Sea surface pCO2 and its variability in the Ulleung Basin, East Sea constrained by a neural network model

Currently available surface seawater partial pressure carbon dioxide () data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the measurements, we construct a neural network (NN) model based on satellite data to map for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where data are best available, to map and estimate the variability of based on in situ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted values. The RMSE (root mean square error) of the NN model output was and much less than the variability of in situ . The variability of with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of increases. When SST is lower than , variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than , the variability of is less sensitive to changes in SST and chlorophyll. The mean rate of the annual increase estimated by the NN model output in the Ulleung Basin is from 2003 to 2014. As NN model can successfully map data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.

Park S., Lee T. & Jo Y.-H., 2016. Sea surface pCO2 and its variability in the Ulleung Basin, East Sea constrained by a neural network model. The Sea 21(1):1-10. Article.

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