Abstract. Studies of coastal seas in Europe have noted the high variability of the CO 2 system. This high variability, generated by the complex mechanisms driving the CO 2 fluxes, complicates the accurate estimation of these mechanisms. This is particularly pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been characterized in as much detail as in the open oceans. In addition, the joint availability of in situ measurements of CO 2 and of seasurface satellite data is limited in the area. In this paper, we used the SOMLO (self-organizing multiple linear output; Sasse et al., 2013) methodology, which combines two existing methods (i.e. self-organizing maps and multiple linear regression) to estimate the ocean surface partial pressure of CO 2 (pCO 2 ) in the Baltic Sea from the remotely sensed sea surface temperature, chlorophyll, coloured dissolved organic matter, net primary production, and mixed-layer depth. The outputs of this research have a horizontal resolution of 4 km and cover the 1998-2011 period. These outputs give a monthly map of the Baltic Sea at a very fine spatial resolution. The reconstructed pCO 2 values over the validation data set have a correlation of 0.93 with the in situ measurements and a root mean square error of 36 µatm. Removing any of the satellite parameters degraded this reconstructed CO 2 flux, so we chose to supply any missing data using statistical imputation. The pCO 2 maps produced using this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data, and we expect to be able to produce even more accurate reconstructions in coming years, given the predicted acquisition of new data.

Abstract. Studies of coastal seas in Europe have brought forth the high variability in the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes makes their accurate estimation an arduous task. This is more pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been as highly detailed as in the open oceans. In adition, the joint availability of in-situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, a combination of two existing methods (Self-Organizing-Maps and Multiple Linear regression) is used to estimate ocean surface pCO2 in the Baltic Sea from remotely sensed surface temperature, chlorophyll, coloured dissolved organic matter, net primary production and mixed layer depth. The outputs of this research have an horizontal resolution of 4 km, and cover the period from 1998 to 2011. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in-situ measurements, and a root mean square error is of 38 μatm. The removal of any of the satellite parameters degraded this reconstruction of the CO2 flux, and we chose therefore to complete any missing data through statistical imputation. The CO2 maps produced by this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data and we expect to be able to produce even more accurate reconstructions in the coming years, in view of the predicted acquisitions of new data.