Seasonal variation of CH₄ emissions from central California

Abstract

[1] We estimate seasonal variations in methane (CH 4 ) emissions from central California from December 2007 through November 2008 by comparing CH 4 mixing ratios measured at a tall tower with transport model predictions based on a global 1 a priori CH 4 emissions map (EDGAR32) and a 10 km seasonally varying California-specific map, calibrated to statewide by CH 4 emission totals. Atmospheric particle trajectories and surface footprints are computed using the Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport models. Uncertainties due to wind velocity and boundary layer mixing depth are evaluated using measurements from radar wind profilers. CH 4 signals calculated using the EDGAR32 emission model are larger than those based on the California-specific model and in better agreement with measurements. However, Bayesian inverse analyses using the California-specific and EDGAR32 maps yield comparable annually averaged posterior CH 4 emissions totaling 1.55 AE 0.24 times and 1.84 AE 0.27 times larger than the California-specific prior emissions, respectively, for a region of central California within approximately 150 km of the tower. If these results are applicable across California, state total CH 4 emissions would account for approximately 9% of state total greenhouse gas emissions. Spatial resolution of emissions within the region near the tower reveal seasonality expected from several biogenic sources, but correlations in the posterior errors on emissions from both prior models indicate that the tower footprints do not resolve spatial structure of emissions. This suggests that including additional towers in a measurement network will improve the regional specificity of the posterior estimates.

Anthropogenic emissions of methane in the United States

“…Our total budget for this region of 3.4 ± 0.7 compares with 1. partially responsible for high emissions over California (41). EDGAR activity datasets are poor over California (42), but several recent studies (34,(36)(37)(38)41) have provided detailed topdown emissions estimates for the state using datasets from state agencies. Existing inventories also greatly underestimate CH 4 sources from the south-central United States (Fig.…”

“…Most regional studies adopted one of three approaches: use a simple box model to estimate an overall CH 4 budget (14), estimate a budget using the relative ratios of different gases (15,(37)(38)(39), or estimate scaling factors for inventories by region or source type (13,(34)(35)(36). The first two methods do not usually give explicit information about geographic distribution.…”

Analysis of the potential of near-ground measurements of CO<sub>2</sub> and CH<sub>4</sub> in London, UK, for the monitoring of city-scale emissions using an atmospheric transport model

“…Previous studies have diagnosed some seasonality for CH 4 emissions (Lowry et al, 2001;McKain et al, 2015). As examples of the potential explanations for this phenomenon, the seasonality of the gas consumption for heating (with large consumption for lower temperatures, especially in winter) could drive seasonal variations in the gas leakage (Jeong et al, 2012), and the seasonal variations in the meteorology (pressure, humidity, temperature) could impact the decomposition and release of CH 4 , and thus the emissions, from the waste storage and waste treatment sector (Börjesson and Svensson, 1997;Masuda et al, 2015;Abushammala et al, 2016). However, characterising such seasonal variations is a difficult task, which may vary substantially depending on the sectors and cities.…”

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

“…3.2. Observations in this category include air samples collected atop telecommunication towers and from aircraft: the NOAA tall-tower observation network , regular NOAA aircraft monitoring , the Environment and Climate Change Canada tower monitoring network (Environment and Climate Change Canada, 2011), the California Greenhouse Gas Research Monitoring Network (e.g., Zhao et al, 2009;Jeong et al, 2012Jeong et al, , 2013Jeong et al, , 2016, and a privately funded tower network operated by Earth Networks (Fig. 2).…”