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Monthly trends of methane emissions in Los Angeles from 2011 to 2015 inferred by CLARS-FTS observations

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This paper presents an analysis of methane emissions from the Los Angeles Basin at monthly timescales across a 4-year time period – from September 2011 to August 2015. Using observations

This paper presents an analysis of methane emissions from the Los Angeles Basin at monthly timescales across a 4-year time period – from September 2011 to August 2015. Using observations acquired by a ground-based near-infrared remote sensing instrument on Mount Wilson, California, combined with atmospheric CH[subscript 4]–CO[subscript 2] tracer–tracer correlations, we observed −18 to +22 % monthly variability in CH[subscript 4] : CO[subscript 2] from the annual mean in the Los Angeles Basin. Top-down estimates of methane emissions for the basin also exhibit significant monthly variability (−19 to +31 % from annual mean and a maximum month-to-month change of 47 %). During this period, methane emissions consistently peaked in the late summer/early fall and winter. The estimated annual methane emissions did not show a statistically significant trend over the 2011 to 2015 time period.

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  • 2016-10-26

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Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO2 emissions

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Megacities are major sources of anthropogenic fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions. The spatial extents of these large urban systems cover areas of 10 000 km[superscript 2] or more with complex

Megacities are major sources of anthropogenic fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions. The spatial extents of these large urban systems cover areas of 10 000 km[superscript 2] or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO[subscript 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[subscript 2] emission product, Hestia-LA, to simulate atmospheric CO[subscript 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[subscript 2] emission products to evaluate the impact of the spatial resolution of the CO[subscript 2] emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO[subscript 2] concentrations. We find that high spatial resolution in the fossil fuel CO[subscript 2] emissions is more important than in the atmospheric model to capture CO[subscript 2] concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO[subscript 2] fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO[subscript 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[subscript 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[subscript 2] emissions monitoring in the LA megacity requires FFCO[subscript 2] emissions modelling with  ∼  1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.

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  • 2016-07-22