Abstract
Vegetation albedo is a critical component of the Earth’s climate system, yet efforts to evaluate and
improve albedo parameterizations in climate models have lagged relative to other aspects of
model development. Here, we calculated growing season albedos for deciduous and evergreen
forests, crops, and grasslands based on over 40 site-years of data from the AmeriFlux network and
compared them with estimates presently used in the land surface formulations of a variety of
climate models. Generally, the albedo estimates used in land surface models agreed well with
this data compilation. However, a variety of models using fixed seasonal estimates of albedo
overestimated the growing season albedo of northerly evergreen trees. In contrast, climatemodels
that rely on a common two-stream albedo submodel provided accurate predictions of boreal
needle-leaf evergreen albedo but overestimated grassland albedos. Inverse analysis showed that
parameters of the two-stream model were highly correlated. Consistent with recent observations
based on remotely sensed albedo, the AmeriFlux dataset demonstrated a tight linear relationship
between canopy albedo and foliage nitrogen concentration (for forest vegetation: albedo
50.0110.071%N, r250.91; forests, grassland, and maize: albedo50.0210.067%N, r250.80).
However, this relationship saturated at the higher nitrogen concentrations displayed by soybean
foliage. We developed similar relationships between a foliar parameter used in the two-stream
albedo model and foliage nitrogen concentration. These nitrogen-based relationships can serve as
the basis for a new approach to land surface albedo modeling that simplifies albedo estimation
while providing a link to other important ecosystem processes.
