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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Deepanshu Malik, Hendrik Andersen, Jan Cermak, Roland Vogt, and Bianca Adler
EGUsphere, https://doi.org/10.5194/egusphere-2025-2645, https://doi.org/10.5194/egusphere-2025-2645, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We investigated cloud base height changes in the Namib Desert and developed a method to estimate it using ground-based humidity data. This improves fog monitoring by distinguishing fog from low clouds, which satellites alone cannot reliably do. Our results reveal diurnal patterns and linkages to coastal proximity in the vertical dynamics of fog and low clouds, highlighting key atmospheric processes with potential importance for future research.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
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Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
Alexandre Mass, Hendrik Andersen, Jan Cermak, Paola Formenti, Eva Pauli, and Julian Quinting
Atmos. Chem. Phys., 25, 491–510, https://doi.org/10.5194/acp-25-491-2025, https://doi.org/10.5194/acp-25-491-2025, 2025
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This study investigates the interaction between smoke aerosols and fog and low clouds (FLCs) in the Namib Desert between June and October. Here, a satellite-based dataset of FLCs, reanalysis data and machine learning are used to systematically analyze FLC persistence under different aerosol loadings. Aerosol plumes are shown to modify local thermodynamics, which increase FLC persistence. But fully disentangling aerosol effects from meteorological ones remains a challenge.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
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Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
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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This study investigates the fractional solubility of iron (Fe) in dust particles along the coast of Namibia, a critical region for the atmospheric Fe supply of the South Atlantic Ocean. Our results suggest a possible two-way interplay whereby marine biogenic emissions from the coastal marine ecosystems into the atmosphere would increase the solubility of Fe-bearing dust by photo-reduction processes. The subsequent deposition of soluble Fe could act to further enhance marine biogenic emissions.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
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This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
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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Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite data are developed and validated for the region of Paris to improve applicability for analyses of urban effects on low clouds. We found that cloud masks obtained from the regional approach are more appropriate for the high-resolution analysis of locally induced cloud processes. Its applicability is tested for the analysis of typical fog conditions over different surface types.
Babak Jahani, Hendrik Andersen, Josep Calbó, Josep-Abel González, and Jan Cermak
Atmos. Chem. Phys., 22, 1483–1494, https://doi.org/10.5194/acp-22-1483-2022, https://doi.org/10.5194/acp-22-1483-2022, 2022
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The change in the state of sky from cloudy to cloudless (or vice versa) comprises an additional phase called
transition zonewith characteristics laying between those of aerosols and clouds. This study presents an approach for the quantification of the broadband longwave radiative effects of the cloud–aerosol transition zone at the top of the atmosphere during daytime over the ocean based on satellite observations and radiative transfer simulations.
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
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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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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