Articles | Volume 10, issue 12
Biogeosciences, 10, 8305–8328, 2013

Special issue: Improving constraints on biospheric feedbacks in Earth system...

Biogeosciences, 10, 8305–8328, 2013

Reviews and syntheses 16 Dec 2013

Reviews and syntheses | 16 Dec 2013

Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art

A. M. Foley1,*, D. Dalmonech2, A. D. Friend1, F. Aires3, A. T. Archibald4, P. Bartlein5, L. Bopp6, J. Chappellaz7, P. Cox8, N. R. Edwards9, G. Feulner10, P. Friedlingstein8, S. P. Harrison11, P. O. Hopcroft12, C. D. Jones13, J. Kolassa3, J. G. Levine14,**, I. C. Prentice15, J. Pyle4, N. Vázquez Riveiros16, E. W. Wolff14,***, and S. Zaehle2 A. M. Foley et al.
  • 1Department of Geography, University of Cambridge, Cambridge, UK
  • 2Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Jena, Germany
  • 3Estellus, Paris, France
  • 4Centre for Atmospheric Science, University of Cambridge, Cambridge, UK
  • 5Department of Geography, University of Oregon, Eugene, Oregon, USA
  • 6Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
  • 7UJF – Grenoble I and CNRS Laboratoire de Glaciologie et Géophysique de l'Environnement, Grenoble, France
  • 8College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
  • 9Environment, Earth and Ecosystems, The Open University, Milton Keynes, UK
  • 10Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 11Department of Biological Sciences, Macquarie University, Sydney, Australia and Geography and Environmental Sciences, School of Human and Environmental Sciences, Reading University, Reading, UK
  • 12BRIDGE, School of Geographical Science, University of Bristol, Bristol, UK
  • 13Met Office Hadley Centre, Exeter, UK
  • 14British Antarctic Survey, Cambridge, UK
  • 15AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences and Grantham Institute for Climate Change, Imperial College, Silwood Park, UK and Department of Biological Sciences, Macquarie University, Sydney, Australia
  • 16Godwin Laboratory for Palaeoclimate Research, Department of Earth Sciences, University of Cambridge, Cambridge, UK
  • *now at: Cambridge Centre for Climate Change Mitigation Research, Department of Land Economy, University of Cambridge, Cambridge, UK
  • **now at: School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
  • ***now at: Department of Earth Sciences, University of Cambridge, Cambridge, UK

Abstract. Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.

Final-revised paper