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Abstract The ocean oxygen (O₂) inventory has declined in recent decades but the estimates of O₂trend are uncertain due to its sparse and irregular sampling. A refined estimate of deoxygenation rate is developed using machine learning techniques and biogeochemical Argo array. The source data includes historical shipboard (bottle and CTD‐O₂) profiles from 1965 to 2020 and biogeochemical Argo profiles after 2005. Neural network and random forest algorithms were trained using approximately 80% of this data and the remaining 20% for validation. The training data is further divided into 5‐fold decadal groups to perform cross validation and hyperparameter tuning. Through different combinations of algorithm types and predictor variable sets, an ensemble of gridded monthly O₂data sets was generated with similar skills (root‐mean‐square error ∼13–18 μmol/kg and R² ∼ 0.9). The largest errors are found in the oxycline and frontal regions with strong lateral and vertical gradients. The mapping was repeated with shipboard data only and with both shipboard and Argo data. The effect of including Argo data on the estimated global deoxygenation trends has a major impact with an 56% increase while reducing the uncertainty by 40% as measured by the ensemble spread. This study demonstrates the importance of new biogeochemical Argo arrays in relatively data‐poor regions such as the Southern Ocean. 

Abstract Estimating fire emissions prior to the satellite era is challenging because observations are limited, leading to large uncertainties in the calculated aerosol climate forcing following the preindustrial era. This challenge further limits the ability of climate models to accurately project future climate change. Here, we reconstruct a gridded dataset of global biomass burning emissions from 1750 to 2010 using inverse analysis that leveraged a global array of 31 ice core records of black carbon deposition fluxes, two different historical emission inventories as a priori estimates, and emission-deposition sensitivities simulated by the atmospheric chemical transport model GEOS-Chem. The reconstructed emissions exhibit greater temporal variabilities which are more consistent with paleoclimate proxies. Our ice core constrained emissions reduced the uncertainties in simulated cloud condensation nuclei and aerosol radiative forcing associated with the discrepancy in preindustrial biomass burning emissions. The derived emissions can also be used in studies of ocean and terrestrial biogeochemistry. 

The global ocean's oxygen content has declined significantly over the past several decades and is expected to continue decreasing under global warming, with far-reaching impacts on marine ecosystems and biogeochemical cycling. Determining the oxygen trend, its spatial pattern, and uncertainties from observations is fundamental to our understanding of the changing ocean environment. This study uses a suite of CMIP6 Earth system models to evaluate the biases and uncertainties in oxygen distribution and trends due to sampling sparseness. Model outputs are sub-sampled according to the spatial and temporal distribution of the historical shipboard measurements, and the data gaps are filled by a simple optimal interpolation method using Gaussian covariance with a constant e-folding length scale. Sub-sampled results are compared to full model output, revealing the biases in global and basin-wise oxygen content trends. The simple optimal interpolation underestimates the modeled global deoxygenation trends, capturing approximately two-thirds of the full model trends. The North Atlantic and subpolar North Pacific are relatively well sampled, and the simple optimal interpolation is capable of reconstructing more than 80% of the oxygen trend in the non-eddying CMIP models. In contrast, pronounced biases are found in the equatorial oceans and the Southern Ocean, where the sampling density is relatively low. The application of the simple optimal interpolation method to the historical dataset estimated the global oxygen loss to be 1.5% over the past 50 years. However, the ratio of the global oxygen trend between the sub-sampled and full model output has increased the estimated loss rate in the range of 1.7% to 3.1% over the past 50 years, which partially overlaps with previous studies. The approach taken in this study can provide a framework for the intercomparison of different statistical gap-filling methods to estimate oxygen content trends and their uncertainties due to sampling sparseness. 

Abstract This study develops a new regional model of the Southern Ocean including an improved representation of the iron biogeochemistry and ecosystem component, nesting within a biogeochemical ocean state estimate, and benchmarking with a suite of observations. The regional domain focuses on the Udintsev Fracture Zone (UFZ) in the central Pacific sector of the Southern Ocean. The UFZ is characterized by the deep gap between the Pacific‐Antarctic Ridge and the East Pacific Rise, which is one of the key “choke points” of the Antarctic Circumpolar Current where major Southern Ocean fronts are constrained within close proximity to this topographic feature. It is also a region of elevated mesoscale eddy activity, especially downstream of the UFZ. The model reproduces observed partial pressure of carbon dioxide in the surface water (pCO₂) remarkably well from seasonal to interannual timescales relative to prior studies (r = 0.89). The seasonality of pCO₂is difficult to simulate correctly because it is a small residual between the opposing influences of temperature and carbon. This model represents an intermittent double peak pattern of pCO₂; one driven by the summertime high temperature and another from the wintertime high of dissolved inorganic carbon. The model also captures the spatial and temporal structure of the regional net primary production with respect to the satellite ocean color products (r = 0.57). The model is further validated by comparing it with biogeochemical float observations from the Southern Ocean Carbon and Climate Observations and Modeling project, revealing the model performance and challenges to accurately represent physical and biogeochemical properties in frontal regions. 

Oxygen inventory of the global ocean has declined in recent decades potentially due to the warming-induced reduction in solubility as well as the circulation and biogeochemical changes associated with ocean warming and increasing stratification. Earth System Models predict continued oxygen decline for this century with profound impacts on marine ecosystem and fisheries. Observational constraint on the rate of oxygen loss is crucial for assessing the ability of models to accurately simulate these changes. There are only a few observational assessments of the global oceanic oxygen inventory reporting a range of oxygen loss. This study develops a gridded data set of dissolved oxygen for the global oceans using optimal interpolation method. The resulting gridded product includes full-depth map of dissolved oxygen as 5-year moving average from 1965 to 2015 with uncertainty estimates. The uncertainty can come from unresolved small-scale and high-frequency variability and mapping errors. The multi-decadal trend of global dissolved oxygen is in the range of −281 to −373 Tmol/decade. This estimate is more conservative than previous works. In this study, the grid points far from the observations are essentially set equal to zero anomaly from the climatology. Calculating global inventory with this approach produces a relatively conservative estimate; thus, the results from this study likely provide a useful lower bound estimate of the global oxygen loss. 

The Southern Ocean is an important region of ocean carbon uptake, and observations indicate its air‐sea carbon flux fluctuates from seasonal to decadal timescales. Carbon fluxes at regional scales remain highly uncertain due to sparse observation and intrinsic complexity of the biogeochemical processes. The objective of this study is to better understand the mechanisms influencing variability of carbon uptake in the Drake Passage. A regional circulation and biogeochemistry model is configured at the lateral resolution of 10 km, which resolves larger mesoscale eddies where the typical Rossby deformation radius is(50 km). We use this model to examine the interplay between mean and eddy advection, convective mixing, and biological carbon export that determines the surface dissolved inorganic carbon and partial pressure of carbon dioxide variability. Results are validated against in situ observations, demonstrating that the model captures general features of observed seasonal to interannual variability. The model reproduces the two major fronts: Polar Front (PF) and Subantarctic Front (SAF), with locally elevated level of eddy kinetic energy and lateral eddy carbon flux, which play prominent roles in setting the spatial pattern, mean state and variability of the regional carbon budget. The uptake of atmospheric CO₂, vertical entrainment during cool seasons, and mean advection are the major carbon sources in the upper 200 m of the region. These sources are balanced by the biological carbon export during warm seasons and mesoscale eddy transfer. Comparing the induced advective carbon fluxes, mean flow dominates in magnitude, however, the amplitude of variability is controlled by the eddy flux. 

Abstract Observations of dissolved iron (dFe) in the subtropical North Atlantic revealed remarkable features: While the near‐surface dFe concentration is low despite receiving high dust deposition, the subsurface dFe concentration is high. We test several hypotheses that might explain this feature in an ocean biogeochemistry model with a refined Fe cycling scheme. These hypotheses invoke a stronger lithogenic scavenging rate, rapid biological uptake, and a weaker binding between Fe and a ubiquitous, refractory ligand. While the standard model overestimates the surface dFe concentration, a 10‐time stronger biological uptake run causes a slight reduction in the model surface dFe. A tenfold decrease in the binding strength of the refractory ligand, suggested by recent observations, starts reproducing the observed dFe pattern, with a potential impact for the global nutrient distribution. An extreme value for the lithogenic scavenging rate can also match the model dFe with observations, but this process is still poorly constrained. 

This study investigates the mechanisms of interannual and decadal variability of dissolved oxygen (O₂) in the North Pacific using historical observations and a hindcast simulation using the Community Earth System Model. The simulated variability of upper ocean (200 m) O₂is moderately correlated with observations where sampling density is relatively high. The dominant mode of O₂variability explains 24.8% of the variance and is significantly correlated with the Pacific Decadal Oscillation (PDO) index (r = 0.68). Two primary mechanisms are hypothesized by which the PDO controls upper ocean O₂variability. Vertical movement of isopycnals (“heave”) drives O₂variations in the deep tropics; isopycnal surfaces are depressed in the eastern tropics under the positive (El Niño‐like) phase of PDO, leading to O₂increases in the upper water column. In contrast to the tropics, changes in subduction are the primary control on extratropical O₂variability. These hypotheses are tested by contrasting O₂anomalies with the heave‐induced component of variability calculated from potential density anomalies. Isopycnal heave is the leading control on O₂variability in the tropics, but heave alone cannot fully explain the amplitude of tropical O₂variability, likely indicating reinforcing changes from the biological O₂consumption. Midlatitude O₂variability indeed reflects ocean ventilation downstream of the subduction region where O₂anomalies are correlated with the depth of winter mixed layer. These mechanisms, synchronized with the PDO, yield a basin‐scale pattern of O₂variability that are comparable in magnitude to the projected rates of ocean deoxygenation in this century under “unchecked” emission scenario.