Satellite remote sensing altimetry data has demonstrated that wintertime low sea level anomaly off northwestern Luzon (NWL-SLA) is a common feature. The genesis of the wintertime low NWL-SLA and its response to the most recent two super El Niño-Southern Oscillation (ENSO) in 1997-1998 and 2015-2016 are explored by conducting a series of numerical experiments based on a well-validated three-dimensional South China Sea (SCS) model. Our analysis emphasizes that the generation of low NWL-SLA and its super-ENSO-related variability are triggered by different dynamical processes. The wintertime local positive wind stress curl off northwestern Luzon serves as the primary driver of low NWL-SLA formation. However, remote signals originating in the tropical western Pacific Ocean represent the dominant source to regulate the super-ENSO-related changes in NWL-SLA, rather than the variabilities of local wind stress curl and ocean currents in the vicinity of Luzon Strait. These remote signals originate from the baroclinic component of sea level changes and propagate westward at speeds of the first-mode baroclinic Rossby wave; subsequently, they enter the SCS clockwise through the southernmost gap of Sibutu Passage and Mindoro Strait at speeds of the baroclinic coastal Kelvin wave. Furthermore, faster wave propagation speeds are observed during La Niña years than El Niño years because of the greater reduced gravity associated with stronger stratification during La Niña years.
Following an international workshop on the topic of research needs in oil spill modeling, arranged by CRRC in Ann Arbor, MI, in September 2024, we present a review of what we find to be the most important and timely directions for oil spill trajectory and fate modeling research.We discuss potential advances in the areas of transport (e.g., advection, diffusion, entrainment), fate (e.g., emulsification, shoreline interaction, countermeasures), uncertainty and calibration (e.g., ensemble simulations, dynamical systems tools, comparison to drifter and historical spills), as well as standardization efforts for oil spill model input and output. We argue that research in these topics will improve the quality and usefulness of oil spill models, as well as give us a better understanding of uncertainty and other limitations.Research projects related to several topics are ongoing and we believe the others should be prioritized. Many of the topics can only be addressed through cross-disciplinary work with researchers outside the oil spill modeling community, ideally through international, collaborative efforts.
The high-resolution, wide-swath sea surface height (SSH) measurements from the Surface Water and Ocean Topography (SWOT) satellite present a significant challenge for data assimilation. How to effectively ingest its multiscale signals, particularly at fine scales, remains an open question. To address this, a scale-separated ensemble optimal interpolation (SS-EnOI) scheme is developed. The method applies a spatial filter (150 km cutoff) to decompose the observation vector, model background, and ensemble samples into large-scale (>150 km) and fine-scale (<150 km) components for stepwise updating. An assimilation experiment in the South China Sea using only SWOT data demonstrates that SS-EnOI effectively alleviates scale aliasing and enhances the representation of fine-scale structures. Given the consistency between SWOT and traditional nadir altimeter gridded products from the Copernicus Marine Environment Monitoring Service (CMEMS) at scales larger than 150 km, a subsequent assimilation experiment combines CMEMS data with the sub-150 km component of SWOT observations within the SS-EnOI framework. Results show a clear complementary impact: while CMEMS assimilation improves large-scale and mesoscale fields, incorporating SWOT’s sub-150 km signals reduces the analysis error of sub-150 km sea level anomaly (SLA) by approximately 39.6% and lowers the upper ocean temperature error by 3.5%. However, SSH forecast experiments indicate that the positive impact of assimilating these SWOT signals on restraining forecast error growth remains limited, due to SWOT’s long orbital revisit period. This study confirms the importance of SWOT’s fine-scale observations in refining ocean state analysis and provides a practical framework for their effective use in operational systems.
Tracking the spread of radionuclides following the accident at the Fukushima-Daiichi Nuclear Power Plant (FDNPP) in 2011 and since the start of controlled discharges of purified water in 2023 is important for assessing radiation risks and for understanding water mass transport in the confluence zone of the Kuroshio Extension (KE) and Oyashio currents. It is widely accepted that the powerful KE acts as a nearly impermeable barrier to cross-jet transport (CJT) of tracers. However, FDNPP-derived radiocesium was observed south of the KE front, raising the question of how such transport can occur. A 13-year particle-tracking simulation showed that virtual tracers released near the FDNPP site could cross the KE by moving southward along transport pathways, either due to shifts in the position of KE meanders or via Kuroshio rings. In both scenarios, tracers may end up on the southern side of the KE. CJT occurs when groups of tracers coherently cross the jet over a short period, taking advantage of transient transport corridors. In 2024, virtual FDNPP tracers were identified inside two cyclonic rings south of the KE. Observations from a cruise in June 2024 confirmed that these rings contained core water of subarctic origin. This finding was further supported by altimetry-based and reanalysis data. Both rings were features with diameters of 200–250 km and geostrophic currents reaching up to 160 cm/s. The ring generated by the first meander facilitated rapid CJT within a few days, while the ring formed at the second meander resulted in a longer transport pathway. The study effectively combines long-term numerical particle tracking and the real-time capture of transient mesoscale features with targeted shipboard observations.
İzmir Bay, a eutrophic semi-enclosed coastal basin in the eastern Aegean Sea, has experienced recurrent phytoplankton blooms associated with increasing nutrient enrichment. Among bloom-forming species, Entomoneis sp. is of particular ecological interest because of its anomalous cold-adapted physiology, exhibiting maximum growth at approximately 13°C rather than the monotonic temperature dependence commonly assumed in standard Eppley-type formulations. Despite its ecological importance, quantitative modelling studies incorporating both species-specific growth behaviour and physiological delay effects for this species remain limited.In this study, a nutrient–phytoplankton delay differential equation (NP-DDE) model is developed to investigate the bloom dynamics of Entomoneis sp. in İzmir Bay. The growth function is calibrated using a 144-point Box–Behnken experimental dataset and is driven by realistic seasonal irradiance and temperature forcing representative of İzmir Bay. The delayed system is solved using a Lucas polynomial spectral collocation method with an algebraic delay-shift formulation, providing an efficient computational framework for delay-dependent bloom analysis.The observed winter dominance of Entomoneis sp. is successfully reproduced by the calibrated growth function, and its strong sensitivity to low-temperature conditions is captured. Maturation delay is further shown to play an important role in bloom regime transitions: a single seasonal bloom is produced for short delay values, whereas a bimodal bloom structure with prolonged duration is produced for larger delay values. A transition threshold between τ₁ ≈ 1.0 and 1.5 d is identified, suggesting a potential link between nutrient-driven physiological delay and bloom stability.The proposed modelling framework provides a quantitative basis for analysing delay-induced bloom transitions in eutrophic coastal systems and offers a practical tool for understanding phytoplankton dynamics in İzmir Bay.
Hurricane-driven extreme sea levels and coastal flooding are major hazards for tropical islands, and understanding the meteorological and oceanic underlying processes is crucial as climate change might intensify these events. On 19 September 2017, Hurricane Maria passed 20 km south of Guadeloupe as a Category 4 on the Saffir–Simpson scale, generating offshore waves of up to 8 m and a maximum recorded still water level of 0.7 m above mean sea level, causing substantial flooding along the seafront. Before Maria, three pressure sensors were deployed in a reef–lagoon system southeast of Guadeloupe: two along a cross-shore transect (forereef slope and lagoon) and one on the backshore of a neighbouring bay. To examine the drivers of extreme sea levels and coastal flooding, data analysis was complemented by numerical modelling. At the regional scale, the phase-averaged SCHISM–WWM modelling system was implemented at the scale of the whole Guadeloupe Archipelago with a resolution reaching 10 m at the study site and reproduced short waves and mean water levels. Numerical experiments revealed that wave setup and atmospheric surge contributed almost equally, but flooding was not predicted, suggesting that additional processes may be missing. To evaluate this hypothesis, a local phase-resolving SWASH model was applied, revealing substantial infragravity (IG) waves (Hm0 = 0.3 m to 0.6 m) at the shoreline. For comparable mean water levels in the lagoon, SWASH predicted greater inundation, matching well available observations, highlighting the critical role of IG dynamics in coastal flooding assessments.
ENSO (El Niño–Southern Oscillation) is a major mode of interannual climate variability in the global climate system, and improving its prediction is essential for understanding and responding to climate anomalies. This study proposes an iterative air–sea flux adjustment scheme and uses multiyear hindcast experiments with the CAS-ESM2.0 coupled model to systematically assess how model climatological state adjustment influences the seasonal prediction skill of ENSO. The results show that flux adjustment markedly improves the simulation of tropical Pacific sea surface temperature climatology and subsurface thermocline structure, thereby providing a more realistic background state for ENSO prediction. After adjustment, the prediction skills of the Niño3.4, Niño3, and Niño4 indices are all significantly improved, with larger gains at forecast ranges beyond four months, and the overall skill approaches the international multimodel ensemble mean. In addition, the adjusted forecasts produce more realistic El Niño peak intensity and seasonal phase locking, and the spring predictability barrier (SPB) is weakened. Further analysis indicates that the improvement in ENSO prediction skill mainly arises from three processes. First, flux adjustment reduces the initial shock during the early forecast stage. Second, it weakens the excessive positive feedback associated with thermocline bias. Third, it stabilizes thermodynamic damping dominated by latent heat and shortwave feedbacks. Together, these changes improve the magnitude and stability of the Bjerknes stability index and thus enhance the stability and skill of ENSO prediction.
The Challenger Deep (CD) is the deepest known point on Earth’s seabed, located at the southern end of the Mariana Trench in the western Pacific Ocean, in which turbulent mixing can ventilate the enclosed trench and help to exchange the abyssal water over the trench. However, turbulent mixing distribution and its dynamics remain to be investigated in this area. Using a three-dimensional and nonhydrostatic numerical model with high resolution (∼1 km), we investigate M2 internal tide and estimate the tide-induced turbulent mixing in the CD based on the method of internal tide energy analysis. Model results show that baroclinic tidal current can exceed 0.02 m/s in the trench. Influenced by the topography, the energy transfer from barotropic tide to baroclinic tide is about 0.11 GW, of which 0.07 GW of the internal tide energy dissipates locally, accounting for about 64% of the total in the CD. As a result, the dissipation of internal tide energy enhances bottom diapycnal mixing. The most intense turbulence occurs over the slope with a dissipation rate of O(10−8∼10−7)Wkg−1and diapycnal diffusivity of O(10−3∼10−2)m2s−1 within the ∼1400 m water column above the bottom. Such intense turbulent mixing can significantly modulate the abyssal circulation in trenches.
Directional wave spectra (DWS) provide a comprehensive description of ocean wave energy across frequencies and directions, serving as the foundation of numerical wave modeling. This work promotes the current artificial intelligence forecasting of ocean waves from integrated parameters such as significant wave height (SWH) to DWS, which captures the comprehensive physical state of waves, enabling the derivation of numerous important dynamical variables such as Stokes drift. However, the massive data volume of DWS presents a significant barrier to direct spatiotemporal prediction using deep learning. To overcome this barrier, we present WaveSpecNet, an energy-conserving deep learning framework for global spatiotemporal DWS prediction. WaveSpecNet consists of a compression-reconstruction module and a prediction module. It first compresses the two-dimensional DWS into a low-dimensional latent space, known as the latent features, then predicts these latent features instead of the original DWS. The predicted DWS is then restored from the predicted latent features. WaveSpecNet achieves comparable accuracy to state-of-the-art numerical wave models when validated against buoy observations, while takeing only 15 s to generate a 5-day global DWS forecast at a 1° × 1° resolution with 6-h intervals. Furthermore, WaveSpecNet also demonstrated the capability to maintain stable, non-accumulating errors during 90-day iterative forecasts driven by wind, indicating its promising potential for coupling with the Earth System Models to reduce computational costs.
Material transport and exchange are crucial to coastal ocean dynamics. Traditional analyses often struggle to capture the fundamental organizing structures of flow, particularly in regions with complex coastal dynamics. This limits the accurate prediction of pollutant trajectories. In this study, we developed a three-dimensional (3-D) ocean model based on the Regional Ocean Modeling System (ROMS) to explore the transport of floating marine debris (FMD) in Xiamen Bay (XMB). Specifically, we applied the Lagrangian coherent structures (LCSs) method to gain insights into the flow structures in this coastal embayment. The model effectively reproduced the thermohaline structure and hydrodynamic characteristics in XMB. The attractive LCSs derived from Finite-Time Lyapunov Exponents (FTLE) revealed multiple flow pathways and potential accumulation regions for FMD throughout the tidal cycle. The analysis of tracer gradient kinematics confirmed that material transport in XMB is primarily controlled by strain-dominated hyperbolic regions, where fluid parcels are intensely stretched and organized along stable manifolds delineated by attracting LCSs. The close correspondence between the Okubo-Weiss (OW) parameter field and the LCSs patterns emphasized that regions of elevated strain rate serve as the dynamical origin of hyperbolic LCSs. Furthermore, the evolution of the principal compressive strain eigenvalues and eigenvectors indicated that tracer filaments tend to intensify and align perpendicular to the local compressive axis defined by the velocity gradient tensor. Moreover, spring tides showed longer LCSs distribution, indicating a greater potential for FMD transport. In contrast, the distribution of FMD was less sensitive to seasonal monsoon variations. This was attributed to the fact that both the strain rate and the principal compressive strain eigenvalues consistently exhibit higher mean values during spring-tide cases than during neap tides, regardless of season. These findings highlight the potential of LCSs as a powerful tool for understanding complex coastal dynamics, while revealing the fundamental role of strain-dominated processes in shaping transport structures in XMB, thereby supporting more effective management of vulnerable regions and marine conservation areas.
This study investigates how wake-induced wind speed (WS) deficits associated with an offshore Proposed Wind Energy Area (PWEA) may modify local wave conditions on the Scotian Shelf. An unstructured-grid spectral wave model based on SCHISM–WWM-III was configured and forced with hourly 10-m winds from ERA5. The WS fields modified by turbine wakes were simulated using PyWake. Model results show good agreement with moored buoy and ADCP observations, with high skill for significant wave height (Hs) and moderate skill for peak wave period (Tp). Constant-wind simulations show that the wind-deficit-induced reduction in Hs is non-monotonic with background WS, with the strongest response occurring at moderate winds (around 8 ms−1) and weaker responses at both lower and higher wind speeds. The mean wave period (Tm) exhibits spatially varying changes, with slight increases inside the PWEA and decreases downstream, and the magnitude of these changes decreases with increasing WS. Turbines also induce a dipole-like pattern in mean wave direction (θm), with the largest directional changes occurring under weak wind conditions. ERA5-forced simulations with turbines quantified the cumulative impact of turbine wakes under realistic atmospheric forcing. For a turbine spacing of 10 rotor diameters (10 D) (402 turbines), reductions in time-mean Hs within and downstream of the PWEA reach about 2.4 cm (1.5%) in summer and 1.9 cm (0.8%) in winter. A denser 5 D layout produces larger wave height reductions, with maximum decreases of up to 7 cm (4.3%). In contrast, time-mean Tm generally increases slightly, by up to 0.07 s for the 10 D layout and up to 0.2 s for the 5 D layout, while changes in time-mean θm are typically less than 1°. This study provides a quantitative assessment of how future offshore wind development may influence regional wave characteristics on the Scotian Shelf, with potential implications for ocean mixing and the dispersal of pelagic particles.
This work presents simplified analytical and numerical predictive models describing the motion of a fully submerged spherical gas bubble released beneath a free surface of fluid and its subsequent impact on the free surface. The proposed approach provides a basis for inferring the properties of underwater bubbly plumes from surface observations alone, which is of practical relevance to a wide range of applications, including methane seepage from the ocean floor, explosive subaqueous volcanism, ruptured underwater gas pipelines, and offshore drilling blowouts. The rise of the bubble is governed by the balance between buoyancy, drag, and gravitational forces. Upon reaching the free surface, the momentum of the bubble and the surrounding accelerated fluid is transferred to the surface layer. Together with the size of the pressure impact, gravity and fluid properties such as density, viscosity, and surface tension, this momentum transfer generates disturbance on the surface and the formation of radially propagating surface waves. The predictions of both simplified models are assessed through comparisons with high-fidelity computational fluid dynamics simulations and existing experimental and theoretical data from the literature.
This study provides the first integrated assessment of the interannual variability, long-term trend, and seasonality of upper ocean temperature across seven Arctic shelf seas over 1995–2022, using an eddy-permitting global coupled ocean-sea ice model (eORCA025) based on the Nucleus for European Modelling of the Ocean (NEMO) framework. The modelled results are evaluated against observations (NOAA OISSTv2 and ARMOR3D) and the ensemble mean of reanalysis products (GLORYS2V4, ORAS5, C-GLORSv7). While confirming previously reported warming in the Barents, Kara, and Laptev Seas associated with Arctic Atlantification, our analysis reveals a new and critical feature of Arctic change – an amplified seasonal cycle of mixed layer temperature (MLT) that is much more pronounced than mean annual warming in the interior shelf seas (East Siberian, Laptev, and Kara Seas). A mixed layer heat budget analysis shows that this amplified seasonality is primarily driven by enhanced surface heat flux forcing, with a secondary contribution from horizontal oceanic heat advection and minimal influence from vertical entrainment. This suggests a greater role of atmospheric drivers than oceanic drivers in modulating the seasonality change in the shelf seas. The extremes of the MLT seasonal cycle are governed by anomalies in summer solar heating and subsequent nonsolar heat loss in the fall. This model study identifies intensifying thermal seasonality and quantifies its underlying drivers, providing crucial insights for assessing their implications on the Arctic marine systems.
