NOAA Integrated Ocean Observing System

Well-founded data management systems are of vital importance for ocean observing systems as they ensure that essential data are not only collected but also retained and made accessible for analysis and application by current and future users. Effective data management requires collaboration across activities including observations, metadata and data assembly, quality assurance and control (QA/QC), and data publication that enables local and interoperable discovery and access, and secure archiving that guarantees long-term preservation. To achieve this, data should be Findable, Accessible, Interoperable, and Reusable (FAIR). Here, we outline how these principles apply to ocean data, and illustrate them with a few examples. In recent decades, ocean data managers, in close collaboration with international organizations, have played an active role in the improvement of environmental data standardization, accessibility and interoperability through different projects, enhancing access to observation data at all stages of the data life cycle and fostering the development of integrated services targeted to research, regulatory and operational users. As ocean observing systems evolve and an increasing number of autonomous platforms and sensors are deployed, the volume and variety of data increases dramatically. For instance, there are more than 70 data catalogues that contain metadata records for the polar oceans, a situation that makes comprehensive data discovery beyond the capacity of most researchers. To better serve research, operational, and commercial users, more efficient turnaround of quality data in known formats and made available through web services is necessary. In particular, automation of data workflows will be critical to reduce friction throughout the data value chain. Adhering to the FAIR principles with free, timely and unrestricted access to ocean observation data is beneficial for the originators, has obvious benefits for users and is an essential foundation for the development of new services made possible with big data technologies.

Zooplankton are major consumers of phytoplankton primary production in marine ecosystems. As such, they represent a critical link for energy and matter transfer between phytoplankton and bacterioplankton to higher trophic levels and play an important role in global biogeochemical cycles. In this Review, we discuss key responses of zooplankton to ocean warming, including shifts in phenology, range, and body size, and assess the implications to the biological carbon pump and interactions with higher trophic levels. Our synthesis highlights key knowledge gaps and geographic gaps in monitoring coverage that need to be urgently addressed. We also discuss an integrated sampling approach that combines traditional and novel techniques to improve zooplankton observation for the benefit of monitoring zooplankton populations and modelling future scenarios under global changes.

Academic, government, and private organizations from around the globe have established High Frequency radar (hereinafter, HFR) networks at regional or national levels. Partnerships have been established to coordinate and collaborate on a single global HFR network (http://global-hfradar.org/). These partnerships were established in 2012 as part of the Group on Earth Observations (GEO) to promote HFR technology and increase data sharing among operators and users. The main product of HFR networks are continuous maps of ocean surface currents within 200 km of the coast at high spatial (1-6 km) and temporal resolution (hourly or higher). Cutting-edge remote sensing technologies are becoming a standard component for ocean observing systems, contributing to the paradigm shift toward ocean monitoring. In 2017 the Global HFR Network was recognized by the Joint Technical WMO-IOC Commission for Oceanography and Marine Meteorology (JCOMM) as an observing network of the Global Ocean Observing System (GOOS). In this paper we will discuss the development of the network as well as establishing goals for the future. The U.S. High Frequency Radar Network (HFRNet) has been in operation for over 13 years, with radar data being ingested from 31 organizations including measurements from Canada and Mexico. HFRNet currently holds a collection from over 150 radar installations totaling millions of records of surface ocean velocity measurements. During the past 10 years in Europe,

Reliable statements about variability and change in marine ecosystems and their underlying causes are needed to report on their status and to guide management. Here we use the Framework on Ocean Observing (FOO) to begin developing ecosystem Essential Ocean Variables (eEOVs) for the Southern Ocean Observing System (SOOS). An eEOV is a defined biological or ecological quantity, which is derived from field observations, and which contributes significantly to assessments of Southern Ocean ecosystems. Here, assessments are concerned with estimating status and trends in ecosystem properties, attribution of trends to causes, and predicting future trajectories. eEOVs should be feasible to collect at appropriate spatial and temporal scales and are useful to the extent that they contribute to direct estimation of trends and/or attribution, and/or development of ecological (statistical or simulation) models to support assessments. In this paper we outline the rationale, including establishing a set of criteria, for selecting eEOVs for the SOOS and develop a list of candidate eEOVs for further evaluation. Other than habitat variables, nine types of eEOVs for Southern Ocean taxa are identified within three classes: state (magnitude, genetic/species, size spectrum), predator–prey (diet, foraging range), and autecology (phenology, reproductive rate, individual growth rate, detritus). Most candidates for the suite of Southern Ocean taxa relate to state or diet. Candidate autecological eEOVs have not been developed other than for marine mammals and birds. We consider some of the spatial and temporal issues that will influence the adoption and use of eEOVs in an observing system in the Southern Ocean, noting that existing operations and platforms potentially provide coverage of the four main sectors of the region — the East and West Pacific, Atlantic and Indian. Lastly, we discuss the importance of simulation modelling in helping with the design of the observing system in the long term. Regional boundary: south of 30°S.

Advancing our understanding of ocean biogeochemistry, biology, and ecosystems relies on the ability to make observations both in the ocean and at the critical boundaries between the ocean and other earth systems at relevant spatial and temporal scales. After decades of advancement in ocean observing technologies, one of the key remaining challenges is how to cost-effectively make measurements at the increased resolution necessary for illuminating complex system processes and rapidly evolving changes. In recent years, biogeochemical and biological in situ sensors have been emerging that are three-fold or more lower in cost than established technologies,; cost reduction for many biological in situ sensors has also been significant, although the absolute costs are still relatively high. with cCost savings in these advancements has been driven by miniaturization, new methods of packaging, and lower-cost mass-produced components such as electronics and materials. Recently, field projects have demonstrated the potential for sciencequality data collection via large-scale deployments using cost-effective sensors and deployment strategies. In the coming decade, it is envisioned that ocean biogeochemistry and biology observations will be revolutionized by continued innovation in sensors with increasingly low price points and the scale-up of deployments of these in situ sensor technologies. The goal of this study is therefore to: (1) provide a review of existing sensor technologies that are already achieving costeffectiveness compared with traditional instrumentation, (2) present case studies of cost-effective in situ deployments that can provide insight into methods for bridging observational gaps, (3) identify key challenge areas where progress in cost reduction is lagging, and (4) present a number of potentially transformative directions for future ocean biogeochemical and biological studies using cost-effective technologies and deployment strategies.

Abstract Ecological forecasting provides a powerful set of methods for predicting short‐ and long‐term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.

Marine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System.

Abstract Bio‐logging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bio‐logging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bio‐logging data into research and management recommendations. We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracy‐of‐use, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security. We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing. Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and inter‐governmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bio‐logging data formats across all fields in animal ecology.

Marine biodiversity is a fundamental characteristic of our planet that depends on and influences climate, water quality, and many ocean state variables. It is also at the core of ecosystem services that can make or break economic development in any region. Our purpose is to highlight the need for marine biological observations to inform science and conservation management and to support the blue economy. We provide ten recommendations, applicable now, to measure and forecast biological Essential Ocean Variables (EOVs) as part of economic monitoring efforts. The UN Decade of Ocean Science for Sustainable Development (2021–2030) provides a timely opportunity to implement these recommendations to benefit humanity and enable the USD 3 trillion global ocean economy expected by 2030.

Biologging through temporary attachment of miniaturized data loggers on marine animals is revolutionizing the science of marine ecology (Block et al., 2011; Hussey et al., 2015). It also offers new capabilities to observe the ocean. In addition to collecting location and depth information useful for studying foraging behavior, these devices can record vertical profiles of temperature and salinity (Fedak, 2004; Costa et al., 2010), data that are used to improve knowledge of ocean state and its variability.

Maintaining healthy, productive ecosystems in the face of pervasive and accelerating human impacts including climate change requires globally coordinated and sustained observations of marine biodiversity. Global coordination is predicated on an understanding of the scope and capacity of existing monitoring programs, and the extent to which they use standardized, interoperable practices for data management. Global coordination also requires identification of gaps in spatial and ecosystem coverage, and how these gaps correspond to management priorities and information needs. We undertook such an assessment by conducting an audit and gap analysis from global databases and structured surveys of experts. Of 371 survey respondents, 203 active, long-term (>5 years) observing programs systematically sampled marine life. These programs spanned about 7% of the ocean surface area, mostly concentrated in coastal regions of the United States, Canada, Europe, and Australia. Seagrasses, mangroves, hard corals, and macroalgae were sampled in 6% of the entire global coastal zone. Two-thirds of all observing programs offered accessible data, but methods and conditions for access were highly variable. Our assessment indicates that the global observing system is largely uncoordinated which results in a failure to deliver critical information required for informed decision-making such as, status and trends, for the conservation and sustainability of marine ecosystems and provision of ecosystem services. Based on our study, we suggest four key steps that can increase the sustainability, connectivity and spatial coverage of biological Essential Ocean Variables in the global ocean: (1) sustaining existing observing programs and encouraging coordination among these; (2) continuing to strive for data strategies that follow FAIR principles (findable, accessible, interoperable, and reusable); (3) utilizing existing ocean observing platforms and enhancing support to expand observing along coasts of developing countries, in deep ocean basins, and near the poles; and (4) targeting capacity building efforts. Following these suggestions could help create a coordinated marine biodiversity observing system enabling ecological forecasting and better planning for a sustainable use of ocean resources.

Scientists who observe and distribute oceanographic data require processes that ensure high-quality data. These processes includes quality assurance, quality control, quality assessment, standards, and best practices. In this paper, quality assurance is regarded as actions taken prior to instrument deployment to improve the probability of generating good data, while quality control is the effort made to examine the resultant data. We focus on quality assurance and strive to guide the oceanographic community by identifying existing quality assurance best practices preferred by the five entities represented by the authors – specifically, the Alliance for Coastal Technology, the AtlantOS project, the Integrated Marine Observing System, the Joint Technical Commission for Oceanographic and Marine Meteorology, and the U.S. IOOS Quality Assurance / Quality Control of Real-Time Oceanographic Data project. The focus has been placed on QA in response to suggestions from the AtlantOS and QARTOD communities. We define the challenges associated with quality assurance, which include a clear understanding of various terms, the overlap in meaning of those terms, establishment of standards, and varying program requirements. Brief, ‘real-world’ case-studies are presented to demonstrate the challenges. Following this is a description of best practices gathered by the authors from hundreds of scientists over the many years or decades the aforementioned entities have been in place. These practices address instrument selection, preparation, deployment, maintenance, and data acquisition. Varying resources and capabilities are considered, and corresponding levels of quality assurance efforts are discussed. We include a comprehensive description of measurement uncertainty with a detailed example of such a calculation. Rigorous estimates of measurement uncertainty are surprisingly complex, necessarily specific, and not provided as often as needed. But they are critical to data users who may have applications not envisioned by the data provider, to ensure appropriate use of the data. The guidance is necessarily generic because of the broad expanse of oceanographic observations. Further, it is platform-agnostic and applies to most deployment scenarios. We identify the recently created Ocean Best Practice System as one means of developing, sharing, documenting, and curating more specific QA processes. Ultimately, our goal here is to foster their development and harmonization.

Strong and strategic collaborations among experts from academia, federal operational centers, and industry have been forged to create a U.S. IOOS Coastal and Ocean Modeling Testbed (COMT). The COMT mission is to accelerate the transition of scientific and technical advances from the coastal and ocean modeling research community to improved operational ocean products and services. This is achieved via the evaluation of existing technology or the development of new technology depending on the status of technology within the research community. The initial phase of the COMT has addressed three coastal and ocean prediction challenges of great societal importance: estuarine hypoxia , shelf hypoxia , and coastal inundation . A fourth effort concentrated on providing and refining the cyberinfrastructure and cyber tools to support the modeling work and to advance interoperability and community access to the COMT archive. This paper presents an overview of the initiation of the COMT, the findings of each team and a discussion of the role of the COMT in research to operations and its interface with the coastal and ocean modeling community in general. Detailed technical results are presented in the accompanying series of 16 technical papers in this special issue.

Climate change has rapidly altered marine ecosystems and is expected to continue to push systems and species beyond historical baselines into novel conditions. Projecting responses of organisms and populations to these novel environmental conditions often requires extrapolations beyond observed conditions, challenging the predictive limits of statistical modeling capabilities. Bioenergetics modeling provides the mechanistic basis for projecting climate change effects on marine living resources in novel conditions, has a long history of development, and has been applied widely to fish and other taxa. We provide our perspective on 4 opportunities that will advance the ability of bioenergetics-based models to depict changes in the productivity and distribution of fishes and other marine organisms, leading to more robust projections of climate impacts. These are (1) improved depiction of bioenergetics processes to derive realistic individual-level response(s) to complex changes in environmental conditions, (2) innovations in scaling individual-level bioenergetics to project responses at the population and food web levels, (3) more realistic coupling between spatial dynamics and bioenergetics to better represent the local- to regional-scale differences in the effects of climate change on the spatial distributions of organisms, and (4) innovations in model validation to ensure that the next generation of bioenergetics-based models can be used with known and sufficient confidence. Our focus on specific opportunities will enable critical advancements in bioenergetics modeling and position the modeling community to make more accurate and robust projections of the effects of climate change on individuals, populations, food webs, and ecosystems.

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/.

Abstract On 30 September 2021, a saildrone uncrewed surface vehicle (USV) was steered into category 4 Hurricane Sam, the most intense storm of the 2021 Atlantic hurricane season. It measured significant wave heights up to 14 m (maximum wave height = 27 m) and near-surface winds exceeding 55 m s −1 . This was the first time in more than seven decades of hurricane observations that in real time a USV transmitted scientific data, images, and videos of the dynamic ocean surface near a hurricane’s eyewall. The saildrone was part of a five-saildrone deployment of the NOAA 2021 Atlantic Hurricane Observations Mission. These saildrones observed the atmospheric and oceanic near-surface conditions of five other tropical storms, of which two became hurricanes. Such observations inside tropical cyclones help to advance the understanding and prediction of hurricanes, with the ultimate goal of saving lives and protecting property. The 2021 deployment pioneered a new practice of coordinating measurements by saildrones, underwater gliders, and airborne dropsondes to make simultaneous and near-collocated observations of the air–sea interface, the ocean immediately below, and the atmosphere immediately above. This experimental deployment opened the door to a new era of using remotely piloted uncrewed systems to observe one of the most extreme phenomena on Earth in a way previously impossible. This article provides an overview of this saildrone hurricane observations mission, describes how the saildrones were coordinated with other observing platforms, presents preliminary scientific results from these observations to demonstrate their potential utility and motivate further data analysis, and offers a vision of future hurricane observations using combined uncrewed platforms.

Abstract 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.

Reflecting on two decades of the U.S. Integrated Ocean Observing System (IOOS) is particularly timely during the OceanObs’19 meeting. Over the past twenty years since the first OceanObs meeting was convened, U.S. IOOS has advanced from regional proofs of concept to a national, sustained enterprise. U.S. IOOS has grown to include 17 Federal partners and 11 Regional Associations (RAs) that implement regional observing systems covering all U.S. coasts and Great Lakes with activities spanning from head of tide to the U.S. exclusive economic zone (EEZ). The National Oceanographic and Atmospheric Administration (NOAA), as lead agency, provides guidance and national-level coordination. An interagency body, the Integrated Ocean Observation Committee (IOOC), communicates across federal agencies and ensures IOOS maintains strong connections to the Global Ocean Observing System (GOOS). Additionally, a federal advisory committee, non-federal association, and various informal partnerships further inform and advance the IOOS enterprise. This governance structure fosters both national consistency, regional flexibility, and global contributions addressing the diverse needs of U.S. coastal and Great Lakes stakeholders.

An ocean modeling program is improving our ability to predict circulation along the U.S. West Coast, dead zones and other coastal ecosystem responses, and storm surges in island environments.

Abstract Marine Life 2030 is a programme endorsed by the United Nations Decade of Ocean Science for Sustainable Development (the Ocean Decade) to establish a globally coordinated system that delivers knowledge of ocean life to those who need it, promoting human well-being, sustainable development, and ocean conservation. It is an open network to unite existing and new programmes into a co-designed, global framework to share information on methods, standards, observations, and applications. Goals include realizing interoperable information and transforming the observation and forecasting of marine life for the benefit of all people. Co-design, sharing local capacity, and coordination between users of ocean resources across regions is fundamental to enable sustainable use and conservation. A novel, bottom-up networking structure is now engaging members of the ocean community to address local issues, with Marine Life 2030 facilitating the linkage between groups across different regions to meet the challenges of the Ocean Decade. A variety of metrics, including those proposed by the Group on Earth Observations, will be used to track the success of the co-design process.