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The continuous uptake of anthropogenic CO2 by the ocean leads to ocean acidification, which is an ongoing threat to the marine ecosystem. The ocean acidification rate was globally documented in the surface ocean but limited below the surface. Here, we present a monthly four-dimensional 1°×1° gridded product of global seawater pH, derived from a machine learning algorithm trained on pH observations at total scale and in-situ temperature from the Global Ocean Data Analysis Project (GLODAP). The constructed pH product covers the years 1992–2020 and depths from the surface to 2 km on 41 levels. Three types of machine learning algorithms were used in the pH product construction, including self-organizing map neural networks for region dividing, a stepwise algorithm for predictor selection, and feed-forward neural networks (FFNN) for non-linear relationship regression. The performance of the machine learning algorithm was validated using real observations by a cross validation method, where four repeating iterations were carried out with 25 % varied observations for each evaluation and 75 % for training. The constructed pH product is evaluated through comparisons to time series observations and the GLODAP pH climatology. The overall root mean square error between the FFNN constructed pH and the GLODAP measurements is 0.028, ranging from 0.044 in the surface to 0.013 at 2000 m. The pH product is distributed through the data repository of the Marine Science Data Center of the Chinese Academy of Sciences at http://dx.doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023). [ABSTRACT FROM AUTHOR]

In recent decades, China's coastal ecological environment has undergone significant changes under multiple pressures such as climate change and high intensity of human activities. The occurrence and scale of harmful algal blooms represented by the red tide and the green tide are increasing rapidly. Moreover environmental problems such as hypoxia and acidification in seawater have become increasingly prominent. It is urgent to find out the occurrence mechanism and prevention measures of marine eco-environmental disasters. Therefore; we explained the connotation of the concept of "marine eco-environmental disaster" for the first time, and systematically interpreted the biogenic element driving mechanisms in the occurrence of eco-environmental disasters in China nearshore area in the aspects of exogenous input of nutrients, the mineralization and decomposition of marine organic matters and nutrient regeneration as well as the abnormal nutrient structure in offshore waters. We pointed out that the drastic increases of terrigenous nutrient discharge caused by enhancing human activities, together with the complex biogeochemical cycle process after biogenic elements entering the sea; led to the frequent occurrence of coastal eco- environmental disasters. On this basis; combined with the latest research progress in this field ; we put forward the regulatory schemes for coastal ecological environment health based on the controlling of seawater biogenic element. That is' based on land-sea integration strategy, the reduction of land-source pollutants, especially inorganic nitrogen from land, coastal zone and into the sea, and the normalization of nutrient structure in coastal seawater through artificial regulation technology, are the key points to reduce and control the occurrence of disasters and improve the quality of the coastal ecosystems. [ABSTRACT FROM AUTHOR]

The ocean could profoundly modulate the ever-increasing atmospheric CO2 by air-sea CO2 exchange process, which is also able to cause significant changes of physical and biogeochemical properties in return. In this study, we assessed the long-term average and spatial-temporal variability of global air-sea CO2 exchange flux (FCO2) since 1980s basing on the results of 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth System Models (ESMs). Our findings indicate that the CMIP6 ESMs simulated global CO2 sink in recent three decades ranges from 1.80 to 2.24 Pg C/a, which is coincidence with the results of cotemporaneous observations. What's more, the CMIP6 ESMs consistently show that the global oceanic CO2 sink has gradually intensified since 1980s as well as the observations. This study confirms the simulated FCO2 could reach agreements with the observations in the aspect of primary climatological characteristics, however, the simulation skills of CIMP6 ESMs in diverse open-sea biomes are unevenness. None of the 18 CMIP6 ESMs could reproduce the observed FCO2 increasement in the central-eastern tropical Pacific and the midlatitude Southern Ocean. Deficiencies of some CMIP6 ESMs in reproducing the atmospheric pressure systems of the Southern Hemisphere and the El Niño-Southern Oscillation (ENSO) mode of the tropical Pacific are probably the major causes. [ABSTRACT FROM AUTHOR]

Various machine learning methods were attempted in the global mapping of surface ocean partial pressure of CO2 (pCO2) to reduce the uncertainty of global ocean CO2 sink estimate due to undersampling of pCO2. In previous researches the predicators of pCO2 were usually selected empirically based on theoretic drivers of surface ocean pCO2 and same combination of predictors were applied in all areas unless lack of coverage. However, the differences between the drivers of surface ocean pCO2 in different regions were not considered. In this work, we combined the stepwise regression algorithm and a Feed Forward Neural Network (FFNN) to selected predicators of pCO2 based on mean absolute error in each of the 11 biogeochemical provinces defined by Self-Organizing Map (SOM) method. Based on the predicators selected, a monthly global 1° × 1° surface ocean pCO2 product from January 1992 to August 2019 was constructed. Validation of different combination of predicators based on the SOCAT dataset version 2020 and independent observations from time series stations was carried out. The prediction of pCO2 based on region-specific predicators selected by the stepwise FFNN algorithm were more precise than that based on predicators from previous researches. Appling of a FFNN size improving algorithm in each province decreased the mean absolute error (MAE) of global estimate to 11.32 μatm and the root mean square error (RMSE) to 17.99 μatm. The script file of the stepwise FFNN algorithm and pCO2 product are distributed through the Institute of Oceanology of the Chinese Academy of Sciences Marine Science Data Center (IOCAS; http://dx.doi.org/10.12157/iocas.2021.0022, Zhong et al., 2021). [ABSTRACT FROM AUTHOR]

The partial pressure of CO2 (pCO2) and air-sea CO2 exchange flux (FCO2) in the southern Yellow Sea (SYS, 120–125°E, 31.5–37°N) were investigated basing on the field surveys conducted in April of 2011, and June, July, October of 2012. With significant spatial variations, surface pCO2 ranged from 243 to 574μatm, 206 to 620μatm, 102 to 655μatm and 328 to 557μatm in April, June, July and October, respectively. Nearshore area of Shandong Peninsula and Jiangsu Shallow (depth<50m) were pCO2-supersaturated (pCO2=400–600μatm), as the result of intensive water mixing which brought the bottom CO2-rich water to the surface layer. Conversely, offshore area of SYS center (depth>50m) was pCO2-undersaturated (pCO2<390μatm) in April, June and October, but supersaturated in July. Phytoplankton production sustained by abundant nutrient and suitable hydrodynamic conditions was of great importance for this undersaturated pCO2. Moreover, extreme low pCO2 (pCO2<300μatm) was observed in the Changjiang plume (32.5–33.5°N, 123–125°E) in July, which was also related with the biological uptake of CO2. Average air-sea CO2 exchange flux of the SYS in April, June, July and October was −3.16±0.40mmolm−2 d−1,−4.56±0.34mmolm−2 d−1, −0.36±0.51mmolm−2 d−1, and 6.67±0.57mmolm−2 d−1, respectively. As a whole, the SYS behaved as a weak CO2 sink during April to October, with an average flux for about −0.35mmolm−2 d−1. As for the controlling factors for pCO2 variation, temperature played the dominant role in October, whereas the non-temperature factors, such as vertical mixing, Changjiang plume and biological activity, were considered as the primary controlling factors in June and July. Spatially, the control of temperature on pCO2 was predominant in the offshore SYS; the non-temperature factors were predominant in the shallow nearshore area, especially in coast of Shandong Peninsula and the Jiangsu Shallow. [ABSTRACT FROM AUTHOR]