Abstract
The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as a carbon sink, and regional and global carbon cycles. This study estimated the fugacity of carbon dioxide (ƒCO2) over the East Sea located between Korea and Japan. In situ measurements, satellite data and products from the Geostationary Ocean Color Imager (GOCI) and the Hybrid Coordinate Ocean Model (HYCOM) reanalysis data were used through stepwise multi-variate nonlinear regression (MNR) and two machine learning approaches (i.e., support vector regression (SVR) and random forest (RF)). We used five ocean parameters—colored dissolved organic matter (CDOM; <0.3 m−1), chlorophyll-a concentration (Chl-a; <21 mg/m3), mixed layer depth (MLD; <160 m), sea surface salinity (SSS; 32–35), and sea surface temperature (SST; 8–28 °C)—and four band reflectance (Rrs) data (400 nm–565 nm) and their ratios as input parameters to estimate surface seawater ƒCO2 (270–430 μatm). Results show that RF generally performed better than stepwise MNR and SVR. The root mean square error (RMSE) of validation results by RF was 5.49 μatm (1.7%), while those of stepwise MNR and SVR were 10.59 μatm (3.2%) and 6.82 μatm (2.1%), respectively. Ocean parameters (i.e., sea surface salinity (SSS), sea surface temperature (SST), and mixed layer depth (MLD)) appeared to contribute more than the individual bands or band ratios from the satellite data. Spatial and seasonal distributions of monthly ƒCO2 produced from the RF model and sea-air CO2 flux were also examined.
Highlights
Carbon dioxide, one of the greenhouse gases, has significantly increased since the industrial revolution due to economic and population growth
The objectives of this study were (1) to estimate surface seawater ƒCO2 in the East Sea of Korea using satellite data and in situ measurements based on multi-variate nonlinear regression (MNR) and machine learning approaches, and (2) to examine ocean parameters contributing to ƒCO2 estimation, and (3) investigate the spatial and temporal variation of surface seawater ƒCO2 and sea-air CO2 flux over the East Sea using satellite data
Individual band reflectance data can be used as good supplements for Variableimportance importanceidentified identified random forest
Summary
One of the greenhouse gases, has significantly increased since the industrial revolution due to economic and population growth. Increase of carbon dioxide concentration in the atmosphere accelerates global warming, which is directly related with on-going climate change. Climate change has brought significant impacts on human society and the natural environment all over the world, in such ways as increasing extreme weather events and a rise in sea levels [1]. The substantial amount of carbon dioxide in the atmosphere is absorbed into the oceans, approximately half of anthropogenic carbon dioxide remains in the atmosphere, increasing its concentration [2]. Since the ocean acts as a buffer for carbon dioxide uptake, temporal and spatial changes of the sea-air carbon dioxide flux are crucial to understanding the global carbon cycle [3]
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