Articles | Volume 15, issue 5
https://doi.org/10.5194/os-15-1191-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-15-1191-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Sep 2019
Research article |
| 06 Sep 2019
| 06 Sep 2019
Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data
Nansen Environmental and Remote Sensing Center, N5006 Bergen, Norway
Roshin P. Raj
Nansen Environmental and Remote Sensing Center, N5006 Bergen, Norway
Bjerknes Centre for
Climate Research, Bergen, N5006 Bergen, Norway
Laurent Bertino
Nansen Environmental and Remote Sensing Center, N5006 Bergen, Norway
Bjerknes Centre for
Climate Research, Bergen, N5006 Bergen, Norway
Annette Samuelsen
Nansen Environmental and Remote Sensing Center, N5006 Bergen, Norway
Bjerknes Centre for
Climate Research, Bergen, N5006 Bergen, Norway
Tsuyoshi Wakamatsu
Nansen Environmental and Remote Sensing Center, N5006 Bergen, Norway
Bjerknes Centre for
Climate Research, Bergen, N5006 Bergen, Norway
Related authors
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-1896, https://doi.org/10.5194/egusphere-2024-1896, 2024
Short summary
Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011–2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes on sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
Marta Umbert, Eva De Andrés, Maria Sánchez, Carolina Gabarró, Nina Hoareau, Veronica González-Gambau, Aina García-Espriu, Estrella Olmedo, Roshin P. Raj, Jiping Xie, and Rafael Catany
Ocean Sci., 20, 279–291, https://doi.org/10.5194/os-20-279-2024, https://doi.org/10.5194/os-20-279-2024, 2024
Short summary
Short summary
Satellite retrievals of sea surface salinity (SSS) offer insights into freshwater changes in the Arctic Ocean. This study evaluates freshwater content in the Beaufort Gyre using SMOS and reanalysis data, revealing underestimation with reanalysis alone. Incorporating satellite SSS measurements improves freshwater content estimation, especially near ice-melting areas. Adding remotely sensed salinity aids in monitoring Arctic freshwater content and in understanding its impact on global climate.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, https://doi.org/10.5194/os-19-269-2023, 2023
Short summary
Short summary
Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
Short summary
Short summary
Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
Short summary
Short summary
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
Short summary
Short summary
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
Short summary
Short summary
The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Jiping Xie, Laurent Bertino, François Counillon, Knut A. Lisæter, and Pavel Sakov
Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, https://doi.org/10.5194/os-13-123-2017, 2017
Short summary
Short summary
The Arctic Ocean plays an important role in the global climate system, but the concerned interpretation about its changes is severely hampered by the sparseness of the observations of sea ice and ocean. The focus of this study is to provide a quantitative assessment of the performance of the TOPAZ4 reanalysis for ocean and sea ice variables in the pan-Arctic region (north of 63 °N) in order to guide the user through its skills and limitations.
Jiping Xie, François Counillon, Laurent Bertino, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 10, 2745–2761, https://doi.org/10.5194/tc-10-2745-2016, https://doi.org/10.5194/tc-10-2745-2016, 2016
Short summary
Short summary
As a potentially operational daily product, the SMOS-Ice can improve the statements of sea ice thickness and concentration. In this study, focusing on the SMOS-Ice data assimilated into the TOPAZ system, the quantitative evaluation for the impacts and the concerned comparison with the present observation system are valuable to understand the further improvement of the accuracy of operational ocean forecasting system.
D. Mignac, C. A. S. Tanajura, A. N. Santana, L. N. Lima, and J. Xie
Ocean Sci., 11, 195–213, https://doi.org/10.5194/os-11-195-2015, https://doi.org/10.5194/os-11-195-2015, 2015
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Vidar S. Lien, Roshin P. Raj, and Sourav Chatterjee
State Planet, 4-osr8, 8, https://doi.org/10.5194/sp-4-osr8-8-2024, https://doi.org/10.5194/sp-4-osr8-8-2024, 2024
Short summary
Short summary
We find that major marine heatwaves are rather coherent throughout the Barents Sea, but surface marine heatwaves occur more frequently while heatwaves on the ocean floor have a longer duration. Moreover, we investigate the sensitivity to the choice of climatological average length when calculating marine heatwave statistics. Our results indicate that severe marine heatwaves may become more frequent in the future Barents Sea due to ongoing climate change.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore
State Planet Discuss., https://doi.org/10.5194/sp-2024-20, https://doi.org/10.5194/sp-2024-20, 2024
Preprint under review for SP
Short summary
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity and currents will evolve over the coming days and weeks, as well as to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet Discuss., https://doi.org/10.5194/sp-2024-24, https://doi.org/10.5194/sp-2024-24, 2024
Preprint under review for SP
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 days ahead – and an outlook of their upcoming developments.
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-1896, https://doi.org/10.5194/egusphere-2024-1896, 2024
Short summary
Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011–2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes on sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
Short summary
Short summary
Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin
The Cryosphere, 18, 1597–1619, https://doi.org/10.5194/tc-18-1597-2024, https://doi.org/10.5194/tc-18-1597-2024, 2024
Short summary
Short summary
Individual satellite passes instead of daily means of sea ice concentration are used to correct the sea ice model forecast in the Barents Sea. The use of passes provides a significantly larger improvement of the forecasts even after a 7 d period due to the more precise information on temporal and spatial variability contained in the passes. One major advantage of the use of satellite passes is that there is no need to wait for the daily mean availability in order to update the forecast.
Marta Umbert, Eva De Andrés, Maria Sánchez, Carolina Gabarró, Nina Hoareau, Veronica González-Gambau, Aina García-Espriu, Estrella Olmedo, Roshin P. Raj, Jiping Xie, and Rafael Catany
Ocean Sci., 20, 279–291, https://doi.org/10.5194/os-20-279-2024, https://doi.org/10.5194/os-20-279-2024, 2024
Short summary
Short summary
Satellite retrievals of sea surface salinity (SSS) offer insights into freshwater changes in the Arctic Ocean. This study evaluates freshwater content in the Beaufort Gyre using SMOS and reanalysis data, revealing underestimation with reanalysis alone. Incorporating satellite SSS measurements improves freshwater content estimation, especially near ice-melting areas. Adding remotely sensed salinity aids in monitoring Arctic freshwater content and in understanding its impact on global climate.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Christoph Heinze, Thorsten Blenckner, Peter Brown, Friederike Fröb, Anne Morée, Adrian L. New, Cara Nissen, Stefanie Rynders, Isabel Seguro, Yevgeny Aksenov, Yuri Artioli, Timothée Bourgeois, Friedrich Burger, Jonathan Buzan, B. B. Cael, Veli Çağlar Yumruktepe, Melissa Chierici, Christopher Danek, Ulf Dieckmann, Agneta Fransson, Thomas Frölicher, Giovanni Galli, Marion Gehlen, Aridane G. González, Melchor Gonzalez-Davila, Nicolas Gruber, Örjan Gustafsson, Judith Hauck, Mikko Heino, Stephanie Henson, Jenny Hieronymus, I. Emma Huertas, Fatma Jebri, Aurich Jeltsch-Thömmes, Fortunat Joos, Jaideep Joshi, Stephen Kelly, Nandini Menon, Precious Mongwe, Laurent Oziel, Sólveig Ólafsdottir, Julien Palmieri, Fiz F. Pérez, Rajamohanan Pillai Ranith, Juliano Ramanantsoa, Tilla Roy, Dagmara Rusiecka, J. Magdalena Santana Casiano, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Miriam Seifert, Anna Shchiptsova, Bablu Sinha, Christopher Somes, Reiner Steinfeldt, Dandan Tao, Jerry Tjiputra, Adam Ulfsbo, Christoph Völker, Tsuyoshi Wakamatsu, and Ying Ye
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-182, https://doi.org/10.5194/bg-2023-182, 2023
Preprint under review for BG
Short summary
Short summary
For assessing the consequences of human-induced climate change for the marine realm, it is necessary to not only look at gradual changes but also at abrupt changes of environmental conditions. We summarise abrupt changes in ocean warming, acidification, and oxygen concentration as the key environmental factors for ecosystems. Taking these abrupt changes into account requires greenhouse gas emissions to be reduced to a larger extent than previously thought to limit respective damage.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Jiping Xie, Roshin P. Raj, Laurent Bertino, Justino Martínez, Carolina Gabarró, and Rafael Catany
Ocean Sci., 19, 269–287, https://doi.org/10.5194/os-19-269-2023, https://doi.org/10.5194/os-19-269-2023, 2023
Short summary
Short summary
Sea ice melt, together with other freshwater sources, has effects on the Arctic environment. Sea surface salinity (SSS) plays a key role in representing water mixing. Recently the satellite SSS from SMOS was developed in the Arctic region. In this study, we first evaluate the impact of assimilating these satellite data in an Arctic reanalysis system. It shows that SSS errors are reduced by 10–50 % depending on areas, encouraging its use in a long-time reanalysis to monitor the Arctic water cycle.
Vidar S. Lien and Roshin P. Raj
State Planet Discuss., https://doi.org/10.5194/sp-2022-13, https://doi.org/10.5194/sp-2022-13, 2022
Preprint withdrawn
Short summary
Short summary
Dense overflow water entering the North Atlantic from the Nordic Seas forms the northern limb of the Atlantic Meridional Overturning Circulation. The formation of dense water in the Nordic Seas is sensitive to the properties of the northward flowing Atlantic Water entering the Nordic Seas to the south. We find that the unprecedented freshwater anomaly in the North Atlantic recent years caused the dense water formed in the Barents Sea to have the lowest density in recorded history.
Pedro Duarte, Jostein Brændshøi, Dmitry Shcherbin, Pauline Barras, Jon Albretsen, Yvonne Gusdal, Nicholas Szapiro, Andreas Martinsen, Annette Samuelsen, Keguang Wang, and Jens Boldingh Debernard
Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, https://doi.org/10.5194/gmd-15-4373-2022, 2022
Short summary
Short summary
Sea ice models are often implemented for very large domains beyond the regions of sea ice formation, such as the whole Arctic or all of Antarctica. In this study, we implement changes in the Los Alamos Sea Ice Model, allowing it to be implemented for relatively small regions within the Arctic or Antarctica and yet considering the presence and influence of sea ice outside the represented areas. Such regional implementations are important when spatially detailed results are required.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
Short summary
Short summary
We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Fabio Mangini, Léon Chafik, Antonio Bonaduce, Laurent Bertino, and Jan Even Ø. Nilsen
Ocean Sci., 18, 331–359, https://doi.org/10.5194/os-18-331-2022, https://doi.org/10.5194/os-18-331-2022, 2022
Short summary
Short summary
We validate the recent ALES-reprocessed coastal satellite altimetry dataset along the Norwegian coast between 2003 and 2018. We find that coastal altimetry and conventional altimetry products perform similarly along the Norwegian coast. However, the agreement with tide gauges slightly increases in terms of trends when we use the ALES coastal altimetry data. We then use the ALES dataset and hydrographic stations to explore the steric contribution to the Norwegian sea-level anomaly.
Martin Horwath, Benjamin D. Gutknecht, Anny Cazenave, Hindumathi Kulaiappan Palanisamy, Florence Marti, Ben Marzeion, Frank Paul, Raymond Le Bris, Anna E. Hogg, Inès Otosaka, Andrew Shepherd, Petra Döll, Denise Cáceres, Hannes Müller Schmied, Johnny A. Johannessen, Jan Even Øie Nilsen, Roshin P. Raj, René Forsberg, Louise Sandberg Sørensen, Valentina R. Barletta, Sebastian B. Simonsen, Per Knudsen, Ole Baltazar Andersen, Heidi Ranndal, Stine K. Rose, Christopher J. Merchant, Claire R. Macintosh, Karina von Schuckmann, Kristin Novotny, Andreas Groh, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, https://doi.org/10.5194/essd-14-411-2022, 2022
Short summary
Short summary
Global mean sea-level change observed from 1993 to 2016 (mean rate of 3.05 mm yr−1) matches the combined effect of changes in water density (thermal expansion) and ocean mass. Ocean-mass change has been assessed through the contributions from glaciers, ice sheets, and land water storage or directly from satellite data since 2003. Our budget assessments of linear trends and monthly anomalies utilise new datasets and uncertainty characterisations developed within ESA's Climate Change Initiative.
Justino Martínez, Carolina Gabarró, Antonio Turiel, Verónica González-Gambau, Marta Umbert, Nina Hoareau, Cristina González-Haro, Estrella Olmedo, Manuel Arias, Rafael Catany, Laurent Bertino, Roshin P. Raj, Jiping Xie, Roberto Sabia, and Diego Fernández
Earth Syst. Sci. Data, 14, 307–323, https://doi.org/10.5194/essd-14-307-2022, https://doi.org/10.5194/essd-14-307-2022, 2022
Short summary
Short summary
Measuring salinity from space is challenging since the sensitivity of the brightness temperature to sea surface salinity is low, but the retrieval of SSS in cold waters is even more challenging. In 2019, the ESA launched a specific initiative called Arctic+Salinity to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, https://doi.org/10.5194/gmd-14-7073-2021, 2021
Short summary
Short summary
The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
Short summary
Short summary
Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
Short summary
Short summary
Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Anna V. Vesman, Igor L. Bashmachnikov, Pavel A. Golubkin, and Roshin P. Raj
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-109, https://doi.org/10.5194/os-2020-109, 2020
Revised manuscript not accepted
Short summary
Short summary
Atlantic Waters carry heat and salt towards Arctic. The goal of this study was to study how the heat flux changes with its journey to the north. It was shown that despite the fact that there is some connection between variability of the heat flux near the shores of Norway and heat fluxes in the northern part of the Fram Strait. There are different processes governing this variability, which results in a different tendencies in the southern and northern regions of the study.
Roshin P. Raj, Sourav Chatterjee, Laurent Bertino, Antonio Turiel, and Marcos Portabella
Ocean Sci., 15, 1729–1744, https://doi.org/10.5194/os-15-1729-2019, https://doi.org/10.5194/os-15-1729-2019, 2019
Short summary
Short summary
In this study we investigated the variability of the Arctic Front (AF), an important biologically productive region in the Norwegian Sea, using a suite of satellite data, atmospheric reanalysis and a regional coupled ocean–sea ice data assimilation system. We show evidence of the two-way interaction between the atmosphere and the ocean at the AF. The North Atlantic Oscillation is found to influence the strength of the AF and may have a profound influence on the region's biological productivity.
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
Short summary
Short summary
This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
Short summary
Short summary
We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
Short summary
Short summary
We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
Short summary
Short summary
Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
Short summary
Short summary
The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
Short summary
Short summary
Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Saleem Shalin, Annette Samuelsen, Anton Korosov, Nandini Menon, Björn C. Backeberg, and Lasse H. Pettersson
Biogeosciences, 15, 1395–1414, https://doi.org/10.5194/bg-15-1395-2018, https://doi.org/10.5194/bg-15-1395-2018, 2018
Short summary
Short summary
This work objectively classified the northern Arabian Sea into six ecological zones based on surface Chl a distribution patterns during winter. Distinct Chl a characteristics within each delineated zone show that this classification method is a good way of separating regions with different phytoplankton dynamics during winter. The study provides improved understanding of how environmental factors control the spatio-temporal variability of the marine Chl a concentration in the area during winter.
Kristoffer Aalstad, Sebastian Westermann, Thomas Vikhamar Schuler, Julia Boike, and Laurent Bertino
The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, https://doi.org/10.5194/tc-12-247-2018, 2018
Short summary
Short summary
We demonstrate how snow cover data from satellites can be used to constrain estimates of snow distributions at sites in the Arctic. In this effort, we make use of data assimilation to combine the information contained in the snow cover data with a simple snow model. By comparing our snow distribution estimates to independent observations, we find that this method performs favorably. Being modular, this method could be applied to other areas as a component of a larger reanalysis system.
Jiping Xie, Laurent Bertino, François Counillon, Knut A. Lisæter, and Pavel Sakov
Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, https://doi.org/10.5194/os-13-123-2017, 2017
Short summary
Short summary
The Arctic Ocean plays an important role in the global climate system, but the concerned interpretation about its changes is severely hampered by the sparseness of the observations of sea ice and ocean. The focus of this study is to provide a quantitative assessment of the performance of the TOPAZ4 reanalysis for ocean and sea ice variables in the pan-Arctic region (north of 63 °N) in order to guide the user through its skills and limitations.
Jiping Xie, François Counillon, Laurent Bertino, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 10, 2745–2761, https://doi.org/10.5194/tc-10-2745-2016, https://doi.org/10.5194/tc-10-2745-2016, 2016
Short summary
Short summary
As a potentially operational daily product, the SMOS-Ice can improve the statements of sea ice thickness and concentration. In this study, focusing on the SMOS-Ice data assimilated into the TOPAZ system, the quantitative evaluation for the impacts and the concerned comparison with the present observation system are valuable to understand the further improvement of the accuracy of operational ocean forecasting system.
A. Samuelsen, C. Hansen, and H. Wehde
Geosci. Model Dev., 8, 2187–2202, https://doi.org/10.5194/gmd-8-2187-2015, https://doi.org/10.5194/gmd-8-2187-2015, 2015
Short summary
Short summary
Biogeochemical models are increasingly used in forecasting systems. They provide parameter fields such as nutrients, chlorophyll and oxygen for scientific use and for marine management. This paper describes a model currently used for forecasting the North Atlantic and Arctic oceans on a weekly basis and the evaluation of this model against observations. The model provides reliable fields of nutrients, while the predicted phytoplankton fields are still connected with large uncertainties.
D. Mignac, C. A. S. Tanajura, A. N. Santana, L. N. Lima, and J. Xie
Ocean Sci., 11, 195–213, https://doi.org/10.5194/os-11-195-2015, https://doi.org/10.5194/os-11-195-2015, 2015
Cited articles
Bertino, L. and Lisæter, K. A.: The TOPAZ monitoring and prediction
system for the Atlantic and Arctic Oceans, J. Oper. Oceanogr., 1, 15–19, https://doi.org/10.1080/1755876X.2008.11020098, 2008.
Boutin, J., Vergely, J. L., Marchand, S., D'Amico, F., Hasson, A., Kolodziejczyk,
N., Reul, N., Reverdin, G., and Vialard, J.: New SMOS Sea Surface Salinity with
reduced systematic errors and improved variability, Remote Sens. Environ., 214, 115–134, https://doi.org/10.1016/j.rse.2018.05.022, 2018.
Buongiorno Nardelli, B., Droghei, R., and Santoleri, R.: Multi-dimensional
interpolation of SMOS sea surface salinity with surface temperature and in situ
salinity data, Remote Sens. Environ., 180, 392–402, https://doi.org/10.1016/j.rse.2015.12.052, 2016
Cabanes, C., Grouazel, A., von Schuckmann, K., Hamon, M., Turpin, V., Coatanoan, C., Paris, F., Guinehut, S., Boone, C., Ferry, N., de Boyer Montégut, C., Carval, T., Reverdin, G., Pouliquen, S., and Le Traon, P.-Y.: The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements, Ocean Sci., 9, 1–18, https://doi.org/10.5194/os-9-1-2013, 2013.
Chassignet, E. P., Smith, L. T., and Halliwell, G. R.: North Atlantic
Simulations with the Hybrid Coordinate Ocean Model (HYCOM): Impact of the
vertical coordinate choice, reference pressure, and thermobaricity, J. Phys. Oceanogr., 33,
2504–2526, https://doi.org/10.1175/1520-0485(2003)033<2504:NASWTH>2.0.CO;2, 2003.
D'Addezio, J. M. and Subrahmanyam, B.: Sea surface salinity variability in
the Agulhas Current region inferred from SMOS and Aquarius, Remote Sens. Environ., 180, 440–452,
https://doi.org/10.1016/j.rse.2016.02.006, 2016.
de Boyer Montegut, C., Madec, G., Fischer, A., Lazar, A., and Iudicone, D.:
Mixed Layer Depth over the Global Ocean: An Examination of Profile Data and
a Profile-Based Climatology, J. Geophys. Res., 109, 1–20,
https://doi.org/10.1029/2004JC002378, 2004.
Drange, H. and Simonsen, K.: Formulation of air-sea fluxes in the ESOP2
version of MICOM, Technical Report No. 125 of Nansen Environmental and
Remote Sensing Center, 1996.
Droghei, R., Buongiorno Nardelli, B., and Santoleri, R.: A new global sea
surface salinity and density dataset from multivariate observations
(1993–2016), Front. Mar. Sci, 5, 84, https://doi.org/10.3389/fmars.2018.00084, 2018.
ESA: SMOS data products, available at:
https://earth.esa.int/documents/10174/1854456/SMOS-Data-Products-Brochure
(last access: 12 December 2018), 2017.
Font, J., Camps, A., Borges, A., Martín-Neira, M., Boutin, J., Reul,
N., Kerr, Y. H., Hahne, A., and Mecklenburg, S.: SMOS: The challenging sea
surface salinity measurement from space, Proc. IEEE, 98, 649–665, https://doi.org/10.1109/JPROC.2009.2033096, 2010.
Furue, R., Takatama, K., Sasaki, H., Schneider, N., Nonaka, M., and Taguchi,
B.: Impacts of sea-surface salinity in an eddy-resolving semi-global OGCM,
Ocean Modell., 122, 36–56, https://doi.org/10.1016/j.ocemod.2017.11.004, 2018.
Hátún, H., Sandø, A. B., Drange, H., Hansen, B., and
Valdimarsson, H.: Influence of the Atlantic Subpolar Gyre on the
Thermohaline Circulation, Science, 309, 1841–1844,
https://doi.org/10.1126/science.1114777, 2005.
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea
ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997.
Johnson, G. C., Schmidtko, S., and Lyman, J. M.: Relative contributions of
temperature and salinity to seasonal mixed layer density changes and
horizontal density gradients, J. Geophys. Res., 117, C04015, https://doi.org/10.1029/2011JC007651, 2012.
Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Delwart, S., Cabot, F.,
Boutin, J., Escorihuela, M. J., Font, J., Reul, N., Gruhier, C., Juglea, S.,
Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg, S.:
The SMOS mission: New tool for monitoring key elements of the global water
cycle, Proc. IEEE, 98, 666–687, https://doi.org/10.1109/JPROC.2010.2043032, 2010.
Kolodziejczyk, N., Boutin, J., Vergely, J.-L., Marchand, S., Martin, N., and
Reverdin, G.: Mitigation of systematic errors in SMOS sea surface salinity,
Remote Sens. Environ., 180, 164–177, https://doi.org/10.1016/j.rse.2016.02.061, 2016.
Latif, M., Roeckner, E., Mikolajewicz, U., and Voss, R.: Tropical
stabilization of the thermohaline circulation in a greenhouse warming
simulation, J. Climate, 13, 1809–1813, 2000.
Macdonald, R. W., Carmack, E. C., McLaughlin, F. A., Falkner, K. K., and
Swift, J. H.: Connections among ice, runoff and atmospheric forcing in the
Beaufort Gyre, Geophys. Res. Lett., 26, 2223–2226, 1999.
Maes, C., Ando, K., Delcroix, T., Kessler, W. S., McPhaden, M. J., and
Roemmich, D.: Observed correlation of surface salinity, temperature and
barrier layer at the eastern edge of the western Pacific warm
pool, Geophys. Res. Lett., 33, L06601, https://doi.org/10.1029/2005GL024772, 2006.
Mathis, J. T. and Monacci, N. M.: Carbon Dioxide and Hydrographic data
obtained during the USCGC Healy Cruise HLY1203 in the Arctic Ocean (October
05–25, 2012), available at: http://cdiac.ess-dive.lbl.gov/ftp/oceans/CARINA/Healy/HLY-12-03/ (last access: March 2019), Oak Ridge
National Laboratory, US Department of Energy, Oak Ridge, Tennessee, https://doi.org/10.3334/CDIAC/OTG.CLIVAR_33HQ20121005, 2014.
McPhee, M. G., Stanton, T. P., Morison, J. H., and Martinson, D. G.:
Freshening of the upper ocean in the Arctic: is perennial sea ice
disappearing?, Geophys. Res. Lett., 25, 1729–1732, 1998.
Mecklenburg, S., Drusch, M., Kerr, Y. H., Font, J., Martiìn-Neira, M.,
Delwart, S., Buenadicha, G., Reul, N., Daganzo-Eusebio, E., Oliva, R., and
Crapolicchio, R.: ESA's soil moisture and ocean salinity mission: Mission
performance and operations, IEEE TGARS, 50, 1354–1366, https://doi.org/10.1109/TGRS.2012.2187666, 2012.
Mignot, J. and Frankignoul, C.: On the interannual variability of surface
salinity in the Atlantic, Clim. Dynam., 20, 555–565, https://doi.org/10.1007/s00382-002-0294-0,
2003.
Morison, J., Kwok, R., Peralta-Ferriz, C., Alkire, M., Rigor, I., Andersen,
R., and Steele, M.: Changing arctic ocean freshwater pathways, Nature, 481, 66–70,
2012.
Olmedo, E., Gabarró, C., González-Gambau, V., Martínez, J.,
Ballabrera-Poy, J., Turiel, A., Portabella, M., Fournier, S., and Lee, T.:
Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in
Arctic and Sub-Arctic Regions, Remote Sens., 10, 1772, https://doi.org/10.3390/rs10111772, 2018.
Reverdin, G., Cayan, D., and Kushnir, Y.: Decadal variability of hydrography
in the upper northern North Atlantic in 1948–1990, J. Geophys. Res., 102, 8505–8531,
https://doi.org/10.1029/96JC03943, 1997.
Sakov, P. and Oke, P. R.: A deterministic formulation of the ensemble
Kalman Filter: an alternative to ensemble square root filters, Tellus A, 60,
361–371, https://doi.org/10.1111/j.1600-0870.2007.00299.x, 2008.
Simmons, A., Uppala, S., Dee, D., and Kobayashi, S.: ERA-Interim: New ECMWF
reanalysis products from 1989 onwards, ECMWF Newsletter, 110, 25–35,
https://doi.org/10.21957/pocnex23c6, 2007.
SMOS Team: SMOS L2 OS Algorithm Theoretical Baseline Document, ESA, Paris,
France, SO-TN-ARG-GS-0007, version 3.13, available at: https://earth.esa.int/documents/10174/1854519/SMOS_L2OS-ATBD (last access: 12 December 2018), 2016.
SMOS-BEC Team: Quality Report: Validation of SMOS-BEC experimental sea
surface salinity products in the Arctic Ocean and high latitudes Oceans,
Years 2011–2013, Barcelona Expert Centre, Spain, Technical note:
BEC-SMOS-0007-QR version 1.0, available at: http://bec.icm.csic.es/doc/BEC-SMOS-0007-QR.pdf (last access: 13 December 2018), 2016.
Steele, M. and Ermold, W.: Salinity Trends on the East Siberian Shelves,
Geophys. Res. Lett., 31, L24308, https://doi.org/10.1029/2004GL021302, 2004.
Steele, M., Morley, R., and Ermold, W.: PHC: A global ocean hydrography with a
high-quality Arctic Ocean, J. Climate, 14, 2079–2087, 2001.
Sumner, D. and Belaineh, G.: Evaporation, Precipitation, and Associated
Salinity Changes at a Humid, Subtropical Estuary, Estuaries, 28, 844–855, available at: http://www.jstor.org/stable/3526951 (last access: July 2019), 2005.
Supply, A., Boutin, J., Vergely, J.-L., Martin, N., Hasson, A., Reverdin, G., Mallet, C., and Viltard, N.: Precipitation Estimates from SMOS
Sea-Surface Salinity, Q. J. Roy. Meteorol. Soc., 144, 103–119, https://doi.org/10.1002/qj.3110, 2018.
Talley, L. D., Johnson, G. C., Purkey, S., Feely, R. A., and Wanninkhof, R.:
Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP)
provides key climate-relevant deep ocean observations, US CLIVAR Variations,
15, available at:
https://www.pmel.noaa.gov/pubs/PDF/tall4659/tall4659.pdf (last access: 19 December 2018), 2017.
Toole, J. M., Krishfield, R. A., Timmermans, M.-L., and Proshutinsky, A.:
The Ice-Tethered Profiler: Argo of the Arctic, Oceanography, 24, 126–135,
https://doi.org/10.5670/oceanog.2011.64, 2011.
Tseng, Y., Bryan, F. O., and Whitney, M. M.: Impacts of the representation
of riverine freshwater input in the community earth system model, Ocean Modell., 105,
71–86, https://doi.org/10.1016/j.ocemod.2016.08.002, 2016.
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A.,
Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., Kauker, F.,
Korhonen, M., Lien, V. S., Marnela, M., Massonnet, F., Mignac, D., Peterson,
A., Sadikn, R., Shi, L., Tietsche, S., Toyoda, T., Xie, J., and Zhang, Z.:
An assessment of ten ocean reanalyses in the polar regions, Clim. Dynam., 52, 1613–1650, https://doi.org/10.1007/s00382-018-4242-z, 2019.
Vancoppenolle, M., Fichefet, T., and Goosse, H.: Simulating the mass balance
and salinity of Arctic and Antarctic sea ice, 2, Importance of sea ice
salinity variations, Ocean Modell., 27, 54–69, https://doi.org/10.1016/j.ocemod.2008.11.003, 2009.
Verbrugge, N., Mulet, S., Guinehut, S., Buongiorno Nardelli, B., and
Droghei, R.: Quality information document for global ocean multi observation
products multiobs_glo_phy_rep_015_002, CMEMS-MOB-QUID-015-002, v1.0,
available at: http://cmems-resources.cls.fr/documents/QUID/CMEMS-MOB-QUID-015-002.pdf
(last access: 14 December 2018), 2018.
Vialard, J. and Delecluse, P.: An OGCM study for the TOGA decade: I. Role of
salinity in the physics of the Western Pacific fresh pool, J. Phys. Oceanogr., 28, 1071–1088,
1998.
Xie, J., Counillon, F., Bertino, L., Tian-Kunze, X., and Kaleschke, L.: Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system, The Cryosphere, 10, 2745–2761, https://doi.org/10.5194/tc-10-2745-2016, 2016.
Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013, Ocean Sci., 13, 123–144, https://doi.org/10.5194/os-13-123-2017, 2017.
Xie, J., Counillon, F., and Bertino, L.: Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis, The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, 2018.
Yu, L.: A global relationship between the ocean water cycle and
near-surface salinity, J. Geophys. Res., 116, C10025, https://doi.org/10.1029/2010JC006937, 2011.
Zweng, M. M., Reagan, J. R., Antonov J. I., Locarnini, R. A., Mishonov, A.
V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov,
D., and Biddle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, edited by: Levitus,
S. and Mishonov, A., Technical Ed. NOAA Atlas NESDIS 74, 39 pp., 2013.
Short summary
Two gridded sea surface salinity (SSS) products have been derived from the European Space Agency’s Soil Moisture and Ocean Salinity mission. The uncertainties of these two products in the Arctic are quantified against two SSS products in the Copernicus Marine Environment Monitoring Services, two climatologies, and other in situ data. The results compared with independent in situ data clearly show a common challenge for the six SSS products to represent central Arctic freshwater masses (<24 psu).
Two gridded sea surface salinity (SSS) products have been derived from the European Space...
