Factors Impacting Observation-Based Estimates of Urban Greenhouse Gas Emissions

Abstract

Urban areas are responsible for a large and increasing fraction of anthropogenic greenhouse gas emissions. Accurate methods for quantifying and monitoring those emissions are needed to suggest and evaluate mitigation policies, as well as for fundamental carbon cycle science as anthropogenic carbon dioxide emissions become a dominant source of uncertainty in closing the global carbon budget. I present investigations into several factors that can impact our ability to characterize urban greenhouse gas emissions using observations in the atmosphere. An automated method is developed for estimating the mixing depth, a key meteorological variable affecting the sensitivity of mole fraction observations to emissions fluxes, using optical remote sensing instruments. In a long time series of mixing depth estimates in Pasadena, California, day-to-day variability is shown to be large in comparison to seasonal trends. Significant mixing depth biases are demonstrated in meteorological models, and the likely impacts on emissions estimation are discussed.

Optimized estimates of methane emissions in the South Coast Air Basin, California, are made using several flux inversion or regularization methods, with four sources of meteorological information, and with all or some of the mole fraction observations taken at nine within-basin observing sites associated with the LA Megacities Carbon Project. Using the full observational dataset in a geostatistical inversion, the capability to detect seasonal and event-driven emissions changes is demonstrated with generic meteorology, opening the door to near-real-time monitoring. Differences in absolute methane emissions flux magnitude according to the source of driving meteorological information are shown to be largely removable by calibration to a trusted model. The choice of inversion or regularization method is shown to have substantial impacts both on the estimated emissions time series and on the capacity to detect emissions changes, especially when the observational constraint is reduced.