Intraseasonal variability of mixed layer depth in the tropical Indian Ocean

In this paper, we use an observational dataset built from Argo in situ profiles to describe the main large-scale patterns of intraseasonal mixed layer depth (MLD) variations in the Indian Ocean. An eddy permitting (0.25A degrees) regional ocean model that generally agrees well with those observed estimates is then used to investigate the mechanisms that drive MLD intraseasonal variations and to assess their potential impact on the related SST response. During summer, intraseasonal MLD variations in the Bay of Bengal and eastern equatorial Indian Ocean primarily respond to active/break convective phases of the summer monsoon. In the southern Arabian Sea, summer MLD variations are largely driven by seemingly-independent intraseasonal fluctuations of the Findlater jet intensity. During winter, the Madden-Julian Oscillation drives most of the intraseasonal MLD variability in the eastern equatorial Indian Ocean. Large winter MLD signals in northern Arabian Sea can, on the other hand, be related to advection of continental temperature anomalies from the northern end of the basin. In all the aforementioned regions, peak-to-peak MLD variations usually reach 10 m, but can exceed 20 m for the largest events. Buoyancy flux and wind stirring contribute to intraseasonal MLD fluctuations in roughly equal proportions, except for the Northern Arabian Sea in winter, where buoyancy fluxes dominate. A simple slab ocean analysis finally suggests that the impact of these MLD fluctuations on intraseasonal sea surface temperature variability is probably rather weak, because of the compensating effects of thermal capacity and sunlight penetration: a thin mixed-layer is more efficiently warmed at the surface by heat fluxes but loses more solar flux through its lower base.

Catalogue of data and platforms at Network GDAC level, including the example of Copernicus In Situ TAC

This document presents catalogue techniques used at network GDAC level to facilitate the discovery of platforms and data files. Some AtlantOS networks are organized as DAC-GDACs that continuously update a catalogue of metadata on observation datasets and platforms: • A DAC is a Data Assembly Centre operating at national or regional scale. It manages data and metadata for its area with a direct link to Scientifics and Operators. The DAC pushes observations to the network GDAC. • A GDAC is a Global Data Assembly Centre. It is designed for a global observation network such as Argo, OceanSITES, DBCP, EGO, Gosud, etc… The GDAC aggregates data and metadata of an observation network, in real-time and delayed mode, provided by DACs.

Developing European operational oceanography for Blue Growth, climate change adaptation and mitigation, and ecosystem-based management

Operational approaches have been more and more widely developed and used for providing marine data and information services for different socio-economic sectors of the Blue Growth and to advance knowledge about the marine environment. The objective of operational oceanographic research is to develop and improve the efficiency, timeliness, robustness and product quality of this approach. This white paper aims to address key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5-10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European Ocean Observations, Modelling and Forecasting Technology, Coastal Operational Oceanography and Operational Ecology. The areas "European Ocean Observations" and "Modelling and Forecasting Technology" focus on the further advancement of the basic instruments and capacities for European operational oceanography, while "Coastal Operational Oceanography" and "Operational Ecology" aim at developing new operational approaches for the corresponding knowledge areas.

Data assimilation for advanced cross-scale unstructured-grid ocean modeling.

The coastal ocean is a complex environment with extremely dynamic processes that require a high-resolution and cross-scale modeling approach in which all hydrodynamic fields and scales are considered integral parts of the overall system. In the last decade, unstructured-grid models have been used to advance in seamless modeling between scales. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas have been developed only recently and need significant advancements. Here, we link the unstructured-grid ocean modeling to the variational data assimilation methods. In particular, we show results from the modeling system SANIFS based on SHYFEM fully-baroclinic unstructured-grid model interfaced with OceanVar, a state-of-art variational data assimilation scheme adopted for several systems based on a structured grid. OceanVar implements a 3DVar DA scheme. The combination of three linear operators models the background error covariance matrix. The vertical part is represented using multivariate EOFs for temperature, salinity, and sea level anomaly. The horizontal part is assumed to be Gaussian isotropic and is modeled using a first-order recursive filter algorithm designed for structured and regular grids. Here we introduced a novel recursive filter algorithm for unstructured grids. A local hydrostatic adjustment scheme models the rapidly evolving part of the background error covariance. We designed two data assimilation experiments using SANIFS implementation interfaced with OceanVar over the period 2017-2018, one with only temperature and salinity assimilation by Argo profiles and the second also including sea level anomaly. The results showed a successful implementation of the approach and the added value of the assimilation for the active tracer fields. While looking at the broad basin, no significant improvements are highlighted for the sea level, requiring future investigations. Furthermore, a Machine Learning methodology based on an LSTM network has been used to predict the model SST increments.

A comparison between vertical mixing parameterizations in the Levantine Sea

The goal of this thesis was the study of an optimal vertical mixing parameterization scheme in a mesoscale dominated field characterized from a strong vorticity and the presence of a layer of colder, less saline water at about 100 m depth (Atlantic Waters); in these conditions we compared six different experiments, that differ by the turbulent closure schemes, the presence or not of an enhanced diffusion parameterization and the presence or not of a double diffusion mixing parameterization. To evaluate the performance of the experiments and the model we compared the simulations with the ARGO observations of temperature and salinity available in our domain, in our period of interest. The conclusions were the following: • the increase of the resolution gives better results in terms of temperature in all the considered cases, and in terms of salinity. • The comparisons between the Pacanovski-Philander and the TKE turbulent closure schemes don’t show significant differences when the simulations are compared to the observations. • The removing of the enhanced diffusion parameterization in presence of the TKE turbulent closure submodel doesn’t give positive results, and show limitations in the resolving of gravitational instabilities near the surface • The k-ϵ turbulent closure model utilized in all the GLS experiments, is the best performing closure model among the three considered, with positive results in all the salinity comparison with the in situ observation and in most of the temperature comparisons. • The double mixing parameterization utilized in the k-ϵ closure submodel improves the results of the experiments improving both the temperature and salinity in comparison with the ARGO data.