The European Space Agency's Sentinel-1 (S-1) satellite mission has captured more than 10 million images of the ocean surface using C-band synthetic aperture radar (SAR WV mode). While machine learning is a promising approach for detecting and quantifying various geophysical signatures in these images, scientists are limited by the cost of manual data annotation for any particular task. We propose to use contrastive self-supervised learning on the full archive of unannotated WV-mode images to train a semantic embedding model named WV-Net. In experiments, we show that WV-Net embeddings outperform those from models that were pretrained with natural images (ImageNet) on four downstream tasks: multilabel classification [0.96 average area under the receiver operating characteristic (AUROC) vs 0.95], wave height regression [0.50 root-mean-square error (RMSE) vs 0.60], near-surface air temperature regression (0.90 RMSE vs 0.97), and unsupervised image retrieval [0.41 class-averaged mean average precision (mAP) vs 0.37]. WV-Net embeddings also scale better in data-sparse settings, and fine-tuned WV-Net models are more robust to hyper-parameter choices. The WV-Net foundation model is publicly available and can be adapted to a variety of data analysis and exploration tasks in geophysical research.

Abstract A dataset of multi‐tagged sea surface roughness synthetic aperture radar (SAR) satellite images was established near Barbados from January to June 2016 to 2019. It is an advancement of the Sentinel‐1 Wave Mode TenGeoP‐SARwv (a labelled SAR imagery dataset of 10 geophysical phenomena from Sentinel‐1 wave mode) dataset that targets SAR marine atmospheric boundary layer (MABL) coherent structures. Twelve tags define roll vortices, convective cells, mixed rolls and convective cells, fronts, rain cells, cold pools and low winds. Examples are provided for each signature. The final dataset is comprised of 2100 Sentinel‐1 wave mode SAR images acquired at 36 incidence angle over an 8° × 8°region centered at 51° W, 15° N. Each image is tagged with one or multiple phenomena by five experts. This strategy extends the TenGeoP‐SARwv by identifying coexisting phenomena within a single SAR image and by the addition of mixed roll/cell states and cold pools. The dataset includes PNG‐formatted SAR image files along with two text files containing the file name, the central latitude/longitude, expert tags for each image, and all dataset metadata. There is a high degree of consensus among expert tags. The dataset complements existing hand‐labelled ocean SAR image datasets and offers the potential for new deep‐learning SAR image classification model developments. Future use is also expected to yield new insights into the tradewind MABL processes such as structure transitions and their relation to the stratification.

The European Space Agency's Copernicus Sentinel-1 (S-1) mission is a constellation of C-band synthetic aperture radar (SAR) satellites that provide unprecedented monitoring of the world's oceans. S-1's wave mode (WV) captures 20x20 km image patches at 5 m pixel resolution and is unaffected by cloud cover or time-of-day. The mission's open data policy has made SAR data easily accessible for a range of applications, but the need for manual image annotations is a bottleneck that hinders the use of machine learning methods. This study uses nearly 10 million WV-mode images and contrastive self-supervised learning to train a semantic embedding model called WV-Net. In multiple downstream tasks, WV-Net outperforms a comparable model that was pre-trained on natural images (ImageNet) with supervised learning. Experiments show improvements for estimating wave height (0.50 vs 0.60 RMSE using linear probing), estimating near-surface air temperature (0.90 vs 0.97 RMSE), and performing multilabel-classification of geophysical and atmospheric phenomena (0.96 vs 0.95 micro-averaged AUROC). WV-Net embeddings are also superior in an unsupervised image-retrieval task and scale better in data-sparse settings. Together, these results demonstrate that WV-Net embeddings can support geophysical research by providing a convenient foundation model for a variety of data analysis and exploration tasks.

ABSTRACTMeasuring boundary layer stratification, wind shear, and turbulence remains challenging for wind resource assessment. In particular, larger eddy scales have the greatest impact on turbine load fluctuations, and there are few in situ methods to observe them adequately. Satellite remote sensing using synthetic aperture radar (SAR) is an alternative approach. In this study, eddy‐related signatures in 704 high‐resolution images are related to stratification through a bulk Richardson number ( ) measured by a buoy near Martha's Vineyard, the US epicenter of offshore wind. Variations in SAR‐observed atmospheric boundary layer eddies, or lack of them, correspond to specific regimes. Accounting for strong vertical wind shear, typically under stable stratification, is critical for energy production and turbine loads, and SAR directly identifies these conditions by the absence of energetic eddies. SAR also provides a regional climatology of atmospheric stratification for offshore wind assessment, complementing other observations, and with potential application worldwide.

There are numerous global ocean wave reanalysis and hindcast products currently being distributed and used across different scientific fields. However, there is not a consistent dataset that can sample across all existing products based on a standardized framework. Here, we present and describe the first coordinated multi-product ensemble of present-day global wave fields available to date. This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, includes general and extreme statistics of significant wave height (Hs), mean wave period (Tm) and mean wave direction (θm) computed across 1980–2014, at different frequency resolutions (monthly, seasonally, and annually). This coordinated global ensemble has been derived from fourteen state-of-the-science global wave products obtained from different atmospheric reanalysis forcing and downscaling methods. This data set has been processed, under a specific framework for consistency and quality, following standard Data Reference Syntax, Directory Structures and Metadata specifications. This new comprehensive dataset provides support to future broad-scale analysis of historical wave climatology and variability as well as coastal risk and vulnerability assessments across offshore and coastal engineering applications.

Historical trends in the direction and magnitude of ocean surface wave height, period, or direction are debated due to diverse data, time-periods, or methodologies. Using a consistent community-driven ensemble of global wave products, we quantify and establish regions with robust trends in global multivariate wave fields between 1980 and 2014. We find that about 30–40% of the global ocean experienced robust seasonal trends in mean and extreme wave height, period, and direction. Most of the Southern Hemisphere exhibited strong upward-trending wave heights (1–2 cm per year) and periods during winter and summer. Ocean basins with robust positive trends are far larger than those with negative trends. Historical trends calculated over shorter periods generally agree with satellite records but vary from product to product, with some showing a consistently negative bias. Variability in trends across products and time-periods highlights the importance of considering multiple sources when seeking robust change analyses.

Invasive species can precede far-reaching environmental and economic consequences. In the Hawai'ian Archipelago Cephalopholis argus (family Serranidae) is an established invasive species, now recognized as the dominant local reef predator, negatively impacting the native ecosystem and local fishery. In this region, no official C. argus fishery exists, due to its association with Ciguatera seafood poisoning (CP); a severe intoxication in humans occurring after eating (primarily) fish contaminated with ciguatoxins (CTXs). Pre-harvest prediction of CP is currently not possible; partly due to the ubiquitous nature of the microalgae producing CTXs and the diverse bioaccumulation pathways of the toxins. This study investigated the perceived risk of CP in two geographically discrete regions (Leeward and Windward) around the main island of Hawai'i, guided by local fishers. C. argus was collected and investigated for CTXs using the U.S. Food and Drug Administration (FDA) CTX testing protocol (in vitro neuroblastoma N2a-assay and LC-MS/MS). Overall, 76% of fish (87/113) exceeded the FDA guidance value for CTX1B (0.01 ng g(-1) tissue equivalents); determined by the N2a-assay. Maximum CTX levels were congruent to 2x higher at the Leeward vs Windward location and, respectively, 95% (64/67) and 54% (25/46) of fish were positive for CTX-like activity. Fisher persons and environmental understandings, regarding the existence of a geographic predictor (Leeward vs Windward) for harvest, were found to be (mostly) accurate as CTXs were detected in both locations and the local designation of C. argus as a risk for CP was confirmed. This study provides additional evidence that supports the previous conclusions that this species is a severe CP risk in the coastal food web of Hawai' i, and that ocean exposure (wave power) may be a prominent factor influencing the CTX content in fish within a hyperendemic region for CP.

MARINE DEBRIS AND PELAGIC ECOSYSTEMS Wood, pumice, drifting kelp, and other natural marine debris have long played important roles in marine ecosystems. Today, oceanic “litter” generated by human activities, notably plastics, constitutes the majority of marine debris and is mostly harmful to those ecosystems. In the twentieth century, plastic became a symbol of technological development and globalization of the world’s economy. Cheap, durable, and long-lasting, with a broad variety of properties that are attractive for an array of human uses, plastic penetrated all parts of business and everyday life. In recent decades, growing demand exponentially increased plastic production. Ironically, the negative environmental impacts of plastic are in part an extension of some of the very properties that make it popular, such as its durability and wide availability. Plastic degrades with time into microscopic particles that have been found in every corner of the natural world—on land, in lakes and rivers, and in the ocean. This phenomenon has led to a new description of the present era as the Plasticene: “an era in Earth’s history, within the Anthropocene, commencing in the 1950s, marked stratigraphically in the depositional record by a new and increasing layer of plastic” (Haram et al., 2020). A significant fraction of plastic in the ocean has sources located on land. Depending on chemical composition, some plastic entering the ocean sinks instantly, but the majority is buoyant and remains floating at the ocean’s surface for various durations. The fate of marine debris depends on ocean currents, winds, and waves, which together move floating objects and can transport them over long distances. Some debris released into the ocean transits between distant locations and pollutes remote

Deep-ocean observing is essential for informing policy making in the arenas of climate, biodiversity, fisheries, energy and minerals extraction, pollution, hazards, and genetic resources. The Deep Ocean Observing Strategy (DOOS), a UN Ocean Decade endorsed programme, is meeting with representatives from relevant international bodies and agreements to strengthen their interface with the deep-ocean science community, ensure that deep observing is responsive to societal needs, identify points of entry for science in policy making, and to develop relevant products for broad use. DOOS collaboration with the Environmental Systems Research Institute (Esri) facilitates this co-design. A DOOS policy liaison team is being formed to link the contacts, voices, and messaging of multiple deep-ocean networks and organizations in reaching international policy makers. The UN Ocean Decade will help to gain the ear of target communities, scale communication channels appropriately, minimize duplicative efforts, maximize limited resources, and organize inclusive and equitable public and private partners in deep-ocean science and policy.

Abstract A three‐state global estimator of marine surface layer atmospheric stratification is demonstrated using more than 600,000 Sentinel‐1 synthetic aperture radar wave mode images at incidence angle ≈36.8°. Stratification is quantified using a bulk Richardson number, Ri, derived from collocated ERA5 surface analyses. The three stratification states are defined as unstable: Ri < −0.012, near‐neutral: −0.012 < Ri < +0.001, and stable: Ri > +0.001. These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. Data from 2016 to 2019 are used to examine spatial and temporal variation in these state estimates in terms of expected wind and thermal forcing. This new satellite‐based approach for detecting air‐sea stratification has implications for weather modeling and air‐sea flux products.

The Deep Ocean Observing Strategy (DUOS) is an international, community-driven initiative that facilitates collaboration across disciplines and fields, elevates a diverse cohort of early career researchers into future leaders, and connects scientific advancements to societal needs. DOOS represents a global network of deep-ocean observing, mapping, and modeling experts, focusing community efforts in the support of strong science, policy, and planning for sustainable oceans. Its initiatives work to propose deep-sea Essential Ocean Variables; assess technology development; develop shared best practices, standards, and cross-calibration procedures; and transfer knowledge to policy makers and deep-ocean stakeholders. Several of these efforts align with the vision of the UN Ocean Decade to generate the science we need to create the deep ocean we want. DOOS works toward (1) a healthy and resilient deep ocean by informing science-based conservation actions, including optimizing data delivery, creating habitat and ecological maps of critical areas, and developing regional demonstration projects; (2) a predicted deep ocean by strengthening collaborations within the modeling community, determining needs for interdisciplinary modeling and observing system assessment in the deep ocean; (3) an accessible deep ocean by enhancing open access to innovative low-cost sensors and open-source plans, making deep-ocean data Findable, Accessible, Interoperable, and Reusable, and focusing on capacity development in developing countries; and finally (4) an inspiring and engaging deep ocean by translating science to stakeholders/end users and informing policy and management decisions, including in international waters.

This study outfitted wild-caught white seabass (Atractoscion nobilis) with electronic data storage tags (DSTs) to evaluate subsequent movements of adult fish captured along Baja California, Mexico (BC). Cefas G5 DSTs were surgically implanted into 89 wild-caught white seabass ranging in size from 66 to 152 cm TL between La Salina (32.11°N/116.90°W) and San Quintin (30.25°N/115.83°W), BC. Twenty-four tagged individuals (27

Wave mode of spaceborne synthetic aperture radar (SAR) is designed for the global ocean wave observations. Despite the fact that the significant wave height inferred from SAR measurements has been validated against model output and in-situ data, SAR's primary and unique capability for operational 2-dimensional spectral description of sea state remains to be fully evaluated. In this study, we extended the previous assessment approaches by introducing a new SAR image spectral parameter, the Mean rAnge Cross-Spectrum (MACS) that focuses on the isolated wave scales along the radar line-of-sight direction. MACS is an efficient variable in that it characterizes the local wave spectra properties without need of the non-linear wave inversion procedure. The assessment is based on the multiple-year data acquired by Envisat/ASAR wave mode, along with the collocated WaveWatch III (WW3) hindcast and the in-situ buoy-observed wave spectra, for which the SAR forward transformation is systematically performed to obtain the simulated image spectra. Inter-comparison between SAR-measured and WW3-simulated MACS demonstrates that the consistency is wavelength (or wavenumber) dependent. Three typical wavelengths, around 62 m for windsea, 168 m for intermediate waves and 342 m for swell, are selected to present the MACS comparison in detail. Comparable magnitude of SAR-measured and the simulated MACS is observed for the intermediate waves and swell, while larger simulation values are predicted for the windsea waves. Spatial distribution of MACS agrees well between these two data sets for all wavelengths with high correlation coefficients (>0.8) in most of the global ocean. One exception is in the extratropics where the quantitative difference is particularly notable. In the contrary, when comparing SAR-measured and buoys-simulated MACS, the agreement increases towards the shorter (<100 m) wavelengths. We also found that the large-scale atmospheric/oceanic features persistent on SAR images lead to the overestimate of SAR MACS at long wavelengths, which is expected to bias the wave inversion. The wave spectra retrieval performance shall advance as long as such impact is properly resolved.

Wood, pumice, drifting kelp, and other natural marine debris have long played important roles in marine ecosystems.Today, oceanic "litter" generated by human activities, notably plastics, constitutes the majority of marine debris and is mostly harmful to those ecosystems.In the twentieth century, plastic became a symbol of technological development and globalization of the world's economy.Cheap, durable, and long-lasting, with a broad variety of properties that are attractive for an array of human uses, plastic penetrated all parts of business and everyday life.In recent decades, growing demand exponentially increased plastic production.Ironically, the negative environmental impacts of plastic are in part an extension of some of the very properties that make it popular, such as its durabil-

The annual cycle and associated harmonics contain the majority global wind and wave field variance. Yet, these annual variations are not studied in detail. Here we develop a non-parametric method to classify the seasons using observations from a multi-platform calibrated altimeter dataset from 1985 to 2016. Our approach makes no assumptions of the seasonal features and handles a wide range of behavior. After classification, we document seasonal averages, standard deviations, slopes, phases, and lengths that reveal a unique view of the wind speed and wave height global seasonal patterns. We find some regions of the ocean have distinct seasonal shapes different from sinusoids. Lastly, we identify several regions of the ocean that have strong seasonal inter-annual variability while other regions have small year-to-year variability which might be important areas to monitor for long-term changes related to variability in solar radiation or climate change.

We investigate a case of ocean waves through a pack ice cover captured by Sentinel-1A synthetic aperture radar (SAR) on 12 October 2015 in the Beaufort Sea. The study domain is 400 km by 300 km, adjacent to a marginal ice zone (MIZ). The wave spectra in this domain were reported in a previous study (Stopa et al., 2018b). In that study, the authors divided the domain into two regions delineated by the first appearance of leads (FAL) and reported a clear change of wave attenuation of the total energy between the two regions. In the present study, we use the same dataset to study the spectral attenuation in the domain. According to the quality of SAR-retrieved wave spectrum, we focus on a range of wave numbers corresponding to 9–15 s waves from the open-water dispersion relation. We first determine the apparent attenuation rates of each wave number by pairing the wave spectra from different locations. These attenuation rates slightly increase with increasing wave number before the FAL and become lower and more uniform against wave number in thicker ice after the FAL. The spectral attenuation due to the ice effect is then extracted from the measured apparent attenuation and used to calibrate two viscoelastic wave-in-ice models. For the Wang and Shen (2010b) model, the calibrated equivalent shear modulus and viscosity of the pack ice are roughly 1 order of magnitude greater than that in grease and pancake ice reported in Cheng et al. (2017). These parameters obtained for the extended Fox and Squire model are much greater, as found in Mosig et al. (2015) using data from the Antarctic MIZ. This study shows a promising way of using remote-sensing data with large spatial coverage to conduct model calibration for various types of ice cover.Highlights. Three key points: The spatial distribution of wave number and spectral attenuation in pack ice are analyzed from SAR-retrieved surface wave spectra. The spectral attenuation rate of 9–15 s waves varies around 10−5 m2 s−1, with lower values in thicker semicontinuous ice fields with leads. The calibrated viscoelastic parameters are greater than those found in pancake ice.

Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data, last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).