Terrestrial groundwater travels through subterranean estuaries before reaching the sea. Groundwater‐derived nutrients drive coastal water quality, primary production, and eutrophication. We determined how dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved organic nitrogen (DON) are transformed within subterranean estuaries and estimated submarine groundwater discharge (SGD) nutrient loads compiling > 10,000 groundwater samples from 216 sites worldwide. Nutrients exhibited complex, nonconservative behavior in subterranean estuaries. Fresh groundwater DIN and DIP are usually produced, and DON is consumed during transport. Median total SGD (saline and fresh) fluxes globally were 5.4, 2.6, and 0.18 Tmol yr⁻¹ for DIN, DON, and DIP, respectively. Despite large natural variability, total SGD fluxes likely exceed global riverine nutrient export. Fresh SGD is a small source of new nutrients, but saline SGD is an important source of mostly recycled nutrients. Nutrients exported via SGD via subterranean estuaries are critical to coastal biogeochemistry and a significant nutrient source to the oceans.

To evaluate biogeochemical impacts of shelf‐scale submarine groundwater discharge (SGD), the northern South China Sea (NSCS) and the East China Sea (ECS) shelf were chosen for this study. Based on a three end‐member mixing model and a Ra box model, SGD fluxes on NSCS (west and east of the Pearl River Estuary, WPRE and EPRE, respectively) and ECS shelf in winter were estimated to be 3.8–9.5 × 10⁹, 1.4–2.2 × 10⁹ and 0.7–2.2 ×10¹⁰ m³ d⁻¹, respectively. Our results were equivalent to the SGD flux to the entire Mediterranean Sea, and were an order of magnitude greater than fluxes to the South Atlantic Bight. SGD associated nutrient fluxes into WPRE, EPRE and ECS were almost in the same order of magnitude, 0.2–2.4 × 10³ mol m⁻¹ d⁻¹ for dissolved inorganic nitrogen, 1.2–9.8 mol m⁻¹ d⁻¹ for soluble reactive phosphorus, and 0.4–3.4 × 10³ mol m⁻¹ d⁻¹ for dissolved silicate. Moreover, SGD delivered 1.1–2.8 × 10⁴, 0.6–0.9 × 10⁴, 1.7–5.1 × 10⁴ mol m⁻¹ d⁻¹ dissolved inorganic carbon and 1.1–2.7 × 10⁴, 0.5–0.8 × 10⁴, 1.6–4.8 × 10⁴ mol m⁻¹ d⁻¹ total alkalinity to WPRE, EPRE and ECS, respectively. Shelf‐scale SGD is a significant source of nutrients and carbon, and may pose great impacts on regional marine ecosystems.

The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO₂ (pCO₂), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface pCO₂ datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface pCO₂ in the SCS with a reasonably high spatial resolution (0.05^∘ × 0.05^∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of pCO₂ constrained by EOF, atmospheric pCO₂, and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed pCO₂ field successfully simulated the spatial and temporal patterns of sea surface pCO₂ observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm, which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed pCO₂ and in situ data ranged from −10 to 4 µatm (−2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface pCO₂ distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface pCO₂ in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface pCO₂ in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO₂, the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at https://doi.org/10.57760/sciencedb.02050 (Wang and Dai, 2022).

Sea surface partial pressure of CO₂ (pCO₂) data with a high spatiotemporal resolution are important in studying the global carbon cycle and assessing the oceanic carbon uptake. However, the observed sea surface pCO₂ data are usually limited in spatial and temporal coverage, especially in marginal seas. This study provides an approach to reconstruct the complete sea surface pCO₂ field in the South China Sea (SCS) with a grid resolution of $\mathrm{0.5}{}^{\circ }\times \mathrm{0.5}{}^{\circ }$ over the period of 2000–2017 using both remote-sensing-derived pCO₂ and observed underway pCO₂, among which the gridded underway pCO₂ data in 2004, 2005, and 2006 are presented for the first time. Empirical orthogonal functions (EOFs) were computed from the remote-sensing-derived pCO₂. Then, a multilinear regression was applied to the observed pCO₂ as the response variable with the EOFs as the explanatory variables. EOF1 explains the general spatial pattern of pCO₂ in the SCS. EOF2 shows the pattern influenced by the Pearl River plume on the northern shelf and slope. EOF3 is consistent with the pattern influenced by coastal upwelling along the northern coast of the SCS. When pCO₂ observations cover a sufficiently large area, the reconstructed fields successfully display a pattern of relatively high pCO₂ in the mid and southern basin. The rate of sea surface pCO₂ increase in the SCS is 2.4±0.8 µatm yr⁻¹ based on the spatial average of the reconstructed pCO₂ over the period of 2000–2017. This is consistent with the temporal trends at Station SEATS (SouthEast Asia Time-series Study; 18^∘ N, 116^∘ E) in the northern basin of the SCS and at Station ALOHA (A Long-Term Oligotrophic Habitat Assessment; $\mathrm{22}{}^{\circ }{\mathrm{45}}^{\prime }$ N, 158^∘ W) in the North Pacific. We validated our reconstruction with a leave-one-out cross-validation approach, which yields the root-mean-square error (RMSE) in the range of 2.4–5.2 µatm, smaller than the spatial standard deviation of our reconstructed data and much smaller than the spatial standard deviation of the observed underway data. The RMSE between the reconstructed summer pCO₂ and the observed underway pCO₂ is no larger than 31.7 µatm, in contrast to (a) the RMSE from 12.8 to 89.0 µatm between the remote-sensing-derived pCO₂ and the underway data and (b) the RMSE from 32.6 to 44.5 µatm between the neural-network-produced pCO₂ and the underway data. The difference between the reconstructed pCO₂ and those calculated from observations at Station SEATS is in the range from −7 to 10 µatm. These comparison results indicate the reliability of our reconstruction method and output. All the data for this paper are openly and freely available at PANGAEA under the link https://doi.org/10.1594/PANGAEA.921210 (Wang et al., 2020).

Stable barium isotopes are a potential proxy for riverine inputs into the ocean that reflect monsoon variability and climate change. However, dissolved Ba isotope (δ¹³⁸Ba_DBa) geochemistry in river estuaries, a dynamic land to ocean transition zone, has rarely been systematically examined to date. Here, we show that significant Ba isotope fractionation occurs at near‐zero salinities in the Yangtze and Pearl River Estuary, whereas conservative mixing dominates δ¹³⁸Ba_DBa distributions beyond low salinities, which are well predicted by an ion exchange model. Elevated δ¹³⁸Ba_DBa in the river endmember results from preferential removal of light Ba isotopes by adsorption to fluvial particles. Subsequently, δ¹³⁸Ba_DBa rapidly drops to minimum signatures at increased salinities indicating particle desorption of isotopically light Ba. Nevertheless, the apparently conservative δ¹³⁸Ba_DBa‐salinity relationship beyond the low‐salinity minimum in both estuaries provides a modern calibration for using Ba isotopes as a proxy for paleosalinity and river water inputs into the ocean.