Synthetic Data Experiments of a Measurement-Modeling Greenhouse Gas Emissions Estimation System for the Washington, DC and Baltimore, MD Metropolitan Area

Climate change threatens humans and natural systems, and consequently, the vast majority of countries signed the Paris Agreement to achieve carbon neutrality by 2050 to limit global warming below two degrees Celsius. To implement successful emissions reduction policies, urban areas are a critical target due to their significant contribution (42-66%) to the total greenhouse gases (GHG) budget. Therefore, accurate and up-to-date city-specific GHG emissions data are crucial to assess progress in achieving GHG reduction goals. A prototype measurement-modeling GHG emissions estimation system is being developed for the Washington, DC and Baltimore, MD metropolitan area utilizing tower-based observations from the NIST Northeast Corridor Urban Testbed, the HYSPLIT dispersion model, and the CarbonTracker-Lagrange inversion model. In this presentation, we describe synthetic data experiments to test the ability of this prototype to improve GHG emissions estimates. This project results from a U.S. interagency effort with the ultimate goal of implementing an operational system for delivering improved, near real-time estimations of GHG emissions and uptake in this urban area. As a preliminary assessment of the prototype, a set of experiments has been implemented using different initial assumed (prior) fluxes to assess the ability of the system to retrieve a realistic anthropogenic CO₂ emission scenario. The synthetic inversion framework, generally recognized as observing-system simulation experiments (OSSEs), relies on the assimilation of synthetic observations created from the convolution of HYSPLIT calculated footprints, or locations where surface fluxes impact the observations through atmospheric transport, with “true” emissions fields, aiming to assess the capacity of the system to retrieve the known fluxes under a set of controlled settings (e.g., sampling network configuration, prior structure, and uncertainties of prior and atmospheric transport). Given this experimental approach within the existing measuring network in the Washington, DC and Baltimore, MD metropolitan area, emissions were optimized over January 2019 using various spatial and temporal emission structures as priors based on previous studies. Examination of the flux estimates of the inversion analysis shows a consistent agreement with the synthetic true flux magnitude in the urban corridor, where the surface sensitivity associated with the existing measuring network is higher than in peripheral areas. These OSSEs based findings suggest that even when the prior does not contain information on the structure of the true emissions (i.e., a spatially and temporally flat prior), the prototype is able to provide improved metrics of CO₂ emissions if there are adequate data to constrain the fluxes.