Model-observations synergy in the coastal ocean

Integration of observations of the coastal ocean continuum, from regional oceans to shelf seas and estuaries/deltas with models, can substantially increase the value of observations and enable a wealth of applications. In particular, models can play a critical role at connecting sparse observations, synthesizing them, and assisting the design of observational networks; in turn, whenever available, observations can guide coastal model development. Coastal observations should sample the two-way interactions between nearshore, estuarine and shelf processes and open ocean processes, while accounting for the different pace of circulation drivers, such as the fast atmospheric, hydrological and tidal processes and the slower general ocean circulation and climate scales. Because of these challenges, high-resolution models can serve as connectors and integrators of coastal continuum observations. Data assimilation approaches can provide quantitative, validated estimates of Essential Ocean Variables in the coastal continuum, adding scientific and socioeconomic value to observations through applications (e.g., sea-level rise monitoring, coastal management under a sustainable ecosystem approach, aquaculture, dredging, transport and fate of pollutants, maritime safety, hazards under natural variability or climate change). We strongly recommend an internationally coordinated approach in support of the proper integration of global and coastal continuum scales, as well as for critical tasks such as community-agreed bathymetry and coastline products

Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level

A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.RP was funded by NASA grant NNH16CT00C. CD was supported by the Australian Research Council (FT130101532 and DP 160103130), the Scientific Committee on Oceanic Research (SCOR) Working Group 148, funded by national SCOR committees and a grant to SCOR from the U.S. National Science Foundation (Grant OCE-1546580), and the Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. SJ was supported by the Natural Environmental Research Council under Grant Agreement No. NE/P01517/1 and by the EPSRC NEWTON Fund Sustainable Deltas Programme, Grant Number EP/R024537/1. RvdW received funding from NWO, Grant 866.13.001. WH was supported by NASA (NNX17AI63G and NNX17AH25G). CL was supported by NASA Grant NNH16CT01C. This work is a contribution to the PIRATE project funded by CNES (to TP). PT was supported by the NOAA Research Global Ocean Monitoring and Observing Program through its sponsorship of UHSLC (NA16NMF4320058). JS was supported by EU contract 730030 (call H2020-EO-2016, “CEASELESS”). JW was supported by EU Horizon 2020 Grant 633211, Atlantos

Space-time structure and dynamics of the forecast error in a coastal circulation model of the Gulf of Lions

The probability density function (pdf) of forecast errors due to several possible error sources is investigated in a coastal ocean model driven by the atmosphere and a larger-scale ocean solution using an Ensemble (Monte Carlo) technique. An original method to generate dynamically adjusted perturbation of the slope current is proposed. The model is a high-resolution 3D primitive equation model resolving topographic interactions, river runoff and wind forcing. The Monte Carlo approach deals with model and observation errors in a natural way. It is particularly well-adapted to coastal non-linear studies. Indeed higher-order moments are implicitly retained in the covariance equation. Statistical assumptions are made on the uncertainties related to the various forcings (wind stress, open boundary conditions, etc.), to the initial state and to other model parameters, and randomly perturbed forecasts are carried out in accordance with the a priori error pdf. The evolution of these errors is then traced in space and time and the a posteriori error pdf can be explored.Third- and fourth-order moments of the pdf are computed to evaluate the normal or Gaussian behaviour of the distribution. The calculation of Central Empirical Orthogonal Functions (Ceofs) of the forecast Ensemble covariances eventually leads to a physical description of the model forecast error subspace in model state space. The time evolution of the projection of the Reference forecast onto the first Ceofs clearly shows the existence of specific model regimes associated to particular forcing conditions. The Ceofs basis is also an interesting candidate to define the Reduced Control Subspace for assimilation and in particular to explore transitions in model state space.We applied the above methodology to study the penetration of the Liguro-Provençal Catalan Current over the shelf of the Gulf of Lions in north-western Mediterranean together with the discharge of the Rhône river. This region is indeed well-known for its intense topographic and atmospheric forcings

Recent Developments of the Mercator Assimilation System (SAM): Towards the Seek Filter

The French MERCATOR project is developing several operational ocean forecasting systems to take part in the Global Ocean Data Assimilation Experiment (GODAE). Prototype systems are designed to simulate (1) the Atlantic and Mediterranean Sea (from 1/3o to 1/15o ), and (2) the global ocean circulation (from 2o to 1/4o ). The first generation assimilation scheme referred to as SAM1 has been implemented in the operational system. It provides routine weekly analyses and forecasts. SAM1 includes an altimetry-only version (SAM1-v1), and a fully multivariate version (SAM1-v2) permitting to assimilate vertical profiles and SST in addition to altimetry (JASON, ERS-2 and GFO). The SAM1 scheme is based on the SOFA reduced order interpolation scheme (LEGOS, Toulouse). It uses vertical/horizontal separation of error statistics, and order reduction in the vertical in terms of multivariate Empirical Orthogonal Functions (EOFs) of temperature: T, salinity: S, and barotropic streamfunction: ψ . The next generation assimilation system referred as SAM2 is being developed from the SEEK (Singular Evolutive Extended Kalman) algorithm (LEGI, Grenoble). This scheme is a Reduced Order Kalman Filter using a 3D multivariate modal decomposition of the forecast error covariance as well as an adaptive scheme to specify parameters of the forecast error. The use of the SEEK filter and its 3D modal representation for the error statistic is intended to overcome some of the limitations of SAM1 in highly inhomogeneous, anisotropic, and nonseparable regions of the world ocean such as shallow areas, as well as in the surface layer. A second objective for SAM2 will be to consistently propagate error estimates between successive assimilation cycles. We also developed several methods in order to generate 3D EOF basis both from local or global EOF calculation. Comparisons between these various multivariate approaches (SAM1 and SAM2) will be presented and discussed

Mise en œuvre de systèmes opérationnels de surveillance et de prévision des océans

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Key future research priorities in ocean forecasting

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Ensemble downscaling of a regional ocean model

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Stochastic study of the temperature response of the upper ocean to uncertainties in the atmospheric forcing in an Atlantic OGCM

International audienceThe impact of errors in atmospheric forcing on the behaviour of ocean models is a fundamental issue for ocean modellers and data assimilation and one that has yet to be fully addressed. In this study, we use a stochastic modelling approach with 50 7-months (September-March) primitive equation eddy permitting (1/4°) integrations. We investigate the response of the oceanic circulation to atmospheric uncertainties, focusing principally on their impact on the upper oceanic temperature field. The ensemble is generated by perturbing the wind, atmospheric temperature and incoming solar radiation of the ERA40 reanalysis. Each perturbation consists of a random combination of the 20 dominant EOFs of the difference between the ERA40 and NCEP/CORE reanalysis datasets. The ensemble standard deviation of various interfacial and oceanic quantities is then examined in the upper 200 m of three distinct regions of the North Atlantic: in the Gulf Stream, in the Northern Tropical band and in the North East Atlantic. These show that even a very small perturbation of the atmospheric variables can lead to significant changes in the ocean properties and that regions of oceanic mesoscale activity are the most sensitive. The ocean response is driven by vertical diffusivity and eddy activity. The role of subsurface currents is also crucial in carrying the eddy signal away from the regions of mesoscale activity. Finally, the decorrelation time scale of the mesoscale activity is critical in determining the amplitude of the oceanic response

How sensitive is a simulated river plume to uncertainties in wind forcing? A case study for the Red River plume (Vietnam)

International audienceThis paper aims at characterizing the sensitivity of a simulated plume's properties to uncertainties in the wind fields which force the ocean model using an ensemble method. The case study is the Red River plume in the Gulf of Tonkin. The variability of the Red River plume in the mid and far field is described in a previous paper using a clustering analysis (Nguyen-Duy et al., 2021) and is shown to be mostly driven by monsoon winds and tides. In the present study, we also aim at assessing the robustness of the classification with respect to the wind forcing uncertainties. The variability of the wind uncertainty is estimated as 60% of the wind variability with a higher variability near the coast. Based on that estimation, two ensembles of 50 simulations each with perturbed wind forcing are run over the summer 2015 period. Then, we examine the ensemble spread (defined as the standard deviation across the members) of the wind stress and of the ocean variables. The coastal current shows similar spread for both meridional and zonal flows, with the highest spread related to the highest wind stress spread. The sensitivity is the largest at the surface for salinity and at the base of the mixed-layer for temperature. The properties of the river plume are analyzed. The spread of the plume area is maximum in August, which is the same time as when the plume is the most spread out. The clustering analysis applied to the ensemble members shows some cluster attribution shifts between different members, but the cluster that is most likely to occur is still the one from the reference simulation (with unperturbed wind). These limited changes suggest that the cluster analysis of the reference simulation in Nguyen-Duy et al. (2021) is indeed robust to the wind forcing errors. The uncertainty on the plume thickness is typically less than 2m, sometimes reaching 4m (for a total thickness of 10m). The freshwater transport mainly follows the variations of the current due to the changes of wind. Possible implications of this study for the assimilation of high-frequency radar data are discussed at the end of the paper. Firstly, the relevance of the ensemble in simulating the model errors is assessed: the comparison with the data suggests that the model suffers from systematic errors that are not represented by the ensemble (by construction). Secondly, the ensemble is used to provide examples of model correction in a hypothetical data assimilation, highlighting its potential to constrain the plume by correcting directly the surface salinity, but also correcting the surface coastal current and the wind stress