Articles | Volume 24, issue 22
https://doi.org/10.5194/acp-24-13025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-13025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Nov 2024
Research article |
| 26 Nov 2024
| 26 Nov 2024
Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Hendrik Andersen
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Jan Cermak
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
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Karine Desboeufs, Paola Formenti, Raquel Torres-Sánchez, Kerstin Schepanski, Jean-Pierre Chaboureau, Hendrik Andersen, Jan Cermak, Stefanie Feuerstein, Benoit Laurent, Danitza Klopper, Andreas Namwoonde, Mathieu Cazaunau, Servanne Chevaillier, Anaïs Feron, Cécile Mirande-Bret, Sylvain Triquet, and Stuart J. Piketh
Atmos. Chem. Phys., 24, 1525–1541, https://doi.org/10.5194/acp-24-1525-2024, https://doi.org/10.5194/acp-24-1525-2024, 2024
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Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
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Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270, https://doi.org/10.5194/amt-15-4257-2022, https://doi.org/10.5194/amt-15-4257-2022, 2022
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Babak Jahani, Hendrik Andersen, Josep Calbó, Josep-Abel González, and Jan Cermak
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Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Roland Stirnberg, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, Julia Fuchs, Miae Kim, Jean-Eudes Petit, and Olivier Favez
Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, https://doi.org/10.5194/acp-21-3919-2021, 2021
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Air pollution endangers human health and poses a problem particularly in densely populated areas. Here, an explainable machine learning approach is used to analyse periods of high particle concentrations for a suburban site southwest of Paris to better understand its atmospheric drivers. Air pollution is particularly excaberated by low temperatures and low mixed layer heights, but processes vary substantially between and within seasons.
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Short summary
We present a near-global observation-based explainable machine learning framework to quantify the response of cloud fraction (CLF) of marine low clouds to cloud droplet number concentration (Nd), accounting for the covariations with meteorological factors. This approach provides a novel data-driven method to analyse the CLF adjustment by assessing the CLF sensitivity to Nd and numerous meteorological factors as well as the dependence of the Nd–CLF sensitivity on the meteorological conditions.
We present a near-global observation-based explainable machine learning framework to quantify...
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