Ocean Acidification

Anthropogenic greenhouse gas emissions cause multiple changes in the ocean and its ecosystems through climate change and ocean acidification. These changes can occur progressively with rising atmospheric carbon dioxide concentrations, but there is also the possibility of large-scale abrupt, and/or potentially irreversible changes, which would leave limited opportunity for marine ecosystems to adapt. Such changes, either progressive or abrupt, pose a threat to biodiversity, food security, and human societies. However, it remains notoriously difficult to determine exact limits of a “safe operating space” for humanity. Here, we map, for a variety of ocean impact metrics, the crossing of limits, which we define using the available literature and to represent a wide range of deviations from the unperturbed state. We assess the crossing of these limits in three future emission pathways: two climate mitigation scenarios, including an overshoot scenario, and one high-emission no-mitigation scenario. These scenarios are simulated by the latest generation of Earth system models and large perturbed-parameter ensembles with two Earth system models of intermediate complexity. Using this comprehensive model database, we estimate the timing and warming level at which 15 different impact metrics exceed 4 limits, along with an assessment of the associated uncertainties. We find that under the high-emissions scenario, the strongest severity of impacts is expected with high probability for marine heatwaves’ duration, loss of Arctic summer sea ice extent, expansion of ocean areas that are undersaturated with respect to aragonite, and decrease in plankton biomass. The probability of exceeding a given limit generally decreases clearly under low-emissions scenario. Yet, exceedance of ambitious limits related to steric sea level rise, Arctic summer sea ice extent, Arctic aragonite undersaturation, and plankton biomass are projected to be difficult to avoid (high probability) even under the low-emissions scenario. Compared to the high-emissions scenario, the scenario including a temporary overshoot reduces with high probability the risk of exceeding limits by year 2100 related to marine heatwave duration, Arctic summer sea ice extent, strength of the Atlantic meridional overturning circulation, aragonite undersaturation, global deoxygenation, plankton biomass, and metabolic index. Our study highlights the urgent need for ambitious mitigation efforts to drastically minimize extensive impacts and potentially irreversible changes to the world’s ocean ecosystems.

The continuous uptake of anthropogenic CO₂ by the ocean leads to ocean acidification, which is an ongoing threat to marine ecosystem. The ocean acidification rate has been globally documented in the surface ocean, but this information is limited below the surface. Here, we present a monthly 4D 1°×1° gridded product of global seawater pH on the total scale and at in situ temperature (without standardization to 25 °C), derived from a machine learning algorithm trained on pH observations from the Global Ocean Data Analysis Project (GLODAP). The proposed pH product covers the years from 1992 to 2020 and depths from the surface to 2 km on 41 levels. A three-step machine-learning-based algorithm was used to construct the pH product, incorporating region division via a self-organizing map neural network, predictor selection via the stepwise regression algorithm that adds and removes variables from network inputs based on their contribution to reducing reconstruction errors, and nonlinear relationship regression by feedforward neural networks (FFNNs). The performance of the machine learning algorithm was validated using real observations with a cross-validation method, in which four repeating iterations were carried out with each iteration utilizing a different 25 % subset of observations for validation and the complementary 75 % subset for training. The proposed pH product is evaluated using comparisons to time-series observations and the GLODAP pH climatology. The overall root-mean-square error between the FFNN-reconstructed pH and the GLODAP measurements is 0.028, ranging from 0.044 at the surface to 0.013 at 2000 m. The pH product is distributed via the Marine Science Data Center of the Chinese Academy of Sciences: https://doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023).

Highlights

• 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.

Abstract

Ocean acidification is transforming marine ecosystems at an unprecedented rate, which in turn requires the estimation of sea surface carbon dioxide partial pressure (pCO₂) 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 pCO₂ 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 (U₁₀), total precipitation (TP), evaporation (E), and significant height of combined wind waves and swell (SHWW), in the reconstruction of sea surface pCO₂. 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 pCO₂ data for the Atlantic Ocean from 2001 to 2020 to investigate the potential and suitability of high-precision reconstruction of the sea surface pCO₂ 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 pCO₂ reconstruction. Notably, the Copernicus pCO₂ and CODC-GOSD pCO₂ contribute the most, with both contributing ∼0.72. These are followed by TP, latitude, longitude, SHWW, U₁₀, 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 pCO₂ and CODC-GOSD pCO₂ products in conjunction with SHWW, U₁₀, 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 pCO₂ or CODC-GOSD pCO₂ 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 pCO₂ reconstruction. The reconstruction of the Atlantic sea surface pCO₂ is conducive to the assessment of global ocean acidification and provides a theoretical basis for the sustainable development of the marine environment.

Ocean pH is a fundamental property regulating various aspects of Earth system evolution. However, early ocean pH remains controversial, with estimates ranging from strongly acidic to alkaline. Here we develop a model integrating global carbon cycling with ocean geochemistry, and incorporating continental growth and mantle thermal evolution. By coupling global carbon cycle with ocean charge balance, and by using solid Earth processes of mantle degassing and crustal evolution to specify the history of volatile distribution and ocean chemistry, we show that a rapid increase in ocean pH is likely during the Hadean to the early Archaean eons, with pH evolving from 5 to neutral by approximately 4.0 Gyr ago. This rapid pH evolution is attributed primarily to elevated rates of both seafloor and continental weathering during the Hadean. This acceleration in weathering rates originates in the unique aspects of Hadean geodynamics, including rapid crust formation, different crustal lithology and fast plate motion. Earth probably transformed from a hostile state to a habitable one by the end of the Hadean, approximately 4.0 Gyr ago, with important implications for planetary habitability and the origin of life.

The oceans mitigate climate change by absorbing approximately 25% of anthropogenic carbon emissions. Decadal variability in the ocean carbon sink, such as a weakening in the 1990s and a strengthening in the 2000s, has been suggested by pCO2-based reconstructions, but its causes remain poorly understood. This variability is also not well represented in climate models, raising concerns about our ability to accurately project future changes. To address potential biases from sparse observational data, machine learning methods have been applied to surface pCO2 and interior dissolved inorganic carbon (DIC), but global reconstructions of full-depth DIC remain lacking. We aim to determine whether ocean carbon sink variability is real and to understand the role of interior DIC inventory changes in the carbon budget. Using neural networks trained on GLODAPv2.2023 observations and predictors like atmospheric CO2, location, temperature, and salinity from EN4 analysis, we reconstruct full-depth global DIC distributions from the 1990s to the 2010s using a residual neural network (ResNet). Validation through prediction of independent datasets show an improvement over previous products. Validation with the ECCO-Darwin dataset results in an average RMSE of 15.1 µmol/kg and bias of -0.3 µmol/kg. The global average uncertainty is 16.85 µmol/kg. The global change in the DIC inventory exhibits pronounced peaks in decadal variability, especially in the early 2000s driven primarily by intermediate waters at depths of 300-1200 m, particularly in the Atlantic, Indian, and Southern Oceans, and to a lesser extent in the Pacific. The accumulation rate of DIC increases steadily from the mid-2000s.