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The Deep Ocean Observing Strategy (DOOS) 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.

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 2016-2019 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 modelling and air-sea flux products. Key Points Common atmospheric boundary layer coherent structures are classified using high-resolution global satellite sea surface radar roughness data Coherent structures partition into separate unstable, near-neutral and stable stratification regimes 3-regime coherent structure and stratification classification can aid air-sea flux estimation and atmosphere boundary layer parameterization Plain Language Summary The air-sea fluxes of momentum, heat, and water vapor are crucial climate data records because they represent lower boundary conditions on the atmospheric circulation and upper boundary conditions of ocean waves and currents. Global measurements of these fluxes using conventional fixed-station systems are impractical making satellite observations attractive. It is very difficult to measure two crucial parameters: the temperature and humidity of the air near the sea surface. The study’s motivation is that external information about the air-sea stratification should improve the

The Deep Ocean Observing Strategy (DOOS) 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.

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 program of the European Space Agency, whose objective is to realize 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) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/. Three products are available: a multi-mission along-track L2P product (https://doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (https://doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (https://doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).

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

Wave hindcasts of long time series (> 30 years) have been instrumental in understanding the wave climate. However it is still difficult to have a consistent reanalysis suitable for study of trends and inter‐annual variability. Here we explore the consistency of a wave hindcast with independent observations from moored buoys, satellite altimeters, and seismic data. We use the Climate Forecast System Reanalysis (CFSR) winds to drive a wave model since extreme events are generally well captured. Unfortunately the original CFSR winds are not homogeneous in time. We systematically modify this wind field in time and space to produce a wave field that has homogeneous differences against the Globwave/SeaStateCCI altimeter wave height database. These corrections to the winds and resulting waves are validated using independent buoy and microseism data. We particularly use seismic data in the dominant double‐frequency band, around 5 s period, that are generated by opposing waves of equal frequencies. The seismic data confirms that our correction of time‐varying biases is consistent, even in remote and under‐sampled region such as the Southern Ocean where the original CFSR biases are strongest. Our analysis is performed on monthly time series and we expect the monthly statistics to be better suited for climate studies. Remaining issues with time consistency of reanalysis products and associated wave hindcasts are further discussed.

Sea state information is needed for many applications, ranging from safety at sea and on the coast, for which real time data are essential, to planning and design needs for infrastructure that require long time series. The definition of the wave climate and its possible evolution requires high resolution data, and knowledge on possible drift in the observing system. Sea state is also an important climate variable that enters in air-sea fluxes parameterizations. Finally, sea state patterns can reveal the intensity of storms and associated climate patterns at large scales, and the intensity of currents at small scales. A synthesis of user requirements leads to requests for spatial resolution at kilometer scales, and estimations of trends of a few centimeters per decade. Such requirements cannot be met by observations alone in the foreseeable future, and numerical wave models can be combined with in situ and remote sensing data to achieve the required resolution. As today's models are far from perfect, observations are critical in providing forcing data, namely winds, currents and ice, and validation data, in particular for frequency and direction information, and extreme wave heights. In situ and satellite observations are particularly critical for the correction and calibration of significant wave heights to ensure the stability of model time series. A number of developments are underway for extending the capabilities of satellites and in situ observing systems. These include the generalization of directional measurements, an easier exchange of moored buoy data, the measurement of waves on drifting buoys, the evolution of satellite altimeter technology, and the measurement of directional wave spectra from satellite radar instruments. For each of these observing systems, the stability of the data is a very important issue. The combination of the different data sources, including numerical models, can help better fulfill the needs of users.

Space‐borne synthetic aperture radar (SAR) measurements have already been proven to be invaluable in detecting and quantifying properties of ocean swell systems. With the improved spatial resolution, Sentinel‐1 wave mode measurements can further be extended towards shorter scale waves, i.e. within the surface wave equilibrium range. In this study, a new parameter is derived from filtering SAR image cross‐spectra around range‐traveling intermediate wind waves (wavelengths 15‐20 m). This parameter captures both the radar cross section variability and its time evolution. Given the dependence of intermediate waves on local wind field, this parameter is statistically documented to confirm its sensitivity to both wind speed and wind direction. Comparable to Doppler estimate, the signed parameter can be used to reduce the wind direction ambiguity in the inversion of high‐resolution wind fields from SAR imagery. In addition, under complex environmental situations over a Polar Low event, this parameter is expected to better capture the dynamics of the surface wind. Globally, our analysis demonstrates regional and seasonal variations of this parameter, associated with those of wind/wave patterns. In particular, its directionality reveals the seasonal migration of intertropical convergence zone. This parameter is valuable to various further applications, for example, to help develop wind retrieval scheme from SAR measurements or to map space‐time variability of ocean waves at various wavelengths. Plain Language Summary Our paper entitled 'A new ocean SAR cross‐spectral parameter: definition and directional property using the global Sentinel‐1 wave mode measurements' defines a new radar parameter to highlight the properties of intermediate scale ocean waves (wavelength of 15m) based on high‐resolution spaceborne synthetic aperture radar (SAR) images. This new parameter is found to be a signed quantity, sensitive to both wind speed and direction. At global scale, we use a one‐ye

Sentinel-1 (S-1) is a two-satellite constellation for continuity of operational synthetic aperture radar (SAR) observations. Wave mode (WV) is the default mode over open ocean for S-1 to monitor global ocean waves and wind field. Therefore, proper radiometric calibration is essential to accurately infer these geophysical quantities. Based on the global data set acquired by S-1A WV, assessment of normalized radar cross section (NRCS) is carried out through comparison with CMOD5.N predictions over open ocean. The calibration accuracy quantified by NRCS residuals between SAR measurements and CMOD5.N demonstrates distinct features for two incidence angles (23.8° and 36.8°). Particularly, NRCS at 23.8° is overall consistent with CMOD5.N, while NRCS at 36.8° displays great deviation. Two recalibration methods are then implemented by examining the backscattering profile over Amazon rain forest and ocean calibration. Both methods show the necessity for recalibration and obtain comparable correction factors for WV1 and WV2, respectively. The NRCS residuals by applying both methods are significantly reduced toward zero. By comparison, ocean calibration is more efficient and practical to implement.

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 123(12), (2018): 8674-8687, doi:10.1002/2018JC013766., A large collaborative program has studied the coupled air‐ice‐ocean‐wave processes occurring in the Arctic during the autumn ice advance. The program included a field campaign in the western Arctic during the autumn of 2015, with in situ data collection and both aerial and satellite remote sensing. Many of the analyses have focused on using and improving forecast models. Summarizing and synthesizing the results from a series of separate papers, the overall view is of an Arctic shifting to a more seasonal system. The dramatic increase in open water extent and duration in the autumn means that large surface waves and significant surface heat fluxes are now common. When refreezing finally does occur, it is a highly variable process in space and time. Wind and wave events drive episodic advances and retreats of the ice edge, with associated variations in sea ice formation types (e.g., pancakes, nilas). This variability becomes imprinted on the winter ice cover, which in turn affects the melt season the following year., This program was supported by the Office of Naval Research, Code 32, under Program Managers Scott Harper and Martin Jeffries. The crew of R/V Sikuliaq provide outstanding support in collecting the field data, and the US National Ice Center, German Aerospace Center (DLR), and European Space Agency facilitated the remote sensing collections and daily analysis products. RADARSAT‐2 Data and Products are from MacDonald, Dettwiler, and Associates Ltd., courtesy of the U.S. National Ice Center. Data, supporting information, and a cruise report can be found at http://www.apl.uw.edu/arcticseastate