ALBERT

Beschreibung: Changes to global net primary production (NPP), vegetation biomass carbon (VegC), and soil organic carbon (SOC) estimated by six global vegetation models (GVM) obtained from an Inter-Sectoral Impact Model Intercomparison Project study were examined. Simulation results were obtained using five global climate models (GCM) forced with four representative concentration pathway (RCP) scenarios. To clarify which component (emission scenarios, climate projections, or global vegetation models) contributes the most to uncertainties in projected global terrestrial C cycling by 2100, analysis of variance (ANOVA) and wavelet clustering were applied to 70 projected simulation sets. In the end of simulation period, the changes from the year of 2000 in all three variables considerably varied from net negative to positive values. ANOVA revealed that the main sources of uncertainty are different among variables and depend on the projection period. We determined that in the global VegC, and SOC projections, GVMs dominate uncertainties (60 and 90%, respectively) rather than climate driving scenarios, i.e., RCPs and GCMs. These results suggested that we don't have still enough resolution among each RCP scenario to evaluate climate change impacts on ecosystem conditions in global terrestrial C cycling. In addition, we found that the contributions of each uncertainty source were spatio-temporally heterogeneous and differed among the GVM variables. The dominant uncertainty source for changes in NPP and VegC varies along the climatic gradient. The contribution of GVM to the uncertainty decreases as the climate division gets cooler (from ca. 80% in the equatorial division to 40% in the snow climatic division). To evaluate the effects of climate change on ecosystems with practical resolution in RCP scenarios, GVMs require further improvement to reduce the uncertainties in global C cycling as much as, if not more than, GCMs. Our study suggests that the improvement of GVMs is a priority for the reduction of total uncertainties in projected C cycling for climate impact assessments.