Articles | Volume 11, issue 5
https://doi.org/10.5194/tc-11-2363-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/tc-11-2363-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue editorial
| 
23 Oct 2017
Special issue editorial |
| 23 Oct 2017
Investigating the local-scale influence of sea ice on Greenland surface melt
National Snow and Ice Data Center, Cooperative Institute for Research
in Environmental Sciences, University of Colorado, 449 UCB, Boulder, CO
80309, USA
Centre for Polar Observation and Modelling, University College London,
Department of Earth Sciences, Gower Street, London, WC1E6BT, UK
John R. Mioduszewski
Center for Climatic Research, University of Wisconsin – Madison, 1225
W. Dayton St., Madison, WI 53706, USA
Asa Rennermalm
Department of Geography, Rutgers, State University of New Jersey,
54 Joyce Kilmer Avenue, Piscataway, NJ 08854-8045, USA
Linette N. Boisvert
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Marco Tedesco
Lamont-Doherty Earth Observation, Columbia University, Palisades, NY 10964-8000, USA
David Robinson
Department of Geography, Rutgers, State University of New Jersey,
54 Joyce Kilmer Avenue, Piscataway, NJ 08854-8045, USA
Related authors
Nicole A. Loeb, Alex Crawford, Brice Noël, and Julienne Stroeve
The Cryosphere, 19, 5403–5422, https://doi.org/10.5194/tc-19-5403-2025, https://doi.org/10.5194/tc-19-5403-2025, 2025
Short summary
Short summary
We examine how extreme precipitation days affect the seasonal mass balance (SMB) of land ice in Greenland and the Eastern Canadian Arctic in historical and future simulations. Past extreme precipitation led to higher SMB with snowfall. Future extreme precipitation may lead to the loss of ice mass as more falls as rain rather than snow in some regions, such as southwestern Greenland. Across the region, extreme precipitation becomes more important to seasonal SMB in the future, warmer climate.
Lanqing Huang, Julienne Stroeve, Thomas Newman, Robbie Mallett, Rosemary Willatt, Lu Zhou, Malin Johansson, Carmen Nab, and Alicia Fallows
EGUsphere, https://doi.org/10.5194/egusphere-2025-5158, https://doi.org/10.5194/egusphere-2025-5158, 2025
Short summary
Short summary
Understanding snow depth on sea ice is key for measuring ice thickness, studying ecosystems, and modeling climate. Using snow and ice thickness measurements from Arctic and Antarctic campaigns, this study examines sub-kilometer-scale (<1 km²) snow depth variations and identifies the most suitable statistical models for different ice ages, thicknesses, and weather conditions. These results can improve sub-grid snow parameterizations in snow models and remote sensing algorithms.
Vaishali Chaudhary, Julienne Stroeve, Vishnu Nandan, and Dustin Isleifson
EGUsphere, https://doi.org/10.5194/egusphere-2025-2851, https://doi.org/10.5194/egusphere-2025-2851, 2025
Preprint archived
Short summary
Short summary
This study examines how changing weather is affecting sea ice near the Arctic community of Tuktoyaktuk in Canada. Using satellite images and weather records, we found that stronger winds from certain directions are causing the sea ice to break more often in winter. These changes pose risks for local people who depend on stable ice for travel and hunting. Our findings help understand how climate change is making Arctic ice less reliable and more dangerous.
Franck Eitel Kemgang Ghomsi, Muharrem Hilmi Erkoç, Roshin P. Raj, Atinç Pirti, Antonio Bonaduce, Babatunde J. Abiodun, and Julienne Stroeve
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-6-2025, 393–397, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-393-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-393-2025, 2025
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
The Cryosphere, 19, 325–346, https://doi.org/10.5194/tc-19-325-2025, https://doi.org/10.5194/tc-19-325-2025, 2025
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 the satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice), but estimating sea surface height from leadless landfast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in situ data after adjusting for tides. Realistic snow depths are retrieved, but differences in roughness, satellite footprints, and snow geophysical properties are identified.
Caroline R. Holmes, Thomas J. Bracegirdle, Paul R. Holland, Julienne Stroeve, and Jeremy Wilkinson
The Cryosphere, 18, 5641–5652, https://doi.org/10.5194/tc-18-5641-2024, https://doi.org/10.5194/tc-18-5641-2024, 2024
Short summary
Short summary
Until recently, satellite data showed an increase in Antarctic sea ice area since 1979, but climate models simulated a decrease over this period. This mismatch was one reason for low confidence in model projections of 21st-century sea ice loss. We show that following low Antarctic sea ice in 2022 and 2023, we can no longer conclude that modelled and observed trends differ. However, differences in the manner of the decline mean that model sea ice projections should still be viewed with caution.
Lu Zhou, Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Shiming Xu, Weixin Zhu, Sahra Kacimi, Stefanie Arndt, and Zifan Yang
The Cryosphere, 18, 4399–4434, https://doi.org/10.5194/tc-18-4399-2024, https://doi.org/10.5194/tc-18-4399-2024, 2024
Short summary
Short summary
Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy snowfall, has a complex stratigraphy and profound impact on the microwave signature. We employ advanced radiation transfer models to analyse the effects of complex snow properties on brightness temperatures over the sea ice in the Southern Ocean. Great potential lies in the understanding of snow processes and the application to satellite retrievals.
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.
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.
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.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
Short summary
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.
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.
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.
Nicole A. Loeb, Alex Crawford, Brice Noël, and Julienne Stroeve
The Cryosphere, 19, 5403–5422, https://doi.org/10.5194/tc-19-5403-2025, https://doi.org/10.5194/tc-19-5403-2025, 2025
Short summary
Short summary
We examine how extreme precipitation days affect the seasonal mass balance (SMB) of land ice in Greenland and the Eastern Canadian Arctic in historical and future simulations. Past extreme precipitation led to higher SMB with snowfall. Future extreme precipitation may lead to the loss of ice mass as more falls as rain rather than snow in some regions, such as southwestern Greenland. Across the region, extreme precipitation becomes more important to seasonal SMB in the future, warmer climate.
Lanqing Huang, Julienne Stroeve, Thomas Newman, Robbie Mallett, Rosemary Willatt, Lu Zhou, Malin Johansson, Carmen Nab, and Alicia Fallows
EGUsphere, https://doi.org/10.5194/egusphere-2025-5158, https://doi.org/10.5194/egusphere-2025-5158, 2025
Short summary
Short summary
Understanding snow depth on sea ice is key for measuring ice thickness, studying ecosystems, and modeling climate. Using snow and ice thickness measurements from Arctic and Antarctic campaigns, this study examines sub-kilometer-scale (<1 km²) snow depth variations and identifies the most suitable statistical models for different ice ages, thicknesses, and weather conditions. These results can improve sub-grid snow parameterizations in snow models and remote sensing algorithms.
Jonathon R. Preece, Patrick Alexander, Thomas L. Mote, Gabriel J. Kooperman, Xavier Fettweis, and Marco Tedesco
EGUsphere, https://doi.org/10.5194/egusphere-2025-4140, https://doi.org/10.5194/egusphere-2025-4140, 2025
Short summary
Short summary
Surface melt of the Greenland Ice Sheet has increased dramatically since the turn of the century, aided by an increase in persistent atmospheric circulation patterns that promote anomalously warm conditions. Through modeling experiments, this study shows that surface mass loss would have been reduced by 62% relative to historical conditions if this shift in atmospheric circulation would have occurred in a preindustrial climate, highlighting the important contribution of anthropogenic warming.
Vaishali Chaudhary, Julienne Stroeve, Vishnu Nandan, and Dustin Isleifson
EGUsphere, https://doi.org/10.5194/egusphere-2025-2851, https://doi.org/10.5194/egusphere-2025-2851, 2025
Preprint archived
Short summary
Short summary
This study examines how changing weather is affecting sea ice near the Arctic community of Tuktoyaktuk in Canada. Using satellite images and weather records, we found that stronger winds from certain directions are causing the sea ice to break more often in winter. These changes pose risks for local people who depend on stable ice for travel and hunting. Our findings help understand how climate change is making Arctic ice less reliable and more dangerous.
Franck Eitel Kemgang Ghomsi, Muharrem Hilmi Erkoç, Roshin P. Raj, Atinç Pirti, Antonio Bonaduce, Babatunde J. Abiodun, and Julienne Stroeve
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-6-2025, 393–397, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-393-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-6-2025-393-2025, 2025
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
The Cryosphere, 19, 325–346, https://doi.org/10.5194/tc-19-325-2025, https://doi.org/10.5194/tc-19-325-2025, 2025
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 the satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice), but estimating sea surface height from leadless landfast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in situ data after adjusting for tides. Realistic snow depths are retrieved, but differences in roughness, satellite footprints, and snow geophysical properties are identified.
Caroline R. Holmes, Thomas J. Bracegirdle, Paul R. Holland, Julienne Stroeve, and Jeremy Wilkinson
The Cryosphere, 18, 5641–5652, https://doi.org/10.5194/tc-18-5641-2024, https://doi.org/10.5194/tc-18-5641-2024, 2024
Short summary
Short summary
Until recently, satellite data showed an increase in Antarctic sea ice area since 1979, but climate models simulated a decrease over this period. This mismatch was one reason for low confidence in model projections of 21st-century sea ice loss. We show that following low Antarctic sea ice in 2022 and 2023, we can no longer conclude that modelled and observed trends differ. However, differences in the manner of the decline mean that model sea ice projections should still be viewed with caution.
Lu Zhou, Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Shiming Xu, Weixin Zhu, Sahra Kacimi, Stefanie Arndt, and Zifan Yang
The Cryosphere, 18, 4399–4434, https://doi.org/10.5194/tc-18-4399-2024, https://doi.org/10.5194/tc-18-4399-2024, 2024
Short summary
Short summary
Snow over Antarctic sea ice, influenced by highly variable meteorological conditions and heavy snowfall, has a complex stratigraphy and profound impact on the microwave signature. We employ advanced radiation transfer models to analyse the effects of complex snow properties on brightness temperatures over the sea ice in the Southern Ocean. Great potential lies in the understanding of snow processes and the application to satellite retrievals.
Joseph Fogarty, Elie Bou-Zeid, Mitchell Bushuk, and Linette Boisvert
The Cryosphere, 18, 4335–4354, https://doi.org/10.5194/tc-18-4335-2024, https://doi.org/10.5194/tc-18-4335-2024, 2024
Short summary
Short summary
We hypothesize that using a broad set of surface characterization metrics for polar sea ice surfaces will lead to more accurate representations in general circulation models. However, the first step is to identify the minimum set of metrics required. We show via numerical simulations that sea ice surface patterns can play a crucial role in determining boundary layer structures. We then statistically analyze a set of high-resolution sea ice surface images to obtain this minimal set of parameters.
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.
Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Åsa K. Rennermalm, Achim Heilig, Jakob Abermann, Dirk van As, Elisa Bjerre, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel R. van den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, https://doi.org/10.5194/tc-18-609-2024, 2024
Short summary
Short summary
How fast is the Greenland ice sheet warming? In this study, we compiled 4500+ temperature measurements at 10 m below the ice sheet surface (T10m) from 1912 to 2022. We trained a machine learning model on these data and reconstructed T10m for the ice sheet during 1950–2022. After a slight cooling during 1950–1985, the ice sheet warmed at a rate of 0.7 °C per decade until 2022. Climate models showed mixed results compared to our observations and underestimated the warming in key regions.
Wenwen Li, Chia-Yu Hsu, and Marco Tedesco
EGUsphere, https://doi.org/10.5194/egusphere-2023-2831, https://doi.org/10.5194/egusphere-2023-2831, 2024
Preprint withdrawn
Short summary
Short summary
This review paper fills a knowledge gap in comprehensive literature review at the junction of AI-Arctic sea ice research. We provide a fine-grained review of AI applications in a variety of sea ice research domains. Based on these analyses, we point out exciting opportunities where the Arctic sea ice community can continue benefiting from cutting-edge AI. These future research directions will foster the continuous growth of the Arctic sea ice–AI research community.
Marco Tedesco, Paolo Colosio, Xavier Fettweis, and Guido Cervone
The Cryosphere, 17, 5061–5074, https://doi.org/10.5194/tc-17-5061-2023, https://doi.org/10.5194/tc-17-5061-2023, 2023
Short summary
Short summary
We developed a technique to improve the outputs of a model that calculates the gain and loss of Greenland and consequently its contribution to sea level rise. Our technique generates “sharper” images of the maps generated by the model to better understand and quantify where losses occur. This has implications for improving models, understanding what drives the contributions of Greenland to sea level rise, and more.
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.
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.
Benjamin E. Smith, Brooke Medley, Xavier Fettweis, Tyler Sutterley, Patrick Alexander, David Porter, and Marco Tedesco
The Cryosphere, 17, 789–808, https://doi.org/10.5194/tc-17-789-2023, https://doi.org/10.5194/tc-17-789-2023, 2023
Short summary
Short summary
We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
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.
Isatis M. Cintron-Rodriguez, Åsa K. Rennermalm, Susan Kaspari, and Sasha Leidman
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-195, https://doi.org/10.5194/tc-2022-195, 2022
Revised manuscript not accepted
Short summary
Short summary
Snow and ice melt driven by solar absorption is enhanced by the presence of light-absorbing particles (LAPs), such as black carbon (BC) and dust. Previous studies have ruled out LAP as an important Greenland's albedo reduction and accelerated mass loss rate factor. However, most simulations only take into consideration LAP direct effects. This study shows that taking into account LAP impact on snow metamorphism leads to albedo reductions 4 to 10 times larger than previously thought.
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.
Raf M. Antwerpen, Marco Tedesco, Xavier Fettweis, Patrick Alexander, and Willem Jan van de Berg
The Cryosphere, 16, 4185–4199, https://doi.org/10.5194/tc-16-4185-2022, https://doi.org/10.5194/tc-16-4185-2022, 2022
Short summary
Short summary
The ice on Greenland has been melting more rapidly over the last few years. Most of this melt comes from the exposure of ice when the overlying snow melts. This ice is darker than snow and absorbs more sunlight, leading to more melt. It remains challenging to accurately simulate the brightness of the ice. We show that the color of ice simulated by Modèle Atmosphérique Régional (MAR) is too bright. We then show that this means that MAR may underestimate how fast the Greenland ice is melting.
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.
Rohi Muthyala, Åsa K. Rennermalm, Sasha Z. Leidman, Matthew G. Cooper, Sarah W. Cooley, Laurence C. Smith, and Dirk van As
The Cryosphere, 16, 2245–2263, https://doi.org/10.5194/tc-16-2245-2022, https://doi.org/10.5194/tc-16-2245-2022, 2022
Short summary
Short summary
In situ measurements of meltwater discharge through supraglacial stream networks are rare. The unprecedentedly long record of discharge captures diurnal and seasonal variability. Two major findings are (1) a change in the timing of peak discharge through the melt season that could impact meltwater delivery in the subglacial system and (2) though the primary driver of stream discharge is shortwave radiation, longwave radiation and turbulent heat fluxes play a major role during high-melt episodes.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
Short summary
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.
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.
Sean Horvath, Linette Boisvert, Chelsea Parker, Melinda Webster, Patrick Taylor, and Robyn Boeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-297, https://doi.org/10.5194/tc-2021-297, 2021
Preprint withdrawn
Short summary
Short summary
Arctic sea ice has been experiencing a dramatic decline since the late 1970s. A database is presented that combines satellite observations with daily sea ice parcel drift tracks. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. This has multiple applications for the scientific community that can shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.
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.
Paolo Colosio, Marco Tedesco, Roberto Ranzi, and Xavier Fettweis
The Cryosphere, 15, 2623–2646, https://doi.org/10.5194/tc-15-2623-2021, https://doi.org/10.5194/tc-15-2623-2021, 2021
Short summary
Short summary
We use a new satellite dataset to study the spatiotemporal evolution of surface melting over Greenland at an enhanced resolution of 3.125 km. Using meteorological data and the MAR model, we observe that a dynamic algorithm can best detect surface melting. We found that the melting season is elongating, the melt extent is increasing and that high-resolution data better describe the spatiotemporal evolution of the melting season, which is crucial to improve estimates of sea level rise.
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.
Colin J. Gleason, Kang Yang, Dongmei Feng, Laurence C. Smith, Kai Liu, Lincoln H. Pitcher, Vena W. Chu, Matthew G. Cooper, Brandon T. Overstreet, Asa K. Rennermalm, and Jonathan C. Ryan
The Cryosphere, 15, 2315–2331, https://doi.org/10.5194/tc-15-2315-2021, https://doi.org/10.5194/tc-15-2315-2021, 2021
Short summary
Short summary
We apply first-principle hydrology models designed for global river routing to route flows hourly through 10 000 individual supraglacial channels in Greenland. Our results uniquely show the role of process controls (network density, hillslope flow, channel friction) on routed meltwater. We also confirm earlier suggestions that large channels do not dewater overnight despite the shutdown of runoff and surface mass balance runoff being mistimed and overproducing runoff, as validated in situ.
Matthew G. Cooper, Laurence C. Smith, Asa K. Rennermalm, Marco Tedesco, Rohi Muthyala, Sasha Z. Leidman, Samiah E. Moustafa, and Jessica V. Fayne
The Cryosphere, 15, 1931–1953, https://doi.org/10.5194/tc-15-1931-2021, https://doi.org/10.5194/tc-15-1931-2021, 2021
Short summary
Short summary
We measured sunlight transmitted into glacier ice to improve models of glacier ice melt and satellite measurements of glacier ice surfaces. We found that very small concentrations of impurities inside the ice increase absorption of sunlight, but the amount was small enough to enable an estimate of ice absorptivity. We confirmed earlier results that the absorption minimum is near 390 nm. We also found that a layer of highly reflective granular "white ice" near the surface reduces transmittance.
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.
Andrea J. Pain, Jonathan B. Martin, Ellen E. Martin, Åsa K. Rennermalm, and Shaily Rahman
The Cryosphere, 15, 1627–1644, https://doi.org/10.5194/tc-15-1627-2021, https://doi.org/10.5194/tc-15-1627-2021, 2021
Short summary
Short summary
The greenhouse gases (GHGs) methane and carbon dioxide can be produced or consumed by geochemical processes under the Greenland Ice Sheet (GrIS). Chemical signatures and concentrations of GHGs in GrIS discharge show that organic matter remineralization produces GHGs in some locations, but mineral weathering dominates and consumes CO2 in other locations. Local processes will therefore determine whether melting of the GrIS is a positive or negative feedback on climate change driven by GHG forcing.
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.
Cited articles
Alexander, P. M., Tedesco, M., Fettweis, X., van de Wal, R. S. W., Smeets, C. J. P. P., and van den Broeke, M. R.: Assessing spatio-temporal variability and trends in modelled and measured Greenland Ice Sheet albedo (2000–2013), The Cryosphere, 8, 2293–2312, https://doi.org/10.5194/tc-8-2293-2014, 2014.
Ballinger, T. J., Hanna, E., Hall, R. J., and Hoyer, J. L.: Greenland coastal air temperatures linked to Baffin Bay and Greenland ice conditions during autumn through regional blocking patterns, Clim. Dyn., https://doi.org/10.1007/s00382-017-3583-3, 2017.
Bennartz, R., Shupe, M. D., Turner, D. D., Walden, V. P., Steffen, K., Cox, C. J., Kullie, M. S., Miller, N. B., and Pettersen, C.: July 2012 Greenland melt extent enhanced by low-level liquid clouds, Nature, 496, 83–86, https://doi.org/10.1038/nature12002, 2013.
Bezeau, P., Sharp, M., and Gascon, G.: Variability in summer anticyclonic circulation over the Canadian Arctic Archipelago and west Greenland in the late 20th/early 21st centuries and its effect on glacier mass balance, Int. J. Climatol., 35, 540–557, https://doi.org/10.1002/joc.4000, 2015.
Bhatt, U. S., Walker, D. A., Raynolds, M. K., Comiso, J. C., Epstein, H. E., Jia, G., Gens, R., Pinzon, J. E., Tucker, C. J., Tweedie, C. E., and Webber, P. J.: Circumpolar Arctic Tundra Vegetation Change Is Linked to Sea Ice Decline, Earth Interact., 14, 1–20, https://doi.org/10.1175/2010EI315.1, 2010.
Boisvert, L. N., Markus, T., Parkinson, C. L., and Vihma, T.: Moisture fluxes derived from EOS Aqua satellite data for the North Water polynya over 2003–2009, J. Geophys. Res., 117, D06119, https://doi.org/10.1029/2011JD016949, 2012.
Boisvert, L. N. and Stroeve, J. C.: The Arctic is becoming warmer and wetter as revealed by the Atmospheric Infrared Sounder, Geophys. Res. Lett., 42, 4439–4446, https://doi.org/10.1002/2015GL063775, 2015.
Boisvert, L. N., Wu, D. L., and Shie, C.-L.: Increasing evaporation amounts seen in the Arctic between 2003–2013 from AIRS data, J. Geophys. Res.-Atmos., 120, 6865–6881, https://doi.org/10.1002/2015JD023258, 2015.
Boisvert, L. N., Petty, A. A., and Stroeve, J. C.: The Impact of the Extreme Winter 2015/16 Arctic Cyclone on the Barents-Kara Seas, Mon. Weather Rev., 144, 4279–4287, https://doi.org/10.1175/MWR-D-16-0234.1, 2016.
Bosilovich, M. G., Robertson, F. R., and Chen, J.: Global Energy and Water Budgets in MERRA, J. Climate, 24, 5721–5739, https://doi.org/10.1175/2011JCLI4175.1, 2011.
Bretherton, C. S., Smith, C., and Wallace, J. M.: An intercomparison of methods for finding coupled patterns in climate data, J. Climate, 5, 451–560, 1992.
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13–22, 1992.
Cassano, E. N., Cassano, J. J., Higgins, M. E., and Serreze, M. C.: Atmospheric impacts of an Arctic sea ice minimum as seen in the Community Atmosphere Model, Int. J. Climatol., 34, 766–779, https://doi.org/10.1002/joc.3723, 2014.
Cavalieri, D., Parkinson, C., Gloersen, P., and Zwally, H. J.: Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data, (1979–2013), Boulder, Colorado USA, National Snow and Ice Data Center, Digital media, 1996.
Cohen, J., Cohen, P., West, S. G., and Aiken, L. S.: Applied multiple regression/correlation analysis for the behavioral sciences (3 Edn.), Routledge, Mahwah, 2003.
Comiso, J. C.: Correlation and trend studies of the sea-ice cover and surface temperatures in the Arctic, Ann. Glaciol., 34, 420–428, 2002.
Cullather, R. I. and Bosilovich, M. G.: The Energy Budget of the Polar Atmosphere in MERRA, J. Climate, 25, 5–24, https://doi.org/10.1175/2011JCLI4138.1, 2011a.
Cullather, R. I. and Bosilovich, M. G.: The Moisture Budget of the Polar Atmosphere in MERRA, J. Climate, 24, 2861–2879, https://doi.org/10.1175/2010JCLI4090.1, 2011b.
Dee, D. P. and Uppala, S.: Variational bias correlation of satellite radiance data in the ERA-Interim reaqnalysis, Q. J. R. Meteorol., Soc., 135, 1830–1841, https://doi.org/10.1002/qj.493, 2009.
De Ridder, K. and Gallée, H.: Land surface-induced regional climate change in southern Israel, J. Appl. Meteorol., 37, 1470–1485, 1998.
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, 2000.
Doyle, S. H., Hubbard, A., van de Wal, R. S. W., Box, J. E., van As, D., Scharrer, K., Meierbachtol, T. W., Smeets, P. C. J. P., Harper, J. T., Johansson, E., Mottram, R. H., Mikkelsen, A. B., Wilhelms, F., Patton, H., Christoffersen, P., and Hubbard, B.: Amplified melt and flow of the Greenland ice sheet driven by late-summer cyclonic rainfall, Nat. Geosci., 8, 647–653, https://doi.org/10.1038/ngeo2482, 2015.
Enderlin, E. M., Howat, I. M., Jeong, S., Noh, M.-J., van Angelen, J. H., and van den Broeke, M. R.: An improved mass budget for the Greenland ice sheet, Geophys. Res. Lett., 41, 866–872, https://doi.org/10.1002/2013GL059010, 2014.
Fang, Z.-F: Statistical relationship between the northern hemisphere sea ice and atmospheric circulation during wintertime, in: Observation, Theory and Modeling of Atmospheric Variability, World Scientific Series on Meteorology of East Asia, edited by: Zhu, X, World Scientific, 2004.
Fausto, R. S., van As, D., Box, J. E., Colgan, W., Langen, P. L., and Mottram, R. H.: The implication of nonradiative energy fluxes dominating Greenland ice sheet exceptional ablation area surface melt in 2012, Geophys. Res. Lett., 43, 2649–2658, https://doi.org/10.1002/2016GL067720, 2016.
Fettweis, X., Gallée, H., Lefebre, F., and van Ypersele, J.-P.: Greenland surface mass balance simulated by a regional climate model and comparison with satellite-derived data in 1990–1991, Clim. Dyn., 24, 623–640. https://doi.org/10.1007/s00382-005-0010-y, 2005.
Fettweis, X., Tedesco, M., van den Broeke, M., and Ettema, J.: Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models, The Cryosphere, 5, 359–375, https://doi.org/10.5194/tc-5-359-2011, 2011.
Francis, J. A. and Vavrus, S. J.: Evidence linking Arctic amplification to extreme weather in mid-latitudes, Geophys. Res. Lett., 39, L06801, https://doi.org/10.1029/2012GL051000, 2012.
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.
Fyke, J. G., Vizcaino, M., Lipscomb, W., and Price, S.: Future climate warming increases Greenland ice sheet surface mass balance variability, Geophys. Res. Lett., 41, 470–475, https://doi.org/10.1002/2013GL058172, 2014a.
Fyke, J. G., Vizcaino, M., and Lipscomb, W. H.: The pattern of anthropogenic signal emergence in Greenland Ice Sheet surface mass balance, Geophys. Res. Lett., 41, 6002–6008, https://doi.org/10.1002/2014GL060735, 2014b.
Gallée, H. and Schayes, G.: Development of a three-dimensional meso-γ primitive equation model – katabatic winds simulation in the area of Terra-Nova Bay, Antarctica, Mon. Weather Rev., 122, 671–685, 1994.
Gallée, H., Peyaud V., and Goodwin, I.: Simulation of the net snow accumulation along the Wilkes Land transect, Antarctica, with a regional climate model, Ann. Glaciol., 41, 17–22, 2005.
Ghatak, D., Frei, A., Gong, G., Stroeve, J., and Robinson, D.: On the emergence of an Arctic amplification signal in terrestrial Arctic snow extent, J. Geophys. Res., 115, D24105, https://doi.org/10.1029/2010JD014007, 2010.
Hanna, E., Fettweis, X., Mernild, S. H., Cappelen, J., Ribergaard, M. H., Shuman, C. A., Steffen, K., Wood, L., and Mote, T. L.: Atmospheric and oceanic climate forcing of the exceptional Greenland ice sheet surface melt in summer 2012, Int. J. Climatol., 34, 1022–1037, https://doi.org/10.1002/joc.3743, 2013.
Hanna, E., Huybrechts, P., Steffen, K., Cappelen, J., Huff, R., Shuman, C., Irvine-Fynn, T., Wise, S., and Griffiths, M.: Increased runoff from melt from the Greenland ice sheet: A response to global warming, J. Climate, 21, 331–341, https://doi.org/10.1175/2007JCLI1964.1, 2008.
Hanna, E., Jonsson, T., and Box, J. E.: An analysis of Icelandic climate since the nineteenth Century, Int. J. Climatol, 24, 1193–1210, doi.10.1002/joc.1051, 2004.
Kay, J. E., Raeder, K., Gettelman, A., and Anderson, J.: The Boundary Layer Response to Recent Arctic Sea Ice Loss and Implications for High-Latitude Climate Feedbacks, J. Climate, 24, 428–447, https://doi.org/10.1175/2010JCLI3651.1, 2011.
Lefebre, F., Gallée, H., van Ypersele, J. P., and Greuell, W.: Modeling of snow and ice melt at ETH-Camp (West Greenland): A study of surface albedo, J. Geophys. Res., 108, 4231, https://doi.org/10.1029/2001JD001160, 2003.
Lefebre, F., Fettweis, X., Galée, H., van Ypersele, J.-P., Marbaix, P., Greuell, W., and Calanca, P.: Evaluation of a high-resolution regional climate simulation over Greenland, Clim. Dynam, 25, 99–116. https://doi.org/10.1007/s00382-005-0005-8, 2005.
Lindsay, R., Wensnahan, M., Schweiger, A., and Zhang, J.: Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic, J. Climate, 27, 2588–2606, https://doi.org/10.1175/JCLI-D-13-00014.1, 2014.
Liu, J., Chen, Z.,l Francis, J., Song, M., Mote, T., and Hu, Y.: Has Arctic Sea Ice Loss Contributed to Increased Surface Melting of the Greenland Ice Sheet?, J. Climate, 29, 3373–3386, https://doi.org/10.1175/JCLI-D-15-0391.1, 2016.
Markus, T., Stroeve, J. C., and Miller, J.: Recent changes in Arctic sea ice melt onset, freezeup and melt season length, J. Geophys. Res., 114, C12024, https://doi.org/10.1029/2009JC005436, 2009.
Mattingly, K. S., Ramseyer, C. A., Rosen, J. J., Mote, T. L., and Muthyala, R.: Increasing water vapor transport to the Greenland Ice Sheet revealed using self-organizing maps, Geophys. Res. Lett., 43, 9250–9258, https://doi.org/10.1002/2016GL070424, 2016.
Mioduszewski, J. R., Rennermalm, A. K., Hammann, A., Tedesco, M., Noble, E. U., Stroeve, J. C., and Mote, T. L.: Atmospheric drivers of Greenland surface melt revealed by self-organizing maps, J. Geophys. Res.-Atmos., 121, 5095–5114, https://doi.org/10.1002/2015JD024550, 2016.
Mortin, J., Svensson, G., Graverson, R., Kapsch, M.-L., Stroeve, J. C., and Boisvert, L. N.: Melt onset over Arctic sea ice controlled by atmospheric moisture transport, Geophys. Res. Lett., 43, 6636–6642, https://doi.org/10.1002/2016GL069330, 2016.
Mote, T. L.: MEaSUREs Greenland Surface Melt Daily 25 km EASE-Grid 2.0, (indicate subset used), Boulder, Colorado USA: NASA DAAC at the National Snow and Ice Data Center, https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0533.001, 2014.
Mote, T. L.: Greenland surface melt trends 1973–2007: Evidence of a large increase in 2007, Geophys. Res. Lett., 34, L22507, https://doi.org/10.1029/2007GL031976, 2007.
Neff, W., Compo, G., Ralph, F. M., and Shupe, M. D.: Continental heat anomalies and the extreme melting of the Greenland ice surface in 2012 and 1889, J. Geophys. Res.-Atmos., 119, 6520–6536, https://doi.org/10.1002/2014JD021470, 2014.
Noël, B., Fettweis, X., van de Berg, W. J., van den Broeke, M. R., and Erpicum, M.: Sensitivity of Greenland Ice Sheet surface mass balance to perturbations in sea surface temperature and sea ice cover: a study with the regional climate model MAR, Cryosph., 8, 1871–1883, https://doi.org/10.5194/tc-8-1871-2014, 2014.
Notz, D. and Stroeve, J.: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission, Science, https://doi.org/10.1126/science.aag2345, 2016.
Ornaheim, I. H., Eldevik, T., Arthun, M., Ingvaldsen, R. B., and Smedsrud, L. H.: Skillful prediction of Barents Sea ice cover, Geophys. Res. Lett., 42, 5364–5371, https://doi.org/10.1002/2015GL064359, 2016.
Overland, J. E. and Wang, M.: Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice, Tellus A, 62, 1–9, https://doi.org/10.1111/j.1600-0870.2009.00421.x, 2010.
Parkinson, C.: Spatially mapped reductions in the length of the Arctic sea ice season, Geophys. Res. Lett., 41, 4316–4322, https://doi.org/10.1002/2014GL060434, 2014.
Polyakov, I. V., Walsh, J. E., and Kwok, R.: Recent Changes of Arctic Multiyear Sea Ice Coverage and the Likely Causes, B. Am. Meteorol. Soc., 93, 145–151, https://doi.org/10.1175/BAMS-D-11-00070.1, 2012.
Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q., Mahanama, S. P. P., and Touré, A.: Assessment and Enhancement of MERRA Land Surface Hydrology Estimates, J. Climate, 24, 6322–6338, https://doi.org/10.1175/JCLI-D-10-05033.1, 2011.
Rennermalm, A. K., Smith, L. C., Stroeve, J. C., and Chu, V. W.: Does sea ice influenced Greenland ice sheet surface melt?, Environ. Res. Lett., https://doi.org/10.1088/1748-9326/4/2/024011, 2009.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA – NASA's Modern-Era Retrospective Analysis for Research and Applications, J. Climate, 24, 3624–3648, https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Rinke, A., Maslowski, W., Dethloff, K., and Clement, J.: Influence of sea ice on the atmosphere: A study with an Arctic atmospheric regional climate model, J. Geophys. Res., 111, D16103, https://doi.org/10.1029/2005JD006957, 2006.
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in recent Arctic temperature amplification, Nature, 464, 1334–1337, https://doi.org/10.1038/nature09051, 2010.
Serreze, M. C., Holland, M. M., and Stroeve, J.: Perspectives on the Arctic's Shrinking Sea Ice Cover, Science, 16, 1533–1536, 2007.
Serreze, M. C., Barrett, A. P., Stroeve, J. C., Kindig, D. N., and Holland, M. M.: The emergence of surface-based Arctic amplification, The Cryosphere, 3, 11–19, https://doi.org/10.5194/tc-3-11-2009, 2009.
Serreze, M. C., Barrett, A. P., and Cassano, J. J.: Circulation and surface controls on the lower tropospheric air temperature field of the Arctic, J. Geophys. Res., 116, D07104, https://doi.org/10.1029/ 2010JD015127, 2011.
Serreze, M. C., Stroeve, J., Barrett, A. P., and Boisvert, L. N.: Summer atmospheric circulation anomalies over the Arctic Ocean and their influences on September sea ice extent: A cautionary tale, J. Geophys. Res.-Atmos., 121, 11463–11485, https://doi.org/10.1002/2016JD025161, 2016.
Stroeve, J. C., Serreze, M. C., Barrett, A., and Kindig, D. N.: Attribution of recent changes in autumn cyclone associated precipitation in the Arctic, Tellus A, 63, 653–663, https://doi.org/10.1111/j.1600-0870.2011.00515.x, 2011.
Stroeve, J. C., Serreze, M. C., Kay, J. E., Holland, M. M., Meier, W. N., and Barrett, A. P.: The Arctic's rapidly shrinking sea ice cover: A research synthesis, Clim. Change, 110, 1005–1027, https://doi.org/10.1007/s10584-011-0101-1, 2012.
Stroeve, J. C., Crawford, A. D., and Stammerjohn, S.: Using timing of ice retreat to predit timing of fall freeze-up in the Arctic, Geophys. Res. Lett., 43, 6332–6340, https://doi.org/10.1002/2016GL069314, 2016.
Stroeve, J. C., Markus, T., Boisvert, L., Miller, J., and Barrett, A.: Changes in Arctic Melt Season and Implications for Sea Ice Loss, Geophys. Res. Lett., 41, 1216–1225, https://doi.org/10.1002/2013GL058951, 2014.
Stroeve, J., Frei, A., McCreight, J., and Ghatak, D.: Arctic sea-ice variability revisited, Ann. Glaciol., 48, 71–81, https://doi.org/10.3189/172756408784700699, 2008.
Tedesco, M.: Snowmelt detection over the Greenland ice sheet from SSM/I brightness temperature daily variations, Geophys. Res. Lett., 34, L02504, https://doi.org/10.1029/2006GL028466, 2007.
Tedesco, M., Serreze, M., and Fettweis, X.: Diagnosing the extreme surface melt event over southwestern Greenland in 2007, The Cryosphere, 2, 159–166, https://doi.org/10.5194/tc-2-159-2008, 2008.
Tedesco, M., Fettweis, X., van den Broeke, M. R., van de Wal, R. S. W., Smeets, C. J. P. P., van de Berg, W. J., Serreze, M. C., and Box, J. E.: The role of albedo and accumulation in the 2010 melting record in Greenland, Environ. Res. Lett., 6, 014005, https://doi.org/10.1088/1748-9326/6/1/014005, 2011.
Tedesco, M. and Fettweis, X.: 21st century projections of surface mass balance changes for major drainage systems of the Greenland ice sheet, Environ. Res. Lett., 7, 045405, https://doi.org/10.1088/1748-9326/7/4/045405, 2012.
Tedesco, M., Fettweis, X., Mote, T., Wahr, J., Alexander, P., Box, J. E., and Wouters, B.: Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data, The Cryosphere, 7, 615–630, https://doi.org/10.5194/tc-7-615-2013, 2013.
Tedesco, M., Fettweis, X., and Alexander, P. M.: MAR v3.2 regional climate model data for Greenland (1958–2013), UCAR/NCAR – CISL – ACADIS, https://doi.org/10.5065/D6JH3J7Z, 2015.
van den Broeke, M., Bamber, J., Ettena, J., Rignot, E., Schrama, E., van de Berg, W.-J., van Meijgaard, E., Velicogna, I., and Wouters, B.: Partitioning recent Greenland mass loss, Science, 326, 984–986, https://doi.org/10.1126/science.1178176, 2009.
van den Broeke, M. and Gallée, H.: Observation and simulation of barrier winds at the western margin of the Greenland ice sheet, Q. J. R. Meteorol. Soc., 122, 1365–1383, https://doi.org/10.1002/qj.49712253407, 1996.
van Tricht, K., Lhermitte, S., Lenaerts, J. T. M., Gorodetskaya, I. V., L'Ecuyer, T. S., Noël, B., van den Broeke, M. R., Turner, D. D., and van Lipzig, N. P. M.: Clouds enhance Greenland ice sheet meltwater runoff, Nat. Commun., 7, 10266, https://doi.org/10.1038/ncomms10266, 2016.
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.
Yulaeva, E., Schneider, N., Pierce, D. W., and Barnet, T. M.: Modeling of North Pacific Climate Variability Forced by Oceanic Heat Flux Anomalies, J. Climate, 14, 4027–4046, https://doi.org/10.1175/1520-0442(2001)014<4027:MONPCV>2.0.CO;2, 2001.
Zib, B. J., Dong, X., Xi, B., and Kennedy, A.: Evaluation and Intercomparison of Cloud Fraction and Radiative Fluxes in Recent Reanalyses over the Arctic Using BSRN Surface Observations, J. Climate, 25, 2291–2305, https://doi.org/10.1175/JCLI-D-11-00147.1, 2012.
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.
As the sea ice has declined strongly in recent years there has been a corresponding increase in...
Special issue