I. Colin Prentice
Sarah Cornell
T Davies-Barnard
Adrien Finzi
Oskar Franklin
Ivan Janssens
Tuula Larmola
Stefano Manzoni
Torgny Nasholm
John Raven
Karin Rebel
Sasha C. Reed
Sara Vicca
Andy Wiltshire
Sonke Zaehle
Benjamin Stocker
2016
<div class="t m0 x0 h4 y6 ff4 fs3 fc1 sc0 ls0 ws0">Understanding the degree to which nitrogen (N) availability limits land carbon (C) uptake under global environmental change represents an unresolved challenge. First-generation ‘C-only’vegetation models, lacking explicit representations of N cycling,projected a substantial and increasing land C sink under rising atmospheric CO<sub>2</sub> concentrations. This prediction was questioned for not taking into account the potentially limiting effect of N availability, which is necessary for plant growth (Hungate et al.,2003). More recent global models include coupled C and N cycles in land ecosystems (C–N models) and are widely assumed to be more realistic. However, inclusion of more processes has not consistently improved their performance in capturing observed responses of the global C cycle (e.g. Wenzel et al., 2014). With the advent of a new generation of global models, including coupled C, N, and phosphorus (P) cycling, model complexity is sure to increase; but model reliability may not, unless greater attention is paid to the correspondence of model process representations ande mpirical evidence. It was in this context that the ‘Nitrogen Cycle Workshop’ at Dartington Hall, Devon, UK was held on 1–5 February 2016. Organized by I. Colin Prentice and Benjamin D. Stocker (Imperial College London, UK), the workshop was funded by the European Research Council,project ‘Earth system Model Bias Reduction and assessing Abrupt Climate change’ (EMBRACE). We gathered empirical ecologists and ecosystem modellers to identify key uncertainties in terrestrial C–N cycling, and to discuss processes that are missing or poorly represented in current models.</div>
application/pdf
10.1111/nph.13997
en
Blackwell Science
Terrestrial nitrogen cycling in Earth system models revisited
article