Sources of uncertainty in future projections of the carbon cycle

This is the final version of the article. Available from the publisher via the DOI in this record.The inclusion of carbon cycle processes within CMIP5 Earth System Models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way ANOVA approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between Representative Concentration Pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modelled processes that determine ocean fluxes are currently better constrained than those of land fluxes, thus we can be more confident in linking different future socio-economic pathways to consequences of ocean carbon uptake than for land carbon uptake. The apparent agreement in atmosphere-ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modelled processes represent an important source of variability in projected regional fluxesMOHC authors were supported by the Joint DECC / Defra Met Office Hadley Centre Cli- mate Programme (GA01101). SY was supported by the Hong Kong Polytechnic University grant “Bayesian Modelling for Quantifying Uncertainty in Climate Predictions” (1-ZV9Z). We acknowl- edge use of R software package (R Core Team 2013). We acknowledge the World Climate Re- search Programme’s Working Group on Coupled Modelling, which is responsible for CMIP and we thank the climate modelling groups for providing their GCM output (listed in Table 1). Support of this dataset was provided by the Office of Science, U.S. Department of Energy