Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1269-2022
© Author(s) 2022. This work is distributed under
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the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-1269-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 Feb 2022
Model description paper |
| 14 Feb 2022
| 14 Feb 2022
NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China
School of Ocean Sciences, China University of Geosciences, Beijing
100083, China
School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China
Institute for Atmospheric and Earth System Research (INAR), Faculty of
Science, University of Helsinki, 00014 Helsinki, Finland
Wei Gu
School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China
Andrea M. U. Gierisch
Danish Meteorological Institute, Research and Development, 2100 Copenhagen, Denmark
Yingjun Xu
School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China
Petteri Uotila
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research (INAR), Faculty of
Science, University of Helsinki, 00014 Helsinki, Finland
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Sofie Hedetoft, Olivia Bang Brinck, Ruth Mottram, Andrea M. U. Gierisch, Steffen Malskær Olsen, Martin Olesen, Nicolaj Hansen, Anders Anker Bjørk, Erik Loebel, Anne Solgaard, and Peter Thejll
The Cryosphere, 20, 5071–5098, https://doi.org/10.5194/tc-20-5071-2026, https://doi.org/10.5194/tc-20-5071-2026, 2026
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Iceberg mélange is the jumble of icebergs in front of some glaciers that calve into the sea. Some studies suggest mélange might help to control the retreat of glaciers. We studied 3 glaciers in NW Greenland where we used GPS sensors and satellites to track ice movement. We found that glaciers push forward and calve all year, including when mélange and landfast sea ice are present, suggesting mélange is not important in supporting glaciers, but may influence the seasonal calving cycle.
Andrea M. U. Gierisch, Bogi Hansen, Karin Margretha. H. Larsen, and Steffen M. Olsen
EGUsphere, https://doi.org/10.5194/egusphere-2026-5283, https://doi.org/10.5194/egusphere-2026-5283, 2026
This preprint is open for discussion and under review for Ocean Science (OS).
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Around the Faroe Islands there is a submarine ridge, which interferes with the large-scale northward flow of warm Atlantic water and the southward flow of cold deep waters. To improve the simulation of these water flows in an ocean model we increase the grid resolution locally in the region of the ridge. Compared to observations, the water flow becomes more realistic and the cold water sinks deeper. Possibly, the same approach could improve the ocean circulation also in climate models.
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
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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.
Guðrið Eriksdóttir, Bogi Hansen, Karin Margretha H. Larsen, Steffen M. Olsen, Andrea M. U. Gierisch, and Sólveig Rósa Ólafsdóttir
Ocean Sci., 22, 2405–2423, https://doi.org/10.5194/os-22-2405-2026, https://doi.org/10.5194/os-22-2405-2026, 2026
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The Atlantic inflow across the Iceland-Faroe Ridge carries almost half of the warm water towards the Arctic. This study explores the modifications of the Atlantic water on its way to and across this underwater ridge. While crossing the ridge, the water is strongly cooled and freshened, which can reduce the ability for deep-water formation. Understanding these processes is important for improving ocean and climate models so more realistic forecasts can be made in a warming climate.
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
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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.
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
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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.
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
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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.
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
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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
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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.
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
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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.
Bogi Hansen, Karin M. H. Larsen, Hjálmar Hátún, Steffen M. Olsen, Andrea M. U. Gierisch, Svein Østerhus, and Sólveig R. Ólafsdóttir
Ocean Sci., 19, 1225–1252, https://doi.org/10.5194/os-19-1225-2023, https://doi.org/10.5194/os-19-1225-2023, 2023
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Based on in situ observations combined with sea level anomaly (SLA) data from satellite altimetry, volume as well as heat (relative to 0 °C) transport of the Iceland–Faroe warm-water inflow towards the Arctic (IF inflow) increased from 1993 to 2021. The reprocessed SLA data released in December 2021 represent observed variations accurately. The IF inflow crosses the Iceland–Faroe Ridge in two branches, with retroflection in between. The associated coupling to overflow reduces predictability.
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
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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.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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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.
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
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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.
Imke Sievers, Andrea M. U. Gierisch, Till A. S. Rasmussen, Robinson Hordoir, and Lars Stenseng
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-84, https://doi.org/10.5194/tc-2022-84, 2022
Preprint withdrawn
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To predict Arctic sea ice models are used. Many ice models exists. They all are skill full, but give different results. Often this differences result from forcing as for example air temperature. Other differences result from the way the physical equations are solved in the model. In this study two commonly used models are compared under equal forcing, to find out how much the models differ under similar external forcing. The results are compared to observations and to eachother.
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
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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
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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.
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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.
In this study, we developed NEMO-Bohai, an ocean–ice model for the Bohai Sea, China. This study...
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