Low Cloud Cover Sensitivity to Biomass-Burning Aerosols and Meteorology over...

The core information for this publication's citation.: 
Adebiyi, A., and P. Zuidema (2018), Low Cloud Cover Sensitivity to Biomass-Burning Aerosols and Meteorology over the Southeast Atlantic, J. Climate, 31, 4329-4346, doi:10.1175/JCLI-D-17-0406.1.
Abstract: 

Shortwave-absorbing aerosols seasonally cover and interact with an expansive low-level cloud deck over the southeast Atlantic. Daily anomalies of the MODIS low cloud fraction, fine-mode aerosol optical depth (AODf), and six ERA-Interim meteorological parameters (lower-tropospheric stability, 800-hPa subsidence, 600-hPa specific humidity, 1000- and 800-hPa horizontal temperature advection, and 1000-hPa geopotential height) are constructed spanning July–October (2001–12). A standardized multiple linear regression, whereby the change in the low cloud fraction to each component’s variability is normalized by one standard deviation, facilitates comparison between the different variables. Most cloud–meteorology relationships follow expected behavior for stratocumulus clouds. Of interest is the low cloud–subsidence relationship, whereby increasing subsidence increases low cloud cover between 108 and 208S but decreases it elsewhere. Increases in AODf increase cloudiness everywhere, independent of other meteorological predictors. The cloud–AODf effect is partially compensated by accompanying increases in the midtropospheric moisture, which is associated with decreases in low cloud cover. This suggests that the free-tropospheric moisture affects the low cloud deck primarily through longwave radiation rather than mixing. The low cloud cover is also more sensitive to aerosol when the vertical distance between the cloud and aerosol layer is relatively small, which is more likely to occur early in the biomass burning season and farther offshore. A parallel statistical analysis that does not include AODf finds altered relationships between the low cloud cover changes and meteorology that can be understood through the aerosol cross-correlations with the meteorological predictors. For example, the low cloud–stability relationship appears stronger if aerosols are not explicitly included.

PDF of Publication: 
Download from publisher's website.
Research Program: 
Radiation Science Program (RSP)
Mission: 
ORACLES