Title: Quantifying uncertainty sources in the gridded data of sea surface CO₂ partial pressure. Wang, GZ; Dai, MH; Shen, SSP; Bai, Y; Xu, Y. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2014. 119: 5181-5189. 

Abstract: The bulk uncertainty in the gridded sea surface pCO₂ data is crucial in assessing the reliability of the CO₂ flux estimated from measurements of air-sea pCO₂ difference, because atmospheric pCO₂ are relatively homogeneous and well defined. The bulk uncertainty results from three different sources: analytical error (E_m), spatial variance ( ), and the bias from undersampling ( ). Common uncertainty quantification by standard deviation may mix up the different sources of uncertainty. We have established a simple procedure to determine these three sources of uncertainty using remote sensing-derived and field-measured pCO₂ data. E_m is constrained by the analytical method and data reduction procedures.  is derived from the remotely sensed pCO₂ field.  is determined by spatial variance and the effective number of observations, considering, for the first time, the geometric bias introduced by pCO₂ sampling. This approach is applied to 1° × 1° gridded pCO₂ data collected from the East China Sea. We demonstrate that the spatial distribution of these biases is uneven and that none of them follow the same spatial trend as the standard deviation.  contributes the most to the uncertainty in gridded pCO₂ data over those grid boxes with good sampling coverage, while   dominates the total uncertainty in the grid boxes with poor sampling coverage. Application of this procedure to other parts of the global ocean will help to better define the inherent spatial variability of the pCO₂ field and thus better interpolate and/or extrapolate pCO₂ data, and eventually better constrain air-sea CO2 fluxes.

Link to full text: http://onlinelibrary.wiley.com/doi/10.1002/2013JC009577/abstract.