Articles | Volume 16, issue 4
https://doi.org/10.5194/gmd-16-1395-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-1395-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
02 Mar 2023
Model evaluation paper |
| 02 Mar 2023
| 02 Mar 2023
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Yafei Nie
Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China
Institute for Atmospheric and Earth System Research (INAR), Faculty of Science, University of Helsinki, Helsinki, Finland
Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Chengkun Li
Department of Computer Science, University of Helsinki, Helsinki, Finland
Martin Vancoppenolle
Laboratoire d'Océanographie et du Climat, CNRS/IRD/MNHN, Sorbonne Université, 75252, Paris, France
Bin Cheng
Finnish Meteorological Institute, Helsinki, Finland
Fabio Boeira Dias
Institute for Atmospheric and Earth System Research (INAR), Faculty of Science, University of Helsinki, Helsinki, Finland
Xianqing Lv
CORRESPONDING AUTHOR
Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China
Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Petteri Uotila
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research (INAR), Faculty of Science, University of Helsinki, Helsinki, Finland
Related authors
No articles found.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 20, 4585–4604, https://doi.org/10.5194/tc-20-4585-2026, https://doi.org/10.5194/tc-20-4585-2026, 2026
Short summary
Short summary
Understanding of the local effects of sea-ice concentration variations on the Arctic atmosphere is a prerequisite for assessing the role of Arctic sea-ice decline in the climate system, including its influence on mid-latitudes. In our study, using data from atmospheric reanalysis, we present how the relationships of sea-ice concentration, temperature, and specific humidity and their direction change depending on region and season over the Arctic.
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.
Léna Champiot-Bayard, Lester Kwiatkowski, and Martin Vancoppenolle
Biogeosciences, 23, 4735–4758, https://doi.org/10.5194/bg-23-4735-2026, https://doi.org/10.5194/bg-23-4735-2026, 2026
Short summary
Short summary
To investigate the future of primary production, we analyzed outputs from several climate models. Changes vary greatly from one region to another: some areas show large increases, while others experience little change or even declines. These differences reflect the balance between water temperature, light availability, and nutrient supply. We also found that the newer generation of climate models projects a much stronger increase than previous models, but with greater uncertainty.
Paola Papapetros, Benjamin K. Galton-Fenzi, David E. Gwyther, Fabio Boeira Dias, Sue Cook, and Chen Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2026-3435, https://doi.org/10.5194/egusphere-2026-3435, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Ocean simulations are used to investigate how freshwater flow beneath Antarctic ice shelves influences melting and seafloor change. Results show that impacts depend strongly on where and how freshwater enters the ocean and on local temperature and salinity conditions. Sediment transport can also modify seafloor shape and reduce melting, improving understanding of processes that affect ice shelf stability and Antarctic ice loss.
Robert A. Massom, Phillip A. Reid, Stephen G. Warren, Bonnie Light, Donald K. Perovich, Luke G. Bennetts, Petteri Uotila, Siobhan P. O'Farrell, Michael H. Meylan, Klaus M. Meiners, Pat Wongpan, Alexander D. Fraser, Alessandro Toffoli, Giulio Passerotti, Peter G. Strutton, Sean M. T. Chua, and Melissa Fedrigo
The Cryosphere, 20, 3271–3298, https://doi.org/10.5194/tc-20-3271-2026, https://doi.org/10.5194/tc-20-3271-2026, 2026
Short summary
Short summary
This paper advances understanding of why and how Antarctic sea ice can retreat so rapidly each summer, and identifies critical gaps in climate models, by highlighting 3 previously-neglected wave-driven processes and 5 associated feedbacks that accelerate seasonal ice melting and link physics to biology (wave flooding, wave pulverisation and wave greening). There are major implications for Earth’s radiation budget, Antarctic ecosystems and the accuracy of future sea-ice and climate projections.
Hugues Goosse, Cecile Davrinche, Benjamin Richaud, Dániel Topál, Stephy Libera, Alberto C. Naveira Garabato, Alessandro Silvano, Martin Vancoppenolle, and Pablo Ortega
EGUsphere, https://doi.org/10.5194/egusphere-2026-1823, https://doi.org/10.5194/egusphere-2026-1823, 2026
Short summary
Short summary
The variability of the winter sea ice edge is much higher in regions close to the main topographic features of the Southern Ocean than over the smoother abyssal plain. The oceanic bathymetry influences mesoscale eddy activity and the Antarctic Circumpolar Current jets, affecting both oceanic heat transport and sea ice velocity and subsequently sea ice variability.
Mengmeng Li, Jianwei Ma, Yang Li, Juha Karvonen, Bin Cheng, Yingfei Wang, Haili Li, Yafei Nie, and Zheng Duan
EGUsphere, https://doi.org/10.5194/egusphere-2026-2504, https://doi.org/10.5194/egusphere-2026-2504, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We retrieved Arctic snow depth (2012–2021) using satellite data and four machine learning methods. A simple linear regression performed best against independent aircraft data, while complex models overfit. A measurement at 89 GHz was key for older ice. Using our snow depth data greatly reduced errors in ice thickness estimates. This provides a reliable Arctic snow depth dataset.
Ziyu Yan, Yufang Ye, Georg Heygster, Bin Cheng, Carolina Gabarró, Ferran Hernández-Macià, and Xiao Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2026-1680, https://doi.org/10.5194/egusphere-2026-1680, 2026
Short summary
Short summary
Passive microwave retrieval of snow depth remains constrained by snow and ice conditions. By combining the Snow Microwave Radiative Transfer model with global sensitivity analysis, this study quantifies how snow grain size, density, and sea ice type affect snow depth retrievability across channels. The small grain size of new snow limits retrieval performance and increases its sensitivity to sea ice type, while the use of the 89 GHz channel may help address this limitation.
Xiyue Zhang, Minghu Ding, Diyi Yang, Bin Cheng, Ian Allison, Kongju Zhu, Yuande Yang, Xiangbin Cui, Petra Heil, Chuanjin Li, and Cunde Xiao
EGUsphere, https://doi.org/10.5194/egusphere-2026-2150, https://doi.org/10.5194/egusphere-2026-2150, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Stake measurements underestimate Antarctic surface mass balance (SMB) due to firn compaction. Using a firn densification model, we correct 2008-2024 stake records from Dome A. The corrected mean annual SMB is 24.14 kg m-2 yr-1, 8.8 % higher than uncorrected values, whereas RACMO2.4p1 gives 17.50 kg m-2 yr-1. The net vapor flux is equivalent to 5.7 % of the total mass input. This framework provides observational benchmarks for climate models, and supports oldest‑ice drilling at Dome A.
Deyu Meng, Yunfei Zhang, Yibo Zhang, Xiaojiang Zhang, and Xianqing Lv
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-233, https://doi.org/10.5194/essd-2026-233, 2026
Preprint under review for ESSD
Short summary
Short summary
In this study, a high-precision dataset of eight major tidal constituents in the central Pacific Ocean using Equidistant Node Orthogonal Polynomial Fitting (ENOPF) model was generated. This high-precision tidal harmonic constant dataset for the Central Pacific Basin offers significant application advantages, providing more reliable data support and technical references for tidal dynamics, ocean circulation, and marine engineering projects in the region.
Cecilia Äijälä, Lucía Gutiérrez-Loza, Wolfgang Dorn, Siv K. Lauvset, Wieslaw Maslowski, and Petteri Uotila
EGUsphere, https://doi.org/10.5194/egusphere-2026-1849, https://doi.org/10.5194/egusphere-2026-1849, 2026
Short summary
Short summary
This study compares coupled ocean–atmosphere models, reanalyses, and satellite data for 1991–2020 in the Barents and Kara Seas, where Arctic sea‑ice loss has been greatest. The results evaluate how well these products asses sea ice and ocean temperature. Reanalyses and satellites show strong sea‑ice decline, while models show little or none – only HIRHAM‑NAOSIM shows a weak decline in the Barents Sea. Reanalyses also indicate stronger mixed‑layer warming then the models, especially in autumn.
Letizia Tedesco, Giulia Castellani, Pedro Duarte, Meibing Jin, Sebastien Moreau, Eric Mortenson, Benjamin Tobey Saenz, Nadja Steiner, and Martin Vancoppenolle
The Cryosphere, 20, 723–736, https://doi.org/10.5194/tc-20-723-2026, https://doi.org/10.5194/tc-20-723-2026, 2026
Short summary
Short summary
Sea ice hosts tiny algae that support polar marine life, yet their growth remains challenging to simulate. We tested six computer models using data from a 2015 Arctic drifting ice expedition to see how well they reproduced spring algae blooms and nutrient changes. While tuning helped models better match algae growth, nutrients remained difficult to capture. Our results highlight key challenges in representing fragile sea‑ice habitats that are expected to become more common as the Arctic warms.
Benjamin K. Galton-Fenzi, Richard Porter-Smith, Sue Cook, Eva Cougnon, David E. Gwyther, Wilma G. C. Huneke, Madelaine G. Rosevear, Xylar Asay-Davis, Fabio Boeira Dias, Michael S. Dinniman, David Holland, Kazuya Kusahara, Kaitlin A. Naughten, Keith W. Nicholls, Charles Pelletier, Ole Richter, Hélène Seroussi, and Ralph Timmermann
The Cryosphere, 19, 6507–6525, https://doi.org/10.5194/tc-19-6507-2025, https://doi.org/10.5194/tc-19-6507-2025, 2025
Short summary
Short summary
Quantifying melt and freeze beneath Antarctica’s floating ice shelves is vital to understanding present-day ice-sheet behavior and its potential to contribute to future sea-level rise. We compare 10 ice-shelf/ocean computer simulations with satellite data, providing the first multi-model estimate of melting and refreezing driven by the ocean. This new estimate offers a valuable tool for assessing ice-shelf roles in current and future ice-sheet changes, informing coastal adaptation strategies.
Yubing Cheng, Bin Cheng, Roberta Pirazzini, Amy R. Macfarlane, Timo Vihma, Wolfgang Dorn, Ruzica Dadic, Martin Schneebeli, Stefanie Arndt, and Annette Rinke
The Cryosphere, 19, 6001–6021, https://doi.org/10.5194/tc-19-6001-2025, https://doi.org/10.5194/tc-19-6001-2025, 2025
Short summary
Short summary
We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.
Hugues Goosse, Stephy Libera, Alberto C. Naveira Garabato, Benjamin Richaud, Alessandro Silvano, and Martin Vancoppenolle
The Cryosphere, 19, 5763–5779, https://doi.org/10.5194/tc-19-5763-2025, https://doi.org/10.5194/tc-19-5763-2025, 2025
Short summary
Short summary
The position of the winter sea ice edge in the Southern Ocean is strongly linked to the one of the Antarctic Circumpolar Current and thus to ocean bathymetry. This is due to the influence of the Antarctic Circumpolar Current on the southward heat flux that limits sea ice expansion, directly through oceanic processes and indirectly through its influence on atmospheric heat transport.
Fabio Boeira Dias, Matthew H. England, Adele K. Morrison, and Benjamin K. Galton-Fenzi
The Cryosphere, 19, 5231–5258, https://doi.org/10.5194/tc-19-5231-2025, https://doi.org/10.5194/tc-19-5231-2025, 2025
Short summary
Short summary
The Antarctic Ice Sheet melting dominates the sea-level projection uncertainties. Much uncertainty arises from our limited understanding of how ice shelves melt from below. Using a detailed ocean–ice-shelf model, we found that East Antarctic ice shelves experience seasonal melting driven by ocean heat transport variability. In contrast, West Antarctic ice shelves show consistent melting due to a steady supply of warm, deep water, indicating a potentially distinct response due to a warming climate.
Kristiina Verro, Cecilia Äijälä, Roberta Pirazzini, Ruzica Dadic, Damien Maure, Willem Jan van de Berg, Giacomo Traversa, Christiaan T. van Dalum, Petteri Uotila, Xavier Fettweis, Biagio Di Mauro, and Milla Johansson
The Cryosphere, 19, 4409–4436, https://doi.org/10.5194/tc-19-4409-2025, https://doi.org/10.5194/tc-19-4409-2025, 2025
Short summary
Short summary
Accurately representing Antarctic sea ice is essential for reliable climate and ocean model predictions. We evaluated how different models simulate the sea ice's sunlight reflectivity (called albedo) using field and satellite data. Models with simple albedo schemes performed well in limited cases but missed key processes. The advanced scheme in the MetROMS-UHel ocean model provided the most accurate results, including observed day–night albedo changes observed during a field campaign.
Théo Brivoal, Virginie Guemas, Martin Vancoppenolle, Clément Rousset, and Bertrand Decharme
Geosci. Model Dev., 18, 6885–6902, https://doi.org/10.5194/gmd-18-6885-2025, https://doi.org/10.5194/gmd-18-6885-2025, 2025
Short summary
Short summary
Snow in polar regions is key to sea ice formation and the Earth's climate, but current climate models simplify snow cover on sea ice. This study integrates an intermediate-complexity snow-physics scheme into a sea ice model designed for climate applications. We show that modeling the temporal changes in properties such as the density and thermal conductivity of the snow layers leads to a more accurate representation of heat transfer between the underlying sea ice and the atmosphere.
Fangru Mu, Chengfei Jiang, Bin Cheng, Keguang Wang, Caixin Wang, Yuhan Chen, Zhiyuan Shao, and Jiechen Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2025-3214, https://doi.org/10.5194/egusphere-2025-3214, 2025
Preprint archived
Short summary
Short summary
Icebergs pose risks to ships and are an important part of the polar environment. We developed an iceberg detection algorithm based on the Swin transformer model (IDAS-Transformer). The IDAS-Transformer is capable of handling complex surface characteristic mixtures, including fast ice, pack ice, and open water, to identify icebergs.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David Anthony Bailey, and Petteri Uotila
Geosci. Model Dev., 18, 4823–4853, https://doi.org/10.5194/gmd-18-4823-2025, https://doi.org/10.5194/gmd-18-4823-2025, 2025
Short summary
Short summary
The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 19, 1031–1046, https://doi.org/10.5194/tc-19-1031-2025, https://doi.org/10.5194/tc-19-1031-2025, 2025
Short summary
Short summary
To better understand the local, regional, and global impacts of the recent rapid sea-ice decline in the Arctic, one of the key issues is to quantify the effects of sea-ice concentration on the surface radiative fluxes. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in the sensitivity of the surface radiative fluxes to sea-ice concentration.
Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li
The Cryosphere, 19, 849–868, https://doi.org/10.5194/tc-19-849-2025, https://doi.org/10.5194/tc-19-849-2025, 2025
Short summary
Short summary
We developed a new method for retrieving lake ice phenology for a lake with complex surface cover. The method is particularly useful for mixed-pixel satellite data. We implement this method on Lake Ulansu, a lake characterized by complex shorelines and aquatic plants in northwestern China. In connection with a random forest model, we reconstructed the longest lake ice phenology in China.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
The Cryosphere, 18, 1419–1441, https://doi.org/10.5194/tc-18-1419-2024, https://doi.org/10.5194/tc-18-1419-2024, 2024
Short summary
Short summary
We retrieved sea ice drift in Fram Strait using the Chinese HaiYang 1D Coastal Zone Imager. The dataset is has hourly and daily intervals for analysis, and validation is performed using a synthetic aperture radar (SAR)-based product and International Arctic Buoy Programme (IABP) buoys. The differences between them are explained by investigating the spatiotemporal variability in sea ice motion. The accuracy of flow direction retrieval for sea ice drift is also related to sea ice displacement.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
Short summary
Short summary
Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 18, 957–976, https://doi.org/10.5194/tc-18-957-2024, https://doi.org/10.5194/tc-18-957-2024, 2024
Short summary
Short summary
A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the sensible and latent heat fluxes in the Arctic. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in SIC and in the sensitivity of the latent and sensible heat fluxes to SIC.
Miao Yu, Peng Lu, Matti Leppäranta, Bin Cheng, Ruibo Lei, Bingrui Li, Qingkai Wang, and Zhijun Li
The Cryosphere, 18, 273–288, https://doi.org/10.5194/tc-18-273-2024, https://doi.org/10.5194/tc-18-273-2024, 2024
Short summary
Short summary
Variations in Arctic sea ice are related not only to its macroscale properties but also to its microstructure. Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
Short summary
Short summary
A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Katherine Hutchinson, Julie Deshayes, Christian Éthé, Clément Rousset, Casimir de Lavergne, Martin Vancoppenolle, Nicolas C. Jourdain, and Pierre Mathiot
Geosci. Model Dev., 16, 3629–3650, https://doi.org/10.5194/gmd-16-3629-2023, https://doi.org/10.5194/gmd-16-3629-2023, 2023
Short summary
Short summary
Bottom Water constitutes the lower half of the ocean’s overturning system and is primarily formed in the Weddell and Ross Sea in the Antarctic due to interactions between the atmosphere, ocean, sea ice and ice shelves. Here we use a global ocean 1° resolution model with explicit representation of the three large ice shelves important for the formation of the parent waters of Bottom Water. We find doing so reduces salt biases, improves water mass realism and gives realistic ice shelf melt rates.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
Short summary
Short summary
This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Xiaoqiao Wang, Zhaoru Zhang, Michael S. Dinniman, Petteri Uotila, Xichen Li, and Meng Zhou
The Cryosphere, 17, 1107–1126, https://doi.org/10.5194/tc-17-1107-2023, https://doi.org/10.5194/tc-17-1107-2023, 2023
Short summary
Short summary
The bottom water of the global ocean originates from high-salinity water formed in polynyas in the Southern Ocean where sea ice coverage is low. This study reveals the impacts of cyclones on sea ice and water mass formation in the Ross Ice Shelf Polynya using numerical simulations. Sea ice production is rapidly increased caused by enhancement in offshore wind, promoting high-salinity water formation in the polynya. Cyclones also modulate the transport of this water mass by wind-driven currents.
Na Li, Ruibo Lei, Petra Heil, Bin Cheng, Minghu Ding, Zhongxiang Tian, and Bingrui Li
The Cryosphere, 17, 917–937, https://doi.org/10.5194/tc-17-917-2023, https://doi.org/10.5194/tc-17-917-2023, 2023
Short summary
Short summary
The observed annual maximum landfast ice (LFI) thickness off Zhongshan (Davis) was 1.59±0.17 m (1.64±0.08 m). Larger interannual and local spatial variabilities for the seasonality of LFI were identified at Zhongshan, with the dominant influencing factors of air temperature anomaly, snow atop, local topography and wind regime, and oceanic heat flux. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Marcel Nicolaus, Fanyi Zhang, Benjamin Rabe, Long Lin, Julia Regnery, and Donald K. Perovich
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-25, https://doi.org/10.5194/tc-2023-25, 2023
Manuscript not accepted for further review
Short summary
Short summary
To characterize the freezing and melting of different types of sea ice, we deployed four IMBs during the MOSAiC second drift. The drifting pattern, together with a large snow accumulation, relatively warm air temperatures, and a rapid increase in oceanic heat close to Fram Strait, determined the seasonal evolution of the ice mass balance. The refreezing of ponded ice and voids within the unconsolidated ridges amplifies the anisotropy of the heat exchange between the ice and the atmosphere/ocean.
Hugues Goosse, Sofia Allende Contador, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Clare Eayrs, Thierry Fichefet, Kenza Himmich, Pierre-Vincent Huot, François Klein, Sylvain Marchi, François Massonnet, Bianca Mezzina, Charles Pelletier, Lettie Roach, Martin Vancoppenolle, and Nicole P. M. van Lipzig
The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, https://doi.org/10.5194/tc-17-407-2023, 2023
Short summary
Short summary
Using idealized sensitivity experiments with a regional atmosphere–ocean–sea ice model, we show that sea ice advance is constrained by initial conditions in March and the retreat season is influenced by the magnitude of several physical processes, in particular by the ice–albedo feedback and ice transport. Atmospheric feedbacks amplify the response of the winter ice extent to perturbations, while some negative feedbacks related to heat conduction fluxes act on the ice volume.
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844, https://doi.org/10.5194/tc-16-1821-2022, https://doi.org/10.5194/tc-16-1821-2022, 2022
Short summary
Short summary
We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic utilizing radar altimeter (RA) measurement lines and C-band SAR imagery. The RA data are from CryoSat-2, and SAR imagery is from Sentinel-1. By combining them we get a SIT grid covering the whole test area instead of only narrow measurement lines from RA. This kind of SIT estimation can be extended to cover the whole Arctic (and Antarctic) for operational SIT monitoring.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
Short summary
Short summary
The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
Short summary
Short summary
A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Yu Yan, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila
Geosci. Model Dev., 15, 1269–1288, https://doi.org/10.5194/gmd-15-1269-2022, https://doi.org/10.5194/gmd-15-1269-2022, 2022
Short summary
Short summary
In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study presented the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of ocean and sea ice. NEMO-Bohai is intended to be a valuable tool for long-term ocean and ice simulations and climate change studies.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
Geosci. Model Dev., 14, 6331–6354, https://doi.org/10.5194/gmd-14-6331-2021, https://doi.org/10.5194/gmd-14-6331-2021, 2021
Short summary
Short summary
This study introduces a new Sea Ice Evaluation Tool (SITool) to evaluate the model skills on the bipolar sea ice simulations by providing performance metrics and diagnostics. SITool is applied to evaluate the CMIP6 OMIP simulations. By changing the atmospheric forcing from CORE-II to JRA55-do data, many aspects of sea ice simulations are improved. SITool will be useful for helping teams managing various versions of a sea ice model or tracking the time evolution of model performance.
Cited articles
Abernathey, R. P., Cerovecki, I., Holland, P. R., Newsom, E., Mazloff, M., and Talley, L. D.: Water-mass transformation by sea ice in the upper branch of the Southern Ocean overturning, Nat. Geosci., 9, 596–601, https://doi.org/10.1038/ngeo2749, 2016.
Baki, H., Chinta, S., Balaji, C., and Srinivasan, B.: Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning, Geosci. Model Dev., 15, 2133–2155, https://doi.org/10.5194/gmd-15-2133-2022, 2022.
Barthélemy, A., Goosse, H., Fichefet, T., and Lecomte, O.: On the sensitivity of Antarctic sea ice model biases to atmospheric forcing uncertainties, Clim. Dynam., 51, 1585–1603, https://doi.org/10.1007/s00382-017-3972-7, 2018.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677, https://doi.org/10.1029/1999jc900100, 1999.
Brandt, R. E., Warren, S. G., Worby, A. P., and Grenfell, T. C.: Surface albedo of the Antarctic sea ice zone, J. Climate, 18, 3606–3622, https://doi.org/10.1175/JCLI3489.1, 2005.
Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of sea ice parameters with the Nimbus 7 SMMR, J. Geophys. Res., 89, 5355–5369, https://doi.org/10.1029/JD089iD04p05355, 1984.
Cavalieri, D. J., Markus, T., and Comiso, J. C.: AMSR-E/AQUA daily L3 12.5 km brightness temperature, sea ice concentration and snow depth polar grids product, version 3, Boulder, Colorado, USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/AMSR-E/AE_SI12.003, 2014.
Comiso, J. C.: Characteristics of Arctic winter sea ice from satellite multispectral microwave observations, J. Geophys. Res., 91, 975–994, https://doi.org/10.1029/JC091iC01p00975, 1986.
Comiso, J. C., Gersten, R. A., Stock, L. V., Turner, J., Perez, G. J., and Cho, K.: Positive trend in the Antarctic sea ice cover and associated changes in surface temperature, J. Climate, 30, 2251–2267, https://doi.org/10.1175/JCLI-D-16-0408.1, 2017.
Copernicus Marine Service: Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF), Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00136, 2017.
Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations, J. Hydrometeorol., 3, 660–687, https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2, 2002.
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. M., 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.
Docquier, D., Massonnet, F., Barthélemy, A., Tandon, N. F., Lecomte, O., and Fichefet, T.: Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6, The Cryosphere, 11, 2829–2846, https://doi.org/10.5194/tc-11-2829-2017, 2017.
Dussin, R., Barnier, B., Brodeau, L., and Molines, J.-M.: The making of the DRAKKAR Forcing Set DFS5, Drakkar/myocean report 01-04-16, Laboratoire de Glaciologie et de Géophysique de l’Environnement, Université de Grenoble, Grenoble, France, https://www.drakkar-ocean.eu/forcing-the-ocean (last access: 22 February 2022), 2016.
Ezraty, R., Girard-Ardhuin, F., Piolle, J. F., Kaleschke, L., and Heygster, G.: Arctic and Antarctic Sea Ice Concentration and Arctic Sea Ice Drift Estimated from Special Sensor Microwave Data, Technical Report, Departement d'Oceanographie Physique et Spatiale, IFREMER, Brest, France, 2007.
Fichefet, T. and Maqueda, M. A. M.: 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.
Fichefet, T., Tartinville, B., and Goosse, H.: Sensitivity of the Antarctic sea ice to the thermal conductivity of snow, Geophys. Res. Lett., 27, 401–404, https://doi.org/10.1029/1999GL002397, 2000.
Geisser, S.: The predictive sample reuse method with applications, J. Am. Stat. Assoc., 70, 320–328, https://doi.org/10.1080/01621459.1975.10479865, 1975.
GPy: A Gaussian process framework in python, http://github.com/SheffieldML/GPy (last access: 1 March 2022), 2012.
Haumann, F. A., Gruber, N., Münnich, M., Frenger, I., and Kern, S.: Sea-ice transport driving Southern Ocean salinity and its recent trends, Nature, 537, 89–92, https://doi.org/10.1038/nature19101, 2016.
Holland, P. R. and Kimura, N.: Observed concentration budgets of Arctic and Antarctic sea ice, J. Climate, 29, 5241–5249, https://doi.org/10.1175/JCLI-D-16-0121.1, 2016.
Holland, P. R. and Kwok, R.: Wind-driven trends in Antarctic sea-ice drift, Nat. Geosci., 5, 872–875, https://doi.org/10.1038/ngeo1627, 2012.
Holmes, C. R., Holland, P. R., and Bracegirdle, T. J.: Compensating Biases and a Noteworthy Success in the CMIP5 Representation of Antarctic Sea Ice Processes, Geophys. Res. Lett., 46, 4299–4307, https://doi.org/10.1029/2018GL081796, 2019.
Joseph, V. R. and Hung, Y.: Orthogonal-maximin Latin hypercube designs, Stat. Sinica, 18, 171–186, 2008.
Kennedy, M. C. and O'Hagan, A.: Predicting the output from a complex computer code when fast approximations are available, Biometrika, 87, 1–13, https://doi.org/10.1093/biomet/87.1.1, 2000.
Kim, J. G., Hunke, E. C., and Lipscomb, W. H.: Sensitivity analysis and parameter tuning scheme for global sea-ice modeling, Ocean Model., 14, 61–80, https://doi.org/10.1016/j.ocemod.2006.03.003, 2006.
Kimmritz, M., Danilov, S., and Losch, M.: The adaptive EVP method for solving the sea ice momentum equation, Ocean Model., 101, 59–67, https://doi.org/10.1016/j.ocemod.2016.03.004, 2016.
Kimmritz, M., Losch, M., and Danilov, S.: A comparison of viscous-plastic sea ice solvers with and without replacement pressure, Ocean Model., 115, 59–69, https://doi.org/10.1016/j.ocemod.2017.05.006, 2017.
Kimura, N., Nishimura, A., Tanaka, Y., and Yamaguchi, H.: Influence of winter sea-ice motion on summer ice cover in the Arctic, Polar Res., 32, 20193, https://doi.org/10.3402/polar.v32i0.20193, 2013.
Large, W. G. and Yeager, S.: Diurnal to decadal global forcing for ocean and sea–ice models: the datasets and flux climatologies, NCAR Technical Note, No. NCAR/TN-460CSTR, https://doi.org/10.5065/D6KK98Q6, 2004.
Lecomte, O., Fichefet, T., Vancoppenolle, M., Domine, F., Massonnet, F., Mathiot, P., Morin, S., and Barriat, P. Y.: On the formulation of snow thermal conductivity in large-scale sea ice models, J. Adv. Model. Earth Sy., 5, 542–557, https://doi.org/10.1002/jame.20039, 2013.
Lecomte, O., Goosse, H., Fichefet, T., Holland, P. R., Uotila, P., Zunz, V., and Kimura, N.: Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models, Ocean Model., 105, 60–70, https://doi.org/10.1016/j.ocemod.2016.08.001, 2016.
Leppäranta, M.: The drift of sea ice, Springer, Berlin, Heidelberg, https://doi.org/10.1007/b138386, 2011.
Liao, S., Luo, H., Wang, J., Shi, Q., Zhang, J., and Yang, Q.: An evaluation of Antarctic sea-ice thickness from the Global Ice-Ocean Modeling and Assimilation System based on in situ and satellite observations, The Cryosphere, 16, 1807–1819, https://doi.org/10.5194/tc-16-1807-2022, 2022.
Loeppky, J. L., Sacks, J., and Welch, W. J.: Choosing the sample size of a computer experiment: A practical guide, Technometrics, 51, 366–376, https://doi.org/10.1198/TECH.2009.08040, 2009.
Lüpkes, C., Gryanik, V. M., Hartmann, J., and Andreas, E. L.: A parametrization, based on sea ice morphology, of the neutral atmospheric drag coefficients for weather prediction and climate models, J. Geophys. Res.-Atmos., 117, 1–18, https://doi.org/10.1029/2012JD017630, 2012.
Madec, G., Delécluse, P., Imbard, M., and Lévy, C.: OPA 8.1 Ocean General Circulation Model reference manual, Notes du pôle de modélisation, laboratoire d’océanographie dynamique et de climatologie, Institut Pierre Simon Laplace des sciences de l’environnement global, 11, 91 pp., 1998.
Marsaleix, P., Auclair, F., Floor, J. W., Herrmann, M. J., Estournel, C., Pairaud, I., and Ulses, C.: Energy conservation issues in sigma-coordinate free-surface ocean models, Ocean Model., 20, 61–89, https://doi.org/10.1016/j.ocemod.2007.07.005, 2008.
Massom, R. A., Eicken, H., Haas, C., Jeffries, M. O., Drinkwater, M. R., Sturm, M., Worby, A. P., Wu, X., Lytle, V. I., Ushio, S., Morris, K., Reid, P. A., Warren, S. G., and Allison, I.: Snow on Antarctic sea ice, Rev. Geophys., 39, 413–445, https://doi.org/10.1029/2000RG000085, 2001.
Massonnet, F., Fichefet, T., Goosse, H., Vancoppenolle, M., Mathiot, P., and König Beatty, C.: On the influence of model physics on simulations of Arctic and Antarctic sea ice, The Cryosphere, 5, 687–699, https://doi.org/10.5194/tc-5-687-2011, 2011.
Massonnet, F., Goosse, H., Fichefet, T., and Counillon, F.: Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter, J. Geophys. Res.-Oceans, 119, 4168–4184, https://doi.org/10.1002/2013JC009705, 2014.
Massonnet, F., Barthélemy, A., Worou, K., Fichefet, T., Vancoppenolle, M., Rousset, C., and Moreno-Chamarro, E.: On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model, Geosci. Model Dev., 12, 3745–3758, https://doi.org/10.5194/gmd-12-3745-2019, 2019.
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, https://doi.org/10.1029/jc076i006p01550, 1971.
McKay, M. D., Beckman, R. J., and Conover, W. J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 42, 55–61, https://doi.org/10.1080/00401706.2000.10485979, 2000.
Meier, W. N., Markus, T., and Comiso, J. C.: AMSR-E/AMSR2 Unified L3 Daily 25.0 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth Polar Grids, Version 1, Boulder, Colorado, USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/TRUIAL3WPAUP, 2018.
Meier, W. N., Fetterer, F., Windnagel, A. K., and Stewart, J. S.: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, Boulder, Colorado, USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.7265/efmz-2t65, 2021.
Merryfield, W. J., Holloway, G., and Gargett, A. E.: A global ocean model with double-diffusive mixing, J. Phys. Oceanogr., 29, 1124–1142, https://doi.org/10.1175/1520-0485(1999)029<1124:AGOMWD>2.0.CO;2, 1999.
Mora, E. B., Spelling, J., and van der Weijde, A. H.: Benchmarking the PAWN distribution-based method against the variance-based method in global sensitivity analysis: Empirical results, Environ. Modell. Softw., 122, 104556, https://doi.org/10.1016/j.envsoft.2019.104556, 2019.
Moreno-Chamarro, E., Ortega, P., and Massonnet, F.: Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model, Geosci. Model Dev., 13, 4773–4787, https://doi.org/10.5194/gmd-13-4773-2020, 2020.
Morris, M. D. and Mitchell, T. J.: Exploratory designs for computational experiments, J. Stat. Plan. Infer., 43, 381–402, https://doi.org/10.1016/0378-3758(94)00035-T, 1995.
Nakawo, M. and Sinha, N. K.: Growth Rate and Salinity Profile of First-Year Sea Ice in the High Arctic, J. Glaciol., 27, 315–330, https://doi.org/10.3189/s0022143000015409, 1981.
National Snow & Data Center: Homepage, National Snow & Data Center [data set], https://nsidc.org/, last access: 1 March 2022.
NEMO: Annual mean of sea surface salinity in 1/12° (NEMO-WRF coupling), NEMO [data set], https://www.nemo-ocean.eu/, last access: 1 March 2022.
NEMO ocean engine: NEMO System Team, Scientific Notes of Climate Modelling Center, 27, Institut Pierre-Simon Laplace (IPSL), ISSN 1288-1619, https://doi.org/10.5281/zenodo.1464816, 2022.
Nie, Y.: Y.Nie/Paper-SICB-SEN, Zenodo, https://doi.org/10.5281/zenodo.6780342, 2022.
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, https://doi.org/10.1007/s00382-022-06260-x, 2022.
Notz, D.: Sea-ice extent and its trend provide limited metrics of model performance, The Cryosphere, 8, 229–243, https://doi.org/10.5194/tc-8-229-2014, 2014.
Notz, D.: How well must climate models agree with observations?, Philos. T. Roy. Soc. A, 373, 20140164, https://doi.org/10.1098/rsta.2014.0164, 2015.
Oakley, J. E. and O'Hagan, A.: Probabilistic sensitivity analysis of complex models: A Bayesian approach, J. Roy. Stat. Soc. B, 66, 751–769, https://doi.org/10.1111/j.1467-9868.2004.05304.x, 2004.
Parkinson, C. L.: A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic, P. Natl. Acad. Sci. USA, 116, 14414–14423, https://doi.org/10.1073/pnas.1906556116, 2019.
Perovich, D. K.: Seasonal evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res., 107, 1–13, https://doi.org/10.1029/2000jc000438, 2002.
Petty, A. A., Webster, M., Boisvert, L., and Markus, T.: The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis, Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, 2018.
Pianosi, F.: Python version of the Sensitivity Analysis for Everybody (SAFE) Toolbox, GitHub [code], https://github.com/SAFEtoolbox/SAFE-python, last access: 24 February 2023.
Pianosi, F. and Wagener, T.: A simple and efficient method for global sensitivity analysis based oncumulative distribution functions, Environ. Modell. Softw., 67, 1–11, https://doi.org/10.1016/j.envsoft.2015.01.004, 2015.
Pianosi, F., Sarrazin, F., and Wagener, T.: A Matlab toolbox for Global Sensitivity Analysis, Environ. Modell. Softw., 70, 80–85, https://doi.org/10.1016/j.envsoft.2015.04.009, 2015.
Rae, J. G. L., Hewitt, H. T., Keen, A. B., Ridley, J. K., Edwards, J. M., and Harris, C. M.: A sensitivity study of the sea ice simulation in the global coupled climate model, HadGEM3, Ocean Model., 74, 60–76, https://doi.org/10.1016/j.ocemod.2013.12.003, 2014.
Raphael, M. N. and Handcock, M. S.: A new record minimum for Antarctic sea ice, Nat. Rev. Earth Environ., 3, 215–216, https://doi.org/10.1038/s43017-022-00281-0, 2022.
Rasmussen, C. E. and Williams, C.: Gaussian Processes for Machine Learning, the MIT Press, 2, https://doi.org/10.7551/mitpress/3206.001.0001, 2006.
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P.: Design and analysis of computer experiments, Stat. Sci., 4, 409–423, https://doi.org/10.1214/ss/1177012413, 1989.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global Sensitivity Analysis: The Primer, John Wiley & Sons, Ltd, Chichester, England, Hoboken, NJ, ISBN 9780470059975, Online ISBN 9780470725184, https://doi.org/10.1002/9780470725184, 2008.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S.: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259–270, https://doi.org/10.1016/j.cpc.2009.09.018, 2010.
Shu, Q., Song, Z., and Qiao, F.: Assessment of sea ice simulations in the CMIP5 models, The Cryosphere, 9, 399–409, https://doi.org/10.5194/tc-9-399-2015, 2015.
Shu, Q., Wang, Q., Song, Z., Qiao, F., Zhao, J., Chu, M., and Li, X.: Assessment of Sea Ice Extent in CMIP6 With Comparison to Observations and CMIP5, Geophys. Res. Lett., 47, 1–9, https://doi.org/10.1029/2020GL087965, 2020.
Sobol, I. M.: On sensitivity estimation for nonlinear mathematical models, Mat. Model., 2, 112–118, https://doi.org/10.18287/0134-2452-2015-39-4-459-461, 1990.
Sobol, I. M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simulat., 55, 271–280, https://doi.org/10.1016/S0378-4754(00)00270-6, 2001.
Sun, S. and Eisenman, I.: Observed Antarctic sea ice expansion reproduced in a climate model after correcting biases in sea ice drift velocity, Nat. Commun., 12, 1060, https://doi.org/10.1038/s41467-021-21412-z, 2021.
Thomas, D. N. and Dieckmann, G. S.: Sea ice, 2nd edn., Wiley, Oxford, https://doi.org/10.1002/9781444317145, 2010.
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/jc080i033p04501, 1975.
Timco, G. W. and Frederking, R. M. W.: A review of sea ice density, Cold Reg. Sci. Technol., 24, 1–6, https://doi.org/10.1016/0165-232X(95)00007-X, 1996.
Turner, J., Bracegirdle, T. J., Phillips, T., Marshall, G. J., and Scott Hosking, J.: An initial assessment of antarctic sea ice extent in the CMIP5 models, J. Climate, 26, 1473–1484, https://doi.org/10.1175/JCLI-D-12-00068.1, 2013.
Uotila, P., O'Farrell, S., Marsland, S. J., and Bi, D.: A sea-ice sensitivity study with a global ocean-ice model, Ocean Model., 51, 1–18, https://doi.org/10.1016/j.ocemod.2012.04.002, 2012.
Uotila, P., Holland, P. R., Vihma, T., Marsland, S. J., and Kimura, N.: Is realistic Antarctic sea-ice extent in climate models the result of excessive ice drift?, Ocean Model., 79, 33–42, https://doi.org/10.1016/j.ocemod.2014.04.004, 2014.
Uotila, P., Iovino, D., Vancoppenolle, M., Lensu, M., and Rousset, C.: Comparing sea ice, hydrography and circulation between NEMO3.6 LIM3 and LIM2, Geosci. Model Dev., 10, 1009–1031, https://doi.org/10.5194/gmd-10-1009-2017, 2017.
Urrego-Blanco, J. R., Urban, N. M., Hunke, E. C., Turner, A. K., and Jeffery, N.: Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model, J. Geophys. Res.-Oceans, 121, 2709–2732, https://doi.org/10.1002/2015JC011558, 2016.
Vancoppenolle, M., Fichefet, T., Goosse, H., Bouillon, S., Madec, G., and Maqueda, M. A. M.: Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation, Ocean Model., 27, 33–53, https://doi.org/10.1016/j.ocemod.2008.10.005, 2009.
Wang, J., Luo, H., Yang, Q., Liu, J., Yu, L., Shi, Q., and Han, B.: An Unprecedented Record Low Antarctic Sea-ice Extent during Austral Summer 2022, Adv. Atmos. Sci., 39, 1591–1597, https://doi.org/10.1007/s00376-022-2087-1, 2022a.
Wang, J., Min, C., Ricker, R., Shi, Q., Han, B., Hendricks, S., Wu, R., and Yang, Q.: A comparison between Envisat and ICESat sea ice thickness in the Southern Ocean, The Cryosphere, 16, 4473–4490, https://doi.org/10.5194/tc-16-4473-2022, 2022b.
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R.: Snow depth on Arctic sea ice, J. Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2, 1999.
Williamson, D. B., Blaker, A. T., and Sinha, B.: Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model, Geosci. Model Dev., 10, 1789–1816, https://doi.org/10.5194/gmd-10-1789-2017, 2017.
Zadeh, K. F., Nossent, J., Sarrazin, F., Pianosi, F., van Griensven, A., Wagener, T., and Bauwens, W.: Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model, Environ. Modell. Softw., 91, 210–222, https://doi.org/10.1016/j.envsoft.2017.02.001, 2017.
Zunz, V., Goosse, H., and Massonnet, F.: How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent?, The Cryosphere, 7, 451–468, https://doi.org/10.5194/tc-7-451-2013, 2013.
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A., Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K., Paver, C. R., and Smolyar, I. V.: World Ocean Atlas 2018. Volume 2: Salinity, Tech. rep., NOAA Atlas NESDIS 81, http://www.nodc.noaa.gov/OC5/indprod.html (last access: 22 February 2022), 2019.
Short summary
State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but...
Special issue