Articles | Volume 18, issue 5
https://doi.org/10.5194/tc-18-2357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-2357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 May 2024
Research article |
| 13 May 2024
| 13 May 2024
The role of upper-ocean heat content in the regional variability of Arctic sea ice at sub-seasonal timescales
Elena Bianco
CORRESPONDING AUTHOR
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University, Venice, Italy
Doroteaciro Iovino
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Simona Masina
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Stefano Materia
CMCC Foundation – Euro-Mediterranean Center on Climate Change, Bologna, Italy
Barcelona Supercomputing Center, Barcelona, Spain
Paolo Ruggieri
Department of Physics and Astronomy, University of Bologna, Bologna, Italy
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Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
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Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
Giulia Bonino, Giuliano Galimberti, Simona Masina, Ronan McAdam, and Emanuela Clementi
Ocean Sci., 20, 417–432, https://doi.org/10.5194/os-20-417-2024, https://doi.org/10.5194/os-20-417-2024, 2024
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This study employs machine learning to predict marine heatwaves (MHWs) in the Mediterranean Sea. MHWs have far-reaching impacts on society and ecosystems. Using data from ESA and ECMWF, the research develops accurate prediction models for sea surface temperature (SST) and MHWs across the region. Notably, machine learning methods outperform existing forecasting systems, showing promise in early MHW predictions. The study also highlights the importance of solar radiation as a predictor of SST.
Francesco Cocetta, Lorenzo Zampieri, Julia Selivanova, and Doroteaciro Iovino
EGUsphere, https://doi.org/10.5194/egusphere-2024-413, https://doi.org/10.5194/egusphere-2024-413, 2024
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Arctic sea ice thinning and retreating because of global warming. Thus, the region is transitioning to a new state featuring an expansion of the marginal ice zone, a region where mobile ice interacts with waves from the open ocean. By analyzing 30 years of sea ice reconstructions that combine numerical models and observations, this paper proves that an ensemble of global ocean and sea ice reanalyses is an adequate tool for investigating the changing Arctic sea ice cover.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Tuula Aalto, Aki Tsuruta, Jarmo Mäkelä, Jurek Mueller, Maria Tenkanen, Eleanor Burke, Sarah Chadburn, Yao Gao, Vilma Mannisenaho, Thomas Kleinen, Hanna Lee, Antti Leppänen, Tiina Markkanen, Stefano Materia, Paul Miller, Daniele Peano, Olli Peltola, Benjamin Poulter, Maarit Raivonen, Marielle Saunois, David Wårlind, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2023-2873, https://doi.org/10.5194/egusphere-2023-2873, 2024
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Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in Northern Europe using ecosystem models, atmospheric inversions and up-scaled flux observations. The ecosystem models differed in their responses to temperature and precipitation and in their seasonality. However, multi-model means, inversions and up-scaled fluxes had similar seasonality, and they suggested co-limitation by temperature and precipitation.
Doroteaciro Iovino, Pier Giuseppe Fogli, and Simona Masina
Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, https://doi.org/10.5194/gmd-16-6127-2023, 2023
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This paper describes the model performance of three global ocean–sea ice configurations, from non-eddying (1°) to eddy-rich (1/16°) resolutions. Model simulations are obtained following the Ocean Model Intercomparison Project phase 2 (OMIP2) protocol. We compare key global climate variables across the three models and against observations, emphasizing the relative advantages and disadvantages of running forced ocean–sea ice models at higher resolution.
Giovanni Coppini, Emanuela Clementi, Gianpiero Cossarini, Stefano Salon, Gerasimos Korres, Michalis Ravdas, Rita Lecci, Jenny Pistoia, Anna Chiara Goglio, Massimiliano Drudi, Alessandro Grandi, Ali Aydogdu, Romain Escudier, Andrea Cipollone, Vladyslav Lyubartsev, Antonio Mariani, Sergio Cretì, Francesco Palermo, Matteo Scuro, Simona Masina, Nadia Pinardi, Antonio Navarra, Damiano Delrosso, Anna Teruzzi, Valeria Di Biagio, Giorgio Bolzon, Laura Feudale, Gianluca Coidessa, Carolina Amadio, Alberto Brosich, Arnau Miró, Eva Alvarez, Paolo Lazzari, Cosimo Solidoro, Charikleia Oikonomou, and Anna Zacharioudaki
Ocean Sci., 19, 1483–1516, https://doi.org/10.5194/os-19-1483-2023, https://doi.org/10.5194/os-19-1483-2023, 2023
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The paper presents the Mediterranean Forecasting System evolution and performance developed in the framework of the Copernicus Marine Service.
Kristian Strommen, Tim Woollings, Paolo Davini, Paolo Ruggieri, and Isla R. Simpson
Weather Clim. Dynam., 4, 853–874, https://doi.org/10.5194/wcd-4-853-2023, https://doi.org/10.5194/wcd-4-853-2023, 2023
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We present evidence which strongly suggests that decadal variations in the intensity of the North Atlantic winter jet stream can be predicted by current forecast models but that decadal variations in its position appear to be unpredictable. It is argued that this skill at predicting jet intensity originates from the slow, predictable variability in sea surface temperatures in the sub-polar North Atlantic.
Michael Mayer, Takamasa Tsubouchi, Susanna Winkelbauer, Karin Margretha H. Larsen, Barbara Berx, Andreas Macrander, Doroteaciro Iovino, Steingrímur Jónsson, and Richard Renshaw
State Planet, 1-osr7, 14, https://doi.org/10.5194/sp-1-osr7-14-2023, https://doi.org/10.5194/sp-1-osr7-14-2023, 2023
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This paper compares oceanic fluxes across the Greenland–Scotland Ridge (GSR) from ocean reanalyses to largely independent observational data. Reanalyses tend to underestimate the inflow of warm waters of subtropical Atlantic origin and hence oceanic heat transport across the GSR. Investigation of a strong negative heat transport anomaly around 2018 highlights the interplay of variability on different timescales and the need for long-term monitoring of the GSR to detect forced climate signals.
Jonathan Andrew Baker, Richard Renshaw, Laura Claire Jackson, Clotilde Dubois, Doroteaciro Iovino, Hao Zuo, Renellys C. Perez, Shenfu Dong, Marion Kersalé, Michael Mayer, Johannes Mayer, Sabrina Speich, and Tarron Lamont
State Planet, 1-osr7, 4, https://doi.org/10.5194/sp-1-osr7-4-2023, https://doi.org/10.5194/sp-1-osr7-4-2023, 2023
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We use ocean reanalyses, in which ocean models are combined with observations, to infer past changes in ocean circulation and heat transport in the South Atlantic. Comparing these estimates with other observation-based estimates, we find differences in their trends, variability, and mean heat transport but closer agreement in their mean overturning strength. Ocean reanalyses can help us understand the cause of these differences, which could improve estimates of ocean transports in this region.
Ali Aydogdu, Pietro Miraglio, Romain Escudier, Emanuela Clementi, and Simona Masina
State Planet, 1-osr7, 6, https://doi.org/10.5194/sp-1-osr7-6-2023, https://doi.org/10.5194/sp-1-osr7-6-2023, 2023
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This paper investigates the salt content, salinity anomaly and trend in the Mediterranean Sea using observational and reanalysis products. The salt content increases overall, while negative salinity anomalies appear in the western basin, especially around the upwelling regions. There is a large spread in the salinity estimates that is reduced with the emergence of the Argo profilers.
Andrea Cipollone, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, and Simona Masina
Ocean Sci., 19, 1375–1392, https://doi.org/10.5194/os-19-1375-2023, https://doi.org/10.5194/os-19-1375-2023, 2023
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Sea-ice volume is characterized by low predictability compared to the sea ice area or the extent. A joint initialization of the thickness and concentration using satellite data could improve the predictive power, although it is still absent in the present global analysis–reanalysis systems. This study shows a scheme to correct the two features together that can be easily extended to include ocean variables. The impact of such a joint initialization is shown and compared among different set-ups.
Ronan McAdam, Giulia Bonino, Emanuela Clementi, and Simona Masina
State Planet Discuss., https://doi.org/10.5194/sp-2023-22, https://doi.org/10.5194/sp-2023-22, 2023
Revised manuscript accepted for SP
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In the summer of 2022, a regional short-term forecasting system was able to predict the onset, spread, peaks and decay of a record-breaking marine heatwave in the Mediterranean Sea, up to 10 days in advance. Satellite data shows that the event was record-breaking in terms of basin-wide intensity and duration. This study demonstrates the potential of state-of-the-art forecasting systems to provide early-warning of marine heatwaves to marine activities (e.g. conservation and aquaculture).
Dimitra Denaxa, Gerasimos Korres, Giulia Bonino, Simona Masina, and Maria Hatzaki
State Planet Discuss., https://doi.org/10.5194/sp-2023-24, https://doi.org/10.5194/sp-2023-24, 2023
Revised manuscript accepted for SP
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This study investigates air-sea heat fluxes during marine heatwaves (MHWs) in the Mediterranean Sea. Surface fluxes drive 44 % of the onset and only 17 % of the decline phases of MHWs, suggesting a key role of oceanic processes. Heat fluxes are more important in warmer months and onset phases, with the latent heat dominating. Shorter events show weaker heat flux contribution. In most cases, mixed layer shoaling occurs over the entire MHW duration, followed by vertical mixing after the MHW end day.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Giulia Bonino, Simona Masina, Giuliano Galimberti, and Matteo Moretti
Earth Syst. Sci. Data, 15, 1269–1285, https://doi.org/10.5194/essd-15-1269-2023, https://doi.org/10.5194/essd-15-1269-2023, 2023
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We present a unique observational dataset of marine heat wave (MHW) macroevents and their characteristics over southern Europe and western Asian (SEWA) basins in the SEWA-MHW dataset. This dataset is the first effort in the literature to archive extremely hot sea surface temperature macroevents. The advantages of the availability of SEWA-MHWs are avoiding the waste of computational resources to detect MHWs and building a consistent framework which would increase comparability among MHW studies.
Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi
Geosci. Model Dev., 16, 179–197, https://doi.org/10.5194/gmd-16-179-2023, https://doi.org/10.5194/gmd-16-179-2023, 2023
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Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of the near-term climate and are useful for informing decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.
Giulia Bonino, Doroteaciro Iovino, Laurent Brodeau, and Simona Masina
Geosci. Model Dev., 15, 6873–6889, https://doi.org/10.5194/gmd-15-6873-2022, https://doi.org/10.5194/gmd-15-6873-2022, 2022
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The sea surface temperature (SST) is highly influenced by the transfer of energy driven by turbulent air–sea fluxes (TASFs). In the NEMO ocean general circulation model, TASFs are computed by means of bulk formulas. Bulk formulas require the choice of a given bulk parameterization, which influences the magnitudes of the TASFs. Our results show that parameterization-related SST differences are primarily sensitive to the wind stress differences across parameterizations.
Marco Reale, Gianpiero Cossarini, Paolo Lazzari, Tomas Lovato, Giorgio Bolzon, Simona Masina, Cosimo Solidoro, and Stefano Salon
Biogeosciences, 19, 4035–4065, https://doi.org/10.5194/bg-19-4035-2022, https://doi.org/10.5194/bg-19-4035-2022, 2022
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Future projections under the RCP8.5 and RCP4.5 emission scenarios of the Mediterranean Sea biogeochemistry at the end of the 21st century show different levels of decline in nutrients, oxygen and biomasses and an acidification of the water column. The signal intensity is stronger under RCP8.5 and in the eastern Mediterranean. Under RCP4.5, after the second half of the 21st century, biogeochemical variables show a recovery of the values observed at the beginning of the investigated period.
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
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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.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Giulia Bonino, Elisa Lovecchio, Nicolas Gruber, Matthias Münnich, Simona Masina, and Doroteaciro Iovino
Biogeosciences, 18, 2429–2448, https://doi.org/10.5194/bg-18-2429-2021, https://doi.org/10.5194/bg-18-2429-2021, 2021
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Seasonal variations of processes such as upwelling and biological production that happen along the northwestern African coast can modulate the temporal variability of the biological activity of the adjacent open North Atlantic hundreds of kilometers away from the coast thanks to the lateral transport of coastal organic carbon. This happens with a temporal delay, which is smaller than a season up to roughly 500 km from the coast due to the intense transport by small-scale filaments.
Daniele Peano, Deborah Hemming, Stefano Materia, Christine Delire, Yuanchao Fan, Emilie Joetzjer, Hanna Lee, Julia E. M. S. Nabel, Taejin Park, Philippe Peylin, David Wårlind, Andy Wiltshire, and Sönke Zaehle
Biogeosciences, 18, 2405–2428, https://doi.org/10.5194/bg-18-2405-2021, https://doi.org/10.5194/bg-18-2405-2021, 2021
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Global climate models are the scientist’s tools used for studying past, present, and future climate conditions. This work examines the ability of a group of our tools in reproducing and capturing the right timing and length of the season when plants show their green leaves. This season, indeed, is fundamental for CO2 exchanges between land, atmosphere, and climate. This work shows that discrepancies compared to observations remain, demanding further polishing of these tools.
Hilla Afargan-Gerstman, Iuliia Polkova, Lukas Papritz, Paolo Ruggieri, Martin P. King, Panos J. Athanasiadis, Johanna Baehr, and Daniela I. V. Domeisen
Weather Clim. Dynam., 1, 541–553, https://doi.org/10.5194/wcd-1-541-2020, https://doi.org/10.5194/wcd-1-541-2020, 2020
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We investigate the stratospheric influence on marine cold air outbreaks (MCAOs) in the North Atlantic using ERA-Interim reanalysis data. MCAOs are associated with severe Arctic weather, such as polar lows and strong surface winds. Sudden stratospheric events are found to be associated with more frequent MCAOs in the Barents and the Norwegian seas, affected by the anomalous circulation over Greenland and Scandinavia. Identification of MCAO precursors is crucial for improved long-range prediction.
Hiroyuki Tsujino, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro Iovino, Who M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, and Zipeng Yu
Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, https://doi.org/10.5194/gmd-13-3643-2020, 2020
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The OMIP-2 framework for global ocean–sea-ice model simulations is assessed by comparing multi-model means from 11 CMIP6-class global ocean–sea-ice models calculated separately for the OMIP-1 and OMIP-2 simulations. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet key improvements in transitioning from OMIP-1 to OMIP-2 are also identified. Thus, the present assessment justifies that future ocean–sea-ice model development and analysis studies use the OMIP-2 framework.
Torben Koenigk, Ramon Fuentes-Franco, Virna Meccia, Oliver Gutjahr, Laura C. Jackson, Adrian L. New, Pablo Ortega, Christopher Roberts, Malcolm Roberts, Thomas Arsouze, Doroteaciro Iovino, Marie-Pierre Moine, and Dmitry V. Sein
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-41, https://doi.org/10.5194/os-2020-41, 2020
Revised manuscript not accepted
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The mixing of water masses into the deep ocean in the North Atlantic is important for the entire global ocean circulation. We use seven global climate models to investigate the effect of increasing the model resolution on this deep ocean mixing. The main result is that increased model resolution leads to a deeper mixing of water masses in the Labrador Sea but has less effect in the Greenland Sea. However, most of the models overestimate the deep ocean mixing compared to observations.
Verena Haid, Doroteaciro Iovino, and Simona Masina
The Cryosphere, 11, 1387–1402, https://doi.org/10.5194/tc-11-1387-2017, https://doi.org/10.5194/tc-11-1387-2017, 2017
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Since the Antarctic sea ice extent shows a recent increase, we investigate the sea ice response to changed amount and distribution of surface freshwater addition in the Southern Ocean with the ocean–sea ice model NEMO/LIM2. We find that freshwater addition within the range of current estimates increases the ice extent, but higher amounts could have an opposing effect. The freshwater distribution is of great influence on the ice dynamics and the ice thickness is strongly influenced by it.
Petteri Uotila, Doroteaciro Iovino, Martin Vancoppenolle, Mikko Lensu, and Clement Rousset
Geosci. Model Dev., 10, 1009–1031, https://doi.org/10.5194/gmd-10-1009-2017, https://doi.org/10.5194/gmd-10-1009-2017, 2017
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We performed ocean model simulations with new and old sea-ice components. Sea ice improved in the new model compared to the earlier one due to better model physics. In the ocean, the largest differences are confined close to the surface within and near the sea-ice zone. The global ocean circulation slowly deviates between the simulations due to dissimilar sea ice in the deep water formation regions, such as the North Atlantic and Antarctic.
Stephen M. Griffies, Gokhan Danabasoglu, Paul J. Durack, Alistair J. Adcroft, V. Balaji, Claus W. Böning, Eric P. Chassignet, Enrique Curchitser, Julie Deshayes, Helge Drange, Baylor Fox-Kemper, Peter J. Gleckler, Jonathan M. Gregory, Helmuth Haak, Robert W. Hallberg, Patrick Heimbach, Helene T. Hewitt, David M. Holland, Tatiana Ilyina, Johann H. Jungclaus, Yoshiki Komuro, John P. Krasting, William G. Large, Simon J. Marsland, Simona Masina, Trevor J. McDougall, A. J. George Nurser, James C. Orr, Anna Pirani, Fangli Qiao, Ronald J. Stouffer, Karl E. Taylor, Anne Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Maria Valdivieso, Qiang Wang, Michael Winton, and Stephen G. Yeager
Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, https://doi.org/10.5194/gmd-9-3231-2016, 2016
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The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This document defines OMIP and details a protocol both for simulating global ocean/sea-ice models and for analysing their output.
Bart van den Hurk, Hyungjun Kim, Gerhard Krinner, Sonia I. Seneviratne, Chris Derksen, Taikan Oki, Hervé Douville, Jeanne Colin, Agnès Ducharne, Frederique Cheruy, Nicholas Viovy, Michael J. Puma, Yoshihide Wada, Weiping Li, Binghao Jia, Andrea Alessandri, Dave M. Lawrence, Graham P. Weedon, Richard Ellis, Stefan Hagemann, Jiafu Mao, Mark G. Flanner, Matteo Zampieri, Stefano Materia, Rachel M. Law, and Justin Sheffield
Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, https://doi.org/10.5194/gmd-9-2809-2016, 2016
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This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
Doroteaciro Iovino, Simona Masina, Andrea Storto, Andrea Cipollone, and Vladimir N. Stepanov
Geosci. Model Dev., 9, 2665–2684, https://doi.org/10.5194/gmd-9-2665-2016, https://doi.org/10.5194/gmd-9-2665-2016, 2016
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An 11-year simulation of a global eddying ocean (1/16) configuration is presented. Model performance is evaluated against observations and a twin 1/4 configuration. The model realistically represents the variability at upper and intermediate depths, the position and strength of the surface circulation, and exchanges of mass through key passages. Sea ice properties are close to satellite observations. This simulation constitutes the groundwork for future applications to short range ocean forecasting.
Italo Epicoco, Silvia Mocavero, Francesca Macchia, Marcello Vichi, Tomas Lovato, Simona Masina, and Giovanni Aloisio
Geosci. Model Dev., 9, 2115–2128, https://doi.org/10.5194/gmd-9-2115-2016, https://doi.org/10.5194/gmd-9-2115-2016, 2016
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The present work aims at evaluating the scalability performance of a high-resolution global ocean biogeochemistry model (PELAGOS025) on massive parallel architectures and the benefits in terms of the time-to-solution reduction. The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, and the load unbalancing due to the memory structure of the biogeochemistry model component.
F. Fattore, T. Bertolini, S. Materia, S. Gualdi, A. Thongo M'Bou, G. Nicolini, R. Valentini, A. De Grandcourt, D. Tedesco, and S. Castaldi
Biogeosciences, 11, 3069–3081, https://doi.org/10.5194/bg-11-3069-2014, https://doi.org/10.5194/bg-11-3069-2014, 2014
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A model for the Arctic mixed layer circulation under a summertime lead: implications for the near-surface temperature maximum formation
Underestimation of oceanic carbon uptake in the Arctic Ocean: ice melt as predictor of the sea ice carbon pump
Uncertainty analysis of single- and multiple-size-class frazil ice models
Wave–sea-ice interactions in a brittle rheological framework
Experimental evidence for a universal threshold characterizing wave-induced sea ice break-up
High-resolution simulations of interactions between surface ocean dynamics and frazil ice
Frazil ice growth and production during katabatic wind events in the Ross Sea, Antarctica
Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone
Wave energy attenuation in fields of colliding ice floes – Part 2: A laboratory case study
Responses of sub-ice platelet layer thickening rate and frazil-ice concentration to variations in ice-shelf water supercooling in McMurdo Sound, Antarctica
Momme C. Hell and Christopher Horvat
The Cryosphere, 18, 341–361, https://doi.org/10.5194/tc-18-341-2024, https://doi.org/10.5194/tc-18-341-2024, 2024
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Sea ice is heavily impacted by waves on its margins, and we currently do not have routine observations of waves in sea ice. Here we propose two methods to separate the surface waves from the sea-ice height observations along each ICESat-2 track using machine learning. Both methods together allow us to follow changes in the wave height through the sea ice.
Gemma Marie Brett, Gregory Howard Leonard, Wolfgang Rack, Christian Haas, Patricia Jean Langhorne, Natalie Robinson, and Anne Irvin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2724, https://doi.org/10.5194/egusphere-2023-2724, 2023
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Glacial meltwater with ice crystals flows from beneath ice shelves, causing thicker sea ice with sub-ice platelet layers (SIPL) beneath. Thicker sea ice and SIPL reveal where and how much meltwater is outflowing. We collected continuous measurements of sea ice and SIPL. In winter, we observed rapid SIPL growth with strong winds. In spring, SIPL grew when tides caused offshore circulation. Wind-driven and tidal circulation influence glacial meltwater outflow from ice shelf cavities.
Sam De Abreu, Rosalie M. Cormier, Mikhail G. Schee, Varvara E. Zemskova, Erica Rosenblum, and Nicolas Grisouard
EGUsphere, https://doi.org/10.5194/egusphere-2023-1756, https://doi.org/10.5194/egusphere-2023-1756, 2023
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Arctic sea ice is becoming more mobile and thinner, which will affect the upper Arctic ocean in unforeseen ways. Using numerical simulations, we find that mixing by ice keels (ridges underlying sea ice) depends significantly on their speeds and depths, and the density structure of the upper ocean. Large uncertainties in our results highlight the need for more realistic numerical simulations and better measurements of ice keel characteristics.
Alberto Alvarez
The Cryosphere, 17, 3343–3361, https://doi.org/10.5194/tc-17-3343-2023, https://doi.org/10.5194/tc-17-3343-2023, 2023
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A near-surface temperature maximum (NSTM) layer is typically observed under different Arctic basins. Although its development seems to be related to solar heating in leads, its formation mechanism is under debate. This study uses numerical modeling in an idealized framework to demonstrate that the NSTM layer forms under a summer lead exposed to a combination of calm and moderate wind periods. Future warming of this layer could modify acoustic propagation with implications for marine mammals.
Benjamin Richaud, Katja Fennel, Eric C. J. Oliver, Michael D. DeGrandpre, Timothée Bourgeois, Xianmin Hu, and Youyu Lu
The Cryosphere, 17, 2665–2680, https://doi.org/10.5194/tc-17-2665-2023, https://doi.org/10.5194/tc-17-2665-2023, 2023
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Sea ice is a dynamic carbon reservoir. Its seasonal growth and melt modify the carbonate chemistry in the upper ocean, with consequences for the Arctic Ocean carbon sink. Yet, the importance of this process is poorly quantified. Using two independent approaches, this study provides new methods to evaluate the error in air–sea carbon flux estimates due to the lack of biogeochemistry in ice in earth system models. Those errors range from 5 % to 30 %, depending on the model and climate projection.
Fabien Souillé, Cédric Goeury, and Rem-Sophia Mouradi
The Cryosphere, 17, 1645–1674, https://doi.org/10.5194/tc-17-1645-2023, https://doi.org/10.5194/tc-17-1645-2023, 2023
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Models that can predict temperature and ice crystal formation (frazil) in water are important for river and coastal engineering. Indeed, frazil has direct impact on submerged structures and often precedes the formation of ice cover. In this paper, an uncertainty analysis of two mathematical models that simulate supercooling and frazil is carried out within a probabilistic framework. The presented methodology offers new insight into the models and their parameterization.
Guillaume Boutin, Timothy Williams, Pierre Rampal, Einar Olason, and Camille Lique
The Cryosphere, 15, 431–457, https://doi.org/10.5194/tc-15-431-2021, https://doi.org/10.5194/tc-15-431-2021, 2021
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In this study, we investigate the interactions of surface ocean waves with sea ice. We focus on the evolution of sea ice after it has been fragmented by the waves. Fragmented sea ice is expected to experience less resistance to deformation. We reproduce this evolution using a new coupling framework between a wave model and the recently developed sea ice model neXtSIM. We find that waves can significantly increase the mobility of compact sea ice over wide areas in the wake of storm events.
Joey J. Voermans, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Aleksey Marchenko, Clarence O. Collins III, Mohammed Dabboor, Graig Sutherland, and Alexander V. Babanin
The Cryosphere, 14, 4265–4278, https://doi.org/10.5194/tc-14-4265-2020, https://doi.org/10.5194/tc-14-4265-2020, 2020
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In this work we demonstrate the existence of an observational threshold which identifies when waves are most likely to break sea ice. This threshold is based on information from two recent field campaigns, supplemented with existing observations of sea ice break-up. We show that both field and laboratory observations tend to converge to a single quantitative threshold at which the wave-induced sea ice break-up takes place, which opens a promising avenue for operational forecasting models.
Agnieszka Herman, Maciej Dojczman, and Kamila Świszcz
The Cryosphere, 14, 3707–3729, https://doi.org/10.5194/tc-14-3707-2020, https://doi.org/10.5194/tc-14-3707-2020, 2020
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Under typical conditions favorable for sea ice formation in many regions (strong wind and waves, low air temperature), ice forms not at the sea surface but within the upper, turbulent layer of the ocean. Although interactions between ice and ocean dynamics are very important for the evolution of sea ice cover, many aspects of them are poorly understood. We use a numerical model to analyze three-dimensional water circulation and ice transport and show that ice strongly modifies that circulation.
Lisa Thompson, Madison Smith, Jim Thomson, Sharon Stammerjohn, Steve Ackley, and Brice Loose
The Cryosphere, 14, 3329–3347, https://doi.org/10.5194/tc-14-3329-2020, https://doi.org/10.5194/tc-14-3329-2020, 2020
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The offshore winds around Antarctica can reach hurricane strength and produce intense cooling, causing the surface ocean to form a slurry of seawater and ice crystals. For the first time, we observed a buildup of heat and salt in the surface ocean, caused by loose ice crystal formation. We conclude that up to 1 m of ice was formed per day by the intense cooling, suggesting that unconsolidated crystals may be an important part of the total freezing that happens around Antarctica.
Guillaume Boutin, Camille Lique, Fabrice Ardhuin, Clément Rousset, Claude Talandier, Mickael Accensi, and Fanny Girard-Ardhuin
The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, https://doi.org/10.5194/tc-14-709-2020, 2020
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We investigate the interactions of surface ocean waves with sea ice taking place at the interface between the compact sea ice cover and the open ocean. We use a newly developed coupling framework between a wave and an ocean–sea ice numerical model. Our results show how the push on sea ice exerted by waves changes the amount and the location of sea ice melting, with a strong impact on the ocean surface properties close to the ice edge.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2901–2914, https://doi.org/10.5194/tc-13-2901-2019, https://doi.org/10.5194/tc-13-2901-2019, 2019
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Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. One of the reasons limiting progress in modelling is a lack of observational data for model validation. The paper presents an analysis of laboratory observations of waves propagating in colliding ice floes. We show that wave attenuation is sensitive to floe size and wave period. A numerical model is calibrated to reproduce this behaviour.
Chen Cheng, Adrian Jenkins, Paul R. Holland, Zhaomin Wang, Chengyan Liu, and Ruibin Xia
The Cryosphere, 13, 265–280, https://doi.org/10.5194/tc-13-265-2019, https://doi.org/10.5194/tc-13-265-2019, 2019
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The sub-ice platelet layer (SIPL) under fast ice is most prevalent in McMurdo Sound, Antarctica. Using a modified plume model, we investigated the responses of SIPL thickening rate and frazil concentration to variations in ice shelf water supercooling in McMurdo Sound. It would be key to parameterizing the relevant process in more complex three-dimensional, primitive equation ocean models, which relies on the knowledge of the suspended frazil size spectrum within the ice–ocean boundary layer.
Cited articles
Aagaard, K.: A synthesis of the Arctic Ocean circulation, Rapp. P. V. Reun. Cons. Int. Explor. Mer, 1188, 11–22, 1989. a
Årthun, M., Eldevik, T., Smedsrud, L. H., Skagseth, and Ingvaldsen, R. B.: Quantifying the influence of atlantic heat on barents sea ice variability and retreat, J. Climate, 25, 4736–4743, https://doi.org/10.1175/JCLI-D-11-00466.1, 2012. a, b, c
Årthun, M., Eldevik, T., and Smedsrud, L. H.: The role of Atlantic heat transport in future Arctic winter sea ice loss, J. Climate, 32, 3327–3341, https://doi.org/10.1175/JCLI-D-18-0750.1, 2019. a
Asbjørnsen, H., Årthun, M., Skagseth, Ø., and Eldevik, T.: Mechanisms underlying recent arctic atlantification, Geophys. Res. Lett., 47, e2020GL088036, https://doi.org/10.1029/2020GL088036, 2020. a, b, c
Aylmer, J., Ferreira, D., and Feltham, D.: Different mechanisms of Arctic and Antarctic sea ice response to ocean heat transport, Clim. Dynam., https://doi.org/10.1007/s00382-021-06131-x, 2022. a
Balmaseda, M. A., Hernandez, F., Storto, A., Palmer, M. D., Alves, O., Shi, L., Smith, G. C., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D., Boyer, T., Chang, Y. S., Chepurin, G. A., Ferry, N., Forget, G., Fujii, Y., Good, S., Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A., Lee, T., Martin, M. J., Masina, S., Masuda, S., Meyssignac, B., Mogensen, K., Parent, L., Peterson, K. A., Tang, Y. M., Yin, Y., Vernieres, G., Wang, X., Waters, J., Wedd, R., Wang, O., Xue, Y., Chevallier, M., Lemieux, J. F., Dupont, F., Kuragano, T., Kamachi, M., Awaji, T., Caltabiano, A., Wilmer-Becker, K., and Gaillard, F.: The ocean reanalyses intercomparison project (ORA-IP), J. Oper. Oceanogr., 8, s80–s97, https://doi.org/10.1080/1755876X.2015.1022329, 2015. a
Beszczynska-Möller, A., Fahrbach, E., Schauer, U., and Hansen, E.: Variability in Atlantic water temperature and transport at the entrance to the Arctic Ocean, 1997–2010, ICES J. Mar. Sci., 69, 852–863, 2012. a
Bliss, A. C., Steele, M., Peng, G., Meier, W. N., and Dickinson, S.: Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record, Environ. Res. Lett., 14, 045003, https://doi.org/10.1088/1748-9326/aafb84, 2019. a, b
Carmack, E., Polyakov, I., Padman, L., Fer, I., Hunke, E., Hutchings, J., Jackson, J., Kelley, D., Kwok, R., Layton, C., Melling, H., Perovich, D., Persson, O., Ruddick, B., Timmermans, M. L., Toole, J., Ross, T., Vavrus, S., and Winsor, P.: Toward quantifying the increasing role of oceanic heat in sea ice loss in the new arctic, B. Am. Meteorol. Soc., 96, 2079–2105, https://doi.org/10.1175/BAMS-D-13-00177.1, 2015. a
Carton, J. A., Penny, S. G., and Kalnay, E.: Temperature and salinity variability in the SODA3, ECCO4r3, and ORAS5 ocean reanalyses, 1993–2015, J. Climate, 32, 2277–2293, 2019. a
Comiso, J. C., Meier, W. N., and Gersten, R.: Variability and trends in the Arctic Sea ice cover: Results from different techniques, J. Geophys. Res.-Oceans, 122, 6883–6900, https://doi.org/10.1002/2017JC012768, 2017. a
Copernicus Climate Change Service, Climate Data Store: ORAS5 global ocean reanalysis monthly data from 1958 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.67e8eeb7, 2021. a
Copernicus Marine Service: Global Ocean Ensemble Physics Reanalysis, Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00024, 2023. a
D'Angelo, G., Hunke, E. C., Pitts, T. R., Price, S. F., and Roberts, A. F.: Sea ice evolution along the Northern Sea Route and implications for trans-Arctic shipping from 2021 through 2060, Tech. Rep., Los Alamos National Lab. (LANL), Los Alamos, NM, United States, https://doi.org/10.2172/1832347, 2021. a
Davis, P. E., Lique, C., Johnson, H. L., and Guthrie, J. D.: Competing effects of elevated vertical mixing and increased freshwater input on the stratification and sea ice cover in a changing Arctic Ocean, J. Phys. Oceanogr., 46, 1531–1553, 2016. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Ding, Q., Schweiger, A., L'Heureux, M., Steig, E. J., Battisti, D. S., Johnson, N. C., Blanchard-Wrigglesworth, E., Po-Chedley, S., Zhang, Q., Harnos, K., Bushuk, M., Markle, B., and Baxter, I.: Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations, Nat. Geosci., 12, 28–33, https://doi.org/10.1038/s41561-018-0256-8, 2019. a
Dörr, J., Årthun, M., Eldevik, T., and Madonna, E.: Mechanisms of regional winter sea-ice variability in a warming arctic, J. Climate, 34, 8635–8653, https://doi.org/10.1175/JCLI-D-21-0149.1, 2021. a
Duarte, P., Sundfjord, A., Meyer, A., Hudson, S. R., Spreen, G., and Smedsrud, L. H.: Warm Atlantic Water Explains Observed Sea Ice Melt Rates North of Svalbard, J. Geophys. Res.-Oceans, 125, e2019JC015662, https://doi.org/10.1029/2019JC015662, 2020. a
England, M., Jahn, A., and Polvani, L.: Nonuniform contribution of internal variability to recent Arctic sea ice loss, J. Climate, 32, 4039–4053, 2019. a
Fichefet, T. and Maqueda, M. A.: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics, J. Geophys. Res.-Oceans, 102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997. a
Gimbert, F., Jourdain, N. C., Marsan, D., Weiss, J., and Barnier, B.: Recent mechanical weakening of the Arctic sea ice cover as revealed from larger inertial oscillations, J. Geophys. Res.-Oceans, 117, C00J12, https://doi.org/10.1029/2011JC007633, 2012. a
Graham, R. M., Itkin, P., Meyer, A., Sundfjord, A., Spreen, G., Smedsrud, L. H., Liston, G. E., Cheng, B., Cohen, L., Divine, D., Fer, I., Fransson, A., Gerland, S., Haapala, J., Hudson, S. R., Johansson, A. M., King, J., Merkouriadi, I., Peterson, A. K., Provost, C., Randelhoff, A., Rinke, A., Rösel, A., Sennéchael, N., Walden, V. P., Duarte, P., Assmy, P., Steen, H., and Granskog, M. A.: Winter storms accelerate the demise of sea ice in the Atlantic sector of the Arctic Ocean, Scientific Reports, 9, 9222, https://doi.org/10.1038/s41598-019-45574-5, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2023. a
Heuzé, C., Zanowski, H., Karam, S., and Muilwijk, M.: The deep Arctic Ocean and Fram Strait in CMIP6 models, J. Climate, 36, 2551–2584, 2023. a
Holland, M. M., Bitz, C. M., and Tremblay, B.: Future abrupt reductions in the summer Arctic sea ice, Geophys. Res. Lett., 33, 23, https://doi.org/10.1029/2006GL028024, 2006. a
Ilıcak, M., Drange, H., Wang, Q., Gerdes, R., Aksenov, Y., Bailey, D., Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E., Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T., Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. J. G., Roth, C., Salas y Mèlia, D., Samuels, B. L., Spence, P., Tsujino, H., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes, Ocean Model., 100, 141–161, 2016. a, b, c
Iovino, D., Selivanova, J., Masina, S., and Cipollone, A.: The Antarctic Marginal Ice Zone and Pack Ice Area in CMEMS GREP Ensemble Reanalysis Product, Front. Earth Sci., 10, 95, https://doi.org/10.3389/feart.2022.745274 2022. a
Ivanov, V., Alexeev, V., Koldunov, N. V., Repina, I., Sandø, A. B., Smedsrud, L. H., and Smirnov, A.: Arctic Ocean heat impact on regional ice decay: A suggested positive feedback, J. Phys. Oceanogr., 46, 1437–1456, 2016. a
Ivanov, V. V., Alexeev, V. A., Repina, I., Koldunov, N. V., and Smirnov, A.: Tracing atlantic water signature in the arctic sea ice cover east of svalbard, Adv. Meteorol., 2012, 201818, https://doi.org/10.1155/2012/201818, 2012. a
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019. a
Karami, M. P., Myers, P. G., de Vernal, A., Tremblay, L. B., and Hu, X.: The role of Arctic gateways on sea ice and circulation in the Arctic and North Atlantic Oceans: a sensitivity study with an ocean-sea-ice model, Clim. Dynam., 57, 2129–2151, 2021. a
Koenigk, T. and Brodeau, L.: Ocean heat transport into the Arctic in the twentieth and twenty-first century in EC-Earth, Clim. Dynam., 42, 3101–3120, 2014. a
Korhonen, M., Rudels, B., Marnela, M., Wisotzki, A., and Zhao, J.: Time and space variability of freshwater content, heat content and seasonal ice melt in the Arctic Ocean from 1991 to 2011, Ocean Sci., 9, 1015–1055, https://doi.org/10.5194/os-9-1015-2013, 2013. a
Lebrun, M., Vancoppenolle, M., Madec, G., and Massonnet, F.: Arctic sea-ice-free season projected to extend into autumn, The Cryosphere, 13, 79–96, https://doi.org/10.5194/tc-13-79-2019, 2019. a
Lenetsky, J. E., Tremblay, B., Brunette, C., and Meneghello, G.: Subseasonal predictability of Arctic Ocean sea ice conditions: Bering Strait and Ekman-driven ocean heat transport, J. Climate, 34, 4449–4462, 2021. a
Li, D., Zhang, R., and Knutson, T. R.: On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline, Nat. Commun., 8, 14991, https://doi.org/10.1038/ncomms14991, 2017. a
Li, Z., Ding, Q., Steele, M., and Schweiger, A.: Recent upper Arctic Ocean warming expedited by summertime atmospheric processes, Nat. Commun., 13, 362, https://doi.org/10.1038/s41467-022-28047-8, 2022. a
Liang, X. and Losch, M.: On the effects of increased vertical mixing on the Arctic Ocean and sea ice, J. Geophys. Res.-Oceans, 123, 9266–9282, 2018. a
Lien, V. S., Hjøllo, S. S., Skogen, M. D., Svendsen, E., Wehde, H., Bertino, L., Counillon, F., Chevallier, M., and Garric, G.: An assessment of the added value from data assimilation on modelled Nordic Seas hydrography and ocean transports, Ocean Model., 99, 43–59, 2016. a
Lien, V. S., Schlichtholz, P., Skagseth, Ø., and Vikebø, F. B.: Wind-Driven Atlantic Water Flow as a Direct Mode for Reduced Barents Sea Ice Cover, J. Climate, 30, 803–812, https://doi.org/10.1175/jcli-d-16-0025.1, 2017. a
Lind, S., Ingvaldsen, R. B., and Furevik, T.: Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import, Nat. Clim. Change, 8, 634–639, https://doi.org/10.1038/s41558-018-0205-y, 2018. a
Long, Z. and Perrie, W.: Changes in ocean temperature in the Barents Sea in the twenty-first century, J. Climate, 30, 5901–5921, 2017. a
MacKinnon, J. A., Simmons, H. L., Hargrove, J., Thomson, J., Peacock, T., Alford, M. H., Barton, B. I., Boury, S., Brenner, S. D., Couto, N., Danielson, S. L., Fine, E. C., Graber, H. C., Guthrie, J., Hopkins, J. E., Jayne, S. R., Jeon, C., Klenz, T., Lee, C. M., Lenn, Y., Lucas, A. J., Lund, B., Mahaffey, C., Norman, L., Rainville, L., Smith, M. M., Thomas, L. N., Torres-Valdès, S., and Wood, K. R.: A warm jet in a cold ocean, Nat. Commun., 12, 2418, https://doi.org/10.1038/s41467-021-22505-5, 2021. a
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., Delrosso, D., Christian, E., Flavoni, S., Graham, T., Harle, J., Iovino, D., Lea, D., Lèvy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Paul, J., Rousset, C., Storkey, D., Storto, A., and Vancoppenolle, M.: NEMO oean engine, Note du Pole de modèlisation de l'Institut Pierre-Simon Laplace No. 27, ISSN 1288-1619, 2017. a
Maslowski, W., Marble, D., Walczowski, W., Schauer, U., Clement, J. L., and Semtner, A. J.: On climatological mass, heat, and salt transports through the Barents Sea and Fram Strait from a pan-Arctic coupled ice-ocean model simulation, J. Geophys. Res.-Oceans, 109, C3, https://doi.org/10.1029/2001JC001039, 2004. a
McAdam, R., Masina, S., Balmaseda, M., Gualdi, S., Senan, R., and Mayer, M.: Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems, Clim. Dynam., 58, 3335–3350, 2022. a
Mikhailova, N., Bayankina, T., and Sizov, A.: Two modes of atmosphere–ocean interaction in the Atlantic sector of the Arctic Basin, Oceanology, 61, 443–449, 2021. a
Mohamed, B., Nilsen, F., and Skogseth, R.: Marine heatwaves characteristics in the barents sea based on high resolution satellite data (1982–2020), Frontiers in Marine Science, 9, 821646, https://doi.org/10.3389/fmars.2022.821646, 2022. a
Muilwijk, M., Smedsrud, L. H., Polyakov, I. V., Nummelin, A., Heuzé, C., and Zanowski, H.: Divergence in CMIP6 projections of future Arctic Ocean stratification, Tech. Rep., Copernicus Meetings, https://doi.org/10.1175/JCLI-D-22-0349.1, 2022. a, b
Nie, Y., Uotila, P., Cheng, B., Massonnet, F., Kimura, N., Cipollone, A., and Lv, X.: Southern Ocean sea ice concentration budgets of five ocean-sea ice reanalyses, Clim. Dynam., 59, 3265–3285, 2022. a
Nummelin, A., Li, C., and Hezel, P. J.: Connecting ocean heat transport changes from the midlatitudes to the Arctic Ocean, Geophys. Res. Lett., 44, 1899–1908, https://doi.org/10.1002/2016GL071333, 2017. a
Olonscheck, D., Mauritsen, T., and Notz, D.: Arctic sea-ice variability is primarily driven by atmospheric temperature fluctuations, Nat. Geosci., 12, 430–434, https://doi.org/10.1038/s41561-019-0363-1, 2019. a
Onarheim, I. H., Smedsrud, L. H., Ingvaldsen, R. B., and Nilsen, F.: Loss of sea ice during winter north of Svalbard, Tellus A, 66, 23933, https://doi.org/10.3402/tellusa.v66.23933, 2014. a, b
Onarheim, I. H., Eldevik, T., Årthun, M., Ingvaldsen, R. B., and Smedsrud, L. H.: Skillful prediction of Barents Sea ice cover, Geophys. Res. Lett., 42, 5364–5371, https://doi.org/10.1002/2015GL064359, 2015. a, b
Onarheim, I. H., Eldevik, T., Smedsrud, L. H., and Stroeve, J. C.: Seasonal and regional manifestation of Arctic sea ice loss, J. Climate, 31, 4917–4932, https://doi.org/10.1175/JCLI-D-17-0427.1, 2018. a, b, c
Pan, R., Shu, Q., Wang, Q., Wang, S., Song, Z., He, Y., and Qiao, F.: Future Arctic Climate Change in CMIP6 Strikingly Intensified by NEMO-Family Climate Models, Geophys. Res. Lett., 50, e2022GL102077, https://doi.org/10.1029/2022GL102077, 2023. a
Peng, G. and Meier, W. N.: Temporal and regional variability of Arctic sea-ice coverage from satellite data, Ann. Glaciol., 59, 191–200, 2018. a
Peralta-Ferriz, C. and Woodgate, R. A.: Seasonal and interannual variability of pan-Arctic surface mixed layer properties from 1979 to 2012 from hydrographic data, and the dominance of stratification for multiyear mixed layer depth shoaling, Prog. Oceanogr., 134, 19–53, 2015. a
Perovich, D. K.: The changing Arctic sea ice cover, Oceanography, 24, 162–173, 2011. a
Perovich, D. K. and Polashenski, C.: Albedo evolution of seasonal Arctic sea ice, Geophys. Res. Lett., 39, 8, https://doi.org/10.1029/2012GL051432, 2012. a, b
Perovich, D. K., Light, B., Eicken, H., Jones, K. F., Runciman, K., and Nghiem, S. V.: Increasing solar heating of the Arctic Ocean and adjacent seas, 1979–2005: Attribution and role in the ice-albedo feedback, Geophys. Res. Lett., 34, L19505, https://doi.org/10.1029/2007GL031480, 2007. a, b
Polyakov, I., Alekseev, G., Timokhov, L., Bhatt, U., Colony, R., Simmons, H., Walsh, D., Walsh, J., and Zakharov, V.: Variability of the intermediate Atlantic water of the Arctic Ocean over the last 100 years, J. Climate, 17, 4485–4497, 2004. a
Polyakov, I., Bhatt, U., Simmons, H., Walsh, D., Walsh, J., and Zhang, X.: Multidecadal variability of North Atlantic temperature and salinity during the twentieth century, J. Climate, 18, 4562–4581, 2005. a
Polyakov, I. V., Timokhov, L. A., Alexeev, V. A., Bacon, S., Dmitrenko, I. A., Fortier, L., Frolov, I. E., Gascard, J. C., Hansen, E., Ivanov, V. V., Laxon, S., Mauritzen, C., Perovich, D., Shimada, K., Simmons, H. L., Sokolov, V. T., Steele, M., and Toole, J.: Arctic ocean warming contributes to reduced polar ice cap, J. Phys. Oceanogr., 40, 2743–2756, https://doi.org/10.1175/2010JPO4339.1, 2010. a
Polyakov, I. V., Pnyushkov, A. V., Alkire, M. B., Ashik, I. M., Baumann, T. M., Carmack, E. C., Goszczko, I., Guthrie, J., Ivanov, V. V., Kanzow, T., Krishfield, R., Kwok, R., Sundfjord, A., Morison, J., Rember, R., and Yulin, A.: Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean, Science, 356, 285–291, 2017. a
Polyakov, I. V., Alkire, M. B., Bluhm, B. A., Brown, K. A., Carmack, E. C., Chierici, M., Danielson, S. L., Ellingsen, I., Ershova, E. A., Gårdfeldt, K., Ingvaldsen, R. B., Pnyushkov, A. V., Slagstad, D., and Wassmann, P.: Borealization of the Arctic Ocean in response to anomalous advection from sub-Arctic seas, Frontiers in Marine Science, 7, 491, https://doi.org/10.3389/fmars.2020.00491, 2020a. a
Polyakov, I. V., Rippeth, T. P., Fer, I., Alkire, M. B., Baumann, T. M., Carmack, E. C., Ingvaldsen, R., Ivanov, V. V., Janout, M., Lind, S., Padman, L., Pnyushkov, A. V., and Rember, R.: Weakening of cold halocline layer exposes sea ice to oceanic heat in the eastern arctic ocean, J. Climate, 33, 8107–8123, https://doi.org/10.1175/JCLI-D-19-0976.1, 2020b. a
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily high-resolution-blended analyses for sea surface temperature, J. Climate, 20, 5473–5496, 2007. a
Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., and Paul, S.: Evidence for an increasing role of ocean heat in arctic winter sea ice growth, J. Climate, 34, 5215–5227, https://doi.org/10.1175/JCLI-D-20-0848.1, 2021. a
Rudels, B.: Arctic Ocean circulation, processes and water masses: A description of observations and ideas with focus on the period prior to the International Polar Year 2007–2009, Prog. Oceanogr., 132, 22–67, 2015. a
Sandø, A. B., Nilsen, J. Ø., Gao, Y., and Lohmann, K.: Importance of heat transport and local air-sea heat fluxes for Barents Sea climate variability, J. Geophys. Res.-Oceans, 115, C7, https://doi.org/10.1029/2009JC005884, 2010. a
Schauer, U. and Beszczynska-Möller, A.: Problems with estimation and interpretation of oceanic heat transport – conceptual remarks for the case of Fram Strait in the Arctic Ocean, Ocean Sci., 5, 487–494, https://doi.org/10.5194/os-5-487-2009, 2009. a, b
Serreze, M. C. and Meier, W. N.: The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic, Ann. NY Acad. Sci., 1436, 36–53, 2019. a
Serreze, M. C., Crawford, A. D., Stroeve, J. C., Barrett, A. P., and Woodgate, R. A.: Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea, J. Geophys. Res.-Oceans, 121, 7308–7325, https://doi.org/10.1002/2016JC011977, 2016. a
Shu, Q., Wang, Q., Song, Z., and Qiao, F.: The poleward enhanced Arctic Ocean cooling machine in a warming climate, Nat. Commun., 12, 2966, https://doi.org/10.1038/s41467-021-23321-7, 2021. a, b, c, d
Shu, Q., Wang, Q., Årthun, M., Wang, S., Song, Z., Zhang, M., and Qiao, F.: Arctic Ocean Amplification in a warming climate in CMIP6 models, Science Advances, 8, eabn9755, https://doi.org/10.1126/sciadv.abn9755, 2022. a, b, c
Simmonds, I.: Comparing and contrasting the behaviour of Arctic and Antarctic sea ice over the 35 year period 1979–2013, Ann. Glaciol., 56, 18–28, https://doi.org/10.3189/2015AoG69A909, 2015. a
Smedsrud, L. H., Ingvaldsen, R., Nilsen, J. E. Ø., and Skagseth, Ø.: Heat in the Barents Sea: transport, storage, and surface fluxes, Ocean Sci., 6, 219–234, https://doi.org/10.5194/os-6-219-2010, 2010. a
Smedsrud, L. H., Esau, I., Ingvaldsen, R. B., Eldevik, T., Haugan, P. M., Li, C., Lien, V. S., Olsen, A., Omar, A. M., Risebrobakken, B., Sandø, A. B., Semenov, V. A., and Sorokina, S. A.: The role of the Barents Sea in the Arctic climate system, Rev. Geophys., 51, 415–449, https://doi.org/10.1002/rog.20017, 2013. a, b
Smedsrud, L. H., Muilwijk, M., Brakstad, A., Madonna, E., Lauvset, S. K., Spensberger, C., Born, A., Eldevik, T., Drange, H., Jeansson, E., Li, C., Olsen, A., Skagseth, Ø., Slater, D. A., Straneo, F., Våge, K., and Årthun, M.: Nordic Seas heat loss, Atlantic inflow, and Arctic sea ice cover over the last century, Rev. Geophys., 60, e2020RG000725, https://doi.org/10.1029/2020RG000725, 2022. a, b, c
Stammerjohn, S., Massom, R., Rind, D., and Martinson, D.: Regions of rapid sea ice change: An inter-hemispheric seasonal comparison, Geophys. Res. Lett., 39, L06501, https://doi.org/10.1029/2012GL050874, 2012. a
Steele, M., Ermold, W., and Zhang, J.: Arctic Ocean surface warming trends over the past 100 years, Geophys. Res. Lett., 35, L02614, https://doi.org/10.1029/2007GL031651, 2008. a
Storto, A. and Masina, S.: C-GLORSv5: an improved multipurpose global ocean eddy-permitting physical reanalysis, Earth Syst. Sci. Data, 8, 679–696, https://doi.org/10.5194/essd-8-679-2016, 2016a. a, b
Storto, A. and Masina, S.: The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980–2014), links to NetCDF files, Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.857995, 2016b. a
Storto, A., Alvera-Azcárate, A., Balmaseda, M. A., Barth, A., Chevallier, M., Counillon, F., Domingues, C. M., Drevillon, M., Drillet, Y., Forget, G., Garric, G., Haines, K., Hernandez, F., Iovino, D., Jackson, L. C., Lellouche, J., Masina, S., Mayer, M., Oke, P. R., Penny, S. G., Peterson, K. A., Yang, C., and Zuo, H.: Ocean reanalyses: recent advances and unsolved challenges, Frontiers in Marine Science, 6, 418, https://doi.org/10.3389/fmars.2019.00418, 2019. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons, Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a
Stroeve, J. C., Crawford, A. D., and Stammerjohn, S.: Using timing of ice retreat to predict timing of fall freeze-up in the Arctic, Geophys. Res. Lett., 43, 6332–6340, https://doi.org/10.1002/2016GL069314, 2016. a
Takahashi, N., Richards, K. J., Schneider, N., Annamalai, H., Hsu, W. C., and Nonaka, M.: Formation Mechanism of Warm SST Anomalies in 2010s Around Hawaii, J. Geophys. Res.-Oceans, 126, e2021JC017763, https://doi.org/10.1029/2021JC017763, 2021. a
Thépaut, J.-N., Dee, D., Engelen, R., and Pinty, B.: The Copernicus programme and its climate change service, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, 1591–1593, Valencia, Spain, 22–27 July 2018, IEEE, https://doi.org/10.1109/IGARSS.2018.8518067, 2018. a
Thomson, R. E. and Fine, I. V.: Estimating Mixed Layer Depth from Oceanic Profile Data, Tech. Rep., https://doi.org/10.1175/1520-0426(2003)020<0319:EMLDFO>2.0.CO;2, 2003. a
Timmermans, M. L., Cole, S., and Toole, J.: Horizontal density structure and restratification of the Arctic Ocean surface layer, J. Phys. Oceanogr., 42, 659–668, https://doi.org/10.1175/JPO-D-11-0125.1, 2012. a
Toole, J. M., Timmermans, M. L., Perovich, D. K., Krishfield, R. A., Proshutinsky, A., and Richter-Menge, J. A.: Influences of the ocean surface mixed layer and thermohaline stratification on Arctic Sea ice in the central Canada Basin, J. Geophys. Res.-Oceans, 115, C10018, https://doi.org/10.1029/2009JC005660, 2010. a, b
Tsubouchi, T., Bacon, S., Naveira Garabato, A., Aksenov, Y., Laxon, S. W., Fahrbach, E., Beszczynska-Möller, A., Hansen, E., Lee, C. M., and Ingvaldsen, R.: The Arctic Ocean in summer: A quasi-synoptic inverse estimate of boundary fluxes and water mass transformation, J. Geophys. Res.-Oceans, 117, C1, https://doi.org/10.1029/2011JC007174, 2012. a, b
Tsubouchi, T., Bacon, S., Aksenov, Y., Naveira Garabato, A. C., Beszczynska-Möller, A., Hansen, E., de Steur, L., Curry, B., and Lee, C. M.: The Arctic Ocean seasonal cycles of heat and freshwater fluxes: Observation-based inverse estimates, J. Phys. Oceanogr., 48, 2029–2055, 2018. a
Tsubouchi, T., von Appen, W., Schauer, U., Kanzow, T., Lee, C., Curry, B., de Steur, L., Ingvaldsen, R., and Woodgate, R.: The Arctic Ocean volume, heat and fresh water transports time series from October 2004 to May 2010, PANGAEA [data set], https://doi.org/10.1594/PANGAEA, 909966, 2019. a, b
Tsubouchi, T., Våge, K., Hansen, B., Larsen, K. M. H., Østerhus, S., Johnson, C., Jónsson, S., and Valdimarsson, H.: Increased ocean heat transport into the Nordic Seas and Arctic Ocean over the period 1993–2016, Nat. Clim. Change, 11, 21–26, https://doi.org/10.1038/s41558-020-00941-3, 2021. a
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A., Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., Krauer, F., Korhinen, M., Lien, V. S., Marnela, M., Massonnet, F., Mignac, D., Peterson, K. A., Sadikni, 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, 2019. a
Wang, Q., Wang, X., Wekerle, C., Danilov, S., Jung, T., Koldunov, N., Lind, S., Sein, D., Shu, Q., and Sidorenko, D.: Ocean heat transport into the Barents Sea: Distinct controls on the upward trend and interannual variability, Geophys. Res. Lett., 46, 13180–13190, 2019. a
Woodgate, R. A. and Peralta-Ferriz, C.: Warming and Freshening of the Pacific Inflow to the Arctic from 1990–2019 implying dramatic shoaling in Pacific Winter Water ventilation of the Arctic water column, Geophys. Res. Lett., 48, e2021GL092528, https://doi.org/10.1029/2021GL092528, 2021. a
Woodgate, R. A., Weingartner, T., and Lindsay, R.: The 2007 Bering Strait oceanic heat flux and anomalous Arctic sea-ice retreat, Geophys. Res. Lett., 37, L01602, https://doi.org/10.1029/2009GL041621, 2010. a, b
Woodgate, R. A., Weingartner, T. J., and Lindsay, R.: Observed increases in Bering Strait oceanic fluxes from the Pacific to the Arctic from 2001 to 2011 and their impacts on the Arctic Ocean water column, Geophys. Res. Lett., 39, 24, https://doi.org/10.1029/2012GL054092, 2012. a
Yang, J.: The seasonal variability of the Arctic Ocean Ekman transport and its role in the mixed layer heat and salt fluxes, J. Climate, 19, 5366–5387, 2006. a
Zhang, J. and Rothrock, D. A.: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, 845–861, https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2, 2003. a
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019. a, b
Short summary
Changes in ocean heat transport and surface heat fluxes in recent decades have altered the Arctic Ocean heat budget and caused warming of the upper ocean. Using two eddy-permitting ocean reanalyses, we show that this has important implications for sea ice variability. In the Arctic regional seas, upper-ocean heat content acts as an important precursor for sea ice anomalies on sub-seasonal timescales, and this link has strengthened since the 2000s.
Changes in ocean heat transport and surface heat fluxes in recent decades have altered the...
