Abstract
Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.
Highlights
The ocean is one of the destinations for anthropogenic carbon, which takes up about30% of emissions from pre-industrial times to 1994 [1]
Some satellite-derived parameters can be used in the study for their features are closely related to them, as follows: (I) sea surface temperature (SST, °C) can reflect the thermodynamic process directly. (II) Some inherent optical properties (IOPs) and apparent optical properties (AOPs), such as dissolved and particulate detrital matter absorption coefficient (Adg, m−1 ), particulate backscattering, absorption by phytoplanktonic (Aph, m−1 ) and diffuse attenuation coefficient of downwelling irradiance (Kd, m−1 ), can measure the mass of carbon which influences the sea surface pressure of CO2 (pCO2) through the process of water mixing and biological activities. (III) Some elements such as sea surface salinity (SSS, dimensionless) and surface chlorophyll-a concentration (Chla, mg·m−3 ) can be deduced by the biological activities and other variables
The results suggest that the CatBoost algorithm has a good generalization ability surface pCO2 inversion model in these cruises, and even for the extensive data coverage in the North Atlantic
Summary
The ocean is one of the destinations for anthropogenic carbon, which takes up about30% of emissions from pre-industrial times to 1994 [1]. Surface pCO2 is controlled by four major factors: the thermodynamic process, physical mixing between different water masses, biological activities, and the air–sea gas exchange [12,13,14]. According to these processes, some satellite-derived parameters can be used in the study for their features are closely related to them, as follows: (I) sea surface temperature (SST, °C) can reflect the thermodynamic process directly. Some satellite-derived parameters can be used in the study for their features are closely related to them, as follows: (I) sea surface temperature (SST, °C) can reflect the thermodynamic process directly. (II) Some inherent optical properties (IOPs) and apparent optical properties (AOPs), such as dissolved and particulate detrital matter absorption coefficient (Adg, m−1 ), particulate backscattering (bbp, m−1 ), absorption by phytoplanktonic (Aph, m−1 ) and diffuse attenuation coefficient of downwelling irradiance (Kd, m−1 ), can measure the mass of carbon which influences the sea surface pCO2 through the process of water mixing and biological activities. (III) Some elements such as sea surface salinity (SSS, dimensionless) and surface chlorophyll-a concentration (Chla, mg·m−3 ) can be deduced by the biological activities and other variables
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