A multiscale ocean data assimilation approach combining spatial and spectral localisation

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 localisation 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 the assimilation systems of the Copernicus Marine Environment Monitoring Service, 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 domain and the spectral localisation provides consistent ensemble estimates of the state of the system (in the spectral domain or after backward transformation to the spatial domain). 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.</p

Influence of geometrical boundary conditions on the estimation of rheological parameters

Improved knowledge of geometrical boundary conditions, such as bedrock geometry and surface topography, can contribute significantly to glaciological studies including ice-sheet-flow modelling. Precise thickness and altimetric data allow an estimation of ice-flow direction, the balance velocity and the basal shear stress. These parameters are calculated along a 1160 km profile in East Antarctica using a relationship between shear stress, basal temperature, the Glen flow exponent and a parameter related to strain rate. Strong variations of the flow-law parameters and basal conditions are found to play a major role in the ice-flow pattern. Sliding, anisotropy and longitudinal stress strongly perturb the validity of the law, but their signature can be identified.Published102-1063.8. Geofisica per l'ambienteJCR Journalreserve

Influence of geometrical boundary conditions on the estimation of rheological parameters

Improved knowledge of geometrical boundary conditions, such as bedrock geometry and surface topography, can contribute significantly to glaciological studies including ice-sheet-flow modelling. Precise thickness and altimetric data allow an estimation of ice-flow direction, the balance velocity and the basal shear stress. These parameters are calculated along a 1160 km profile in East Antarctica using a relationship between shear stress, basal temperature, the Glen flow exponent and a parameter related to strain rate. Strong variations of the flow-law parameters and basal conditions are found to play a major role in the ice-flow pattern. Sliding, anisotropy and longitudinal stress strongly perturb the validity of the law, but their signature can be identified.Published102-1063.8. Geofisica per l'ambienteJCR Journalreserve

Lakes and subglacial hydrological networks around Dome C, East Antarctica

Precise topography from European Remote-sensing Satellite radar altimetry and high density of airborne radio-echo sounding in the area surrounding Dome C, Antarctica, show a link between surface features and subglacial lakes. In this paper, we extend the study to fine structures by computing a curvature-based coefficient (cy) related to surface undulations. These coefficient variations reveal many surface undulations, and some elongated features of this parameter seem to link known subglacial lakes. A population of high values of this coefficient, assumed to correspond to transitions between sliding and non-sliding flow regime, strengthen the appearance of a network which would link most of the lakes in the area. The existence of such a network impacts on ice-flow dynamics and on subglacial-lake studies.Published252-2563.8. Geofisica per l'ambienteJCR Journalreserve

Lakes and subglacial hydrological networks around Dome C, East Antarctica

Precise topography from European Remote-sensing Satellite radar altimetry and high density of airborne radio-echo sounding in the area surrounding Dome C, Antarctica, show a link between surface features and subglacial lakes. In this paper, we extend the study to fine structures by computing a curvature-based coefficient (cy) related to surface undulations. These coefficient variations reveal many surface undulations, and some elongated features of this parameter seem to link known subglacial lakes. A population of high values of this coefficient, assumed to correspond to transitions between sliding and non-sliding flow regime, strengthen the appearance of a network which would link most of the lakes in the area. The existence of such a network impacts on ice-flow dynamics and on subglacial-lake studies.Published252-2563.8. Geofisica per l'ambienteJCR Journalreserve

Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations

Abstract. The objective of this paper is to investigate if the description of ocean uncertainties can be significantly improved by applying a local anamorphic transformation to each model variable, and by making the assumption of joint Gaussianity for the transformed variables, rather than for the original variables. For that purpose, it is first argued that a significant improvement can already be obtained by deriving the local transformations from a simple histogram description of the marginal distributions. Two distinctive advantages of this solution for large size applications are the conciseness and the numerical efficiency of the description. Second, various oceanographic examples are used to evaluate the effect of the resulting piecewise linear local anamorphic transformations on the spatial correlation structure. These examples include (i) stochastic ensemble descriptions of the effect of atmospheric uncertainties on the ocean mixed layer, and of wind uncertainties or parameter uncertainties on the ecosystem, and (ii) non-stochastic ensemble descriptions of forecast uncertainties in current sea ice and ecosystem pre-operational developments. The results indicate that (i) the transformation is accurate enough to faithfully preserve the correlation structure if the joint distribution is already close to Gaussian, and (ii) the transformation has the general tendency of increasing the correlation radius as soon as the spatial dependence between random variables becomes nonlinear, with the important consequence of reducing the number of degrees of freedom in the uncertainties, and thus increasing the benefit that can be expected from a given observation network. </jats:p