Ensemble projections elucidate effects of uncertainty in terrestrial nitrogen limitation on future carbon uptake

Abstract

The magnitude of the nitrogen (N) limitation of terrestrial carbon (C) storage over the 21st century is highly uncertain because of the complex interactions between the terrestrial C and N cycles. We use an ensemble approach to quantify and attribute process-level uncertainty in C-cycle projections by analysing a 30-member ensemble representing published alternative representations of key N cycle processes (stoichiometry, biological nitrogen fixation (BNF) and ecosystem N losses) within the framework of one terrestrial biosphere model. Despite large differences in the simulated present-day N cycle, primarily affecting simulated productivity north of 40°N, ensemble members generally conform with global C-cycle benchmarks for present-day conditions. Ensemble projections for two representative concentration pathways (RCP 2.6 and RCP 8.5) show that the increase in land C storage due to CO₂ fertilization is reduced by 24 ± 15% due to N constraints, whereas terrestrial C losses associated with climate change are attenuated by 19 ± 20%. As a result, N cycling reduces projected land C uptake for the years 2006-2099 by 19% (37% decrease to 3% increase) for RCP 2.6, and by 21% (40% decrease to 9% increase) for RCP 8.5. Most of the ensemble spread results from uncertainty in temperate and boreal forests, and is dominated by uncertainty in BNF (10% decrease to 50% increase for RCP 2.6, 5% decrease to 100% increase for RCP 8.5). However, choices about the flexibility of ecosystem C:N ratios and processes controlling ecosystem N losses regionally also play important roles. The findings of this study demonstrate clearly the need for an ensemble approach to quantify likely future terrestrial C-N cycle trajectories. Present-day C-cycle observations only weakly constrain the future ensemble spread, highlighting the need for better observational constraints on large-scale N cycling, and N cycle process responses to global change.

Keywords: CO2 fertilization; biogeochemical modelling; carbon-climate feedbacks; land surface models; model evaluation; terrestrial ecosystem modelling.