A statistical gap-filling method to interpolate global monthly surface ocean 
carbon dioxide data

Jones, Steve D.; Le Quere, Corinne; Rödenbeck, Christian; Manning, Andrew C.; Olsen, Are

doi:10.1002/2014MS000416

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A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data

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Rödenbeck, Christian
Inverse Data-driven Estimation, Dr. C. Rödenbeck, Department Biogeochemical Systems, Prof. M. Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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http://dx.doi.org/10.1002/2014MS000416
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Citation

Jones, S. D., Le Quere, C., Rödenbeck, C., Manning, A. C., & Olsen, A. (2016). A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data. Journal of Advances in Modeling Earth Systems, 7(4), 1554 -1575. doi:10.1002/2014MS000416.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-03EB-8

Abstract

We have developed a statistical gap-filling method adapted to the specific coverage and properties of observed fugacity of surface ocean CO2 (fCO2). We have used this method to interpolate the Surface Ocean CO2 Atlas (SOCAT) v2 database on a 2.5°×2.5° global grid (south of 70°N) for 1985–2011 at monthly resolution. The method combines a spatial interpolation based on a “radius of influence” to determine nearby similar fCO2 values with temporal harmonic and cubic spline curve-fitting, and also fits long-term trends and seasonal cycles. Interannual variability is established using deviations of observations from the fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on the spatial and temporal range of the interpolation. Tests of the method using model data show that it performs as well as or better than previous regional interpolation methods, but in addition it provides a near-global and interannual coverage.