LETKF-ROMS: An improved predictability system for the Indian Ocean

We have developed the assimilation scheme Local Ensemble Transform Kalman Filter (LETKF) and interfaced with the present basin-wide operational ROMS set-up ( 1/12 degree horizontal resolution ) that assimilates in-situ temperature and salinity from RAMA moorings, NIOT buoys and Argo floats. The system also assimilate satellite track data of sea-surface temperature from AMSR-E. The speciality of this assimilation system is that it comprises of ensembles that are initialized with different model coefficients like diffusion parameters and the ensemble members also respond to two different mixing schemes - K profile parameterization and Mellor-Yamada. This aids in maintaining the spread of the ensemble intact - which has always been a challenging task. We have also employed a localization radius of ~200 km, i.e., observations influence the prognostic state variables that fall within this range. The assimilation system is also bestowed with better representative error estimates - a method developed in-house along the likes of Etherton et al. The ensemble members were forced with ensemble atmospheric fluxes provided by National Centre for Medium Range Weather Forecast (NCMRWF). Assimilation was performed every five day. We show that the assimilated system simulates the ocean state better than the present operational basin-wide ROMS. We validate it extensively against multiple observations ranging from RAMA moorings to ADCP observations across both dependent variables like temperature and salinity and independent variables like sealevel anomaly and currents. We show that assimilation improves the overall ocean state except at few isolated locations. It improves the correlation with respect to observations and reduces the root-mean-squared error. We also show that assimilation improves the estimation of mixed layer depth and 20 degree isotherm (which are diagnostic variables) thereby proving that the subsurface conditions are better simulated

Copernicus Ocean State Report, issue 6

The 6th issue of the Copernicus OSR incorporates a large range of topics for the blue, white and green ocean for all European regional seas, and the global ocean over 1993–2020 with a special focus on 2020

Bivariate sea-ice assimilation for Global Ocean Analysis/Reanalysis

&amp;lt;p&amp;gt;Recent intercomparison studies among ocean/sea-ice Reanalyses (such as ORA-IP) have shown large discrepancies in many sea-ice-related fields, despite a rather general agreement in the sea-ice extension. The low accuracy of sea-ice thickness measurements together with the highly non-gaussian distributions of related uncertainty, made multivariate sea-ice data assimilation (DA) strategies still at an early stage, although nearly twenty years of thickness observations are now available. In a standard multivariate scheme, the break of Gaussianity can generate un-realistic corrections due to the poor linear relationship driven by the B matrix.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;One approach to solve the problem is the implementation of anamorphous transformations that modify the probability density functions of ice anomalies into Gaussian ones (Brankart et al. 2012). In this study, a 3DVar DA scheme (called OceanVar), employed in the routinely production of global/regional ocean reanalysis CGLORS (Storto et al, 2016), has been recently extended to ingest sea-ice concentration (SIC) and thickness (SIT) data. An anamorphous operator, firstly developed and made freely available within the SANGOMA project (http://www.data-assimilation.net/), has been updated and adapted for the bivariate assimilation of SIC/SIT within the OceanVar framework.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present the comparison among several sensitivity experiments that were performed assimilating different observation datasets and using different DA configurations at 1/4 degree global resolution. Specifically, we assess the impact of ingesting different SIT products, such as SMOS and CRYOSAT-2 data or the merged product CS2SMOS.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We show that the sole assimilation of SIC improves the spatial representation of SIT with respect to a free run. The inclusion of thickness correction, determined by empirical relations, appears to improve the sea ice characteristics in the Atlantic sector and degrade them in the Siberian region; therefore a refined tuning could probably be beneficial. The spatial error reduces sharply only once CRYOSAT-2 data are assimilated jointly with SIC data. In the present set up, all the experiments generally tend to overestimate the sea-ice volume in the case SMOS data are not assimilated. However, observational errors associated with SMOS data are generally too small, leading to jumps in the volume time series at the beginning of the accretion period if not calibrated correctly.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The proposed approach is suitable to be used for covarying ocean/sea-ice variables in future coupled ocean/sea-ice DA.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Storto, A. and Masina, S. (2016), Earth Syst. Sci. Data, 8, 679, doi: 0.5194/essd-8-679-2016&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Brankart, et al. (2012), Ocean Sci., 8, 121, doi: 10.5194/os-8-121-2012&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;</jats:p

Bivariate sea-ice assimilation for global ocean Analysis/Reanalysis

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. </jats:p

The Atlantic Meridional Overturning Circulation forcing the mean sea level in the Mediterranean Sea through the Gibraltar transport&amp;#160;

&amp;lt;p&amp;gt;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.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;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&amp;amp;#176;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.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;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.&amp;lt;/p&amp;gt;</jats:p