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This study presents results from numerical model experiments with a high-resolution regional forecast system to evaluate model predictability of the Loop Current (LC) system and assess the added value of different types of observations. The experiments evaluate the impact of surface versus subsurface observations as well as different combinations and spatial coverage of observations on the forecasts of the LC variability. The experiments use real observations (observing system experiments) and synthetic observations derived from a high-resolution independent simulation (observing system simulation experiments). Model predictability is assessed based on a saturated error growth model. The forecast error is computed for the sea surface height fields and the LC frontal positions derived from the forecasts and control fields using two metrics. Estimated model predictability of the LC ranges from 2 to 3 months. Predictability limit depends on activity state of the LC, with shorter predictability limit during active LC configurations. Assimilation of subsurface temperature and salinity profiles in the LC area have notable impact on the medium-range forecasts (2–3 months), whereas the impact is less prominent on shorter scales. The forecast error depends on the uncertainty of the initial state; therefore, on the accuracy of the analysis providing the initial fields. Forecasts with the smallest initial error have the best predictive skills with reliable predictability beyond 2 months suggesting that the impact of the model error is less prominent than the initial error. Hence, substantial improvements in forecasts up to 3 months can be achieved with increased accuracy of initialization.

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen’s formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA’s) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts’ newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA’s) meso-scale forecasting data. We analyzed the accuracy of each model’s results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

In numerical ocean modeling, dynamical downscaling is the approach consisting in generating high-resolution regional simulations exploiting the information from coarser resolution models for initial and boundary conditions. Here we evaluate the impacts of downscaling the 1/16o (~ 6–7 km) CMEMS Mediterranean reanalysis model solution into a high-resolution 2-km free-run simulation over the Western Mediterranean basin, focusing on the surface circulation and mesoscale activity. Multi-platform observations from satellite-borne altimeters, high-frequency radar, fixed moorings, and gliders are used for this evaluation, providing insights into the variability from basin to coastal scales. Results show that the downscaling leads to an improvement of the time-averaged surface circulation, especially in the topographically complex area of the Balearic Sea. In particular, the path of the Balearic current is improved in the high-resolution model, also positively affecting transports through the Ibiza Channel. While the high-resolution model produces a similar number of large eddies as CMEMS Med Rea and altimetry, it generates a much larger number of small-scale eddies. Looking into the variability, in the absence of data assimilation, the high-resolution model is not able to properly reproduce the observed phases of mesoscale structures, especially in the southern part of the domain. This negatively affects the representation of the variability of the surface currents interacting with these eddies, highlighting the importance of data assimilation in the high-resolution ocean model in this region to constrain the evolution of these mesoscale structures. [ABSTRACT FROM AUTHOR]

Quality-controlled current observations from a High Frequency radar (HFR) network deployed in the Ebro River Delta (NW Mediterranean) were combined with outputs from IBI operational ocean forecasting system in order to comprehensively portray the ocean state and its variability during 2014. Accurate HFR data were used as benchmark for a rigorous validation of the Iberia-Biscay-Ireland (IBI) regional system, routinely operated in the frame of the Copernicus Marine Environment Monitoring Service (CMEMS). The analysis of skill metrics and monthly averaged current maps showed that IBI reasonably captured the prevailing dynamic features of the coastal circulation previously observed by the HFR, according to the moderate resemblance found in circulation patterns and the spatial distribution of eddy kinetic energy. The model skill assessment was completed with an exploration of dominant modes of spatiotemporal variability. The EOF analysis confirmed that the modeled surface current field evolved both in space and time according to three significantly dominant modes of variability which accounted for the 49.2% of the total variance, in close agreement with the results obtained for HFR (46.1%). The response of the subtidal surface current field to prevailing wind regime in the study area was examined in terms of induced circulation structures and immediacy of reaction by performing a conditional averaging approach and a time-lagged vector correlation analysis, respectively. This observations-model synergistic strategy has proved to be valid to operationally monitor the complex coastal circulation in Ebro Delta despite the observed model drawbacks in terms of reduced energy content in surface currents and some inaccuracies in the wind-driven low frequency response. This integrated methodology aids to improve the prognostic capabilities of IBI ocean forecasting system and also to facilitate high-stakes decision-making for coastal management in the Ebro River Delta marine protected area. [ABSTRACT FROM AUTHOR]

The Salish Sea, located in the Pacific Northwest region of North America has complex currents and circulation features distributed over numerous interconnected deep basins with islands. Increased risk of exposure to oil spills and untreated wastewater from maritime emergencies and treatment plant failures have led to a need for quantifying residence and flushing characteristics at a sub-basin scale using a shoreline resolving hydrodynamic model. An unstructured grid model of the Salish Sea was developed using FVCOM with a ≈75–100m shoreline resolution. In addition to 7 tides and 23 salinity and temperature monitoring stations, an extensive currents data set from 135 stations collected over a span of three years was used for skill assessment and validation. Explicit forward computations were then conducted to define and quantify residence and flushing times in various sub-basins of interest using (a) Lagrangian particles and (b) Numerical/virtual dye experiments. The results in most basins show expected seasonal variability with longer flushing time associated with summer lower tides and lower freshwater inflows. However, contrary to expectation, flushing time is significantly longer in wintertime in large fjord-like basins such as Hood Canal (≈138 days), likely due to increased stratification and reduced mixing. The flushing time for the Puget Sound region of the Salish Sea is ≈ 115 days, while Georgia Basin is 240 days when analyzed as stand-alone basins with zero background concentrations. When examined as part of the flushing of the entire Puget Sound filled with virtual dye, the compounded flushing times for embedded sub-basins can be significantly longer in order of magnitudes and largely dictated by the flushing time of Puget Sound. The computed residence and flushing time scales tabulated over 36 sub-basins provide an improved understanding of water renewal in the system, informing pollution management actions. • Robust validation of a shoreline resolving hydrodynamic model for the Salish Sea using a comprehensive currents data set. • Lagrangian particle-based methods and Eulerian virtual dye experiments for computing residence and flushing times. • Seasonal variations of residence and flushing times in the sub-basin scale for greater Salish Sea. • Compounded flushing times for smaller sub-basins are largely dictated by the flushing time scale of the parent basin. [ABSTRACT FROM AUTHOR]

All numerical codes developed to solve the advection-diffusion-reaction (ADR) equation need to be verified before they are moved to the operational phase. In this paper, we initially provide four new one-dimensional analytical solutions designed to help code verification; these solutions are able to handle the challenges of the scalar transport equation including nonlinearity and spatiotemporal variability of the velocity and dispersion coefficient, and of the source term. Then, we present a solution of Burgers' equation in a novel setup. Proposed solutions satisfy the continuity of mass for the ambient flow, which is a crucial factor for coupled hydrodynamics-transport solvers. By the end of the paper, we solve hypothetical test problems for each of the solutions numerically, and we use the derived analytical solutions for code verification. Finally, we provide assessments of results accuracy based on well-known model skill metrics. [ABSTRACT FROM AUTHOR]

Abstract: Although the purpose of models is to simplify complex reality to allow the investigation of patterns, processes and relationships, many ecosystem models retain high levels of complexity. The outputs from such models are highly multivariate. Taking the view that a perfect model simulation of a spatial domain over a determinate time period will reproduce observed variables from the same place over the same period perfectly, we demonstrate how appropriate multivariate methods may be used to elucidate patterns within observations and model outputs, to compare patterns between them, and to explore the nature and spatio-temporal distribution of model errors. Analyses based on observations collected from the southern North Sea in 1988–89 are compared to analyses based on an equivalent dataset extracted from the output of the POLCOMS-ERSEM model. A combination of PCA and nonparametric multivariate approaches is used to demonstrate that in broad terms the model performs well, simulating patterns in, and interrelationships between, a range of variables. Errors are greatest in late winter and early spring, and are associated with inaccurate estimation of the magnitude of primary production in coastal waters and the amount of suspended particulate matter in the water column. [Copyright &y& Elsevier]

The Bay of Bengal is a semi-enclosed ocean basin situated in the eastern part of the North Indian Ocean. Though the physical dynamical features of the Bay of Bengal have been studied and measured in detail, the carbonate chemistry of this basin has been less explored, and very few reliable data-sets exist. This paucity of data has emerged as a major challenge in modeling and understanding the carbonate system parameters for this region. In this study, a coupled physical-biogeochemical (ROMS-PISCES) model has been configured and run to emulate the surface carbonate system parameters (DIC, TALK, pCO 2 , and pH) for the Bay of Bengal region. Model skill assessment analysis has been performed using available observational data-sets. Two different numerical experiments have been performed (WB indicating the use of default bulk formulae of ROMS to calculate wind stress and WoB indicating the calculated wind stresses of QuikSCAT climatology product using different bulk formula), to understand which one reproduces the carbonate parameters better. Both the numerical experiments are rigorously compared for physical as well as carbonate system parameters. The numerical experiments have been passed through exhaustive statistical analysis by comparing it with the observed data-sets. The temperature, the primary driver affecting pH and pCO 2 has been reproduced by both the experiments excellently, and the correlation value is more than 0.9 with RAMA buoy data (15 o N, 90 o E). The salinity, when compared with the NIOA climatology data, shows that the WoB experiment has better captured both the spatial and temporal variation of salinity. Both the numerical experiments have been compared individually with three sets of observed carbonate data. The WoB run has been found to emulate carbonate system parameters satisfactorily than the WB run. The pCO 2 and pH show a good positive correlation with RAMA data and the values are 0.87, and 0.93, respectively. • A coupled physical-biogeochemical model has been set up for the Bay of Bengal region to emulate its carbonate chemistry. • The model has been rigorously evaluated with the available data sets using 8 statistical indices to establish its skills. • The model is excellent in simulating the spatial heterogeneity and temporal variation of all the carbonate parameters. • The importance of modeling physical characteristics to reproduce the carbonate chemistry for Bay of Bengal is highlighted. [ABSTRACT FROM AUTHOR]

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll- a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll- a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.