This study investigates the impact of dynamical representational error (RE) on the analysis of an ocean ensemble Kalman filter‐based data assimilation system, LETKF‐ROMS (Local Ensemble Transform Kalman Filter – Regional Ocean Modelling System) configured for the Indian Ocean and assimilating in situ temperature and salinity observations from Argo. Three different approaches to account for the RE are studied and inter‐compared: (a) static RE (varies in horizontal and vertical direction), (b) dynamic RE (varies in space and time) estimated from concurrent observations, and (c) dynamic RE estimated using concurrent high‐resolution model outputs. RE estimated from the model outputs exhibits rich spatial and temporal variability with an estimated temporal mean RE for temperature below 0.5 and 0.2 °C in the surface and deep layers, respectively, and reaching up to 1 °C in the thermocline layers. The region encompassing the Great Whirl displays a large seasonal variability reaching up to 0.8 °C, and the South Equatorial Current (SEC) a large interannual variability reaching up to 0.4 °C. Neglecting such spatio‐temporal variations of RE and assimilating with a static RE limited the benefits of assimilation by entertaining over‐fitting issues that caused degradations in the Bay of Bengal, the western parts of the Arabian Sea, and the equatorial Indian Ocean. Assimilating with the observations‐based dynamic RE improved the results in these regions, but the best performances were obtained with the configuration using the model‐based dynamic RE, which yielded further improvements, e.g. reduction of sea surface height root‐mean‐square errors reaches 30% with respect to the observations‐based dynamic RE. The latter also better handled the rich spatial variability regions and areas not well sampled by the observations. Improved estimates of the spatial and temporal variations of RE helped to better exploit the assimilated observations and provided enhanced analyses less prone to assimilation shocks.

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<p>Understanding the causes of the variability of the North Atlantic and Mediterranean overturning circulations, and the possible correlation between them is important to disentangle the processes which link the two ocean basins. In this study, we hypothesize that the Gibraltar inflow transport is the main driver of the basin-mean sea surface height variability in the Mediterranean Sea and that they are both anti-correlated to the Atlantic Meridional Overturning Circulation (AMOC) in the North Atlantic.</p><p>We analyze here the AMOC and the Mediterranean mean sea surface height (SSH) in an ensemble of eddy-permitting global ocean reanalyses and the Gibraltar inflow transport using an eddy-resolving Mediterranean Reanalysis over the period 1993-2019. In this contribution, firstly we extend the results obtained in past literature with observations (2004-2017 period) and confirm the anti-correlation between the Mediterranean mean sea level and the upper branch of the AMOC at 26.5°N over the 1993-2019 period. Secondly, for the first time, we examine the correlation of the different components of the AMOC and the Gibraltar inflow transport and find significant anti-correlations at interannual time scales.</p><p>We show that during years of weaker/stronger AMOC and higher/lower SSH in the Mediterranean Sea, a stronger/weaker Azores Current results in stronger/weaker Gibraltar inflow transport. We argue that the anticorrelation between AMOC and the mean sea level of the Mediterranean Sea is explained by the anticorrelation between AMOC and the Gibraltar inflow transport which in turn is changed by the wind driven Azores current strength.</p>

Abstract. In the last decade, various satellite missions have been monitoring the status of cryoshopere and its evolution over time. Beside sea-ice concentration data, available since the 80s, sea-ice thickness retrievals are now ready to be used in operational prediction and reanalysis systems. Nevertheless, a straightforward ingestion of multiple sea-ice characteristics in a multivariate framework is prevented by the highly non-gaussian distribution of such variables together with the low accuracy of thickness observations. This study describes an extension of OceanVar, a 3Dvar system routinely employed in the production of global/regional operational/reanalysis products, designed to include sea-ice variables. Those variables are treated through an anamorphosis operator that transforms sea-ice anomalies into gaussian control variables, the benefit brought by such transformation is described. Several sensitivity experiments are carried out using a suite of diverse datasets. The assimilation of the sole Cryosat-2 provides a good spatial representation of thickness distribution but still overestimates the total volume that requires the inclusion of SMOS data to be properly constrained. The intermittent availability of thickness data along the year, leads to potential discontinuities in the integrated quantities that requires a dedicated tuning. The use of merged L4 product CS2SMOS produces similar skill score when validated against independent mooring data, compared to the ingestion of L3 CryoSat-2 and L3 SMOS data. The new sea-ice module is meant to simplify the future coupling with ocean variables.