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How Cloud Droplet Number Concentration Impacts Liquid Water Path and Precipitation in Marine Stratocumulus Clouds—A Satellite-Based Analysis Using Explainable Machine Learning

Zipfel, Lukas 1,2; Andersen, Hendrik ORCID iD icon 1,2; Grosvenor, Daniel Peter ; Cermak, Jan ORCID iD icon 1,3
1 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)
2 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)
3 Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF), Karlsruher Institut für Technologie (KIT)

Abstract:

Aerosol–cloud–precipitation interactions (ACI) are a known major cause of uncertainties in simulations of the future climate. An improved understanding of the in-cloud processes accompanying ACI could help in advancing their implementation in global climate models. This is especially the case for marine stratocumulus clouds, which constitute the most common cloud type globally. In this work, a dataset composed of satellite observations and reanalysis data is used in explainable machine learning models to analyze the relationship between the cloud droplet number concentration (𝑁$_𝑑$), cloud liquid water path (LWP), and the fraction of precipitating clouds (PF) in five distinct marine stratocumulus regions. This framework makes use of Shapley additive explanation (SHAP) values, allowing to isolate the impact of 𝑁$_𝑑$ from other confounding factors, which proved to be very difficult in previous satellite-based studies. All regions display a decrease of PF and an increase in LWP with increasing 𝑁$_𝑑$, despite marked inter-regional differences in the distribution of 𝑁$_𝑑$. Polluted (high 𝑁$_𝑑$) conditions are characterized by an increase of 12 gm$^{−2}$ in LWP and a decrease of 0.13 in PF on average when compared to pristine (low 𝑁$_𝑑$) conditions. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000171702
Veröffentlicht am 17.06.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Institut für Meteorologie und Klimaforschung (IMK)
Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 2073-4433
KITopen-ID: 1000171702
Erschienen in Atmosphere
Verlag MDPI
Band 15
Heft 5
Seiten Art.-Nr.: 596
Vorab online veröffentlicht am 14.05.2024
Nachgewiesen in Web of Science
Dimensions
Scopus
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