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
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
Related authors
No articles found.
Yushi Morioka, Doroteaciro Iovino, Andrea Cipollone, Andrea Storto, Takeshi Doi, Masami Nonaka, and Swadhin K. Behera
EGUsphere, https://doi.org/10.5194/egusphere-2026-5070, https://doi.org/10.5194/egusphere-2026-5070, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Arctic sea ice has been shrinking for the last four decades. As global warming continues, predicting summer sea ice is becoming more important. Here we create a seasonal forecast system using the SINTEX-F3 model. Results show that summer sea ice in the Barents-Kara Sea is best predicted when accurate sea ice and snow conditions are provided from spring. It is found that initial sea ice thickness acts as a memory for the climate system, enabling accurate summer sea ice prediction from spring.
Francesco Cocetta, Doroteaciro Iovino, Andrea Cipollone, and Daniela Flocco
EGUsphere, https://doi.org/10.5194/egusphere-2026-4349, https://doi.org/10.5194/egusphere-2026-4349, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Arctic sea ice is rapidly changing toward a new climate state characterized by thinner and more seasonal ice. To understand these changes, we use reconstructions of ocean and sea ice states to investigate the transition zone between consolidated pack ice and open ocean. Our results show clear differences from central pack ice, highlighting the need for better representation in climate models and opening the path toward identifying specific indicators of its evolution.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O'Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Oluwayemi Garuba, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
Geosci. Model Dev., 19, 6043–6078, https://doi.org/10.5194/gmd-19-6043-2026, https://doi.org/10.5194/gmd-19-6043-2026, 2026
Short summary
Short summary
The ocean and sea ice are central to Earth's climate system, influencing global heat and carbon cycles, weather patterns, and sea level rise. Here the earth system model variables needed for studies of the ocean and sea ice are prioritized and requested. These requested variables were developed through an international, community-based process.
Monalisa Sahoo, Stefano Materia, and Markus G. Donat
EGUsphere, https://doi.org/10.5194/egusphere-2026-3206, https://doi.org/10.5194/egusphere-2026-3206, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study compares several gridded observation-based datasets of soil moisture, evapotranspiration, potential evapotranspiration, and air temperature during summer across Europe from 2003–2022. It shows that datasets differ in actual values and seasonal variations, but most datasets show coherent temporal variations and coupling relationships. However, timeseries differences found for some datasets also affect their representation of land-atmosphere coupling relationships.
Gokhan Danabasoglu, Frederic S. Castruccio, Burcu Boza, Alice M. Barthel, Arne Biastoch, Adam Blaker, Alexandra Bozec, Diego Bruciaferri, Frank O. Bryan, Eric P. Chassignet, Yao Fu, Ian Grooms, Catherine Guiavarc'h, Hakase Hayashida, Andrew McC. Hogg, Ryan M. Holmes, Doroteaciro Iovino, Andrew E. Kiss, M. Susan Lozier, Gustavo Marques, Alex Megann, Franziska U. Schwarzkopf, Dave Storkey, Luke van Roekel, Jon Wolfe, Xiaobiao Xu, and Rong Zhang
Geosci. Model Dev., 19, 5071–5117, https://doi.org/10.5194/gmd-19-5071-2026, https://doi.org/10.5194/gmd-19-5071-2026, 2026
Short summary
Short summary
A comparison of simulated and observed overturning transports across the Overturning in the Subpolar North Atlantic Program sections is presented. Eighteen ocean simulations participate in the study with resolutions ranging from coarse to eddy-resolving. The simulated transports are in general agreement with observations, with better agreement at high resolution. Analyzing overturning circulations in both depth and density space together provides a more complete picture.
Francesco De Rovere, Giulia Bonino, Ronan McAdam, Enrico Scoccimarro, Samuel Somot, Iván Manuel Parras-Berrocal, Bodo Ahrens, Vladimir Djurdjevic, Laurent Li, and Simona Masina
EGUsphere, https://doi.org/10.5194/egusphere-2026-2752, https://doi.org/10.5194/egusphere-2026-2752, 2026
Short summary
Short summary
Marine heatwaves in the Mediterranean Sea are intensifying, posing growing threats to marine life and coastal communities. We evaluated whether high-resolution regional climate models better capture these extremes compared to the global models driving them. Regional models reliably improve sea surface temperature patterns and marine heatwave duration, but struggle more with heatwave intensity. Higher resolution alone is not enough, advances in other aspects of model design are equally needed.
Rita Lecci, Robyn Gwee, Kun Yan, Sanne Muis, Nadia Pinardi, Jun She, Martin Verlaan, Simona Masina, Wenshan Li, Hui Wang, Salvatore Causio, Antonio Novellino, Marco Alba, Etiënne Kras, Sandra Gaytan Aguilar, and Jan-Bart Calewaert
Nat. Hazards Earth Syst. Sci., 26, 1767–1783, https://doi.org/10.5194/nhess-26-1767-2026, https://doi.org/10.5194/nhess-26-1767-2026, 2026
Short summary
Short summary
This study explored how sea level is changing along the China-Europe Sea Route. By combining satellite and in-situ observations with advanced modeling, the research identified ongoing sea level rise and an increasing frequency of extreme water level events in some regions. These findings underscore the importance of continued monitoring and provide useful knowledge to support long-term planning, coastal resilience, and informed decision-making.
Simona Masina, Andrea Cipollone, Doroteaciro Iovino, Stefania Ciliberti, Rita Lecci, Sergio Cretí, Vladyslav Lyubartsev, Giovanni Coppini, and Emanuela Clementi
EGUsphere, https://doi.org/10.5194/egusphere-2026-887, https://doi.org/10.5194/egusphere-2026-887, 2026
Short summary
Short summary
The paper presents GOFS16, an eddy-resolving global operational ocean and sea ice forecasting system which provides 6-day forecasts of three-dimensional temperature, salinity, currents, sea level, and sea ice properties. The system assimilates satellite and in situ observations using a 3D variational data assimilation scheme. Validation is conducted routinely using global and regional metrics. Results indicate that GOFS16 performs within the expected range of skill for current global systems.
Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
Geosci. Model Dev., 19, 423–445, https://doi.org/10.5194/gmd-19-423-2026, https://doi.org/10.5194/gmd-19-423-2026, 2026
Short summary
Short summary
This study present a data assimilation system that combines ocean observational data with ocean model results to better understand the ocean and predict its future state. The method uses a three dimensional incremental variational approach focusing on the physical relationships between all the state vector variables errors. Testing in the Mediterranean Sea showed that a complex sea level operator based on a barotropic model works best.
Alex C. Ruane, Charlotte L. Pascoe, Claas Teichmann, David J. Brayshaw, Carlo Buontempo, Ibrahima Diouf, Jesus Fernandez, Paula L. M. Gonzalez, Birgit Hassler, Vanessa Hernaman, Ulas Im, Doroteaciro Iovino, Martin Juckes, Iréne L. Lake, Timothy Lam, Xiaomao Lin, Jiafu Mao, Negin Nazarian, Sylvie Parey, Indrani Roy, Wan-Ling Tseng, Briony Turner, Andrew Wiebe, Lei Zhao, and Damaris Zurell
Geosci. Model Dev., 18, 9497–9540, https://doi.org/10.5194/gmd-18-9497-2025, https://doi.org/10.5194/gmd-18-9497-2025, 2025
Short summary
Short summary
This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.
Daniele Peano, Deborah Hemming, Christine Delire, Yuanchao Fan, Hanna Lee, Stefano Materia, Julia E. M. S. Nabel, Taejin Park, David Wårlind, Andy Wiltshire, and Sönke Zaehle
Biogeosciences, 22, 7117–7135, https://doi.org/10.5194/bg-22-7117-2025, https://doi.org/10.5194/bg-22-7117-2025, 2025
Short summary
Short summary
Earth System Models are the principal tools for scientists to study past, present, and future climate changes. This work investigates the ability of a set of them to represent the observed changes in vegetation, which are vital to estimating the impact of future climate mitigation and adaptation strategies. This study highlights the main limitations in correctly representing vegetation variability. These tools still need further development to improve our understanding of future changes.
Arnau Garcia Mesa, Lluís Palma, Markus Donat, Stefano Materia, and Raül Marcos Matamoros
EGUsphere, https://doi.org/10.5194/egusphere-2025-5392, https://doi.org/10.5194/egusphere-2025-5392, 2025
Short summary
Short summary
Explainable machine learning was used to quantify what drives summer heat extremes at six sites in Europe and North Africa. Large-scale atmospheric circulation, especially mid-tropospheric geopotential, dominates, while soil drying amplifies heat in temperate and northern regions, and rising carbon dioxide contributes to long-term trends. Results were robust across data sources and help target climate risk and model improvements.
Tuula Aalto, Aki Tsuruta, Jarmo Mäkelä, Jurek Müller, Maria Tenkanen, Eleanor Burke, Sarah Chadburn, Yao Gao, Vilma Mannisenaho, Thomas Kleinen, Hanna Lee, Antti Leppänen, Tiina Markkanen, Stefano Materia, Paul A. Miller, Daniele Peano, Olli Peltola, Benjamin Poulter, Maarit Raivonen, Marielle Saunois, David Wårlind, and Sönke Zaehle
Biogeosciences, 22, 323–340, https://doi.org/10.5194/bg-22-323-2025, https://doi.org/10.5194/bg-22-323-2025, 2025
Short summary
Short summary
Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in northern Europe using ecosystem models, atmospheric inversions, and upscaled flux observations. The ecosystem models differed in their responses to temperature and precipitation and in their seasonality. However, multi-model means, inversions, and upscaled fluxes had similar seasonality, and they suggested co-limitation by temperature and precipitation.
Francesco Cocetta, Lorenzo Zampieri, Julia Selivanova, and Doroteaciro Iovino
The Cryosphere, 18, 4687–4702, https://doi.org/10.5194/tc-18-4687-2024, https://doi.org/10.5194/tc-18-4687-2024, 2024
Short summary
Short summary
Arctic sea ice is 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.
Ronan McAdam, Giulia Bonino, Emanuela Clementi, and Simona Masina
State Planet, 4-osr8, 13, https://doi.org/10.5194/sp-4-osr8-13-2024, https://doi.org/10.5194/sp-4-osr8-13-2024, 2024
Short summary
Short summary
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 d in advance. Satellite data show 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 for marine activities (e.g. conservation and aquaculture).
Dimitra Denaxa, Gerasimos Korres, Giulia Bonino, Simona Masina, and Maria Hatzaki
State Planet, 4-osr8, 11, https://doi.org/10.5194/sp-4-osr8-11-2024, https://doi.org/10.5194/sp-4-osr8-11-2024, 2024
Short summary
Short summary
We investigate the air–sea heat flux during marine heatwaves (MHWs) in the Mediterranean Sea. Surface heat flux drives 44 % of the onset and only 17 % of the declining MHW phases, suggesting a key role of oceanic processes. Heat flux is more important in warmer months and onset phases, with latent heat dominating. Shorter events show a 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.
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
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...
