Articles | Volume 12, issue 6
Biogeosciences, 12, 1833–1848, 2015
Biogeosciences, 12, 1833–1848, 2015

Research article 20 Mar 2015

Research article | 20 Mar 2015

Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models

M. Baudena1, S. C. Dekker1, P. M. van Bodegom2,3, B. Cuesta4, S. I. Higgins5, V. Lehsten6, C. H. Reick7, M. Rietkerk1, S. Scheiter8, Z. Yin9, M. A. Zavala4, and V. Brovkin7 M. Baudena et al.
  • 1Copernicus Institute of Sustainable Development, Environmental Sciences Group, Utrecht University, 3508 TC Utrecht, the Netherlands
  • 2VU University Amsterdam, Department of Ecological Science, de Boelelaan 1081, 1081 HV Amsterdam, the Netherlands
  • 3Leiden University, Institute of Environmental Sciences, Einsteinweg 2, 2333 CC Leiden, the Netherlands
  • 4Forest Ecology and Restoration Group, Department of Life Sciences, Ctra. Madrid–Barcelona km. 33.6, University of Alcalá, 28805 Alcalá de Henares, Madrid, Spain
  • 5Department of Botany, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand
  • 6Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62 Lund, Sweden
  • 7Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
  • 8Biodiversity and Climate Research Centre (LOEWE BiK-F), Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, 60325 Frankfurt am Main, Germany
  • 9Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, the Netherlands

Abstract. The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree–grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass–fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.

Final-revised paper