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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

Conventional radar altimeter makes measurement of sea surface height (SSH) in one-dimensional profiles along the ground tracks of a satellite. Such profiles are combined via various mapping techniques to construct two-dimensional SSH maps, providing a valuable data record over the past two decades for studying the global ocean circulation and sea level change. However, the spatial resolution of the SSH is limited by both coarse sampling across the satellite tracks and the instrument error in the profile measurements. A new satellite mission based on radar interferometry offers the capability of making high-resolution wide-swath measurement of SSH. This mission is called Surface Water and Ocean Topography (SWOT), which will demonstrate the application of swath altimeter to both oceanography and land hydrology. This paper presents a brief introduction to the design of SWOT, its performance specification for SSH, and the anticipated spatial resolution and coverage, demonstrating the promise of SWOT for fundamental advancement in observing SSH. A main objective of the paper is to address issues in the anticipated transition of conventional profile altimetry to swath altimetry in the future—in particular, the need for consistency of the new observing system with the old for extending the existing data record into the future. A viable approach is to carry a profile altimeter in the SWOT payload to provide calibration and validation of the new measurement against the old at large scales. This is the baseline design of SWOT. The unique advantages of the approach are discussed in the context of a new standard for observing the global SSH in the future.

During the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields that are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standard Developing Use of Altimetry for Climate Studies (DUACS) products are created with an optimal interpolation method that is effective for both low temporal and low spatial resolution. However, the upcoming next-generation SWOT mission will provide very high spatial resolution but with low temporal resolution. The present paper makes the case that this temporal–spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging (BNF), to interpolate altimetric data while respecting quasigeostrophic (QG) dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40 km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadir data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.

Observing, modelling and understanding the climate-scale variability of the deep water formation (DWF) in the North-Western Mediterranean Sea remains today very challenging. In this study, we first characterize the interannual variability of this phenomenon by a thorough reanalysis of observations in order to establish reference time series. These quantitative indicators include 31 observed years for the yearly maximum mixed layer depth over the period 1980–2013 and a detailed multi-indicator description of the period 2007–2013. Then a 1980–2013 hindcast simulation is performed with a fully-coupled regional climate system model including the high-resolution representation of the regional atmosphere, ocean, land-surface and rivers. The simulation reproduces quantitatively well the mean behaviour and the large interannual variability of the DWF phenomenon. The model shows convection deeper than 1000 m in 2/3 of the modelled winters, a mean DWF rate equal to 0.35 Sv with maximum values of 1.7 (resp. 1.6) Sv in 2013 (resp. 2005). Using the model results, the winter-integrated buoyancy loss over the Gulf of Lions is identified as the primary driving factor of the DWF interannual variability and explains, alone, around 50 % of its variance. It is itself explained by the occurrence of few stormy days during winter. At daily scale, the Atlantic ridge weather regime is identified as favourable to strong buoyancy losses and therefore DWF, whereas the positive phase of the North Atlantic oscillation is unfavourable. The driving role of the vertical stratification in autumn, a measure of the water column inhibition to mixing, has also been analyzed. Combining both driving factors allows to explain more than 70 % of the interannual variance of the phenomenon and in particular the occurrence of the five strongest convective years of the model (1981, 1999, 2005, 2009, 2013). The model simulates qualitatively well the trends in the deep waters (warming, saltening, increase in the dense water volume, increase in the bottom water density) despite an underestimation of the salinity and density trends. These deep trends come from a heat and salt accumulation during the 1980s and the 1990s in the surface and intermediate layers of the Gulf of Lions before being transferred stepwise towards the deep layers when very convective years occur in 1999 and later. The salinity increase in the near Atlantic Ocean surface layers seems to be the external forcing that finally leads to these deep trends. In the future, our results may allow to better understand the behaviour of the DWF phenomenon in Mediterranean Sea simulations in hindcast, forecast, reanalysis or future climate change scenario modes. The robustness of the obtained results must be however confirmed in multi-model studies.

Neural networks offer novel ways to parameterize unresolved ocean mixing but are challenging to interpret. Here, we derive compact equations that reproduce the behavior of neural networks trained on a second‐moment closure data set. The resulting interpretable expressions employed in a physics‐based first order closure scheme match neural‐network performance in global forced simulations. They expose a structural error in the baseline physics‐based scheme and describe how surface friction velocity, buoyancy flux, rotation, and boundary layer depth regulate diffusivity. The equations reveal a shift in the mixing peak toward the surface under stabilizing conditions and toward the mid‐boundary layer depth under convective conditions. The diffusivity amplitude (set by the velocity scale) is controlled by surface shear and buoyancy flux. Equations yield a transparent, efficient, and physically grounded vertical diffusivity applicable for ocean models.

Conventional altimetry measures a one-dimensional profile of sea surface height (SSH) along the satellite track. Two-dimensional SSH can be reconstructed using mapping techniques; however, the spatial resolution is quite coarse even when data from several altimeters are analyzed. A new satellite mission based on radar interferometry is scheduled to be launched in 2020. This mission, called Surface Water and Ocean Topography (SWOT), will measure SSH at high resolution along a wide swath, thus providing two-dimensional images of the ocean surface topography. This new capability will provide a large amount of data even though they are contaminated with instrument noise and geophysical errors. This paper presents a tool that simulates synthetic observations of SSH from the future SWOT mission using SSH from any ocean general circulation model (OGCM). SWOT-like data have been generated from a high-resolution model and analyzed to investigate the sampling and accuracy characteristics of the future SWOT data. This tool will help explore new ideas and methods for optimizing the retrieval of information from future SWOT missions.

Modeled global warming is often quantified using global near-surface air temperature (T as). Meanwhile, long-term temperature datasets combine observations of T as over land with sea surface temperature (SST) over ocean. Modeled ocean T as warms more than SST, which can bias model–observation comparisons. Skin temperature (Ts ), which is typically warmer than T as, follows SST changes so the ocean surface temperature discontinuity δTs = Ts − T as decreases with warming. Here I show that under CO2 forcing, decreased δTs is consistently simulated for nonpolar ocean within ±60°S/N, but not for other regions. I investigate the causes of oceanic δTs decrease using a LongRunMIP climate simulation, radiative kernels, and standard methods for diagnosing forcing and feedbacks from the CMIP5 ensemble. CO2 forcing establishes longwave heating of the lower atmosphere and subsequent adjustments that result in a small T as increase, and therefore a δTs decrease. During the subsequent warming in response to CO2 forcing, the model-mean surface evaporation feedback is 3.6 W m−2 °C−1 over oceans, which reduces Ts warming relative to T as and further shrinks δTs . Present-day forcing and feedback contributions are of similar magnitude, and both contribute to small differences in model–observation comparisons of global warming rates when these differences are not accounted for.

Real-time observation of ocean surface topography is essential for various oceanographic applications. Historically, these observations have mainly relied on satellite nadir altimetry data, which were limited to observation scales greater than approximately 60 km. However, the recent launch of the wide-swath Surface Water Ocean Topography (SWOT) mission in December 2022 marks a significant advancement, enabling the two-dimensional global observation of finer-scale oceanic scales (∼ 15 km). While the direct analysis of the two-dimensional content of these swaths can provide valuable insights into ocean surface dynamics, integrating such data into mapping systems presents several challenges. This study focuses on integrating the SWOT mission into multi-mission mapping systems. Specifically, it examines the contribution of the SWOT mission to both the current nadir altimetry constellation (six/seven nadirs) and a reduced nadir altimetry constellation (three nadirs). Our study indicates that within the current nadir altimetry constellation, SWOT's impact is moderate, as existing nadir altimeters effectively constrain surface dynamics. However, in a hypothetical scenario where a reduced nadir altimetry constellation is envisioned to be operational by 2030, the significance of wide-swath data in mapping becomes more pronounced. Alternatively, we found that data-driven and dynamical mapping systems can significantly participate in refining the resolution of the multi-mission gridded products. Consequently, integrating high-resolution ocean surface topography observations with advanced mapping techniques can enhance the resolution of satellite-derived products, providing promising solutions for studying and monitoring sea-level variability at finer scales. However, to fully exploit SWOT's capabilities, future research will need to focus on innovations in data gridding and assimilation to extend mapping beyond geostrophically balanced flows.

More than 30 years of observations from an international suite of satellite altimeter missions continue to provide key data enabling research discoveries and a broad spectrum of operational and user-driven applications. These missions were designed to advance technologies and to answer scientific questions about ocean circulation, ocean heat content, and the impact of climate change on these Earth systems. They are also a valuable resource for the operational needs of oceanographic and weather forecasting agencies that provide information to shipping and fishing vessels and offshore operations for route optimization and safety, as well as for other decision makers in coastal, water resources, and disaster management fields. This time series of precise measurements of ocean surface topography (OST)—the “hills and valleys” of the ocean surface—reveals changes in ocean dynamic topography, tracks sea level variations at global to regional scales, and provides key information about ocean trends reflecting climate change in our warming world. Advancing technologies in new satellite systems allows measurements at higher spatial resolution ever closer to coastlines, where the impacts of storms, waves, and sea level rise on coastal communities and infrastructure are manifest. We review some collaborative efforts of international space agencies, including NASA, CNES, NOAA, ESA, and EUMETSAT, which have contributed to a collection of use cases of satellite altimetry in operational and decision-support contexts. The extended time series of ocean surface topography measurements obtained from these satellite altimeter missions, along with advances in satellite technology that have allowed for higher resolution measurements nearer to coasts, has enabled a range of such applications. The resulting body of knowledge and data enables better assessments of storms, waves, and sea level rise impacts on coastal communities and infrastructure amongst other key contributions for societal benefit. Although not exhaustive, this review provides a broad overview with specific examples of the important role of satellite altimetry in ocean and coastal applications, thus justifying the significant resource contributions made by international space agencies in the development of these missions.

The Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1.5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyze fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. Results indicate a modest degradation in a retrospective prediction test for 2021–2022. The generated fCO2 and fluxes for January–August 2023 show a progressive reduction in the Equatorial Pacific source following the La Niña retreat. A breaking‐record decrease in the northeastern Atlantic CO2 sink has been diagnosed on account of the marine heatwave event in June 2023. Plain Language Summary There is a growing need to monitor carbon emissions and removals over the globe in near real time in order to correctly interpret changes in CO2 concentrations as they unfold. For the oceans, the best information comes from measurements of the surface ocean CO2 fugacity (fCO2) by the international marine carbon research community. So far, this data is mostly available 6 to 18 months behind real time after collection, qualification, harmonization, and processing. Here, we show that a set of biological, chemical, and physical predictors available in near real time, allows the information contained in the “old” fCO2 measurements to be transferred over time. Based on a statistical technique, we combine all these data sources to estimate global monthly maps of fCO2 and of CO2 fluxes at the air‐sea interface within one month behind real time and with good accuracy. Key Points We demonstrate the capacity of statistical models to generate global maps of fCO2 and air‐sea flux with a latency reduced to one month A decrease in the CO2 source for January to August 2023 diagnosed in the tropical Pacific coheres with the retreat of the La Niña event An unusual northeastern Atlantic sink reduction diagnosed for June 2023 is linked to record heat and exceptionally low winds