Takamitsu Ito

We investigate the response and reversibility of Southern Ocean air–sea CO ₂ fluxes to a symmetric atmospheric CO ₂ increase and removal experiment, where atmospheric CO ₂ is increased by 1% annually for 140 years, followed by a symmetric 1% annual decrease over 140 years, and then held at preindustrial levels for 50 years. During positive emissions, the Southern Ocean CO ₂ sink strengthens and shifts poleward, with the Antarctic region becoming the dominant uptake area. Carbon dioxide removal reverses this spatial redistribution within approximately 100 years, restoring a subtropical‐dominated uptake structure; however, the Southern Ocean remains a net CO ₂ source for at least 50 years after mitigation. This delayed recovery arises from the inefficient removal of anthropogenic dissolved inorganic carbon relative to heat, leading to persistent subsurface carbon storage and sustained reductions in CO ₂ solubility. Model responses show a pronounced asymmetry between emission and removal phases, with a faster intensification of CO ₂ uptake during ramp‐up than its recovery during ramp‐down. These differences are strongly controlled by preindustrial seasonal pCO ₂ dynamics and ocean CO ₂ buffering capacity: models with a weaker buffering capacity exhibit up to twice the sensitivity of air–sea CO ₂ fluxes to atmospheric forcing. Seasonal cycle representation in the Subantarctic and Antarctic regions further separates models into thermally and non‐thermally dominated regimes, which govern both the magnitude and reversibility of the CO ₂ sink under forcing. Overall, our results demonstrate that Southern Ocean carbon sink recovery under negative emissions is slow, spatially heterogeneous, and controlled by interior ocean memory and seasonal carbon‐cycle processes.

Ocean deoxygenation is a consequence of anthropogenic ocean warming. Global historical deoxygenation estimates are based on gap-filled reconstructions from sparse and unevenly distributed observations and span a wide and uncertain range between 0.8-2.4% (equivalent to 0.23-0.7 micromol/kg/decade oxygen loss. Here, we quantify errors in historical deoxygenation estimates arising from observational subsampling and reconstruction by applying six state-of-the-art reconstruction frameworks to 14 Earth System Models (ESMs) subsampled at historical observation locations from 1965-2021, and comparing the reconstructed oxygen changes with their fully sampled counterparts. We find that five out of six reconstruction frameworks reduce deoxygenation estimates between 64% (i.e., 0.12 micromol/kg/decade) to 13% (i.e., 0.02 micromol/kg/decade), with one framework increasing the deoxygenation estimate by 7% (i.e., 0.01 micromol/kg/decade). The largest errors occur in poorly sampled regions, including the deep ocean (below 2000 m) and the tropical oceans. We leverage across-ESM relationships between reconstructed and fully sampled oxygen trends to correct the subsampling-reconstruction error in observation-based estimates. The correction in the upper 2000 m is small due to regional compensations. Model-based corrections weaken deoxygenation estimates in the tropical Atlantic (by 25%) and the tropical Pacific (by 19%), but strengthen deoxygenation in other regions including the Southern Ocean (by 37%). In contrast, corrections below 2000 m indicate a 69% [15-117%, 95% confidence interval] stronger deoxygenation, equivalent to an additional oxygen loss of 0.14 [0.03 to 0.25] micromol/kg/decade. Our results call for targeted observations in poorly-sampled regions to better quantify future ocean deoxygenation.

Machine learning (ML) methods have emerged as a promising approach for dissolved oxygen (DO) mapping. However, the uncertainties arising from model configurations such as hyperparameter setting and random initialization remain insufficiently characterized. Here we quantify this uncertainty through an efficient ensemble intercomparison. ML-based DO mapping uncertainty spatially peaks in the oxycline and temporally increases during the periods of historical observation scarcity. These patterns are primarily driven by intrinsic DO variability and extrinsic observing strategy. Our results further show that with the deployment of the Argo-O ₂ floats, global uncertainty has been reduced by 37.8% over the last two decades. However, uncertainty reductions are regionally uneven: substantial decreases occur mainly where sampling is spatially uniform, including the Atlantic, Indian, and Southern Oceans. These findings emphasize that enhancing spatial uniformity is equally important as increasing the number of observations, positioning uncertainty analysis as a bridge between understanding DO variability and designing future observing-systems.

Dissolved nitrate, phosphate, and silicate are fundamental drivers of marine primary productivity and the biological carbon pump. However, the development of continuous, long-term global datasets has long been severely hindered by extreme historical data sparsity and complex biogeochemical dynamics. Statistical interpolation methods struggle to simultaneously fill the severely sparse data gaps and capture the non-linear interactions, necessitating advanced artificial intelligence (AI) to explicitly learn and leverage their underlying relationships. Nevertheless, most existing AI methods reconstruct nutrients independently (i.e., Single-Task Learning), failing to exploit the synergistic effects inherent in cross-nutrient stoichiometry. In this study, we present Jingwei-Nutrients , a four-dimensional global monthly data product at 1 ° ×1 ° resolution from 0 to 2000 m depth spanning 1965 to 2023, reconstructed using a Transformer-based Multi-Task Learning (MTL) framework trained on a comprehensive, quality-controlled multi-source observational database. Evaluation on chronological K -fold cross-validation yields mean validation R ² values across folds of 0.991, 0.969, and 0.990, with RMSEs of 1.295, 0.163, and 4.109 µmol kg ⁻¹ for nitrate, phosphate, and silicate, respectively. Temporal K -fold cross-validation reveals that the MTL framework consistently achieves higher R ² and lower RMSE for all three nutrients compared to single-task models, with larger accuracy gains in data-sparse earlier decades such as 1965–1975. Our data product reproduces consistent global climatology patterns and seasonal cycles with World Ocean Atlas (WOA). Furthermore, independent evaluations against long-term monitoring stations (HOT and KERFIX) and GO-SHIP cruise sections (P16N, P16S, and P06E) demonstrate our effectiveness across multi-decadal temporal trends, spatial variability and vertical changes. Additionally, an ensemble-based uncertainty analysis reveals interpretable spatial heterogeneities and a long-term decreasing trend in global uncertainty, which directly mirrors the historical transition from sparse early sampling to modern observing networks. This product fills a critical gap in historical ocean biogeochemical observations, providing a reliable, physically consistent foundation for marine biogeochemical modeling and climate change studies. The product is openly available at https://doi.org/10.5281/zenodo.21066027 (Wang et al., 2026).

Marine heatwaves (MHWs), characterized by anomalously high sea surface temperatures, are increasing in frequency and intensity and strongly impact ocean circulation, biogeochemistry, and marine ecosystems. During the 2014–2015 MHW (commonly called the Blob) in the NE subarctic Pacific, moored observations at Ocean Station Papa (OSP; 145° W, 50° N) showed a moderate decrease in oceanic pCO₂, contrary to the increase expected from warming-induced solubility reduction alone. Using a regional model that reproduces the observed pCO₂ variability and trend at OSP, we show that this decline resulted from a decrease in dissolved inorganic carbon (DIC) supply that outweighed the warming-driven increase in pCO₂. The DIC reduction was primarily caused by weakened vertical transport associated with enhanced upper-ocean stratification and reduced Ekman pumping prior to the onset of the Blob, which suppressed the upwelling of DIC-rich subsurface waters. Horizontal transport also contributed locally, particularly at OSP. These results demonstrate that anomalous physical circulation, rather than biological processes, was the primary driver of the enhanced CO₂ uptake during the Blob and highlight the importance of resolving physical transport mechanisms when assessing carbon cycle responses to extreme warming events.

The current generation of earth system models (ESMs) exhibit substantial biases and inter-model spread in representing ocean deoxygenation and the expansion of tropical oxygen minimum zones (OMZs). Improving dissolved oxygen simulation is challenging as it depends on uncertain physical and biogeochemical processes and their interactions. A machine learning-based emulator, O ₂ EMU, aims to reduce model biases and inter-model spread by replacing ESMs’ biogeochemical parameterizations with the learned relationships between dissolved oxygen and physical variables. The emulator first learns from the historical shipboard and autonomous observations of dissolved oxygen and temperature and salinity from ocean reanalyses. The learned relationships are then applied to the temperature and salinity outputs from an ensemble of ESM historical simulations. The results show significantly improved climatological OMZ structure and long-term oxygen trends including the shoaling and expansion of the tropical Atlantic OMZs. Application of O ₂ EMU to ESM simulations can reduce the inter-model spread by 70%–75% and the root mean square error by 50%–60%, offering a computationally efficient and scalable alternative to standard biogeochemical models, and providing a stepping stone towards hybrid biogeochemical projections that blend mechanistic ESM dynamics with observationally constrained tracer distributions.

Dissolved nitrate, phosphate, and silicate are fundamental drivers of marine primary productivity and the biological carbon pump. However, the development of continuous, long-term global datasets has long been severely hindered by extreme historical data sparsity and complex biogeochemical dynamics. Statistical interpolation methods struggle to simultaneously fill the severely sparse data gaps and capture the non-linear interactions, necessitating advanced artificial intelligence (AI) to explicitly learn and leverage their underlying relationships. Nevertheless, most existing AI methods reconstruct nutrients independently (i.e., Single-Task Learning), failing to exploit the synergistic effects inherent in cross-nutrients stoichiometry. In this study, we present Jingwei-Nutrients, a global monthly dataset at resolution from 0 to 2000 m depth spanning 1965 to 2023, reconstructed using a Transformer-based Multi-Task Learning (MTL) framework trained on a comprehensive, quality-controlled multi-source observational database. Evaluation on the validation set yields values of 0.980, 0.961, and 0.983, with RMSEs of 2.21, 0.23, and 6.35 for nitrate, phosphate, and silicate, respectively. Temporal K-fold cross-validation reveals that the MTL framework consistently achieves higher and lower RMSE for all three nutrients compared to single-task models, with larger accuracy gains in data-sparse earlier decades such as 1965–1975. Our dataset reproduces consistent global climatology patterns and seasonal cycles with World Ocean Atlas (WOA). Furthermore, independent evaluations against long-term monitoring stations (HOT and KERFIX) and GO-SHIP cruise sections (P16N, P16S, and P06E) demonstrate our effectiveness across multi-decadal temporal trend, spatial variability and vertical changes. Additionally, an ensemble-based uncertainty analysis reveals interpretable spatial heterogeneities and a long-term decreasing trend in global uncertainty, which directly mirrors the historical transition from sparse early sampling to modern observing networks. This dataset fills a critical gap in historical ocean biogeochemical observations, providing a reliable, physically consistent foundation for marine biogeochemical modeling and climate change studies. The dataset is openly available at https://doi.org/10.5281/zenodo.19491198.

Ocean dissolved oxygen (O₂) is an essential climate variable crucial for sustaining the marine life; thus, changes of O₂ at various spatiotemporal scales should be quantified and understood. Here, we study the climatology and annual cycle of O₂ at regional to global scales using eight available gridded observational products. These datasets are generated by different groups using different primary data selection, quality control, bias correction, and interpolation methods, including statistical and machine-learning-based mapping methods. A common set of metrics was collaboratively developed by the community of the Gridded Observational Dataset Intercomparison Project-Dissolved Oxygen (GODIP-DO) to facilitate the inter-comparison. We find that global mean O₂ profiles are consistent among all products (±3 µmol kg⁻¹), with the well-established decrease from high surface values to a minimum ∼ 1000 m, and subsequent increase to higher O₂ at depth, although local differences could reach ±25 µmol kg⁻¹ (0–1000 m). The hemispheric O₂ annual cycle correlates strongly with ocean temperature changes, suggesting the key driver of temperature for the O₂ annual cycle. However, there is substantial variation in the global mean 0–100 m O₂ annual cycle, the magnitude ranges from −1 to 0.8 µmol kg⁻¹, with a standard deviation of the datasets of ∼ 0.3 µmol kg⁻¹. Average oxygen minimum zones (OMZ) volume among the products is 80.92 × 10⁶ km³ (±1.95 %) for a 60 µmol kg⁻¹ threshold and 152.00 × 10⁶ km³ (±1.72 %) for a 90 µmol kg⁻¹ threshold. Our results help to depict and understand the spread among the available O₂ gridded datasets.

Marine heatwaves (MHWs), characterized by anomalously high sea surface temperatures, are occurring with increasing frequency and intensity, profoundly impacting ocean circulation, biogeochemistry, and marine ecosystems. The MHW known as the Blob, which persisted in the subarctic NE Pacific from 2014 to 2015, significantly affected surrounding ecosystems. Warming-induced solubility reduction is expected to raise the partial pressure of carbon dioxide (pCO₂) in the surface water, causing outgassing of CO₂ to the atmosphere. Outgassing of CO₂ is another source of atmospheric CO₂ in addition to anthropogenic fossil fuel burning. However, moored observations at Ocean Station Papa (OSP; 145° W, 50° N) shows a moderate decrease in oceanic pCO₂ during the Blob, resisting the warming-induced outgassing of CO₂. This response is opposite of what is expected from warming alone, and instead has been attributed to reductions in dissolved inorganic carbon (DIC), although the mechanisms driving this reduction have remained unclear. We employed a regional model that accurately reproduces the temporal variability of oceanic pCO₂ at OSP to investigate the cause of decrease pCO₂ during the Blob. The analysis of model outputs indicates that the observed oceanic pCO₂ decline resulted from the offset between warming-induced solubility reduction (increasing pCO₂) and weakened physical transport of DIC (decreasing pCO₂), with the latter dominating. Both horizontal and vertical transports played important roles. The near-surface carbon budget over the broad region was primarily driven by changes in the vertical transport. The decrease in DIC during the Blob resulted from the suppression of upwelling of DIC-rich subsurface waters in the winter of 2013. In this period, the horizontal transport also contributed substantially to DIC reduction. In particular, at OSP, the effect of the horizontal transport was comparable to that of the vertical transport, reflecting the northward advection of low-DIC water masses. These findings indicate that changes in physical circulation were the primary driver of the moderately enhanced CO₂ uptake observed during the Blob. This study provides a critical insight into the complexity of biogeochemical response to extreme warming events and underscores the importance of resolving physical transport processes in assessing oceanic carbon uptake during MHWs.

Marine heatwaves (MHWs), characterized by anomalously high sea surface temperatures, are occurring with increasing frequency and intensity, profoundly impacting ocean circulation, biogeochemistry, and marine ecosystems. The MHW known as the Blob, which persisted in the subarctic NE Pacific from 2014 to 2015, significantly affected surrounding ecosystems. Warming-induced solubility reduction is expected to raise the partial pressure of carbon dioxide (pCO₂) in the surface water, causing outgassing of CO₂ to the atmosphere. Outgassing of CO₂ is another source of atmospheric CO₂ in addition to anthropogenic fossil fuel burning. However, moored observations at Ocean Station Papa (OSP; 145° W, 50° N) shows a moderate decrease in oceanic pCO₂ during the Blob, resisting the warming-induced outgassing of CO₂. This response is opposite of what is expected from warming alone, and instead has been attributed to reductions in dissolved inorganic carbon (DIC), although the mechanisms driving this reduction have remained unclear. We employed a regional model that accurately reproduces the temporal variability of oceanic pCO₂ at OSP to investigate the cause of decrease pCO₂ during the Blob. The analysis of model outputs indicates that the observed oceanic pCO₂ decline resulted from the offset between warming-induced solubility reduction (increasing pCO₂) and weakened physical transport of DIC (decreasing pCO₂), with the latter dominating. Both horizontal and vertical transports played important roles. The near-surface carbon budget over the broad region was primarily driven by changes in the vertical transport. The decrease in DIC during the Blob resulted from the suppression of upwelling of DIC-rich subsurface waters in the winter of 2013. In this period, the horizontal transport also contributed substantially to DIC reduction. In particular, at OSP, the effect of the horizontal transport was comparable to that of the vertical transport, reflecting the northward advection of low-DIC water masses. These findings indicate that changes in physical circulation were the primary driver of the moderately enhanced CO₂ uptake observed during the Blob. This study provides a critical insight into the complexity of biogeochemical response to extreme warming events and underscores the importance of resolving physical transport processes in assessing oceanic carbon uptake during MHWs.