A multiyear, global gridded fossil fuel CO2 emissions data product: evaluation and analysis of results

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Monday, 5 January 2015: 2:00 PM
124A (Phoenix Convention Center - West and North Buildings)
salvi Asefi-Najafabady, Arizona State University, Tempe, AZ; and P. Rayner, K. Gurney, A. McRobert, Y. Song, K. Coltin, J. Huang, C. Elvidge, and K. Baugh

As international and intra-national agreements to limit greenhouse gas emissions emerge, it is important to be able to quantify emissions and to independently measure, monitor, and verify reported emissions. The largest annual single net source of CO2 into the Earth's atmosphere is due to the combustion of fossil fuel and therefore accurate quantification of fossil fuel emissions is needed to better address the concern of rising atmospheric greenhouse gas concentrations. In the last decade, there has been a growing need, from both carbon cycle science and policymaking communities for quantification of global fossil fuel CO2 emissions at finer space and time resolutions. Motivated by this concern we have built a high-resolution global fossil fuel CO2 emission inventory for the years of 1997 2010 using a combination of in situ and remotely sensed data in a fossil fuel data assimilation system (FFDAS). A suite of observations are used to constrain the FFDAS model which include saturation-corrected nightlights, recently released multiyear LandScan population data, sectoral national emissions and an updated and improved pointwise database of global power plant emissions that includes improved location information and individual power plant uncertainties. FFDAS is a technique that combines some elements of downscaling, bottom-up information, and an assimilation approach that utilizes a model of fossil fuel CO2 emissions to optimally assign national emissions to a global grid. The underlying model is based on a modified Kaya identity, which expresses emissions as the product of areal population density, per capita economic activity, energy intensity of economic activity, and carbon intensity of energy consumption. An important advantage of FFDAS is the ability to incorporate prior uncertainties and estimate posterior uncertainties. We have developed techniques to estimate prior uncertainties for the input observation datasets of national emissions and point source power plant emissions. Results are compared to the Vulcan data product in the United States, the most intensive bottom-up fossil fuel CO2 emissions effort to date [Gurney et al., 2009]. Finally, we have analyzed trends across the globe. Long-term trend analysis of the resulting global emissions shows sub-national spatial structure in large active economies such as the United States, China and India. These three countries, in particular, show different long-term trends and exploration of the trends in nighttime lights and population reveal a decoupling of population and emissions at the sub-national level. Analysis of shorter-term variations reveals the impact of the 2008/2009 Global Financial Crisis with widespread negative emission anomalies across the US and Europe.