Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration
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, climate models 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=0.01+0.071%N, r²=0.91; forests, grassland, and maize: albedo=0.02+0.067%N, r²=0.80). 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.
Hollinger, DY; Ollinger, SV; Richardson, AD; Meyers, TP; Dail, DB; Martin, ME; Scott, NA; Arkebauer, TJ; Baldocchi, DD; Clark, KL; Curtis, PS; Davis, KJ; Desai, AR; Dragoni, D; Goulden, ML; Gu, L; Katul, GG; Pallard, SG; Paw U., KT; Schmid, HP; Stoy, PC; Suyker, AE; Verma, SB
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