The World Ocean Database 2023 (WOD23) is the world's most complete and representative digital collection to date of near real time and delayed mode oceanographic in situ profile measurements collected from ocean observing systems over the 1772 to 2022 instrumental record. It is a collection of irreplaceable data records containing ~18.6 million water column profiles with ~3.6 billion measurements of 27 commonly measured physical and chemical variables, including 17 essential ocean and 11 climate variables, ~22.7 million meteorological and sea state observations, and more than 245 thousand plankton tows. WOD23 serves as a foundational and reliable data resource by and for global marine communities by making globally scattered and heterogeneous data FAIR, uniformly formatted, quality controlled, and searchable by means of extensive granular metadata. The data were sourced from long-term archived primary data, thus preserving its provenance, traceability, and authoritativeness. New and updated data are made available as quarterly updates to WOD23. The data are used in research applications, including earth system models, climate data reanalysis, and diagnostic studies. WOD23 is an activity of the International Oceanographic Data and Information Exchange, World Data System, and Center for Marine Meteorology and Ocean Climate Data.

In this study, we present ocean heat content/salt content results with and without gliders, in part concentrating on the western North Atlantic, including the continental shelf area where there are typically numerous glider observations. The impact that the disparity in the number of these two platforms has on the calculation of Upper Ocean Heat Content at NCEI is discussed. Because gliders (vs. profiling floats) generally occupy small geographic regions on short time scales, the impact on global estimates vs. local estimates is examined in the context of those two ocean observing systems. We also look at the impact of NAS UGOS profiling floats vs. non UGOS floats vs. gliders in the Gulf of Mexico. The NAS program has funded the effort that has resulted in the collection of over 9000 ocean in situ profiles of temperature and salinity since 2019, and the value of those profiles is assessed both in terms of Ocean Heat Content, as well as ocean model forecast skill. Again, the different space-time sampling of gliders vs. profiling floats is highlighted. Finally, an overview of fully blended ocean products, including glider observations that come through the IOOS glider DAC to NCEI, Argo, and other observations, is presented.

Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.

Forecasting mesoscale variability, such as the Loop Current (LC) growth and Loop Current Eddy (LCE) shedding in the Gulf of Mexico, poses challenges due to the large uncertainty in initial conditions and the development of non-linear instabilities from LC-cyclone interactions, which are crucial for LC/LCE separation. An ensemble forecast system may account for this uncertainty and filter out unconstrained scales and thereby extend the predictability of the mesoscale variability. A 32-member ensemble forecast system is employed to investigate the predictability of LC/LCE separation, with a specific focus on the December 2019 – March 2020 period. The forecasts demonstrated predictability of LC/LCE separation out to 7 to 13 weeks. During this period, significant changes occurred in the LC. The LC transitioned from being an extended LC to LCE separation on January 27, 2020. Subsequently, in March 2020, the LCE deformed and nearly split into two separate eddies. Detailed analyses of individual forecasts during this timeframe revealed that these transformations were influenced by two main interactions: (a) the interaction between the LC and a cyclone along its eastern edge, which caused the LC/LCE separation, and (b) the interaction between the LCE and a cyclone along its northern side, leading to the potential splitting of the LCE. These interactions were further intensified by the coupling between surface and deep cyclones. The validity of these findings is supported by a variety of observations, including drifters, current meters, and sea surface height, as well as verifying analysis.

Abstract NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) and other similar sea surface temperature (SST) products indicate that the globally averaged SST set a new daily record in March 2023. The record‐high SST in March was immediately broken in April, and new daily records were set again in July and August 2023. The SST anomaly (SSTA) persisted at a record high from mid‐March to the remainder of 2023. Our analysis indicates that the record‐high SSTs, and associated marine heatwaves (MHWs) and even super‐MHWs, are attributed to three factors: (a) a long‐term warming trend, (b) a shift to the warm phase of the multi‐decadal Pacific‐Atlantic‐Arctic (PAA) mode, and (c) the transition from the triple‐dip succession of La Niña events to the 2023–24 El Niño event.

NOAA’s Daily Optimum Interpolation Sea Surface Temperature (DOISST) indicates that globally averaged sea surface temperature (SST) broke record in March 2023 and set new record highs in April, July, and August 2023. This has raised intense media interest and public concern about causes and connections to climate change. Our analysis indicates that the record high SSTs qualified as marine heatwaves (MHWs) and even super-MHWs as defined in this study, and are attributed to three factors: (i) a linear trend, (ii) a shift to the warm phase of the multi-decadal Pacific-Atlantic-Arctic Oscillation(PAO) pattern which is identified in this study, and (iii) the transition from the triple-dip succession of La Niña events to the 2023 El Niño event. One-Sentence Summary The extreme warm SSTs in 2023 resulted from linear warming trends, a pattern of low-frequency oscillation, and the El Niño event.

The Ocean Carbon and Acidification Data System (OCADS) is a data management system at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI). It manages a wide range of ocean carbon and acidification data, including chemical, physical, and biological observations collected from research vessels, ships of opportunity, and uncrewed platforms, as well as laboratory experiment results, and model outputs. Additionally, OCADS serves as a repository for related Global Ocean Observing System (GOOS) biogeochemistry Essential Ocean Variables (EOVs), e.g., oxygen, nutrients, transient tracers, and stable isotopes. OCADS endeavors to be one of the world's leading providers of ocean carbon and acidification data, information, products, and services. To provide the best data management services to the ocean carbon and acidification research community, OCADS prioritizes adopting a customer-centric approach and gathering knowledge and expertise from the research community to improve its data management practices. OCADS aims to make all ocean carbon and acidification data accessible via a single portal, and welcomes submissions from around the world: https://www.ncei.noaa.gov/products/ocean-carbon-acidification-data-system/.

We estimate ocean heat content (OHC) change in the upper 2000 m in the Gulf of Mexico (GOM) from 1950 to 2020 to improve understanding of regional warming. Our estimates are based on 192 890 temperature profiles from the World Ocean Database. Warming occurs at all depths and in most regions except for a small region at northeastern GOM between 200 and 600 m. GOM OHC in the upper 2000 m increases at a rate of 0.38 +/- 0.13 ZJ decade-1 between 1970 and 2020, which is equivalent to 1.21 +/- 0.41 terawatts (TW). The GOM sea surface temperature (SST) increased ;1.0 degrees +/- 0.25 degrees C between 1970 and 2020, equivalent to a warming rate of 0.19 degrees +/- 0.05 degrees C decade-1. Although SST in the GOM increases at a rate approximately twice that for the global ocean, the full-depth ocean heat storage rate in the GOM (0.86 +/- 0.26 W m-2) applied to the entire GOM surface is comparable to that for the global ocean (0.82-1.11 W m-2). The upper-1000-m layer accounts for approximately 80%-90% of the total warming and variations in the upper 2000 m in the GOM. The Loop Current advective net heat flux is estimated to be 40.7 +/- 6.3 TW through the GOM. A heat budget analy-sis shows the difference between the advective heat flux and the ocean heat storage rate (1.76 +/- 1.36 TW, 1992-2017) can be roughly balanced with the annual net surface heat flux from ECCO (-37.9 TW).

Abstract Accurately predicting future ocean acidification (OA) conditions is crucial for advancing OA research at regional and global scales, and guiding society's mitigation and adaptation efforts. This study presents a new model‐data fusion product covering 10 global surface OA indicators based on 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three recent observational ocean carbon data products. The indicators include fugacity of carbon dioxide, pH on total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. The evolution of these OA indicators is presented on a global surface ocean 1° × 1° grid as decadal averages every 10 years from preindustrial conditions (1750), through historical conditions (1850–2010), and to five future Shared Socioeconomic Pathways (2020–2100): SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. These OA trajectories represent an improvement over previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs. The generated data product offers a state‐of‐the‐art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification. The gridded data product is available in NetCDF at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html, and global maps of these indicators are available in jpeg at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/surface-oa-indicators.html.

The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean’s role in Earth’s climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements.

Mesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.

Patterns of correlations between the ocean and the atmosphere are examined using a high-resolution (1/12° ocean and ice, 1/3° atmosphere) ensemble of data assimilative, coupled, global, ocean–atmosphere forecasts. This provides a unique perspective into atmosphere–ocean interactions constrained by assimilated observations, allowing for the contrast of patterns of coupled processes across regions and the examination of processes affected by ocean mesoscale eddies. Correlations during the first 24 h of the coupled forecast between the ocean surface temperature and atmospheric variables, and between the ocean mixed layer depth and surface winds are examined as a function of region and season. Three distinct coupling regimes emerge: 1) regions characterized by strong sea surface temperature fronts, where uncertainty in the ocean mesoscale influences ocean–atmosphere exchanges; 2) regions with intense atmospheric convection over the tropical oceans, where uncertainty in the modeled atmospheric convection impacts the upper ocean; and 3) regions where the depth of the seasonal mixed layer (MLD) determines the magnitude of the coupling, which is stronger when the MLD is shallow and weaker when the MLD is deep. A comparison with models at lower horizontal (1/12° vs 1° and 1/4°) and vertical (1- vs 10-m depth of the first layer) ocean resolution reveals that coupling in the boundary currents, the tropical Indian Ocean, and the warm pool regions requires high levels of horizontal and vertical resolution. Implications for coupled data assimilation and short-term forecasting are discussed.

Abstract This paper describes the new global Navy Earth System Prediction Capability (Navy‐ESPC) coupled atmosphere‐ocean‐sea ice prediction system developed at the Naval Research Laboratory (NRL) for operational forecasting for timescales of days to the subseasonal. Two configurations of the system are validated: (1) a low‐resolution 16‐member ensemble system and (2) a high‐resolution deterministic system. The Navy‐ESPC ensemble system became operational in August 2020, and this is the first time the NRL operational partner, Fleet Numerical Meteorology and Oceanography Center, will provide global coupled atmosphere‐ocean‐sea ice forecasts, with atmospheric forecasts extending past 16 days, and ocean and sea ice ensemble forecasts. A unique aspect of the Navy‐ESPC is that the global ocean model is eddy resolving at 1/12° in the ensemble and at 1/25° in the deterministic configurations. The component models are current Navy operational systems: NAVy Global Environmental Model (NAVGEM) for the atmosphere, HYbrid Coordinate Ocean Model (HYCOM) for the ocean, and Community Ice CodE (CICE) for the sea ice. Physics updates to improve the simulation of equatorial phenomena, particularly the Madden‐Julian Oscillation (MJO), were introduced into NAVGEM. The low‐resolution ensemble configuration and high‐resolution deterministic configuration are evaluated based on analyses and forecasts from January 2017 to January 2018. Navy‐ESPC ensemble forecast skill for large‐scale atmospheric phenomena, such as the MJO, North Atlantic Oscillation (NAO), Antarctic Oscillation (AAO), and other indices, is comparable to that of other numerical weather prediction (NWP) centers. Ensemble forecasts of ocean sea surface temperatures perform better than climatology in the tropics and midlatitudes out to 60 days. In addition, the Navy‐ESPC Pan‐Arctic and Pan‐Antarctic sea ice extent predictions perform better than climatology out to about 45 days, although the skill is dependent on season.

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

Operational oceanography can be described as the provision of routine oceanographic information needed for decision-making purposes. It is dependent upon sustained research and development through the end-to-end framework of an operational service, from observation collection to delivery mechanisms. The core components of operational oceanographic systems are a multi-platform observation network, a data management system, a data assimilative prediction system, and a dissemination/accessibility system. These are interdependent, necessitating communication and exchange between them, and together provide the mechanism through which a clear picture of ocean conditions, in the past, present, and future, can be seen. Ocean observations play a critical role in all aspects of operational oceanography, not only for assimilation but as part of the research cycle, and for verification and validation of products. Data assimilative prediction systems are advancing at a fast pace, in tandem with improved science and the growth in computing power. To make best use of the system capability these advances would be matched by equivalent advances in operational observation coverage. This synergy between the prediction and observation systems underpins the quality of products available to stakeholders, and justifies the need for sustained ocean observations. In this white paper, the components of an operational oceanographic system are described, highlighting the critical role of ocean observations, and how the operational systems will evolve over the next decade to improve the characterization of ocean conditions, including at finer spatial and temporal scales.

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.