Copernicus Marine Service Ocean State Report, Issue 4

Traon, Pierre‐Yves Le; Smith, Neville; Pascual, Ananda; Djavidnia, Samuel; Gattuso, Jean‐Pierre; Grégoire, Marilaure; Nolan, Glenn; Aaboe, Signe; Fanjul, Enrique Álvarez; Aouf, Lotfi; Aznar, Roland; Badewien, Thomas H.; Behrens, Arno; Berta, Maristella; Bertino, Laurent; Blackford, Jeremy; Bolzon, Giorgio; Borile, Federica; Bretagnon, Marine; Brewin, Robert J. W.; Canu, Donata Melaku; Cessi, Paola; Ciavatta, Stefano; Chapron, Bertrand; Chau, Thi Tuyet Trang; Chevallier, F.; Chtirkova, Boriana; Ciliberti, Stefania Angela; Clark, James R.; Clementi, Emanuela; Combot, Clément; Comerma, Eric; Conchon, Anna; Coppini, Giovanni; Corgnati, Lorenzo; Cossarini, Gianpiero; Cravatte, Sophie; Alfonso, Marta de; Montégut, C. de Boyer; Fernández, Christian De Lera; Santos, Francisco J. de los; Denvil-Sommer, Anna; Collar, Álvaro de Pascual; Nunes, Paulo Alonso Lourenco Dias; Biagio, Valeria Di; Drudi, Massimiliano; Embury, Owen; Falco, Pierpaolo; d’Andon, O. Fanton; Ferrer, Luis Gabriel; Ford, David; Freund, Holger; García-León, M.; Sotillo, Marcos García; García-Valdecasas, José María; Garnesson, Philippe; Garric, Gilles; Gasparin, Florent; Gehlen, Marion; Genua‐Olmedo, Ana; Geyer, Gerhard; Ghermandi, Andrea; Good, Simon; Gourrion, Jérôme; Greiner, Eric; González, Manuel; Griffa, Annalisa; Hernández‐Carrasco, Ismael; Isoard, Stéphane; Kennedy, John J.; Kay, Susan; Korosov, Anton; Laanemäe, Kaari; Land, Peter E.; Lee, Thomas; Lazzari, Paolo; Legeais, Jean-François; Lemieux, B.; Levier, Bruno; Llovel, William; Lyubartsev, Vladyslav; Lien, Vidar S.; Lima, Leonardo; Lorente, Pablo; Mader, Julien; Magaldi, Marcello G.; Maljutenko, Ilja; Mangin, Antoine; Mantovani, Carlo; Marinova, Veselka; Masina, Simona; Mauri, Elena; Meyerjürgens, Jens; Mignot, Alexandre; McEwan, Robert; Mejía, Carlos; Melet, Angélique; Menna, Milena; Meyssignac, Benoît; Mouche, Alexis; Mourre, Baptiste; Müller, Malte; Notarstefano, Giulio; Orfila, Alejandro; Pardo, Silvia; Peneva, Elisaveta; Pérez-Gómez, Begoña; Perruche, Coralie; Peterlin, Monika; Poulain, Pierre-Marie; Pinardi, Nadia; Quilfen, Yves; Raudsepp, Urmas; Renshaw, Richard; Révelard, Adèle; Reyes, Emma; Ricker, Marcel; Rodríguez-Rubio, Pablo; Rotllán, Paz; Gelabert, Eva Royo; Rubio, Anna; Ruiz-Parrado, Inmaculada; Sathyendranath, Shubha; She, Jun; Schuckmann, Karina von; Solidoro, Cosimo; Stanev, Emil V.; Staneva, Joanna; Storto, Andrea; Su, Jian; Bakhsh, Tayebeh Tajalli; Tilstone, Gavin H.; Tintoré, Joaquı́n; Toledano, Cristina; Tournadre, Jean; Tranchant, B.; Uiboupin, Rivo; Valcarcel, Arnaud François; Valcheva, Nadezhda; Verbrugge, Nathalie; Vrac, Mathieu; Wolff, Jörg‐Olaf; Zambianchi, Enrico; Zielinski, Oliver; Zinck, Ann-Sofie Priergaard; Zunino, Serena

doi:10.1080/1755876x.2020.1785097

Cited by 64 publications

(41 citation statements)

References 503 publications

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“…For this reason, we use a very simple trajectory model and do not consider the errors in Lagrangian modeling (e.g., van Sebille et al, 2018). However, it has been verified that the trajectories simulated by COSMO using WMOP currents agreed with the ones simulated using CDrift (Sayol et al, 2014), as shown in Reyes et al (2020a).…”

Section: Design Of the Skill Assessment Methodsmentioning

confidence: 58%

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

et al. 2021

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Search and rescue (SAR) modeling applications, mostly based on Lagrangian tracking particle algorithms, rely on the accuracy of met-ocean forecast models. Skill assessment methods are therefore required to evaluate the performance of ocean models in predicting particle trajectories. The Skill Score (SS), based on the Normalized Cumulative Lagrangian Separation (NCLS) distance between simulated and satellite-tracked drifter trajectories, is a commonly used metric. However, its applicability in coastal areas, where most of the SAR incidents occur, is difficult and sometimes unfeasible, because of the high variability that characterizes the coastal dynamics and the lack of drifter observations. In this study, we assess the performance of four models available in the Ibiza Channel (Western Mediterranean Sea) and evaluate the applicability of the SS in such coastal risk-prone regions seeking for a functional implementation in the context of SAR operations. We analyze the SS sensitivity to different forecast horizons and examine the best way to quantify the average model performance, to avoid biased conclusions. Our results show that the SS increases with forecast time in most cases. At short forecast times (i.e., 6 h), the SS exhibits a much higher variability due to the short trajectory lengths observed compared to the separation distance obtained at timescales not properly resolved by the models. However, longer forecast times lead to the overestimation of the SS due to the high variability of the surface currents. Findings also show that the averaged SS, as originally defined, can be misleading because of the imposition of a lower limit value of zero. To properly evaluate the averaged skill of the models, a revision of its definition, the so-called SS∗, is recommended. Furthermore, whereas drifters only provide assessment along their drifting paths, we show that trajectories derived from high-frequency radar (HFR) effectively provide information about the spatial distribution of the model performance inside the HFR coverage. HFR-derived trajectories could therefore be used for complementing drifter observations. The SS is, on average, more favorable to coarser-resolution models because of the double-penalty error, whereas higher-resolution models show both very low and very high performance during the experiments.

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Section: Design Of the Skill Assessment Methodsmentioning

confidence: 58%

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

et al. 2021

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“…They also highlighted the uneven spatial pattern of the SST trend with values increasing from 0.036+/−0.006 • C/year in the western basin to 0.048+/−0.006 • C/year in the Levantine/Aegean basin. The CMEMS OMI also found a positive trend for the Mediterranean SST over the period 1993-2019 of approximately 0.037+/−0.003 • C/year (Mulet et al, 2018;von Schuckmann et al, 2020). The recently updated indicators presented in this paper show positive trends in SST over the 39-year period around 0.038+/−0.002 • C/year in the whole basin, with higher mean value of 0.044+/−0.002 • C/year in the eastern Mediterranean (EMED) than in the western part (WMED) with mean value of 0.032+/−0.002 • C/year.…”

Section: Ocean Warming and Marine Heat Wavesmentioning

confidence: 98%

“…Ocean indicators provide information and estimations of the state and temporal evolution of EOVs or key (regional) derived quantities that are scientifically relevant and sufficiently simple to be suitable for widespread public communication (Hayes et al, 2015;von Schuckmann et al, 2019;Trewin et al, 2021). For the global ocean and European Seas, the Copernicus Marine Service (CMEMS) Ocean State Report (von Schuckmann et al, 2016(von Schuckmann et al, , 2018(von Schuckmann et al, , 2019(von Schuckmann et al, , 2020) is reporting the so-called "Ocean Monitoring Indicators" (OMIs). They address the monitoring of various essential variables (chlorophyll-a concentration, ocean heat content, sea level, sea ice, transports, water mass and heat exchange, ocean currents, temperature and salinity), which are integrated spatially over the global ocean and/or the European Seas (Artic Ocean, Baltic Sea, European North-West Shelf Seas, Iberia-Biscay-Irish regional seas, Black Sea and Mediterranean Sea).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Sub-Regional Ocean Indicators in the Mediterranean Sea: From Event Detection to Climate Change Estimations

Juza

Tintoré

2021

Front. Mar. Sci.

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The increasing science and society requests for ocean monitoring from global to regional and local scales, the need for integration and convergence into a globally consistent ocean observing system as well as the need for improvement of access to information are now internationally recognized goals to progress toward the sustainable management of a healthy ocean. To respond to these challenges at regional level, the Balearic Islands Coastal Observing and Forecasting System (SOCIB) is developing a comprehensive set of ocean indicators in the Mediterranean Sea and around the Balearic Islands, key environments that are strongly affected by climate change and human pressure. This new SOCIB value-added product addresses the sub-regional ocean variability from daily (events) to interannual/decadal (climate) scales. A user-friendly interface has been implemented to monitor, visualize and communicate ocean information that is relevant for a wide range of sectors, applications and regional end-users. These sub-regional indicators allowed us to detect specific events in real time. Remarkable events and features identified include marine heat waves, atmospheric storm, extreme river discharge, mesoscale eddy, deep convection among others, all of them being oceanic phenomena that directly impact the ocean circulation and marine ecosystems. The long-term variations, in response to climate change, are also addressed highlighting and quantifying trends in physical and biogeochemical components of the ocean as well as sub-regional differences. At both (sub-) regional, national and international levels, a society-aligned science will have stronger impact on policy decision-makings and will support society to implement specific actions to address worldwide environmental challenges.

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“…The Baltic Sea is seasonally ice-covered. Inter-annually variable and dynamic ice coverage (Raudsepp et al, 2020) has 95 considerable effect on the evolution of the thermohaline fields in the Baltic Sea.…”

Section: The Baltic Seamentioning

confidence: 99%

A method for assessment of the general circulation model quality using K-means clustering algorithm

Raudsepp

Maljutenko

2021

Preprint

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Abstract. The model's ability to reproduce the state of the simulated object or particular feature or phenomenon is always a subject of discussion. Multidimensional model quality assessment is usually customized to the specific focus of the study and often to a limited number of locations. In this paper, we propose a method that provides information on the accuracy of the model in general, while all dimensional information for posterior analysis of the specific tasks is retained. The main goal of the method is to perform clustering of the multivariate model errors. The clustering is done using the K-means algorithm of unsupervised machine learning. In addition, the potential application of the K-means clustering of model errors for learning and predicting is shown. The method is tested on the 40-year simulation results of the general circulation model of the Baltic Sea. The model results are evaluated with the measurement data of temperature and salinity from more than one million casts by forming a two-dimensional error space and performing a clustering procedure in it. The optimal number of clusters that consist of four clusters was determined using the Elbow cluster selection criteria and based on the analysis of the different number of error clusters. In this particular model, the error cluster of good quality of the model with a bias of 0.4 °C (std = 0.8 °C) for temperature and 0.6 g kg−1 (std = 0.7 g kg−1) for salinity made up 57 % of all comparison data pairs. The prediction of centroids from a limited number of randomly selected data showed that the obtained centroids gained a stability of at least 100 000 error pairs in the learning dataset.

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Copernicus Marine Service Ocean State Report, Issue 4

Cited by 64 publications

References 503 publications

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

Multivariate Sub-Regional Ocean Indicators in the Mediterranean Sea: From Event Detection to Climate Change Estimations

A method for assessment of the general circulation model quality using K-means clustering algorithm

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