@PhDThesis{Hoffman_ESS-Dissertation_20150605,
 author         = {Forrest M. Hoffman},
 title          = {Quantification and Reduction of Uncertainties Associated with Carbon Cycle--Climate System Feedbacks},
 school         = {University of California - Irvine, Department of Earth System Science},
 address	= {Irvine, California, USA},
 day		= 5,
 month		= jun,
 year           = 2015,
 url		= {https://escholarship.org/uc/item/53h2942d},
 abstract	= {
Anthropogenic perturbation of global biogeochemical cycles, particularly
through emissions of radiatively active greenhouse gases into the
atmosphere---chiefly carbon dioxide (CO$_2$), methane (CH$_4$), and
nitrous oxide (N$_2$O)---is altering the Earth's climate and inducing
feedbacks from the terrestrial biosphere and oceans on future CO$_2$
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 CO$_2$ 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 CO$_2$ 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 CO$_2$ levels were computed prognostically.
Comparison of ESM prognostic atmospheric CO$_2$ over the historical
period with observations indicated that ESMs, on average, had a small
positive bias in predictions of contemporary atmospheric CO$_2$, due in
part to weak ocean carbon uptake.
I found a significant linear relationship between contemporary atmospheric
CO$_2$ biases and future CO$_2$ levels for the multimodel ensemble,
and used this emergent constraint to create a contemporary CO$_2$
tuned model (CCTM) to estimate an atmospheric CO$_2$ trajectory for the
21$^\textnormal{st}$ century for the Representative Concentration Pathway
(RCP)~8.5.
The CCTM yielded CO$_2$ estimates of $600 \pm14$~ppm at 2060 and $947
\pm 35$~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 CO$_2$ during the 21$^\textnormal{st}$ 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~km$^2$
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 CO$_2$
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 CO$_2$,
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 $\mathrm{FC} - \mathrm{BGC}$
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 CO$_2$;
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.}
}