Variability of projected terrestrial biosphere responses to elevated levels of atmospheric CO2 due to uncertainty in biological nitrogen fixation

Abstract

Including a terrestrial nitrogen (N) cycle in Earth system models has led to substantial attenuation of predicted biosphere-climate feedbacks. However, the magnitude of this attenuation remains uncertain. A particularly important but highly uncertain process is biological nitrogen fixation (BNF), which is the largest natural input of N to land ecosystems globally. In order to quantify this uncertainty and estimate likely effects on terrestrial biosphere dynamics, we applied six alternative formulations of BNF spanning the range of process formulations in current state-of-the-art biosphere models within a common framework, the O-CN model: a global map of static BNF rates, two empirical relationships between BNF and other ecosystem variables (net primary productivity and evapotranspiration), two process-oriented formulations based on plant N status, and an optimality-based approach. We examined the resulting differences in model predictions under ambient and elevated atmospheric [CO2] and found that the predicted global BNF rates and their spatial distribution for contemporary conditions were broadly comparable, ranging from 108 to 148 Tg N yr-1 (median: 128 Tg N yr-1), despite distinct regional patterns associated with the assumptions of each approach. Notwithstanding, model responses in BNF rates to elevated levels of atmospheric [CO2] (+200 ppm) ranged between -4 Tg N yr-1 (-3 %) and 56 Tg N yr-1 (+42 %) (median: 7 Tg N yr-1 (+8 %)). As a consequence, future projections of global ecosystem carbon (C) storage (+281 to +353 Pg C, or +13 to +16 %) as well as N2O emission (-1.6 to +0.5 Tg N yr-1, or -19 to +7 %) differed significantly across the different model formulations. Our results emphasize the importance of better understanding the nature and magnitude of BNF responses to change-induced perturbations, particularly through new empirical perturbation experiments and improved model representation.