Characterizing natural and anthropogenic carbon flux spatiotemporal variability at regional scales using a dense network of atmospheric CO2 observations over North America [electronic resource]

The anthropogenic and the natural biosphere components of the carbon cycle play critically important roles in determining the future status of earth's climate. While our knowledge of CO2 fluxes, at large (global) and small (~1 km2) scales are fairly well known, much uncertainty exists at intermediate/regional-scales (i.e., sub-continental, biome-level). Knowledge at these regional scales is vital for informing relevant climate change mitigation strategies (e.g., land management, fossil fuel emissions reduction policies) and for improving the characterization of key carbon-climate feedback mechanisms. This dissertation leverages the information content of a recent 4-fold increase in atmospheric CO2 observation locations over North America to investigate carbon fluxes, both anthropogenic and natural, at the regional scale (e.g., biome/sub-continental). This work specifically emphasizes the exploration and characterization of spatial and temporal patterns of surface fluxes rather than focusing solely on carbon budgets. With the growing interest in independently verifying fossil fuel CO2 (FFCO2) emissions using atmospheric CO2 emissions, there exists a crucial need to understand the capabilities of the atmospheric observation network to isolate the FF signal. The first study explores and identifies when and where the space-time patterns of monthly sub-continental FFCO2 emissions are detectable using the atmospheric CO2 observation network. Winter months and regions with a relatively high density of observations (e.g., Midwest) offer the best opportunity to detect FFCO2 emissions patterns. The combined impact of the natural biospheric signal and atmospheric transport model related issues are identified as severely hampering the detection of the FFCO2 signal in spring, summer, and autumn. This first study provides key guidance for future efforts to independently estimate FFCO2 emissions using atmospheric CO2 observations through a systematic examination of the various factors hampering detectability. The second study quantifies the ability of a promising new remote sensing data set, solar-induced fluorescence (SIF), to inform spatiotemporal patterns at previously unexplored regional scales. The potential of SIF lies in its ability to measure an emission that, unlike previous reflectance based vegetation indices, is directly related to photosynthesis rather than a measure of the "greenness" of the land surface. While SIF has been explored at local and global scales, here, SIF is shown to explain CO2 flux patterns within continental and biome regions better than existing vegetation and climate indicators as well as most process-based terrestrial biosphere models (TBMs). By incorporating SIF into an inverse model, SIF is found to inform a significant increase in the net uptake of CO2 over croplands as well as a significant decrease over needleleaf forests. The final study explores the regional drivers of interannual variability (IAV) for the net North American CO2 land sink using inverse estimates of net CO2 flux derived from six years of observations. Understanding the current drivers of IAV is crucial for improving predictions of the future response of the terrestrial biosphere to climate change. This work identifies the deciduous broadleaf and mixed forest biomes as the primary regional drivers of IAV over North America, which differs from the dominant drivers identified for the globe and for the northern hemisphere. When comparing the inverse-modelling-derived estimates to a suite of ten TBMs, the large spread in TBM based biome-level contributions to North American IAV make identifying a dominant biome-level driver not possible. The wide spread among TBM based biome-level IAV contributions is attributed to a trade-off between the contribution of IAV in forested vs non-forested regions in a given TBM. This trade-off corresponds with emergent regional sensitivities to environmental drivers (temperature, precipitation, and radiation) where TBMs with IAV dominated by forested regions exhibit stronger sensitivity to environmental drivers in forested regions relative to non-forested regions and vice-versa. This trade-off helps to explain the inability of TBMs to agree on a dominant regional driver of IAV and calls into question the ability of TBMs to inform regional-scale carbon flux IAV dynamics.

Advisor: Kitanidis P. K. (Peter K.) primary advisor. Thesis advisor
Michalak Anna M.
Jacobson Mark Z. (Mark Zachary) advisor. Thesis advisor