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The Kuroshio takes a large meander (LM) path since summer of 2017 for the first time since the 2004–2005 event and is the sixth LM event since 1965. It has been commonly recognized that a cool water pool is distributed broadly in the inshore region between the Kuroshio and southern coast of the Tokai district, Japan, during the LM periods. By using the recently-developed 1-km high-resolution sea surface temperature data, here we show marked coastal warming off the Tokai district during the LM periods, despite the Kuroshio not passing through the coastal area. The archived temperature-salinity profiles reveal that large positive anomalies off the Tokai district exist not only at the sea surface but also below 300 m and the water properties of which are those of the offshore Kuroshio water. The warm, salty waters are transported inshore by the westward Kuroshio which bifurcates at around 138° E, 34° N, during the LM path periods. We detect an increased upward heat release via turbulent heat fluxes along the coastal warming region from the new-generation atmosphere reanalysis data on a 25 km grid. These are common features to the past LMs and, furthermore, the region around the Kanto-Tokai districts becomes warmer than usual in warm seasons during the LM events. Our result reveals that the LM event can exert an influence upon the Japanese climate via the coastal air-sea interaction.

The Argo programme was developed to provide near real-time observations of the ocean and contribute significantly to understanding the changes occurring in ocean temperature and salinity in the upper 2000 m. Riser et al. showed that, for profiles sampling both temperature and salinity to 1000 m or deeper, the Argo programme produced, in just under 16 years, three times as many profiles as all other shipboard observations in the past 100 years. Furthermore, Argo observations have occurred globally, albeit with higher resolution in regions where deployment opportunities were more prevalent.

In the past two decades, the Argo Program has collected, processed and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002°C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.

A nonlinear empirical method, called the generalized regression neural network with the fruit fly optimization algorithm (FOAGRNN), is proposed to estimate subsurface salinity profiles from sea surface parameters in the Pacific Ocean. The purpose is to evaluate the ability of the FOAGRNN methodology and satellite salinity data to reconstruct salinity profiles. Compared with linear methodology, the estimated salinity profiles from the FOAGRNN method are in better agreement with the measured profiles at the halocline. Sensitivity studies of the FOAGRNN estimation model shows that, when applied to various types of sea surface parameters, latitude is the most significant variable in estimating salinity profiles in the tropical Pacific Ocean (correlation coefficient R greater than 0.9). In comparison, sea surface temperature (SST) and height (SSH) have minimal effects on the model. Based on FOAGRNN modeling, Pacific Ocean three-dimensional salinity fields are estimated for the year 2014 from remote sensing sea surface salinity (SSS) data. The performance of the satellite-based salinity field results and possible sources of error associated with the estimation methodology are briefly discussed. These results suggest a potential new approach for salinity profile estimation derived from sea surface data. In addition, the potential utilization of satellite SSS data is discussed.

This paper proposes a new method to retrieve salinity profiles from the sea surface salinity （SSS） observed by the Soil Moisture and Ocean Salinity （SMOS） satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS （SMOS_SSS） with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error （RMSE） of the linear and nonlinear model are 0.08-0.16 and 0.08-0.14 for the upper 400 m, which are 0.01-0.07 and 0.01-0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.

Using temperature and salinity profiles from the Argo data repository, a detailed volumetric temperature‐salinity diagram is compiled for the upper 2,000 m layer of the Atlantic Ocean. It is generally accepted that, unlike the Pacific and Indian Oceans where the Equatorial Water is present, there is no Equatorial Water in the Atlantic Ocean and its place is occupied by the South Atlantic Central Water (SACW). However, the detailed volumetric T‐S diagram shows that the main thermocline in the latitude range of 10°S–10°N is characterized by its own tight T‐S relationship which is relatively close to but clearly distinguishable from the tight T–S relationship of SACW in the latitude range of 10°S–40°S. We argue that the Atlantic Equatorial Water can be considered as a separate water mass which is probably formed by isopycnal mixing of SACW and the North Atlantic Central Water (NACW) in proportion approx. 3.5:1. Plain Language Summary One of the most astonishing properties of water in the ocean is the presence of a so‐called tight temperature‐salinity relationship, when the vertical profiles of temperature and salinity, sampled over a distance of hundreds and thousands of kilometers, express similar dependence of temperature versus salinity. The volume of oceanic water expressing similar temperature‐salinity relationship is considered a separate water mass. In the World Ocean, the water masses differ depending on their geographic location and origin. Oceanic water masses have been actively studied since the 1930s, and it would seem that everything is known about them. However, in 1998 the Argo program was launched, that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid‐water level. Re‐examination of water masses using previously unavailable high‐quality large volume Argo data allowed us to distinguish a formerly unnoticed water mass in the main thermocline of the Equatorial Atlantic and thereby complete the phenomenological pattern of basic water masses of the World Ocean. Key Points Main thermocline in the equatorial Atlantic in the depth range of 150–500 m is characterized by its own distinct tight T‐S relationship The low thermoclinicity water body in the equatorial Atlantic thermocline is separated from SACW and NACW by thermohaline fronts The newly introduced Atlantic Equatorial Water (AEW) can be thought of as a mixture of SACW and NACW in proportion 3.5:1

This article is a Commentary on Arsova et al., 225: 1111–1119; Che‐Othman et al., 225: 1166–1180; Fricke, 225: 1152–1165; Munns et al., 225: 1072–1090; Munns et al., 225: 1091–1096; Rubio et al., 225: 1097–1104; Shabala et al., 225: 1105–1110.

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 1.1–0.05m. For the 35 rain events sampled with the SSP in 2016 and 2017, the maximum value of ΔS 1.1–0.05m is well correlated with the accumulated rainfall. The maximum value of ΔS 1.1–0.05m 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.

A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993–2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state.

Salt gradient solar ponds (SGSPs) are a highly economical and sustainable renewable energy technology. However, their optimal design is not trivial due to often conflicting objectives presented in their intended utility, environmental operational settings, and stability requirements. Here, we present the problem of finding the optimal salt density profiles (SDPs) as a vital aspect of SGSP design as a many‐objective optimization problem. We then propose a systematic integer‐coded, order‐preserving mutation and crossover evolutionary operator‐based nondominated sorting genetic search algorithm (ONSGA) that automatically generates optimal SDPs. Specifically, the proposed method aims to maximize solar insolation transmission and thermal efficiency while minimizing changes in SDP and total salt consumption, all while meeting the overall criterion for pond stability. The proposed approach is general; in addition to the mentioned criteria, any other relevant criteria, such as climatic conditions, could also be considered. The proposed approach is compared with the traditional multiobjective covariance adaptive evolution strategy (MOCMA) and the competitive and cooperative swarm optimization constrained multiobjective optimization algorithm (CMOCSO) in terms of hypervolume, spread, and Pareto optimal solutions, using the initial SDP measured at the RMIT SGSP pond and studied under five typical initial temperature scenarios. The proposed algorithm outperforms the others in optimizing the SDPs, achieving 45 wins for hypervolume and 43 wins for spread across 65 experiments. Additionally, the optimal profiles yield improvements of up to 94% in some aspects over the currently implemented SDPs. The variety of optimal solutions also confirms that optimal profiles are not unique, and different in‐situ criteria surrounding a given solar pond lead to other desirable profiles.