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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 5
Geosci. Model Dev., 7, 1901–1918, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 7, 1901–1918, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 03 Sep 2014

Development and technical paper | 03 Sep 2014

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

J. Ray1, V. Yadav2, A. M. Michalak2, B. van Bloemen Waanders3, and S. A. McKenna4 J. Ray et al.
  • 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 idealized 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 if fossil-fuel-only CO2 observations are available.

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