Assessing the impacts of assimilating satellite SST in addition to along-track SLA into a HYCOM of the Agulhas System

The greater Agulhas Current System, is considered to be the largest western boundary current in the Southern Hemisphere, with only the Gulf Stream, and possibly the Kuroshio, considered to be larger globally (Bryden et al., 2005). The Current System is a crucial factor for determining the mean state and variability of the regional marine environment, resources and ecosystems in the region, regional weather, as well as the global climate on a broad range of temporal and spatial scales. Due to an absence of a coherent in situ and satellite-based observing system in the area, modelling and data assimilation techniques are utilised. These both further the quantitative understanding of the ocean dynamics as well as providing better forecasts of this complicated western boundary current system. In this study, we compare two assimilation experiments using the Ensemble Optimal Interpolation (EnOI) data assimilation scheme in a regional implementation of the Hybrid Coordinate Ocean Model (HYCOM). In the first experiment, we assimilate along-track satellite sea level anomaly (SLA) data only, and in the second experiment we assimilate both along-track sea level anomaly (SLA) as well as satellite sea surface temperature (SST) data. The objectives of the study are to investigate the impacts of assimilating SST along with SLA into the regional HYCOM model, with the hopes of improving the model performance. The long term aim of this experiment is to develop a regional ocean prediction system. The additional assimilation of SST along with SLA into the HYCOM model, has improved upon the representation of the SST field across the region by reducing the error. However, with regards to velocity, surface eddy kinetic energies (EKE), as well as subsurface velocities, the updated SST model shows less improvement. A velocity bias can be seen as the reason for underperformance in these aspects. The model still struggles to recreate subsurface water masses, underestimating salinity in the upper 500 m; assimilating T/S profiles in the future could improve on this. The assimilation of SST has improved upon the SST-SSH correlation in the model, as well as the spatial distribution and accuracy. The assimilation of SST along with SLA has had many positive impacts, with unfortunately, a few negatives. The shortcomings of the numerical model will have to be improved upon and additional assimilation variables should be tested in further studies, to provide a solid forecasting system

Evaluation of oil spill trajectory model with the observed SVP drifter track

Indian National Centre for Ocean Information Services (INCOIS) collaborated with Indian Coast Guard(ICG) and conducted Surface Velocity Program (SVP) drifter experiment at Mumbai High region for evaluating the operational oil spill trajectory model. INCOIS adopted General National Oceanic and Atmospheric Administration(NOAA) Operational Modelling Environment(GNOME) from NOAA and customised it in diagnostic mode for Indian ocean. GNOME was operationalised during May 2014. The objective of this experiment is to compare the drift pattern obtained from an oil spill trajectory model with the observed drifter track. SVP drifter was procured from M/s. Pacific Gyre, USA. It gives the Lagrangian trajectory path over the ocean. It was deployed by officials of Indian Coast Guard at Mumbai High region on 20 November 2016,12.45 hrs at 72.2295º E, 18.91035º N off Mumbai. It gave its drifted path along the west coast of India for ten days, before it beached near Diu on 3 December 2016.This observed track was considered for comparing the simulated positions obtained from GNOME when forced with currents of different ocean general circulation models. The results show that the positions of the drifter obtained, while forced with analysed currents of GODAS -MOM4p1 (GM4p1) and Hybrid Co-ordinate Ocean Model (HYCOM) are found to be in better agreement with the actual position of the drifter

Validation of FOAM near-surface ocean current forecasts using Lagrangian drifting buoys

In this study, the quality of near-surface current forecasts from the FOAM ocean forecasting system is assessed using the trajectories of Lagrangian drifting buoys. A method is presented for deriving pseudo-Eulerian estimates of ocean currents from the positions of Surface Velocity Program drifters and the resulting data are compared to velocities observed by the global tropical moored buoy array. A quantitative analysis of the global FOAM velocities is performed for the period 2007 and 2008 using currents derived from over 3000 unique drifters (providing an average of 650 velocity observations per day). A potential bias is identified in the Southern Ocean which appears to be caused by wind-slip in the drifter dataset as a result of drogue loss. The drifter-derived currents are also used to show how the data assimilation scheme and a recent system upgrade impact upon the quality of FOAM current forecasts

An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

The upper ocean circulation in the South China Sea (SCS) is driven by the Asian monsoon, the Kuroshio intrusion through the Luzon Strait, strong tidal currents, and a complex topography. Here, we demonstrate the benefit of assimilating along-track altimeter data into a nested configuration of the HYbrid Coordinate Ocean Model that includes tides. Including tides in models is important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscale features contribute to the elevation of ocean surface at different time scales and require different corrections. To address this issue, tides are filtered out of the model output and only the mesoscale variability is corrected with a computationally cheap data assimilation method: the Ensemble Optimal Interpolation (EnOI). This method uses a running selection of members to handle the seasonal variability and assimilates the track data asynchronously. The data assimilative system is tested for the period 1994–1995, during which time a large number of validation data are available. Data assimilation reduces the Root Mean Square Error of Sea Level Anomalies from 9.3 to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme reduces the errors quantitatively with an improvement at intermediate depth and deterioration at deeper depth. The comparison to surface drifters shows an improvement of surface current by approximately −9% in the Northern SCS and east of Vietnam. Results are improved compared to an assimilative system that does not include tides and a system that does not consider asynchronous assimilation

The impact of assimilating along-track SLA data on simulated Eddy characteristics in the Agulhas system

The Agulhas Current System is a vital element of the global ocean-climate system by virtue of its role in the transfer of energy, nutrients and organic material. In the context of working towards better climate change projections, it is necessary to develop a robust understanding of the complex dynamical mechanisms which facilitate this transfer. Mesoscale cyclonic and anticyclonic eddies transport heat, salt, organic matter and nutrients from the Indian Ocean into the South Atlantic Ocean. In so doing, they are key drivers of the Atlantic Meridional Overturning Circulation (AMOC). As such, it is important that they are adequately simulated by numerical models in order to advance the accuracy of climate prediction. In the absence of spatially and temporally coherent observing systems, numerical models provide the capacity to describe the oceanographic conditions of the region. Given the complexity of the regional dynamics, and the challenges it presents to free-running numerical models, data assimilation is a valuable tool in improving simulation quality. An important step in this continuing process is the objective, quantitative evaluation of model configurations, such that they can be continuously refined. In this study, the impact of assimilating along-track sea level anomaly (SLA) data is investigated with regard to the simulation of mesoscale eddies in the Agulhas System. Two configurations of a Hybrid Coordinate Ocean Model (HYCOM) configuration are analysed; one free run (hereafter 'Free') and one with along-track SLA data from satellite altimetry assimilated (hereafter 'Assim.') via an Ensemble Optimal Interpolation (EnOI) data assimilation scheme. The results of these two configurations are compared with each other, and against a set of corresponding observational data from satellite altimetry (hereafter 'Aviso'). To this end, an automatic eddy detection and tracking algorithm is implemented, in order to quantify eddy characteristics in a coherent and consistent manner

The South Atlantic in the Fine-Resolution Antarctic Model

The geographical area covered by the Fine-Resolution Antarctic Model (FRAM) includes that part of the South Atlantic south of 24°S. A description of the dynamics and thermodynamics of this region of the model is presented. Both the mean and eddy fields in the model are in good agreement with reality, although the magnitude of the transients is somewhat reduced. The heat flux is northward and in broad agreement with many other estimates. Agulhas eddies are formed by the model and propagate westward into the Atlantic providing a mechanism for fluxing heat from the Indian Ocean. The confluence of the Brazil and Falkland currents produces a strong front and a large amount of mesoscale activity. In the less stratified regions to the south, topographic steering of the Antarctic circumpolar current is important

Global in situ observations of essential climate and ocean variables at the air–sea interface

The air–sea interface is a key gateway in the Earth system. It is where the atmosphere sets the ocean in motion, climate/weather-relevant air–sea processes occur, and pollutants (i.e., plastic, anthropogenic carbon dioxide, radioactive/chemical waste) enter the sea. Hence, accurate estimates and forecasts of physical and biogeochemical processes at this interface are critical for sustainable blue economy planning, growth, and disaster mitigation. Such estimates and forecasts rely on accurate and integrated in situ and satellite surface observations. High-impact uses of ocean surface observations of essential ocean/climate variables (EOVs/ECVs) include (1) assimilation into/validation of weather, ocean, and climate forecast models to improve their skill, impact, and value; (2) ocean physics studies (i.e., heat, momentum, freshwater, and biogeochemical air–sea fluxes) to further our understanding and parameterization of air–sea processes; and (3) calibration and validation of satellite ocean products (i.e., currents, temperature, salinity, sea level, ocean color, wind, and waves). We review strengths and limitations, impacts, and sustainability of in situ ocean surface observations of several ECVs and EOVs. We draw a 10-year vision of the global ocean surface observing network for improved synergy and integration with other observing systems (e.g., satellites), for modeling/forecast efforts, and for a better ocean observing governance. The context is both the applications listed above and the guidelines of frameworks such as the Global Ocean Observing System (GOOS) and Global Climate Observing System (GCOS) (both co-sponsored by the Intergovernmental Oceanographic Commission of UNESCO, IOC–UNESCO; the World Meteorological Organization, WMO; the United Nations Environment Programme, UNEP; and the International Science Council, ISC). Networks of multiparametric platforms, such as the global drifter array, offer opportunities for new and improved in situ observations. Advances in sensor technology (e.g., low-cost wave sensors), high-throughput communications, evolving cyberinfrastructures, and data information systems with potential to improve the scope, efficiency, integration, and sustainability of the ocean surface observing system are explored