A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data
| Type | Article | ||||||||
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| Date | 2015-12 | ||||||||
| Language | English | ||||||||
| Author(s) | Jones Steve D.1, Le Quere Corinne1, Roedenbeck Christian2, Manning Andrew C.3, Olsen Are4, 5 | ||||||||
| Affiliation(s) | 1 : Univ E Anglia, Tyndall Ctr Climate Change Res, Norwich NR4 7TJ, Norfolk, England. 2 : Max Planck Inst Biochem, Jena, Germany. 3 : Univ E Anglia, Sch Environm Sci, Ctr Ocean & Atmospher Sci, Norwich NR4 7TJ, Norfolk, England. 4 : Univ Bergen, Inst Geophys, Bergen, Norway. 5 : Bjerknes Ctr Climate Res, Bergen, Norway. |
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| Source | Journal Of Advances In Modeling Earth Systems (1942-2466) (Amer Geophysical Union), 2015-12 , Vol. 7 , N. 4 , P. 1554-1575 | ||||||||
| DOI | 10.1002/2014MS000416 | ||||||||
| WOS© Times Cited | 27 | ||||||||
| Keyword(s) | ocean, CO2, interpolation | ||||||||
| 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. | ||||||||
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