Cloud transition across the daily cycle illuminates model responses of trade cumuli to warming.

daily cycle general circulation models low-cloud feedback observations trade-wind cumulus

Journal

Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876

Informations de publication

Date de publication:
21 Feb 2023
Historique:
entrez: 13 2 2023
pubmed: 14 2 2023
medline: 14 2 2023
Statut: ppublish

Résumé

The response of trade cumulus clouds to warming remains a major source of uncertainty for climate sensitivity. Recent studies have highlighted the role of the cloud-convection coupling in explaining this spread in future warming estimates. Here, using observations from an instrumented site and an airborne field campaign, together with high-frequency climate model outputs, we show that i) over the course of the daily cycle, a cloud transition is observed from deeper cumuli during nighttime to shallower cumuli during daytime, ii) the cloud evolution that models predict from night to day reflects the strength of cloud sensitivity to convective mass flux and exhibits many similarities with the cloud evolution they predict under global warming, and iii) those models that simulate a realistic cloud transition over the daily cycle tend to predict weak trade cumulus feedback. Our findings thus show that the daily cycle is a particularly relevant testbed, amenable to process studies and anchored by observations, to assess and improve the model representation of cloud-convection coupling and thus make climate projections more reliable.

Identifiants

pubmed: 36780519
doi: 10.1073/pnas.2209805120
pmc: PMC9974475
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2209805120

Références

J Adv Model Earth Syst. 2016 Dec;8(4):1892-1911
pubmed: 28239438
Nature. 2022 Dec;612(7941):696-700
pubmed: 36450982
Science. 2016 Apr 8;352(6282):224-7
pubmed: 27124459
Q J R Meteorol Soc. 2021 Jul;147(738):2850-2873
pubmed: 34588710
J Adv Model Earth Syst. 2019 Oct;11(10):3148-3166
pubmed: 31894190
Nature. 2014 Jan 2;505(7481):37-42
pubmed: 24380952
Surv Geophys. 2017;38(6):1529-1568
pubmed: 31997845

Auteurs

Jessica Vial (J)

Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace (LMD IPSL), Sorbonne Université, CNRS 75005, Paris, France.

Anna Lea Albright (AL)

Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace (LMD IPSL), Sorbonne Université, CNRS 75005, Paris, France.

Raphaela Vogel (R)

Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace (LMD IPSL), Sorbonne Université, CNRS 75005, Paris, France.
University of Hamburg, 20148 Hamburg, Germany.

Ionela Musat (I)

Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace (LMD IPSL), Sorbonne Université, CNRS 75005, Paris, France.

Sandrine Bony (S)

Laboratoire de Météorologie Dynamique Institut Pierre Simon Laplace (LMD IPSL), Sorbonne Université, CNRS 75005, Paris, France.

Classifications MeSH