The interannual variability of Africa's ecosystem productivity: a multi-model analysis
- 1Department of Forest Science and Environment,University of Tuscia, Viterbo, Italy
- 2Max Planck Institute for Biogeochemistry, Jena, Germany
- 3GeoBiosphere Science Centre, Lund University, Sweden
- 4Department of Geography, University of Leicester, UK
- 5Laboratoire des Sciences du Climate et de l' Environnement, Gif-sur-Yvette, France
- 6European Commission – DG Joint Research Centre, Institute for Environment and Sustainability, Global Environment Monitoring Unit, Ispra (VA), Italy
Abstract. We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data.
While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are partly corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.