How do CMIP6 models simulate future changes in the terrestrial carbon cycle?
Uncertainties in projections of atmospheric carbon dioxide concentrations are largely driven by uncertainties in the land carbon cycle (e.g., Friedlingstein et al. 2014). The C4MIP simulations used CMIP5 models to compare concentration-driven versus emissions-driven RCP8.5 simulations. The resulting spread for land carbon uptake was large, in part due to uncertainties in model structure and parameters. How has this changed with CMIP6 models?
Figure 4d from Friedlingstein et al., 2014. Annual global air to land carbon flux for 11 emissions-driven CMIP5 models.
A limited number of CMIP6 models have released results from C4MIP emissions driven simulations using the SSP5-8.5 scenario. These results can be compared with the concentration driven SSP5-8.5 runs, with a focus on land carbon fluxes.
Anticipated Data Needs
Monthly land carbon fluxes (e.g., GPP, NPP, NBP) for esm-ssp585 (C4MIP) and ssp585.
Anticipated Software Tools
Standard python analysis tools (xarray, numpy, matplotlib).
Anyone interested in terrestrial climate changes, carbon cycle modeling, and/or uncertainty quantification.