Articles | Volume 16, issue 5
https://doi.org/10.5194/tc-16-1653-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-1653-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 May 2022
Research article |
| 05 May 2022
| 05 May 2022
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
William Gregory
CORRESPONDING AUTHOR
Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, WC1E 6BT, UK
Julienne Stroeve
Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, WC1E 6BT, UK
National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, 80309-0449, USA
Centre for Earth Observation Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
Michel Tsamados
Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, WC1E 6BT, UK
Related authors
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, https://doi.org/10.5194/tc-15-2857-2021, 2021
Short summary
Short summary
Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.
Wiebke Margitta Kolbe, Rasmus T. Tonboe, and Julienne Stroeve
Earth Syst. Sci. Data, 16, 1247–1264, https://doi.org/10.5194/essd-16-1247-2024, https://doi.org/10.5194/essd-16-1247-2024, 2024
Short summary
Short summary
Current satellite-based sea-ice climate data records (CDRs) usually begin in October 1978 with the first multichannel microwave radiometer data. Here, we present a sea ice dataset based on the single-channel Electrical Scanning Microwave Radiometer (ESMR) that operated from 1972-1977 onboard NASA’s Nimbus 5 satellite. The data were processed using modern methods and include uncertainty estimations in order to provide an important, easy-to-use reference period of good quality for current CDRs.
Lu Zhou, Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Shiming Xu, Weixin Zhu, Sahra Kacimi, Stefanie Arndt, and Zifan Yang
EGUsphere, https://doi.org/10.5194/egusphere-2024-81, https://doi.org/10.5194/egusphere-2024-81, 2024
Short summary
Short summary
Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy snowfall, has complex stratigraphy and profound impact over the microwave signature. We employ advanced radiation transfer models to analyze the effects of complex snow properties on brightness temperatures over the sea ice in Southern Oceans. Great potential lies in the understanding of snow processes and the application to satellite retrievals.
Caroline R. Holmes, Thomas J. Bracegirdle, Paul R. Holland, Julienne Stroeve, and Jeremy Wilkinson
EGUsphere, https://doi.org/10.5194/egusphere-2023-2881, https://doi.org/10.5194/egusphere-2023-2881, 2023
Short summary
Short summary
Until recently, observed Antarctic sea ice was increasing, while in contrast numerical climate models simulated a decrease over the same period (1979–2014). This apparent mismatch was one reason for low confidence in model projections of large 21st century sea ice loss and related aspects of Southern Hemisphere climate. Here we show that, with the inclusion of several low Antarctic sea ice years (notably 2017, 2022 and 2023), we can no longer conclude that modelled and observed trends differ.
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-2509, https://doi.org/10.5194/egusphere-2023-2509, 2023
Short summary
Short summary
Snow on sea ice is vital for near-shore sea ice geophysical and biological processes. Past studies have measured snow depths using satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice) but estimating sea surface height from lead-less land-fast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in-situ after adjusting for tides. Realistic snow depths are retrieved but difference in roughness, satellite footprints and snow geophysical properties are identified as challenges.
Alistair Duffey, Robbie Mallett, Peter J. Irvine, Michel Tsamados, and Julienne Stroeve
Earth Syst. Dynam., 14, 1165–1169, https://doi.org/10.5194/esd-14-1165-2023, https://doi.org/10.5194/esd-14-1165-2023, 2023
Short summary
Short summary
The Arctic is warming several times faster than the rest of the planet. Here, we use climate model projections to quantify for the first time how this faster warming in the Arctic impacts the timing of crossing the 1.5 °C and 2 °C thresholds defined in the Paris Agreement. We show that under plausible emissions scenarios that fail to meet the Paris 1.5 °C target, a hypothetical world without faster warming in the Arctic would breach that 1.5 °C target around 5 years later.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
Short summary
Short summary
In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023, https://doi.org/10.5194/tc-17-233-2023, 2023
Short summary
Short summary
During 1979–2020, four winter polynyas occurred in December 1986 and February 2011, 2017, and 2018 north of Greenland. Instead of ice melting due to the anomalous warm air intrusion, the extreme wind forcing resulted in greater ice transport offshore. Based on the two ensemble runs, representing a 1980s thicker ice vs. a 2010s thinner ice, a dominant cause of these winter polynyas stems from internal variability of atmospheric forcing rather than from the forced response to a warming climate.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
Short summary
Short summary
Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
Short summary
Short summary
Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
Short summary
Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, https://doi.org/10.5194/tc-15-2857-2021, 2021
Short summary
Short summary
Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
Short summary
Short summary
We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
Short summary
Short summary
A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Lu Zhou, Julienne Stroeve, Shiming Xu, Alek Petty, Rachel Tilling, Mai Winstrup, Philip Rostosky, Isobel R. Lawrence, Glen E. Liston, Andy Ridout, Michel Tsamados, and Vishnu Nandan
The Cryosphere, 15, 345–367, https://doi.org/10.5194/tc-15-345-2021, https://doi.org/10.5194/tc-15-345-2021, 2021
Short summary
Short summary
Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analyses on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in situ and remote sensing observations.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
Short summary
Short summary
The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
Short summary
Short summary
This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
Short summary
Short summary
In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Robbie D. C. Mallett, Isobel R. Lawrence, Julienne C. Stroeve, Jack C. Landy, and Michel Tsamados
The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, https://doi.org/10.5194/tc-14-251-2020, 2020
Short summary
Short summary
Soils store large carbon and are important for global warming. We do not know what factors are important for soil carbon storage in the alpine Andes and how they work. We studied how rainfall affects soil carbon storage related to soil structure. We found soil structure is not important, but soil carbon storage and stability controlled by rainfall are dependent on rocks under the soils. The results indicate that we should pay attention to the rocks when studying soil carbon storage in the Andes.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
Short summary
Short summary
This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Isobel R. Lawrence, Michel C. Tsamados, Julienne C. Stroeve, Thomas W. K. Armitage, and Andy L. Ridout
The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, https://doi.org/10.5194/tc-12-3551-2018, 2018
Short summary
Short summary
In this paper we estimate the thickness of snow cover on Arctic sea ice from space. We use data from two radar altimeter satellites, AltiKa and CryoSat-2, that have been operating synchronously since 2013. We produce maps of monthly average snow depth for the four growth seasons (October to April): 2012–2013, 2013–2014, 2014–2015, and 2015–2016. Snow depth estimates are essential for the accurate retrieval of sea ice thickness from satellite altimetry.
Graham D. Quartly, Eero Rinne, Marcello Passaro, Ole B. Andersen, Salvatore Dinardo, Sara Fleury, Kevin Guerreiro, Amandine Guillot, Stefan Hendricks, Andrey A. Kurekin, Felix L. Müller, Robert Ricker, Henriette Skourup, and Michel Tsamados
The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-148, https://doi.org/10.5194/tc-2018-148, 2018
Revised manuscript not accepted
Short summary
Short summary
Radar altimetry is a high-precision technique for measuring sea level and sea ice thickness from space, which are important for monitoring ocean circulation, sea level rise and changes in the Arctic ice cover. This paper reviews the processing techniques needed to best extract the information from complicated radar echoes, and considers the likely developments in the coming decade.
Julienne C. Stroeve, David Schroder, Michel Tsamados, and Daniel Feltham
The Cryosphere, 12, 1791–1809, https://doi.org/10.5194/tc-12-1791-2018, https://doi.org/10.5194/tc-12-1791-2018, 2018
Short summary
Short summary
This paper looks at the impact of the warm winter and anomalously low number of total freezing degree days during winter 2016/2017 on thermodynamic ice growth and overall thickness anomalies. The approach relies on evaluation of satellite data (CryoSat-2) and model output. While there is a negative feedback between rapid ice growth for thin ice, with thermodynamic ice growth increasing over time, since 2012 that relationship is changing, in part because the freeze-up is happening later.
Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, and Walter N. Meier
The Cryosphere, 12, 433–452, https://doi.org/10.5194/tc-12-433-2018, https://doi.org/10.5194/tc-12-433-2018, 2018
Short summary
Short summary
There was significant scientific and media attention surrounding Arctic sea ice in 2016, due primarily to the record-warm air temperatures and low sea ice conditions observed at the start of the year. Here we quantify and assess the record-low monthly sea ice cover in winter, spring and fall, and the lack of record-low sea ice conditions in summer. We explore the primary drivers of these monthly sea ice states and explore the implications for improved summer sea ice forecasting.
Julienne C. Stroeve, John R. Mioduszewski, Asa Rennermalm, Linette N. Boisvert, Marco Tedesco, and David Robinson
The Cryosphere, 11, 2363–2381, https://doi.org/10.5194/tc-11-2363-2017, https://doi.org/10.5194/tc-11-2363-2017, 2017
Short summary
Short summary
As the sea ice has declined strongly in recent years there has been a corresponding increase in Greenland melting. While both are likely a result of changes in atmospheric circulation patterns that favor summer melt, this study evaluates whether or not sea ice reductions around the Greenland ice sheet are having an influence on Greenland summer melt through enhanced sensible and latent heat transport from open water areas onto the ice sheet.
Thomas W. K. Armitage, Sheldon Bacon, Andy L. Ridout, Alek A. Petty, Steven Wolbach, and Michel Tsamados
The Cryosphere, 11, 1767–1780, https://doi.org/10.5194/tc-11-1767-2017, https://doi.org/10.5194/tc-11-1767-2017, 2017
Short summary
Short summary
We present a new 12-year record of geostrophic currents at monthly resolution in the ice-covered and ice-free Arctic Ocean and characterise their seasonal to decadal variability. We also present seasonal climatologies of eddy kinetic energy, and examine the changing location of the Beaufort Gyre. Geostrophic current variability highlights the complex interplay between seasonally varying forcing and sea ice conditions, changing ice–ocean coupling and increasing ocean surface stress in the 2000s.
Lars H. Smedsrud, Mari H. Halvorsen, Julienne C. Stroeve, Rong Zhang, and Kjell Kloster
The Cryosphere, 11, 65–79, https://doi.org/10.5194/tc-11-65-2017, https://doi.org/10.5194/tc-11-65-2017, 2017
Short summary
Short summary
Export of Arctic sea ice area southwards through the Fram Strait from 1935 to 2014 is calculated based on satellite radar images and surface pressure observations. The annual mean export is 880 000 km2, representing 10 % of the Arctic sea ice area. In recent years the export has been above 1 million km2, and there are positive trends over the last 30 years. Increased ice export during spring and summer contributes to more open water in September, and this correlations has increased over time.
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.
Julienne C. Stroeve, Stephanie Jenouvrier, G. Garrett Campbell, Christophe Barbraud, and Karine Delord
The Cryosphere, 10, 1823–1843, https://doi.org/10.5194/tc-10-1823-2016, https://doi.org/10.5194/tc-10-1823-2016, 2016
Short summary
Short summary
Sea ice variability within the marginal ice zone and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore mapping their spatial extent as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Assessments are complicated, however, by which sea ice algorithm is used, with impacts on interpretations on seabird populations.
Alek A. Petty, Michel C. Tsamados, Nathan T. Kurtz, Sinead L. Farrell, Thomas Newman, Jeremy P. Harbeck, Daniel L. Feltham, and Jackie A. Richter-Menge
The Cryosphere, 10, 1161–1179, https://doi.org/10.5194/tc-10-1161-2016, https://doi.org/10.5194/tc-10-1161-2016, 2016
Short summary
Short summary
This study presents an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper (ATM) laser altimeter, flown as part of NASA's Operation IceBridge mission. We describe and implement a newly developed sea ice surface feature-picking algorithm and derive novel information regarding the height, volume and geometry of surface features over the western Arctic sea ice cover.
Daniela Flocco, Daniel L. Feltham, David Schroeder, and Michel Tsamados
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-118, https://doi.org/10.5194/tc-2016-118, 2016
Preprint withdrawn
Short summary
Short summary
Melt ponds form over the sea ice cover in the Arctic and impact the surface albedo inducing a positive feedback leading to further melting.
While they refreeze, ponds delay basal sea ice growth in Autumn impacting the internal sea ice temperature and therefore its basal growth rate. By using a numerical model we estimate an inhibited basal growth of up to 228 km3, which represents 25 % of the basal sea ice growth estimated by PIOMAS during the months of September and October.
Marco Tedesco, Sarah Doherty, Xavier Fettweis, Patrick Alexander, Jeyavinoth Jeyaratnam, and Julienne Stroeve
The Cryosphere, 10, 477–496, https://doi.org/10.5194/tc-10-477-2016, https://doi.org/10.5194/tc-10-477-2016, 2016
Short summary
Short summary
Summer surface albedo over Greenland decreased at a rate of 0.02 per decade between 1996 and 2012. The decrease is due to snow grain growth, the expansion of bare ice areas, and trends in light-absorbing impurities on snow and ice surfaces. Neither aerosol models nor in situ observations indicate increasing trends in impurities in the atmosphere over Greenland. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening.
J. Stroeve, A. Barrett, M. Serreze, and A. Schweiger
The Cryosphere, 8, 1839–1854, https://doi.org/10.5194/tc-8-1839-2014, https://doi.org/10.5194/tc-8-1839-2014, 2014
Related subject area
Discipline: Sea ice | Subject: Climate Interactions
Forced and internal components of observed Arctic sea-ice changes
The contribution of melt ponds to enhanced Arctic sea-ice melt during the Last Interglacial
Analyzing links between simulated Laptev Sea sea ice and atmospheric conditions over adjoining landmasses using causal-effect networks
Clouds damp the radiative impacts of polar sea ice loss
Jakob Simon Dörr, David B. Bonan, Marius Årthun, Lea Svendsen, and Robert C. J. Wills
The Cryosphere, 17, 4133–4153, https://doi.org/10.5194/tc-17-4133-2023, https://doi.org/10.5194/tc-17-4133-2023, 2023
Short summary
Short summary
The Arctic sea-ice cover is retreating due to climate change, but this retreat is influenced by natural (internal) variability in the climate system. We use a new statistical method to investigate how much internal variability has affected trends in the summer and winter Arctic sea-ice cover using observations since 1979. Our results suggest that the impact of internal variability on sea-ice retreat might be lower than what climate models have estimated.
Rachel Diamond, Louise C. Sime, David Schroeder, and Maria-Vittoria Guarino
The Cryosphere, 15, 5099–5114, https://doi.org/10.5194/tc-15-5099-2021, https://doi.org/10.5194/tc-15-5099-2021, 2021
Short summary
Short summary
The Hadley Centre Global Environment Model version 3 (HadGEM3) is the first coupled climate model to simulate an ice-free summer Arctic during the Last Interglacial (LIG), 127 000 years ago, and yields accurate Arctic surface temperatures. We investigate the causes and impacts of this extreme simulated ice loss and, in particular, the role of melt ponds.
Zoé Rehder, Anne Laura Niederdrenk, Lars Kaleschke, and Lars Kutzbach
The Cryosphere, 14, 4201–4215, https://doi.org/10.5194/tc-14-4201-2020, https://doi.org/10.5194/tc-14-4201-2020, 2020
Short summary
Short summary
To better understand the connection between sea ice and permafrost, we investigate how sea ice interacts with the atmosphere over the adjacent landmass in the Laptev Sea region using a climate model. Melt of sea ice in spring is mainly controlled by the atmosphere; in fall, feedback mechanisms are important. Throughout summer, lower-than-usual sea ice leads to more southward transport of heat and moisture, but these links from sea ice to the atmosphere over land are weak.
Ramdane Alkama, Patrick C. Taylor, Lorea Garcia-San Martin, Herve Douville, Gregory Duveiller, Giovanni Forzieri, Didier Swingedouw, and Alessandro Cescatti
The Cryosphere, 14, 2673–2686, https://doi.org/10.5194/tc-14-2673-2020, https://doi.org/10.5194/tc-14-2673-2020, 2020
Short summary
Short summary
The amount of solar energy absorbed by Earth is believed to strongly depend on clouds. Here, we investigate this relationship using satellite data and 32 climate models, showing that this relationship holds everywhere except over polar seas, where an increased reflection by clouds corresponds to an increase in absorbed solar radiation at the surface. This interplay between clouds and sea ice reduces by half the increase of net radiation at the surface that follows the sea ice retreat.
Cited articles
Abe, S. and Suzuki, N.: Complex-network description of seismicity, Nonlin. Processes Geophys., 13, 145–150, https://doi.org/10.5194/npg-13-145-2006, 2006. a
Albert, R. and Barabási, A.-L.: Statistical mechanics of complex networks,
Rev. Mod. Phys., 74, 47, https://doi.org/10.1103/RevModPhys.74.47, 2002. a
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz,
N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft, A. J.:
Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean
modeling system, Adv. Space Res., 62, 1265–1280,
https://doi.org/10.1016/j.asr.2017.12.030, 2018. a
Årthun, M., Onarheim, I. H., Dörr, J., and Eldevik, T.: The seasonal
and regional transition to an ice-free Arctic, Geophys. Res. Lett.,
48, e2020GL090825, https://doi.org/10.1029/2020GL090825, 2021. a
Balan-Sarojini, B., Tietsche, S., Mayer, M., Balmaseda, M., Zuo, H., de Rosnay, P., Stockdale, T., and Vitart, F.: Year-round impact of winter sea ice thickness observations on seasonal forecasts, The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, 2021. a
Blockley, E. W. and Peterson, K. A.: Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness, The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, 2018. a
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D.-U.: Complex
networks: Structure and dynamics, Phys. Rep., 424, 175–308,
https://doi.org/10.1016/j.physrep.2005.10.009, 2006. a
Boccaletti, S., Bianconi, G., Criado, R., Del Genio, C. I.,
Gómez-Gardenes, J., Romance, M., Sendina-Nadal, I., Wang, Z., and
Zanin, M.: The structure and dynamics of multilayer networks, Phys.
Rep., 544, 1–122, https://doi.org/10.1016/j.physrep.2014.07.001, 2014. a
Boers, N., Bookhagen, B., Barbosa, H. M. J., Marwan, N., Kurths, J., and
Marengo, J. A.: Prediction of extreme floods in the eastern Central Andes
based on a complex networks approach, Nat. Commun., 5, 5199,
https://doi.org/10.1038/ncomms6199, 2014. a
Bonan, D. and Blanchard-Wrigglesworth, E.: Nonstationary teleconnection between
the Pacific Ocean and Arctic sea ice, Geophys. Res. Lett., 47,
e2019GL085666, https://doi.org/10.1029/2019GL085666, 2020. a
Bonan, D. B., Bushuk, M., and Winton, M.: A spring barrier for regional
predictions of summer Arctic sea ice, Geophys. Res. Lett., 46,
5937–5947, https://doi.org/10.1029/2019GL082947, 2019. a
Bushuk, M. and Giannakis, D.: The seasonality and interannual variability of
Arctic sea ice reemergence, J. Climate, 30, 4657–4676,
https://doi.org/10.1175/JCLI-D-16-0549.1, 2017. a, b
Bushuk, M., Msadek, R., Winton, M., Vecchi, G. A., Gudgel, R., Rosati, A., and
Yang, X.: Skillful regional prediction of Arctic sea ice on seasonal
timescales, Geophys. Res. Lett., 44, 4953–4964,
https://doi.org/10.1002/2017GL073155, 2017. a
Bushuk, M., Msadek, R., Winton, M., Vecchi, G., Yang, X., Rosati, A., and
Gudgel, R.: Regional Arctic sea–ice prediction: Potential versus operational
seasonal forecast skill, Clim. Dynam., 52, 2721–2743,
https://doi.org/10.1007/s00382-018-4288-y, 2019. a, b
Bushuk, M., Winton, M., Bonan, D. B., Blanchard-Wrigglesworth, E., and
Delworth, T. L.: A mechanism for the Arctic sea ice spring predictability
barrier, Geophys. Res. Lett., 47, e2020GL088335,
https://doi.org/10.1029/2020GL088335, 2020. a
Cattiaux, J. and Cassou, C.: Opposite CMIP3/CMIP5 trends in the wintertime
Northern Annular Mode explained by combined local sea ice and remote tropical
influences, Geophys. Res. Lett., 40, 3682–3687,
https://doi.org/10.1002/grl.50643, 2013. a
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H. J.: Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA [data set], https://doi.org/10.5067/8GQ8LZQVL0VL, 1996. a, b
Chevallier, M. and Salas-Mélia, D.: The role of sea ice thickness
distribution in the Arctic sea ice potential predictability: A diagnostic
approach with a coupled GCM, J. Climate, 25, 3025–3038,
https://doi.org/10.1175/JCLI-D-11-00209.1, 2012. a
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou,
D., Francis, J., Dethloff, K., Entekhabi, D., Overland, J., and Jones, J.: Recent
Arctic amplification and extreme mid-latitude weather, Nat. Geosci., 7,
627–637, https://doi.org/10.1038/ngeo2234, 2014. a
Cohen, J., Zhang, X., Francis, J., Jung, T., Kwok, R., Overland, J., Ballinger,
T., Bhatt, U., Chen, H., Coumou, D., Feldstein, S., Gu, H., Handorf, D., Henderson, G., Ionita, M., Kretschmer, M., Laliberte, F., Lee, S., Linderholm, H. W., Maslowski, W., Peings, Y., Pfeiffer, K., Rigor, I., Semmler, T., Stroeve, J., Taylor, P. C., Vavrus, S., Vihma, T., Wang, S., Wendisch, M., Wu, Y., and Yoon, J.: Divergent consensuses on Arctic
amplification influence on midlatitude severe winter weather, Nat. Clim.
Change, 10, 20–29, https://doi.org/10.1038/s41558-019-0662-y, 2020. a
Cohen, R. and Havlin, S.: Complex networks: structure, robustness and function, 1st edn.,
Cambridge University Press, ISBN (Hardback) 978-0-521-84156-6,
ISBN (Online) 9780511780356,
https://doi.org/10.1017/CBO9780511780356, 2010. a
Collow, T. W., Wang, W., Kumar, A., and Zhang, J.: Improving Arctic sea ice
prediction using PIOMAS initial sea ice thickness in a coupled
ocean–atmosphere model, Mon. Weather Rev., 143, 4618–4630,
https://doi.org/10.1175/MWR-D-15-0097.1, 2015. a
Comiso, J.: Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA [data set], https://doi.org/10.5067/7Q8HCCWS4I0R,
2017. a, b
Comiso, J. C., Meier, W. N., and Gersten, R.: Variability and trends in the
Arctic Sea ice cover: Results from different techniques, J.
Geophys. Res.-Oceans, 122, 6883–6900, https://doi.org/10.1002/2017JC012768,
2017. a
Crawford, A. D., Horvath, S., Stroeve, J., Balaji, R., and Serreze, M. C.:
Modulation of sea ice melt onset and retreat in the Laptev Sea by the timing
of snow retreat in the West Siberian Plain, J. Geophys. Res.-Atmos., 123, 8691–8707, https://doi.org/10.1029/2018JD028697, 2018. a
Day, 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
delEtoile, J. and Adeli, H.: Graph theory and brain connectivity in
Alzheimer’s disease, Neuroscientist, 23, 616–626,
https://doi.org/10.1177/1073858417702621, 2017. a
Deser, C., Walsh, J. E., and Timlin, M. S.: Arctic sea ice variability in the
context of recent atmospheric circulation trends, J. Climate, 13,
617–633, https://doi.org/10.1175/1520-0442(2000)013<0617:ASIVIT>2.0.CO;2, 2000. a
Dijkstra, H. A., Hernández-García, E., Masoller, C., and Barreiro, M.:
Networks in Climate, 1st edn., Cambridge University Press, ISBN (Hardback) 9781107111233, ISBN (Online) 9781316275757,
https://doi.org/10.1017/9781316275757,
2019. a
Doblas-Reyes, F. J., García-Serrano, J., Lienert, F., Biescas, A. P., and
Rodrigues, L. R.: Seasonal climate predictability and forecasting: status and
prospects, WIRES Clim. Change, 4, 245–268,
https://doi.org/10.1002/wcc.217, 2013. a
Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: Complex networks in
climate dynamics: Comparing linear and nonlinear network construction
methods, Eur. Phys. J.-Spec. Top., 174, 157–179,
https://doi.org/10.1140/epjst/e2009-01098-2, 2009. a, b
Donges, J. F., Petrova, I., Loew, A., Marwan, N., and Kurths, J.: How complex
climate networks complement eigen techniques for the statistical analysis of
climatological data, Clim. Dynam., 45, 2407–2424,
https://doi.org/10.1007/s00382-015-2479-3, 2015. a, b
EUMETSAT Ocean and Sea Ice Satellite Application Facility: Global sea ice concentration interim climate data record 2016–onwards (v2.0, 2017), OSI-430-b, OSI SAF FTP server/EUMETSAT Data Center [data set], https://osi-saf.eumetsat.int/products/osi-430-b-complementing-osi-450, (last access: 1 June 2021), 2016. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fountalis, I., Bracco, A., and Dovrolis, C.: Spatio-temporal network analysis
for studying climate patterns, Clim. Dynam., 42, 879–899,
https://doi.org/10.1007/s00382-013-1729-5, 2014. a, b, c
Fountalis, I., Bracco, A., and Dovrolis, C.: ENSO in CMIP5 simulations: network
connectivity from the recent past to the twenty-third century, Clim.
Dynam., 45, 511–538, https://doi.org/10.1007/s00382-014-2412-1, 2015. a, b, c
Francis, J. A., Chan, W., Leathers, D. J., Miller, J. R., and Veron, D. E.:
Winter Northern Hemisphere weather patterns remember summer Arctic sea-ice
extent, Geophys. Res. Lett., 36, L07503, https://doi.org/10.1029/2009GL037274, 2009. a
Fritz, M., Vonk, J. E., and Lantuit, H.: Collapsing arctic coastlines, Nat.
Clim. Change, 7, 6–7, https://doi.org/10.1038/nclimate3188, 2017. a
Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019. a
Giesse, C., Notz, D., and Baehr, J.: On the origin of discrepancies between
observed and simulated memory of Arctic sea ice, Geophys. Res.
Lett., 48, e2020GL091784, https://doi.org/10.1029/2020GL091784, 2021. a
Gong, H., Wang, L., Chen, W., Chen, X., and Nath, D.: Biases of the wintertime
Arctic Oscillation in CMIP5 models, Environ. Res. Lett., 12,
014001, https://doi.org/10.1088/1748-9326/12/1/014001, 2016. a, b
Gong, H., Wang, L., Chen, W., Wu, R., Zhou, W., Liu, L., Nath, D., and Lan, X.:
Diversity of the wintertime Arctic Oscillation pattern among CMIP5 models:
Role of the stratospheric polar vortex, J. Climate, 32, 5235–5250,
https://doi.org/10.1175/JCLI-D-18-0603.1, 2019. a
Graham, R. M., Cohen, L., Ritzhaupt, N., Segger, B., Graversen, R. G., Rinke,
A., Walden, V. P., Granskog, M. A., and Hudson, S. R.: Evaluation of six
atmospheric reanalyses over Arctic sea ice from winter to early summer,
J. Climate, 32, 4121–4143, https://doi.org/10.1175/JCLI-D-18-0643.1, 2019. a
Gregory, W.: William-gregory/CMIP6: Accompanying code for: “Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations” (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.6514306, 2022. a, b
Gregory, W., Tsamados, M., Stroeve, J., and Sollich, P.: Regional September Sea
Ice Forecasting with Complex Networks and Gaussian Processes, Weather
Forecast., 35, 793–806, https://doi.org/10.1175/WAF-D-19-0107.1, 2020. a, b, c, d
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional
climate predictions, B. Am. Meteorol. Soc., 90,
1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2019. a, b
Holland, M. M., Bailey, D. A., and Vavrus, S.: Inherent sea ice predictability
in the rapidly changing Arctic environment of the Community Climate System
Model, version 3, Clim. Dynam., 36, 1239–1253,
https://doi.org/10.1007/s00382-010-0792-4, 2011. a
Hubert, L. and Arabie, P.: Comparing partitions, J. Classif., 2,
193–218, https://doi.org/10.1007/BF01908075, 1985. a, b, c
Hurrell, J. W., Kushnir, Y., Ottersen, G., and Visbeck, M.: An overview of the
North Atlantic oscillation, in: Geophysical Monograph Series, Volume 134, 1–35, https://doi.org/10.1029/134GM01, 2003. a
Jahn, A.: Reduced probability of ice-free summers for 1.5 ∘C compared to 2 ∘C
warming, Nat. Clim. Change, 8, 409–413, https://doi.org/10.1038/s41558-018-0127-8,
2018. 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
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) large ensemble project: A community
resource for studying climate change in the presence of internal climate
variability, B. Am. Meteorol. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1, 2015. a
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses
and coupled variability (1958–2018), Environ. Res. Lett., 13,
105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Larsen, J. N., Schweitzer, P., Abass, K., Doloisio, N., Gartler, S.,
Ingeman-Nielsen, T., Ingimundarson, J. H., Jungsberg, L., Meyer, A., Rautio,
A., Scheer, J., Timlin, U., Vanderlinden, J.-P., and Vullierme, M.: Thawing permafrost in arctic coastal communities: A framework for
studying risks from climate change, Sustainability, 13, 2651,
https://doi.org/10.3390/su13052651, 2021. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a
Malik, N., Bookhagen, B., Marwan, N., and Kurths, J.: Analysis of spatial and
temporal extreme monsoonal rainfall over South Asia using complex networks,
Clim. Dynam., 39, 971–987, 2012. a
Mallett, R., Stroeve, J., Cornish, S., Crawford, A., Lukovich, J., Serreze, M.,
Barrett, A., Meier, W., Heorton, H., and Tsamados, M.: Record winter winds in
2020/21 drove exceptional Arctic sea ice transport, Commun. Earth
Environ., 2, 149, https://doi.org/10.1038/s43247-021-00221-8, 2021. a
Maslanik, J., Fowler, C., Stroeve, J., Drobot, S., Zwally, J., Yi, D., and
Emery, W.: A younger, thinner Arctic ice cover: Increased potential for
rapid, extensive sea-ice loss, Geophys. Res. Lett., 34, L24501,
https://doi.org/10.1029/2007GL032043, 2007. a
Maslanik, J. A., Serreze, M. C., and Barry, R. G.: Recent decreases in Arctic
summer ice cover and linkages to atmospheric circulation anomalies,
Geophys. Res. Lett., 23, 1677–1680, https://doi.org/10.1029/96GL01426, 1996. a
Massonnet, F., Vancoppenolle, M., Goosse, H., Docquier, D., Fichefet, T., and
Blanchard-Wrigglesworth, E.: Arctic sea-ice change tied to its mean state
through thermodynamic processes, Nat. Clim. Change, 8, 599–603,
https://doi.org/10.1038/s41558-018-0204-z, 2018. a
Matsumura, S., Zhang, X., and Yamazaki, K.: Summer Arctic atmospheric
circulation response to spring Eurasian snow cover and its possible linkage
to accelerated sea ice decrease, J. Climate, 27, 6551–6558,
https://doi.org/10.1175/JCLI-D-13-00549.1, 2014. a
Miller, R., Schmidt, G., and Shindell, D.: Forced annular variations in the
20th century intergovernmental panel on climate change fourth assessment
report models, J. Geophys. Res.-Atmos., 111, D18101,
https://doi.org/10.1029/2005JD006323, 2006. a
Mioduszewski, J. R., Vavrus, S., Wang, M., Holland, M., and Landrum, L.: Past and future interannual variability in Arctic sea ice in coupled climate models, The Cryosphere, 13, 113–124, https://doi.org/10.5194/tc-13-113-2019, 2019. a
Morabito, F. C., Campolo, M., Labate, D., Morabito, G., Bonanno, L., Bramanti,
A., De Salvo, S., Marra, A., and Bramanti, P.: A longitudinal EEG study of
Alzheimer's disease progression based on a complex network approach,
Int. J. Neural Syst., 25, 1550005,
https://doi.org/10.1142/S0129065715500057, 2015. a
Newman, M. E.: The structure and function of complex networks, SIAM Rev., 45,
167–256, https://doi.org/10.1137/S003614450342480, 2003. a
Notz, D.: How well must climate models agree with observations?, Philosophical
Transactions of the Royal Society A: Mathematical, Physical and Engineering
Sciences, 373, 20140164, https://doi.org/10.1098/rsta.2014.0164, 2015. a
Notz, D. and SIMIP-Community: Arctic sea ice in CMIP6, Geophys. Res.
Lett., 47, e2019GL086749, https://doi.org/10.1029/2019GL086749,
2020. a
Notz, D. and Stroeve, J.: Observed Arctic sea-ice loss directly follows
anthropogenic CO2 emission, Science, 354, 747–750,
https://doi.org/10.1126/science.aag2345, 2016. a
Olonscheck, D., Mauritsen, T., and Notz, D.: Arctic sea-ice variability is
primarily driven by atmospheric temperature fluctuations, Nat. Geosci.,
12, 430–434, https://doi.org/10.1038/s41561-019-0363-1, 2019. a
Onarheim, I. H., Eldevik, T., Smedsrud, L. H., and Stroeve, J. C.: Seasonal and
regional manifestation of Arctic sea ice loss, J. Climate, 31,
4917–4932, https://doi.org/10.1175/JCLI-D-17-0427.1, 2018. a
Ono, J., Komuro, Y., and Tatebe, H.: Impact of sea-ice thickness initialized in
April on Arctic sea-ice extent predictability with the MIROC climate model,
Ann. Glaciol., 61, 97–105, https://doi.org/10.1017/aog.2020.13, 2020. a
OSI-SAF: Global Sea Ice Concentration Climate Data Record v2.0 – Multimission,
EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_0008, 2017. 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
Park, H.-S., Stewart, A. L., and Son, J.-H.: Dynamic and thermodynamic impacts
of the winter Arctic Oscillation on summer sea ice extent, J.
Climate, 31, 1483–1497, https://doi.org/10.1175/JCLI-D-17-0067.1, 2018. a, b
Polar Science Center: PIOMAS Variables on Model Grid, Polar Science Center [data set], http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/model_grid, last access: 2 March 2021. a
Rand, W. M.: Objective criteria for the evaluation of clustering methods,
J. Am. Stat. Assoc., 66, 846–850,
https://doi.org/10.1080/01621459.1971.10482356, 1971. a, b
Ricker, R., Girard-Ardhuin, F., Krumpen, T., and Lique, C.: Satellite-derived sea ice export and its impact on Arctic ice mass balance, The Cryosphere, 12, 3017–3032, https://doi.org/10.5194/tc-12-3017-2018, 2018. a
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, b
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D.:
Detecting and quantifying causal associations in large nonlinear time series
datasets, Science Advances, 5, eaau4996, https://doi.org/10.1126/sciadv.aau4996, 2019. a, b
Sakshaug, E., Bjørge, A., Gulliksen, B., Loeng, H., and Mehlum, F.:
Structure, biomass distribution, and energetics of the pelagic ecosystem in
the Barents Sea: a synopsis, Polar Biol., 14, 405–411,
https://doi.org/10.1007/BF00240261, 1994. a
Schröder, D., Feltham, D. L., Flocco, D., and Tsamados, M.: September
Arctic sea-ice minimum predicted by spring melt-pond fraction, Nat. Clim.
Change, 4, 353–357, https://doi.org/10.1038/nclimate2203, 2014. a
Schröder, D., Feltham, D. L., Tsamados, M., Ridout, A., and Tilling, R.: New insight from CryoSat-2 sea ice thickness for sea ice modelling, The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, 2019. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys.
Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. a
Serreze, M. C., Maslanik, J. A., Key, J. R., Kokaly, R. F., and Robinson,
D. A.: Diagnosis of the record minimum in Arctic sea ice area during 1990 and
associated snow cover extremes, Geophys. Res. Lett., 22, 2183–2186,
https://doi.org/10.1029/95GL02068, 1995. a
Steinley, D.: Properties of the Hubert-Arable Adjusted Rand Index,
Psychol. Methods, 9, 386–396, https://doi.org/10.1037/1082-989X.9.3.386, 2004. a
Stirling, I.: The importance of polynyas, ice edges, and leads to marine
mammals and birds, J. Marine Syst., 10, 9–21,
https://doi.org/10.1016/S0924-7963(96)00054-1, 1997. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons,
Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a, b
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., and Serreze, M.: Arctic
sea ice decline: Faster than forecast, Geophys. Res. Lett., 34, L09501,
https://doi.org/10.1029/2007GL029703, 2007. a
Stroeve, J., Barrett, A., Serreze, M., and Schweiger, A.: Using records from submarine, aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness, The Cryosphere, 8, 1839–1854, https://doi.org/10.5194/tc-8-1839-2014, 2014. a, b, c
Stroeve, J., Vancoppenolle, M., Veyssiere, G., Lebrun, M., Castellani, G.,
Babin, M., Karcher, M., Landy, J., Liston, G. E., and Wilkinson, J.: A
multi-sensor and modeling approach for mapping light under sea ice during the
ice-growth season, Frontiers in Marine Science, 7, 592337,
https://doi.org/10.3389/fmars.2020.592337, 2021. a
Thompson, D. W. and Wallace, J. M.: The Arctic Oscillation signature in the
wintertime geopotential height and temperature fields, Geophys. Res.
Lett., 25, 1297–1300, https://doi.org/10.1029/98GL00950, 1998. a, b
Tietsche, S., Day, J. J., Guemas, V., Hurlin, W., Keeley, S., 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
Tsonis, A. A. and Roebber, P. J.: The architecture of the climate network,
Physica A, 333, 497–504,
https://doi.org/10.1016/j.physa.2003.10.045, 2004. a, b, c
Tsonis, A. A., Swanson, K. L., and Roebber, P. J.: What do networks have to do
with climate?, B. Am. Meteorol. Soc., 87, 585–596,
https://doi.org/10.1175/BAMS-87-5-585, 2006. a
Venegas, S. A. and Mysak, L. A.: Is there a dominant timescale of natural
climate variability in the Arctic?, J. Climate, 13, 3412–3434,
https://doi.org/10.1175/1520-0442(2000)013<3412:ITADTO>2.0.CO;2, 2000. a
Vinje, T.: Anomalies and trends of sea-ice extent and atmospheric circulation
in the Nordic Seas during the period 1864–1998, J. Climate, 14,
255–267, https://doi.org/10.1175/1520-0442(2001)014<0255:AATOSI>2.0.CO;2, 2001. a
Watts, M., Maslowski, W., Lee, Y. J., Kinney, J. C., and Osinski, R.: A spatial
evaluation of Arctic sea ice and regional limitations in CMIP6 historical
simulations, J. Climate, 34, 6399–6420,
https://doi.org/10.1175/JCLI-D-20-0491.1, 2021. a, b
Williams, J., Tremblay, B., Newton, R., and Allard, R.: Dynamic preconditioning
of the minimum September sea-ice extent, J. Climate, 29, 5879–5891,
https://doi.org/10.1175/JCLI-D-15-0515.1, 2016. a, b
Zhang, J. and Rothrock, D.: Modeling global sea ice with a thickness and
enthalpy distribution model in generalized curvilinear coordinates, Mon.
Weather Rev., 131, 845–861,
https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2, 2003. a
Zhang, R.: Mechanisms for low-frequency variability of summer Arctic sea ice
extent, P. Natl. Acad. Sci., 112, 4570–4575,
https://doi.org/10.1073/pnas.1422296112, 2015. a
Zhou, C., Zemanová, L., Zamora-Lopez, G., Hilgetag, C. C., and Kurths, J.:
Structure–function relationship in complex brain networks expressed by
hierarchical synchronization, New J. Phys., 9, 178,
https://doi.org/10.1088/1367-2630/9/6/178, 2007. a
Zuo, J.-Q., Li, W.-J., and Ren, H.-L.: Representation of the Arctic
Oscillation in the CMIP5 models, Advances in Climate Change Research, 4,
242–249, https://doi.org/10.3724/SP.J.1248.2013.242, 2013. a, b
Short summary
This research was conducted to better understand how coupled climate models simulate one of the large-scale interactions between the atmosphere and Arctic sea ice that we see in observational data, the accurate representation of which is important for producing reliable forecasts of Arctic sea ice on seasonal to inter-annual timescales. With network theory, this work shows that models do not reflect this interaction well on average, which is likely due to regional biases in sea ice thickness.
This research was conducted to better understand how coupled climate models simulate one of the...
