Hydrodynamics of Mar Menor through field observations and numerical modeling: A wind-forced coastal lagoon

Abstract. We describe the operational forecasting system “Norkyst”, now in version 3, which is used for predicting the ocean circulation along the coast of mainland Norway and in the fjords. The forecasting system is based on the Regional Ocean Modeling System (ROMS), and has sub-kilometric horizontal resolution to resolve mesoscale features. Here we describe the basic configuration and report verification statistics of unconstrained model runs. The main features of the circulation and hydrography, including seasonal variations, are well represented in Norkyst v.3, making the forecast system suitable for its intended use as an open service for users in public or private sectors such as aquaculture, fishery, shipping, research, consulting, environmental management, and others who needs detailed predictions of the physical state of the Norwegian coastal ocean.

Cold-water corals (CWCs) are key ecosystem engineers in deep-sea habitats, yet their distribution in the Gulf of Naples remains poorly known. Here, we applied a Maximum Entropy (MaxEnt) model with high-resolution environmental predictors to investigate the fine-scale suitability of scleractinian CWCs within the Gulf of Naples and adjacent areas. Presence records were derived from Remotely Operated Vehicles (ROV) video analyses, while predictors included bottom current velocity from a Regional Ocean Modeling System (ROMS) simulation and geomorphological variables from multibeam bathymetry (bathymetric position index, slope, roughness, aspect, and backscatter). The model predicted ∼0.43km2 of suitable habitat (suitability index >0.6) corresponding to 0.09% of the entire study area, mainly along canyon walls and elevated seabed features of the Dohrn Canyon. Additional suitable areas were identified in the deeper canyon sectors and south of Ischia Island. Current velocity at the bottom influenced the most our model, with high suitability values obtained from 0.10 to 0.18m/s, suggesting these as favorable conditions for sediment removal and food supply. The variable response curves documented that Bathymetric position index and roughness contributed to the model, with preferences for elevated seabed features and heterogeneous seafloor topography. These findings highlight the role of bottom current velocity and topographic complexity in shaping CWCs habitats in the study region and reveal unexplored areas with high potential for coral occurrence. Model outputs provide a scientific basis for Natura 2000 site designation and support conservation and restoration strategies for vulnerable deep-sea ecosystems in the area.

Abstract Analysis of satellite‐derived sea surface salinity (SSS) from SMAP and SMOS between 2015 and 2023 reveals distinct patterns of spatial and temporal variability in the Gulf of Anadyr (GA) in the northwestern Bering Sea. In particular, both products consistently capture anomalously fresher SSS patterns in summer 2021. Similar patterns are reproduced in a 2‐km resolution coupled ice‐ocean model simulation of the Bering Sea using the Regional Ocean Modeling System. The model is analyzed to understand the mechanisms driving SSS variability in the GA. In 2021, ice persists in the GA into June, leading to the anomalously fresher surface conditions when the ice eventually melts. The gulf‐averaged sea ice volume equation term balance analysis is performed to understand the mechanisms driving changes in the sea ice volume that in turn drive SSS anomalies. On the seasonal time scale, during the freezing season, sea ice volume in the GA increases primarily due to local thermodynamic production. Transport in and out of the GA is a substantial contributor to shorter term variability. Large ice production and less ice export result in persistent ice in 2021. Melting this ice in the GA creates the SSS anomaly pattern detected from space. Model results show that area‐averaged SSS and volume‐averaged salinity anomalies vary similarly during the freezing season but decouple during the melt season. Ice freeze/melt, boundary transport, and Anadyr River discharge are of comparable importance for the volume‐averaged salinity budget in the GA.

Abstract. Cold-water corals form complex three-dimensional structures on the seafloor, providing habitat for numerous species, and act as a carbon cycling hotspot in the deep-sea. The distribution of these important ecosystems is often predicted by statistical habitat suitability models, using variables such as terrain characteristics, temperature, salinity, and surface productivity. While useful, these models do not provide a mechanistic understanding of the processes that facilitate cold-water coral occurrence, and how this may change in the future. Here, we present the results of a mechanistic process-based model in which coral biomass and respiration are predicted based on hydrodynamics, organic matter transport and coral physiology. The model domain comprises the cold-water coral mounds of south-east Rockall Bank in the north-east Atlantic Ocean. Hydrodynamic forcing is provided by a high-resolution Regional Ocean Modelling System (ROMS) model, which drives the transport of reactive suspended particulate organic matter in the region. The physiological cold-water coral model, with coral food uptake, assimilation, and respiration as key variables and with model parameters estimated from available experimental reports, is coupled to the reactive transport model of suspended particulate organic matter. Cold-water coral biomass was mainly predicted on coral mounds and ridges in the area. Model predictions agree with coral reef biomass and respiration observations in the study area and coral occurrences agree with predictions from previously published habitat suitability models. Filter feeding activity by cold-water corals proved to strongly deplete food particles in the bottom waters. Replenishment of food particles by tidal currents was therefore vital for cold-water coral growth. This mechanistic modelling approach has the advantage over statistical and machine learning-based predictions that it can be used to obtain an understanding of the effect of changing environmental conditions such as ocean temperature, surface production export, or ocean currents on cold-water coral biomass distribution and can be applied to other study areas and/or species.

Abstract. We present a 4D-Var data assimilation (DA) system covering the North-Western Mediterranean Sea implemented with the Regional Ocean Modeling System (ROMS). We study, throughout the year 2022, its ability to improve the description of the overall circulation and the capability to constrain the transport across the Corsica Channel (CC), the dynamics of which are crucial in determining the circulation throughout the region. The system assimilates Sea Surface Temperature (SST) and Sea Level Anomaly (SLA) observations from satellites, surface velocity data from High-Frequency Radars (HFRs), and in situ temperature, salinity and velocity observations, the latter from a mooring located in the CC. For all the observed state variables, DA is able to improve the forecast and the analysis compared to a free run without DA, with root mean squared error reduction up to 60 % and correlation increase up to 0.4. The general circulation after DA is characterized by a reduction of the Eastern Corsica Current (ECC) and an increase of the Western Corsica Current (WCC). An adjoint sensitivity-based method was used to evaluate the impact of observations on the CC transport state estimates. The net reduction in transport induced by DA, changed the annual average value of 0.49 Sv for the free run, to 0.31 and 0.28 Sv for the forecast and analysis, respectively. The observations that contribute most to the transport changes are the in situ velocity data and those from HFRs. The observation impacts were found to vary seasonally, and sometimes act in competition to shape the circulation pathways across the CC. The sensitivity of the transport to SST and in situ temperature and salinity observations indicates that remote measurements (e.g. those from the Gulf of Lion) can potentially play a significant role in constraining the CC transport. Transport variations are largely affected by free surface gradients contained in the increment, and promoted by modifications to the open boundary conditions. This indicates that the CC dynamic is controlled by mechanisms operating at basin scales.

Abstract High-resolution (HR) sea surface temperature (SST) is crucial for understanding ocean dynamics, climate variability, and nearshore ecosystems. While demand for fine-scale SST data in coastal regions grows, HR observations remain limited, and numerical modeling is constrained by prohibitive computational costs. Efficient and accurate downscaling approaches are therefore essential, and recent advances in artificial intelligence (AI) offer promising alternatives. However, current AI-based methods often use artificially degraded low-resolution (LR) data, overlooking the systematic mismatches where LR simulations misrepresent complex, fine-scale ocean features. This study employed a Convolutional Block Attention Module-enhanced UNet model (CBAM-UNet) trained on realistic LR-HR data pairs from the three-layer nested regional ocean model system (ROMS) simulations to enhance practical reliability. The LR multi-mode inputs include SST and sea surface currents, which help encode essential physical ocean processes. Compared with bilinear interpolation and a traditional UNet model using SST-only input, the proposed model reduced Root Mean Square Error (RMSE) by 21.93% and achieved a spatial Pearson Correlation Coefficient (R) of 0.86. In addition, interpretability analysis revealed the contribution of each input channel, aligning well with temperature transport and seasonal variability, and confirming the underlying natural physical constraints learned from real-world data. Beyond pixel-value precision, the physically interpretable behavior of the multi-mode downscaling method demonstrates its capability to reconstruct accurate and dynamically consistent HR SST fields, which is vital for operational applications.

Coupled current-wave simulation reveals sea surface heat fluxes responses to diurnal skin sea surface temperature modulation in the Sunda Strait

The pre-flood season precipitation in South China during 2019 exhibited significant anomalies, characterized by an earlier onset, prolonged duration, and increased rainfall volume. Concurrently, the northern South China Sea experienced the most intense marine heatwave in nearly 30 years, which had a pronounced impact on the local short-term climate and ecosystems. In this paper, we utilize multi-source observational data and reanalysis datasets, in conjunction with the coupled Weather Research and Forecasting + Regional Ocean Modeling System (WRF + ROMS) model, to conduct a diagnostic analysis of this process. The results indicate that (1) the persistent heavy rainfall in early March was key to the early onset of the pre-flood season. The precipitation displayed the characteristics of warm-sector torrential rainfall. On the synoptic scale, the circulation featured coordinated upper and lower-level jets and the transport of warm and moist airflow along the edge of the subtropical high, providing favorable conditions for rainfall. (2) Atmospheric rivers were closely related to the pre-flood season precipitation in South China. Based on the PanLu2.0 algorithm, eight atmospheric river events were identified over South China in early March. The atmospheric rivers increased the atmospheric moisture and precipitable water through moisture convergence and vertical transport while also enhancing convective instability in the lower atmosphere, thereby storing energy for heavy rainfall. (3) The extreme marine heatwave in the northern South China Sea was a major driving factor of the anomalous precipitation. By affecting the lower atmosphere, the marine heatwave induced an anticyclonic circulation over eastern South China, enhancing the transport of water vapor from the South China Sea to the land and thereby significantly strengthening the atmospheric rivers. From the perspective of local ocean–atmosphere interactions, this study reveals the interaction mechanisms of marine heatwave, atmospheric river, and flood season precipitation. It provides a new perspective for understanding the anomalies of pre-flood season precipitation in South China and serves as a reference for studying the local climatic effects of marine heatwaves.

Modeling the distribution of biogeochemical components in the ocean is essential for further understanding climate change impacts and assess the functioning of marine ecosystems. This requires robust and efficient physical-biological simulations of coupled ocean-ecosystem models, which are often hindered by limited data availability and computational resources. The option of running biological tracer fields offline, independently from the physical ocean simulation, is appealing due to increased computational efficiency. Here, we present an assessment and implementation of an offline biogeochemical model — the Offline Fennel model — within the Regional Ocean Modeling System (ROMS). Our methodology employs ROMS hydrodynamic outputs to run the biogeochemical model offline. This work also includes the first ground-truthing exercise of the referred offline biogeochemical model. We use a variety of skill metrics to compare the simulated surface chlorophyll to an ocean color dataset (Copernicus Marine Service Mediterranean Ocean Color) and BGC-Argo floats for the 2015–2020 period. The model is able to reproduce the temporal and spatial structures of the main chlorophyll fluctuation patterns in the study area, the Northwestern Mediterranean Sea. This area is of particular interest as it is one of the most productive regions in the entire Mediterranean Basin, with open-ocean upwellings and deep winter convection events occurring seasonally. The typical behavior of the region is likewise effectively represented in the implementation, including offshore primary production, nutrient supplies from the Rhone and Ebro rivers, and mesoscale hydrographic structures. This study provides a baseline for ROMS users in need of executing more biogeochemical simulations independently from more computationally demanding physical simulations. • Developed an offline biogeochemical model within ROMS: the Offline Fennel model. • Validated the model with satellite ocean color data and BGC-ARGO floats (2015–2020). • The model captured key chlorophyll and nutrient patterns in the NW Mediterranean Sea. • Findings support efficient offline biogeochemical modeling for ROMS users.

Abstract Satellite‐derived sea ice products and ice model simulations are analyzed in the Bering Sea during the winters of 2018–2019, featuring historically low sea ice coverage on the Eastern Bering Sea shelf, and 2019–2020, when ice conditions were close to average. Hindcast simulations are conducted using the Community sea Ice CodE (CICE) as a standalone regional ice model forced with ERA5 atmospheric fields and oceanic fields derived from realizations of the Regional Ocean Modeling System (ROMS) internally coupled with its own ice model component. CICE is able to predict the observed seasonal variability in ice concentration and ice thickness provided that ocean mixed layer temperature and salinity are strongly nudged toward an accurate ocean state. Ice mass balance analysis shows that sustained periods of southerly wind during the 2018–19 season led to increased melting and a larger ice export into the Arctic when compared to 2019–20. More ice was produced in 2018–19, the season with lower sea ice areal extent, offset by larger melting and transport out of the domain. The volume of ice exported to the Russian shelf and basin is comparable to the net transport through the Bering Strait. The consistency between the model and satellite products demonstrates that the representation of sea ice thermodynamic and dynamic models present in CICE are adequate for representing key physical processes driving sea ice variability in this region during both low and normal ice coverage years.

Abstract Suspended sediment fluxes on continental shelves impact geomorphology, habitats, and biogeochemistry. In the coastal Arctic, the rate at which sediment is transported to locations where it can be sequestered also impacts the fate of carbon from thawing permafrost. This study used a numerical model to analyze the role of wave events on open water suspended sediment fluxes over hourly to monthly timescales. A coupled hydrodynamic—sediment transport model, the Regional Ocean Modeling System—Community Sediment Transport Modeling System, was implemented within the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport (COAWST) Modeling System for the 2020 open water season on the Alaskan Beaufort Sea shelf. Results showed that wave‐ and current‐induced bed shear stresses were frequently capable of resuspending sediment. Waves dominated bed shear stresses in depths shallower than 10 m and currents dominated in depths deeper than 20 m. Suspended sediment flux directions oscillated with the currents, which were eastward on average. However, since large waves tended to occur during westward currents, time‐averaged suspended sediment fluxes on the inner shelf were westward. Sensitivity tests were performed where significant wave heights were (a) set to zero and (b) doubled, which showed that waves increased the fraction of time that sediment could be resuspended by up to 50% and increased westward suspended sediment fluxes on the inner shelf. Overall, the results improve our understanding of how waves impact sediment fluxes on the Beaufort Sea shelf during the open water season and suggest that terrestrially derived sediment may be transported westward along the inner shelf.

Accurate prediction of weather and climate conditions is vital for ensuring the safety of human environments. In this study, we developed a regional air-sea coupled weather forecasting model and conducted a preliminary evaluation of its performance concerning basic variables such as precipitation, typhoons, and 10-meter wind fields. The forecasting system covers the region from 15° to 40°N latitude and 108° to 146°E longitude, utilizing the Weather Research and Forecasting Model (WRF) for atmospheric components and the Regional Ocean Modeling System (ROMS) for oceanic components, integrated via the National Center for Atmospheric Research Coupler version 7 (CPL7). The system operates at a horizontal resolution of approximately 3 km. We performed daily rolling 96-hour forecast experiments, starting at 00:00 each day from January 1, 2024, to December 31, 2024. The results indicate that the annual mean rainfall root mean square error (RMSE) for the entire region is 13.7 mm/day for a 24-hour forecast and 16.3 mm/day for a 96-hour forecast. Spatially, the RMSE is generally smaller in the northwest land area of the region (inland China) compared to the ocean, with notably larger RMSE near Taiwan and the Philippines due to higher average precipitation in these areas. Southern Japan also exhibits relatively large RMSE values. The forecast skill demonstrates significant seasonal variation, with higher RMSE in summer compared to winter. For typhoon forecasts, the mean error distance is 74.1 km for 24–48 hours and 118.9 km for 48–72 hours. The RMSE of 10-meter wind over the oceans shows similar patterns to rainfall, with an annual mean RMSE of 1.5 m/s for a 24-hour forecast and 2.5 m/s for a 96-hour forecast.

Abstract Observations of ocean surface currents from the JPL Doppler Scatterometer (DopplerScatt) during the S-MODE campaigns reveal unexpectedly shallow second-order velocity structure function (SF) slopes at submesoscale separation scales ( r < 10 km), deviating from classical turbulence theory and prior modeling results. This discrepancy suggests missing physics in current submesoscale-resolving numerical ocean models or an incomplete interpretation of the DopplerScatt observations. To investigate this, we analyze high-resolution Regional Ocean Modeling System (ROMS) simulations across a range of configurations that isolate the influence of model resolution, season, high-frequency forcings, and surface gravity wave effects on currents. We find that high-frequency motions associated with near-inertial waves reduce the transverse SF amplitude, driving the ratio of longitudinal to transverse SFs close to unity at submesoscales independently of the season. Additionally, the inclusion of wave-current interactions, often omitted in standard submesoscale-resolving models, can produce energetic small-scale motions, leading to broadband shallow structure function slopes. These results reveal a broader mechanism by which shallow structure function slopes can emerge: any process that injects kinetic energy at small scales over a narrow range of wavenumbers will appear broadband in structure function space and produce shallow scalings. Wave effects are one such candidate and offer a plausible interpretation of the DopplerScatt observations under energetic wave conditions. However, under low wave conditions, other processes with similar spectral characteristics are required to account for the observed shallowness. Finally, the relatively large transverse-to-longitudinal SF ratio in DopplerScatt may reflect its lateral averaging over part of an inertial period, a sampling strategy not replicated in models and warranting further study.

As a strong air-sea interaction phenomenon, typhoons significantly impact the physical and biogeochemical processes of the upper ocean. Based on the Regional Ocean Modeling System - Carbon, Silicate, Nitrate Ecosystem model (ROMS-CoSINE) coupled model, the influence of Typhoon Bolaven (2012) on physical and ecological variables in the East China Sea and the underlying mechanisms are investigated. The results show that the typhoon has induced intense vertical mixing in the upper ocean, leading to sea surface cooling, increased salinity and nutrient concentrations, as well as a phytoplankton bloom. Conversely, warming, reduced salinity, and decreased nutrient concentrations occurred in the subsurface layer. In the Yangtze River Estuary, the typhoon's passage effectively affected the wind and current directions, thereby shaping the dipole distribution patterns of environmental elements. Diagnostic analysis indicates that TC-induced horizontal advection played a dominant role in driving the changes in both physical and ecological variables within the estuary region. This study provides novel insights into the physical-ecological coupling processes and driving mechanisms governing oceanic environmental changes during typhoon events, particularly in the waters adjacent to the Yangtze River Estuary.

In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications.

Abstract Compound extremes of temperature and acidity that extend over substantial fractions of the water column can be particularly damaging to marine organisms, as they experience not only additional stress by the potentially synergistic effects of these two stressors, but also a reduction in habitable vertical space. Here, we detect and analyze such column‐compound extremes (CCX) in the Southern Ocean between 1980 and 2019, and characterize their duration, intensity, and spatial extent. To this end, we use daily output from a hindcast simulation of the Regional Ocean Modeling System (ROMS), coupled with the Biological Elemental Cycling (BEC) model. We first detect extremes in temperature and acidity ([]) within the top 300 m using a relative threshold of 95% and then identify CCX where conditions are extreme for both stressors for at least 50 m of the water column. When analyzed on a fixed baseline, positive trends in ocean warming and acidification caused CCX to last longer, intensify, and expand throughout the Southern Ocean. In the Antarctic zone, CCX expanded between 1980 and 2019 more than ten times in volume, lasted up to 120 days longer, and doubled in anomaly. Some of the largest and longest events occurred in Antarctic Marine Protected Areas (MPAs), covering more than 200,000 km 2 and persisting for over 500 days. CCX in the Subantarctic and Northern zones quadrupled in volume and increased by more than 30% in anomaly. Across the Southern Ocean, the increasing occurrence of CCX exacerbates the risks to marine ecosystems from warming and acidification.

In recent years, the Northwest Pacific has seen a decline in Pacific saury (Cololabis saira) catch and an eastward shift of fishing grounds, both of which have posed increasing challenges for effective resource management. To identify environmental drivers underlying the formation of Pacific saury fishing grounds, we developed machine learning-based prediction models using spatial environmental variables. Our models combined fishing site and pseudo-absence data with high-resolution oceanographic data from the Japan Fisheries Research and Education Agency Regional Ocean Modeling System (FRA-ROMS). We employed three machine learning methods to evaluate three types of explanatory variable representations: averaged, vectorized, and spatially structured. The results demonstrated that preserving spatial structure using a two-dimensional grid layout improved model performance. Our prediction results reflected the recent eastward shifting fishing grounds, suggesting a strong influence of environmental factors, particularly water temperature derived from the ocean circulation model. The convolutional neural network model, which best replicated the eastward shift of fishing sites, achieved a recall of 45.0% and a precision of 95.4%, although its performance declined under higher environmental novelty, which was associated with low-catch years (2020-2022). By evaluating how different spatial representations of environmental variables affect model performance, this study demonstrates that incorporating spatial structure improves predictive ability and enables models to capture recent eastward shifts in fishing activity under changing ocean conditions.

Abstract. El Niño–Southern Oscillation (ENSO) constitutes the most prominent interannual climate variation mode in the climate system that originates from ocean–atmosphere interactions in the tropical Pacific. Accurately modeling ENSO variation has consistently posed a great challenge, exhibiting strongly model-dependent representations and simulations of ENSO. This study presents a novel hybrid coupled model (HCM), denoted HCMROMS, built upon the Regional Ocean Modeling System (ROMS) that has been widely used for regional modeling studies. For basin-wide applications to the tropical Pacific, here, the ROMS is coupled with a statistical atmospheric model. The statistical atmospheric model is based on singular value decomposition (SVD), capturing interannual relationships of atmospheric perturbations such as wind stress and freshwater flux anomalies with sea surface temperature (SST) anomalies. The model is constructed in a flexible way so that various components representing atmospheric forcing and oceanic biogeochemistry can be easily included as a module in the HCMROMS. Results demonstrate that the HCMROMS can simulate a stable quasi-3-year ENSO cycle when the interannual wind stress coupling coefficient, ατ, is set to 1.5. The HCMROMS reproduces the three-dimensional (3D) evolution of ENSO-related anomalies, revealing that the most pronounced temperature anomalies occur beneath the surface at 150 m. The interannual temperature anomaly budget highlights the dominance of the advection process in simulated ENSO. Vertical mixing contributes negatively to ENSO anomalies, damping temperature anomalies from the surface due to the turbulent heat flux feedback. This newly developed HCMROMS is poised to serve as an efficient modeling tool for ENSO research in the future.

Quantile mapping enhances the sea surface temperature prediction accuracy in the northern coastal region of Penang using ROMS