Pierre De Mey

Mean sea level and surface circulation variability of the Mediterranean Sea from 2 years of TOPEX/POSEIDON altimetry

We describe the circulation and mean sea level variations of the Mediterranean Sea from 2 years of TOPEX/POSEIDON altimetric data. It is first shown that the response of the Mediterranean Sea to atmospheric pressure forcing is close to an inverse barometer (except at high frequencies) which means that the adjustment is accompanied by a flow through the Straits of Sicily and Gibraltar. We then use TOPEX/POSEIDON to study the mean sea level variations, representing steric effects and integrated large-scale changes of the mass of the Mediterranean Sea. We observe an annual cycle with a fast drop during winter. Steric effects account for about half of the observed variations. The remaining signal is believed to be driven by evaporation minus precipitation (E - P) forcing and internal hydraulic control in the Straits of Gibraltar. Using suboptimal space-time objective analysis, the classic components of the Mediterranean surface circulation are recovered, despite low signal-to-noise ratio (the rms of sea level variability is less than 10 cm). The variable Mediterranean circulation is seen as a complex combination of mesoscale and large-scale variations. The surface circulation is more complex in the eastern basin than in the western basin. In the east it is composed of subbasin-scale gyres, such as the so-called Mersa-Matruh and Shikmona gyres, which do not have an obvious recurrence period. We also observe an intensification of the large-scale cyclonic winter circulation in the western and in the Ionian basins. Several mesoscale structures, such as the Alboran gyres and the Ierepetra gyre, show a clear seasonal cycle, with a maximum in summer. The good qualitative and quantitative agreement of the results with previous data from the Mediterranean illustrates the improved accurary of TOPEX/POSEIDON over its predecessors.

A Multivariate Reduced-order Optimal Interpolation Method and its Application to the Mediterranean Basin-scale Circulation

For more than a decade, the Ocean Circulation and Prediction Team at LEGOS, Toulouse, has been developing data assimilation methods and conducting data assimilation experiments in various basins of the World Ocean, and in particular in the Mediterranean. Our aims are to study the feasibility of multivariate control of a model trajectory, and to characterize the predictability of the general circulation, seasonal and interannual variability mesoscale eddies, meanders, sub-basin-scale gyres, and response to wind. This chapter deals with the design and modus operandi of a practical algorithm for data assimilation and application to the Mediterranean.

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

Abstract 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-Provencal Catalan Current over the shelf of the Gulf of Lions in north-western Mediterranean together with the discharge of the Rhone river. This region is indeed well-known for its intense topographic and atmospheric forcings.

Continuous assimilation in an open domain of the northeast Atlantic: 1. Methodology and application to AthenA‐88

A scheme to perform suboptimal intermittent assimilation in an open-ocean, quasi-geostrophic model is presented and applied to the assimilation of altimeter data in a domain of the northeast Atlantic west of Ireland. Both simulated and real altimeter data are used. Three-dimensional (3-D) synoptic observation fields and error variance fields are derived from synoptic dynamic topography anomaly estimates, using an empirical orthogonal mode (EOF) vertical extension technique. The 3-D estimates are suboptimally combined with the time-dependent part of the forecast fields. Suboptimality here means that the combination does not directly use spatial correlations of errors; however, the 3-D synoptic observation estimates used in the combination do contain error spatial statistical information. The resulting fields added to a mean model climatology are used as initial conditions for the model, which is integrated until a new dynamic topography anomaly estimate is available. Locally, the combination is optimal in the sense that it is based on the observational error variances, which reflect the measurement noise and the space-time distribution of data, and that it minimizes the error variance of the results. A similar combination scheme using past as well as future observations is used to update the boundaries during model integration. Simulated surface topography anomaly maps, typical of the northeast Atlantic, are assimilated every 20 days for a 300-day period, with different noise characteristics typical of altimetric sampling errors, in a three-level version of the model. The assimilation fields, especially the vorticity at deeper levels, converge toward the reference fields. The convergence is obtained after O(100 days), regardless of the observational noise levels tested (up to −2 dB). Using a relevant observational error model seems to matter, in particular, the error level should not be underestimated. In the case of an 80-day gap in the data inflow, the model predictability limits the reliability of the forecast beyond 20 days. The scheme makes the model converge back as soon as new observations are entered, with limited convergence loss due to the gap. The case of an unknown model climatology is also studied. Finally, the scheme is applied to Geosat altimeter data, added to the Robinson, Bauer and Schroeder annual climatology, in the AthenA-88 cruise area, which is contained in the model box. The assimilation results compare favorably with hydrographical data from the cruise and help the synoptic interpretation of the observed phenomena.

Adjoint assimilation of altimetric, surface drifter, and hydrographic data in a quasi-geostrophic model of the Azores Current

The flow characteristics in the region of the Azores Current are investigated by assimilating TOPEX/POSEIDON and ERS 1 altimeter data into the multilevel Harvard quasigeostrophic (QG) model with open boundaries (Miller et al., 1983) using an adjoint variational scheme (Moore, 1991). The study site lies in the path of the Azores Current, where a branch retroflects to the south in the vicinity of the Madeira Rise. The region was the site of an intensive field program in 1993, SEMAPHORE. We had two main aims in this adjoint assimilation project. The first was to see whether the adjoint method could be applied locally to optimize an initial guess field, derived from the continous assimilation of altimetry data using optimal interpolation (OI). The second aim was to assimilate a variety of different data sets and evaluate their importance in constraining our QG model. The adjoint assimilation of surface data was effective in optimizing the initial conditions from OI. After 20 iterations the cost function was generally reduced by 50–80%, depending on the chosen data constraints. The primary adjustment process was via the barotropic mode. Altimetry proved to be a good constraint on the variable flow field, in particular, for constraining the barotropic field. The excellent data quality of the TOPEX/POSEIDON (T/P) altimeter data provided smooth and reliable forcing; but for our mesoscale study in a region of long decorrelation times O(30 days), the spatial coverage from the combined T/P and ERS 1 data sets was more important for constraining the solution and providing stable flow at all levels. Surface drifters provided an excellent constraint on both the barotropic and baroclinic model fields. More importantly, the drifters provided a reliable measure of the mean field. Hydrographic data were also applied as a constraint; in general, hydrography provided a weak but effective constraint on the vertical Rossby modes in the model. Finally, forecasts run over a 2-month period indicate that the initial conditions optimized by the 20-day adjoint assimilation provide more stable, longer-term forecasts.

Assimilation of satellite altimeter data in a primitive-equation model of the Azores–Madeira region

Abstract The aim of this study is to implement satellite altimetric assimilation into a high-resolution primitive-equation ocean model and check the validity and sensitivity of the results. Beyond this paper, the remote objective is to get a dynamical tool capable of simulating the surface ocean processes linked to the air–sea interactions as well as to perform mesoscale ocean forecasting. For computational cost and practical reasons, this study takes place in a 1000 by 1000 sq km open domain of the Canary basin. The assimilation experiments are carried out with the combined TOPEX/POSEIDON and ERS-1 data sets between June 1993 and December 1993. The space–time domain overlaps with in situ data collected during the SEMAPHORE experiment and thus enables an objective validation of the results. A special boundary treatment is applied to the model by creating a surrounding recirculating area separated from the interior by a buffer zone. The altimetric assimilation is done by implementing a reduced-order optimal interpolation algorithm with a special vertical projection of the surface model/data misfits. We perform a first experiment with a vertical projection onto an isopycnal EOF representing the Azores Current vertical variability. An objective validation of the models velocities with Lagrangian float data shows good results (the correlation is 0.715 at 150 dbar). The question of the sensitivity to the vertical projection is addressed by performing similar experiments using a method for lifting/lowering of the water column, and using an EOF in Z-coordinates. Some comparisons with in situ temperature data do not show any significant difference between the three projections, after five months of assimilation. However, in order to preserve the large-scale water characteristics, we felt that the isopycnal projection was a more physically consistent choice. Then, the complementary character of the two satellites is assessed with two additional experiments which use each altimeter data sets separately. There is an evidence of the benefit of combining the two data sets. Otherwise, an experiment assimilating long-wavelength bias-corrected CLS altimetric maps every 10 days exhibits the best correlation scores and emphasizes the importance of reducing the orbit error and biases in the altimetric data sets. The surface layers of the model are forced using realistic daily wind stress values computed from ECMWF analyses. Although we resolve small space and time scales, in our limited domain the wind stress does not significantly influence the quality of the results obtained with the altimetric assimilation. Finally, the relative effects of the data selection procedure and of the integration times (cycle lengths) is explored by performing data window experiments. A value of 10 days seems to be the most satisfactory cycle length.

Contribution of a Wide-Swath Altimeter in a Shelf Seas Assimilation System: Impact of the Satellite Roll Errors

Abstract The authors investigate the potential qualitative improvement brought by wide-swath, interferometry-based ocean altimetry measurements with respect to classical nadir altimeters in a coastal/shelf data assimilation system. In addition, particular attention is paid to roll errors, which could significantly reduce the expected benefits of wide-swath altimetry. A barotropic, nonlinear free-surface model is set up over the European shelf as part of an ensemble Kalman filter. Experiments assimilating simulated data are performed over the North Sea to test the ability of altimeter configurations to reduce model errors due to the action of meteorological forcing in the presence of bathymetric uncertainties. A simplified wide-swath observation scheme is used, composed of nadir altimeter height plus a nadir-centered cross-track sea level slope measurement. The simplified wide-swath measurements are found to be able to constrain events unsampled by a single nadir altimeter owing to a wider domain of influe...

Synthesis of new scientific challenges for GODAE OceanView

The marine environment plays an increasingly important role in shaping economies and infrastructures, and touches upon many aspects of our lives, including food supplies, energy resources, national security and recreational activities. Global Ocean Data Assimilation Experiment (GODAE) and GODAE OceanView have provided platforms for international collaboration that significantly contribute to the scientific development and increasing uptake of ocean forecasting products by end users who address societal issues such as those listed above. Many scientific challenges and opportunities remain to be tackled in the ever-changing field of operational oceanography, from the observing system to modelling, data assimilation and product dissemination. This paper provides a brief overview of past achievements in GODAE OceanView, but subsequently concentrates on the future scientific foci of GODAE OceanView and its Task Teams, and provides a vision for the future of ocean forecasting.

Objective assessment of the contribution of the RECOPESCA network to the monitoring of 3D coastal ocean variables in the Bay of Biscay and the English Channel

In the Bay of Biscay and the English Channel, in situ observations represent a key element to monitor and to understand the wide range of processes in the coastal ocean and their direct impacts on human activities. An efficient way to measure the hydrological content of the water column over the main part of the continental shelf is to consider ships of opportunity as the surface to cover is wide and could be far from the coast. In the French observation strategy, the RECOPESCA programme, as a component of the High frequency Observation network for the environment in coastal SEAs (HOSEA), aims to collect environmental observations from sensors attached to fishing nets. In the present study, we assess that network using the Array Modes (ArM) method (a stochastic implementation of Le Hénaff et al. Ocean Dyn 59: 3–20. doi: 10.1007/s10236-008-0144-7, 2009). That model ensemble-based method is used here to compare model and observation errors and to quantitatively evaluate the performance of the observation network at detecting prior (model) uncertainties, based on hypotheses on error sources. A reference network, based on fishing vessel observations in 2008, is assessed using that method. Considering the various seasons, we show the efficiency of the network at detecting the main model uncertainties. Moreover, three scenarios, based on the reference network, a denser network in 2010 and a fictive network aggregated from a pluri-annual collection of profiles, are also analysed. Our sensitivity study shows the importance of the profile positions with respect to the sheer number of profiles for ensuring the ability of the network to describe the main error modes. More generally, we demonstrate the capacity of this method, with a low computational cost, to assess and to design new in situ observation networks.

Science in support of coastal ocean forecasting—part 1

In parallel to the ever increasing activities and population in the coastal regions, and to the critical growth of the associated stresses on the coastal environment, the need for monitoring and forecasting of currents and marine parameters in the coastal and shelf seas is becoming more pressing (e.g. De Mey et al. 2009; De Mey and Kourafalou 2014). Largescale ocean modeling, observational, and forecasting projects provide products which are available everywhere, but the relevance of those products in coastal regions is often found inadequate (De Mey and Proctor 2009). Because of the smaller-scale, higher-frequency, coupled coastal dynamical, and biogeochemical processes found in coastal regions, and because of the presence of the coastline, shelves, coastal rivers, and other unique elements, specific coastal modeling, observational, and forecasting strategies have to be designed, within a general paradigm of integration between large-scale, regional, and coastal ocean forecasting systems (e.g., Stanev et al. 2016). Science has to be advanced to that end, as a community effort: this is what the Coastal Ocean and Shelf Seas Task Team (COSS-TT) aims to do within GODAE Ocean View (GOV, www.godae-oceanview.org), as illustrated in Kourafalou et al. (2015a, b). Five successful international COSS-TT meetings so far helped define priority areas where science is needed for the development of Coastal Ocean Forecasting Systems: (a) monitoring of physical and biogeochemical parameters in coastal regions (including active links with the coastal altimetry community); (b) development of fine-scale coastal ocean models; (c) integration topics: downscaling the ocean estimation problem from large-scale to coastalscale models, data and forcings, coastal data assimilation and prediction, and consistent validation metrics; (d) coastal-scale atmosphere-wave-ocean couplings; (e) ecosystem response to the physical drivers; (f) probabilistic approaches and risk assessment in the coastal ocean, including extreme events. Papers in this collection have been gathered in themes which cut across those boundaries: (1) coastal monitoring and array design, (2) coastal modeling, integration, and model-data synergy, (3) coastal data assimilation and prediction, and (4) coastal applications. A summary of all contributions is included below.