Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks
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 mon...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-08-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/17/9535/2017/acp-17-9535-2017.pdf |
| Summary: | 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. |
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| ISSN: | 1680-7316 1680-7324 |