KIT | KIT-Bibliothek | Impressum | Datenschutz

Machine-Learning Based Analysis of Liquid Water Path Adjustments to Aerosol Perturbations in Marine Boundary Layer Clouds Using Satellite Observations

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

Abstract:

Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration (N$_d$, a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and N$_d$ are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and N$_d$ is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: N$_d$–LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs.


Verlagsausgabe §
DOI: 10.5445/IR/1000144633
Veröffentlicht am 08.04.2022
Originalveröffentlichung
DOI: 10.3390/atmos13040586
Scopus
Zitationen: 3
Web of Science
Zitationen: 2
Dimensions
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2073-4433
KITopen-ID: 1000144633
HGF-Programm 12.11.26 (POF IV, LK 01) Aerosol-Cloud-Climate-Interaction
Erschienen in Atmosphere
Verlag MDPI
Band 13
Heft 4
Seiten Art.-Nr.: 586
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 05.04.2022
Schlagwörter aerosol–cloud interactions; liquid water path; cloud droplet number concentration; machine learning; gradient boosting regression trees; marine boundary layer clouds; remote sensing; satellite observations; Southeast Atlantic
Nachgewiesen in Web of Science
Scopus
Dimensions
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page