Articles | Volume 16, issue 3
https://doi.org/10.5194/esd-16-683-2025
© Author(s) 2025. 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/esd-16-683-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2025
Research article |
| 06 May 2025
| 06 May 2025
Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm
Jerome Sauer
CORRESPONDING AUTHOR
Earth and Climate Research Center, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
François Massonnet
Earth and Climate Research Center, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Giuseppe Zappa
National Research Council of Italy, Institute of Atmospheric Sciences and Climate, Bologna, Italy
Francesco Ragone
School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
Related authors
No articles found.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O’Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3083, https://doi.org/10.5194/egusphere-2025-3083, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The earth system model variables needed for studies of the ocean and sea ice are prioritized and requested.
Benjamin Richaud, François Massonnet, Thierry Fichefet, Dániel Topál, Antoine Barthélemy, and David Docquier
EGUsphere, https://doi.org/10.5194/egusphere-2025-886, https://doi.org/10.5194/egusphere-2025-886, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Sea ice covers in the Arctic and Antarctic experienced intense reduction during specific recent years. Using an ocean-sea ice model, we found similarities between hemispheres and years to explain the ice reduction, such as ice melt (or lack of growth) at the ice-ocean interface. Differences between years and regions are also evident, such as increased ice transport or snow precipitation. This highlights the importance of heat stored by the ocean to explain ice melt in a warming climate.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Bianca Mezzina, Hugues Goosse, François Klein, Antoine Barthélemy, and François Massonnet
The Cryosphere, 18, 3825–3839, https://doi.org/10.5194/tc-18-3825-2024, https://doi.org/10.5194/tc-18-3825-2024, 2024
Short summary
Short summary
We analyze years with extraordinarily low sea ice extent in Antarctica during summer, until the striking record in 2022. We highlight common aspects among these events, such as the fact that the exceptional melting usually occurs in two key regions and that it is related to winds with a similar direction. We also investigate whether the summer conditions are preceded by an unusual state of the sea ice during the previous winter, as well as the physical processes involved.
Annelies Sticker, François Massonnet, Thierry Fichefet, Patricia DeRepentigny, Alexandra Jahn, David Docquier, Christopher Wyburn-Powell, Daphne Quint, Erica Shivers, and Makayla Ortiz
EGUsphere, https://doi.org/10.5194/egusphere-2024-1873, https://doi.org/10.5194/egusphere-2024-1873, 2024
Short summary
Short summary
Our study analyses rapid Arctic sea ice loss events (RILEs), which are significant reductions in sea ice extent. RILEs are expected throughout the year, varying in frequency and duration with the seasons. Our research gives a year-round analysis of their characteristics in climate models and suggests that summer RILEs could begin before the mid-century. Understanding these events is crucial as they can have profound impacts on the Arctic environment.
Federico Fabiano, Paolo Davini, Virna L. Meccia, Giuseppe Zappa, Alessio Bellucci, Valerio Lembo, Katinka Bellomo, and Susanna Corti
Earth Syst. Dynam., 15, 527–546, https://doi.org/10.5194/esd-15-527-2024, https://doi.org/10.5194/esd-15-527-2024, 2024
Short summary
Short summary
Even after the concentration of greenhouse gases is stabilized, the climate will continue to adapt, seeking a new equilibrium. We study this long-term stabilization through a set of 1000-year simulations, obtained by suddenly "freezing" the atmospheric composition at different levels. If frozen at the current state, global warming surpasses 3° in the long term with our model. We then study how climate impacts will change after various centuries and how the deep ocean will warm.
Steve Delhaye, Rym Msadek, Thierry Fichefet, François Massonnet, and Laurent Terray
EGUsphere, https://doi.org/10.5194/egusphere-2023-1748, https://doi.org/10.5194/egusphere-2023-1748, 2023
Preprint archived
Short summary
Short summary
The climate impact of Arctic sea ice loss may depend on the region of sea ice loss and the methodology used to study this impact. This study uses two approaches across seven climate models to investigate the winter atmospheric circulation response to regional sea ice loss. Our findings indicate a consistent atmospheric circulation response to pan-Arctic sea ice loss in most models and across both approaches. In contrast, more uncertainty emerges in the responses linked to regional sea ice loss.
Mukesh Gupta, Leandro Ponsoni, Jean Sterlin, François Massonnet, and Thierry Fichefet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1560, https://doi.org/10.5194/egusphere-2023-1560, 2023
Preprint archived
Short summary
Short summary
We explored the relationship of Arctic September minimum sea ice extent with mid-summer melt pond area fraction, under the present-day climate. We confirm through the advanced numerical modelling, with an explicit melt pond scheme in the global climate model, EC-EARTH3, that melt pond fraction in mid-summer (June–July, not May) shows a strong relationship with the Arctic September sea ice extent. Satellite-based inferences validated our findings of this association.
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.
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.
Steve Delhaye, Thierry Fichefet, François Massonnet, David Docquier, Rym Msadek, Svenya Chripko, Christopher Roberts, Sarah Keeley, and Retish Senan
Weather Clim. Dynam., 3, 555–573, https://doi.org/10.5194/wcd-3-555-2022, https://doi.org/10.5194/wcd-3-555-2022, 2022
Short summary
Short summary
It is unclear how the atmosphere will respond to a retreat of summer Arctic sea ice. Much attention has been paid so far to weather extremes at mid-latitude and in winter. Here we focus on the changes in extremes in surface air temperature and precipitation over the Arctic regions in summer during and following abrupt sea ice retreats. We find that Arctic sea ice loss clearly shifts the extremes in surface air temperature and precipitation over terrestrial regions surrounding the Arctic Ocean.
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.
Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sébastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
Geosci. Model Dev., 15, 553–594, https://doi.org/10.5194/gmd-15-553-2022, https://doi.org/10.5194/gmd-15-553-2022, 2022
Short summary
Short summary
We present PARASO, a circumpolar model for simulating the Antarctic climate. PARASO features five distinct models, each covering different Earth system subcomponents (ice sheet, atmosphere, land, sea ice, ocean). In this technical article, we describe how this tool has been developed, with a focus on the
coupling interfacesrepresenting the feedbacks between the distinct models used for contribution. PARASO is stable and ready to use but is still characterized by significant biases.
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.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
Short summary
Short summary
Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Ann Keen, Ed Blockley, David A. Bailey, Jens Boldingh Debernard, Mitchell Bushuk, Steve Delhaye, David Docquier, Daniel Feltham, François Massonnet, Siobhan O'Farrell, Leandro Ponsoni, José M. Rodriguez, David Schroeder, Neil Swart, Takahiro Toyoda, Hiroyuki Tsujino, Martin Vancoppenolle, and Klaus Wyser
The Cryosphere, 15, 951–982, https://doi.org/10.5194/tc-15-951-2021, https://doi.org/10.5194/tc-15-951-2021, 2021
Short summary
Short summary
We compare the mass budget of the Arctic sea ice in a number of the latest climate models. New output has been defined that allows us to compare the processes of sea ice growth and loss in a more detailed way than has previously been possible. We find that that the models are strikingly similar in terms of the major processes causing the annual growth and loss of Arctic sea ice and that the budget terms respond in a broadly consistent way as the climate warms during the 21st century.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
Short summary
Short summary
The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Marlene Kretschmer, Giuseppe Zappa, and Theodore G. Shepherd
Weather Clim. Dynam., 1, 715–730, https://doi.org/10.5194/wcd-1-715-2020, https://doi.org/10.5194/wcd-1-715-2020, 2020
Short summary
Short summary
The winds in the polar stratosphere affect the weather in the mid-latitudes, making it important to understand potential changes in response to global warming. However, climate model projections disagree on how this so-called polar vortex will change in the future. Here we show that sea ice loss in the Barents and Kara (BK) seas plays a central role in this. The time when the BK seas become ice-free differs between models, which explains some of the disagreement regarding vortex projections.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, https://doi.org/10.5194/tc-14-3479-2020, 2020
Short summary
Short summary
We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change.
The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
Eduardo Moreno-Chamarro, Pablo Ortega, and François Massonnet
Geosci. Model Dev., 13, 4773–4787, https://doi.org/10.5194/gmd-13-4773-2020, https://doi.org/10.5194/gmd-13-4773-2020, 2020
Short summary
Short summary
Climate models need to capture sea ice complexity to represent it realistically. Here we assess how distributing sea ice in discrete thickness categories impacts how sea ice variability is simulated in the NEMO3.6–LIM3 model. Simulations and satellite observations are compared by using k-means clustering of sea ice concentration in winter and summer between 1979 and 2014 at both poles. Little improvements in the modeled sea ice lead us to recommend using the standard number of five categories.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Leandro Ponsoni, François Massonnet, David Docquier, Guillian Van Achter, and Thierry Fichefet
The Cryosphere, 14, 2409–2428, https://doi.org/10.5194/tc-14-2409-2020, https://doi.org/10.5194/tc-14-2409-2020, 2020
Short summary
Short summary
The continuous melting of the Arctic sea ice observed in the last decades has a significant impact at global and regional scales. To understand the amplitude and consequences of this impact, the monitoring of the total sea ice volume is crucial. However, in situ monitoring in such a harsh environment is hard to perform and far too expensive. This study shows that four well-placed sampling locations are sufficient to explain about 70 % of the inter-annual changes in the pan-Arctic sea ice volume.
François Massonnet, Martin Ménégoz, Mario Acosta, Xavier Yepes-Arbós, Eleftheria Exarchou, and Francisco J. Doblas-Reyes
Geosci. Model Dev., 13, 1165–1178, https://doi.org/10.5194/gmd-13-1165-2020, https://doi.org/10.5194/gmd-13-1165-2020, 2020
Short summary
Short summary
Earth system models (ESMs) are one of the cornerstones of modern climate science. They are usually run on high-performance computers (HPCs). Whether the choice of HPC can affect the model results is a question of importance for optimizing the design of scientific studies. Here, we introduce a protocol for testing the replicability of the EC-Earth3 ESM across different HPCs. We find the simulation results to be replicable only if specific precautions are taken at the compilation stage.
François Massonnet, Antoine Barthélemy, Koffi Worou, Thierry Fichefet, Martin Vancoppenolle, Clément Rousset, and Eduardo Moreno-Chamarro
Geosci. Model Dev., 12, 3745–3758, https://doi.org/10.5194/gmd-12-3745-2019, https://doi.org/10.5194/gmd-12-3745-2019, 2019
Short summary
Short summary
Sea ice thickness varies considerably on spatial scales of several meters. However, contemporary climate models cannot resolve such scales yet. This is why sea ice models used in climate models include an ice thickness distribution (ITD) to account for this unresolved variability. Here, we explore with the ocean–sea ice model NEMO3.6-LIM3 the sensitivity of simulated mean Arctic and Antarctic sea ice states to the way the ITD is discretized.
Leandro Ponsoni, François Massonnet, Thierry Fichefet, Matthieu Chevallier, and David Docquier
The Cryosphere, 13, 521–543, https://doi.org/10.5194/tc-13-521-2019, https://doi.org/10.5194/tc-13-521-2019, 2019
Short summary
Short summary
The Arctic is a main component of the Earth's climate system. It is fundamental to understand the behavior of Arctic sea ice coverage over time and in space due to many factors, e.g., shipping lanes, the travel and tourism industry, hunting and fishing activities, mineral resource extraction, and the potential impact on the weather in midlatitude regions. In this work we use observations and results from models to understand how variations in the sea ice thickness change over time and in space.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96, https://doi.org/10.5194/tc-13-79-2019, https://doi.org/10.5194/tc-13-79-2019, 2019
Short summary
Short summary
The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
Camille Li, Clio Michel, Lise Seland Graff, Ingo Bethke, Giuseppe Zappa, Thomas J. Bracegirdle, Erich Fischer, Ben J. Harvey, Trond Iversen, Martin P. King, Harinarayan Krishnan, Ludwig Lierhammer, Daniel Mitchell, John Scinocca, Hideo Shiogama, Dáithí A. Stone, and Justin J. Wettstein
Earth Syst. Dynam., 9, 359–382, https://doi.org/10.5194/esd-9-359-2018, https://doi.org/10.5194/esd-9-359-2018, 2018
Short summary
Short summary
This study investigates the midlatitude atmospheric circulation response to 1.5°C and 2.0°C of warming using modelling experiments run for the HAPPI project (Half a degree Additional warming, Prognosis & Projected Impacts). While the chaotic nature of the atmospheric flow dominates in these low-end warming scenarios, some local changes emerge. Case studies explore precipitation impacts both for regions that dry (Mediterranean) and regions that get wetter (Europe, North American west coast).
David Docquier, François Massonnet, Antoine Barthélemy, Neil F. Tandon, Olivier Lecomte, and Thierry Fichefet
The Cryosphere, 11, 2829–2846, https://doi.org/10.5194/tc-11-2829-2017, https://doi.org/10.5194/tc-11-2829-2017, 2017
Short summary
Short summary
Our study provides a new way to evaluate the performance of a climate model regarding the interplay between sea ice motion, area and thickness in the Arctic against different observation datasets. We show that the NEMO-LIM model is good in that respect and that the relationships between the different sea ice variables are complex. The metrics we developed can be used in the framework of the Coupled Model Intercomparison Project 6 (CMIP6), which will feed the next IPCC report.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
Short summary
Short summary
The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
P. J. Hezel, T. Fichefet, and F. Massonnet
The Cryosphere, 8, 1195–1204, https://doi.org/10.5194/tc-8-1195-2014, https://doi.org/10.5194/tc-8-1195-2014, 2014
V. Zunz, H. Goosse, and F. Massonnet
The Cryosphere, 7, 451–468, https://doi.org/10.5194/tc-7-451-2013, https://doi.org/10.5194/tc-7-451-2013, 2013
Related subject area
Topics: Climate dynamics and variability | Interactions: Cryosphere/atmosphere interactions | Methods: Earth system and climate modeling
Sea-ice thermodynamics can determine waterbelt scenarios for Snowball Earth
Johannes Hörner and Aiko Voigt
Earth Syst. Dynam., 15, 215–223, https://doi.org/10.5194/esd-15-215-2024, https://doi.org/10.5194/esd-15-215-2024, 2024
Short summary
Short summary
Snowball Earth refers to a climate in the deep past of the Earth where the whole planet was covered in ice. Waterbelt states, where a narrow region of open water remains at the Equator, have been discussed as an alternative scenario, which might explain how life was able to survive these periods. Here, we demonstrate how waterbelt states are influenced by the thermodynamical sea-ice model used. The sea-ice model modulates snow on ice, ice albedo and ultimately the stability of waterbelt states.
Cited articles
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical process of the UCLA general circulation model, Meth. Comp. Phys., 17, 173–265, 1977. a
Årthun, M., Eldevik, T., Smedsrud, L. H., Skagseth, Ø., 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
Baldwin, M. P., Stephenson, D. B., Thompson, D. W. J., Dunkerton, T. J., Charlton, A. J., and O'Neill, A.: Stratospheric memory and skill of extended-range weather forecasts, Science, 301, 636–640, https://doi.org/10.1126/science.1087143, 2003. a
Baxter, I., Ding, Q., Schweiger, A., L'Heureux, M., Baxter, S., Wang, T., Zhang, Q., Harnos, K., Markle, B., Topal, D., and Lu, J.: How tropical Pacific surface cooling contributed to accelerated sea ice melt from 2007 to 2012 as ice is thinned by anthropogenic forcing, J. Climate, 32, 8583–8602, https://doi.org/10.1175/JCLI-D-18-0783.1, 2019. a, b
Bouchet, F., Marston, J. B., and Tangarife, T.: Fluctuations and large deviations of Reynolds stresses in zonal jet dynamics, Phys. Fluids, 30, 015110, https://doi.org/10.1063/1.4990509, 2018. a
Chevallier, M., Massonnet, F., Goessling, H., Guémas, V., and Thomas, J.: The role of sea ice in sub-seasonal predictability, in: Sub-Seasonal to Seasonal Prediction: The Gap Between Weather and Climate Forecasting, edited by: Robertson, A. W. and Vitart, F., 201–221, Elsevier Inc., https://doi.org/10.1016/B978-0-12-811714-9.00010-3, 2019. a, b, c, d
Cini, M., Zappa, G., Ragone, F., and Corti, S.: Simulating AMOC tipping driven by internal climate variability with a rare event algorithm, npj Climate and Atmospheric Science, 7, 2024, https://doi.org/10.1038/s41612-024-00568-7, 2023. a
Coles, S.: An Introduction to Statistical Modelling of Extreme Values, Springer-Verlag, London, ISBN 978-1-85233-459-8, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a
Day, J. J., Tietsche, S., and Hawkins, E.: Pan-Arctic and regional sea ice predictability: initialization month dependence, J. Climate, 27, 4371–4390, https://doi.org/10.1175/JCLI-D-13-00614.1, 2014. a, b, c
Del Moral, P.: Feynman-kac Formulae Genealogical and Interacting Particle Systems with Applications, Springer New York, ISBN 978-0-387-20268-6, https://doi.org/10.1007/978-1-4684-9393-1, 2004. a
Del Moral, P. and Garnier, J.: Genealogical particle analysis of rare events, Ann. Appl. Probab., 15, 2496–2534, https://doi.org/10.1214/105051605000000566, 2005. a
Ding, Q., Schweiger, A., L'Heureux, M., Battisti, D. S., P.-Chedley, S., Johnson, N. C., Blanchard Wrigglesworth, E., Harnos, K., Zhang, Q., Eastman, R., and Steig, E. J.: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice, Nat. Clim. Change, 7, 289–295, https://doi.org/10.1038/NCLIMATE3241, 2017. a, b, c
Docquier, D., Grist, J. P., Roberts, M. J., Roberts, C. D., Semmler, T., Ponsoni, L., Massonnet, F., Sidorenko, D., Sein, D. V., Iovino, D., Bellucci, A., and Fichefet, T.: Impact of model resolution on Arctic sea ice and North Atlantic Ocean heat transport, Clim. Dynam., 53, 4989–5017, https://doi.org/10.1007/s00382-019-04840-y, 2019. a
Drijfhout, S. S., Maier Reimer, E., and Mikolajewicz, U.: Tracing the conveyor belt in the Hamburg large-scale geostrophic ocean general circulation model, J. Geophys. Res.-Oceans, 101, 22563–22575, https://doi.org/10.1029/96JC02162, 1996. a
England, M., Alexandra, J., and Polvani, L.: Nonuniform contribution of internal variability to recent Arctic sea ice loss, J. Climate, 32, 4039–4053, https://doi.org/10.1175/JCLI-D-18-0864.1, 2019. a, b
Fetterer, F., Knowles, K., Meier, W. N., Savoie, M., and Windnagel, A. K.: Sea Ice Index, Version 3. National Snow and Ice Data Center, Boulder, CO, digital media, https://doi.org/10.7265/N5K072F8, 2017. a
Fraedrich, K., Jansen, H., Kirk, E., Luksch, U., and Lunkeit, F.: The planet simulator: towards a user friendly model, Meteorol. Z., 14, 299–304, https://doi.org/10.1127/0941-2948/2005/0043, 2005. a
Francis, J. A. and Wu, B.: Why has no new record-minimum Arctic sea-ice extent occurred since September 2012?, Environ. Res. Lett., 15, 114034, https://doi.org/10.1088/1748-9326/abc047, 2020. a, b
Giardina, C., Kurchan, J., Lecomte, V., and Tailleur, J.: Simulating rare events in dynamical processes, J. Stat. Phys., 145, 787–811, https://doi.org/10.1007/s10955-011-0350-4, 2011. a, b, c
Grafke, T. and Vanden-Eijnden, E.: Numerical compuation of rare events via large deviation theory, Chaos, 29, 063118, https://doi.org/10.1063/1.5084025, 2019. a
Grafke, T., Grauer, R., and Schäfer, T.: The instanton method and its numerical implementation in fluid mechanics, J. Phys. A-Math. Theor., 48, 333001, https://doi.org/10.1088/1751-8113/48/33/333001, 2015. a
Gregory, J. M., Stott, P. A., Cresswell, D. J., Rayner, N. A., Gordon, C., and Sexton, D. M. H.: Recent and future changes in Arctic sea ice simulated by the HadCM3 AOGCM, Geophys. Res. Lett., 29, 2175, https://doi.org/10.1029/2001GL014575, 2002. a
Guemas, V., Doblas Reyes, F., Germe, A., Chevallier, M., and y Mélia, D. S.: September 2012 Arctic sea ice minimum: discriminating between sea ice memory, the August 2012 extreme storm, and prevailing warm conditions, B. Am. Meteorol. Soc., 94, S20–S22, 2013. a
Hoven, V. D. I.: Power spectrum of horizontal wind speed in the frequency range from 0.0007 to 900 cycles per hour, J. Atmos. Sci., 14, 160–164, https://doi.org/10.1175/1520-0469(1957)014<0160:PSOHWS>2.0.CO;2, 1957. a
Kahn, H. and Harris, T. E.: Estimation of particle transmission by random sampling, Natl. Bur. Stand. Appl. Math. Ser., 12, 27–30, 1951. a
Kapsch, M.-L., Graversen, R. G., and Tjernström, M.: Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent, Nat. Clim. Change, 3, 744–748, https://doi.org/10.1038/NCLIMATE1884, 2013. a, b
Kapsch, M.-L., Skific, N., Graversen, R. G., Tjernström, M., and Francis, J. A.: Summers with low Arctic sea ice linked to persistence of spring atmospheric circulation patterns, Clim. Dynam., 52, 2497–2512, https://doi.org/10.1007/s00382-018-4279-z, 2019. a, b, c
Kauker, F., Kaminski, T., Karcher, M., Giering, R., Gerdes, R., and Voßbeck, M.: Adjoint analysis of the 2007 all time Arctic sea-ice minimum, Geophys. Res. Lett., 36, L03707, https://doi.org/10.1029/2008GL036323, 2009. a, b
Kirchmeier-Young, M., Zwiers, F. W., and Gillet, N. P.: Attribution of extreme events in Arctic sea ice extent, J. Climate, 30, 553–571, https://doi.org/10.1175/JCLI-D-16-0412.s1, 2017. a, b, c
Lestang, T., Ragone, F., Bréhier, C.-E., Herbert, C., and Bouchet, F.: Computing return times or return periods with rare event algorithms, J. Stat. Mech.-Theory E., 2018, 043213, https://doi.org/10.1088/1742-5468/aab856, 2018. a
Lestang, T., Bouchet, F., and Lévêque, E.: Numerical study of extreme mechanical force exerted by a turbulent flow on a bluff body by direct and rare-event sampling techniques, J. Fluid Mech., 895, A19, https://doi.org/10.1017/jfm.2020.293, 2020. a
Lindsay, R. W., Zhang, J., Schweiger, A. J., and Steele, M. A.: Seasonal predictions of ice extent in the Arctic Ocean, J. Geophys. Res.-Oceans, 113, 1–11, https://doi.org/10.1029/2007JC004259, 2008. a, b, c
Lindsay, R. W., Zhang, J., Schweiger, A., Steele, M., and Stern, H.: Arctic sea ice retreat in 2007 follows thinning trend, J. Climate, 22, 165–176, https://doi.org/10.1175/2008JCLI2521.1, 2009. a, b
Maier-Reimer, E., Mikolajewicz, U., and Hasselmann, K.: Mean circulation of the Hamburg LSG OGCM and its sensitivity to the thermohaline surface forcing, J. Phys. Oceanogr., 23, 731–757, https://doi.org/10.1175/1520-0485(1993)023<0731:MCOTHL>2.0.CO;2, 1993. a
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017. a
Notz, D. and Marotzke, J.: Observations reveal external driver for Arctic sea-ice retreat, Geophys. Res. Lett., 39, 1–6, https://doi.org/10.1029/2012GL051094, 2012. a
Notz, D. and SIMIP Community: Arctic sea ice in CMIP6, Geophys. Res. Lett., 47, 1–11, https://doi.org/10.1029/2019GL086749, 2020. a
Ogi, M., Rysgaard, S., and Barber, D. G.: Importance of combined winter and summer Arctic Oscillation (AO) on September sea ice extent, Environ. Res. Lett., 11, 034019, https://doi.org/10.1088/1748-9326/11/3/034019, 2016. a, b, c
Ono, J., Tatebe, H., and Komuro, Y.: Mechanisms for and predictability of a drastic reduction in the Arctic sea ice: APPOSITE data with climate model MIROC, J. Climate, 32, 1361–1380, https://doi.org/10.1175/JCLI-D-18-0195.1, 2019. a, b
Overland, J. E., Francis, J. A., Hanna, E., and Wang, M.: The recent shift in early summer Arctic atmospheric circulation, Geophys. Res. Lett., 39, L19804, https://doi.org/10.1029/2012GL053268, 2012. a
Parkinson, C. L. and Comiso, J.: On the 2012 record low Arctic sea ice cover: combined impact of preconditioning and an August storm, Geophys. Res. Lett., 40, 1356–1361, https://doi.org/10.1002/grl.50349, 2013. a
Plotkin, D. A., Webber, R. J., O'Neill, M. E., Waere, J., and Abbot, D. S.: Maximizing simulated tropical cyclone intensity with action minimization, J. Adv. Model. Earth Sy., 11, 863–891, https://doi.org/10.1029/2018ms001419, 2019. a, b
Rigor, I. G., Wallace, J. M., and Colony, R. L.: Response of sea ice to the Arctic oscillation, J. Climate, 15, 2648–2663, https://doi.org/10.1175/1520-0442(2002)015<2648:ROSITT>2.0.CO;2, 2002. a
Sauer, J., Massonnet, F., Zappa, G., and Ragone, F.: Supplementary Materials for “Ensemble design for seasonal climate predictions: Studying extreme Arctic sea ice lows with a rare event algorithm”, Zenodo [data set], https://doi.org/10.5281/zenodo.14858944, 2024b. a
Schweiger, A. J., Zhang, J., Lindsay, R. W., and Steele, M.: Did unusually sunny skies help drive the record sea ice minimum of 2007?, Geophys. Res. Lett., 35, L10503, https://doi.org/10.1029/2008GL033463, 2008. a
Semtner, A. J.: A model for the thermodynamic growth of sea ice in numerical investigations of climate, J. Phys. Oceanogr., 6, 379–389, 1976. a
Serreze, M. C. and Barry, R. G.: The Arctic climate system, in: Cambridge Atmospheric and Space Science Series, edited by: Houghton, J. T., Rycroft, M. J., and Dessler, A. J., Cambridge University Press, 1–385, ISBN 9780521814188, 2014. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons, Environ. Res. Lett., 13, 1–23, https://doi.org/10.1088/1748-9326/aade56, 2018. a
Tietsche, S., Notz, D., Jungclaus, J. H., and Marotzke, J.: Recovery mechanisms of Arctic summer sea ice, Geophys. Res. Lett., 38, 1–4, https://doi.org/10.1029/2010GL045698, 2011. a
Tietsche, S., Day, J. J., Guemas, V., W. J. Hurlin, S. P. E. K., Matei, D., Msadek, R., Collins, M., and Hawkins, E.: Seasonal to interannual Arctic sea ice predictability in current global climate models, Geophys. Res. Lett., 41, 1035–1043, https://doi.org/10.1002/2013GL058755, 2014. a, b, c
Wang, J., Zhang, J., Watanabe, E., Ikeda, M., Mizobata, K., Walsh, J. E., Bai, X., and Wu, B.: Is the Dipole Anomaly a major driver to record lows in Arctic summer sea ice extent?, Geophys. Res. Lett., 36, L05706, https://doi.org/10.1029/2008GL036706, 2009. a, b, c
Webber, R. J., Plotkin, D. A., O'Neill, M. E., Abbot, D. S., and Waere, J.: Practical rare event smapling for extreme mesoscale weather, Chaos, 29, 053109, https://doi.org/10.1063/1.5081461, 2019. 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
Wouters, J., Schiemann, R. K. H., and Shaffrey, L. C.: Rare event simulation of extreme European winter rainfall in an intermediate complexity climate model, J. Adv. Model. Earth Sy., 15, e2022MS003537, https://doi.org/10.1029/2022MS003537, 2023. a
Zhang, J., Lindsay, R., Steele, M., and Schweiger, A.: What drove the dramatic retreat of arctic sea ice during summer 2007?, Geophys. Res. Lett., 35, L11505, https://doi.org/10.1029/2008GL034005, 2008. a
Zhang, J., Lindsay, R., Schweiger, A., and Steele, M.: The impact of an intense summer cyclone on 2012 Arctic sea ice retreat, Geophys. Res. Lett., 40, 720–726, https://doi.org/10.1002/grl.50190, 2013. a, b, c
Short summary
An obstacle in studying climate extremes is the lack of robust statistics. We use a rare event algorithm to gather robust statistics on extreme Arctic sea ice lows with probabilities below 0.1 % and to study drivers of events with amplitudes larger than observed in 2012. The work highlights that the most extreme sea ice reductions result from the combined effects of preconditioning and weather variability, emphasizing the need for thoughtful ensemble design when turning to real applications.
An obstacle in studying climate extremes is the lack of robust statistics. We use a rare event...
Altmetrics
Final-revised paper
Preprint