The shallow water masses of the South China Sea (SCS) play a critical role in regulating regional climate and sustaining marine ecosystems. Using Argo float observations, we examine SCS water mass properties and eddy structures on isopycnal surfaces. By analyzing data in density space, we separate true water mass changes from vertical displacement of isopycnals. This approach helps reveal previously undocumented interannual variability in temperature and salinity together. We find that during the study period (2005-23), the shallow waters of the SCS, including within eddies, are strongly linked to El Ni & ntilde;o-Southern Oscillation (ENSO) variability. During El Ni & ntilde;o events, SCS waters become warmer and saltier, and during La Ni & ntilde;a, they become colder and fresher. We show that the water mass variability can be explained by anomalies in local surface fluxes with enhanced heating and reduced precipitation during El Ni & ntilde;o and weaker heating with increased precipitation during La Ni & ntilde;a. The water mass response is observed throughout the SCS but appears amplified within cyclonic eddies, likely due to their shallower mixed layer and greater sensitivity to surface fluxes. Our results demonstrate the sensitivity of SCS water masses to large-scale climate variability and provide new insight into the sensitivity of the SCS to ENSO.

Context Argo Australia is a national program contributing to the international Argo array, providing sustained, global observations of subsurface ocean properties to support marine research, operational forecasting and climate monitoring. Aims We describe the structure, operations, performance and impacts of Argo Australia, highlighting how the program supports the global Argo array and delivers value to Australian and international users. Methods We detail key program components, including float procurement and testing, deployment planning, real-time and delayed-mode data processing, and technical and scientific coordination within the international Argo framework. Key results Australia manages ~7.5% of the global Argo array, with most floats operating in Australian waters and the Southern Ocean. Sustained investment has delivered reliable real-time processing, comprehensive delayed-mode quality, and long float lifetimes. Together, these capabilities underpin a diverse and influencial body of scientific research delivered by scientists associated with Argo Australia. Conclusions Argo Australia provides a cost-effective, resilient national contribution to a critical international observing system, delivering high-quality ocean observations that underpin a broad range of scientific, operational and climate applications. Implications Sustained support for Argo Australia is required to maintain continuity of climate-quality subsurface records, and preserving Australia’s ability to contribute strategically to the global ocean observing system.

Accurate ocean forecasting is essential for many marine industries, including oil and gas, search and rescue, and Defence. Traditional forecasting systems typically produce analyses that are not dynamically consistent – leading to initialisation shock that degrades forecasts. These systems are computationally intensive and generate vast amounts of data, making it difficult for end users to interpret and exploit. Here, we develop a data-driven alternative using analog forecasting. We use along-track sea-level anomaly observations to identify past ocean states that most closely match present conditions in a large archive of model simulations. These historical cases serve as analogs to the present state. The subsequent evolution of each analog is then assembled into an ensemble forecast. We generate 15-day sea-level anomaly forecasts for twelve 5°x5° regions around Australia and demonstrate that our system outperforms traditional operational forecasts in 40-60% of cases, performs equally well (no statistical difference) in about 30% of cases, and is outperformed in about 10-25% of cases. By offering a computationally efficient approach to predicting mesoscale ocean circulation, analog forecasting presents a viable and practical alternative or compliment for ocean prediction.

The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.

The East Australian Current (EAC) system includes a poleward jet that flows adjacent to the continental shelf, a southward and eastward extension, and a complex eddy field. The EAC jet is often observed to be subsurface intensified. Here, we explain that there are two factors that cause the EAC to develop a subsurface maximum. First, the EAC flows as a narrow current, carrying low-density water from the Coral Sea into the denser waters of the Tasman Sea. This results in horizontal density gradients with a different sign on either side of the jet, negative onshore and positive offshore. According to the thermal wind relation, this produces vertical gradients in southward current that are surface intensified onshore and subsurface intensified offshore. Second, we show that the winds over the shelf are mostly downwelling favorable, drawing the surface EAC waters onshore. This aligns the region of positive horizontal density gradients with the EAC core, producing a subsurface velocity maximum. The presence of a subsurface maximum may produce baroclinic instabilities that play a role in eddy formation and EAC separation from the coast. Significance Statement Observations of the East Australian Current (EAC) show that the strongest currents are often below the surface at about 100-m depth. Two factors cause this subsurface maximum. First, because the EAC is a narrow jet, carrying warm water southward from the Coral Sea, the density gradient across the jet changes sign, causing surface-intensified currents onshore and subsurface-intensified currents offshore. Second, the wind field over the shelf often pulls the shallow waters shoreward, shifting the waters that cause subsurface intensification to align with the center of the jet, resulting in a subsurface maximum of the EAC. This process may be responsible for the generation of eddies in the Tasman Sea.

Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed , maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical , Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800-1200 dbar layer at 1 degrees horizontal resolution. This dataset pro-vides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of abso-lute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems.SIGNIFICANCE STATEMENT: The aim of this study is to provide observation-based estimates of the large-scale, subsurface ocean circulation. We exploit the drift of autonomous profiling floats to carefully isolate the inferred circula-tion at the parking depth, and combine observations from over 11000 floats, sampling between 2001 and 2020, to deliver a new dataset with unprecedented accuracy. The new estimates of subsurface currents are suitable for assessing global models, reanalyses, and forecasts, and for constraining ocean circulation in data-assimilating models.

The ocean is the main heat reservoir in Earth's climate system, absorbing most of the top-of-the-atmosphere excess radiation. As the climate warms, anomalously warm and fresh ocean waters in the densest layers formed near Antarctica spread northward through the abyssal ocean, while successions of warming and cooling events are seen in the deep-ocean layers formed near Greenland. The abyssal warming and freshening expands the ocean volume and raises sea level. While temperature and salinity characteristics and large-scale circulation of upper 2000 m ocean waters are well monitored, the present ocean observing network is limited by sparse sampling of the deep ocean below 2000 m. Recently developed autonomous robotic platforms, Deep Argo floats, collect profiles from the surface to the seafloor. These instruments supplement satellite, Core Argo float, and ship-based observations to measure heat and freshwater content in the full ocean volume and close the sea level budget. Here, the value of Deep Argo and planned strategy to implement the global array are described. Additional objectives of Deep Argo may include dissolved oxygen measurements, and testing of ocean mixing and optical scattering sensors. The development of an emerging ocean bathymetry dataset using Deep Argo measurements is also described.

Abstract Since the Argo program began, 568 floats returned almost 31,000 profiles, at high‐southern latitudes, with no measured position. These data are either disseminated with positions linearly interpolated between known positions, or with no geographic positions. Here, we present a simple method for estimating unknown Argo float trajectories. We try to identify trajectories that approximately follow contours along three different properties: potential vorticity (f/H), sea‐level, and density at 1,000 m. No single property‐constraint can be used to estimate trajectories for all position‐gaps. Each constraint fails for 9%–18% of gaps, where no continuous contour between the end‐points exists. But all constraints fail for the same position‐gap, for fewer than 1% of cases. For a given position‐gap, when a trajectory is identified using two or three different constraints, we select the shortest trajectory to be used to “fill the gap”. This selection process could be performed better by an Expert Operator, inspecting each estimated trajectory, and selecting the trajectory that is most consistent with a priori knowledge of the circulation in the vicinity of the position‐gap. Nonetheless, using the objective metric for selection, we find that 41.2% of position‐gaps use the f/H‐constraint, 32.1% use density, and 25.8% use sea‐level. We assess the estimated trajectories for consistency, by comparing bottom depths beneath trajectories to the deepest measurements in each profile. We find inconsistencies for 11.6% of position‐gaps using our method, compared to 28.0% using linearly‐interpolated trajectories. Adoption of the estimated trajectories for measurements under ice may yield benefits to many applications.

Using data from Argo floats, satellite altimetry, and satellite sea surface temperature (SST), we investigate the merging of two anti-cyclonic eddies in the Tasman Sea. The eddies are of different size and different density. Once the distance between the eddies falls below a critical separation distance, water from the smaller, denser eddy is observed to flow around the larger, lighter eddy, sink to greater than 400 m depth, and slowly spiral toward the eddy-center. The merging event is characterized by filaments around the eddy perimeter, evident in high-resolution, satellite SST data. The merged eddy develops multiple well-mixed layers, each almost 400 m thick, with large anomalies in temperature and salinity that penetrate to about 1,000 m depth. After the merging is complete, water from the lighter eddy is stacked on top of water from the denser eddy. Using a simple conceptual model, we show that the merged profiles can be simply explained by subduction of surface waters from the denser eddy, and mixing with subsurface waters from the lighter eddy. The stacked eddy has a distinct signature in TS-space. Using this TS-signature, together with altimetry for context, we identify over 20 other examples when Argo floats sample stacked eddies in the Tasman Sea. We speculate that merged eddies may be more common than previously thought, with implications for ocean productivity, underwater acoustics, and the transfer of energy across scales.

Here we introduce a new tool, the Spectral Diagram (SD), for the comparison of time series in the frequency domain. The SD provides a novel way to display the coherence function, power, amplitude, phase, and skill score of discrete frequencies of two time series. Each SD summarises these quantities in a single plot for multiple targeted frequencies. The versatility of SDs is demonstrated through a series of sea-level comparisons between observations from tide gauges and the model results from a global eddy-permitting ocean general circulation model (MOM5) with explicit tidal forcing. Phase information for the eight principal lunisolar constituents (M2, S2, N2, K2, K1, O1, P1, Q1) is added to the default configuration of MOM5. Inaccurate estimation of phase information is an important source of barotropic errors in ocean modelling, thereby compromising the skill scores in regions where amplitudes are close to the tidal gauge datasets. The greatest contribution of SD analysis is the indication that some diurnal estimates can be improved by adjusting the phase lag in the model as severe underestimation of semidiurnal amplitudes is the main reason for lower skill scores despite higher coherence. Although the SD has been designed for tidal analysis, it is a powerful tool for detecting co-oscillating patterns in multi-scale analyses, and this approach might provide guidance in devising skill scores for inter-comparing model results.

Argo, an international, global observational array of nearly 4,000 autonomous robotic profiling floats, each measuring ocean temperature and salinity from 0 to 2,000 m on nominal 10-day cycles, has revolutionized physical oceanography. Argo started at the turn of the millennium,growing out of advances in float technology over the previous several decades. After two decades, with well over 2 million profiles made publicly available in real time, Argo data have underpinned more than 4,000 scientific publications and improved countless nowcasts, forecasts, and projections. We review a small subset of those accomplishments, such as elucidating remarkable zonal jets spanning the deep tropical Pacific; increasing understanding of ocean eddies and the roles of mixing in shaping water masses and circulation; illuminating interannual to decadal ocean variability; quantifying, in concert with satellite data, contributions of ocean warming and ice melting to sea level rise; improving coupled numerical weather predictions; and underpinning decadal climate forecasts.

BRAN2020 (2020 version of the Bluelink ReANalysis) is an ocean reanalysis that combines observations with an eddy-resolving, near-global ocean general circulation model to produce a four-dimensional estimate of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. There is a separation in the scales that are corrected in the two steps: the high-resolution step corrects the mesoscale dynamics in the same way as previous versions of BRAN, while the extra coarse step is effective at correcting biases that develop at large scales. The reanalysis currently spans January 1993 to December 2019 and assimilates observations of in situ temperature and salinity, as well as of satellite sea-level anomaly and sea surface temperature. BRAN2020 is planned to be updated to within months of real time after this initial release, until an updated version of BRAN is available. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with misfits between reanalysed and observed fields reduced by over 30 % for some variables, for subsurface temperature and salinity in particular. The BRAN2020 dataset is comprised of daily averaged fields of temperature, salinity, velocity, mixed-layer depth and sea level. Reanalysed fields realistically represent all of the major current systems within 75∘ S and 75∘ N, excluding processes relating to sea ice but including boundary currents, equatorial circulation, Southern Ocean variability and mesoscale eddies. BRAN2020 is publicly available at https://doi.org/10.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.

Forecast errors of subsurface temperature and salinity are substantially reduced with an efficient, two-step, multiscale Ensemble Optimal Interpolation (EnOI) system, applied to a near-global eddy-resolving ocean model. A critical element of any data assimilation system is the background error covariance, which for EnOI is typically a static ensemble of anomalies from a long model run. Here, we construct two ensembles — one based on intraseasonal anomalies from a free run of the same eddy-resolving ocean model used to underpin the forecasts, and a second ensemble of climatogical anomalies calculated using a relatively coarse, 1-degree global ocean model. For each assimilation cycle, the coarse-resolution ensemble is used to "correct" the broad-scales, and the high-resolution ensemble is used to "correct" the eddy-scales. Corrections from the coarse steps are more effective at reducing systematic errors in the subsurface ocean whereas the high-resolution steps typically produce vertically coherent corrections associated with mesoscale eddies. We compare two configurations of multiscale data assimilation with different localisation radii in the coarse data assimilation step. The best performance and slowest error growth was found with localisation that was large enough to encompass neighbouring profiles in each assimilation cycle. The efficacy of the approach is demonstrated in ocean reanalyses over 2017-8 that assimilate data every 3 days. We demonstrate clear improvements in the representation of temperature and salinity at all depths around Australia. Model-observation differences are particularly improved in and below the thermocline. The corrections to the ocean state with multiscale data assimilation follow water mass structures. The increased computational cost of this multiscale approach is modest (about double the analysis step), but the performance improvement is significant, making this approach suitable for research and operational applications.

Citation: Testor P, Young Bd, Rudnick DL, Glenn S, Hayes D, Lee CM, Pattiaratchi C, Hill K, Heslop E, Turpin V, Alenius P, Barrera C, Barth JA, Beaird N, Bécu G, Bosse A, Bourrin F, Brearley JA, Chao Y, Chen S, Chiggiato J, Coppola L, Crout R, Cummings J, Curry B, Curry R, Davis R, Desai K, DiMarco S, Edwards C, Fielding S, Fer I, Frajka-Williams E, Gildor H, Goni G, Gutierrez D, Haugan P, Hebert D, Heiderich J, Henson S, Heywood K, Hogan P, Houpert L, Huh S, Inall ME, Ishii M, Ito S-i, Itoh S, Jan S, Kaiser J, Karstensen J, Kirkpatrick B, Klymak J, Kohut J, Krahmann G, Krug M, McClatchie S, Marin F, Mauri E, Mehra A, Meredith MP, Meunier T, Miles T, Morell JM, Mortier L, Nicholson S, O’Callaghan J, O’Conchubhair D, Oke P, Pallàs-Sanz E, Palmer M, Park J, Perivoliotis L, Poulain P-M, Perry R, Queste B, Rainville L, Rehm E, Roughan M, Rome N, Ross T, Ruiz S, Saba G, Schaeffer A, Schönau M, Schroeder K, Shimizu Y, Sloyan BM, Smeed D, Snowden D, Song Y, Swart S, Tenreiro M, Thompson A, Tintore J, Todd RE, Toro C, Venables H, Wagawa T, Waterman S, Watlington RA and Wilson D (2021) Corrigendum: OceanGliders: A Component of the Integrated GOOS. Front. Mar. Sci. 8:696100. doi: 10.3389/fmars.2021.696100 Corrigendum: OceanGliders: A Component of the Integrated GOOS

Blue Maps aims to exploit the versatility of an ensemble data assimilation system to deliver gridded estimates of ocean temperature, salinity, and sea-level with the accuracy of an observation-based product. Weekly maps of ocean properties are produced on a 1/10°, near-global grid by combining Argo profiles and satellite observations using ensemble optimal interpolation (EnOI). EnOI is traditionally applied to ocean models for ocean forecasting or reanalysis, and usually uses an ensemble comprised of anomalies for only one spatiotemporal scale (e.g., mesoscale). Here, we implement EnOI using an ensemble that includes anomalies for multiple space- and time-scales: mesoscale, intraseasonal, seasonal, and interannual. The system produces high-quality analyses that produce mis-fits to observations that compare well to other observation-based products and ocean reanalyses. The accuracy of Blue Maps analyses is assessed by comparing background fields and analyses to observations, before and after each analysis is calculated. Blue Maps produces analyses of sea-level with accuracy of about 4 cm; and analyses of upper-ocean (deep) temperature and salinity with accuracy of about 0.45 (0.15) degrees and 0.1 (0.015) practical salinity units, respectively. We show that the system benefits from a diversity of ensemble members with multiple scales, with different types of ensemble members weighted accordingly in different dynamical regions.

The operational Australian Bluelink ocean forecast system is used to transform physical oceanographic observations into coherent analyses and predictions. These analyses and predictions form the basis for information services about the marine environment and its ecosystem, and can provide boundary data for weather predictions. Bluelink information services are available to marine industries (e.g. commercial fishing, aquaculture, shipping, oil and gas, renewable energy), government agencies (e.g. search and rescue, defence, coastal management, environmental protection), and other stakeholders (e.g. recreation, water sports, artisanal and sport fishing) who depend on timely and accurate information about the marine environment. This review highlights the last 15 years of Bluelink achievements delivering mesoscale (eddy-resolving) to sub-mesoscale and short- to medium-range (days to weeks) ocean forecasts and reanalyses. Key achievements include the development of a global ocean forecasting and reanalysis system, a relocatable ocean-atmosphere model and a littoral zone analysis and forecasting capability. Beyond the traditional short-term forecasting of physical ocean properties (temperature, salinity, surface height, currents, waves), marine activities such as water quality and habitat management as well as climate monitoring increasingly rely on operational oceanographic data and products. These are areas of active research of the Bluelink team in collaboration with national and international partners.

In the past two decades, the Argo Program has collected, processed and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002°C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.

We introduce ACCESS-OM2, a new version of the ocean–sea ice model of the Australian Community Climate and Earth System Simulator. ACCESS-OM2 is driven by a prescribed atmosphere (JRA55-do) but has been designed to form the ocean–sea ice component of the fully coupled (atmosphere–land–ocean–sea ice) ACCESS-CM2 model. Importantly, the model is available at three different horizontal resolutions: a coarse resolution (nominally 1∘ horizontal grid spacing), an eddy-permitting resolution (nominally 0.25∘), and an eddy-rich resolution (0.1∘ with 75 vertical levels); the eddy-rich model is designed to be incorporated into the Bluelink operational ocean prediction and reanalysis system. The different resolutions have been developed simultaneously, both to allow for testing at lower resolutions and to permit comparison across resolutions. In this paper, the model is introduced and the individual components are documented. The model performance is evaluated across the three different resolutions, highlighting the relative advantages and disadvantages of running ocean–sea ice models at higher resolution. We find that higher resolution is an advantage in resolving flow through small straits, the structure of western boundary currents, and the abyssal overturning cell but that there is scope for improvements in sub-grid-scale parameterizations at the highest resolution.