ABSTRACT The seasonal and interannual variability of physicochemical (temperature, salinity, dissolved oxygen, and nutrients) and productivity characteristics of the Arabian Sea (AS) are studied using 30 years (1993–2022) of datasets. Chlorophyll-a (Chl-a) variance at specific depths identified four core variability regions: Western AS (WAS), Eastern AS (EAS), Northern AS (NAS), and Central AS (CAS). The highest Chl-a content was observed in the WAS region during the summer monsoon, followed by the NAS region, where the winter monsoon dominated productivity. Nutrient entrainment from the WAS to the open ocean enhances productivity in the CAS, which lags by over a month compared to other regions. It appears that nitrate and phosphate contribute to productivity in all regions. However, silicate has no contribution in the EAS region, but iron does. Parameters like Chl-a, net primary production, nitrate, and phosphate in all regions have decreased, and on the contrary, iron has increased, but increase of silicate in the EAS region. This study also unveils the El-Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on biogeochemistry parameters across AS regions. During ENSO/IOD years, Chl-a anomaly reveals a strong correlation between IOD and EAS suggesting more substantial influence on the productivity of this region.

ABSTRACT The French Sea Surface Salinity (SSS) Observation Service is the main provider of thermosalinograph (TSG) observations from ships of opportunity at global scale, in real time (RT) for operational oceanography and in delayed time (DT) for research. We develop here a near real time (NRT) processing chain, which aims at transposing the human-operated DT processing into automatic algorithms to deliver data with optimal quality in less than a week. Quality Control (QC) flags are based on realistic instrumental thresholds, and comparison with a SSS climatology and colocated satellite temperature data. To correct instrumental SSS biases due to fouling, water samples used in DT are replaced by colocated Argo data in NRT. The method includes detection of ship harbour calls, when the largest biases appear. The NRT processing chain is retrospectively applied on a 2014–2019 TSG database, then evaluated and optimised by comparison to the same TSG database processed in DT, in terms of QC flags and corrections. Using a SSS climatology based on Mercator Global Reanalysis, NRT QC flags reach an 88% agreement with DT QC flags. Compared to unprocessed data, the differences with DT processed data are 4–5 times smaller after NRT QC and correction.

ABSTRACT Continued greenhouse gas emissions have caused global sea levels to rise, leading to coastal erosion and flooding. However, some regions worldwide are experiencing coastline expansion, while the drivers are often overlooked. In this study, we conducted an intensive analysis of significantly expanding (>1 km) coastlines worldwide based on the Global Long-term Shoreline Evolution (GLSE) dataset between 1986 and 2016. The study employs the interpretation of remote sensing imagery to identify and categorise coastline types comprehensively, focusing on the main drivers of coastline expansion. Coastline types include natural (e.g. rocky, sandy, muddy, and biological) and artificial (e.g. traffic, aquaculture, port, and embankment) coastlines. At the global scale, 2939 km of coastline experienced significant expansion, with aquaculture coastlines dominant at 22%, followed by biological (17%) and muddy coastlines (15%). At the national level, China accounted for 27% of global expansion, driven primarily by artificial factors (43% aquaculture), while Brazil exhibited the highest natural expansion (97% biological coastlines). At the continental scale, Asia’s coastline expansion was predominantly artificial (34%), whereas Africa and South America were dominated by natural drivers (88% and 94%, respectively). Among deltas, aquaculture constituted 36% of expansion, with the Yellow River Delta showing the highest mean expansion (3369.7 m) and the Nile Delta reaching a maximum of 9920.6 m. This study not only deepens our understanding of how sea-level rise and human activities affect coastlines but also offers valuable insights into the sustainable management and utilisation of coastal resources.

ABSTRACT This study evaluates the impact of assimilating satellite Sea Surface Salinity (SSS) data into the Community Earth System Model (CESM), a fully coupled general circulation model, using the Ensemble Adjustment Kalman Filter (EAKF). Two data assimilation experiments spanning from January 2010 to December 2019 are compared: EXP1, assimilating Sea Surface Temperature (SST) and Temperature-Salinity (T-S) profiles, and EXP2, which adds satellite SSS assimilation. Results highlight EXP2′s notable enhancement in sea surface salinity accuracy and variability, particularly in coastal and tropical regions, compared to EXP1. Moreover, the assimilation of satellite SSS contributes to improved sea surface velocity in the analysis, indicating enhanced ocean dynamics. However, the impact of extra assimilation of SSS on seawater temperature, sea surface height, and sea ice is relatively minor when other data is assimilated. This study pioneers the evaluation of satellite SSS assimilation within the ensemble-based data assimilation system using CESM, promising advancements in both data assimilation and numerical forecasting based on CESM.

ABSTRACT The Arabian Sea (AS), a basin in the North Indian Ocean (NIO), possesses strong coastal upwelling in its eastern and western margins. The spatio-temporal variability of upwelling over Somalia, Oman and the southwest coast of India is analysed using observed datasets. The spectral analysis of upwelling indices highlights that the semi-annual and annual frequencies were dominant by two orders of magnitude compared with the inter-annual variability over these upwelling zones. The inter-annual variability of coastal upwelling was demarcated with variations in the causative forcing of upwelling associated with the positive modes of Indian Ocean Dipole (pIOD), El-Niño Southern Oscillation (El-Niño) and combined pIOD and El-Niño events over the southwest coast of India (SWCI) and Somalia. Along SWCI, the sea surface temperature and meridional wind stress weakened leading to warming associated with the suppression of upwelling during El-Niño, pIOD and combined events. Along Somalia, the upwelling favourable wind stress showed slight weakening due to pIOD and strong decline due to El-Niño and combined modes. Along Oman, the upwelling favourable wind stress indicated negligible changes due to pIOD, an increase due to El-Niño and a slight decline due to combined modes.

ABSTRACT In this paper, the spatio-temporal variations of Yemen, Oman and west coast of India upwelling are studied using wind and Sea Surface Temperature (SST) based upwelling indices. The relative contributions of offshore Ekman transport and Ekman pumping are compared in these upwelling regions. It has been found that offshore Ekman transport is the more dominant mechanism in coastal areas of Oman from 17°N to 21°N with upwelling indices reaching up to 3–4 m2/s during June-July while for the Yemen coast (13–16.5°N), Ekman pumping is stronger. The west coast of India has much weaker upwelling during the southwest monsoon. Offshore Ekman transport is the main mechanism for upwelling activity during the southwest monsoon for the west coast of India. A combined upwelling index was developed, adding the contributions of Ekman transport and integrated Ekman pumping. Chlorophyll data from 1998 to 2017 showed an increase in chlorophyll concentration during southwest monsoon season for all three upwelling regions. The maximum increase in southwest monsoon chlorophyll was observed for northern Yemen (15–16.5°N) with an increase of 10–20 times. This is followed by coastal region of Oman (17–19°N) and southern part of west coast of India (8–10°N).

ABSTRACT Partitioning is a process used to separate wind-sea and swell contributions in an ocean wave directional spectrum to simplify, and hence make more useful, the interpretation of the spectrum for users of wave data. HF radar systems can measure the wave spectrum over regions of the coastal ocean from the coast to over 100 km offshore with good spatial and temporal resolution depending on the operating frequency and bandwidth. Such systems can measure hundreds of directional spectra across the field of view of the radar, so there is very strong motivation to reduce the dimensionality of the data set for practical applications using partitioning. For similar reasons partitioning methods are increasingly being used for wave model and satellite-measured spectra. A partitioning method, which extends the method of Waters J, Wyatt LR, Wolf J, Hines A. [2013. Data assimilation of partitioned HF radar wave data into Wavewatch III. Ocean Model. 72:17–31.] for HF radar data, is described, assessed using buoy data and used to demonstrate the spatial variability of both swell and wind waves in three coastal regions. The results are very encouraging. HF radar systems could therefore provide very useful data for wave model and satellite partitioning validations in coastal waters where model and satellite measurements are most challenged by wave-current interactions, coastal topography and bathymetry.

ABSTRACT During the passage of two consecutive atmospheric cold fronts that migrated eastward along the northern Gulf of Mexico, currents, waves, and seafloor elevation were observed from instrumentation attached to a metal structure called ‘quadpod’ at the 7.5 m bathymetric contour off the coast of Panama City, Florida. During the passage of the first front, significant wave height (Hs) increased from 0.15 m to 1.2 m, and seafloor elevation relative to the quadpod increased by up to 5 cm at the quadpod location. During the passage of the second front, Hs peaked at 2 m, and seafloor elevation increased by up to 15 cm over 24 h. The increase in the seafloor elevation is consistent with the burial of surrogate munitions observed with sonar imagery and with sediment accretion from a Delft3D simulation. The model predicts cross-shore seafloor changes with erosion nearshore and accretion offshore, starting at approximately 250 m from the coast. The seafloor elevation increase is attributed to sediment accretion driven by wave forcing. The results of this study have important implications for morphological changes and object burial during a period of front-driven waves in the northern Gulf of Mexico, which is typically a lower energetic environment.

ABSTRACT Data from three satellites (JASON2, CRYOSAT, and SARAL/AltiKa) and two buoys were used to examine the performance of wind and wave forecasting models in the Persian Gulf. The quality of the satellite data was first examined by comparing them with buoy data. Wind speed data from all three satellites were found to be close to in situ measurements; for wave heights, SARAL/AltiKa data showed the best comparisons. Model forecasts and buoy data indicated correlation coefficients between 0.72 and 0.86 for wind speeds and 0.85–0.94 for wave heights. The forecast was more accurate at the open sea buoy than at the coastal buoy. Relative to SARAL/AltiKa data, which was preferred for assessing modelled wave heights, correlation coefficients were between 0.86 and 0.90. For modelled wind speeds, correlation coefficients with the consolidated satellite dataset were between 0.80 and 0.88. Some anomalous results were partly attributed to the exclusion of air–sea temperature differences. The likelihood of a given forecast representing a given sea state was estimated. Generally, for lead times up to 48 h, a user could have a 78–92% expectation that a SWH forecast > 1 m would lie within ±30% of a sea-state represented by the SARAL/AltiKa measurements.

ABSTRACT Sea-level reanalysis data fields from the Iberia-Biscay-Ireland Monitoring and Forecasting Center (IBIre) were analysed and validated with coastal tide-gauge and vertical positioning data from 110 experimental stations, complemented with the Technical University of Denmark global ocean tide model and mean dynamic topography DTU10, in order to establish the reference hydrographic surface (RHS) for Spanish waters. IBIre fitting to experimental tidal ranges, with mean errors about 10 cm, improves the DTU10 results along the Iberian Mediterranean shore due to an adequate reproduction of the ‘radiational’ tides. Regarding the mean dynamic topography, errors were also about 10 cm both for IBIre and DTU10 fields, and about 50% of them is plausibly related to the experimental uncertainness arising from the different averaging periods of observed series. Fitting of IBIre results to experimental data provides orthometric/ellipsoidal heights of the lowest astronomical tide with mean errors between 7 and 10 cm. IBIre sea-level data have enough quality to constitute a reliable basis for an operative RHS in Spanish waters, and plausibly in other regions within its spatial coverage. Moreover, IBIre sea-level data have other potential applications to the study of climate/sea-level trends, and improvements of the accuracy in de-tiding satellite-altimetry data in the western Mediterranean Sea.