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
Atmos. Chem. Phys., 26, 681–701, https://doi.org/10.5194/acp-26-681-2026, https://doi.org/10.5194/acp-26-681-2026, 2026
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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.
Viola Hipler, Hendrik Andersen, Robert Spirig, Roland Vogt, Stuart Piketh, Bianca Adler, and Jan Cermak
EGUsphere, https://doi.org/10.5194/egusphere-2025-5816, https://doi.org/10.5194/egusphere-2025-5816, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Fog is a key component of the Namib Desert ecosystem, and is mostly associated with the advection of marine stratus clouds. Here, we use local measurements near the coast to distinguish fog situations from lifted low-cloud situations. Using reanalysis data, we find synoptic and regional-scale processes over the ocean and over the continent that are connected to stratus altitude and therefore fog occurrence patterns. The results lead to a better understanding of the coastal desert fog system.
Paulo Ceppi, Sarah Wilson Kemsley, Hendrik Andersen, Timothy Andrews, Ryan J. Kramer, Peer Nowack, Casey J. Wall, and Mark D. Zelinka
EGUsphere, https://doi.org/10.5194/egusphere-2025-5206, https://doi.org/10.5194/egusphere-2025-5206, 2025
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Recent decades have seen a marked decrease in global low-level cloud cover, leading to more sunlight heating the Earth. This trend is poorly understood, raising the concern that clouds may amplify global warming more than previously thought. We show that the cloud decrease is mostly caused by human forcing on climate, and that it agrees with previous estimates of how clouds respond to decreasing aerosol pollution, increasing greenhouse gas concentration, and their effects on global temperature.
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.
Cited articles
Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The impact of humidity above stratiform clouds on indirect aerosol climate forcing, Nature, 432, 1014–1017, https://doi.org/10.1038/nature03174, 2004. a
Adebiyi, A. A. and Zuidema, P.: The role of the southern African easterly jet in modifying the southeast Atlantic aerosol and cloud environments, Q. J. Roy. Meteorol. Soc., 142, 1574–1589, https://doi.org/10.1002/qj.2765, 2016. a
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Andersen, H. and Cermak, J.: How thermodynamic environments control stratocumulus microphysics and interactions with aerosols, Environ. Res. Lett., 10, 024004, https://doi.org/10.1088/1748-9326/10/2/024004, 2015. a
Andersen, H., Cermak, J., Fuchs, J., and Schwarz, K.: Global observations of cloud-sensitive aerosol loadings in low-levelmarine clouds, J. Geophys. Res., 121, 936–12, https://doi.org/10.1002/2016JD025614, 2016. a
Andersen, H., Cermak, J., Stirnberg, R., Fuchs, J., Kim, M., and Pauli, E.: Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning, Tellus B, 73, 1–14, https://doi.org/10.1080/16000889.2021.1971925, 2021. a
Andersen, H., Cermak, J., Zipfel, L., and Myers, T. A.: Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors, Geophys. Res. Lett., 49, 1–10, https://doi.org/10.1029/2021gl096498, 2022. a, b, c, d
Andersen, H., Cermak, J., Douglas, A., Myers, T. A., Nowack, P., Stier, P., Wall, C. J., and Wilson Kemsley, S.: Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations, Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, 2023. a, b, c, d, e, f
Arola, A., Lipponen, A., Kolmonen, P., Virtanen, T. H., Bellouin, N., Grosvenor, D. P., Gryspeerdt, E., Quaas, J., and Kokkola, H.: Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors, Nat. Commun., 13, 7357, https://doi.org/10.1038/s41467-022-34948-5, 2022. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A. L., Dufresne, J. L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, 1–45, https://doi.org/10.1029/2019RG000660, 2020. a, b, c
Bender, F. A., Frey, L., McCoy, D. T., Grosvenor, D. P., and Mohrmann, J. K.: Assessment of aerosol–cloud–radiation correlations in satellite observations, climate models and reanalysis, Clim. Dynam., 52, 4371–4392, https://doi.org/10.1007/s00382-018-4384-z, 2019. a
Bender, F. A., Lord, T., Staffansdotter, A., Jung, V., and Undorf, S.: Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties, Tellus B, 76, 1–18, https://doi.org/10.16993/tellusb.1868, 2024. a
Bennartz, R. and Rausch, J.: Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations, Atmos. Chem. Phys., 17, 9815–9836, https://doi.org/10.5194/acp-17-9815-2017, 2017. a
Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M., and Gentine, P.: Machine Learning for Clouds and Climate, in: Clouds and Their Climatic Impacts, Geophysical Monograph Series, 325–345, ISBN 9781119700357, https://doi.org/10.1002/9781119700357.ch16, 2023. a, b
Blossey, P. N., Bretherton, C. S., Zhang, M., Cheng, A., Endo, S., Heus, T., Liu, Y., Lock, A. P., de Roode, S. R., and Xu, K.-M.: Marine low cloud sensitivity to an idealized climate change: The CGILS LES intercomparison, J. Adv. Model. Earth Sy., 5, 234–258, https://doi.org/10.1002/jame.20025, 2013. a
Bretherton, C. S.: Insights into low-latitude cloud feedbacks from high-resolution models, Philos. T. R. Soc. A, 373, 20140415, https://doi.org/10.1098/rsta.2014.0415, 2015. a
Bretherton, C. S. and Wyant, M. C.: Moisture Transport, Lower-Tropospheric Stability, and Decoupling of Cloud-Topped Boundary Layers, J. Atmos. Sci., 54, 148–167, https://doi.org/10.1175/1520-0469(1997)054<0148:MTLTSA>2.0.CO;2, 1997. a
Bretherton, C. S., Blossey, P. N., and Uchida, J.: Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo, Geophys. Res. Lett., 34, L03813, https://doi.org/10.1029/2006GL027648, 2007. a
Bretherton, C. S., Blossey, P. N., and Jones, C. R.: Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single-LES exploration extending the CGILS cases, J. Adv. Model. Earth Sy., 5, 316–337, https://doi.org/10.1002/jame.20019, 2013. a
Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A., Forster, P. M., Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L. A., and Pierce, J. R.: Large contribution of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67–71, https://doi.org/10.1038/nature12674, 2013. a
Ceppi, P. and Nowack, P.: Observational evidence that cloud feedback amplifies global warming, P. Natl. Acad. Sci. USA, 118, e2026290118, https://doi.org/10.1073/pnas.2026290118, 2021. a
Cesana, G., Del Genio, A. D., Ackerman, A. S., Kelley, M., Elsaesser, G., Fridlind, A. M., Cheng, Y., and Yao, M.-S.: Evaluating models' response of tropical low clouds to SST forcings using CALIPSO observations, Atmos. Chem. Phys., 19, 2813–2832, https://doi.org/10.5194/acp-19-2813-2019, 2019. a
Cesana, G. V. and Del Genio, A. D.: Observational constraint on cloud feedbacks suggests moderate climate sensitivity, Nat. Clim. Change, 11, 213–218, https://doi.org/10.1038/s41558-020-00970-y, 2021. a
Chen, H., Janizek, J. D., Lundberg, S., and Lee, S.-I.: True to the Model or True to the Data?, ArXiv, abs/2006.16234, https://arxiv.org/abs/2006.16234 (last access: 17 November 2024), 2020. a
Chen, H., Lundberg, S. M., and Lee, S.-I.: Explaining a series of models by propagating Shapley values, Nat. Commun., 13, 4512, https://doi.org/10.1038/s41467-022-31384-3, 2022. a
Chen, H., Covert, I. C., Lundberg, S. M., and Lee, S.-I.: Algorithms to estimate Shapley value feature attributions, Nat. Mach. Intell., 5, 590–601, https://doi.org/10.1038/s42256-023-00657-x, 2023. a
Chen, T. and Guestrin, C.: XGBoost: A scalable tree boosting system, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco California USA, 13–17 August, Association for Computing Machinery, New York, NY, United States, 785–794, https://doi.org/10.1145/2939672.2939785, 2016. a, b
Chen, Y., Haywood, J., Wang, Y., Malavelle, F., Jordan, G., Partridge, D., Fieldsend, J., De Leeuw, J., Schmidt, A., Cho, N., Oreopoulos, L., Platnick, S., Grosvenor, D., Field, P., and Lohmann, U.: Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover, Nat. Geosci., 15, 609–614, https://doi.org/10.1038/s41561-022-00991-6, 2022. a, b, c, d
Chen, Y., Haywood, J., Wang, Y., Malavelle, F., Jordan, G., Peace, A., Partridge, D. G., Cho, N., Oreopoulos, L., Grosvenor, D., Field, P., Allan, R. P., and Lohmann, U.: Substantial cooling effect from aerosol-induced increase in tropical marine cloud cover, Nat. Geosci., 17, 404–410, https://doi.org/10.1038/s41561-024-01427-z, 2024. a, b
Chen, Y. C., Christensen, M. W., Stephens, G. L., and Seinfeld, J. H.: Satellite-based estimate of global aerosol-cloud radiative forcing by marine warm clouds, Nat. Geosci., 7, 643–646, https://doi.org/10.1038/ngeo2214, 2014. a, b, c
Christensen, M. W., Chen, Y.-C., and Stephens, G. L.: Aerosol indirect effect dictated by liquid clouds, J. Geophys. Res.-Atmos., 121, 614–636, https://doi.org/10.1002/2016JD025245, 2016. a
Christensen, M. W., Neubauer, D., Poulsen, C. A., Thomas, G. E., McGarragh, G. R., Povey, A. C., Proud, S. R., and Grainger, R. G.: Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate, Atmos. Chem. Phys., 17, 13151–13164, https://doi.org/10.5194/acp-17-13151-2017, 2017. a, b, c
Dadashazar, H., Painemal, D., Alipanah, M., Brunke, M., Chellappan, S., Corral, A. F., Crosbie, E., Kirschler, S., Liu, H., Moore, R. H., Robinson, C., Scarino, A. J., Shook, M., Sinclair, K., Thornhill, K. L., Voigt, C., Wang, H., Winstead, E., Zeng, X., Ziemba, L., Zuidema, P., and Sorooshian, A.: Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors, Atmos. Chem. Phys., 21, 10499–10526, https://doi.org/10.5194/acp-21-10499-2021, 2021. a, b, c, d
de Szoeke, S. P., Verlinden, K. L., Yuter, S. E., and Mechem, D. B.: The Time Scales of Variability of Marine Low Clouds, J. Clim., 29, 6463–6481, https://doi.org/10.1175/JCLI-D-15-0460.1, 2016. a
Dey, S., Di Girolamo, L., Zhao, G., Jones, A. L., and McFarquhar, G. M.: Satellite-observed relationships between aerosol and trade-wind cumulus cloud properties over the Indian Ocean, Geophys. Res. Lett., 38, L01804, https://doi.org/10.1029/2010GL045588, 2011. a
Douglas, A. R. and L'Ecuyer, T.: Possible evidence of increased global cloudiness due to aerosol-cloud interactions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-688, 2022. a, b
Elith, J., Leathwick, J. R., and Hastie, T.: A working guide to boosted regression trees, J. Anim. Ecol., 77, 802–813, https://doi.org/10.1111/j.1365-2656.2008.01390.x, 2008. a
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of aerosol-cloud interactions: Mechanisms, significance, and challenges, J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1, 2016. a, b, c
Fons, E., Runge, J., Neubauer, D., and Lohmann, U.: Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach, npj Clim. Atmos. Sci., 6, 1–10, https://doi.org/10.1038/s41612-023-00452-w, 2023. a
Forster, P. M., Storelvmo, T., Armour, K., Collins, W., Dufresne, J. L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, H.: Chapter 7: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a, b
Frye, C., Rowat, C., and Feige, I.: Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability, Adv. Neural Informat. Process. Syst., 33, 1229–1239, https://doi.org/10.48550/arXiv.1910.06358, 2021. a, b
Fuchs, J., Cermak, J., and Andersen, H.: Building a cloud in the southeast Atlantic: Understanding low-cloud controls based on satellite observations with machine learning, Atmos. Chem. Phys., 18, 16537–16552, https://doi.org/10.5194/acp-18-16537-2018, 2018. a, b, c, d
Ghan, S., Wang, M., Zhang, S., Ferrachat, S., Gettelman, A., Griesfeller, J., Kipling, Z., Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., and Zhang, K.: Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability, P. Natl. Acad. Sci. USA, 113, 5804–5811, https://doi.org/10.1073/PNAS.1514036113, 2016. a
Goren, T., Kazil, J., Hoffmann, F., Yamaguchi, T., and Feingold, G.: Anthropogenic Air Pollution Delays Marine Stratocumulus Breakup to Open Cells, Geophys. Res. Lett., 46, 14135–14144, https://doi.org/10.1029/2019GL085412, 2019. a
Grandey, B. S. and Stier, P.: A critical look at spatial scale choices in satellite-based aerosol indirect effect studies, Atmos. Chem. Phys., 10, 11459–11470, https://doi.org/10.5194/acp-10-11459-2010, 2010. a
Grise, K. M. and Kelleher, M. K.: Midlatitude Cloud Radiative Effect Sensitivity to Cloud Controlling Factors in Observations and Models: Relationship with Southern Hemisphere Jet Shifts and Climate Sensitivity, J. Clim., 34, 5869–5886, https://doi.org/10.1175/JCLI-D-20-0986.1, 2021. a, b
Grise, K. M. and Medeiros, B.: Understanding the Varied Influence of Midlatitude Jet Position on Clouds and Cloud Radiative Effects in Observations and Global Climate Models, J. Clim., 29, 9005–9025, https://doi.org/10.1175/JCLI-D-16-0295.1, 2016. a
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018. a, b, c, d
Gryspeerdt, E. and Stier, P.: Regime-based analysis of aerosol-cloud interactions, Geophys. Res. Lett., 39, 1–5, https://doi.org/10.1029/2012GL053221, 2012. a
Gryspeerdt, E., Quaas, J., Ferrachat, S., Gettelman, A., Ghan, S., Lohman, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., Wang, M., and Zhang, K.: Constraining the instantaneous aerosol influence on cloud albedo, P. Natl. Acad. Sci. USA, 114, 4899–4904, https://doi.org/10.1073/pnas.1617765114, 2017. a
Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., Unglaub, C., Gettelman, A., and Christensen, M.: Constraining the aerosol influence on cloud liquid water path, Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp-19-5331-2019, 2019. a
Gryspeerdt, E., McCoy, D. T., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J., Sorooshian, A., and Ziemba, L.: The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data, Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, 2022. a, b
Hamby, D. M.: A review of techniques for parameter sensitivity analysis of environmental models, Environ. Monit. Assess., 32, 135–154, https://doi.org/10.1007/BF00547132, 1994. a
Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: The Effect of Cloud Type on Earth's Energy Balance: Global Analysis, J. Clim., 5, 1281–1304, https://doi.org/10.1175/1520-0442(1992)005<1281:TEOCTO>2.0.CO;2, 1992. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., 25 Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023a. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023b. a
Heskes, T., Sijben, E., Bucur, I. G., and Claassen, T.: Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models, Adv. Neural Informat. Process. Syst., 33, 4778–4789, https://doi.org/10.48550/arXiv.2011.01625, 2020. a
Jiang, H., Xue, H., Teller, A., Feingold, G., and Levin, Z.: Aerosol effects on the lifetime of shallow cumulus, Geophys. Res. Lett., 33, L14806, https://doi.org/10.1029/2006GL026024, 2006. a
Kapoor, S., Cantrell, E., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., Hofman, J. M., Hullman, J., Lones, M. A., Malik, M. M., Nanayakkara, P., Poldrack, R. A., Raji, I. D., Roberts, M., Salganik, M. J., Serra-Garcia, M., Stewart, B. M., Vandewiele, G., and Narayanan, A.: REFORMS: Reporting Standards for Machine Learning Based Science, Arxiv, https://arxiv.org/abs/2308.07832, 2023. a
Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H. A., and Kumar, V.: Machine Learning for the Geosciences: Challenges and Opportunities, Arxiv, https://arxiv.org/abs/1711.04708, 2017. a
Kaufman, Y. J. and Koren, I.: Smoke and Pollution Aerosol Effect on Cloud Cover, Science, 313, 655–658, https://doi.org/10.1126/science.1126232, 2006. a
Kazil, J., Feingold, G., Wang, H., and Yamaguchi, T.: On the interaction between marine boundary layer cellular cloudiness and surface heat fluxes, Atmos. Chem. Phys., 14, 61–79, https://doi.org/10.5194/acp-14-61-2014, 2014. a
Kelleher, M. K. and Grise, K. M.: Examining Southern Ocean Cloud Controlling Factors on Daily Time Scales and Their Connections to Midlatitude Weather Systems, J. Clim., 32, 5145–5160, https://doi.org/10.1175/JCLI-D-18-0840.1, 2019. a, b
Kim, M., Brunner, D., and Kuhlmann, G.: Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning, Remote Sens. Environ., 264, 112573, https://doi.org/10.1016/j.rse.2021.112573, 2021. a
Klein, S. A. and Hartmann, D. L.: The Seasonal Cycle of Low Stratiform Clouds, J. Clim., 6, 1587–1606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993. a
Leahy, L. V., Wood, R., Charlson, R. J., Hostetler, C. A., Rogers, R. R., Vaughan, M. A., and Winker, D. M.: On the nature and extent of optically thin marine low clouds, J. Geophys. Res.-Atmos., 117, D22201, https://doi.org/10.1029/2012JD017929, 2012. a
Li, W., Migliavacca, M., Forkel, M., Denissen, J. M. C., Reichstein, M., Yang, H., Duveiller, G., Weber, U., and Orth, R.: Widespread increasing vegetation sensitivity to soil moisture, Nat. Commun., 13, 3959, https://doi.org/10.1038/s41467-022-31667-9, 2022. a, b, c
Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S.: Explainable AI: A Review of Machine Learning Interpretability Methods, Entropy, 23, 18, https://doi.org/10.3390/e23010018, 2021. a
Liu, Y., Lin, T., Zhang, J., Wang, F., Huang, Y., Wu, X., Ye, H., Zhang, G., Cao, X., and de Leeuw, G.: Opposite effects of aerosols and meteorological parameters on warm clouds in two contrasting regions over eastern China, Atmos. Chem. Phys., 24, 4651–4673, https://doi.org/10.5194/acp-24-4651-2024, 2024. a
Loeb, N. G. and Schuster, G. L.: An observational study of the relationship between cloud, aerosol and meteorology in broken low-level cloud conditions, J. Geophys. Res.-Atmos., 113, D14214, https://doi.org/10.1029/2007JD009763, 2008. a
Lundberg, S. M. and Lee, S. I.: A unified approach to interpreting model predictions, Adv. Neur. In., abs/1705.07874, https://doi.org/10.48550/arXiv.1705.07874, 2017. a, b
Lundberg, S. M., Erion, G. G., and Lee, S.-I.: Consistent Individualized Feature Attribution for Tree Ensembles, ArXiv, http://arxiv.org/abs/1802.03888, 2018. a
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I.: From local explanations to global understanding with explainable AI for trees, Nat. Mach. In., 2, 56–67, https://doi.org/10.1038/s42256-019-0138-9, 2020. a, b, c
McCoy, D. T., Bender, F. A.-M., Mohrmann, J. K. C., Hartmann, D. L., Wood, R., and Grosvenor, D. P.: The global aerosol-cloud first indirect effect estimated using MODIS, MERRA, and AeroCom, J. Geophys. Res.-Atmos., 122, 1779–1796, https://doi.org/10.1002/2016JD026141, 2017a. a
McCoy, D. T., Eastman, R., Hartmann, D. L., and Wood, R.: The Change in Low Cloud Cover in a Warmed Climate Inferred from AIRS, MODIS, and ERA-Interim, J. Clim., 30, 3609–3620, https://doi.org/10.1175/JCLI-D-15-0734.1, 2017b. a
Merk, D., Deneke, H., Pospichal, B., and Seifert, P.: Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations, Atmos. Chem. Phys., 16, 933–952, https://doi.org/10.5194/acp-16-933-2016, 2016. a
Michibata, T., Suzuki, K., Sato, Y., and Takemura, T.: The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals, Atmos. Chem. Phys., 16, 15413–15424, https://doi.org/10.5194/acp-16-15413-2016, 2016. a
Miyamoto, A., Nakamura, H., and Miyasaka, T.: Influence of the Subtropical High and Storm Track on Low-Cloud Fraction and Its Seasonality over the South Indian Ocean, J. Clim., 31, 4017–4039, https://doi.org/10.1175/JCLI-D-17-0229.1, 2018. a, b
Molnar, C.: Interpretable Machine Learning, 2nd Edn., https://christophm.github.io/interpretable-ml-book (last access: 17 November 2024), 2022. a
Myers, T. A. and Norris, J. R.: Observational Evidence That Enhanced Subsidence Reduces Subtropical Marine Boundary Layer Cloudiness, J. Clim., 26, 7507–7524, https://doi.org/10.1175/JCLI-D-12-00736.1, 2013. a, b, c
Myers, T. A. and Norris, J. R.: On the Relationships between Subtropical Clouds and Meteorology in Observations and CMIP3 and CMIP5 Models, J. Clim., 28, 2945–2967, https://doi.org/10.1175/JCLI-D-14-00475.1, 2015. a, b
Myers, T. A. and Norris, J. R.: Reducing the uncertainty in subtropical cloud feedback, Geophys. Res. Lett., 43, 2144–2148, https://doi.org/10.1002/2015GL067416, 2016. a
Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., and Caldwell, P. M.: Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity, Nat. Clim. Change, 11, 501–507, https://doi.org/10.1038/s41558-021-01039-0, 2021. a, b
Padarian, J., McBratney, A. B., and Minasny, B.: Game theory interpretation of digital soil mapping convolutional neural networks, Soil, 6, 389–397, https://doi.org/10.5194/soil-6-389-2020, 2020. a
Platnick, S. and Twomey, S.: Determining the Susceptibility of Cloud Albedo to Changes in Droplet Concentration with the Advanced Very High Resolution Radiometer, J. Appl. Meteorol. Climatol., 33, 334–347, https://doi.org/10.1175/1520-0450(1994)033<0334:DTSOCA>2.0.CO;2, 1994. a
Platnick, S., King, M., and Hubanks, P.: MODIS Atmosphere L3 Daily Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD08_D3.061, 2015. a
Qu, X., Hall, A., Klein, S. A., and Caldwell, P. M.: The strength of the tropical inversion and its response to climate change in 18 CMIP5 models, Clim. Dynam., 45, 375–396, https://doi.org/10.1007/s00382-014-2441-9, 2015a. a
Qu, X., Hall, A., Klein, S. A., and Deangelis, A. M.: Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors, Geophys. Res. Lett., 42, 7767–7775, https://doi.org/10.1002/2015GL065627, 2015b. a, b, c, d
Quaas, J., Boucher, O., and Lohmann, U.: Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data, Atmos. Chem. Phys., 6, 947–955, https://doi.org/10.5194/acp-6-947-2006, 2006. a, b
Rieck, M., Nuijens, L., and Stevens, B.: Marine Boundary Layer Cloud Feedbacks in a Constant Relative Humidity Atmosphere, J. Atmos. Sci., 69, 2538–2550, https://doi.org/10.1175/JAS-D-11-0203.1, 2012. a, b
Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., and Yu, S.: Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363, 6427, https://doi.org/10.1126/science.aav0566, 2019. a, b, c
Sato, Y., Goto, D., Michibata, T., Suzuki, K., Takemura, T., Tomita, H., and Nakajima, T.: Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model, Nat. Commun., 9, 1–7, https://doi.org/10.1038/s41467-018-03379-6, 2018. a
Schwarz, K., Cermak, J., Fuchs, J., and Andersen, H.: Mapping the Twilight Zone – What We Are Missing between Clouds and Aerosols, Remote Sens., 9, 577, https://doi.org/10.3390/rs9060577, 2017. a
Scott, R. C., Myers, T. A., Norris, J. R., Zelinka, M. D., Klein, S. A., Sun, M., and Doelling, D. R.: Observed Sensitivity of Low-Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans, J. Clim., 33, 7717–7734, https://doi.org/10.1175/JCLI-D-19-1028.1, 2020. a, b, c, d, e, f, g
Seifert, A., Heus, T., Pincus, R., and Stevens, B.: Large-eddy simulation of the transient and near-equilibrium behavior of precipitating shallow convection, J. Adv. Model. Earth Sy., 7, 1918–1937, https://doi.org/10.1002/2015MS000489, 2015. a
Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J., Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas, I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather, K. A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R., Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system, P. Natl. Acad. Sci. USA, 113, 5781–5790, https://doi.org/10.1073/pnas.1514043113, 2016. a
Small, J. D., Chuang, P. Y., Feingold, G., and Jiang, H.: Can aerosol decrease cloud lifetime?, Geophys. Res. Lett., 36, 1–5, https://doi.org/10.1029/2009GL038888, 2009. a
Small, J. D., Jiang, J. H., Su, H., and Zhai, C.: Relationship between aerosol and cloud fraction over Australia, Geophys. Res. Lett., 38, L23802, https://doi.org/10.1029/2011GL049404, 2011. a, b
Snoek, J., Larochelle, H., and Adams, R. P.: Practical Bayesian Optimization of Machine Learning Algorithms, in: Advances in Neural Information Processing Systems, edited by Pereira, F., Burges, C. J., Bottou, L., and Weinberger, K. Q., vol. 25, Curran Associates, Inc., 9 pp., https://arxiv.org/abs/1206.2944 (last access: 17 November 2024), 2012. a
Stirnberg, R., Cermak, J., Kotthaus, S., Haeffelin, M., Andersen, H., Fuchs, J., Kim, M., Petit, J. E., and Favez, O.: Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning, Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, 2021. a, b, c
Su, W., Loeb, N. G., Xu, K.-M., Schuster, G. L., and Eitzen, Z. A.: An estimate of aerosol indirect effect from satellite measurements with concurrent meteorological analysis, J. Geophys. Res.-Atmos., 115, D18219, https://doi.org/10.1029/2010JD013948, 2010. a, b
Sundararajan, M. and Najmi, A.: The many shapley values for model explanation, in: 37th International Conference on Machine Learning, ICML 2020, PartF16814, 9210–9220, ISBN 9781713821120, 2020. a
Toll, V., Christensen, M., Quaas, J., and Bellouin, N.: Weak average liquid-cloud-water response to anthropogenic aerosols, Nature, 572, 51–55, https://doi.org/10.1038/s41586-019-1423-9, 2019. a
Turner, D. D.: Improved ground-based liquid water path retrievals using a combined infrared and microwave approach, J. Geophys. Res.-Atmos., 112, D15204, https://doi.org/10.1029/2007JD008530, 2007. a
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:tiopot>2.0.co;2, 1977. a
van der Dussen, J. J., de Roode, S. R., Dal Gesso, S., and Siebesma, A. P.: An LES model study of the influence of the free tropospheric thermodynamic conditions on the stratocumulus response to a climate perturbation, J. Adv. Model. Earth Sy., 7, 670–691, https://doi.org/10.1002/2014MS000380, 2015. a
Wall, C. J., Hartmann, D. L., and Ma, P.-L.: Instantaneous Linkages between Clouds and Large-Scale Meteorology over the Southern Ocean in Observations and a Climate Model, J. Clim., 30, 9455–9474, https://doi.org/10.1175/JCLI-D-17-0156.1, 2017. a
Wang, S., Wang, Q., and Feingold, G.: Turbulence, Condensation, and Liquid Water Transport in Numerically Simulated Nonprecipitating Stratocumulus Clouds, J. Atmos. Sci., 60, 262–278, https://doi.org/10.1175/1520-0469(2003)060<0262:TCALWT>2.0.CO;2, 2003. a
Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423, https://doi.org/10.1175/MWR-D-11-00121.1, 2012. a
Wood, R. and Bretherton, C. S.: On the relationship between stratiform low cloud cover and lower-tropospheric stability, J. Clim., 19, 6425–6432, https://doi.org/10.1175/JCLI3988.1, 2006. a, b
Wood, R. and Hartmann, D. L.: Spatial Variability of Liquid Water Path in Marine Low Cloud: The Importance of Mesoscale Cellular Convection, J. Clim., 19, 1748–1764, https://doi.org/10.1175/JCLI3702.1, 2006. a
Xue, H. and Feingold, G.: Large-Eddy Simulations of Trade Wind Cumuli: Investigation of Aerosol Indirect Effects, J. Atmos. Sci., 63, 1605–1622, https://doi.org/10.1175/JAS3706.1, 2006. a
Young, G. S., Kristovich, D. A. R., Hjelmfelt, M. R., and Foster, R. C.: Rolls, Streets, Waves, And More: A Review of Quasi-Two-Dimensional Structures in the Atmospheric Boundary Layer, Bull. Am. Meteorol. Soc., 83, 997–1002, https://doi.org/10.1175/1520-0477(2002)083<0997:RSWAMA>2.3.CO;2, 2002. a
Yuan, T. and Oreopoulos, L.: On the global character of overlap between low and high clouds, Geophys. Res. Lett., 40, 5320–5326, https://doi.org/10.1002/grl.50871, 2013. a
Yuan, T., Remer, L. A., and Yu, H.: Microphysical, macrophysical and radiative signatures of volcanic aerosols in trade wind cumulus observed by the A-Train, Atmos. Chem. Phys., 11, 7119–7132, https://doi.org/10.5194/acp-11-7119-2011, 2011. a
Yuan, T., Song, H., Wood, R., Oreopoulos, L., Platnick, S., Wang, C., Yu, H., Meyer, K., and Wilcox, E.: Observational evidence of strong forcing from aerosol effect on low cloud coverage, Sci. Adv., 9, eadh7716, https://doi.org/10.1126/sciadv.adh7716, 2023. a, b, c
Zelinka, M. D., Andrews, T., Forster, P. M., and Taylor, K. E.: Quantifying components of aerosol-cloud-radiation interactions in climate models, J. Geophys. Res.-Atmos., 119, 7599–7615, https://doi.org/10.1002/2014JD021710, 2014. a
Zelinka, M. D., Grise, K. M., Klein, S. A., Zhou, C., DeAngelis, A. M., and Christensen, M. W.: Drivers of the Low-Cloud Response to Poleward Jet Shifts in the North Pacific in Observations and Models, J. Clim., 31, 7925–7947, https://doi.org/10.1175/JCLI-D-18-0114.1, 2018. a, b
Zhang, Z. and Platnick, S.: An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands, J. Geophys. Res.-Atmos., 116, D20215, https://doi.org/10.1029/2011JD016216, 2011. a
Zhang, Z., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue, H.: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large-eddy simulations, J. Geophys. Res.-Atmos., 117, D19208, https://doi.org/10.1029/2012JD017655, 2012. a
Zheng, G., Wang, Y., Wood, R., Jensen, M. P., Kuang, C., McCoy, I. L., Matthews, A., Mei, F., Tomlinson, J. M., Shilling, J. E., Zawadowicz, M. A., Crosbie, E., Moore, R., Ziemba, L., Andreae, M. O., and Wang, J.: New particle formation in the remote marine boundary layer, Nat. Commun., 12, 527, https://doi.org/10.1038/s41467-020-20773-1, 2021. a
Zhu, Y., Rosenfeld, D., and Li, Z.: Under What Conditions Can We Trust Retrieved Cloud Drop Concentrations in Broken Marine Stratocumulus?, J. Geophys. Res.-Atmos., 123, 8754–8767, https://doi.org/10.1029/2017JD028083, 2018. a
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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