B11C-0438
Large-Scale Variation in Forest Carbon Turnover Rate and its Relation to Climate – Remote Sensing vs. Global Vegetation Models
Monday, 14 December 2015
Poster Hall (Moscone South)
Martin Thurner1, Christian Beer1, Nuno Carvalhais2,3, Matthias Forkel3, Maurizio Santoro4, Markus Tum5 and Christiane Schmullius6, (1)Stockholm University, Stockholm, Sweden, (2)Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Departamento de Ciências e Engenharia do Ambiente, DCEA, Caparica, Portugal, (3)Max Planck Institute for Biogeochemistry, Jena, Germany, (4)Gamma Remote Sensing, Guemligen, Switzerland, (5)German Aerospace Center DLR Oberpfaffenhofen, Oberpfaffenhofen, Germany, (6)Friedrich Schiller University of Jena, Department for Earth Observation, Jena, Germany
Abstract:
While vegetation productivity is known to be strongly correlated to climate, there is a need for an improved understanding of the underlying processes of vegetation carbon turnover and their importance at a global scale. This shortcoming has been due to the lack of spatially extensive information on vegetation carbon stocks, which we recently have been able to overcome by a biomass dataset covering northern boreal and temperate forests originating from radar remote sensing. Based on state-of-the-art products on biomass and NPP, we are for the first time able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests. The implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current global vegetation models. In contrast to our observation-based findings, investigated models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce spatial climate – turnover rate relationships only to a limited extent. While most of the models compare relatively well to observation-based NPP, simulated vegetation carbon stocks are severely biased compared to our biomass dataset. Current limitations lead to considerable uncertainties in the estimated vegetation carbon turnover, contributing substantially to the forest feedback to climate change. Our results are the basis for improving mortality concepts in global vegetation models and estimating their impact on the land carbon balance.
