Bello, Richard2016-09-202016-09-202015-12-232016-09-20http://hdl.handle.net/10315/32208Atmospheric CO2 concentrations are influenced by surface fluxes, as well as advection and vertical mixing on the way to the measurement tower. The capability of transport models to accurately represent air parcel trajectories and footprints is crucial in inverse analysis. This study employs the Stochastic Time-inverted Lagrangian Transport model (STILT), driven by meteorological inputs from the North American Regional Reanalysis (NARR), to simulate atmospheric CO2 in the Hudson Bay Lowlands. The primary objectives include: (1) Characterize daily, seasonal and interannual variations of atmospheric CO2 for a 5-year (2008-2012) period; (2) Evaluate the performance of the STILT model, and CarbonTracker flux estimates. STILT-modelled CO2 concentrations compare reasonably against observations. The mean model bias was -0.57 ppm at Churchill, and -2.44 ppm at Fraserdale. Smoothed seasonal curves fitted to the daily afternoon data revealed that model bias was highest during summertime, particularly over the Fraserdale region. This disparity between modelled and observed results are attributed to transport errors related to advection and PBL mixing.enAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Climate changeElectronic Thesis or Dissertation2016-09-20Atmospheric CO2 concentrationBiosphere-atmosphere interactionsMeteorology and Atmospheric DynamicsBoundary layer processesCarbon fluxesCO2 sources and sinksTrajectory modelingStochastic Time-Inverted Lagrangian Transport ModelSTILTLagrangian Particle Dispersion ModelsLPDMCarbon TrackerHudson Bay LowlandsCanada's Boreal ForestChurchill ManitobaFraserdale OntarioGlobal Change Science