Using MODIS BRDF/Albedo Data to Evaluate and Improve Land Surface Albedo in Weather and Climate Models

Abstract

Land surface albedo plays a key role in the surface-atmosphere internaction, because it greatly influences the shortwave radiation absorbed by the surface. Surface albedo depends on soil characteristics and vegetation types. Error in the specification of albedos of soil and vegetation may cause biases in the computation of ground temperature and surface fluxes, therefore accurate albedo estimates are essential for an accurate simulation of the Earth's climate. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterization in weather forecast and climate models as well as the remote sensing retrieval of surface solar fluxes through a series of three papers. First, the NCAR Community Climate System Model (CCSM2) albedo is evaluated using the MODIS BRDF and albedo data. The model and MODIS albedo differences are related to the deficiences in the model simulation of snow cover and soil moisture and in the model's specification of leaf and stem area indexes. They are also partially caused by the deficiency of the two-stream method. Second, motivated by these analyses, a new formulation for surface albedo is developed. Over desert, most land models assume that the bare soil albedo is a function of soil color and soil moisture but independent of solar zenith angle (SZA). However, analysis of MODIS BRDF/albedo data and in situ data indicates that bare soil albedo does vary with SZA. Furthermore this SZA dependence is found to affect the surface energy fluxes and temperature in the offline land surface model sensitivity tests. Finally, the MODIS BRDF algorithm is reformulated to derive a new two-parameter scheme for the computation of land surface albedo and its SZA dependence for use in weather and climate models as well as the remote sensing retrieval of surface solar fluxes. In this formulation, the season- and pixel-dependent black-sky albedo at 60 deg SZA can be directly prescribed using the MODIS BRDF data while the two parameters are taken as a function of vegetation type only. Comparison of this formulation with those used in weather, climate, and data assimilation models (at NCAR, NCEP, and NASA) as well as those used in remote sensing groups (University of Maryland, ISCCP-FD, and CERES/TRMM) reveals the deficiencies in the land surface albedo treatment in these models and remote sensing retrieval algorithm along with suggestions for improvement.