Evaluating the economic impact of improving uncertainty in the carbon cycle

Uncertainty in the Earth's carbon-cycle, how carbon is partitioned between the land, ocean and the atmosphere, contributes to uncertainty in future temperature projections. This in turn leads to uncertainty in costs of reducing the atmospheric CO2 concentrations to limit the climate warming to 2C. In this study we assess the economic value of constraining the carbon-cycle uncertainty with observational products, and hence reduce uncertainty in the cost of limiting climate change and the date and magnitude at which carbon emissions must peak before declining to zero or less.

We used the Global Change Assessment Model (GCAM), coupled to a reduced complexity carbon-climate model-Hector with quasi random combinations of carbon-cycle (CO2 fertilization and respiration response) and climate parameters (equilibrium climate sensitivity and ocean heat diffusion). The resulting scenarios were filtered using a variance-based distance test statistic used to compare results with HadCRUT4 global mean temperature, MODIS Net Primary Production, and NOAA atmospheric CO2 concentrations. Parameter combinations that produced scenarios that passed through the observational products were used as inputs within GCAM to solve for a radiative forcing target of 2.6 Wm-2 by year 2100. The uncertainty in year 2100 carbon price spans a range of 129% and results from the emission pathways show uncertainty in the timeframe when carbon emissions must peak in order to limit climate forcing by year 2100.

Future work will include quantifying the uncertainty resulting from individual carbon-cycle parameters was well as utilizing this method to investigate the impact of NASA carbon products on narrowing down this uncertainty. Finally, we will continue engaging and working alongside stakeholders to assess the benefits of our research results and NASA carbon products are having on stakeholder organizations.