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Quantification and Reduction of Uncertainties Associated with Carbon Cycle–Climate System Feedbacks

  • Author(s): Hoffman, Forrest McCoy
  • Advisor(s): Randerson, James T
  • et al.
Creative Commons Attribution 4.0 International Public License
Abstract

Anthropogenic perturbation of global biogeochemical cycles, particularly through emissions of radiatively active greenhouse gases into the atmosphere—chiefly carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)—is altering the Earth's climate and inducing feedbacks from the terrestrial biosphere and oceans on future CO2 levels and the climate system. Identifying and quantifying these feedbacks and quantifying and reducing uncertainties associated with them in process-rich Earth system models (ESMs) are important for advancing our understanding of the Earth system, predicting future atmospheric CO2 levels, informing carbon management and energy policies, and fostering the future of life on Earth. This dissertation presents three studies designed to advance our understanding of biogeochemical processes and their interactions with climate under conditions of increasing atmospheric CO2 and to offer an approach for understanding observational representativeness and for scaling up measurements.

In the first investigation, I analyzed emission-driven simulations of ESMs from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) in which atmospheric CO2 levels were computed prognostically. Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2, due in part to weak ocean carbon uptake. I found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2 levels for the multimodel ensemble, and used this emergent constraint to create a contemporary CO2 tuned model (CCTM) to estimate an atmospheric CO2 trajectory for the 21st century for the Representative Concentration Pathway (RCP) 8.5. The CCTM yielded CO2 estimates of 600 ± 14 ppm at 2060 and 947 ± 35 ppm at 2100, which were 21 ppm and 32 ppm below the multi-model mean during these two time periods, respectively. This analysis indicated that much of the model-to-model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era and that model differences in the representation of concentration–carbon feedbacks and other slowly varying carbon cycle processes appear to be the primary driver of this variability.

In the second study, I extended a quantitative methodology for stratifying sampling domains and understanding the representativeness of measurements, measurement sites, and observational networks. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km2 resolution to define multiple sets of ecoregions across two decadal time periods and to identify optimal sampling locations for those ecoregions. I developed a representativeness metric and used it to characterize environmental dissimilarity between potential sampling sites. This analysis provided insights into optimal sampling strategies and offered a framework for up-scaling measurements that can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns.

In the third investigation, I applied a feedback analysis framework to three sets of long-term climate change simulations from the Community Earth System Model version 1.0 (CESM1(BGC)) to quantify drivers of nonlinear terrestrial and ocean responses of carbon uptake. In the biogeochemically coupled simulation (BGC), the effects of CO2 fertilization and nitrogen deposition were expressed in the biosphere. In the radiatively coupled simulation (RAD), the effects of rising temperature and circulation changes due to radiative forcing from CO2, other greenhouse gases, and aerosols were expressed in the atmosphere. In the third, fully coupled simulation (FC), both the bigoeochemical and radiative coupling effects acted simultaneously. I found that climate–carbon sensitivities derived from RAD simulations produced a net ocean carbon storage climate sensitivity that is weaker and a net land carbon storage climate sensitivity that is stronger than those diagnosed from the FC and BGC simulations. For the ocean, this nonlinearity was associated with warming-induced weakening of ocean circulation and mixing that limited exchange of dissolved inorganic carbon between surface and deeper water masses. For the land, this nonlinearity was associated with strong gains in vegetation productivity in the FC simulation that were driven by enhancements in the hydrological cycle and increased nutrient availability. I developed and applied a nonlinearity metric for individual model variables to rank nonlinear responses and drivers. For these simulations, the overall climate–carbon cycle feedback gain at 2300 was 28% lower when estimated from climate–carbon sensitivities derived from the RAD simulation than when derived from the difference between the FC and BGC simulations. The gain estimated from compatible emissions calculations corresponded well with the gain estimated from FC − BGC climate–carbon sensitivity parameters, confirming the validity of the larger gain. This difference has direct implications for carbon management and energy policies because underestimating the climate–carbon cycle feedback gain would result in allowable emissions estimates that would be too low to meet climate change targets.

In these studies, I have shown that 1) we can reduce uncertainties in future climate projections by improving models to more closely match the long-term time series of observed atmospheric CO2; 2) we can reduce sampling biases and partition important environmental gradients to design an optimized network of sampling sites at desired scales; and 3) we can reduce uncertainties in the assessment of climate–carbon cycle feedbacks due to nonlinear terrestrial and marine responses by deriving climate–carbon sensitivities from fully coupled and biogeochemically coupled simulations.

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