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Biologically generated turbulence has been proposed as an important contributor to nutrient transport and ocean mixing 1 – 3 . However, to produce non-negligible transport and mixing, such turbulence must produce eddies at scales comparable to the length scales of stratification in the ocean. It has previously been argued that biologically generated turbulence is limited to the scale of the individual animals involved 4 , which would make turbulence created by highly abundant centimetre-scale zooplankton such as krill irrelevant to ocean mixing. Their small size notwithstanding, zooplankton form dense aggregations tens of metres in vertical extent as they undergo diurnal vertical migration over hundreds of metres 3 , 5 , 6 . This behaviour potentially introduces additional length scales—such as the scale of the aggregation—that are of relevance to animal interactions with the surrounding water column. Here we show that the collective vertical migration of centimetre-scale swimmers—as represented by the brine shrimp Artemia salina —generates aggregation-scale eddies that mix a stable density stratification, resulting in an effective turbulent diffusivity up to three orders of magnitude larger than the molecular diffusivity of salt. These observed large-scale mixing eddies are the result of flow in the wakes of the individual organisms coalescing to form a large-scale downward jet during upward swimming, even in the presence of a strong density stratification relative to typical values observed in the ocean. The results illustrate the potential for marine zooplankton to considerably alter the physical and biogeochemical structure of the water column, with potentially widespread effects owing to their high abundance in climatically important regions of the ocean 7 . Laboratory experiments with the brine shrimp Artemia salina illustrate the potential for turbulence generated by the diurnal vertical migrations of aggregations of centimetre-scale zooplankton to affect the physical and biogeochemical structure of oceanic water columns.

Drifting buoy observations of ocean surface waves in hurricanes are combined with modeled surface wind speeds. The observations include targeted aerial deployments into Hurricane Ian (2022) and opportunistic measurements from the Sofar Ocean Spotter global network in Hurricane Fiona (2022). Analysis focuses on the slope of the waves, as quantified by the spectral mean square slope. At low‐to‐moderate wind speeds (<15 m s−1), slopes increase linearly with wind speed. At higher winds (>15 m s−1), slopes continue to increase, but at a reduced rate. At extreme winds (>30 m s−1), slopes asymptote. The mean square slopes are directly related to the wave spectral shapes, which over the resolved frequency range (0.03–0.5 Hz) are characterized by an equilibrium tail (f−4${f}^{-4}$ ) at moderate winds and a saturation tail (f−5${f}^{-5}$ ) at higher winds. The asymptotic behavior of wave slope as a function of wind speed could contribute to the reduction of surface drag at high wind speeds. Plain Language Summary Drifting buoy observations of ocean surface waves in Hurricanes Ian and Fiona (2022) are combined with modeled wind speed to explore the evolution of the sea surface from moderate to extreme winds (up to 54 m s−1). The sea surface is characterized using the physical slope of the waves, or the ratio of a wave's height to its length, which has previously only been well‐understood up to moderate wind speeds of 15–20 m s−1. At lower wind speeds, the average slopes increase proportional to the wind speed, meaning the waves continually steepen as the wind strengthens. At higher winds, the slopes continue to increase, but at a reduced rate. The slopes eventually reach a maximum value at the most extreme winds (i.e., the slopes saturate). This phenomenon is accompanied by a change in sea surface character from one that is patterned by occasional wave breaking to one that is almost entirely covered by whitecaps and foam. Using wave slope as a measure of the roughness of the ocean surface, the observed wave slope saturation could help to explain the relative reduction in wind surface forcing at extreme wind speeds. Key Points Buoy observations of waves in hurricanes show the dependence of wave slope on wind speed changes above 15 m s−1 and saturates beyond 30 m s−1 Wave spectra become dominated by the saturation range at high winds suggesting wave breaking is ubiquitous and thereby limits wave slope This effect is a plausible cause for the reduction of surface drag at high wind speeds

Severe storms produce ocean waves with periods of 18–26 s, corresponding to wavelengths 500–1,055 m. These waves radiate globally as swell, generating microseisms and affecting coastal areas. Despite their significance, long waves often elude detection by existing remote sensing systems when their height is below 0.2 m. The new Surface Water Ocean Topography (SWOT) satellite offers a breakthrough by resolving these waves in global sea level measurements. Here we show that SWOT can detect 25‐s waves with heights as low as 3 cm, and resolves period and direction better than in situ buoys. SWOT provides detailed maps of wave height, wavelength, and direction across ocean basins. These measurements unveil intricate spatial patterns, shedding light on wave generation in storms, currents that influence propagation, and refraction, diffraction and reflection in shallow regions. Notably, the magnitude of reflections exceeds previous expectations, illustrating SWOT's transformative impact. Plain Language Summary Wind storms at sea make waves that increase in size with wind speed, and with the distance over which the high winds have been able to amplify the waves. Once generated these waves propagate as swell around the world ocean: in that stage the wave period remains constant while the wave height decay away from the source. Waves with periods longer than 18 s are relatively infrequent, but they are an important source of seismic waves and coastal impacts. However, current remote sensing techniques miss long waves under 0.2 m high. The Surface Water Ocean Topography (SWOT) satellite mission changes this, spotting 25‐s waves with heights as low as 3 cm. SWOT maps wave height, wavelength, and direction worldwide, revealing the influence of winds, currents and water depth. For example, We found stronger than expected coastal reflection, which will help revise wave forecasting models and their application in seismology. Key Points Surface Water Ocean Topography (SWOT) data provide the first open ocean spatial measurements of phase‐resolved swells with wavelength 500–1,050 m Swells with heights as low as 3 cm are well detected by SWOT, allowing tracking across oceans Swell reflection off the coast can be separated from incident waves

In the equilibrium range of the wave spectrum’s high-frequency tail, energy levels are proportional to the wind friction velocity. As a consequence of this intrinsic coupling, spectral tail energy levels can be used as proxy observations of surface stress and wind speed when direct observations are unavailable. Proxy observations from drifting wave-buoy networks can therefore augment existing remote sensing capabilities by providing long dwell observations of surface winds. Here we consider the skill of proxy wind estimates obtained from observations recorded by the globally distributed Sofar Spotter network (observations from 2021 to 2022) when compared with collocated observations derived from satellites (yielding over 20 000 collocations) and reanalysis data. We consider physics-motivated parameterizations (based on frequency −4 universal tail assumption), inverse modeling (estimate wind speed from spectral energy balance), and a data-driven approach (artificial neural network) as potential methods. Evaluation of trained/calibrated models on unseen test data reveals comparable performance across methods with generally of order 1 m s −1 root-mean-square difference with satellite observations.

Daily vertical migrations of zooplankton have been shown to affect nutrient distributions and dissolved gas concentrations in lakes and oceans via active internal transport and metabolic consumption. Additionally, mixing generated by these migrations has been shown to have the capacity to alter the physical structure of a water column, with potential further implications for its biogeochemical structure. In this work, we use laboratory experiments to investigate the importance of biologically generated mixing relative to other processes in determining the biogeochemical structure of a water column inhabited by migrating zooplankton. Specifically, we consider oxygen, a highly ecologically relevant scalar, and the competition between metabolic consumption and biogenic mixing in a stably stratified water column with a hypoxic layer. Using laboratory experiments and a one-dimensional model informed by those measurements, we illustrate the potential for migrating animals to alleviate hypoxia, introducing complex feedbacks between the presence of animals and the biogeochemical state of their surroundings. Furthermore, we demonstrate the feasibility of oxygen as a potential indicator of biogenic mixing for future in situ investigations given its low diffusivity and higher signal-to-noise ratio.

A distributed sensor network of over 100 free-drifting, real-time marine weather sensors was deployed in the Pacific Ocean beginning in early 2019. The Spotter buoys used in the network represent a next-generation ocean weather sensor designed to measure surface waves, wind, currents, and sea surface temperature. Large distributed sensor networks like these provide much needed long-dwell sensing capabilities in open-ocean regions. Despite the demand for better weather forecasts and climate data in the oceans, direct in situ measurements of marine surface weather (waves, winds, currents) remain exceedingly sparse in the open oceans. Because of the large expanse of Earth’s oceans, distributed paradigms are necessary to create sufficient data density at global scale, similar to advances in sensing on land and in space. Here we discuss initial findings from this long-dwell open-ocean distributed sensor network. Through triple-collocation analysis, we determine errors in collocated satellite-derived observations and model estimates. The correlation analysis shows that the Spotter network provides wave height data with lower errors than both satellites and models. The wave spectrum was also further used to infer wind speed. Buoy drift dynamics are similar to established drogued drifters, particularly when accounting for windage. We find a windage correction factor for the Spotter buoy of approximately 1%, which is in agreement with theoretical estimates. Altogether, we present a completely new open-ocean weather dataset and characterize the data quality against other observations and models to demonstrate the broad value for ocean monitoring and forecasting that can be achieved using large-scale distributed sensor networks in the oceans.

The 10th Surface Water and Ocean Topography (SWOT) Applications Meeting, held one year after the satellite's launch, highlighted significant milestones in mission progress and showcased the innovative work of SWOT Early Adopters (EA) using mission data products. Over 100 participants from diverse sectors convened to discuss operational applications leveraging SWOT's unprecedented water surface measurements. The meeting emphasized applied science efforts to enhance hydrology and oceanographic models. This summary highlights the breadth of operational and private‐sector uses of SWOT data, emphasizing its potential to drive new innovations and deliver societal benefits, such as improved water resource management, flood prediction, and climate resilience.

Biologically generated turbulence has been proposed as an important contributor to nutrient transport and ocean mixing. However, for swimming animals to produce non-negligible transport and mixing, they must produce eddies at scales comparable to the length scales of stratification in the ocean. It has previously been argued that biologically generated turbulence is limited to the scale of the individual animals involved, which would make turbulence created by highly abundant centimeter-scale zooplankton such as krill irrelevant to ocean mixing. Their small size notwithstanding, zooplankton form dense aggregations tens of meters in vertical extent as they undergo diurnal vertical migration over hundreds of meters. In this work, we investigate the potential for this behavior to introduce additional length scales - such as the scale of the aggregation - that are of relevance to animal interactions with the surrounding water column. Utilizing laboratory experiments, we show that the collective vertical migration of centimeter-scale swimmers generates aggregation-scale eddies that mix a stable density stratification, resulting in a significantly enhanced effective turbulent diffusivity. The large-scale fluid transport similarly enhances mixing of other relevant scalars, such as dissolved oxygen, leading to cascading biogeochemical effects upon the water column. Altogether, the results illustrate the potential for marine zooplankton to considerably alter the physical and biogeochemical structure of the water column, with potentially widespread effects owing to their frequent vertical migrations and high abundance in climatically important regions of the ocean.

A three-dimensional (3D) scanning velocimetry system is developed to quantify the 3D configurations of particles and their surrounding volumetric, three-component velocity fields. The approach uses a translating laser sheet to rapidly scan through a volume of interest and sequentially illuminate slices of the flow containing both tracers seeded in the fluid and particles comprising the aggregation of interest. These image slices are captured by a single high-speed camera, encoding information about the third spatial dimension within the image time-series. Where previous implementations of scanning systems have been developed for either volumetric flow quantification or 3D object reconstruction, we evaluate the feasibility of accomplishing these tasks concurrently with a single-camera, which can streamline data collection and analysis. The capability of the system was characterized using a study of induced vertical migrations of millimeter-scale brine shrimp (Artemia salina). Identification and reconstruction of individual swimmer bodies and 3D trajectories within the migrating aggregation were achieved up to the maximum number density studied presently, \\(8 \\, \\times\\,10^5\\) animals per \\(\\textrm{m}^3\\). This number density is comparable to the densities of previous depth-averaged 2D measurements of similar migrations. Corresponding velocity measurements of the flow indicate that the technique is capable of resolving the 3D velocity field in and around the swimming aggregation. At these animal number densities, instances of coherent flow induced by the migrations were observed. The accuracy of these flow measurements was confirmed in separate studies of a free jet at \\(Re_D = 50\\).

Variability in the El Nino-Southern Oscillation has global impacts on seasonal temperatures and rainfall. Current detection methods for extreme phases, which occur with irregular periodicity, rely upon sea surface temperature anomalies within a strictly defined geographic region of the Pacific Ocean. However, under changing climate conditions and ocean warming, these historically motivated indicators may not be reliable into the future. In this work, we demonstrate the power of data clustering as a robust, automatic way to detect anomalies in climate patterns. Ocean temperature profiles from Argo floats are partitioned into similar groups utilizing unsupervised machine learning methods. The automatically identified groups of measurements represent spatially coherent, large-scale water masses in the Pacific, despite no inclusion of geospatial information in the clustering task. Further, temporal dynamics of the clusters are strongly indicative of El Nino events, the Pacific warming phase of the El Nino-Southern Oscillation. The unsupervised clustering task successfully identifies changes in the vertical structure of the temperature profiles through reassignment to a different group, concisely capturing physical changes to the water column during an El Nino event, such as tilting of the thermocline. Clustering proves to be an effective tool for analysis of the irregularly sampled (in space and time) data from ocean floats and may serve as a novel approach for detecting future anomalies given the freedom from thresholding decisions. Unsupervised machine learning approaches could be particularly valuable due to their ability to identify patterns in datasets without user-imposed expectations, facilitating further discovery of anomaly indicators.