Downscaling

Clouds exert a significant impact on global temperatures and climate change. Cloud-radiative feedback (CRF) is one of the major sources of climate change uncertainty. Understanding CRF is therefore crucial 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 to constrain the cloud feedback uncertainties in the tropical and sub-tropical Atlantic region. We use regional climate model (RCM) simulations with convection permitting resolution, driven by debiased driving fields from three different global climate models (GCMs). Bias-corrected downscaling significantly reduces biases in ITCZ intensity and position, eliminating the double-ITCZ bias across all six experiments (three GCMs for historical and future periods). We explore the new methodology's potential to investigate the CRF in comparison to that of the driving GCMs. Results indicate that additional GCMs and RCMs are necessary for a more comprehensive uncertainty estimation and more conclusive results, while our simulations suggest a potentially narrower range of CRF over the tropical and subtropical Atlantic, primarily due to an improved representation of stratocumulus clouds. Our study highlights the potential of bias-corrected downscaling in constraining the uncertainty of simulations and estimates of cloud feedback and equilibrium climate sensitivity. The results advocate for further simulations with additional RCMs and domains for a more comprehensive analysis.