A. Alvera-Azcárate

The Surface Circulation of the Caribbean Sea and the Gulf of Mexico as Inferred from Satellite Altimetry

The surface circulation of the Caribbean Sea and Gulf of Mexico is studied using 13 years of satellite altimetry data. Variability in the Caribbean Sea is evident over several time scales. At the annual scale, sea surface height (SSH) varies mainly by a seasonal steric effect. Interannually, a longer cycle affects the SSH slope across the current and hence the intensity of the Caribbean Current. This cycle is found to be related to changes in the wind intensity, the wind stress curl, and El Nino-Southern Oscillation. At shorter time scales, eddies and meanders are observed in the Caribbean Current, and their propagation speed is explained by baroclinic instabilities under the combined effect of vertical shear and the b effect. Then the Loop Current (LC) is considered, focusing on the anticyclonic eddies shed by it and the intrusion of the LC into the Gulf of Mexico through time. Twelve of the 21 anticyclonic eddies observed to detach from the LC are shed from July to September, suggesting a seasonality in the timing of these events. Also, a relation is found between the intrusion of the LC into the Gulf of Mexico and the size of the eddies shed from it: larger intrusions trigger smaller eddies. A series of extreme LC intrusions into the Gulf of Mexico, when the LC is observed as far as 928W, are described. The analyses herein suggest that the frequency of such events has increased in recent years, with only one event occurring in 1993 versus three from 2002 to 2006. Transport through the Straits of Florida appears to decrease during these extreme intrusions.

Reconstruction of sea surface temperature by means of DINEOF: a case study during the fishing season in the Bay of Biscay

The Spanish surface fishery operates mainly during the summer season in the waters of the Bay of Biscay. Sea surface temperature (SST) data recovered from satellite images are being used to improve the operational efficiency of fishing vessels (e.g. reduce search time and increase catch rate) and to improve the understanding of the variations in catch distribution and rate needed to properly manage fisheries. The images used for retrieval of SST often present gaps due to the existence of clouds or satellite malfunction periods. The data gaps can totally or partially affect the area of interest. Within this study, an application of a technique for the reconstruction of missing data called DINEOF (data interpolating empirical orthogonal functions) is analysed, with the aim of testing its applicability in operational SST retrieval during summer months. In this case study, the Bay of Biscay is used as the target area. Three months of SST Moderate Resolution Imaging Spectroradiometer (MODIS) images, ranging from 1 May 2006 to 31 July 2006, were used. The main objective of this work is to test the overall performance of this technique, under potential operational use for the support of the fleet during the summer fishing season. The study is designed to analyse the sensitivity of the results of this technique to several details of the methodology used in the reconstruction of SST, such as the number of empirical orthogonal functions (EOFs) retained, the handling of the seasonal cycle or the length (number of images) of the SST database used. The results are tested against independent SST data from International Comprehensive Ocean–Atmosphere Data Set (ICOADS) ship reports and standing buoys and estimations of the error of the reconstructed SST fields are given. Conclusions show that over this area three months of data are enough for efficient SST reconstruction, which yields four EOFs as the optimal number needed for this case study. An extended EOF experiment with SST and SST with a lag of one day was carried out to analyse whether the autocorrelation of the SST data allows better performance in the SST reconstruction, although the experiment did not improve the results. The validation studies show that the reconstructed SSTs can be trusted, even when the amount of missing data is very high. The mean absolute deviation maps show that the error is greatest near to the coast and mainly in the upwelling areas close to the French and north-western Spanish coasts.

Approximate and Efficient Methods to Assess Error Fields in Spatial Gridding with Data Interpolating Variational Analysis (DIVA)

AbstractThis paper presents new approximate methods to provide error fields for the spatial analysis tool Data Interpolating Variational Analysis (DIVA). The first method shows how to replace the costly analysis of a large number of covariance functions with a single analysis for quick error computations. Then another method is presented where the error is only calculated in a small number of locations, and from there the spatial error field itself is interpolated by the analysis tool. The efficiency of the methods is illustrated on simple schematic test cases and a real application in the Mediterranean Sea. These examples show that with these methods, one has the possibility for quick masking of regions void of sufficient data and the production of “exact” error fields at reasonable cost. The error-calculation methods can also be generalized for use with other analysis methods such as three-dimensional variational data assimilation (3DVAR) and are therefore potentially interesting for other implementations.

Local ensemble assimilation scheme with global constraints and conservation

Ensemble assimilation schemes applied in their original, global formulation respect linear conservation properties if the ensemble perturbations are set up accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is therefore necessary to filter out spurious long-range correlations. The conservation of the global properties will be lost if the assimilation is performed locally, since the conservation requires a coupling between all model grid points which is removed by the localization. The distribution of ocean observations is often highly inhomogeneous. Systematic errors of the observed parts of the ocean state can lead to spurious adjustment of the non-observed parts via data assimilation and thus to a spurious increase or decrease in long-term simulations of global properties which should be conserved. In this paper, we propose a local assimilation scheme (with different variants and assumptions) which can satisfy global conservation properties. The proposed scheme can also be used for non-local observation operators. Different variants of the proposed scheme are tested in an idealized model and compared to the traditional covariance localization with an ad-hoc step enforcing conservation. It is shown that the inclusion of the conservation property reduces the total RMS error and that the presented stochastic and deterministic schemes avoiding error space rotation provide better results than the traditional covariance localization.

Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR).