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Climate Models Underestimate Global Decreases in High‐Cloud Amount With Warming

Kemsley, Wilson S. ; Nowack, P. ORCID iD icon 1,2; Ceppi, P.
1 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)
2 Institut für Meteorologie und Klimaforschung Atmosphärische Spurengase und Fernerkundung (IMKASF), Karlsruher Institut für Technologie (KIT)

Abstract:

Cloud feedback has prevailed as a leading source of uncertainty in climate model projections under increasing atmospheric carbon dioxide. Cloud-controlling factor (CCF) analysis is an approach used to observationally constrain cloud feedback, and subsequently the climate sensitivity. Although high clouds contribute significantly toward uncertainty, they have received comparatively little attention in CCF and other observational analyses. Here we use CCF analysis for the first time to constrain the high-cloud radiative feedback, focusing on the cloud amount component owing to its dominant contribution to uncertainty in high-cloud feedback. Globally, observations indicate larger decreases in high cloudiness than state-of-the-art climate models suggest. In fact, half of the 16 models considered here predict radiative feedbacks inconsistent with observations, likely due to misrepresenting the stability iris mechanism. Despite the suggested strong high-cloud amount decreases with warming, observations point toward a near-neutral net high-cloud amount radiative feedback, owing to almost canceling longwave and shortwave contributions.


Verlagsausgabe §
DOI: 10.5445/IR/1000180952
Veröffentlicht am 10.04.2025
Originalveröffentlichung
DOI: 10.1029/2024GL113316
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Atmosphärische Spurengase und Fernerkundung (IMKASF)
Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 16.04.2025
Sprache Englisch
Identifikator ISSN: 0094-8276, 1944-8007
KITopen-ID: 1000180952
HGF-Programm 12.98.07 (POF IV, LK 01) KIT Climate and Environment Center
Weitere HGF-Programme 12.11.34 (POF IV, LK 01) Improved predictions from weather to climate scales
Erschienen in Geophysical Research Letters
Verlag John Wiley and Sons
Band 52
Heft 7
Seiten e2024GL113316
Vorab online veröffentlicht am 09.04.2025
Schlagwörter cloud feedback, machine learning, climate modeling, satellite data, observational constraints
Nachgewiesen in Dimensions
OpenAlex
Web of Science
Scopus
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