Impacts of trait variation through observed trait–climate relationships on performance of an Earth system model: a conceptual analysis
- 1VU University Amsterdam, Systems Ecology, Department of Ecological Science, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands
- 2Max Planck Institute for Meteorology, Bundesstrasse 55, 20146 Hamburg, Germany
- 3Max Planck Institute for Biogeochemistry, Hans Knoell Strasse 10, 07745 Jena, Germany
- 4University of Minnesota, Department of Forest Resources, 1530 Cleveland Avenue North, St Paul, MN 55108 USA
- 5University of Western Sydney, Hawkesbury Institute for the Environment, Penrith NSW 2753, Australia
- 6Macquarie University, Department of Biological Sciences, Sydney, NSW 2109, Australia
Abstract. In many current dynamic global vegetation models (DGVMs), including those incorporated into Earth system models (ESMs), terrestrial vegetation is represented by a small number of plant functional types (PFTs), each with fixed properties irrespective of their predicted occurrence. This contrasts with natural vegetation, in which many plant traits vary systematically along geographic and environmental gradients. In the JSBACH DGVM, which is part of the MPI-ESM, we allowed three traits (specific leaf area (SLA), maximum carboxylation rate at 25 °C (Vcmax25) and maximum electron transport rate at 25 °C (Jmax25)) to vary within PFTs via trait–climate relationships based on a large trait database. The R2adjusted of these relationships were up to 0.83 and 0.71 for Vcmax25 and Jmax25, respectively. For SLA, more variance remained unexplained, with a maximum R2adjusted of 0.40. Compared to the default simulation, allowing trait variation within PFTs resulted in gross primary productivity differences of up to 50% in the tropics, in > 35% different dominant vegetation cover, and a closer match with a natural vegetation map. The discrepancy between default trait values and natural trait variation, combined with the substantial changes in simulated vegetation properties, together emphasize that incorporating climate-driven trait variation, calibrated on observational data and based on ecological concepts, allows more variation in vegetation responses in DGVMs and as such is likely to enable more reliable projections in unknown climates.