Person:

Wecht, Kevin James

We estimate methane emissions from North America with high spatial resolution by inversion of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite observations using the Goddard Earth Observing System Chemistry (GEOS-Chem) chemical transport model and its adjoint. The inversion focuses on summer 2004 when data from the Intercontinental Chemical Transport Experiment-North America (INTEX-A) aircraft campaign over the eastern U.S. are available to validate the SCIAMACHY retrievals and evaluate the inversion. From the INTEX-A data we identify and correct a water vapor-dependent bias in the SCIAMACHY data. We conduct an initial inversion of emissions on the horizontal grid of GEOS-Chem (1/2° × 2/3°) to identify correction tendencies relative to the Emission Database for Global Atmospheric Research (EDGAR) v4.2 emission inventory used as a priori. We then cluster these grid cells with a hierarchical algorithm to extract the maximum information from the SCIAMACHY observations. A 1000 cluster ensemble can be adequately constrained, providing ~100 km resolution across North America. Analysis of results indicates that the Hudson Bay Lowland wetlands source is 2.1 Tg a−1, lower than the a priori but consistent with other recent estimates. Anthropogenic U.S. emissions are 30.1 ± 1.3 Tg a−1, compared to 25.8 Tg a−1 and 28.3 Tg a−1 in the EDGAR v4.2 and Environmental Protection Agency (EPA) inventories, respectively. We find that U.S. livestock emissions are 40% greater than in these two inventories. No such discrepancy is apparent for overall U.S. oil and gas emissions, although this may reflect some compensation between overestimate of emissions from storage/distribution and underestimate from production. We find that U.S. livestock emissions are 70% greater than the oil and gas emissions, in contrast to the EDGAR v4.2 and EPA inventories where these two sources are of comparable magnitude.

We apply a continental-scale inverse modeling system for North America based on the GEOS-Chem model to optimize California methane emissions at 1/2° × 2/3° horizontal resolution using atmospheric observations from the CalNex aircraft campaign (May–June 2010) and from satellites. Inversion of the CalNex data yields a best estimate for total California methane emissions of 2.86 ± 0.21 Tg a−1, compared with 1.92 Tg a−1 in the EDGAR v4.2 emission inventory used as a priori and 1.51 Tg a−1 in the California Air Resources Board (CARB) inventory used for state regulations of greenhouse gas emissions. These results are consistent with a previous Lagrangian inversion of the CalNex data. Our inversion provides 12 independent pieces of information to constrain the geographical distribution of emissions within California. Attribution to individual source types indicates dominant contributions to emissions from landfills/wastewater (1.1 Tg a−1), livestock (0.87 Tg a−1), and gas/oil (0.64 Tg a−1). EDGAR v4.2 underestimates emissions from livestock, while CARB underestimates emissions from landfills/wastewater and gas/oil. Current satellite observations from GOSAT can constrain methane emissions in the Los Angeles Basin but are too sparse to constrain emissions quantitatively elsewhere in California (they can still be qualitatively useful to diagnose inventory biases). Los Angeles Basin emissions derived from CalNex and GOSAT inversions are 0.42 ± 0.08 and 0.31 ± 0.08 Tg a−1 that the future TROPOMI satellite instrument (2015 launch) will be able to constrain California methane emissions at a detail comparable to the CalNex aircraft campaign. Geostationary satellite observations offer even greater potential for constraining methane emissions in the future.

Nigeria has a high population density and large fossil fuel resources but very poorly managed energy infrastructure. Satellite observations of formaldehyde (HCHO) and glyoxal (CHOCHO) reveal very large sources of anthropogenic nonmethane volatile organic compounds (NMVOCs) from the Lagos megacity and oil/gas operations in the Niger Delta. This is supported by aircraft observations over Lagos and satellite observations of methane in the Niger Delta. Satellite observations of carbon monoxide (CO) and nitrogen dioxide (NO2) show large seasonal emissions from open fires in December–February (DJF). Ventilation of central Nigeria is severely restricted at that time of year, leading to very poor ozone air quality as observed from aircraft (MOZAIC) and satellite (TES). Simulations with the GEOS-Chem chemical transport model (CTM) suggest that maximum daily 8-h average (MDA8) ozone exceeds 70 ppbv over the region on a seasonal mean basis, with significant contributions from both open fires (15–20 ppbv) and fuel/industrial emissions (7–9 ppbv). The already severe ozone pollution in Nigeria could worsen in the future as a result of demographic and economic growth, although this would be offset by a decrease in open fires.

We validate satellite methane observations from the Tropospheric Emission Spectrometer (TES) with 151 aircraft vertical profiles over the Pacific from the HIAPER Pole-to-Pole Observation (HIPPO) program. We find that a collocation window of ±750 km and ±24 h does not introduce significant error in comparing TES and aircraft profiles. We validate both the TES standard product (V004) and an experimental product with two pieces of information in the vertical (V005). We determine a V004 mean bias of 65.8 ppb and random instrument error of 43.3 ppb. For V005 we determine a mean bias of 42.3 ppb and random instrument error of 26.5 ppb in the upper troposphere, and mean biases (random instrument errors) in the lower troposphere of 28.8 (28.7) and 16.9 (28.9) ppb at high and low latitudes respectively. Even when V005 cannot retrieve two pieces of information it still performs better than V004. An observation system simulation experiment (OSSE) with the GEOS-Chem chemical transport model (CTM) and its adjoint shows that TES V004 has only limited value for constraining methane sources. Our successful validation of V005 encourages its production as a standard retrieval to replace V004.

Methane is the second most influential anthropogenic greenhouse gas. There are large uncertainties in the magnitudes and trends of methane emissions from different source types and source regions. Satellite observations of methane offer dense spatial coverage unachievable by suborbital observations. This thesis evaluates the capabilities of using satellite observations of atmospheric methane to provide high-resolution constraints on continental scale methane emissions. In doing so, I seek to evaluate the supporting role of suborbital observations, to inform the emission inventories on which policy decisions are based, and to enable inverse modeling of the next generation of satellite observations.