inventories, required under the United Nations Framework Convention on Climate Change, are self-reported and are not required regularly for all countries. Verification requires checking these self-reported emissions estimates. However, independent data against which to verify the statistics used to estimate CO2 emissions, such as fossil fuel consumption, are not available. Existing instruments and methods for remote monitoring of atmospheric CO2 are not able, with useful accuracy, to distinguish fossil fuel emissions from natural fluxes or to verify trends in fossil fuel emissions, such as reductions against a baseline.

Atmospheric CO2 measurements by ground stations, aircraft, and satellites can be combined with atmospheric circulation models to infer emissions from the land surface, a method known as tracer-transport inversion. The principle is that an emission source located between two sites will cause the abundance of the gas to be higher at the downwind site than at the upwind site by an amount proportional to the source strength. However, estimated changes in atmospheric CO2 abundance due to fossil fuel sources are confounded by errors in the reconstruction of atmospheric transport, by sparse CO2 observations, and by the much larger changes due to biological sources and sinks.4 Because of these complications, the tracer-transport inversion method is currently able to estimate emissions with a useful accuracy only for some large continents. The method’s accuracy could be improved by expanding the CO2 sampling network on the ground and from space, and OCO was in fact designed to improve tracer-transport inversions.

A complementary approach to tracer-transport inversion is to measure the increased atmospheric abundance on top of large local sources such as cities or power plants. The majority of fossil fuel emissions emanate from such sources and would likely be a target of mitigation measures. These large sources increase the local CO2 abundance in the atmosphere by 1-10 ppm, a signal large enough to overwhelm the signal from natural sources and sinks, reducing this source of uncertainty.5 Because the increased abundances are largest over the source of emissions and disperse within a few tens of kilometers, they can usually be attributed unambiguously to their country of origin. Statistical or systematic sampling of CO2 from large local sources would thus support treaty verification by providing independent data against which to compare trends in emissions reported by countries, at least for the fossil fuel emissions from cities and power plants.

The existing atmospheric CO2 sampling network of ground stations, aircraft, and satellites is not well designed for estimation of emissions from large local sources distributed around the globe. Ground stations and aircraft were purposefully deployed away from large fossil fuel sources to better detect natural sources and sinks, but could be deployed to monitor CO2 emitted from selected cities and power plants. However, this would require international cooperation and such nationally operated stations would still have the verification challenges associated with self-reporting. Satellites obviate these problems. As shown in Attachment A, Japan’s GOSAT is the

4

Fossil fuel emissions from the United States change the average abundance of atmospheric CO2 by only ~0.7 parts per million (ppm; less than 0.2 percent) as air moves across the U.S. continent. Depending on season, analogous changes from biological sources will be two to five times larger. The signals produced by most countries are significantly smaller than these. See Tans, P.P., P.S. Bakwin, and D.W. Guenther, 1996, A feasible global carbon cycle observing system: A plan to decipher today’s carbon cycle based on observations, Global Change Biology, 2, 309-318.

5

Riley, W.J., D.Y. Hsueh, J.T. Randerson, M.L. Fischer, J.G. Hatch, D.E. Pataki, W. Wang, and M.L. Goulden, 2008, Where do fossil fuel carbon dioxide emissions from California go? An analysis based on radiocarbon observations and an atmospheric transport model, Journal of Geophysical Research, 113, G04002, doi:10.1029/2007JG000625.



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