RECCAP2 Future Component: Consistency and Potential for Regional Assessment to Constrain Global Projections

Jones, C.D., Ziehn, T., Anand, J., Bastos, A., Burke, E., Canadell, J.G., Cardoso, M., Ernst, Y., et al. (2023). RECCAP2 Future Component: Consistency and Potential for Regional Assessment to Constrain Global Projections. AGU Advances 4 (6) e2023AV001024. 10.1029/2023AV001024.

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Abstract

Projections of future carbon sinks and stocks are important because they show how the world's ecosystems will respond to elevated CO2 and changes in climate. Moreover, they are crucial to inform policy decisions around emissions reductions to stay within the global warming levels identified by the Paris Agreement. However, Earth System Models from the 6th Coupled Model Intercomparison Project (CMIP6) show substantial spread in future projections—especially of the terrestrial carbon cycle, leading to a large uncertainty in our knowledge of any remaining carbon budget (RCB). Here we evaluate the global terrestrial carbon cycle projections on a region-by-region basis and compare the global models with regional assessments made by the REgional Carbon Cycle Assessment and Processes, Phase 2 activity. Results show that for each region, the CMIP6 multi-model mean is generally consistent with the regional assessment, but substantial cross-model spread exists. Nonetheless, all models perform well in some regions and no region is without some well performing models. This gives confidence that the CMIP6 models can be used to look at future changes in carbon stocks on a regional basis with appropriate model assessment and benchmarking. We find that most regions of the world remain cumulative net sources of CO2 between now and 2100 when considering the balance of fossil-fuels and natural sinks, even under aggressive mitigation scenarios. This paper identifies strengths and weaknesses for each model in terms of its performance over a particular region including how process representation might impact those results and sets the agenda for applying stricter constraints at regional scales to reduce the uncertainty in global projections.

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