Surface salinity variability on O(1–10) km lateral scales (the submesoscale) generates density variability and thus has implications for submesoscale dynamics. Satellite salinity measurements represent a spatial average over horizontal scales of approximately 40–100 km but are compared to point measurements for validation, so submesoscale salinity variability also complicates validation of satellite salinities. Here, we combine several databases of historical thermosalinograph (TSG) measurements made from ships to globally characterize surface submesoscale salinity, temperature, and density variability. In river plumes; regions affected by ice melt or upwelling; and the Gulf Stream, South Atlantic, and Agulhas Currents, submesoscale surface salinity variability is large. In these regions, horizontal salinity variability appears to explain some of the differences between surface salinities from the Aquarius and SMOS satellites and salinities measured with Argo floats. In other words, apparent satellite errors in highly variable regions in fact arise because Argo point measurements do not represent spatially averaged satellite data. Salinity dominates over temperature in generating submesoscale surface density variability throughout the tropical rainbands, in river plumes, and in polar regions. Horizontal density fronts on 10-km scales tend to be compensated (salinity and temperature have opposing effects on density) throughout most of the global oceans, with the exception of the south Indian and southwest Pacific oceans between 20° and 30°S, where fronts tend to be anticompensated.

Rain-generated lenses of fresher water at the ocean surface affect satellite remote sensing of salinity, mixed-layer dynamics, and air-sea exchange of heat, momentum, and gases. Understanding how rain and wind generate turbulence at the ocean surface is important in modeling the generation and evolution of these fresh lenses. This paper discusses the use of the active controlled flux technique (ACFT) to determine relative levels of turbulence in the top centimeter of the ocean surface in the presence of rain. ACFT measurements were made during the 2016 second Salinity Processes in the Upper-ocean Regional Study (SPURS-2) in the eastern equatorial Pacific Ocean. The data show that at wind speeds below 4 m s^-1, the turbulence dissipation rate at the ocean surface (as parameterized by the water-side surface renewal time constant) is correlated with the instantaneous rain rate. However, at higher wind speeds, the wind stress dominates turbulence production and rain is not a significant source of turbulence. There is also evidence that internal waves can be a significant source of turbulence at the ocean surface under non-raining conditions when a diurnal warm layer is present.

Ship-based X-band radar observations of rain were collected with high spatial resolution during the 2016 and 2017 Salinity Processes in the Upper-ocean Regional Study 2 (SPURS-2) field experiments in the eastern tropical Pacific Ocean. These observations were collected with a repurposed marine radar that is not typically used for weather monitoring. The radar images captured during SPURS-2 show the spatial extent and variable intensity of rain at a horizontal resolution of 180 m within 30 km of the ship. When analyzed alongside collocated measurements of oceanic and atmospheric properties collected during SPURS-2, the radar-derived rain maps enable a clearer understanding of the impact of spatially and temporally varying freshwater fluxes on ocean salinity. Ocean surface freshening, measured by ship gauges, is found to be affected by local rain accumulation, and also by prior rain accumulation in surrounding locations that was measured by radar. In one example, the X-band marine radar measured rain directly ahead of the ship’s path. The ship then sampled a near-surface freshening signature within the time period expected based on the ship speed, ship heading, and rain area measured by the radar. ­

SPURS-2 (Salinity Processes in the Upper-ocean Regional Study 2) used the schooner Lady Amber, a small sailing research vessel, to deploy, service, maintain, and recover a variety of oceanographic and meteorological instruments in the eastern Pacific Ocean. Low operational costs allowed us to frequently deploy floats and drifters to collect data necessary for resolving the regional circulation of the eastern tropical Pacific. The small charter gave us the opportunity to deploy drifters in locations chosen according to current conditions, to recover and deploy various autonomous instruments in a targeted and adaptive manner, and to collect additional near-surface and atmospheric measurements in the remote SPURS-2 region.

During the second Salinity Processes in the Upper-ocean Regional Study (SPURS-2) field experiments in 2016 and 2017 in the eastern tropical Pacific Ocean, the surface salinity profiler (SSP) measured temperature and salinity profiles in the upper 1.1 m of the ocean. The SSP captured the response of the ocean surface to 35 rain events, providing insight into the generation and evolution of rain-formed fresh layers. This paper describes the measurements made with the SSP during SPURS-2 and quantifies the fresh layers in terms of their vertical salinity gradients between 0.05 m and 1.1 m, ΔS_{1.1 - 0.05 m}. For the 35 rain events sampled with the SSP in 2016 and 2017, the maximum value of ΔS_{1.1 - 0.05 m} is well correlated with the accumulated rainfall. The maximum value of ΔS_{1.1 - 0.05 m} is shown to be linearly proportional to the maximum rain rate and inversely proportional to the wind speed. This wind speed-dependent relationship shows a high degree of scatter, reflecting that the vertical salinity gradient formed during any individual rain event depends on the complex interaction between the local ocean dynamics and the highly variable forcing from rain and wind.

The Zeng and Beljaars (2005) sea surface temperature prognostic scheme, developed to represent diurnal warming, is extended to represent rain-induced freshening and cooling. Effects of rain on salinity and temperature in the molecular skin layer (first few hundred micrometers) and the near-surface turbulent layer (first few meters) are separately parameterized by taking into account rain-induced fluxes of sensible heat and freshwater, surface stress, and mixing induced by droplets penetrating the water surface. Numerical results from this scheme are compared to observational data from two field studies of near-surface ocean stratifications caused by rain, to surface drifter observations and to previous computations with an idealized ocean mixed layer model, demonstrating that the scheme produces temperature variations consistent with in situ observations and model results. It reproduces the dependency of salinity on wind and rainfall rate and the lifetime of fresh lenses. In addition, the scheme reproduces the observed lag between temperature and salinity minimum at low wind speed and is sensitive to the peak rain rate for a given amount of rain. Finally, a first assessment of the impact of these fresh lenses on ocean surface variability is given for the near-equatorial western Pacific. In particular, the variability due to the mean rain-induced cooling is comparable to the variability due to the diurnal warming so that they both impact large-scale horizontal surface temperature gradients. The present parameterization can be used in a variety of models to study the impact of rain-induced fresh and cool lenses at different spatial and temporal scales.

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 x 100 km². In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

Recent measurements of the strength of the Atlantic overturning circulation at 26°N show a 1 year drop and partial recovery amid a gradual weakening. To examine the extent and impact of the slowdown on basin wide heat and freshwater transports for 2004–2012, a box model that assimilates hydrographic and satellite observations is used to estimate heat transport and freshwater convergence as residuals of the heat and freshwater budgets. Using an independent transport estimate, convergences are converted to transports, which show a high level of spatial coherence. The similarity between Atlantic heat transport and the Agulhas Leakage suggests that it is the source of the surface heat transport anomalies. The freshwater budget in the North Atlantic is dominated by a decrease in freshwater flux. The increasing salinity during the slowdown supports modeling studies that show that heat, not freshwater, drives trends in the overturning circulation in a warming climate.

In this paper, we use an observational dataset built from Argo in situ profiles to describe the main large-scale patterns of intraseasonal mixed layer depth (MLD) variations in the Indian Ocean. An eddy permitting (0.25°) regional ocean model that generally agrees well with those observed estimates is then used to investigate the mechanisms that drive MLD intraseasonal variations and to assess their potential impact on the related SST response. During summer, intraseasonal MLD variations in the Bay of Bengal and eastern equatorial Indian Ocean primarily respond to active/break convective phases of the summer monsoon. In the southern Arabian Sea, summer MLD variations are largely driven by seemingly-independent intraseasonal fluctuations of the Findlater jet intensity. During winter, the Madden–Julian Oscillation drives most of the intraseasonal MLD variability in the eastern equatorial Indian Ocean. Large winter MLD signals in northern Arabian Sea can, on the other hand, be related to advection of continental temperature anomalies from the northern end of the basin. In all the aforementioned regions, peak-to-peak MLD variations usually reach 10 m, but can exceed 20 m for the largest events. Buoyancy flux and wind stirring contribute to intraseasonal MLD fluctuations in roughly equal proportions, except for the Northern Arabian Sea in winter, where buoyancy fluxes dominate. A simple slab ocean analysis finally suggests that the impact of these MLD fluctuations on intraseasonal sea surface temperature variability is probably rather weak, because of the compensating effects of thermal capacity and sunlight penetration: a thin mixed-layer is more efficiently warmed at the surface by heat fluxes but loses more solar flux through its lower base.

Rain falling on the ocean produces a layer of buoyant fresher surface water, or "fresh lens." Fresh lenses can have significant impacts on satellite-in situ salinity comparisons and on exchanges between the surface and the bulk mixed layer. However, because these are small, transient features, relatively few observations of fresh lenses have been made. Here the Generalized Ocean Turbulence Model (GOTM) is used to explore the response of the upper few meters of the ocean to rain events. Comparisons with observations from several platforms demonstrate that GOTM can reproduce the main characteristics of rain-formed fresh lenses. Idealized sensitivity tests show that the near-surface vertical salinity gradient within fresh lenses has a linear dependence on rain rate and an inverse dependence on wind speed. Yearlong simulations forced with satellite rainfall and reanalysis atmospheric parameters demonstrate that the mean salinity difference between 0.01 and 5 m, equivalent to the measurement depths of satellite radiometers and Argo floats, is –0.04 psu when averaged over the 20°S–20°N tropical band. However, when averaged regionally, the mean vertical salinity difference exceeds –0.15 psu in the Indo-Pacific warm pool, in the Pacific and Atlantic intertropical convergence zone, and in the South Pacific convergence zone. In most of these regions, salinities measured by the Aquarius satellite instrument have a fresh bias relative to Argo measurements at 5 m depth. These results demonstrate that the fresh bias in Aquarius salinities in rainy, low-wind regions may be caused by the presence of rain-produced fresh lenses.

We investigate the processes responsible for the intraseasonal displacements of the eastern edge of the western Pacific warm pool (WPEE), which appear to play a role in the onset and development of El Niño events. We use 25 years of output from an ocean general circulation model experiment that is able to accurately capture the observed displacements of the WPEE, sea level anomalies, and upper ocean zonal currents at intraseasonal time scales in the western and central Pacific Ocean. Our results confirm that WPEE displacements driven by westerly wind events (WWEs) are largely controlled by zonal advection. This paper has also two novel findings: first, the zonal current anomalies responsible for the WPEE advection are driven primarily by local wind stress anomalies and not by intraseasonal wind-forced Kelvin waves as has been shown in most previous studies. Second, we find that intraseasonal WPEE fluctuations that are not related to WWEs are generally caused by intraseasonal variations in net heat flux, in contrast to interannual WPEE displacements that are largely driven by zonal advection. This study hence raises an interesting question: can surface heat flux-induced zonal WPEE motions contribute to El Niño%u—Southern Oscillation evolution, as WWEs have been shown to be able to do?

The Mediterranean and Black Seas are semi-enclosed basins characterized by high environmental variability and growing anthropogenic pressure. This has led to an increasing need for a bioregionalization of the oceanic environment at local and regional scales that can be used for managerial applications as a geographical reference. We aim to identify biogeochemical subprovinces within this domain, and develop synthetic indices of the key oceanographic dynamics of each subprovince to quantify baselines from which to assess variability and change. To do this, we compile a data set of 101 months (2002%u20132010) of a variety of both %u201Cclassical%u201D (i.e., sea surface temperature, surface chlorophyll-a, and bathymetry) and %u201Cmesoscale%u201D (i.e., eddy kinetic energy, finite-size Lyapunov exponents, and surface frontal gradients) ocean features that we use to characterize the surface ocean variability. We employ a k-means clustering algorithm to objectively define biogeochemical subprovinces based on classical features, and, for the first time, on mesoscale features, and on a combination of both classical and mesoscale features. Principal components analysis is then performed on the oceanographic variables to define integrative indices to monitor the environmental changes within each resultant subprovince at monthly resolutions. Using both the classical and mesoscale features, we find five biogeochemical subprovinces for the Mediterranean and Black Seas. Interestingly, the use of mesoscale variables contributes highly in the delineation of the open ocean. The first axis of the principal component analysis is explained primarily by classical ocean features and the second axis is explained by mesoscale features. Biogeochemical subprovinces identified by the present study can be useful within the European management framework as an objective geographical framework of the Mediterranean and Black Seas, and the synthetic ocean indicators developed here can be used to monitor variability and long-term change.

Observations from 35 tropical moorings are used to characterize the diurnal cycle in salinity at 1 m depth. The amplitude of diurnal salinity anomalies is up to 0.01 psu and more typically ~0.005 psu. Diurnal variations in precipitation and vertical entrainment appear to be the dominant drivers of diurnal salinity variability, with evaporation also contributing. Areas where these processes are strong are expected to have relatively strong salinity cycles: the eastern Atlantic and Pacific equatorial regions, the southwestern Bay of Bengal, the Amazon outflow region, and the Indo-Pacific warm pool. We hypothesize that salinity anomalies resulting from precipitation and evaporation are initially trapped very near the surface and may not be observed at the 1 m instrument depths until they are mixed downward. As a result, the pattern of diurnal salinity variations is not only dependent on the strength of the forcing terms, but also on the phasing of winds and convective overturning. A comparison of mixed-layer depth computed with hourly and with daily averaged salinity reveals that diurnal salinity variability can have a significant effect on upper ocean stratification, suggesting that representing diurnal salinity variability could potentially improve air-sea interaction in climate models. Comparisons between salinity observations from moorings and from the Aquarius satellite (level 2 version 3.0 data) reveal that the typical difference between ascending-node and descending-node Aquarius salinity is an order of magnitude greater than the observed diurnal salinity anomalies at 1 m depth.