A document by Peter Chu. Click on the document to view its contents.

A document by Poulomi Ganguli. Click on the document to view its contents.

The Antarctic Circumpolar Current is the world's strongest ocean current and plays a disproportionate role in the climate system due to its role as a conduit for major ocean basins. This vast current system is linked to the ocean's vertical overturning circulation, and is thus pivotal to the uptake of heat and CO 2 in the ocean. The strength of the Antarctic Circumpolar Current has varied substantially across warm and cold climates in Earth's past, but the exact dynamical drivers of this change remain elusive. This is in part because ocean models were not able to adequately resolve the eddies and dense shelf water formation that control current strength. Here, we use a global ocean model which resolves such processes to diagnose the impact of future thermal, haline and wind conditions on the strength of the Antarctic Circumpolar Current. By 2050, our model suggests the strength of the Antarctic Circumpolar Current will decline by ∼ 20% in an extreme scenario. This decline is further supported by simple scaling theory, and is driven by ice shelf melting around Antarctica, which weakens the zonal density stratification historically supported by surface temperature gradients. Such a strong decline in transport would have critical implications for the entire global ocean circulation, and hence the Earth's climate system. Southern Ocean | Antarctic Circumpolar Current | Ocean Freshening | Antarctic Bottom Water

A document by Zacharie Gousseau. Click on the document to view its contents.

Basal melting of ice shelves is fundamental to Antarctic Ice Sheet mass loss, yet direct observations are sparse. We present the first melt record (2017 to 2021) from a phase-sensitive radar at Fimbulisen, East Antarctica, one of the fastest flowing ice shelves in Dronning Maud Land. The observed long-term mean ablation below the central part of the ice shelf was 1.0 ±0.4 m yr–1, marked by substantial sub-weekly variability ranging from 0.3 to 3.8 m yr–1. 36-h filtered fluctuations in basal melt exhibit a close alignment with ocean velocity, revealing shear-driven turbulent heat transfer as the predominant driver of melt variability at sub-weekly to monthly timescale. Seasonally, basal melt rates are highest in the austral summer, when ocean temperature is higher. Our observed in-situ melt rates show threefold lower amplitudes and a 3-month delay in seasonality compared to satellite-derived melt rates, however, the long-term multi-year mean is of similar magnitude (1.0 m yr–1 vs 0.8 m yr–1). Our detailed ice–ocean observations provide essential validation data for remote sensing and numerical models aiming to measure and project ice-shelf response to ocean forcing. In-situ measurements and continued monitoring are crucial for accurately assessing and modelling future basal melt rates, as well as understanding the complex dynamics driving ice-shelf stability and sea-level change.

North Atlantic Subtropical Mode Water (NASTMW) serves as a major conduit for dissolved carbon to penetrate into the ocean interior by its wintertime outcropping events. Prior research on NASTMW has concentrated on its physical formation and destruction, as well as Lagrangian pathways and timescales of water into and out of NASTMW. In this study, we examine how dissolved inorganic carbon (DIC) concentrations are modified along Lagrangian pathways of NASTMW on subannual timescales. We introduce Lagrangian parcels into a physical-biogeochemical model and release these parcels annually over two decades. For different pathways into, out of, and within NASTMW, we calculate changes in DIC concentrations along the path (ΔDIC), distinguishing contributions from vertical mixing and biological processes. Subduction leaves the most distinctive fingerprint on DIC concentrations (+101 µmol/L in one year), followed by export out of NASTMW due to densification (+10 µmol/L). Most DIC enrichment and depletion regimes span timescales of less than ~30 days, related to algal blooms. However, varying physical and biological processes often oppose one another at short timescales, so the largest net DIC changes occur at timescales of more than 30 days. While the mean ΔDIC for parcels that persist within NASTMW in one year is relatively small at +6 µmol/L, this masks underlying complexity: individual parcels undergo interspersed DIC depletion and enrichment, spanning several timescales and magnitudes. Since biological and physical processes both strongly influence DIC concentrations in NASTMW, refining process understanding and models of both domains is important for accurate projections of carbon cycling and sequestration.

Seawater intrusion poses a substantial threat to water security in coastal regions, where numerical models play a pivotal role in supporting groundwater management and protection. However, the inherent heterogeneity of coastal aquifers introduces significant uncertainties into model predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by incorporating various types of observational data to characterize these heterogeneous coastal aquifers. Traditional DA techniques, like ensemble smoother using the Kalman formula (ESK) and Markov chain Monte Carlo, face challenges when confronted with the non-linearity, non-Gaussianity, and high-dimensionality issues commonly encountered in aquifer characterization. In this study, we introduce a novel DA approach rooted in deep learning (DL), referred to as ESDL, aimed at effectively characterizing coastal aquifers with varying levels of heterogeneity. We systematically investigate a range of factors that impact the performance of ESDL, including the number and types of observations, the degree of aquifer heterogeneity, the structure and training options of the DL models, etc. Our findings reveal that ESDL excels in characterizing heterogeneous aquifers, particularly when faced with non-Gaussian conditions. Comparison between ESDL and ESK under different experimentation settings underscores the robustness of ESDL. Conversely, in certain scenarios, ESK displays noticeable biases in the characterizing results, especially when measurement data from nonlinear and discontinuous processes are used. To optimize the efficacy of ESDL, meticulous attention must be given to the design of the DL model and the selection of training options, which are crucial to ensure the universal applicability of this DA method.

To improve current estimates of phytoplankton specific carbon in the Ross Sea, we calculated a regionally specific algorithm from in situ particulate organic carbon (POC) concentrations and backscatter sensor data. These data come from three independent Seaglider deployments during the austral summer. Algal-specific POC (Cphyto) accounted for between 19.8-61.0% of total POC in the Ross Sea with an average Cphyto concentration of 84.2 µg C L-1. As a result, Cphyto:chlorophyll a ratios were less than POC:chlorophyll a ratios and ranged from 9.00-257 µg C (µg chlorophyll a) L-1. This regionally-specific method is substantially more accurate (average Cphyto concentrations are 10-78 µg C L-1 greater) than estimates derived from published algorithms. Our findings highlight the value of regionally-specific algorithms for measuring inherent optical properties and how such approaches can inform our current understanding of particulate carbon partitioning and food web dynamics.

The advent of under-ice profiling float and biologging techniques has enabled year-round observation of the Southern Ocean and its Antarctic margin. These under-ice data are often overlooked in widely used oceanographic datasets, despite their importance in understanding the seasonality and its role in sea ice changes, bottom water formation, and glacial melt. We develop a four-dimensional climatology of the Southern Ocean (south of 40°S and above 2,000 m) using Data Interpolating Variational Analysis, which excels in multi-dimensional interpolation and consistent handling of topography and advection. The climatology captures thermohaline variability under sea ice, previously hard to obtain, and outperforms other products in data fidelity with smaller root-mean-square errors and biases. Our dataset will be instrumental for investigating seasonality and for improving ocean models. This work further highlights the quantitative significance of under-ice data in reproducing ocean conditions, advocating for their increased use to achieve a better Southern Ocean observing system.

A document by Fiz F. Pérez. Click on the document to view its contents.

Increased anthropogenic stressors (e.g., warming, acidification, wildfires and other extreme events) present complex observational challenges for Earth science, and no one sensor can ‘do it all.’ While many remote sensing technologies are available at present, scientific disciplines are often trained to use only a specific subset, greatly limiting scientific advancements. Here we present open-source software (‘icepyx’) that lowers the barrier for entry for two remote platforms offering vertically-resolved information about the ocean’s subsurface: ICESat-2 (Ice, Cloud, and land Elevation Satellite 2) and Argo floats. icepyx provides object-oriented code for querying and downloading ICESat-2 and Argo data within a single analysis workflow. icepyx natively handles ICESat-2 data access and read-in; here we introduce the Query, Unify, Explore SpatioTemporal (QUEST) module as a framework for adapting icepyx to easily access and ingest other datasets and present Argo data as the initial use case. Seamless retrieval of coincident data from ICESat-2 and Argo enables improved targeted and exploratory studies across the cryosphere and open ocean realms. We close with recommendations for future work, a discussion of the value of open science, relevance of our work to upcoming satellite missions, and an invitation to join our programming community.

A document by Florent Grasso. Click on the document to view its contents.

A document by Aidin Jabbari. Click on the document to view its contents.

This study investigates the impact of glacial water discharges on the hydrodynamics of Admiralty Bay (AB) in the South Shetland Islands, a wide bay adjacent to twenty marine-terminating glaciers. From December 2018 until February 2023, AB water properties were measured on 136 days. This dataset showed that a maximally two-layered stratification occurs in AB, and that glacial water is always the most buoyant water mass. Using the Delft3D Flow, a three-dimensional hydrodynamical model of AB was developed. During tests, the vertical position and initial velocity of glacial discharges have been shown to be insignificant for the overall bay circulation. Fourteen model scenarios have been calculated with an increasing glacial influx added. The AB general circulation pattern consists of two cyclonic cells. Even in scenarios with significant glacial input, water level shifts and circulation are predominantly controlled by the ocean. Glacial freshwater is carried out of AB along its eastern boundary in a surface layer no thicker than 60 m. Within the inner AB inlets, significant glacial influx produces buoyancy-driven vertical circulation. Using an innovative approach combining hydrographic and modeling data, a four-year, unprecedentedly high-resolution timeseries of glacial influx volumes into AB has been produced. On average, glacial influx summer values are >10 times greater than in spring and winter and 3 times higher than in autumn. The annual glacial influx into AB was estimated at 0.525 Gt. Overall, it was demonstrated how the topography and forcing controlling the hydrodynamics of an Antarctic bay differs from that of well-studied northern-hemisphere fjords.

Transformation of light to dense waters by atmospheric cooling is key to the Atlantic Meridional Overturning in the Subpolar Gyre. Convection in the center of the Irminger Gyre determines the transformation of the densest waters east of Greenland. We present a 19-year (2002-2020) weekly time series of hydrography and convection in the central Irminger Sea based on (bi-)daily mooring profiles supplemented with Argo profiles. A 70-year annual time series of shipboard hydrography shows that this mooring period is representative of longer term variability. The depth of convection varies strongly from winter to winter (288-1500 dbar), with a mean March climatogical mixed layer depth of 470 dbar and a mean maximum density reached of 27.70 ± 0.05 kg m-3. The densification of the water column by local convection directly impacts the sea surface height in the center of the Irminger Gyre and thus large-scale circulation patterns. Both the observations and a Price-Weller-Pinkel (PWP) mixed layer model analysis show that the main cause of interannual variability in mixed layer depth is the strength of the winter atmospheric surface forcing. Its role is three times as important as that of the strength of the maximum stratification in the preceeding summer. Strong stratification as a result of a fresh surface anomaly similar to the one observed in 2010 can weaken convection by approximately 170 m on average, but changes in surface forcing will need to be taken into account as well when considering the evolution of Irminger Sea convection under climate change.

Submesoscale flows (0.1 - 10 km) are often associated with large vertical velocities, which can have a significant impact on the transport of surface tracers, such as carbon. However, global models do not adequately account for these small-scale effects, which still require a proper parameterization. In this study, we introduced a passive tracer into the mixed layer of the northern Atlantic Ocean using a CROCO simulation with a high horizontal resolution of Δx = 800 m, aiming to investigate the seasonal submesoscale effects on vertical transport. Using surface vorticity and strain criteria, we identified regions with submesoscale fronts and quantified the associated subduction, that is the export of tracer below the mixed layer depth. The results suggest that the tracer vertical distribution and the contribution of frontal subduction can be estimated from surface strain and vorticity. Notably, we observed significant seasonal variations. In winter, the submesoscale fronts contribute up to 40% of the vertical advective transport of tracer below the mixed layer, while representing only 5% of the domain. Conversely, in summer, fronts account for less than 1% of the domain and do not contribute significantly to the transport below the mixed layer. The findings of this study contribute to a better understanding of the seasonal water subduction due to fronts in the region.

The Atlantic Meridional Overturning Circulation (AMOC) entails vigorous thermohaline transformations in the subpolar North Atlantic (SPNA). There, warm and saline waters originating in the subtropics are converted into cooler and fresher waters by a combination of surface fluxes and sub-surface thermohaline mixing. Using microstructure measurements and a small-scale variance conservation framework, we quantify the diapycnal and isopycnal contributions to thermohaline mixing within the eastern SPNA. Isopycnal stirring is found to account for 65% of thermal and 84% of haline variance dissipation in the upper 400 m of the eastern SPNA, suggesting an important role of isopycnal stirring in regional water-mass transformations. By applying the tracer variance method to two tracers, we underscore the special significance of isopycnal stirring for tracers weakly coupled to density, such as biologically-active tracers. Our findings thus highlight the central role of isopycnal stirring in both the AMOC and biogeochemical dynamics within the SPNA.

A document by Raj Pandya. Click on the document to view its contents.

Examination of historical simulations from CMIP6 models shows substantial pre-industrial to present-day changes in ocean heat (ΔH), salinity (ΔS), oxygen (ΔO2), dissolved inorganic carbon (ΔDIC), chlorofluorocarbon-12 (ΔCFC12), and sulfur hexafluoride (ΔSF6). The spatial structure of the changes and the consistency among models differ among tracers: ΔDIC, ΔCFC12, and ΔSF6 all are largest near the surface, are positive throughout the thermocline with weak changes below, and there is good agreement amongst the models. In contrast, the largest ΔH, ΔS, and ΔO2 are not necessarily at the surface, their sign varies within the thermocline, and there are large differences among models. These differences between the two groups of tracers are linked to climate-driven changes in the ocean transport, with this tracer “redistribution” playing a significant role in changes in ΔH, ΔS, and ΔO2 but not the other tracers. Tracer redistribution is prominent in the southern subtropics, in a region where apparent oxygen utilization and ideal age indicate increased ventilation time scales. The tracer changes are linked to a poleward shift of the peak Southern Hemisphere westerly winds, which causes a similar shift of the subtropical gyres, and anomalous upwelling in the subtropics. This wind - tracer connection is also suggested to be a factor in the large model spread in some tracers, as there is also a large model spread in wind trends. A similar multi-tracer analysis of observations could provide insights into the relative role of the addition and redistribution of tracers in the ocean.

A document by Yilang Xu. Click on the document to view its contents.

Diapycnal mixing in the ocean interior is largely fueled by internal tides. Mixing schemes that represent the breaking of internal tides are now routinely included in ocean and earth system models applied to the modern and future. However, this is more rarely the case in climate simulations of deep-time intervals of the Earth, for which estimates of the energy dissipated by the tides are not always available. Here, we present and analyze two IPSL-CM5A2 earth system model simulations of the Early Eocene made under the framework of DeepMIP. One simulation includes mixing by locally dissipating internal tides, while the other does not. We show how the inclusion of tidal mixing alters the shape of the deep ocean circulation, and thereby of large-scale biogeochemical patterns, in particular dioxygen distributions. In our simulations, the absence of tidal mixing leads to a deep North Atlantic basin mostly disconnected from the global ocean circulation, which promotes the development of a basin-scale pool of oxygen-deficient waters, at the limit of complete anoxia. The absence of large-scale anoxic records in the deep ocean posterior to the Cretaceous anoxic events suggests that such an ocean state most likely did not occur at any time across the Paleogene. This highlights how crucial it is for climate models applied to the deep-time to integrate the spatial variability of tidally-driven mixing as well as the potential of using biogeochemical models to exclude aberrant dynamical model states for which direct proxies do not exist.

The use of large ensembles of model simulations is growing due to the need to minimize the influence of internal variability in evaluation of climate models and the detection of climate change induced trends. Yet, exactly how many ensemble members are required to effectively separate internal variability from climate change varies from model to model and metric to metric. Here we analyze the first three statistical moments (i.e., mean, variance and skewness) of detrended precipitation and sea surface temperature (interannual anomalies for variance and skewness) in the eastern equatorial Pacific from observations and ensembles of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate simulations. We then develop/assess the equations, based around established statistical theory, for estimating the required ensemble size for a user defined uncertainty range. Our results show that — as predicted by statistical theory — the uncertainties in ensemble means of these statistics decreases with the square root of the time series length and/or ensemble size. Further to this, as the uncertainties of these ensemble-mean statistics are generally similar when computed using pre-Industrial control runs versus historical runs, the pre-industrial runs can sometimes be used to estimate: i) the number of realizations and years needed for a historical ensemble to adequately characterize a given statistic; or ii) the expected uncertainty of statistics computed from an existing historical simulation or ensemble, if a large ensemble is not available.

Mesoscale eddies are ubiquitous in the global ocean. Most of them are materially coherent: they advect a different water mass in their core than in the surrounding water, according to studies based on in situ observations and Lagrangian techniques. In parallel, laboratory experiments have shown that eddies have the ability to locally modify the stratification according to the thermal wind balance, without necessarily contain heterogeneous water. These two types of density anomalies associated with mesoscale eddies are often erroneously confused in the literature. Here we propose a new theoretical decomposition of the potential density field in the core of eddies to assess their respective amplitude and dynamical effect. This allows the modelling of their 3D shape and the estimation of the importance of each term. This decomposition is applied to 6 anticyclonic eddies sampled during the EUREC4A-OA, METEOR 124 and PHYSINDIEN 2011 in situ experiments. We show that the anomaly corresponding to the slope of the isopycnals is the largest contributor to the total density anomaly. Its vertical shape is nearly Gaussian, but also depends on the local background stratification. The horizontal density gradient associated with the trapped water mass adds a second order term to the total anomaly and can be neglected for the study of eddy dynamics. The horizontal structures of the eddies studied are consistent with previous studies and show an exponential-alpha shape.

Global and coastal ocean surface water elevation prediction skill has advanced considerably with improved algorithms, more refined discretizations and high-performance parallel computing. Model skill is tied to mesh resolution, the accuracy of specified bathymetry/topography, dissipation parameterizations, air-sea drag formulations, and the fidelity of forcing functions. Wind forcing skill can be particularly prone to errors, especially at the land-ocean interface. The resulting biases and errors can be addressed holistically with a machine-learning (ML) approach. Herein, we weakly couple the Temporal Fusion Transformer to the National Oceanic and Atmospheric Administration’s (NOAA) Storm and Tide Operational Forecast System (STOFS 2D Global) to improve its forecasting skill throughout a 7-day horizon. We demonstrate the transformer’s ability to enrich the hydrodynamic model’s output at 228 observed water level stations operated by NOAA’s National Ocean Service. We conclude that the transformer is a rapid way to correct STOFS 2D Global forecasted water levels provided that sufficient covariates are supplied. For stations in wind-dominant areas, we demonstrate that including past and future wind-speed covariates make for a more skillful forecast. In general, while the transformer renders consistent corrections at both tidally and wind-dominant stations, it does so most aggressively at tidally-dominant stations. We show notable improvements in Alaska and the Atlantic and Pacific seaboards of the United States. We evaluate several transformers instantiated with different hyperparameters, covariates, and training data to provide guidance on how to enhance performance.