1Sandia National Laboratories, P.O. Box 969, Livermore CA 94551, USA
2Carnegie Institution for Science, Stanford, CA 94305, USA
3Sandia National Laboratories, P.O. Box 5800, Albuquerque NM 87185-0751, USA
4IBM Research, Smarter Cities Technology Centre, Bldg 3, Damastown Industrial Estate, Mulhuddart, Dublin 15, Ireland
Abstract. The characterization of fossil-fuel CO2 (ffCO2) emissions is paramount to carbon cycle studies, but the use of atmospheric inverse modeling approaches for this purpose has been limited by the highly heterogeneous and non-Gaussian spatiotemporal variability of emissions. Here we explore the feasibility of capturing this variability using a low-dimensional parameterization that can be implemented within the context of atmospheric CO2 inverse problems aimed at constraining regional-scale emissions. We construct a multiresolution (i.e., wavelet-based) spatial parameterization for ffCO2 emissions using the Vulcan inventory, and examine whether such a parameterization can capture a realistic representation of the expected spatial variability of actual emissions. We then explore whether sub-selecting wavelets using two easily available proxies of human activity (images of lights at night and maps of built-up areas) yields a low-dimensional alternative. We finally implement this low-dimensional parameterization within an inversion, where a sparse reconstruction algorithm, an extension of Stagewise Orthogonal Matching Pursuit (StOMP), is used to identify the wavelet coefficients. We find that (i) the spatial variability of fossil fuel emission can indeed be represented using a low-dimensional wavelet-based parameterization, (ii) that images of lights at night can be used as a proxy for sub-selecting wavelets for such analysis, and (iii) that implementing this parameterization within the described inversion framework makes it possible to quantify fossil fuel emissions at regional scales under some simplifying conditions.