Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 17, 9535-9546, 2017
https://doi.org/10.5194/acp-17-9535-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
08 Aug 2017
Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks
Hendrik Andersen1,2, Jan Cermak1,2, Julia Fuchs1,2, Reto Knutti3, and Ulrike Lohmann3 1Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
2Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
3ETH Zürich, Institute of Atmospheric and Climate Science, Zurich, Switzerland
Abstract. The role of aerosols, clouds and their interactions with radiation remain among the largest unknowns in the climate system. Even though the processes involved are complex, aerosol–cloud interactions are often analyzed by means of bivariate relationships. In this study, 15 years (2001–2015) of monthly satellite-retrieved near-global aerosol products are combined with reanalysis data of various meteorological parameters to predict satellite-derived marine liquid-water cloud occurrence and properties by means of region-specific artificial neural networks. The statistical models used are shown to be capable of predicting clouds, especially in regions of high cloud variability. On this monthly scale, lower-tropospheric stability is shown to be the main determinant of cloud fraction and droplet size, especially in stratocumulus regions, while boundary layer height controls the liquid-water amount and thus the optical thickness of clouds. While aerosols show the expected impact on clouds, at this scale they are less relevant than some meteorological factors. Global patterns of the derived sensitivities point to regional characteristics of aerosol and cloud processes.

Citation: Andersen, H., Cermak, J., Fuchs, J., Knutti, R., and Lohmann, U.: Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks, Atmos. Chem. Phys., 17, 9535-9546, https://doi.org/10.5194/acp-17-9535-2017, 2017.
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Short summary
Aerosol-cloud interactions continue to contribute large uncertainties to our climate system understanding. In this study, we use near-global satellite and reanalysis data sets to predict marine liquid-water clouds by means of artificial neural networks. We show that on the system scale, lower-tropospheric stability and boundary layer height are the main determinants of liquid-water clouds. Aerosols show the expected impact on clouds but are less relevant than some meteorological factors.
Aerosol-cloud interactions continue to contribute large uncertainties to our climate system...
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