We identified anomalously warm sea surface temperature (SST) events during 1980–2019 near the major upwelling center at Punta Lavapié in the central Chile-Peru Current System, using the European Centre for Medium-Range Weather Forecasts reanalysis and focusing on time scales of 10 days to 6 months. Extreme warm SST anomalies on these time scales mostly occurred in the austral summer, December through February, and had spatial scales of 1000s of km. By compositing over the 37 most extreme warm events, we estimated terms in a heat budget for the ocean surface mixed layer at the times of strongest warming preceding the events. The net surface heat flux anomaly is too small to explain the anomalous warming, even when allowing for uncertainty in mixed-layer depth. The composite mean anomaly of wind stress, from satellite ocean vector wind swath data, during the 37 anomalous warming periods has a spatial pattern similar to the resulting warm SST anomalies, analogous to previous studies in the California Current System. The weakened surface wind stress suggests reduced entrainment of cold water from below the mixed layer. Within 100-200 km of the coast, the typical upwelling-favorable wind stress curl decreases, suggesting reduced upwelling of cold water. In a 1000-km area of anomalous warming offshore, the typical downwelling-favorable wind stress curl also decreases, implying reduced downward Ekman pumping, which would allow mixed-layer shoaling and amplify the effect of the positive climatological summertime net surface heat flux.

The causes of decadal variations in global warming are poorly understood, however it is widely understood that variations in ocean heat content are linked with variations in surface warming. To investigate the forced response of ocean heat content (OHC) to anthropogenic aerosols (AA), we use an ensemble of historical simulations, which were carried out using a range of anthropogenic aerosol forcing magnitudes in a CMIP6-era global circulation model. We find that the centennial scale linear trends in historical ocean heat content are significantly sensitive to AA forcing magnitude ($-3.0\pm0.1$ x10$^{5}$ (J m$^{-3}$ century$^{-1}$)/(W m$^{-2}$), R$^2$=0.99), but interannual to multi-decadal variability in global ocean heat content appear largely independent of AA forcing magnitude. Comparison with observations find consistencies in different depth ranges and at different time scales with all but the strongest aerosol forcing magnitude, at least partly due to limited observational accuracy. We find broad negative sensitivity of ocean heat content to increased aerosol forcing magnitude across much of the tropics and sub-tropics. The polar regions and North Atlantic show the strongest heat content trends, and also show the strongest dependence on aerosol forcing magnitude. However, the ocean heat content response to increasing aerosol forcing magnitude in the North Atlantic and Southern Ocean is either dominated by internal variability, or strongly state dependent, showing different behaviour in different time periods. Our results suggest the response to aerosols in these regions is a complex combination of influences from ocean transport, atmospheric forcings, and sea ice responses.

Satellite observations are used to establish the dominant magnitudes, scales, and mechanisms of intraseasonal variability in ocean dynamic sea level (ζ) in the Persian Gulf over 2002-2015. Empirical orthogonal function (EOF) analysis applied to altimetry data reveals a basin-wide, single-signed intraseasonal fluctuation that contributes importantly to ζ variance in the Persian Gulf at monthly to decadal timescales. An EOF analysis of Gravity Recovery and Climate Experiment (GRACE) observations over the same period returns a similar large-scale mode of intraseasonal variability, suggesting that the basin-wide intraseasonal ζ variation has a predominantly barotropic nature. A linear barotropic theory is developed to interpret the data. The theory represents Persian-Gulf-average ζ () in terms of local freshwater flux, barometric pressure, and wind stress forcing, as well as ζ at the boundary in the Gulf of Oman. The theory is tested using a multiple linear regression with these freshwater flux, barometric pressure, wind stress, and boundary ζ quantities as input, and as output. The regression explains 70%+/-9% (95% confidence interval) of the intraseasonal variance. Numerical values of regression coefficients computed empirically from the data are consistent with theoretical expectations from first principles. Results point to a substantial non-isostatic response to surface loading. The Gulf of Oman ζ boundary condition shows lagged correlation with ζ upstream along the Indian Subcontinent, Maritime Continent, and equatorial Indian Ocean, suggesting a large-scale Indian-Ocean influence on intraseasonal variation mediated by coastal and equatorial waves, and hinting at potential predictability. This study highlights the value of GRACE for understanding sea level in an understudied marginal sea.

A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, non-overlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a user-specified similarity threshold. Unlike many clustering algorithms, there is no assumption that natural clusters exist in the dataset, though clusters, when present, may be preferentially assigned to one or more classes. The method also does not require data objects to be compared within any coordinate system but rather permits the user to define pairwise similarity using almost any conceivable criterion. The method therefore lends itself to certain geoscientific applications for which conventional clustering methods are unsuited, including two non-trivial and distinctly different datasets presented as examples. In addition to identifying large classes containing numerous similar dataset members, it is also well-suited for isolating rare or anomalous members of a dataset. The method is inductive, in that prototypes identified in representative subset of a larger dataset can be used to classify the remainder.

We present a Python package geared towards the intuitive analysis and visualization of paleoclimate timeseries, Pyleoclim. The code is open-source, object-oriented, and built upon the standard scientific Python stack, allowing users to take advantage of a large collection of existing and emerging techniques. We describe the code’s philosophy, structure and base functionalities, and apply it to three paleoclimate problems: (1) orbital-scale climate variability in a deep-sea core, illustrating spectral, wavelet and coherency analysis in the presence of age uncertainties; (2) correlating a high-resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (3) model-data confrontations in the frequency domain, illustrating the characterization of scaling behavior. We show how the package may be used for transparent and reproducible analysis of paleoclimate and paleoceanographic datasets, supporting FAIR software and an open science ethos. The package is supported by an extensive documentation and a growing library of tutorials shared publicly as videos and cloud-executable Jupyter notebooks, to encourage adoption by new users.

The Lagrangian and Eulerian surface current signatures of a low-mode internal tide propagating through a turbulent balanced flow are compared in idealized numerical simulations. Lagrangian and Eulerian total (i.e. coherent plus incoherent) tidal amplitudes are found to be similar. Compared to Eulerian diagnostics, the Lagrangian tidal signal is more incoherent with comparable or smaller incoherence timescales and larger incoherent amplitudes. The larger level of incoherence in Lagrangian data is proposed to result from the deformation of Eulerian internal tide signal induced by drifter displacements. Based on the latter hypothesis, a theoretical model successfully predicts Lagrangian autocovariances by relating Lagrangian and Eulerian autocovariances and the properties of the internal tides and jet. These results have implications for the separation of balanced flow and internal tides signals in the sea level data collected by the future Surface Water and Ocean Topography (SWOT) satellite mission.

Reaction with iodide (I-) at the sea surface is an important sink for atmospheric ozone, and causes sea-air emission of reactive iodine which in turn drives further ozone destruction. To incorporate this process into chemical transport models, improved understanding of the factors controlling marine iodine speciation, and especially sea-surface iodide concentrations, is needed. The oxidation of I- to iodate (IO3-) is the main sink for oceanic I-, but the mechanism for this remains unknown. We demonstrate for the first time that marine nitrifying bacteria mediate I- oxidation to IO3-. A significant increase in IO3- concentrations compared to media-only controls was observed in cultures of the ammonia-oxidising bacteria Nitrosomonas sp. (Nm51) and Nitrosoccocus oceani (Nc10) supplied with 9-10 mM I-, indicating I- oxidation to IO3-. Cell-normalised production rates were 15.69 (±4.71) fmol IO3- cell-1 d-1 for Nitrosomonas sp., and 11.96 (±6.96) fmol IO3- cell-1 d-1 for Nitrosococcus oceani, and molar ratios of iodate-to-nitrite production were 9.2±4.1 and 1.88±0.91 respectively. Preliminary experiments on nitrite-oxidising bacteria showed no evidence of I- to IO3- oxidation. If the link between ammonia and I- oxidation observed here is representative, our ocean iodine cycling model predicts that future changes in marine nitrification could alter global sea surface I- fields with potential implications for atmospheric chemistry and air quality.

Tsunami deposits provide information for estimating the magnitude and flow conditions of paleotsunamis, and inverse models have potential for reconstructing hydraulic conditions of tsunamis from their deposits. The majority of the previously proposed models are based on oversimplified assumptions and possess some limitations. We present a new inverse model based on the FITTNUSS model, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses a deep neural network (DNN) for the inversion method. In this method, forward model calculations are repeated for random initial flow conditions (e.g., maximum inundation length, flow velocity, maximum flow depth and sediment concentration) to produce artificial training data sets of depositional characteristics such as thickness and grain size distribution. The DNN was then trained to establish a general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that this trained DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku-Oki tsunami deposits. The predicted results of flow conditions were verified by the observational records at Sendai plain. Jackknife resampling was applied to estimate the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.4\pm0.140 m/s and 4.11\pm0.152 m, respectively after the uncertainty analysis. The DNN shows promise for reconstruction of tsunami characteristics from its deposits, which would help in estimating the hydraulic conditions of paleotsunamis.

Warm, subsurface ocean waters that access ice shelves in the Amundsen Sea are likely to be a key driver of high meltrates and ice shelf thinning. Numerical models of the ocean circulation have been essential for gaining understanding of the mechanisms responsible for heat delivery and meltrate response, but a number of challenges remain for simulations that incorporate this region. Here, we develop a suite of numerical experiments to explore how sub ice shelf cavity circulation and meltrate patterns are impacted by parameterization schemes for (1) subgrid-scale ocean turbulence, and (2) ice-ocean interactions. To provide a realistic context, our experiments are developed to simulate the ocean circulation underneath the Pine Island ice shelf, and validated against mooring observations and satellite derived meltrate estimates. Each experiment is forced with data-informed open boundary conditions that bear the imprint of the gyre in Pine Island Bay. We find that even at a ~600 m grid resolution, flow aware ocean parameterizations for subgrid-scale momentum and tracer transfer are crucial for representing the circulation and meltrate pattern accurately. Our simulations show that enhanced meltwater diffusion near the ice-ocean interface intensifies near wall velocities via thermal wind, which subsequently increases meltrates near the grounding line. Incorporating a velocity dependent ice-ocean transfer coefficient together with a flow aware ocean turbulence parameterization therefore seems to be necessary for modelling the ocean circulation underneath ice shelves in the Amundsen Sea at this resolution.

We present a method for predicting wave dissipation by sea ice that is based on the dimensional analysis of data with a scaling defined by ice thickness. Applying the method to an extensive dataset from the measurements during the “Polynyas, Ice Production, and seasonal Evolution in the Ross Sea” (PIPERS) cruise in 2017, we derive a new model of wave dissipation which not only describes a nonlinear dependence on ice thickness but also reveals its relation with the dependence on frequency. This nonlinear dependence on ice thickness can have more implications on predicting low-frequency waves. The root-mean-square error of the prediction is significantly reduced using the new model, compared with other existing parametric models that are also calibrated for the PIPERS dataset. The new model also explicitly describes a condition of similarity between large- and small-scale observations, which is shown to exist when various laboratory datasets collapse onto the prediction.

A realistic numerical model is used to study the circulation and mixing of the Salish Sea, a large, complex estuarine system on the United States and Canadian west coast. The Salish Sea is biologically productive and supports many important fisheries but is threatened by recurrent hypoxia and ocean acidification, so a clear understanding of its circulation patterns and residence times is of value. The estuarine exchange flow is quantified at 39 sections over three years (2017-2019) using the Total Exchange Flow method. Vertical mixing in the 37 segments between sections is quantified as opposing vertical transports: the efflux and reflux. Efflux refers the rate at which deep, landward-flowing water is mixed up to become part of the shallow, seaward-flowing layer. Similarly, reflux refers to the rate at which upper layer water is mixed down to form part of the landward inflow. These horizontal and vertical transports are used to create a box model to explore residence times in a number of different sub-volumes, seasons, and years. Residence times from the box model are generally found to be longer than those based on simpler calculations of flushing time. The longer residence times are partly due to reflux, and partly due to incomplete tracer homogenization in sub-volumes. The methods presented here are broadly applicable to other estuaries.

The isotopic composition of dissolved oxygen offers a family of potentially unique tracers of respiration and transport in the subsurface ocean. Uncertainties in transport parameters and isotopic fractionation factors, however, have limited the strength of the constraints offered by 18O/16O and 17O/16O ratios in dissolved oxygen. In particular, puzzlingly low 17O/16O ratios observed for some low-oxygen samples have been difficult to explain. To improve our understanding of oxygen cycling in the ocean’s interior, we investigated the systematics of oxygen isotopologues in the subsurface Pacific using new data and a 2-D isotopologue-enabled isopycnal reaction-transport model. We measured 18O/16O and 17O/16O ratios, as well as the “clumped” 18O18O isotopologue in the northeast Pacific, and compared the results to previously published data. We find that transport and respiration rates constrained by O2 concentrations in the oligotrophic Pacific yield good measurement-model agreement across all O2 isotopologues only when using a recently reported set of respiratory isotopologue fractionation factors that differ from those most often used for oxygen cycling in the ocean. These fractionation factors imply that an elevated proportion of 17O compared to 18O in dissolved oxygen―i.e., its triple-oxygen isotope composition―does not uniquely reflect gross primary productivity and mixing. For all oxygen isotopologues, transport, respiration, and photosynthesis comprise important parts of their respective budgets. Mechanisms of oxygen removal in the subsurface ocean are discussed.

Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010-2019 decade, sometimes by more than +/-0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI dataset excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original dataset. SSS CCI data are freely available and will be updated and extended as more satellite data become available.

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their topographic structure is poorly mapped due to a reliance upon expensive and time intensive methods. Space-borne bathymetric mapping has helped address these issues, but has remained dependent upon in situ measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine geospatial cloud platform, to create wall-to-wall high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10-15%) when compared with in situ NOAA DEM data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for fully space-borne estimates of nearshore bathymetry in shallow, clear water environments.

Alkalinization of natural waters by the dissolution of natural or artificial minerals is a promising solution to sequester atmospheric CO$_2$ and counteract acidification. Here we address the alkalinization carbon capture efficiency (ACCE) by deriving an analytical factor that quantifies the increase in dissolved inorganic carbon in the water due to variations in alkalinity. We show that ACCE strongly depends on the water pH, with a sharp transition from minimum to maximum in a narrow interval of pH values. We also compare ACCE in surface freshwater and seawater and discuss potential bounds for ACCE in the soil water. Finally, we present two applications of ACCE. The first is a local application to 156 lakes in an acid-sensitive region, highlighting the great sensitivity of ACCE to the lake pH. The second is a global application to the surface ocean, revealing a latitudinal pattern of ACCE driven by differences in temperature and salinity.

The shape of ice-shelf cavities are a major source of uncertainty in understanding ice-ocean interactions. This limits assessments of the response of the Antarctic ice sheets to climate change. Here we use vibroseis seismic reflection surveys to map the bathymetry beneath the Ekström Ice Shelf, Dronning Maud Land. The new bathymetry reveals an inland-sloping trough, reaching depths of 1100 m below sea level, near the current grounding line, which we attribute to erosion by palaeo-ice streams. The trough does not cross-cut the outer parts of the continental shelf. Conductivity-temperature-depth profiles within the ice-shelf cavity reveal the presence of cold water at shallower depths and tidal mixing at the ice-shelf margins. It is unknown if warm water can access the trough. The new bathymetry is thought to be representative of many ice shelves in Dronning Maud Land, which together regulate the ice loss from a substantial area of East Antarctica.

A description and assessment of the first release of the Arctic Subpolar gyre sTate Estimate (ASTE_R1), a data-constrained ocean-sea ice model-data synthesis is presented. ASTE_R1 has a nominal resolution of 1/3o and spans the period 2002-2017. The fit of the model to an extensive (O(10^9)) set of satellite and in situ observations was achieved through adjoint-based nonlinear least-squares optimization. The improvement of the solution compared to an unconstrained simulation is reflected in misfit reductions of 77% for Argo, 50% for satellite sea surface height, 58% for the Fram Strait mooring, 65% for Ice Tethered Profilers, and 83% for sea ice extent. Exact dynamical and kinematic consistency is a key advantage of ASTE_R1, distinguishing the state estimate from existing ocean reanalyses. Through strict adherence to conservation laws, all sources and sinks within ASTE_R1 can be accounted for, permitting meaningful analysis of closed budgets at the grid-scale, such as contributions of horizontal and vertical convergence to the tendencies of heat and salt. ASTE_R1 thus serves as the biggest effort undertaken to date of producing a specialized Arctic ocean-ice estimate over the 21st century. Transports of volume, heat, and freshwater are consistent with published observation-based estimates across important Arctic Mediterranean gateways. Interannual variability and low frequency trends of freshwater and heat content are well represented in the Barents Sea, western Arctic halocline, and east subpolar North Atlantic. Systematic biases remain in ASTE_R1, including a warm bias in the Atlantic Water layer in the Arctic and deficient freshwater inputs from rivers and Greenland discharge.

Oceanography has entered an era of new observing platforms, such as biogeochemical Argo floats and gliders, some of which will provide three-dimensional maps of essential ecosystem variables on the North-West European (NWE) Shelf. In a foreseeable future operational centres will use multi-platform assimilation to integrate those valuable data into ecosystem reanalyses and forecast systems. Here we address some important questions related to glider biogeochemical data assimilation and introduce multi-platform data assimilation in a (pre)operational model of the NWE Shelf-sea ecosystem. We test the impact of the different multi-platform system components (glider vs satellite, physical vs biogeochemical) on the simulated biogeochemical variables. To characterize the model performance we focus on the period around the phytoplankton spring bloom, since the bloom is a major ecosystem driver on the NWE Shelf. We found that the timing and magnitude of the phytoplankton bloom is insensitive to the physical data assimilation, which is explained in the study. To correct the simulated phytoplankton bloom one needs to assimilate chlorophyll observations from glider or satellite Ocean Color (OC) into the model. Although outperformed by the glider chlorophyll assimilation, we show that OC assimilation has mostly desirable impact on the sub-surface chlorophyll. Since the OC assimilation updates chlorophyll only in the mixed layer, the impact on the sub-surface chlorophyll is the result of the model dynamical response to the assimilation. We demonstrate that the multi-platform assimilation combines the advantages of its components and always performs comparably to its best performing component.

A novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model free parameters can vary in time, adjusting to potential non-stationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (Dual State-Parameter Ensemble Kalman Filter) within the established shoreline evolution model, ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real-world shoreline dataset, revealing that time-varying model free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modelling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate.

Geologists have documented at least fourteen occurrences of “giant ooids”, a geologically rare type of carbonate allochem, in Neoproterozoic successions at low paleo-latitudes. Recent experiments and modeling demonstrated that ooid size reflects an equilibrium between precipitation and abrasion rates, such that ooid size could be used as a geological proxy for CaCO3 mineral saturation state (Ω). Here, the documented sizes of Neoproterozoic giant ooids were applied to estimate seawater , which provided a novel approach to constraining temperature, partial pressure of CO2, and alkalinity preceding Neoproterozoic glaciations. The results suggest that giant ooid formation was most plausible with seawater alkalinity elevated over its present value by at least a factor of two, and either much warmer (40C) or much colder (0C) climate than modern tropical carbonate platforms, which have important and divergent implications for climate states and ecosystem responses prior to the initiation of each Neoproterozoic glaciation.

In austral winter, biological productivity at the Angolan shelf reaches its maximum. The alongshore winds, however, reach their seasonal minimum suggesting that processes other than local wind-driven upwelling contribute to near-coastal cooling and upward nutrient supply, one possibility being mixing induced by internal tides (ITs). Here, we apply a three-dimensional ocean model to simulate the generation, propagation and dissipation of ITs at the Angolan continental slope and shelf. Model results are validated against moored acoustic Doppler current profiler and other observations. Simulated ITs are mainly generated in regions with a critical/supercritical slope typically between the 200- and 500-m isobaths. Mixing induced by ITs is found to be strongest close to the coast and gradually decreases offshore thereby contributing to the establishment of cross-shore temperature gradients. The available seasonal coverage of hydrographic data is used to design simulations to investigate the influence of seasonally varying stratification characterized by low stratification in austral winter and high stratification in austral summer. The results show that IT characteristics, such as their wavelengths, sea surface convergence patterns and baroclinic structure, have substantial seasonal variations and additionally strong spatial inhomogeneities. However, seasonal variations in the spatially-averaged generation, onshore flux and dissipation of IT energy are weak. By evaluating the change of potential energy, it is shown, nevertheless, that mixing due to ITs is more effective during austral winter. We argue this is because the weaker background stratification in austral winter than in austral summer acts as a preconditioning for IT mixing.

North African dust is known to be deposited in the Gulf of Mexico, but its deposition rate and associated supply of lithogenic dissolved metals, such as the abiotic metal thorium or the micronutrient metal iron, have not been well-quantified. 232Th is an isotope with similar sources as iron and its input can be quantified using radiogenic 230Th. By comparing dissolved 232Th fluxes at three sites in the northern Gulf of Mexico with upwind sites in the North Atlantic, we place an upper bound on North African dust contributions to 232Th and Fe in the Gulf of Mexico, which is about 30% of the total input. Precision on this bound is hindered by uncertainty in the relative rates of dust deposition in the North Atlantic and the northern Gulf of Mexico. Based on available radium data, shelf sources, including rivers, submarine groundwater discharge and benthic sedimentary releases are likely as important if not more important than dust in the budget of lithogenic metals in the Gulf of Mexico. In other words, it is likely there is no one dominant source of Th and Fe in the Gulf of Mexico. Finally, our estimated Fe input in the northern Gulf of Mexico implies an Fe residence time of less than 6 months, similar to that in the North Atlantic despite significantly higher supply rates in the Gulf of Mexico.

Previous studies have interpreted Last Interglacial (LIG; ~129-116 ka) sea-level estimates in multiple different ways to calibrate projections of future Antarctic ice-sheet (AIS) mass loss and associated sea-level rise. This study systematically explores the extent to which LIG constraints could inform future Antarctic contributions to sea-level rise. We develop a Gaussian process emulator of an ice-sheet model to produce continuous probabilistic projections of Antarctic sea-level contributions over the LIG and a future high-emissions scenario. We use a Bayesian approach conditioning emulator projections on a set of LIG constraints to find associated likelihoods of model parameterizations. LIG estimates inform both the probability of past and future ice-sheet instabilities and projections of future sea-level rise through 2150. Although best-available LIG estimates do not meaningfully constrain Antarctic mass loss projections or physical processes until 2060, they become increasingly informative over the next 130 years. Uncertainties of up to 50 cm remain in future projections even if LIG Antarctic mass loss is precisely known (+/-5 cm), indicating there is a limit to how informative the LIG could be for ice-sheet model future projections. The efficacy of LIG constraints on Antarctic mass loss also depends on assumptions about the Greenland ice sheet and LIG sea-level chronology. However, improved field measurements and understanding of LIG sea levels still have potential to improve future sea-level projections, highlighting the importance of continued observational efforts.

Turbulent mixing induced by breaking internal waves is key to the ocean circulation and global tracer budgets. While the classic marginal shear instability of Richardson number ∼ 1/4 has been considered as potentially relevant to turbulence wave breaking, its relevance to energetic zones where tides, winds, and buoyancy gradients excite non-linearly interacting processes has been suspect. We show that shear instability is indeed relevant in the ocean interior and propose an alternative generalized marginal stability criterion, based on the ratio of Ozmidov and Thorpe turbulence scales, which not only applies to the ocean interior, but remains relevant within turbulent boundary layers. This allows for accurate quantification of the transition from downwelling to upwelling zones in a recently emerged paradigm of ocean circulation. Our results help climate models more accurately calculate the mixing-driven deep ocean circulation and fluxes of tracers in the ocean interior.