Quantifying uncertainty sources in the gridded data of sea surface CO 2 partial pressure

Abstract

The bulk uncertainty in the gridded sea surface p CO 2 data is crucial in assessing the reliability of the CO 2 flux estimated from measurements of air‐sea p CO 2 difference, because atmospheric p CO 2 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 p CO 2 data. E m is constrained by the analytical method and data reduction procedures. is derived from the remotely sensed p CO 2 field. is determined by spatial variance and the effective number of observations, considering, for the first time, the geometric bias introduced by p CO 2 sampling. This approach is applied to 1° × 1° gridded p CO 2 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 p CO 2 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 p CO 2 field and thus better interpolate and/or extrapolate p CO 2 data, and eventually better constrain air‐sea CO 2 fluxes. Three sources of uncertainty in gridded p CO 2 data are quantified Remote sensing‐derived and underway p CO 2 data are combined The uncertainties in gridded p CO 2 data from the East China Sea are estimated