2016 
Charrois, L., Cosme, E., Dumont, M., Lafaysse, M., Morin, S., Libois, Q., et al. (2016). On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model. Cryosphere, 10(3), 1021–1038.
Abstract: This paper examines the ability of optical reflectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multilayer snowpack model Crocus now including a twostream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms.Data assimilation is performed with an ensemblebased method, the Sequential Importance Resampling Particle filter, to represent simulation uncertainties. In snowpack modeling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates.Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODISlike data reduces by 45aEuro% the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODISlike reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70aEuro%. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works.


Garnier, F., Brankart, J. M., Brasseur, P., & Cosme, E. (2016). Stochastic parameterizations of biogeochemical uncertainties in a 1/4 degrees NEMO/PISCES model for probabilistic comparisons with ocean color data. Journal Of Marine Systems, 155, 59–72.
Abstract: In spite of recent advances, biogeochemical models are still unable to represent the full complexity of natural ecosystems. Their formulations are mainly based on empirical laws involving many parameters. Improving biogeochemical models therefore requires to properly characterize model uncertainties and their consequences. Subsequently, this paper investigates the potential of using random processes to simulate some uncertainties of the 1/4 degrees coupled PhysicalBiogeochemical NEMO/PISCES model of the North Atlantic ocean. Starting from a deterministic simulation performed with the original PISCES formulation, we propose a generic method based on AR(1) random processes to generate perturbations with temporal and spatial correlations. These perturbations are introduced into the model formulations to simulate 2 classes of uncertainties: the uncertainties on biogeochemical parameters and the uncertainties induced by unresolved scales in the presence of nonlinear processes. Using these stochastic parameterizations, a probabilistic version of PISCES is designed and a 60member ensemble simulation is performed. With respect to the simulation of chlorophyll, the relevance of the probabilistic configuration and the impacts of these stochastic parameterizations are assessed. In particular, it is shown that the ensemble simulation is in good agreement with the SeaWIFS ocean color data. Using these observations, the statistical consistency (reliability) of the ensemble is evaluated with rank histograms. Finally, the benefits expected from the probabilistic description of uncertainties (model error) are discussed in the context of future ocean color data assimilation. (C) 2015 Elsevier B.V. All rights reserved.


Sanchez, S., Fournier, A., Aubert, J., Cosme, E., & Gallet, Y. (2016). Modelling the archaeomagnetic field under spatial constraints from dynamo simulations: a resolution analysis. Geophysical Journal International, 207(2), 983–1002.
Abstract: Archaeomagnetic observations are key to recovering the behaviour of the geomagnetic field over the past few millennia. The corresponding data set presents a highly heterogeneous distribution in both space and time. Furthermore, the data are affected by substantial age and experimental uncertainties. In order to mitigate these detrimental properties, timedependent global archaeomagnetic field models are usually constructed under spatial and temporal regularization constraints, with the use of bootstrap techniques to account for data uncertainties. The models so obtained are the product of an adjustable tradeoff between goodnessoffit and model complexity. The spatial complexity is penalized by means of a norm reflecting the minimization of Ohmic dissipation within the core. We propose in this study to resort to alternative spatial constraints relying on the statistics of a numerical dynamo simulation with Earthlike features. To that end, we introduce a dynamo norm in an ensemble leastsquares iterative framework, the goal of which is to produce singleepoch models of the archaeomagnetic field. We first validate this approach using synthetic data. We next construct a redistributed archaeomagnetic data set between 1200 BC and 2000 AD by binning the data in windows of 40yr width. Since the dynamo norm is not adjustable, we can legitimately calculate a resolution matrix to quantify the resolving power of the available archaeomagnetic data set. Gauss coefficients are resolved up to spherical harmonic degree 3 for the first thousand years of the interval, to degree 4 for the next thousand years and to degree 5 during the last millennium. These conclusions are based on the distribution and uncertainties that characterize the data set, and do not take into account the possible presence of outliers. Comparison between our model, called AmR, and previously published archaeomagnetic field models confirms the archaeomagnetic resolution analysis: it highlights the dichotomy between datadriven coefficients for which model predictions coincide (within their respective uncertainties), and priordriven coefficients. This study opens the way to physicsbased models of the archaeomagnetic field; future work will be devoted to integrating the framework here introduced into a timedependent ensemble assimilation scheme.


2015 
Cosme, E. (2015). Méthodes d'assimilation rétrospective de données et application à l'océanographie. Habilitation thesis, UJFGrenoble 1, Grenoble.
Abstract: L'assimilation de données a pour objectif de combiner différentes sources d'information sur un système –des observations et un modèle, généralement− pour en obtenir la meilleure description possible. Les méthodes d'assimilation sont nombreuses, mais le choix d'une méthode est guidée d'une part par le problème à résoudre, d'autre part par la différence de nature entre les observations et le modèle.
L'océanographie moderne, notamment sa branche opérationnelle, est caractérisée par des modèles de grande taille, coûteux, et un réseau d'observations très peu dense en comparaison de la résolution spatiale et temporelle des modèles. Dans la perspective de construire des réanalyses de la circulation de l'océan à haute résolution, la méthode d'assimilation doit être relativement peu coûteuse en moyens de calcul tout en tirant le profit maximal des observations existantes. Ainsi, pour estimer l'état de l'océan d'un jour donné, il faut idéalement tenir compte des observations futures. L'assimilation de ces observations est alors rétrospective.
L'essentiel de ma présentation portera sur le développement de ces méthodes d'assimilation rétrospectives de données. Dans un premier temps, je présenterai ces méthodes dans un formalisme général (Bayésien) puis dans le formalisme appliqué aux méthodes habituellement utilisées en océanographie, et plus généralement en géophysique. Dans un second temps, j'illustrerai les bénéfices de cette assimilation rétrospective de données en océanographie, d'abord dans le cadre d'une circulation océanique idéalisée, puis avec un modèle réaliste de la circulation en Atlantique Tropical.
Je consacrerai aussi une part significative de mon exposé aux recherches que j'ai amorcées récemment et que je propose de conduire dans le futur, portant sur l'assimilation des observations océaniques de nouvelle génération : couleur de l'eau et altimétrie et à large fauchée. Ces observations présenteront une nature singulièrement différente des observations actuelles, puisqu'elles seront de très haute résolution, localement denses en surface mais très peu denses en profondeur et/ou en temps. Je décrirai les quelques pistes que j'ai commencé à explorer, et celles que j'envisage d'explorer, pour développer les méthodes d'assimilation spécifiques à ces observations.


Ruggiero, G. A., Ourmieres, Y., Cosme, E., Blum, J., Auroux, D., & Verron, J. (2015). Data assimilation experiments using diffusive backandforth nudging for the NEMO ocean model. Nonlinear Processes In Geophysics, 22(2), 233–248.
Abstract: The diffusive backandforth nudging (DBFN) is an easytoimplement iterative data assimilation method based on the wellknown nudging method. It consists of a sequence of forward and backward model integrations, within a given time window, both of them using a feedback term to the observations. Therefore, in the DBFN, the nudging asymptotic behaviour is translated into an infinite number of iterations within a bounded time domain. In this method, the backward integration is carried out thanks to what is called backward model, which is basically the forward model with reversed time step sign. To maintain numeral stability, the diffusion terms also have their sign reversed, giving a diffusive character to the algorithm. In this article the DBFN performance to control a primitive equation ocean model is investigated. In this kind of model nonresolved scales are modelled by diffusion operators which dissipate energy that cascade from large to small scales. Thus, in this article, the DBFN approximations and their consequences for the data assimilation system setup are analysed. Our main result is that the DBFN may provide results which are comparable to those produced by a 4Dvar implementation with a much simpler implementation and a shorter CPU time for convergence. The conducted sensitivity tests show that the 4Dvar profits of long assimilation windows to propagate surface information downwards, and that for the DBFN, it is worth using short assimilation windows to reduce the impact of diffusioninduced errors. Moreover, the DBFN is less sensitive to the first guess than the 4Dvar.


2014 
Metref, S., Cosme, E., Snyder, C., & Brasseur, P. (2014). A nonGaussian analysis scheme using rank histograms for ensemble data assimilation. Nonlinear Processes In Geophysics, 21(4), 869–885.
Abstract: One challenge of geophysical data assimilation is to address the issue of nonGaussianities in the distributions of the physical variables ensuing, in many cases, from nonlinear dynamical models. NonGaussian ensemble analysis methods fall into two categories, those remapping the ensemble particles by approximating the best linear unbiased estimate, for example, the ensemble Kalman filter (EnKF), and those resampling the particles by directly applying Bayes' rule, like particle filters. In this article, it is suggested that the most common remapping methods can only handle weakly nonGaussian distributions, while the others suffer from sampling issues. In between those two categories, a new remapping method directly applying Bayes' rule, the multivariate rank histogram filter (MRHF), is introduced as an extension of the rank histogram filter (RHF) first introduced by Anderson (2010). Its performance is evaluated and compared with several data assimilation methods, on different levels of nonGaussianity with the Lorenz 63 model. The method's behavior is then illustrated on a simple density estimation problem using ensemble simulations from a coupled physicalbiogeochemical model of the North Atlantic ocean. The MRHF performs well with lowdimensional systems in strongly nonGaussian regimes.


2012 
Cosme, E., Verron, J., Brasseur, P., Blum, J., & Auroux, D. (2012). Smoothing Problems in a Bayesian Framework and Their Linear Gaussian Solutions. Monthly Weather Review, 140(2), 683–695.
Abstract: Smoothers are increasingly used in geophysics. Several linear Gaussian algorithms exist, and the general picture may appear somewhat confusing. This paper attempts to stand back a little, in order to clarify this picture by providing a concise overview of what the different smoothers really solve, and how. The authors begin addressing this issue from a Bayesian viewpoint. The filtering problem consists in finding the probability of a system state at a given time, conditioned to some past and present observations (if the present observations are not included, it is a forecast problem). This formulation is unique: any different formulation is a smoothing problem. The two main formulations of smoothing are tackled here: the joint estimation problem (fixed lag or fixed interval), where the probability of a series of system states conditioned to observations is to be found, and the marginal estimation problem, which deals with the probability of only one system state, conditioned to past, present, and future observations. The various strategies to solve these problems in the Bayesian framework are introduced, along with their deriving linear Gaussian, Kalman filterbased algorithms. Their ensemble formulations are also presented. This results in a classification and a possible comparison of the most common smoothers used in geophysics. It should provide a good basis to help the reader find the most appropriate algorithm for his/her own smoothing problem.


Freychet, N., Cosme, E., Brasseur, P., Brankart, J. M., & Kpemlie, E. (2012). Obstacles and benefits of the implementation of a reducedrank smoother with a high resolution model of the tropical Atlantic Ocean. Ocean Science, 8(5), 797–811.
Abstract: Most of oceanographic operational centers use threedimensional data assimilation schemes to produce reanalyses. We investigate here the benefits of a smoother, i.e. a fourdimensional formulation of statistical assimilation. A squareroot sequential smoother is implemented with a tropical Atlantic Ocean circulation model. A simple twin experiment is performed to investigate its benefits, compared to its corresponding filter. Despite model's nonlinearities and the various approximations used for its implementation, the smoother leads to a better estimation of the ocean state, both on statistical (i.e. mean error level) and dynamical points of view, as expected from linear theory. Smoothed states are more in phase with the dynamics of the reference state, an aspect that is nicely illustrated with the chaotic dynamics of the North Brazil Current rings. We also show that the smoother efficiency is strongly related to the filter configuration. One of the main obstacles to implement the smoother is then to accurately estimate the error covariances of the filter. Considering this, benefits of the smoother are also investigated with a configuration close to situations that can be managed by operational center systems, where covariances matrices are fixed (optimal interpolation). We define here a simplified smoother scheme, called halffixed basis smoother, that could be implemented with current reanalysis schemes. Its main assumption is to neglect the propagation of the error covariances matrix, what leads to strongly reduce the cost of assimilation. Results illustrate the ability of this smoother to provide a solution more consistent with the dynamics, compared to the filter. The smoother is also able to produce analyses independently of the observation frequency, so the smoothed solution appears more continuous in time, especially in case of a low frenquency observation network.


Ubelmann, C., Verron, J., Brankart, J. M., Brasseur, P., & Cosme, E. (2012). Assimilating altimetric data to control the tropical instability waves: an observing system simulation experiment study. Ocean Dynamics, 62(6), 867–880.
Abstract: Tropical instability waves (TIWs) are not easily simulated by ocean circulation models primarily because such waves are very sensitive to wind forcing. In this study, we investigate the impact of assimilating sea surface height (SSH) observations on the control of TIWs in an observing system simulation experiment (OSSE) context based on a regional model configuration of the tropical Atlantic. A Kalman filtering method with suitable adaptations is found to be successful when altimetric data are assimilated in conjunction with sea surface temperature and some in situ temperature/salinity profiles. In this rather realistic system, the TIW phase is roughly controlled with a single nadir observing satellite. However, a right correction of the TIW structure and amplitude requires at least two nadir observing satellites or a wide swath observing satellite. The significant impact of orbital parameters is also demonstrated: in particular, the Jason or GFO satellite orbits are found to be more suitable than the ENVISAT orbit. More generally, it is found that as soon as adequate subsampling exists (with periods of 510 days), the length of the repetitivity cycle of orbits does not have a significant impact.


2011 
Brankart, J. M., Cosme, E., Testut, C. E., Brasseur, P., & Verron, J. (2011). Efficient Local Error Parameterizations for Square Root or Ensemble Kalman Filters: Application to a BasinScale Ocean Turbulent Flow. Monthly Weather Review, 139(2), 474–493.
Abstract: In largesized atmospheric or oceanic applications of square root or ensemble Kalman filters, it is often necessary to introduce the prior assumption that longrange correlations are negligible and force them to zero using a local parameterization, supplementing the ensemble or reducedrank 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 highrank 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 basinscale ocean turbulent flow. Even with this moderatesized system (a 2200kmwide square basin with 100kmwide 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 moderatesized 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.


Krysta, M., Blayo, E., Cosme, E., & Verron, J. (2011). A Consistent Hybrid VariationalSmoothing Data Assimilation Method: Application to a Simple ShallowWater Model of the Turbulent Midlatitude Ocean. Monthly Weather Review, 139(11), 3333–3347.
Abstract: In the standard fourdimensional variational data assimilation (4DVar) algorithm the background error covariance matrix B remains static over time. It may therefore be unable to correctly take into account the information accumulated by a system into which data are gradually being assimilated. A possible method for remedying this flaw is presented and tested in this paper. A hybrid variationalsmoothing algorithm is based on a reducedrank incremental 4DVar. Its consistent coupling to a singular evolutive extended Kalman (SEEK) smoother ensures the evolution of the B matrix. In the analysis step, a lowdimensional error covariance matrix is updated so as to take into account the increased confidence level in the state vector it describes, once the observations have been introduced into the system. In the forecast step, the basis spanning the corresponding control subspace is propagated via the tangent linear model. The hybrid method is implemented and tested in twin experiments employing a shallowwater model. The background error covariance matrix is initialized using an EOF decomposition of a sample of model states. The quality of the analyses and the information content in the bases spanning control subspaces are also assessed. Several numerical experiments are conducted that differ with regard to the initialization of the B matrix. The feasibility of the method is illustrated. Since improvement due to the hybrid method is not universal, configurations that benefit from employing it instead of the standard 4DVar are described and an explanation of the possible reasons for this is proposed.


2010 
Brankart, J. M., Cosme, E., Testut, C. E., Brasseur, P., & Verron, J. (2010). Efficient Adaptive Error Parameterizations for Square Root or Ensemble Kalman Filters: Application to the Control of Ocean Mesoscale Signals. Monthly Weather Review, 138(3), 932–950.
Abstract: In Kalman filter applications, an adaptive parameterization of the error statistics is often necessary to avoid filter divergence, and prevent error estimates from becoming grossly inconsistent with the real error. With the classic formulation of the Kalman filter observational update, optimal estimates of general adaptive parameters can only be obtained at a numerical cost that is several times larger than the cost of the state observational update. In this paper, it is shown that there exists a few types of important parameters for which optimal estimates can be computed at a negligible numerical cost, as soon as the computation is performed using a transformed algorithm that works in the reduced control space defined by the square root or ensemble representation of the forecast error covariance matrix. The set of parameters that can be efficiently controlled includes scaling factors for the forecast error covariance matrix, scaling factors for the observation error covariance matrix, or even a scaling factor for the observation error correlation length scale. As an application, the resulting adaptive filter is used to estimate the time evolution of ocean mesoscale signals using observations of the ocean dynamic topography. To check the behavior of the adaptive mechanism, this is done in the context of idealized experiments, in which model error and observation error statistics are known. This ideal framework is particularly appropriate to explore the illconditioned situations (inadequate prior assumptions or uncontrollability of the parameters) in which adaptivity can be misleading. Overall, the experiments show that, if used correctly, the efficient optimal adaptive algorithm proposed in this paper introduces useful supplementary degrees of freedom in the estimation problem, and that the direct control of these statistical parameters by the observations increases the robustness of the error estimates and thus the optimality of the resulting Kalman filter.


Cosme, E., Brankart, J. M., Verron, J., Brasseur, P., & Krysta, M. (2010). Implementation of a reduced rank squareroot smoother for high resolution ocean data assimilation. Ocean Modelling, 33(12), 87–100.
Abstract: Optimal smoothers enable the use of future observations to estimate the state of a dynamical system. In this paper, a squareroot smoother algorithm is presented, extended from the Singular Evolutive Extended Kalman (SEEK) filter, a squareroot Kalman filter routinely used for ocean data assimilation. With this filter algorithm, the smoother extension appears almost costfree. A modified algorithm implementing a particular parameterization of model error is also described. The smoother is applied with an ocean circulation model in a doublegyre, 1/4 degrees configuration, able to represent midlatitude mesoscale dynamics. Twin experiments are performed: the true fields are drawn from a simulation at a 1/6 degrees resolution, and noised. Then, altimetric satellite tracks and sparse vertical profiles of temperature are extracted to form the observations. The smoother is efficient in reducing errors, particularly in the regions poorly covered by the observations at the filter analysis time. It results in a significant reduction of the global error: the Root Mean Square Error in Sea Surface Height from the filter is further reduced by 20% by the smoother. The actual smoothing of the global error through time is also verified. Three essential issues are then investigated: (i) the time distance within which observations may be favourably used to correct the state estimates is found to be 8 days with our system. (ii) The impact of the model error parameterization is stressed. When this parameterization is spuriously neglected, the smoother can deteriorate the state estimates. (iii) Iterations of the smoother over a fixed time interval are tested. Although this procedure improves the state estimates over the assimilation window, it also makes the subsequent forecast worse than the filter in our experiment. (C) 2009 Elsevier Ltd. All rights reserved.


2009 
Brankart, J. M., Ubelmann, C., Testut, C. E., Cosme, E., Brasseur, P., & Verron, J. (2009). Efficient Parameterization of the Observation Error Covariance Matrix for Square Root or Ensemble Kalman Filters: Application to Ocean Altimetry. Monthly Weather Review, 137(6), 1908–1927.
Abstract: 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.


Castebrunet, H., Martinerie, P., Genthon, C., & Cosme, E. (2009). A threedimensional model study of methanesulphonic acid to non sea salt sulphate ratio at mid and highsouthern latitudes. Atmos. Chem. Phys., 9(24), 9449–9469.
Abstract: The Antarctic and subAntarctic methanesulphonic acid (MSA) to non sea salt sulphate (nssSO(4)) ratio is simulated with the Laboratoire de M,t,orologie Dynamique Atmospheric General Circulation Model including an atmospheric sulphur chemistry module. Spatial variations of the MSA/nssSO(4) ratio in different regions have been suggested to be mostly dependent on temperature or sulphur source contributions. Its past variations in ice cores have been interpreted as related to the DMS precursor source location. Our model results are compared with available field measurements in the Antarctic and subAntarctic regions. This suggests that the MSA/nssSO(4) ratio in the extratropical south hemisphere is mostly dependent on the relative importance of various DMS oxidation pathways. In order to evaluate the effect of a rapid conversion of dimethyl sulphoxide (DMSO) into MSA, not implemented in the model, the MSA+DMSO to nssSO(4) ratio is also discussed. Using this modified ratio, the model mostly captures the seasonal variations of MSA/nssSO(4) at mid and highsouthern latitudes. In addition, the model qualitatively reproduces the bell shaped meridional variations of the ratio, which is highly dependent on the adopted relative reaction rates for the DMS+OH addition and abstraction pathways, and on the assumed reaction products of the MSIA+OH reaction. MSA/nssSO(4) ratio in Antarctic snow is fairly well reproduced except at the most inland sites characterized with very low snow accumulation rates. Our results also suggest that atmospheric chemistry plays an important role in the observed decrease of the ratio in snow between coastal regions and central Antarctica. The still insufficient understanding of the DMS oxidation scheme limits our ability to model the MSA/nssSO(4) ratio. Specifically, reaction products of the MSIA+OH reaction should be better quantified, and the impact of a fast DMSO conversion to MSA in spring to fall over Antarctica should be evaluated. A better understanding of BrO source processes is needed in order to include DMS + BrO chemistry in global models. Completing the observations of DMS, BrO and MSA at Halley Bay with DMSO measurements would better constrain the role of BrO in DMS oxidation. Direct measurements of MSA and nssSO(4) dry deposition velocities on Antarctic snow would improve our ability to model MSA and nssSO(4) in ice cores.


Ubelmann, C., Verron, J., Brankart, J. M., Cosme, E., & Brasseur, P. (2009). Impact of data from upcoming altimetric missions on the prediction of the threedimensional circulation in the tropical Atlantic Ocean. Journal Of Operational Oceanography, 2(1), 3–14.
Abstract: The use of Sea Surface Height (SSH) satellite measurements in ocean models is a key element the efficient control of the threedimensional circulation through data assimilation and therefore in the quality of operational oceanography products. This paper attempts to evaluate the impact of future satellite data, particularly from the upcoming JASON2, SARAL and SWOT missions, introduced into a model through a sophisticated data assimilation procedure. For this purpose, Observing System Simulation Experiments (OSSEs) are performed in the tropical Atlantic Ocean. The NEMO model is used (at a 1/4 degrees resolution) in a configuration covering the tropical Atlantic from 15 degrees S to 17 degrees N, and the assimilation scheme is a reducedorder Kalman (SEEK) filter. The study focuses principally on controlling of the circulation of the North Brazil Current and the propagation of Tropical Instability Waves (TIW). Among the orbits tested for altimetric satellites, the JASON2 orbit (10day repeat period) is found to give the best single satellite sampling for data assimilation. The addition of a second or third satellite to JASON2 is particularly useful in the TIW region and is even required to properly control the Brazil rings. A SWOT satellite would provide benefits that are equivalent to the contribution of two or three satellites, depending on the case.


2005 
Cosme, E., Hourdin, F., Genthon, C., & Martinerie, P. (2005). Origin of dimethylsulfide, nonseasalt sulfate, and methanesulfonic acid in eastern Antarctica. J. Geophys. Res.Atmos., 110(D3), 20 pp.
Abstract: [1] Ignoring the origin of atmospheric chemicals is often a strong limitation to the full interpretation of their measurement. In this article, this question is addressed in the case of the sulfur species in Antarctica, with an original method of retrotransport of tracers. The retrotransport model is derived from the Laboratoire de Meteorologie Dynamique ZoomTracers (LMDZT) atmospheric general circulation model, optimized for polar climate and expanded to simulate atmospheric sulfur chemistry. For two East Antarctic scientific stations (Dumont d'Urville and Vostok) the effects of transport and chemistry and the influence of oceanic, volcanic, and anthropogenic sources on dimethylsulfide (DMS), nonseasalt (nss) sulfate, and methanesulfonic acid (MSA) concentrations are evaluated in summer and winter. The oceanic source largely dominates, but other sources can episodically be significant. The meridional origin and the age of DMS, MSA, and biogenic nss sulfate are also estimated. The latitudes of origin of MSA and nss sulfate are similar in summer, but they differ markedly in winter. This is a signature of their different chemical production scheme. Also, the interannual variability of the origin of the sulfur species at Vostok is weak compared to that at Dumont d'Urville. Acknowledging that the DMS concentrations in the ocean have no interannual variability in the model, this result suggests unsurprisingly that inland Antarctic stations may be better observation sites to monitor largescale DMS bioproductivity variability than coastal sites are. The combination of slower chemistry and more intense atmospheric circulation in winter leads to unexpected results, such as a younger DMS in winter than in summer at Vostok.


2003 
Boucher, O., Moulin, C., Belviso, S., Aumont, O., Bopp, L., Cosme, E., et al. (2003). DMS atmospheric concentrations and sulphate aerosol indirect radiative forcing: a sensitivity study to the DMS source representation and oxidation. Atmos. Chem. Phys., 3, 49–65.
Abstract: The global sulphur cycle has been simulated using a general circulation model with a focus on the source and oxidation of atmospheric dimethylsulphide (DMS). The sensitivity of atmospheric DMS to the oceanic DMS climatology, the parameterisation of the seaair transfer and to the oxidant fields have been studied. The importance of additional oxidation pathways (by O3 in the gas and aqueousphases and by BrO in the gas phase) not incorporated in global models has also been evaluated. While three different climatologies of the oceanic DMS concentration produce rather similar global DMS fluxes to the atmosphere at 2427 Tg S yr()1(,) there are large differences in the spatial and seasonal distribution. The relative contributions of OH and NO3 radicals to DMS oxidation depends critically on which oxidant fields are prescribed in the model. Oxidation by O3 appears to be significant at high latitudes in both hemispheres. Oxidation by BrO could be significant even for BrO concentrations at subpptv levels in the marine boundary layer. The impact of such refinements on the DMS chemistry onto the indirect radiative forcing by anthropogenic sulphate aerosols is also discussed.


Genthon, C., & Cosme, E. (2003). Intermittent signature of ENSO in westAntarctic precipitation. Geophys. Res. Lett., 30(21), 4 pp.
Abstract: Precipitation data from the new ERA40 reanalyses and from a 200year simulation confirm a robust main mode of precipitation variability in west Antarctica. An intermittently strong ENSO signature is found in this mode. However, high correlation with ENSO indices appears infrequent. Thus, the high correlation found in ERA40, and previously in other chronologically realistic data, in the late 1980s and the 1990s may not be expected to last. Unlike previously suggested by others, the sign of the correlation between ENSO indices and west Antarctic precipitation, when significant, does not appear to change in time: Precipitation variability at the ENSO pace in the BellingshausenWeddell (RossAmunsden) region is consistently in phase (phase opposition, respectively) with the Southern Oscillation Index. This is consistent with a tropospheric wave train connecting the tropical Pacific and west Antarctic regions, which modulates in phase opposition the advection of air and moisture in the 2 regions.
Keywords: Antarctic precipitation; interannual/interdecadal variability; El Nino Southern Oscillation; ERA40 reanalysis; surface mass balance


2002 
Cosme, E. (2002). Cycle du soufre des moyennes et hautes latitudes Sud dans un modèle de circulation générale atmosphériqueThèse de l'Université JosephFourier, Grenoble 1. Ph.D. thesis, , .
Keywords: soufre, circulation génégale atmosphérique, moyennes et hautes latitudes sud, modèle LMDZT


Cosme, E., Genthon, C., Martinerie, P., Boucher, O., & Pham, M. (2002). The sulfur cycle at highsouthern latitudes in the LMDZT General Circulation Model. J. Geophys. Res.Atmos., 107(D23), 19 pp.
Abstract: [1] This modeling study was motivated by the recent publication of yearround records of dimethylsulfide (DMS) and dimethylsulfoxide (DMSO) in Antarctica, completing the available series of sulfate and methanesulfonic acid (MSA). Sulfur chemistry has been incorporated in the Laboratoire de Meteorologie DynamiqueZoom Tracers (LMDZT) Atmospheric General Circulation Model (AGCM), with highresolution and improved physics at highsouthern latitudes. The model predicts the concentration of six major sulfur species through emissions, transport, wet and dry deposition, and chemistry in both gas and aqueous phases. Model results are broadly realistic when compared with measurements in air and snow or ice, as well as to results of other modeling studies, at highand middlesouthern latitudes. Atmospheric MSA concentrations are underestimated and DMSO concentrations are overestimated in summer, reflecting the lack of a DMSO heterogeneous sink leading to MSA. Experiments with various recently published estimates of the rate of this sink are reported. Although not corrected in this work, other defects are identified and discussed: DMS concentrations are underestimated in winter, MSA and nonseasalt (nss) sulfate concentrations may be underestimated at the South Pole, the deposition scheme used in the model may not be adapted to polar regions, and the model does not adequately reproduces interannual variability. Oceanic DMS sources have a major contribution to the variability of sulfur in these regions. The model results suggest that in a large part of central Antarctica groundlevel atmospheric DMS concentrations are larger in winter than in summer. At highsouthern latitudes, high loads of DMS and DMSO are found and the main chemical sink of sulfur dioxide (SO2) is aqueous oxidation by ozone (O3), whereas oxidation by hydrogen peroxide (H2O2) dominates at the global scale. A comprehensive modeled sulfur budget of Antarctica is provided.
Keywords: Antarctica; sulfur cycle; general circulation model; dimethylsulfide; oceanatmosphere flux; tropospheric chemistry


Genthon, C., Krinner, G., & Cosme, E. (2002). Free and laterally nudged antarctic climate of an atmospheric general circulation model. Mon. Weather Rev., 130(6), 1601–1616.
Abstract: Because many of the synoptic cyclones south of the 60degreesS parallel originate from 60degreesS and lower latitudes, nudging an atmospheric general circulation model (AGCM) with meteorological analyses at the periphery of the Antarctic region may be expected to exert a strong control on the atmospheric circulation inside the region. Here, the ECMWF reanalyses are used to nudge the atmospheric circulation of the Laboratoire de Meteorologie Dynamique Zoom (LMDZ) stretchedgrid AGCM in a 15yr simulation spanning the 197993 period. The horizontal resolution (grid spacing) in the model reaches similar to100 km south of 60degreesS. Nudging is exerted along the 60degreesS parallel, and this is called lateral nudging for the Antarctic region. Nudging is also performed farther north, near 50degrees and 40degreesS, but this is not essential for the results discussed here. Surface pressure and winds in the atmospheric column are nudged without relaxation to maximize control by the meteorological analyses, at the expense of some "noise'' confined to the latitudes where nudging is exerted. The performances of lateral nudging are evaluated with respect to station observations, the free (unnudged) model, the ECMWF reanalyses, and in limited instances with respect to nudging the surface pressure only. It is shown that the free model has limited but persistent surface pressure and geopotential defects in the Antarctic region, which are efficiently corrected by lateral nudging. Also, the laterally nudged simulations confirm, and to some extent correct, a geopotential deficiency of the ECMWF reanalyses over the east Antarctic continent previously identified by others. The monthly mean variability of surface climate at several stations along a coasttopole transect is analyzed. A significant fraction of the observed variability of surface pressure and temperature is reproduced. The fraction is often less than in the reanalyses. However, the differences are not large considering that the nudged model is forced at distances of hundreds to thousands of kilometers whereas the reanalyses are forced at much shorter distances, in principle right at each station site by the very station data. The variability of surface wind is significantly less well reproduced than that of pressure and temperature in both the nudged model and the reanalyses. Carefully adjusted polar physics in the LMDZ model seems to compensate for a distant observational constraint in the cases when the nudged model results appear similar or even superior to the reanalyses. Lateral nudging is less computationally intensive than global nudging, and it induces realistic variability and chronology while leaving full expression of the model physics in the region of interest. Laterally nudging an AGCM with meteorological analyses can offer complementary value over the analyses themselves, not only by producing additional atmospheric information not available from the analyses, but also by correcting possible regional defects in the analyses.
