Design of the Observing System Simulation Experiments with multi-platform in situ data and impact on fine- scale structures

This report presents the work plan of the Task 2.3: Observing System Simulation Experiments: impact of multi-platform observations for the validation of satellite observation

The Copernicus Marine Environment Monitoring Service Ocean State Report

The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean

Argo data 1999-2019: two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats.

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Wong, A. P. S., Wijffels, S. E., Riser, S. C., Pouliquen, S., Hosoda, S., Roemmich, D., Gilson, J., Johnson, G. C., Martini, K., Murphy, D. J., Scanderbeg, M., Bhaskar, T. V. S. U., Buck, J. J. H., Merceur, F., Carval, T., Maze, G., Cabanes, C., Andre, X., Poffa, N., Yashayaev, I., Barker, P. M., Guinehut, S., Belbeoch, M., Ignaszewski, M., Baringer, M. O., Schmid, C., Lyman, J. M., McTaggart, K. E., Purkey, S. G., Zilberman, N., Alkire, M. B., Swift, D., Owens, W. B., Jayne, S. R., Hersh, C., Robbins, P., West-Mack, D., Bahr, F., Yoshida, S., Sutton, P. J. H., Cancouet, R., Coatanoan, C., Dobbler, D., Juan, A. G., Gourrion, J., Kolodziejczyk, N., Bernard, V., Bourles, B., Claustre, H., D'Ortenzio, F., Le Reste, S., Le Traon, P., Rannou, J., Saout-Grit, C., Speich, S., Thierry, V., Verbrugge, N., Angel-Benavides, I. M., Klein, B., Notarstefano, G., Poulain, P., Velez-Belchi, P., Suga, T., Ando, K., Iwasaska, N., Kobayashi, T., Masuda, S., Oka, E., Sato, K., Nakamura, T., Sato, K., Takatsuki, Y., Yoshida, T., Cowley, R., Lovell, J. L., Oke, P. R., van Wijk, E. M., Carse, F., Donnelly, M., Gould, W. J., Gowers, K., King, B. A., Loch, S. G., Mowat, M., Turton, J., Rama Rao, E. P., Ravichandran, M., Freeland, H. J., Gaboury, I., Gilbert, D., Greenan, B. J. W., Ouellet, M., Ross, T., Tran, A., Dong, M., Liu, Z., Xu, J., Kang, K., Jo, H., Kim, S., & Park, H. Argo data 1999-2019: two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats. Frontiers in Marine Science, 7, (2020): 700, doi:10.3389/fmars.2020.00700.In the past two decades, the Argo Program has collected, processed, and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1,000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002°C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.AW, SR, and other scientists at the University of Washington (UW) were supported by the US Argo Program through the NOAA Grant NA15OAR4320063 to the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) at the UW. SW and other scientists at the Woods Hole Oceanographic Institution (WHOI) were supported by the US Argo Program through the NOAA Grant NA19OAR4320074 (CINAR/WHOI Argo). The Scripps Institution of Oceanography's role in Argo was supported by the US Argo Program through the NOAA Grant NA15OAR4320071 (CIMEC). Euro-Argo scientists were supported by the Monitoring the Oceans and Climate Change with Argo (MOCCA) project, under the Grant Agreement EASME/EMFF/2015/1.2.1.1/SI2.709624 for the European Commission

Copernicus Marine In Situ TAC quality information document for near real-time current (QUID INSITU_GLO_UV_NRT_OBSERVATIONS_013_048)

The document describes the Quality of the Near Real Time (NRT) WAVES product delivered by the Copenicus Marine In Situ TAC. It applies to the INSITU_GLO_UV_NRT_OBSERVATIONS_013_048 product. It consists of four datasets each dedicated to near-surface currents measurements: - drifter: near-surface zonal and meridional raw velocities measured by drifting buoys, wind & wind stress components, surface temperature if available (see Table 1). These surface observations are part of the DBCP’s Global Drifter Program. - drifter_filt: near-surface zonal and meridional velocities and 3-days filtered velocities measured by drifting buoys. All the platforms are gathered together and concatenated in concatenated daily files. - radar_total: near-surface zonal and meridional raw velocities measured by High Frequency radars (HF radars, as acronym HFR), standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata. These surface observations are part of the European HF radar Network (see Mader et al., 2017 and Corgnati et al., 2018) - radar_radial: near-surface zonal and meridional components of raw radial velocities measured by High Frequency radars (HF radars, as acronym HFR), magnitude and direction of near-surface zonal and meridional components of raw radial velocities, standard deviation of near-surface zonal and meridional components of raw radial velocities, quality flags and metadata. These surface observations are part of the European HF radar Network (see Mader et al., 2017 and Corgnati et al., 2018) - argo: ocean currents derived from Argo floats trajectorie

Optimizing multi-platform sampling strategies to anticipate SWOT validation

Trabajo presentado en la AGU Fall Meeting (2020), celebrada online del 1 al 17 de diciembre de 2020.Funded by the European Commission, the H2020 EuroSea project has the objective to improve the European ocean observing system as an integrated entity within a global context, delivering ocean observations and forecasts to advance scientific knowledge about ocean climate, marine ecosystems, and their vulnerability to human impacts and to demonstrate the importance of the ocean to an economically viable and healthy society. In the framework of this project, our goal is to improve the design of multi-platform in situ experiments for validation of high-resolution SWOT observations with the aim of optimizing the utility of these observing platforms. To achieve this goal, a set of Observing System Simulation Experiments (OSSEs) will be developed to evaluate different sampling strategies and their impact on the reconstruction of fine-scale sea surface height fields and currents. Observations from CTD, ADCP, gliders, and altimetry will be simulated from three nature run models to study the sensitivity of the results to the model used. Different sampling strategies will be evaluated to analyze the impact of the spatial and temporal resolution of the observations, the depth of the measurements, the season of the multi-platform experiment, and the impact of changing rosette CTD casts for a continuous underway CTD, and adding gliders. After generating the simulated observations in different scenarios, three methods of reconstruction will be tested: multivariate reconstruction analysis, machine-learning techniques, and modelling data assimilation. To assess the best sampling strategies to validate SWOT observations during the fast-sampling phase, the reconstructed fields will be compared to (i) the ocean “truth” from the nature run models, (ii) simulated SWOT observations, and (iii) simulated observations of drifters, Argo buoys and moorings. The regions of study are the western Mediterranean Sea and the northwestern Atlantic Ocean

Multi-Observation Thematic Assembly: existing products and future evolutions

International audience<div> <p><span data-contrast="none">Producing comprehensive information about the ocean has become a top priority to monitor and predict the ocean and climate change.</span><span data-contrast="none"> Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is developed thought the Copernicus Marine Service MultiOBservation Thematic Assembly (</span><span data-contrast="auto">MOB TAC). Recent advances in data fusion techniques and use of machine-learning approach open the possibility of producing estimators of ocean physic and biogeochemistry (BGC) operationally, using input data from diverse sensors, satellites and in-situ programs.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":160,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">MOB TAC provides the following multi observations products at global scale: </span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":60,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">Blue ocean</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":60,"335559740":259}"> </span></p> </div> <div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">3D temperature, salinity, geopotential height and geostrophic current fields, both in near-real-time (NRT) and as long time series (REP=Reprocessing) in delayed-mode;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">2D sea surface salinity and sea surface density fields, both in NRT and as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">2D total surface and near-surface currents, both in NRT and as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="4" data-aria-level="1"><span data-contrast="auto">3D Vertical velocity fields as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="5" data-aria-level="1"><span data-contrast="auto">L2Q and L4 sea surface salinity from SMOS in REP and NRT (only L2Q)</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> </ul> </div> </div> <div> <div> <p><span data-contrast="auto">Green ocean</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559685":0,"335559739":200,"335559740":276}"> </span></p> </div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">2D surface carbon data sets of FCO2, pCO2, DIC, Alkalinity, saturation states of surface waters with respect to calcite and aragonite as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">Nutrient and Carbon vertical distribution (including Nitrates, Phosphates, Silicates, pH, pCO2, Alkalinity, DIC) profiles as REP and NRT;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">3D Particulate Organic Carbon (POC), particulate backscattering coefficient (bbp) and Chlorophyll a (Chl-a) fields as REP.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> </ul> </div> <div> <p><span data-contrast="auto">Parallel to its portfolio, MOB TAC has and will further develop specific expertise about the integration of multiple satellites and in-situ based observations coming from the other CMEMS TACs and projects. </span><span data-contrast="none">Furthermore, MOB TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor and the global ocean carbon sink. </span></p> </div> </div&gt

Upper ocean variability between Iceland and Newfoundland, 1993-1998

International audienceWe investigate the variability of upper ocean temperature and surface salinity between Iceland and Newfoundland along World Ocean Circulation Experiment line AX2 in 1993-1998. North of 54°N, as well as close to Newfoundland, deviations from the seasonal cycle indicate upper ocean warming from 1994 to 1998, with largest anomalous warming during late 1995/early 1996 and during the winter 1997/1998. North of 54°N, surface salinity increases after mid-1996. The winter of 1995/1996 is the first winter of weak westerlies after more than 20 years of strong westerly conditions in the subarctic gyre, and the ocean loses less heat during that winter than during a normal year. The magnitude of upper ocean heat content change is at least twice as large as what results from the change in air-sea fluxes, which indicates a complementary contribution to heat content change by advection. Altimetric sea level data indicate also an increase of sea level of the order of 6.5 cm during that year, out of which more than 4 cm is contributed by the steric change associated with the temperature increase. A principal component analysis of the sea level data suggests that the increase sensed along line AX2 west of the Reykjanes ridge is found in most of the subpolar gyre, although to a lesser extent in most other areas

L’information dans les organisations : dynamique et complexité

Le système d’information devient le pivot du pilotage des organisations confrontées à un environnement complexe. La dimension informationnelle des différentes activités, le caractère stratégique de l’information, la prise en compte du jeu des acteurs, la question centrale de la mémoire des organisations, l’impact du numérique… Autant de facteurs qui incitent les chercheurs en sciences de l’information et de la communication à développer les recherches dans ce contexte managérial. Cet ouvrage rassemble les textes issus du colloque international qui a permis d’alimenter la réflexion scientifique sur l’information dans les organisations. Il aborde des questions relatives : - au lien information-organisation, - aux systèmes d’information, - à la représentation des connaissances, - à la construction collective du savoir et au travail coopératif, - à la compréhension du besoin informationnel et à la modélisation des pratiques, - à l’impact du numérique et à la « mémoire sociale ». La diversité des approches proposées, le réel recul critique exercé et la réflexion scientifique engagée permettent de contribuer de façon originale aux questionnements en cours