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Browsing by Author "Denning, Scott, advisor"

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    Productivity and phenology in a process-driven carbon cycle model
    (Colorado State University. Libraries, 2018) Cheeseman, Michael J., author; Denning, Scott, advisor; O'Dell, Chris, advisor; Barnes, Libby, committee member; Klein, Julia, committee member
    The carbon cycle is a major source of uncertainty in predicting future climate, especially with regard to changes in the terrestrial biosphere. One obstacle in predicting the sources and sinks of the carbon cycle is accurately predicting phenological transitions of the terrestrial biosphere with a global process-driven model. We hypothesize that the terrestrial biosphere and its phenological transitions can be simulated using a set of universal biological strategies and a simple set of plant functional types in the Simple Biosphere (SiB4) model. In order to test our hypothesis, we compare the SiB4 output to a suite of satellite observations of the terrestrial biosphere including solar induced fluorescence (SIF) from the Orbiting Carbon Observatory (OCO-2), MODIS-based LAI, and AVHRR-based NDVI. Our first analysis compares modeled canopy SIF to aggregated satellite observed SIF over different biomes. We find that the model consistently over predicts pixel-scale SIF. Modeled SIF over evergreen needleleaf forests has an especially high bias during the winter. Our second analysis compares modeled and observed phenology over different regions around the globe. We find that SiB4 is generally successful in simulating growing season onset, but often simulates late senescence, especially in grasslands. We also find that SiB4 simulates crops well in the United States but fails to properly predict the planting and harvesting time of crops in other regions, especially the developing world.
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    Recovering spatially and temporally dynamic regional scale carbon flux estimates
    (Colorado State University. Libraries, 2009) Schuh, Andrew, author; Denning, Scott, advisor
    This dissertation presents two review type chapters and three new research chapters that contribute to our theoretical and practical knowledge about terrestrial carbon fluxes on the regional scale. This research expands on previous carbon dioxide inversion work by providing estimates of ecosystem respiration and gross primary productivity, as opposed to only net ecosystem exchange, and provides estimates on scales in time and space not previously available. The first two chapters provide an introduction and review material. This is necessary to provide the reader with an understanding of the relatively complex geostatistical atmospheric inversion process which uses carbon dioxide concentration data to provide terrestrial carbon flux estimates. Issues of scale are discussed as well previous work which was fundamental to the research presented here. The third and fourth chapters use simulated data to present an analysis of the methodology to a case study of North America in 2004. In particular, simulated data is used to investigate the sensitivity of the inversion to theoretical components of the inversion process and it is concluded that reasonably robust estimates of ecosystem respiration and gross primary productivity can be achieved by using a limited network of eight carbon dioxide observing towers. Chapter 4 specifically looks at the issue of small scale variability in carbon fluxes and the impact it has on obtaining larger scale regional estimates. Chapter five contains an analysis of real collected CO2 observation data from 2004 at the aforementioned eight observing sites. Results show significant seasonal and annual corrections to the a priori carbon flux estimates, in particular to the individual components of net ecosystem exchange, ecosystem respiration and gross primary productivity. Furthermore, the annual net ecosystem exchange, when presented spatially, provides clues to annual sources and sinks in 2004. Sensitivity is investigated with respect to numerous components of the inversion. Although large confidence bounds on estimates indicate statistical uncertainty in the mean estimate of net ecosystem exchange, estimates match reasonably well with previously conducted research as well as observational data. The research provides the estimates within a spatial context (and resolution) that was not previously available, allowing for the construction, and support, of much more descriptive hypotheses about carbon fluxes than was previously possible. Chapter six contains a summary of the results of the dissertation.
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    Time-filtered inverse modeling of land-atmosphere carbon exchange
    (Colorado State University. Libraries, 2015) Geyer, Nicholas M., author; Denning, Scott, advisor; Hoeting, Jennifer, committee member; O'Dell, Christopher, committee member
    The sources and sinks of biospheric carbon dioxide represent one of the least understood and most critical processes in carbon science. Since the 1990's, carbon dioxide inversion models have estimated the magnitude, location, and uncertainty of carbon sources and sinks. These inversions are underconstrained statistical problems that employ aggressive statistical regularizations in both space and time to estimate quantities like net ecosystem exchange (NEE) on weekly timescales over fine spatial scales. This study developed and tested a new regularization that leverages the available observational information toward a small number of estimates associated with the longer-lived slowly varying biospheric processes, which control time-averaged sources and sinks of carbon dioxide. This approach multiplicatively adjusts the longer lived component fluxes, gross primary production (GPP) and total respiration (RESP), using several timescale harmonics. This methodology was tested by estimating adjustments to either net or component fluxes from Simple Biosphere Model 4 (SiB4) using observational data from 8 different eddy-covariance flux towers selected from the North American Carbon Program (NACP) site synthesis dataset. The time-filtering methodology was robustly capable of accurately estimating both net and component fluxes given high observational uncertainty. Furthermore, the methodology was flexible of correctly producing estimates of all three fluxes when given a component flux as an additional observational constraint.