Investigating Seasonal Emissions of Carbon Dioxide and Methane in Northern California Using Airborne Measurements and Inverse Modeling
Abstract:Greenhouse gas (GHG) concentrations have increased over the past decades and are linked to increasing global surface temperatures and climate change. To counteract the trend of increasing atmospheric concentrations of GHGs, the state of California has passed the California Global Warming Solutions Act of 2006 (AB-32). This requires that by 2020, GHG (e.g., carbon dioxide (CO2) and methane (CH4)) emissions will be reduced to 1990 levels. Currently, California emits ~500 Tg yr-1 of CO2eq GHGs, with CO2 and CH4 contributing ~90% of the total. To quantify the success of AB-32, GHG emission rates must be more thoroughly quantified in California. Presently, uncertainties remain in the existing “bottom-up” emission inventories in California due to many contributing factors not being fully understood. To help alleviate these uncertainties, we have analyzed airborne GHG measurements and applied inverse modeling techniques to quantify GHG spatiotemporal concentration patterns and “top-down” emission rates.
To assess the magnitude/spatial variation of GHGs, and to identify local emission “hot spots”, airborne measurements of CO2 and CH4 were made by the Alpha Jet Atmospheric eXperiment (AJAX) in the boundary layer of the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV) in Jan.-Feb. 2013 and July-Aug. 2014. To quantify/constrain GHG emissions we applied the WRF-STILT model and inverse modeling techniques, in conjunction with AJAX data, to estimate “top-down” SFBA/SJV GHG emission rates. Model simulations utilized source apportioned a priori CO2 and CH4 emission inventories from the Vulcan Project (including NASA Carnegie Ames Stanford Approach (NASA-CASA) model CO2 biosphere fluxes) and the California Greenhouse Gas Emissions Measurement (CALGEM) Project, respectively. Results from the evaluation of a priori and posterior GHG concentrations/emissions in northern California using AJAX data, along with the analysis of CO2 and CH4 concentration spatiotemporal patterns and source attribution, will be presented.