Papers on Climate and Atmospheric Physics
Albedo bias and the horizontal variability of clouds in subtropical marine boundary layers: Observations from ships and satellites
Article first published online: 21 SEP 2012
Copyright 1999 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 104, Issue D6, pages 6183–6191, 27 March 1999
How to Cite
1999), Albedo bias and the horizontal variability of clouds in subtropical marine boundary layers: Observations from ships and satellites, J. Geophys. Res., 104(D6), 6183–6191, doi:10.1029/1998JD200125., , and (
- Issue published online: 21 SEP 2012
- Article first published online: 21 SEP 2012
- Manuscript Accepted: 3 DEC 1998
- Manuscript Received: 12 JUL 1998
Cloud optical properties vary dramatically at spatial scales smaller than typical grid cells in large-scale models, which can cause a significant overestimate of cloud albedo by the model. This plane parallel homogeneous (PPH) albedo bias exist may be reduced if the mean cloud optical thickness and the amount of variability are available, but little is known about how much variability exists in nature and to what factors it is sensitive. The authors combine 1331 observations made by volunteer surface observers with satellite imagery to assess the relationships between cloud fraction, cloud optical properties, and cloud type in marine boundary layer clouds off the coast of California during summer. Estimates of cloud fraction from the two datasets are in best agreement when a reflectance threshold between 0.09 and 0.10 is used. Satellite-derived cloud fraction increases slowly with sensor resolution at spatial scales from 1 to 32 km. Cloud fraction in scenes dominated by cumulus is much more sensitive to the reflectance threshold used for cloud detection than are scenes containing stratiform clouds. The mean magnitude of the PPH bias found here, 0.025, is considerably smaller than those found in other recent studies. When fit to the observed distributions of optical thickness both log-normal and gamma distributions substantially reduce the PPH bias. The mean and dispersion of log optical thickness are related to cloud type: optical thickness increases as cloud type changes from cumuliform to stratiform, while the relative amount of variability decreases. The authors suggest a basis for the parameterization of unresolved variability in large scale models.