A very high-resolution (1 km×1 km) global fossil fuel CO<sub>2</sub> emission inventory derived using a point source database and satellite observations of nighttime lights

Abstract

Abstract. Emissions of CO 2 from fossil fuel combustion are a critical quantity that must be accurately given in established flux inversion frameworks. Work with emerging satellite-based inversions requires spatiotemporally-detailed inventories that permit analysis of regional natural sources and sinks. Conventional approaches for disaggregating national emissions beyond the country and city levels based on population distribution have certain difficulties in their application. We developed a global 1 km×1 km annual fossil fuel CO 2 emission inventory for the years 1980-2007 by combining a worldwide point source database and satellite observations of the global nightlight distribution. In addition to estimating the national emissions using global energy consumption statistics, emissions from point sources were estimated separately and were spatially allocated to exact locations indicated by the point source database. Emissions from other sources were distributed using a special nightlight dataset that had fewer saturated pixels compared with regular nightlight datasets. The resulting spatial distributions differed in several ways from those derived using conventional population-based approaches. Because of the inherent characteristics of the nightlight distribution, source regions corresponding to human settlements and land transportation were well articulated. Our distributions showed good agreement with a high-resolution inventory across the US at spatial resolutions that were adequate for regional flux inversions. The inventory can be extended to the future using updated data, and is expected to be incorporated into models for operational flux inversions that use observational data from the Japanese Greenhouse Gases Observing SATellite (GOSAT).

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

“…State and national governments in the US use this strategy to construct official emission estimates (e.g., California Air Resources Board, 2015; US EPA, 2016c). A number of academic and government efforts have produced bottom-up CO 2 and CH 4 emission estimates at local-regional (e.g., Gately et al, 2013;Jeong et al, 2014;Lyon et al, 2015;California Air Resources Board, 2015), national (e.g., Pétron et al, 2008;Gurney et al, 2009;Gately et al, 2015;US EPA, 2013;Environment and Climate Change Canada, 2016;Maasakkers et al, 2016), and global scales (e.g., Rayner et al, 2010;Andres et al, 2011;Oda and Maksyutov, 2011;Olivier et al, 2014;EC JRC/PBL, 2016). In this section, we primarily discuss bottom-up data with an eye toward how this information can be combined with top-down strategies.…”

“…For example, Oda and Maksyutov (2011) develop ODIAC (Open-source Data Inventory of Anthropogenic CO 2 ), a global gridded CO 2 inventory constructed using a database of CO 2 point sources and satellite images of lights at night. Rayner et al (2010) and Asefi-Najafabady et al (2014) develop a data assimilation framework known as FFDAS (Fossil Fuel Data Assimilation System).…”

VIIRS night-time lights

“…Global satellite-observed night-time lights have emerged as one of the widely used geospatial data products (Amaral et al 2005;Small, Pozzi, and Elvidge 2005;Sutton et al 2007;Bharti et al 2009;Chand et al 2009;Ghosh et al 2010;Oda and Maksyutov 2011;Witmer and O'loughlin 2011;He et al 2012;Mazor et al 2013;Min et al 2013;Falchi et al 2016). These products show the locations where artificial lighting is present and a measure of the brightness as observed from space.…”

A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions

“…Gridded inventory estimates of ffCO 2 emissions can be derived using socio-economic data (Oda and Maksyutov, 2011;Rayner et al, 2010), and such "bottom-up" estimates have been proposed as a means of monitoring international agreements aimed at mitigating ffCO 2 emissions (Pacala et al, 2010). Gridded inventory estimates are derived from ffCO 2 budgets and produced by a few institutions; see Andres et al (2012) for a list.…”

“…These large-scale estimates can then be downscaled to finer spatiotemporal scales using easily observed proxies of human activity (and consequently ffCO 2 emissions) such as images of lights at night (henceforth "nightlights"), population density, etc. (Oda and Maksyutov, 2011;Rayner et al, 2010;Doll et al, 2000). More sophisticated…”