In the dark ocean, respiring organisms are the main sink for dissolved oxygen. The respiration rate in a given seawater volume can be quantified through dissolved oxygen drawdown or organic matter consumption as a function of time. Estimates of dissolved oxygen utilization rates (OUR) abound in the literature, but are typically obtained using proxies of questionable accuracy, often with low vertical resolution, and neglecting key regions such as the Southern and Indian oceans. Respiration rates based on particulate (POC) or dissolved (DOC) organic carbon are also sparsely observed and for DOC unavailable in many regions. Consequently, the relative contributions of POC or DOC as a respiration substrate in the dark ocean are unknown. Here we use recent datasets of true oxygen utilization, seawater age, and DOC to derive OUR and DOC consumption-rate profiles in 10 oceanic regions. We demonstrate that although DOC and POC consumption rates are globally consistent with OUR, they underestimate OUR in the deep, suggesting strong oxygen utilization at the seafloor. In the abyss, we find a negative correlation of DOC consumption rate with seawater age, suggesting that DOC reactivity decreases along the deep branch of the conveyor circulation. Our results highlight that benthic organisms are sensitive to perturbations in the surface production of organic matter and to large-scale circulation changes that affect its supply to the abyss.

Mixing parameters can be inaccurate in ocean data assimilation systems, even if there is close agreement between observations and mixing parameters in the same modeling system when data are not assimilated. To address this, we investigate whether there are additional observations that can be assimilated by ocean modeling systems to improve their representation of mixing parameters and thereby gain knowledge of the global ocean’s mixing parameters. Observationally-derived diapycnal diffusivities–using a strain-based parameterization of finescale hydrographic structure–are included in the Estimating the Circulation & Climate of the Ocean (ECCO) framework and the GEOS-5 coupled Earth system model to test if adding observational diffusivities can reduce model biases. We find that adjusting ECCO-estimated and GEOS-5-calculated diapycnal diffusivity profiles toward profiles derived from Argo floats using the finescale parameterization improves agreement with independent diapycnal diffusivity profiles inferred from microstructure data. Additionally, for the GEOS-5 hindcast, agreement with observed mixed layer depths and temperature/salinity/stratification (i.e., hydrographic) fields improves. Dynamic adjustments arise when we make this substitution in GEOS-5, causing the model’s hydrographic changes. Adjoint model-based sensitivity analyses suggest that the assimilation of dissolved oxygen concentrations in future ECCO assimilation efforts would improve estimates of the diapycnal diffusivity field. Observationally-derived products for horizontal mixing need to be validated before conclusions can be drawn about them through similar analyses.

Because spatio-temporal variations in ocean heat content (OHC) are strongly predicted by ocean conductivity content (OCC) over most of the global ocean, we analyze the dynamical budget and behavior of the electrical conductivity of seawater. To perform these analyses, we use an ocean-model state estimate designed to accurately represent long-term variations in ocean properties in a dynamically and kinematically consistent way. We show that this model accurately reproduces the spatio-temporal variations in electrical conductivity seen in satellite-derived and in a seasonal climatology product derived from in-situ data, justifying use of the model data to perform further analyses. An empirical orthogonal function analysis suggests that the vast majority of the variance in OHC and OCC can be explained by similar mechanisms. The electrical conductivity budget’s most important term is the temperature forcing tendency term, suggesting that ocean heat uptake is the mechanism responsible for the strong relationship between OCC and OHC.

Mixing parameters can be inaccurate in ocean data assimilation systems, even if there is close agreement between observations and mixing parameters in the same modeling system when data are not assimilated. To address this, we investigate whether there are additional observations that can be assimilated by ocean modeling systems to improve their representation of mixing parameters and thereby gain knowledge of the global ocean’s mixing parameters. Observationally-derived diapycnal diffusivities–using a strain-based parameterization of finescale hydrographic structure–are included in the Estimating the Circulation & Climate of the Ocean (ECCO) framework and the GEOS-5 coupled Earth system model to test if adding observational diffusivities can reduce model biases. We find that adjusting ECCO-estimated and GEOS-5-calculated diapycnal diffusivity profiles toward profiles derived from Argo floats using the finescale parameterization improves agreement with independent diapycnal diffusivity profiles inferred from microstructure data. Additionally, for the GEOS-5 hindcast, agreement with observed mixed layer depths and temperature/salinity/stratification (i.e., hydrographic) fields improves. Dynamic adjustments arise when we make this substitution in GEOS-5, causing the model’s hydrographic changes. Adjoint model-based sensitivity analyses suggest that the assimilation of dissolved oxygen concentrations in future ECCO assimilation efforts would improve estimates of the diapycnal diffusivity field. Observationally-derived products for horizontal mixing need to be validated before conclusions can be drawn about them through similar analyses.

A new approach to monitor ocean heat content (OHC) is proposed to overcome challenges with observing OHC over the entire ocean. The output of an ocean state estimate (ECCO) is sampled along historical hydrographic transects, a machine learning algorithm (GAM) is trained on these samples, and OHC is estimated everywhere using information inferable from various global satellite coverage. Assuming the ECCO output is perfect observational data, a GAM can estimate OHC within 0.15% spatial root-mean-square error (RMSE). This RMSE is sensitive to the spatial variance in OHC that gets sampled by hydrographic transects, the variables included in the GAM, and their measurement errors when inferred from satellite data. OHC could be remotely monitored over sufficiently long time scales when enough spatial variance in OHC is explained in the training data over those time scales.