Predicting ocean circulation in strong currents remains challenging because of limits in modelling capabilities such as resolution. Coastal ocean circulation models typically have horizontal resolution starting from 1 km. To address this matter, we have developed a high resolution three-dimensional computational fluid dynamics (CFD) model for strong ocean currents such as the Gulf Stream. Our model domain contains three inlets and an outlet and has been verified with field data from the Straits of Florida. For model verification, a 6 ADCP mooring array in a rectangular shape was deployed 8 miles offshore on the Miami Terrace. The data from 5 ADCP moorings were used to produce the inlet boundary conditions, which were updated every 1 minute. The sixth ADCP in the center of the outlet was used for model verification. This approach has demonstrated good predictive ability for ocean circulation in the challenging environment of a strong western boundary current. We anticipate our work to be a starting point for the development of sophisticated prediction models applicable to western boundary currents in the range from small-scales to sub-mesoscales, based on advanced data assimilation techniques.

How to cite: Vanderplow, B., Kluge, J., Soloviev, A., Dodge, R., Wood, J., Evans, J., Venezia, W., and Ferrar, M.: Measurement and modeling of small-scale to mesoscale ocean circulation in the Straits of Florida, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6451, https://doi.org/10.5194/egusphere-egu22-6451, 2022.

We present a post-hoc smoothing algorithm for use with sequentially generated reanalysis products, utilizing the archive of “future” assimilation increments to update the “current” analysis. This is applied to the Lorenz 1963 model and then to the Met Office GloSea5 Global ¼° ocean reanalysis during 2016.  A decay time parameter is applied to the sequential increments which assumes that background error covariances remain spatially unchanged but decay exponentially away from analysis times. Only increments are smoothed so the reanalysis product retains modelled high-frequency variability, e.g., from atmospheric forcing. Results show significant improvement over the original reanalysis in the 3D temperature and salinity variability, as well as in the sea surface height (SSH) and ocean currents. Spatial gap filling from future data is particularly beneficial. The impact on the time variability of ocean heat and salt content, as well as kinetic energy and the Atlantic Meridional Overturning Circulation (AMOC), is demonstrated. 

How to cite: Dong, B., Haines, K., and Martin, M.: Improving High Resolution Ocean Reanalyses Using a Smoother Algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4313, https://doi.org/10.5194/egusphere-egu22-4313, 2022.

Eight of the top ten most populated cities in the world are located by the coast. The improvement of the coastal ocean representation is a key topic to understand the  present and near-future ocean state and predict its evolution under climate change conditions.

The coastal ocean is difficult to model due to the presence of complex coastlines, interaction with inland waters, rapid changes in  topography and highly space-time variability of the phenomena involved. Unstructured-grid models are used to partially attenuate this source of errors in cross-scale (from open sea to coastal regions) oceanographic modelling. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas is being developed only recently (e.g., Aydogdu et al., 2018; Bajo et al., 2019) and needs more advancements.  

Here, we show preliminary results from the coastal ocean forecasting system SANIFS (Southern Adriatic Northern Ionian coastal Forecasting System, Federico et al., 2017) based on SHYFEM fully-baroclinic unstructured-grid model (Umgiesser et al., 2004)  interfaced with OceanVar (Dobricic and Pinardi, 2008; Storto et al., 2014), a state-of-art variational data assimilation scheme, adopted for several systems based on structured grid (e.g. regional CMEMS for Mediterranean and Black Seas, marine.cmems.eu).

In OceanVar, Empirical Orthogonal Functions (EOFs) method is used to reduce the dimensionality of computation removing the statistically less significant modes and to correlate observations and model background in the water column;  while the increments are spread horizontally using the recursive filter method. While this method is typically only used to model covariances between neighbouring points in a structured grid, the algorithm has now been generalised and successfully implemented also for unstructured grids.

Preliminary results show that temperature and salinity observations from Argo profilers improve the ocean state. Future steps will also include sea level assimilation. 

This work is a starting point in order to improve our forecast of local extreme events (e.g. heat waves and storm surge) which are statistically increasing in number and intensity in the Mediterranean region due to climate change.

How to cite: Stefanelli, M., Jansen, E., Aydogdu, A., Federico, I., Coppini, G., and Pinardi, N.: Variational data assimilation for advanced cross-scale ocean modelling., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4741, https://doi.org/10.5194/egusphere-egu22-4741, 2022.

The correct reproduction of sea-level dynamics is crucial for forecasting floods and managing the associated risk. On the other hand, sea-level monitoring through observations can provide a description only of past events and it is challenging and costly, both of time and money. In this context, oceanographic models are increasingly used to describe the sea dynamics, providing a spatial/temporal extension to the observations. The best solution, which merges the observation accuracy and the model spatial/temporal resolution, is the data assimilation analysis, which is particularly important in coastal regions with scarce monitoring resources. In this study, we investigate the benefits of assimilating sparse observations from tide gauges in an unstructured hydrodynamic model for simulating the sea level in the Mediterranean Sea. We use the Ensemble Kalman filter, both to obtain an analysis of the past and to produce accurate forecasts. In the analysis we tested the assimilation in storm-surge simulations, only-tide simulations, and total-level simulations, using the observations in the stations. The results of storm-surge simulations were compared with those of total-level simulations, by adding the tide obtained from harmonic analysis of the observations. RMSE and correlation show improvements for all the components of the sea level and all the stations considered (not assimilated). The averaged-over-station RMSE reduces from 9.1 to 3.4 cm for the total level. The greatest improvements happen when the model without assimilation, due to an error of the wind-pressure forcing, did not reproduce some barotropic free modes of oscillation triggered by an initial surge. The preliminary forecast simulations of storm surge show improvements due to the data assimilation extending up to 5 days of forecasting. Even in this case, the longer improvements seem to happen when a free mode of oscillation is triggered. The results of this study will be used to improve the sea level forecasting system in the Adriatic Sea, developed within the framework of the Interreg Italy-Croatia STREAM project (Strategic development of flood management, project ID 10249186).

How to cite: Ferrarin, C., Bajo, M., and Umgiesser, G.: Sea-level modelling in the Mediterranean Sea using data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-732, https://doi.org/10.5194/egusphere-egu22-732, 2022.

The second version of the Arctic ocean and sea ice reanalysis is based on the coupled ensemble data assimilation system (TOPAZ4b). Compared to its predecessor (Xie et al. 2017) it has benefited from enhancements to observation, model vertical resolution, and forcing datasets. TOPAZ4 relies on version 2.2 of the HYCOM ocean model and the ensemble Kalman filter data assimilation using 100 dynamical members. A 30-years reanalysis of the Arctic ocean and sea ice has been completed starting in 1991, and made available as the multi-year physical product by the Arctic Marine Forecasting Center (ARC MFC) under the Copernicus Marine Environment Monitoring Service. Contrary to the previous version of the Arctic reanalysis, the systematic errors due to fragmented time series of assimilated observations have been removed by using consistent ESA CCI data. The comparison to in situ profiles shows that the temperature and salinity stratification has been considerably improved by the increased vertical resolution in HYCOM, for example in the East Greenland Sea, the temperature root mean square error (RMSE) from surface to 1400 m has been reduced by 50%. These improvements encourage the use of this Arctic reanalysis for climate studies.

How to cite: Xie, J. and Bertino, L.: TOPAZ4b: a new version of the ocean and sea-ice Arctic reanalysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-923, https://doi.org/10.5194/egusphere-egu22-923, 2022.

As part of the ongoing development of a data assimilation system for reconstructing the Arctic ocean-sea ice state, we incorporated an adjoint of sea ice rheology, which was approximated by free drift assumption due to stability problem, into an adjoint model of a coupled ocean-sea ice model. The adjoint sensitivity experiments show that the internal stress effect, represented by the adjoint rheology, induced remarkable differences in the sensitivities to ice drift and wind stress in the central Arctic Ocean. In contrast, ice is mostly free drift in the marginal ice zone. The assimilation experiments reveal that including the adjoint of ice rheology helps extract observational information, especially the ice drift observations, which improves the estimation of the sea ice decline process in 2012. The results suggested great potentials for further improving the Arctic ocean-ice state estimation in the framework of the adjoint method with the adjoint sea ice rheology included. 

How to cite: Lyu, G. and Zhou, M.: Effects of inclusion of adjoint sea ice rheology on estimating ocean-sea ice state, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6848, https://doi.org/10.5194/egusphere-egu22-6848, 2022.