Emissions and topographic effects on column CO_2 (XCO_2) variations, with a focus on the Southern California Megacity

Within the California South Coast Air Basin (SoCAB), X_(CO)_2 varies significantly due to atmospheric dynamics and the nonuniform distribution of sources. X_(CO)_2 measurements within the basin have seasonal variation compared to the “background” due primarily to dynamics, or the origins of air masses coming into the basin. We observe basin-background differences that are in close agreement for three observing systems: Total Carbon Column Observing Network (TCCON) 2.3 ± 1.2 ppm, Orbiting Carbon Observatory-2 (OCO-2) 2.4 ± 1.5 ppm, and Greenhouse gases Observing Satellite 2.4 ± 1.6 ppm (errors are 1σ). We further observe persistent significant differences (∼0.9 ppm) in X_(CO)_2 between two TCCON sites located only 9 km apart within the SoCAB. We estimate that 20% (±1σ confidence interval (CI): 0%, 58%) of the variance is explained by a difference in elevation using a full physics and emissions model and 36% (±1σ CI: 10%, 101%) using a simple, fixed mixed layer model. This effect arises in the presence of a sharp gradient in any species (here we focus on CO_2) between the mixed layer (ML) and free troposphere. Column differences between nearby locations arise when the change in elevation is greater than the change in ML height. This affects the fraction of atmosphere that is in the ML above each site. We show that such topographic effects produce significant variation in X_(CO)_2 across the SoCAB as well

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO_2 emissions

Megacities are major sources of anthropogenic fossil fuel CO_2 (FFCO_2) emissions. The spatial extents of these large urban systems cover areas of 10000 km^2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO_2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO_2 emission product, Hestia-LA, to simulate atmospheric CO_2 concentrations across the LA megacity at spatial resolutions as fine as  ∼ 1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO_2 emission products to evaluate the impact of the spatial resolution of the CO_2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO_2 concentrations. We find that high spatial resolution in the fossil fuel CO_2 emissions is more important than in the atmospheric model to capture CO_2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO_2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO_2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO_2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO_2 emissions monitoring in the LA megacity requires FFCO_2 emissions modelling with  ∼ 1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates