Abstract. A major challenge for cleanup operations in the Great Pacific Garbage Patch is the daily prediction of plastic concentrations that allows identifying hotspots of marine debris. Lagrangian simulations of large particle ensembles are the method in use and effectively reproduce observed particle distributions at synoptic scales 𝒪(1000 km). However, they lose accuracy at operational scales 𝒪(1–10 km), and operators regularly experience differences between predicted and encountered debris accumulations within the garbage patch. In Lagrangian methods, it would be common to ask this question: where do objects go as they follow the current? Here, we take a different approach and instead ask this question: which locations attract material? The recently developed concept of Transient Attracting Profiles (TRAPs) provides answers because TRAPs uncover the most attracting regions of the flow. TRAPs are the attracting form of hyperbolic Objective Eulerian Coherent Structures and are computable from the instantaneous strain field on the ocean surface. They describe flow features which attract drifting objects and could facilitate offshore cleanups that are currently taking place in the Great Pacific Garbage Patch. However, the concept remains unapplied since little is known about the persistence and attraction of these features, specifically within the Pacific. Therefore, we compute a 20-year dataset of daily TRAP detections from satellite-derived mesoscale velocities within the North Pacific subtropical gyre. We are the first to track these instantaneous flow features as they propagate through space and time. It allows us to study the life cycle of TRAPs, which can range from days to seasons and lasts an average of 6 d. We show how long-living TRAPs with lifetimes beyond 30 d intensify and weaken over their life cycle, and we demonstrate that the evolution stage of TRAPs affects the motion of nearby surface drifters. Our findings indicate that, at the mesoscale, operators in the Great Pacific Garbage Patch should search for long-living TRAPs that are at an advanced stage of their life cycle. These TRAPs are the most likely to induce a large-scale confluence of drifting objects and their streamlining into hyperbolic pathways. Such a streamlined bypass takes, on average, 5 d and creates an opportunity to filter the flow around TRAPs. But we also find TRAPs that retain material over multiple weeks where we suspect material clustering at the submesoscale. Prospective research could investigate this further by applying our algorithms to soon-available high-resolution observations of the flow. Our analysis contributes to a better understanding of TRAPs, which can even benefit other offshore operations besides ocean cleanups, such as optimal drifter deployment, oil spill containment, and humanitarian search and rescue.

Abstract. In tropical regions, the freshwater flux entering the ocean originates primarily from precipitation and, to a lesser extent when considering basin-scale averages, from continental rivers. Nevertheless, at the regional scale, river flows can have a significant impact on the surface ocean dynamics. Riverine freshwater modifies salinity and, therefore, density, stratification, and circulation. With its particular coastline and high cumulative river discharge, as well as its being in the vicinity of the intertropical convergence zone (ITCZ), the eastern part of the North Tropical Atlantic (e-NTA) region off northwestern Africa is a particularly interesting location to study the linkage between precipitation, river outflow, and sea surface salinity (SSS). Here, we focus on the regional e-NTA SSS seasonal cycle and interannual variability and on the impact of using various river runoff and precipitation forcing data sets to simulate SSS with a regional model. The simulated SSS values are compared with the Climate Change Initiative (CCI) satellite SSS values; in situ SSS values from Argo floats, ships, and a coastal mooring; and the GLORYS reanalysis SSS values. An analysis of the mixed-layer salinity budget is then conducted. Overall, the simulations reproduce the seasonal cycle and interannual variability well despite a positive mean model bias north of 15° N. The seasonal cycle is impacted by the phasing of the different runoff products. The mixed-layer SSS decrease during the rainy season is mainly driven by precipitation followed by runoff by means of horizontal advection and is partly compensated for by vertical mixing. In terms of interannual anomalies, river runoff has a more direct impact on SSS than precipitation. This study highlights the importance of properly constraining river runoff and precipitation to simulate realistic SSS values and the importance of observing SSS in coastal regions to validate such constraints.

Abstract. A well-validated storm surge numerical model is crucial, offering precise coastal hazard information and serving as a basis for extensive databases and advanced data-driven algorithms. However, selecting the best model setup based solely on common error indicators like the root-mean-square error (RMSE) or Pearson correlation does not always yield optimal results. To illustrate this, we conducted 34-year high-resolution simulations for storm surge under barotropic (BT) and baroclinic (BC) configurations using atmospheric data from ERA5 and a high-resolution downscaling of the Climate Forecast System Reanalysis (CFSR) developed by the University of Genoa (UniGe). We combined forcing and configurations to produce three datasets: (1) BT-ERA5, (2) BC-ERA5, and (3) BC-UniGe. The model performance was assessed against nearshore station data using various statistical metrics. While RMSE and Pearson correlation suggest BT-ERA5, i.e., the coarsest and simplest setup, is the best model (followed by BC-ERA5), we demonstrate that these indicators are not always reliable for performance assessment. The most sophisticated model (BC-UniGe) shows worse values of RMSE or Pearson correlation due to the so-called “double penalty” effect. Here we propose new skill indicators that assess the ability of the model to reproduce the distribution of the observations. This, combined with an analysis of values above the 99th percentile, identifies BC-UniGe as the best model, while ERA5 simulations tend to underestimate the extremes. Although the study focuses on the accurate representation of storm surge by the numerical model, the analysis and proposed metrics can be applied to any problem involving the comparison between time series of simulation and observation.

Abstract. This study investigates the contributions of the ocean's chaotic intrinsic variability (CIV) and atmospherically forced variability to the interannual fluctuations in the North Atlantic Subtropical Mode Water (STMW) properties. Utilizing a 0.25° regional 50-member ocean–sea-ice ensemble simulation driven by an original surface forcing method and perturbed initially, the forced variability in STMW properties is estimated from ensemble mean fluctuations, while CIV is determined from deviations around the ensemble mean within each member. The model successfully captures the main features of STMW, showing correct agreement with observation-based ARMOR3D data in terms of location, seasonality, mean temperature and volume, as well as interannual variance of STMW's main properties. CIV significantly impacts STMW, explaining 10 %–13 % and 28 %–44 % of the interannual variance of its geometric and thermohaline mean properties, respectively, with a maximum imprint on STMW temperature. Observation-based and simulated intrinsic-to-total variance ratios are mostly consistent, dispelling concerns about a signal-to-noise paradox. This study also illustrates the advantages of ensemble simulations over single simulations in understanding oceanic fluctuations and attributing them to external drivers, while also cautioning against overreliance on individual simulation assessments.

Abstract. Seasonal reversal of winds and equatorial remote forcing influences the circulation of the Arabian Sea (AS) and Bay of Bengal (BoB) basins in the northern Indian Ocean. In this study, we numerically modelled the physical characteristics of the AS and BoB, using the Massachusetts Institute of Technology general circulation model (MITgcm) at a high spatial resolution of 1/20° forced with climatological initial and boundary conditions. The simulated temperature, salinity, and flow fields were validated with satellite and in situ datasets. We then studied the exchange of coastal waters by evaluating transports computed from the model simulations. The alongshore volume transport on the eastern coast is stronger with high seasonal variability due to the poleward-flowing western boundary current and equatorward-flowing East Indian Coastal Current. West coast transport is influenced by large intraseasonal oscillations. The alongshore freshwater transport is an order less than the alongshore volume transport. Out of the net volume transport, freshwater accounts for a maximum of 6.03 % during the southwestern monsoon season, followed by 4.85 % in the post-monsoon season. We observe an inverse relationship between alongshore freshwater and volume transport on the western coast and a direct relationship on the eastern coast. The contribution of eddy-induced heat and freshwater transport was also examined. The relation between net heat transport and net heat flux illustrates the role of coastal currents and equatorial forcing in dissipating heat within the coastal waters. We observed that meridional heat transport over the AS is stronger than over the BoB. Both basins act as a heat source during the summer monsoon and heat sink during the winter. This high-resolution model setup simulates all the important physical climatological patterns, leading to a better understanding of the state of the northern Indian Ocean.

Abstract. Transport and dispersion processes in the ocean are crucial, as they determine the lifetime and fate of biological and chemical quantities drifting with ocean currents. Due to the complexity of the coastal ocean environment, numerical circulation models have difficulties to accurately simulate highly turbulent flows and dispersion processes, especially in highly energetic tidal basins such as the eastern English Channel. A method of improving the results of coastal circulation modeling and tracer dispersion in the Dover Strait is proposed. Surface current velocities derived from Lagrangian drifter measurements in November 2020 and May 2021 were optimally interpolated in time and space to constrain a high-resolution coastal circulation MARS model, with careful attention given to selecting ensemble members composing the model covariance matrix. The space–time velocity covariances derived from model simulations were utilized by the optimal interpolation algorithm to determine the most likely evolution of the velocity field under constraints provided by Lagrangian observations and their error statistics. The accuracy of the velocity field reconstruction was evaluated at each time step. The results of the fusion of model outputs with surface drifter velocity measurements show a significant improvement (by ∼ 50 %) of the model capability to simulate the drift of passive tracers in the Dover Strait. Optimized velocity fields were used to quantify the absolute dispersion in the study area. The implications of these results are important, as they can be used to improve existing decision-making support tool or design new tools for monitoring the transport and dispersion in a coastal ocean environment.

Abstract. The Fram Strait is the only deep gateway between the Arctic and the rest of the World Ocean, and it is thus a key region to understand how the deep Arctic will evolve. However, studies and data regarding the deep ocean are scarce, making it difficult to understand its role in the climate system. Here, we analyse oceanographic data obtained close to the Fram Strait sill depth of 2500 m by two long-term mooring locations (F11 and HG-FEVI) in the Fram Strait between 2010–2023 to investigate long-term changes in the hydrographic properties. For additional context, we compile hydrographic profile data from the 1980s for the adjacent basins: the Greenland Sea and the Eurasian Basin. At mooring F11 in the western Fram Strait, we find a clear seasonality, with increased Greenland Sea Deep Water (GSDW) presence during summer and increased Eurasian Basin Deep Water (EBDW) presence during winter. Evaluating long-term changes, we find a modest temperature increase of ∼ 0.1 °C for EBDW from the 1980s. For GSDW, south of the Fram Strait, we find a strong temperature increase of ∼ 0.4–0.5 °C for the same period. The different warming rates have led to GSDW becoming warmer than EBDW since ∼ 2017–2018. This means that the Greenland Sea is no longer a heat sink for the Arctic Ocean at depth but is rather a heat source. It is therefore possible that EBDW temperatures will increase faster in the future.

Abstract. North Pacific subtropical mode water (NPSTMW) is formed as a low-stratification water mass in the wintertime mixed layer south of the Kuroshio Extension (KE). In a recent period of 2018–2021, the KE jet was in a persistent stable dynamic state. But based on analysis of Argo observation, the mean volume of NPSTMW in the ventilation region dropped anomalously by ∼ 21 % during 2018–2021 relative to 2012–2015, when the KE jet was likewise stable. Moreover, the NPSTMW volume in a denser density range (approximately σθ>25.2 kg m−3) has started to decrease since 2018. The decreases in the NPSTMW subduction and formation rate are associated with anomalously shallow wintertime mixed-layer depth (MLD) and weak heat loss in the NPSTMW formation region. The decrease in air–sea heat exchange acts to weaken the vertical mixing and decrease the MLD, resulting in the weakening of subduction. The interannual variations in the air–sea heat exchange and wintertime MLD reflect the variability in the overlying atmosphere, which is correlated with a Pacific Decadal Oscillation (PDO) shift in 2018–2021. When the PDO shifted from its positive phase to a negative phase in the analysis period, the effects of local wind stress anomalies seemed to play an evident role in driving the variability in NPSTMW on interannual timescales. The MLD and heat loss change during the cold season in 2018–2021 were strongly coupled with the poleward shift of the westerlies – which cause weaker wintertime wind and easterly wind anomalies over the NPSTMW formation region. The declines in heat loss and southward Ekman transport, owing to the wind stress anomalies, further prohibit upper-ocean convection and mixed-layer deepening and cooling. Additionally, the insufficient development of wintertime MLD in 2018–2021 may also be associated with the significantly intensified preconditioning of near-surface stratification (< 150 m depth) due to the persistent near-surface warming and the weak vertical entrainment process in winter.

Abstract. We explore historical variability in the volume of Labrador Sea Water (LSW) using ECCO, an ocean state estimate configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). The model's adjoint, a linearization of the MITgcm, is set up to output the lagged sensitivity of the water mass volume to surface boundary conditions. This allows us to reconstruct the evolution of LSW volume over recent decades using historical surface wind stress, heat, and freshwater fluxes. Each of these boundary conditions contributes significantly to the LSW variability that we recover, but these impacts are associated with different geographical fingerprints and arise over a range of time lags. We show that the volume of LSW accumulated in the Labrador Sea exhibits a delayed response to surface wind stress and buoyancy forcing outside the convective interior of the Labrador Sea at important locations in the North Atlantic Ocean. In particular, patterns of wind and surface density anomalies can act as a “traffic controller” and regulate the North Atlantic Current's (NAC's) transport of warm and saline subtropical water masses that are precursors for the formation of LSW. This propensity for a delayed response of LSW to remote forcing allows us to predict a limited yet substantial and significant fraction of LSW variability at least 1 year into the future. Our analysis also enables us to attribute LSW variability to different boundary conditions and to gain insight into the major mechanisms that contribute to volume anomalies in this deep water mass. We point out the important role of key processes that promote the formation of LSW in both the Irminger and Labrador seas: buoyancy loss and preconditioning along the NAC pathway and in the Iceland Basin, the Irminger Sea, and the Nordic Seas.

Abstract. Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.