The Plane-Parallel Albedo Bias of Liquid Clouds from MODIS Observations
Journal of Climate
We present the global plane-parallel shortwave albedo bias of liquid clouds for two months, July 2003 and January 2004. The cloud optical properties necessary to perform the bias calculations come from the operational MODIS Terra and MODIS Aqua Level-3 datasets. These data, along with ancillary surface albedo and atmospheric information consistent with the MODIS retrievals, are inserted into a broadband shortwave radiative transfer model to calculate the fluxes at the atmospheric column
... eric column boundaries. The Plane-Parallel Homogeneous (PPH) calculations are based on the mean cloud properties, while Independent Column Approximation (ICA) calculations are based either on 1D histograms of optical thickness or joint 2D histograms of optical thickness and effective radius. The (positive) PPH albedo bias is simply the difference between PPH and ICA albedo calculations. Two types of biases are therefore examined: (1) the bias due to the horizontal inhomogeneity of optical thickness alone (the effective radius is set to the grid mean value); and (2) the bias due to simultaneous variations of optical thickness and effective radius as derived from their joint histograms. We find that the global bias of albedo (liquid cloud portion of the gridboxes only) is ~+0.03 which corresponds to roughly 8% of the global liquid cloud albedo, and is only modestly sensitive to the inclusion of horizontal effective radius variability and time of day, but depends strongly on season and latitude. This albedo bias translates to ~3-3.5 Wm -2 of bias (stronger negative values) in the diurnally-averaged global shortwave cloud radiative forcing, assuming homogeneous conditions for the fraction of the gridbox not covered by liquid clouds; zonal values can be as high as 8 Wm -2 . Finally, the (positive) broadband atmospheric absorptance bias is about an order of magnitude smaller than the albedo bias. The substantial magnitude of the PPH bias underlines the importance of predicting subgrid variability in GCMs and accounting for its effects in cloud-radiation interactions.