Assessing Cloud Feedbacks Over the Atlantic With Bias‐Corrected Downscaling
Name
J Adv Model Earth Syst - 2025 - Liu - Assessing Cloud Feedbacks Over the Atlantic With Bias‐Corrected Downscaling.pdf
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11 MB
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Adobe PDF
Checksum (MD5)
02bcfc44f1ef57045908412a914e6a4d
Author(s)
Liu, Shuchang
Zeman, Christian
Schär, Christoph
Date Issued
June 16, 2025
Journal
Journal of Advances in Modeling Earth Systems
Citation
Liu, S., Zeman, C., & Schär, C. (2025). Assessing cloud feedbacks over the Atlantic with bias-corrected downscaling. Journal of Advances in Modeling Earth Systems, 17, e2024MS004661.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Massachusetts Institute of Technology. Program in Atmospheres, Oceans, and Climate
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2024MS004661
