predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data
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2018
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Abstract
Cloud droplet number concentration (CDNC) is the key state
variable that moderates the relationship between aerosol and the radiative
forcing arising from aerosol–cloud interactions. Uncertainty related to the
effect of anthropogenic aerosol on cloud properties represents the largest
uncertainty in total anthropogenic radiative forcing. Here we show that
regionally averaged time series of the Moderate-Resolution Imaging
Spectroradiometer (MODIS) observed CDNC of low, liquid-topped clouds is
well predicted by the MERRA2 reanalysis near-surface sulfate mass concentration
over decadal timescales. A multiple linear regression between MERRA2
reanalyses masses of sulfate (SO4), black carbon (BC), organic carbon
(OC), sea salt (SS), and dust (DU) shows that CDNC across many different
regimes can be reproduced by a simple power-law fit to near-surface
SO4, with smaller contributions from BC, OC, SS, and DU. This confirms
previous work using a less sophisticated retrieval of CDNC on monthly timescales. The analysis is supported by an examination of remotely sensed sulfur
dioxide (SO2) over maritime volcanoes and the east coasts of North
America and Asia, revealing that maritime CDNC responds to changes in
SO2 as observed by the ozone monitoring instrument (OMI). This
investigation of aerosol reanalysis and top-down remote-sensing observations
reveals that emission controls in Asia and North America have decreased CDNC
in their maritime outflow on a decadal timescale.
| Reference Key |
mccoy2018atmosphericpredicting
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|---|---|
| Authors | ;D. T. McCoy;F. A.-M. Bender;D. P. Grosvenor;D. P. Grosvenor;J. K. Mohrmann;D. L. Hartmann;R. Wood;P. R. Field;P. R. Field |
| Journal | Journal of agricultural and food chemistry |
| Year | 2018 |
| DOI |
10.5194/acp-18-2035-2018
|
| URL | |
| Keywords |
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