Remote sensing the sea surface CO2 of the Baltic Sea using the SOMLO methodology
- 1Department of Earth Sciences, Uppsala University, Uppsala, Sweden
- 2Centre d'études et de recherche en informatique, Conservatoire des Arts et Métiers, Paris, France
- 3Laboratoire d'océanographie et du climat: expérimentations et approches numériques, Université Pierre et Marie Curie, Paris, France
Abstract. Studies of coastal seas in Europe have noted the high variability of the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 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 CO2 and of sea-surface 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 CO2 (pCO2) 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 pCO2 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 CO2 flux, so we chose to supply any missing data using statistical imputation. The pCO2 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.