In-situ CO2 monitoring network evaluation and design: A criterion based on...

Shiga, Y. P., A. M. Michalak, S. R. Kawa, and R. J. Engelen (2013), In-situ CO2 monitoring network evaluation and design: A criterion based on atmospheric CO2 variability, J. Geophys. Res., 118, 2007-2018, doi:10.1002/jgrd.50168.

Estimates of surface fluxes of carbon dioxide (CO2) can be derived from atmospheric CO2 concentration measurements through the solution of an inverse problem, but the sparseness of the existing CO2 monitoring network is often cited as a main limiting factor in constraining fluxes. Existing methods for assessing or designing monitoring networks either primarily rely on expert knowledge, or are sensitive to the large number of modeling choices and assumptions inherent to the solution of inverse problems. This study proposes a monitoring network evaluation and design approach based on the quantification of the spatial variability in modeled atmospheric CO2. The approach is used to evaluate the 2004–2008 North American network expansion and to create two hypothetical further expansions. The less stringent expansion guarantees a monitoring tower within one correlation length (CL) of each location (1 CL), requiring an additional eight towers relative to 2008. The more stringent network includes a tower within one half of a CL (½ CL) and requires 35 towers beyond the 1 CL network. The two proposed networks are evaluated against the network in 2008, which temporarily had the most continuous monitoring sites in North America thanks to the Mid-Continent Intensive project. Evaluation using a synthetic data inversion shows a marked improvement in the ability to constrain both continental- and biome-scale fluxes, especially in areas that are currently under-sampled. The proposed approach is flexible, computationally inexpensive, and provides a quantitative design tool that can be used in concert with existing tools to inform atmospheric monitoring needs.

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Research Program: 
Interdisciplinary Science Program (IDS)
Modeling Analysis and Prediction Program (MAP)