Research on Atlantic surface pCO2 reconstruction based on machine learning

Ocean acidification is transforming marine ecosystems at an unprecedented rate, which in turn requires the estimation of sea surface carbon dioxide partial pressure (pCO 2 ) as a crucial metric to gauge acidification. This has substantial implications for marine resource assessment and management, marine ecosystems, and global climate change research. This study utilizes SOCAT cruise survey data to assess the accuracy of global sea surface pCO 2 products offered by Copernicus Marine Service and the Chinese Academy of Sciences Ocean Science Research Center. Through the application of a geographic information analysis method—geographical detector—the study quantitatively reveals the significance of environmental influencing factors, such as longitude, latitude, sea surface 10 m wind speed (U 10 ), total precipitation (TP), evaporation (E), and significant height of combined wind waves and swell (SHWW), in the reconstruction of sea surface pCO 2 . Subsequently, various machine learning models, which include convolutional neural network (CNN), back propagation neural network (BP), long short-term memory network (LSTM), extreme learning machine (ELM), support vector regression (SVR), and extreme gradient boosting tree (XGBoost), are used to reconstruct the monthly sea surface pCO 2 data for the Atlantic Ocean from 2001 to 2020 to investigate the potential and suitability of high-precision reconstruction of the sea surface pCO 2 dataset for this sea area. The findings indicate that: (1) The geographical detector effectively quantifies the contribution of various environmental factors used in sea surface pCO 2 reconstruction. Notably, the Copernicus pCO 2 and CODC-GOSD pCO 2 contribute the most, with both contributing ~0.72. These are followed by TP, latitude, longitude, SHWW, U 10 , and E. (2) After comprehensive data testing, the six machine learning models select the optimal hyperparameters for reconstruction. Among these, the XGBoost model notably improved the quality of the original dataset when using Copernicus pCO 2 and CODC-GOSD pCO 2 products in conjunction with SHWW, U 10 , and TP environmental variable data. Compared with SOCAT data, the overall reconstruction accuracy in the Atlantic Ocean reached an impressive 94 %, outperforming the standalone use of either Copernicus pCO 2 or CODC-GOSD pCO 2 products. Furthermore, the XGBoost model demonstrated strong applicability in regions with numerous outliers, maintaining a reconstruction accuracy of ≥95 %. (3) Stability test results reveal that the XGBoost model exhibits low sensitivity to uncertainties in all input variables. This indicates that the model can accommodate environmental data errors induced by abrupt changes in marine environments. Such robustness enhances its reliability in sea surface pCO 2 reconstruction. The reconstruction of the Atlantic sea surface pCO 2 is conducive to the assessment of global ocean acidification and provides a theoretical basis for the sustainable development of the marine environment. • The XGBoost machine learning model has been refined, leading to a superior prediction of atmospheric carbon dioxide partial pressure over the Atlantic Ocean. • We introduced the GeoDetector, a spatial statistical analysis method, to quantitatively assess the influence of various factors on the atmospheric carbon dioxide partial pressure over the ocean surface. • Comprehensive validation ensures the robustness and reliability of the XGBoost model in this region. • Investigations into the atmospheric carbon dioxide partial pressure over the Atlantic Ocean offer valuable insights into global marine ecosystems.