The partial pressure of ocean surface CO₂ (pCO₂) plays an important role in quantifying the carbon budget and assessing ocean acidification. For such a vast and complex ocean system as the global ocean, most current research practices tend to study the ocean into regions. In order to reveal the overall characteristics of the global ocean and avoid mutual influence between zones, a holistic research method was used to detect the correlation of twelve predictive factors, including chlorophyll concentration (Chlor_a), diffuse attenuation coefficient at 490 nm (Kd_490), density ocean mixed layer thickness (Mlotst), eastward velocity (East), northward velocity (North), salinity (Sal), temperature (Temp), dissolved iron (Fe), dissolved silicate (Si), nitrate (NO₃), potential of hydrogen (pH), phosphate (PO₄), at the global ocean scale. Based on measured data from the Global Surface pCO₂ (LDEO) database, combined with National Aeronautics and Space Administration (NASA) Ocean Color satellite data and Copernicus Ocean reanalysis data, an improved optimized random forest (ORF) method is proposed for the overall reconstruction of global ocean surface pCO₂, and compared with various machine learning methods. The results indicate that the ORF method is the most accurate in overall modeling at the global ocean scale (mean absolute error of 6.27μatm, root mean square error of 15.34μatm, R² of 0.92). Based on independent observations from the LDEO dataset and time series observation stations, the ORF model was further validated, and the global ocean surface pCO₂ distribution map of 0.25° × 0.25° for 2010 to 2019 was reconstructed, which is of significance for the global ocean carbon cycle and carbon assessment.

Accurate estimation of the distribution of POC in the sea surface is an important issue in understanding the carbon cycle at the basin scale in the ocean. This study explores the best machine learning approach to determine the distribution of POC in the ocean surface layer based on data obtained using satellite remote sensing. In order to estimate and verify the accuracy of this method, it is necessary to obtain a large amount of POC data from field observations, so this study was conducted in the Mediterranean Sea, where such data have been obtained and published. The research initially utilizes the Geographic Detector (GD) method to identify spatial correlations between POC and 47 environmental factors in the region. Four machine learning models of a Bayesian optimized random forest (BRF), a backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Model validation yielded that the BRF exhibited superior performance in estimating sea-surface POC. To build a more accurate tuneRanger random forest (TRRF) model, we introduced the tuneRanger R package for further optimization, resulting in an R² of 0.868, a mean squared error of 1.119 (mg/m³)², and a mean absolute error of 1.041 mg/m³. It was employed to estimate the surface POC concentrations in the Mediterranean for May and June 2017. Spatial analysis revealed higher concentrations in the west and north and lower concentrations in the east and south, with higher levels near the coast and lower levels far from the coast. Additionally, we deliberated on the impact of human activities on the surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in this area.

The global ocean carbon dioxide flux (air-sea) has shown a slow upward trend. Based on more than 160,000 quality-controlled measurements of surface ocean carbon dioxide fugacity from 2000 to 2020, a satellite-based ocean–atmosphere carbon dioxide fugacity (fCO ₂ ) retrieval algorithm developed using machine learning methods. A comparative analysis was conducted among various machine learning methods, including XGBoost, random forest, light gradient boosting machine, feedforward neural network, convolutional neural network, and backpropagation neural network. Based on the best performance, the random forest algorithm selected for model construction. The results of independent on-site validation indicated that the model had a low root mean square error (RMSE = 14.35 µatm), mean absolute percentage error (MAPE = 2.61%), and coefficient of determination (R² = 0.86). The distribution of global air-sea carbon dioxide fugacity from 2000 to 2020 was reconstructed at a resolution of 0.25° × 0.25°, and the air–sea carbon dioxide flux (FCO ₂ ) of the global ocean during the period of 2000–2020 was further estimated at a resolution of 0.25°×0.25°. During the period of 2000–2020, the global ocean CO ₂ uptake increased from 1.443 PgC/year in 2000 to 1.894 PgC/year in 2020, and the air-sea carbon dioxide flux in the entire study area increased by 31.2% over the 20 years. These comprehensive oceanic carbon sink datasets and new insights will help further research on ocean carbon sequestration and its climate regulation potential.

Particulate organic carbon (POC) is a pivotal component within the marine carbon cycle, actively involved in diverse biogeochemical processes. This study focuses on Mediterranean as the research region, selecting the most influential factors from 47 potential ones influencing surface POC. Using geographic detector for factor identification, ten primary influencers were identified. Preliminarily optimized random forest (BRF), backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Notably BRF exhibited superior performance in estimating the sea surface POC. Further optimization of RF using the tuneRanger R package resulted in R² of 0.868, mean squared error of 1.119 (mg/m³) ², and mean absolute error of 1.041 (mg/m³). Surface POC concentrations in the Mediterranean for May and June 2017 were estimated. Spatial distribution analysis unveiled higher concentrations in the west, lower in the east, higher in the north, lower in the south, with higher levels near the coast and lower far from the coast. Additionally, the study deliberated on the impact of human activities on surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in the area.