ALBERT

Description: The coastal circulation in the Gulf of Lions (GoL) is influenced by the Northern Current (NC), forced by a complex wind system and also affected by important river discharges from the Rhône River. Correct modelling of this current is therefore important for obtaining a good representation of the gulf circulation. An observing system simulation experiment using the SEEK filter data assimilation method was used in a regional 1/16° configuration of the GoL in the NEMO model. The synthetic observation database used for the experiment comprised altimetric data in addition to in-situ temperature and salinity profiles. Statistical diagnostics and other physical criteria based on the improvement of NC representation were set up in order to assess the quality of this experiment. Comparisons between the free 1/16° simulation and the experience with assimilation show that data assimilation significantly improved the description of the characteristics of the NC as well as its seasonal and mesoscale variability, which in turn improved the description of the water exchanges between the coastal region and the open sea.

Description: Most data assimilation algorithms require the inverse of the covariance matrix of the observation errors. In practical applications, the cost of computing this inverse matrix with spatially correlated observation errors is prohibitive. Common practices are therefore to subsample or combine the observations so that the errors of the assimilated observations can be considered uncorrelated. As a consequence, a large fraction of the available observational information is not used in practical applications. In this study, a method is developed to account for the correlations of the errors that will be present in the wide-swath sea surface height measurements, for example, the Surface Water and Ocean Topography (SWOT) mission. It basically consists of the transformation of the observation vector so that the inverse of the corresponding covariance matrix can be replaced by a diagonal matrix, thus allowing to genuinely take into account errors that are spatially correlated in physical space. Numerical experiments of ensemble Kalman filter analysis of SWOT-like observations are conducted with three different observation error covariance matrices. Results suggest that the proposed method provides an effective way to account for error correlations in the assimilation of the future SWOT data. The transformation of the observation vector proposed herein yields both a significant reduction of the root-mean-square errors and a good consistency between the filter analysis error statistics and the true error statistics.

Description: This study investigates the origin and features of interannual–decadal Atlantic meridional overturning circulation (AMOC) variability from several ocean simulations, including a large (50 member) ensemble of global, eddy-permitting (1/4°) ocean–sea ice hindcasts. After an initial stochastic perturbation, each member is driven by the same realistic atmospheric forcing over 1960–2015. The magnitude, spatiotemporal scales, and patterns of both the atmospherically forced and intrinsic–chaotic interannual AMOC variability are then characterized from the ensemble mean and ensemble spread, respectively. The analysis of the ensemble-mean variability shows that the AMOC fluctuations north of 40°N are largely driven by the atmospheric variability, which forces meridionally coherent fluctuations reaching decadal time scales. The amplitude of the intrinsic interannual AMOC variability never exceeds the atmospherically forced contribution in the Atlantic basin, but it reaches up to 100% of the latter around 35°S and 60% in the Northern Hemisphere midlatitudes. The intrinsic AMOC variability exhibits a large-scale meridional coherence, especially south of 25°N. An EOF analysis over the basin shows two large-scale leading modes that together explain 60% of the interannual intrinsic variability. The first mode is likely excited by intrinsic oceanic processes at the southern end of the basin and affects latitudes up to 40°N; the second mode is mostly restricted to, and excited within, the Northern Hemisphere midlatitudes. These features of the intrinsic, chaotic variability (intensity, patterns, and random phase) are barely sensitive to the atmospheric evolution, and they strongly resemble the “pure intrinsic” interannual AMOC variability that emerges in climatological simulations under repeated seasonal-cycle forcing. These results raise questions about the attribution of observed and simulated AMOC signals and about the possible impact of intrinsic signals on the atmosphere.

Description: Satellite-derived surface chlorophyll data are assimilated daily into a three-dimensional 24-member ensemble configuration of an online-coupled NEMO (Nucleus for European Modeling of the Ocean)–PISCES (Pelagic Interaction Scheme of Carbon and Ecosystem Studies) model for the North Atlantic Ocean. A 1-year multivariate assimilation experiment is performed to evaluate the impacts on analyses and forecast ensembles. Our results demonstrate that the integration of data improves surface analysis and forecast chlorophyll representation in a major part of the model domain, where the assimilated simulation outperforms the probabilistic skills of a non-assimilated analogous simulation. However, improvements are dependent on the reliability of the prior free ensemble. A regional diagnosis shows that surface chlorophyll is overestimated in the northern limit of the subtropical North Atlantic, where the prior ensemble spread does not cover the observation's variability. There, the system cannot deal with corrections that alter the equilibrium between the observed and unobserved state variables producing instabilities that propagate into the forecast. To alleviate these inconsistencies, a 1-month sensitivity experiment in which the assimilation process is only applied to model fluctuations is performed. Results suggest the use of this methodology may decrease the effect of corrections on the correlations between state vectors. Overall, the experiments presented here evidence the need of refining the description of model's uncertainties according to the biogeochemical characteristics of each oceanic region.

Description: In this paper, we investigate the potential of using a probabilistic modelling approach in the prospect of ocean colour data assimilation. The main objective of the study is to assess the benefits of using error covariances based on an explicit simulation of model uncertainties. The relevance of this approach is evaluated by considering 3D observational updates of the ensemble (one update at one time step) performed every 5 days (over one year) using the statistics of a North Atlantic coupled NEMO/PISCES stochastic ensemble simulation involving 60 members, as previously described in Garnier et al, 2016. In this experiment, SeaWIFS ocean colour data are used to update the ensemble with a low rank ensemble Kalman Filter analysis scheme. The non-Gaussian behaviour of the model variables is taken into account using anamorphic transformations. Comparisons between the updated ensemble and the MERIS satellite observations shows that the integration of high resolution SeaWIFS data significantly improves the representation and the ensemble statistics of chlorophyll concentrations. We also show that these improvements consistently cascade in the water column chlorophyll distributions and on non-observed variables closely linked with the primary production. In addition, we present first results illustrating the potential of our approach for biogeochemical forecasts. The objective is to examine the model response to data assimilation in the perspective of future operational applications. For this purpose, we perform a 60 member simulation initiated from updated biogeochemical states. This forecast simulation shows that ocean colour data assimilation would be skillful considering integration cycles of the order of a day. Finally, the intend of this article is to point out the feasibility of operational biogeochemical data assimilation in the near future.

Description: Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localization algorithm in ensemble data assimilation systems), so that the large scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in CMEMS assimilation systems, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large scale components of the signal by an explicit observational constraint applied directly on the large scales, and to restrict the use of spatial localisation to the small scale components of the signal. To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the JASON tracks), assimilated in a 1/4° model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral space and the spectral localization provides consistent ensemble estimates of the state of the system (in the spectral space,or after backward transformation to the spatial space). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.

Description: In the Kalman filter standard algorithm, the computational complexity of the observational update is proportional to the cube of the number y of observations (leading behavior for large y). In realistic atmospheric or oceanic applications, involving an increasing quantity of available observations, this often leads to a prohibitive cost and to the necessity of simplifying the problem by aggregating or dropping observations. If the filter error covariance matrices are in square root form, as in square root or ensemble Kalman filters, the standard algorithm can be transformed to be linear in y, providing that the observation error covariance matrix is diagonal. This is a significant drawback of this transformed algorithm and often leads to an assumption of uncorrelated observation errors for the sake of numerical efficiency. In this paper, it is shown that the linearity of the transformed algorithm in y can be preserved for other forms of the observation error covariance matrix. In particular, quite general correlation structures (with analytic asymptotic expressions) can be simulated simply by augmenting the observation vector with differences of the original observations, such as their discrete gradients. Errors in ocean altimetric observations are spatially correlated, as for instance orbit or atmospheric errors along the satellite track. Adequately parameterizing these correlations can directly improve the quality of observational updates and the accuracy of the associated error estimates. In this paper, the example of the North Brazil Current circulation is used to demonstrate the importance of this effect, which is especially significant in that region of moderate ratio between signal amplitude and observation noise, and to show that the efficient parameterization that is proposed for the observation error correlations is appropriate to take it into account. Adding explicit gradient observations also receives a physical justification. This parameterization is thus proved to be useful to ocean data assimilation systems that are based on square root or ensemble Kalman filters, as soon as the number of observations becomes penalizing, and if a sophisticated parameterization of the observation error correlations is required.

Description: In large-sized atmospheric or oceanic applications of square root or ensemble Kalman filters, it is often necessary to introduce the prior assumption that long-range correlations are negligible and force them to zero using a local parameterization, supplementing the ensemble or reduced-rank representation of the covariance. One classic algorithm to perform this operation consists of taking the Schur product of the ensemble covariance matrix by a local support correlation matrix. However, with this parameterization, the square root of the forecast error covariance matrix is no more directly available, so that any observational update algorithm requiring this square root must include an additional step to compute local square roots from the Schur product. This computation generates an additional numerical cost or produces high-rank square roots, which may deprive the observational update from its original efficiency. In this paper, it is shown how efficient local square root parameterizations can be obtained, for use with a specific square root formulation (i.e., eigenbasis algorithm) of the observational update. Comparisons with the classic algorithm are provided, mainly in terms of consistency, accuracy, and computational complexity. As an application, the resulting parameterization is used to estimate maps of dynamic topography characterizing a basin-scale ocean turbulent flow. Even with this moderate-sized system (a 2200-km-wide square basin with 100-km-wide mesoscale eddies), it is observed that more than 1000 ensemble members are necessary to faithfully represent the global correlation patterns, and that a local parameterization is needed to produce correct covariances with moderate-sized ensembles. Comparisons with the exact solution show that the use of local square roots is able to improve the accuracy of the updated ensemble mean and the consistency of the updated ensemble variance. With the eigenbasis algorithm, optimal adaptive estimates of scaling factors for the forecast and observation error covariance matrix can also be obtained locally at negligible additional numerical cost. Finally, a comparison of the overall computational cost illustrates the decisive advantage that efficient local square root parameterizations may have to deal simultaneously with a larger number of observations and avoid data thinning as much as possible.