Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-3933-2023
© Author(s) 2023. 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-17-3933-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
13 Sep 2023
Research article | Highlight paper |
| 13 Sep 2023
| 13 Sep 2023
Atmospheric drivers of melt-related ice speed-up events on the Russell Glacier in southwest Greenland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver BC, Canada
Valentina Radić
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver BC, Canada
Andrew Tedstone
Department of Geosciences, University of Fribourg, Fribourg, Switzerland
James M. Lea
Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom
Stephen Brough
Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom
Mauro Hermann
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Related authors
Raphael Portmann, Timo Schmid, Leonie Villiger, David N. Bresch, and Pierluigi Calanca
Nat. Hazards Earth Syst. Sci., 24, 2541–2558, https://doi.org/10.5194/nhess-24-2541-2024, https://doi.org/10.5194/nhess-24-2541-2024, 2024
Short summary
Short summary
The study presents an open-source model to determine the occurrence of hail damage to field crops and grapevines after hailstorms in Switzerland based on radar, agricultural land use data, and insurance damage reports. The model performs best at 8 km resolution for field crops and 1 km for grapevine and in the main production areas. Highlighting performance trade-offs and the relevance of user needs, the study is a first step towards the assessment of risk and damage for crops in Switzerland.
Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 847–872, https://doi.org/10.5194/nhess-24-847-2024, https://doi.org/10.5194/nhess-24-847-2024, 2024
Short summary
Short summary
Hailstorms cause severe damage to buildings and cars, which motivates a detailed risk assessment. Here, we present a new open-source hail damage model based on radar data in Switzerland. The model successfully estimates the correct order of magnitude of car and building damages for most large hail events over 20 years. However, large uncertainty remains in the geographical distribution of modelled damages, which can be improved for individual events by using crowdsourced hail reports.
Horst Machguth, Andrew Tedstone, Peter Kuipers Munneke, Max Brils, Brice Noël, Nicole Clerx, Nicolas Jullien, Xavier Fettweis, and Michiel van den Broeke
EGUsphere, https://doi.org/10.5194/egusphere-2024-2750, https://doi.org/10.5194/egusphere-2024-2750, 2024
Short summary
Short summary
Due to increasing air temperatures, surface melt expands to higher elevations on the Greenland ice sheet. This is visible on satellite imagery in the form of rivers of meltwater running across the surface of the ice sheet. We compare model results of meltwater at high elevations on the ice sheet to satellite observations. We find that each of the models shows strengths and weaknesses. A detailed look into the model results reveals potential reasons for the differences between models.
Ian Delaney, Andrew Tedstone, Mauro A. Werder, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2024-2580, https://doi.org/10.5194/egusphere-2024-2580, 2024
Short summary
Short summary
Sediment transport in rivers and under glaciers depends on water velocity and channel width. In rivers, water discharge changes affect flow depth, width, and velocity. Under glaciers, pressurized water changes velocity more than shape. Due to these differences, this study shows that sediment transport under glaciers varies widely and peaks before water flow does, creating a complex relationship. Understanding these dynamics helps interpret sediment discharge from glaciers in different climates.
Raphael Portmann, Timo Schmid, Leonie Villiger, David N. Bresch, and Pierluigi Calanca
Nat. Hazards Earth Syst. Sci., 24, 2541–2558, https://doi.org/10.5194/nhess-24-2541-2024, https://doi.org/10.5194/nhess-24-2541-2024, 2024
Short summary
Short summary
The study presents an open-source model to determine the occurrence of hail damage to field crops and grapevines after hailstorms in Switzerland based on radar, agricultural land use data, and insurance damage reports. The model performs best at 8 km resolution for field crops and 1 km for grapevine and in the main production areas. Highlighting performance trade-offs and the relevance of user needs, the study is a first step towards the assessment of risk and damage for crops in Switzerland.
Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 847–872, https://doi.org/10.5194/nhess-24-847-2024, https://doi.org/10.5194/nhess-24-847-2024, 2024
Short summary
Short summary
Hailstorms cause severe damage to buildings and cars, which motivates a detailed risk assessment. Here, we present a new open-source hail damage model based on radar data in Switzerland. The model successfully estimates the correct order of magnitude of car and building damages for most large hail events over 20 years. However, large uncertainty remains in the geographical distribution of modelled damages, which can be improved for individual events by using crowdsourced hail reports.
An Li, Michelle Koutnik, Stephen Brough, Matteo Spagnolo, and Iestyn Barr
EGUsphere, https://doi.org/10.5194/egusphere-2023-2568, https://doi.org/10.5194/egusphere-2023-2568, 2024
Short summary
Short summary
On Earth, glacial cirques are a type of landform eroded by wet-based glaciers, which are glaciers with liquid water at the base of a glacier. While select alcoves have been interpreted as glacial cirques on Mars, we map and assess a large-scale population of ~2000 alcoves as potential cirques in the northern mid-latitudes of Mars. From physical measurements and characteristics, we find 386 cirque-like alcoves. This extends our knowledge of the extent and type of glaciation in the region.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
Short summary
Short summary
We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Mauro Hermann, Matthias Röthlisberger, Arthur Gessler, Andreas Rigling, Cornelius Senf, Thomas Wohlgemuth, and Heini Wernli
Biogeosciences, 20, 1155–1180, https://doi.org/10.5194/bg-20-1155-2023, https://doi.org/10.5194/bg-20-1155-2023, 2023
Short summary
Short summary
This study examines the multi-annual meteorological history of low-forest-greenness events in Europe's temperate and Mediterranean biome in 2002–2022. We systematically identify anomalies in temperature, precipitation, and weather systems as event precursors, with noteworthy differences between the two biomes. We also quantify the impact of the most extensive event in 2022 (37 % coverage), underlining the importance of understanding the forest–meteorology interaction in a changing climate.
Connor J. Shiggins, James M. Lea, and Stephen Brough
The Cryosphere, 17, 15–32, https://doi.org/10.5194/tc-17-15-2023, https://doi.org/10.5194/tc-17-15-2023, 2023
Short summary
Short summary
Iceberg detection is spatially and temporally limited around the Greenland Ice Sheet. This study presents a new, accessible workflow to automatically detect icebergs from timestamped ArcticDEM strip data. The workflow successfully produces comparable output to manual digitisation, with results revealing new iceberg area-to-volume conversion equations that can be widely applied to datasets where only iceberg outlines can be extracted (e.g. optical and SAR imagery).
Nicole Clerx, Horst Machguth, Andrew Tedstone, Nicolas Jullien, Nander Wever, Rolf Weingartner, and Ole Roessler
The Cryosphere, 16, 4379–4401, https://doi.org/10.5194/tc-16-4379-2022, https://doi.org/10.5194/tc-16-4379-2022, 2022
Short summary
Short summary
Meltwater runoff is one of the main contributors to mass loss on the Greenland Ice Sheet that influences global sea level rise. However, it remains unclear where meltwater runs off and what processes cause this. We measured the velocity of meltwater flow through snow on the ice sheet, which ranged from 0.17–12.8 m h−1 for vertical percolation and from 1.3–15.1 m h−1 for lateral flow. This is an important step towards understanding where, when and why meltwater runoff occurs on the ice sheet.
Sophie Goliber, Taryn Black, Ginny Catania, James M. Lea, Helene Olsen, Daniel Cheng, Suzanne Bevan, Anders Bjørk, Charlie Bunce, Stephen Brough, J. Rachel Carr, Tom Cowton, Alex Gardner, Dominik Fahrner, Emily Hill, Ian Joughin, Niels J. Korsgaard, Adrian Luckman, Twila Moon, Tavi Murray, Andrew Sole, Michael Wood, and Enze Zhang
The Cryosphere, 16, 3215–3233, https://doi.org/10.5194/tc-16-3215-2022, https://doi.org/10.5194/tc-16-3215-2022, 2022
Short summary
Short summary
Terminus traces have been used to understand how Greenland's glaciers have changed over time; however, manual digitization is time-intensive, and a lack of coordination leads to duplication of efforts. We have compiled a dataset of over 39 000 terminus traces for 278 glaciers for scientific and machine learning applications. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for the Greenland Ice Sheet.
David W. Ashmore, Douglas W. F. Mair, Jonathan E. Higham, Stephen Brough, James M. Lea, and Isabel J. Nias
The Cryosphere, 16, 219–236, https://doi.org/10.5194/tc-16-219-2022, https://doi.org/10.5194/tc-16-219-2022, 2022
Short summary
Short summary
In this paper we explore the use of a transferrable and flexible statistical technique to try and untangle the multiple influences on marine-terminating glacier dynamics, as measured from space. We decompose a satellite-derived ice velocity record into ranked sets of static maps and temporal coefficients. We present evidence that the approach can identify velocity variability mainly driven by changes in terminus position and velocity variation mainly driven by subglacial hydrological processes.
Rachel K. Smedley, David Small, Richard S. Jones, Stephen Brough, Jennifer Bradley, and Geraint T. H. Jenkins
Geochronology, 3, 525–543, https://doi.org/10.5194/gchron-3-525-2021, https://doi.org/10.5194/gchron-3-525-2021, 2021
Short summary
Short summary
We apply new rock luminescence techniques to a well-constrained scenario of the Beinn Alligin rock avalanche, NW Scotland. We measure accurate erosion rates consistent with independently derived rates and reveal a transient state of erosion over the last ~4000 years in the wet, temperate climate of NW Scotland. This study shows that the new luminescence erosion-meter has huge potential for inferring erosion rates on sub-millennial scales, which is currently impossible with existing techniques.
William D. Smith, Stuart A. Dunning, Stephen Brough, Neil Ross, and Jon Telling
Earth Surf. Dynam., 8, 1053–1065, https://doi.org/10.5194/esurf-8-1053-2020, https://doi.org/10.5194/esurf-8-1053-2020, 2020
Short summary
Short summary
Glacial landslides are difficult to detect and likely underestimated due to rapid covering or dispersal. Without improved detection rates we cannot constrain their impact on glacial dynamics or their potential climatically driven increases in occurrence. Here we present a new open-access tool (GERALDINE) that helps a user detect 92 % of these events over the past 38 years on a global scale. We demonstrate its ability by identifying two new, large glacial landslides in the Hayes Range, Alaska.
Mauro Hermann, Lukas Papritz, and Heini Wernli
Weather Clim. Dynam., 1, 497–518, https://doi.org/10.5194/wcd-1-497-2020, https://doi.org/10.5194/wcd-1-497-2020, 2020
Short summary
Short summary
We find, by tracing backward in time, that air masses causing extensive melt of the Greenland Ice Sheet originate from further south and lower altitudes than usual. Their exceptional warmth further arises due to ascent and cloud formation, which is special compared to near-surface heat waves in the midlatitudes or the central Arctic. The atmospheric systems and transport pathways identified here are crucial in understanding and simulating the atmospheric control of the ice sheet in the future.
Andrew J. Tedstone, Joseph M. Cook, Christopher J. Williamson, Stefan Hofer, Jenine McCutcheon, Tristram Irvine-Fynn, Thomas Gribbin, and Martyn Tranter
The Cryosphere, 14, 521–538, https://doi.org/10.5194/tc-14-521-2020, https://doi.org/10.5194/tc-14-521-2020, 2020
Short summary
Short summary
Albedo describes how much light that hits a surface is reflected without being absorbed. Low-albedo ice surfaces melt more quickly. There are large differences in the albedo of bare-ice areas of the Greenland Ice Sheet. They are caused both by dark glacier algae and by the condition of the underlying ice. Changes occur over centimetres to metres, so satellites do not always detect real albedo changes. Estimates of melt made using satellite measurements therefore tend to be underestimates.
Joseph M. Cook, Andrew J. Tedstone, Christopher Williamson, Jenine McCutcheon, Andrew J. Hodson, Archana Dayal, McKenzie Skiles, Stefan Hofer, Robert Bryant, Owen McAree, Andrew McGonigle, Jonathan Ryan, Alexandre M. Anesio, Tristram D. L. Irvine-Fynn, Alun Hubbard, Edward Hanna, Mark Flanner, Sathish Mayanna, Liane G. Benning, Dirk van As, Marian Yallop, James B. McQuaid, Thomas Gribbin, and Martyn Tranter
The Cryosphere, 14, 309–330, https://doi.org/10.5194/tc-14-309-2020, https://doi.org/10.5194/tc-14-309-2020, 2020
Short summary
Short summary
Melting of the Greenland Ice Sheet (GrIS) is a major source of uncertainty for sea level rise projections. Ice-darkening due to the growth of algae has been recognized as a potential accelerator of melting. This paper measures and models the algae-driven ice melting and maps the algae over the ice sheet for the first time. We estimate that as much as 13 % total runoff from the south-western GrIS can be attributed to these algae, showing that they must be included in future mass balance models.
Alexandra T. Holland, Christopher J. Williamson, Fotis Sgouridis, Andrew J. Tedstone, Jenine McCutcheon, Joseph M. Cook, Ewa Poniecka, Marian L. Yallop, Martyn Tranter, Alexandre M. Anesio, and The Black & Bloom Group
Biogeosciences, 16, 3283–3296, https://doi.org/10.5194/bg-16-3283-2019, https://doi.org/10.5194/bg-16-3283-2019, 2019
Short summary
Short summary
This paper provides a preliminary data set for dissolved nutrient abundance in the Dark Zone of the Greenland Ice Sheet. This 15-year marked darkening has since been attributed to glacier algae blooms, yet has not been accounted for in current melt rate models. We conclude that the dissolved organic phase dominates surface ice environments and that factors other than macronutrient limitation control the extent and magnitude of the glacier algae blooms.
Joseph M. Cook, Andrew J. Hodson, Alex S. Gardner, Mark Flanner, Andrew J. Tedstone, Christopher Williamson, Tristram D. L. Irvine-Fynn, Johan Nilsson, Robert Bryant, and Martyn Tranter
The Cryosphere, 11, 2611–2632, https://doi.org/10.5194/tc-11-2611-2017, https://doi.org/10.5194/tc-11-2611-2017, 2017
Short summary
Short summary
Biological growth darkens snow and ice, causing it to melt faster. This is often referred to as
bioalbedo. Quantifying bioalbedo has not been achieved because of difficulties in isolating the biological contribution from the optical properties of ice and snow, and from inorganic impurities in field studies. In this paper, we provide a physical model that enables bioalbedo to be quantified from first principles and we use it to guide future field studies.
Andrew J. Tedstone, Jonathan L. Bamber, Joseph M. Cook, Christopher J. Williamson, Xavier Fettweis, Andrew J. Hodson, and Martyn Tranter
The Cryosphere, 11, 2491–2506, https://doi.org/10.5194/tc-11-2491-2017, https://doi.org/10.5194/tc-11-2491-2017, 2017
Short summary
Short summary
The bare ice albedo of the south-west Greenland ice sheet varies dramatically between years. The reasons are unclear but likely involve darkening by inorganic particulates, cryoconite and ice algae. We use satellite imagery to examine dark ice dynamics and climate model outputs to find likely climatological controls. Outcropping particulates can explain the spatial extent of dark ice, but the darkening itself is likely due to ice algae growth controlled by meltwater and light availability.
Evan J. Gowan, Paul Tregoning, Anthony Purcell, James Lea, Oscar J. Fransner, Riko Noormets, and J. A. Dowdeswell
Geosci. Model Dev., 9, 1673–1682, https://doi.org/10.5194/gmd-9-1673-2016, https://doi.org/10.5194/gmd-9-1673-2016, 2016
Short summary
Short summary
We present a program that can create paleo-ice sheet reconstructions, using an assumed basal shear stress, margin location and basal topography as input. This allows for the quick determination of relatively realistic past ice sheet configurations without reliance on highly uncertain factors such as climate and ice dynamics. This is ideal for modelling Earth deformation due to the loading of ice sheets. The subsequent ice sheet configurations can be used as an input for climate modelling.
B. Hubbard, C. Souness, and S. Brough
The Cryosphere, 8, 2047–2061, https://doi.org/10.5194/tc-8-2047-2014, https://doi.org/10.5194/tc-8-2047-2014, 2014
Short summary
Short summary
We address the dynamic glaciology of glacier-like forms (GLFs) on Mars, over 1300 of which are located in the planet's midlatitude regions. We present case studies to gain insight into (i) the former extent of GLFs, (ii) GLF motion and surface crevassing, (iii) GLF debris transfer (suggesting a best-estimate surface velocity of 7.5 mm/a over the past 2 Ma), and (iv) putative GLF surface hydrology. Finally, we present several possible research directions for the future study of Martian GLFs.
Related subject area
Discipline: Ice sheets | Subject: Atmospheric Interactions
Extending the Center for Western Weather and Water Extremes (CW3E) atmospheric river scale to the polar regions
Understanding the drivers of near-surface winds in Adélie Land, East Antarctica
Control of the temperature signal in Antarctic proxies by snowfall dynamics
Amundsen Sea Embayment accumulation variability measured with GNSS-IR
Climatology and surface impacts of atmospheric rivers on West Antarctica
Continuous monitoring of surface water vapour isotopic compositions at Neumayer Station III, East Antarctica
Mapping the aerodynamic roughness of the Greenland Ice Sheet surface using ICESat-2: evaluation over the K-transect
Reconciling the surface temperature–surface mass balance relationship in models and ice cores in Antarctica over the last 2 centuries
Melting over the northeast Antarctic Peninsula (1999–2009): evaluation of a high-resolution regional climate model
Multi-year analysis of distributed glacier mass balance modelling and equilibrium line altitude on King George Island, Antarctic Peninsula
Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
Cécile Davrinche, Anaïs Orsi, Cécile Agosta, Charles Amory, and Christoph Kittel
The Cryosphere, 18, 2239–2256, https://doi.org/10.5194/tc-18-2239-2024, https://doi.org/10.5194/tc-18-2239-2024, 2024
Short summary
Short summary
Coastal surface winds in Antarctica are amongst the strongest winds on Earth. They are either driven by the cooling of the surface air mass by the ice sheet (katabatic) or by large-scale pressure systems. Here we compute the relative contribution of these drivers. We find that seasonal variations in the wind speed come from the katabatic acceleration, but, at a 3-hourly timescale, none of the large-scale or katabatic accelerations can be considered as the main driver.
Aymeric P. M. Servettaz, Cécile Agosta, Christoph Kittel, and Anaïs J. Orsi
The Cryosphere, 17, 5373–5389, https://doi.org/10.5194/tc-17-5373-2023, https://doi.org/10.5194/tc-17-5373-2023, 2023
Short summary
Short summary
It has been previously observed in polar regions that the atmospheric temperature is warmer during precipitation events. Here, we use a regional atmospheric model to quantify the temperature changes associated with snowfall events across Antarctica. We show that more intense snowfall is statistically associated with a warmer temperature anomaly compared to the seasonal average, with the largest anomalies seen in winter. This bias may affect water isotopes in ice cores deposited during snowfall.
Andrew O. Hoffman, Michelle Maclennan, Jan Lenaerts, Kristine M. Larson, and Knut Chrsitianson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-114, https://doi.org/10.5194/tc-2023-114, 2023
Revised manuscript accepted for TC
Short summary
Short summary
Traditionally, glaciologists have used GNSS to measure the surface elevation, and velocity of glaciers to understand processes associated with ice flow. Using the interference of GNSS signals that bounce off of the ice sheet surface, we measure the surface height change of several receivers in the Amundsen Sea Embayment. From surface height change, we infer accumulation records and use these records to understand the drivers of extreme precipitation on Thwaites Glacier.
Michelle L. Maclennan, Jan T. M. Lenaerts, Christine A. Shields, Andrew O. Hoffman, Nander Wever, Megan Thompson-Munson, Andrew C. Winters, Erin C. Pettit, Theodore A. Scambos, and Jonathan D. Wille
The Cryosphere, 17, 865–881, https://doi.org/10.5194/tc-17-865-2023, https://doi.org/10.5194/tc-17-865-2023, 2023
Short summary
Short summary
Atmospheric rivers are air masses that transport large amounts of moisture and heat towards the poles. Here, we use a combination of weather observations and models to quantify the amount of snowfall caused by atmospheric rivers in West Antarctica which is about 10 % of the total snowfall each year. We then examine a unique event that occurred in early February 2020, when three atmospheric rivers made landfall over West Antarctica in rapid succession, leading to heavy snowfall and surface melt.
Saeid Bagheri Dastgerdi, Melanie Behrens, Jean-Louis Bonne, Maria Hörhold, Gerrit Lohmann, Elisabeth Schlosser, and Martin Werner
The Cryosphere, 15, 4745–4767, https://doi.org/10.5194/tc-15-4745-2021, https://doi.org/10.5194/tc-15-4745-2021, 2021
Short summary
Short summary
In this study, for the first time, water vapour isotope measurements in Antarctica for all seasons of a year are performed. Local temperature is identified as the main driver of δ18O and δD variability. A similar slope of the temperature–δ18O relationship in vapour and surface snow points to the water vapour isotope content as a potential key driver. This dataset can be used as a new dataset to evaluate the capability of isotope-enhanced climate models.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Bert Wouters, Jakob F. Steiner, Emile J. Nieuwstraten, Walter W. Immerzeel, and Michiel R. van den Broeke
The Cryosphere, 15, 2601–2621, https://doi.org/10.5194/tc-15-2601-2021, https://doi.org/10.5194/tc-15-2601-2021, 2021
Short summary
Short summary
We developed a method to estimate the aerodynamic properties of the Greenland Ice Sheet surface using either UAV or ICESat-2 elevation data. We show that this new method is able to reproduce the important spatiotemporal variability in surface aerodynamic roughness, measured by the field observations. The new maps of surface roughness can be used in atmospheric models to improve simulations of surface turbulent heat fluxes and therefore surface energy and mass balance over rough ice worldwide.
Marie G. P. Cavitte, Quentin Dalaiden, Hugues Goosse, Jan T. M. Lenaerts, and Elizabeth R. Thomas
The Cryosphere, 14, 4083–4102, https://doi.org/10.5194/tc-14-4083-2020, https://doi.org/10.5194/tc-14-4083-2020, 2020
Short summary
Short summary
Surface mass balance (SMB) and surface air temperature (SAT) are correlated at the regional scale for most of Antarctica, SMB and δ18O. Areas with low/no correlation are where wind processes (foehn, katabatic wind warming, and erosion) are sufficiently active to overwhelm the synoptic-scale snow accumulation. Measured in ice cores, the link between SMB, SAT, and δ18O is much weaker. Random noise can be removed by core record averaging but local processes perturb the correlation systematically.
Rajashree Tri Datta, Marco Tedesco, Cecile Agosta, Xavier Fettweis, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 12, 2901–2922, https://doi.org/10.5194/tc-12-2901-2018, https://doi.org/10.5194/tc-12-2901-2018, 2018
Short summary
Short summary
Surface melting on the East Antarctic Peninsula (East AP) has been linked to ice shelf collapse, including the Larsen A (1995) and Larsen B (2002) ice shelves. Regional climate models (RCMs) are a valuable tool to understand how wind patterns and general warming can impact the stability of ice shelves through surface melt. Here, we evaluate one such RCM (Modèle Atmosphérique Régionale) over the East AP, including the remaining Larsen C ice shelf, by comparing it to satellite and ground data.
Ulrike Falk, Damián A. López, and Adrián Silva-Busso
The Cryosphere, 12, 1211–1232, https://doi.org/10.5194/tc-12-1211-2018, https://doi.org/10.5194/tc-12-1211-2018, 2018
Short summary
Short summary
The present study address the glacier–atmosphere relation on King George Island (South Shetland Islands) at the northern Antarctic Peninsula. The focus is on 5 years of glacier mass balance observations and the adaptation of a spatially distributed, physically based mass balance model. The focus is on the analysis of equilibrium line altitude and catchment runoff. The observed changes are expected to have a direct impact on environmental conditions in coastal waters and biota.
Cited articles
Alduchov, O. A. and Eskridge, R. E.: Improved Magnus Form Approximation of
Saturation Vapor Pressure, J. Appl. Meteorol. and Climatology, 35, 601–609, https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2, 1996. a
Ashmore, D. W., Mair, D. W. F., Higham, J. E., Brough, S., Lea, J. M., and Nias, I. J.: Proper orthogonal decomposition of ice velocity identifies drivers of flow variability at Sermeq Kujalleq (Jakobshavn Isbræ), The Cryosphere, 16, 219–236, https://doi.org/10.5194/tc-16-219-2022, 2022. a
Barnes, E. A. and Screen, J. A.: The impact of Arctic warming on the
midlatitude jet-stream: Can it? Has it? Will it?, Wires. Clim.
Change, 6, 277–286, https://doi.org/10.1002/wcc.337, 2015. a
Bartholomew, I., Nienow, P., Mair, D., Hubbard, A., King, M. A., and Sole, A.:
Seasonal evolution of subglacial drainage and acceleration in a Greenland
outlet glacier, Nature Geosci., 3, 408–411, https://doi.org/10.1038/ngeo863, 2010. a, b
Bartholomew, I., Nienow, P., Sole, A., Mair, D., Cowton, T., Palmer, S., and
Wadham, J.: Supraglacial forcing of subglacial drainage in the ablation zone
of the Greenland ice sheet, Geophys. Res. Lett., 38, L08502,
https://doi.org/10.1029/2011GL047063, 2011b. a
Bartholomew, I., Nienow, P., Sole, A., Mair, D., Cowton, T., and King, M. A.:
Short-term variability in Greenland Ice Sheet motion forced by
time-varying meltwater drainage, J. Geophys. Res.-Earth., 117, F03002,
https://doi.org/10.1029/2011JF002220, 2012. a, b, c, d
Bonne, J., Steen-Larsen, H. C., Risi, C., Werner, M., Sodemann, H., Lacour,
J.-L., Fettweis, X., Cesana, G., Delmotte, M., Cattani, O., Vallelonga, P.,
Kjaer, H. A., Clerbaux, C., Sveinbjörnsdóttir, A. E., and
Masson-Delmotte, V.: The summer 2012 Greenland heat wave: In situ and
remote sensing observations of water vapor isotopic composition during an
atmospheric river event, J. Geophys. Res.-Atmos., 120, 2970–2989,
https://doi.org/10.1002/2014jd022602, 2015. a, b
Brayshaw, D. J., Hoskins, B., and Blackburn, M.: The basic ingredients of
the North Atlantic storm track. Part I: Land–sea contrast
and orography, J. Atmos. Sci., 66, 2539–2558, https://doi.org/10.1175/2009JAS3078.1,
2009. a
Charalampidis, C., van As, D., Box, J. E., van den Broeke, M. R., Colgan, W. T., Doyle, S. H., Hubbard, A. L., MacFerrin, M., Machguth, H., and Smeets, C. J. P. P.: Changing surface–atmosphere energy exchange and refreezing capacity of the lower accumulation area, West Greenland, The Cryosphere, 9, 2163–2181, https://doi.org/10.5194/tc-9-2163-2015, 2015. a
Chen, L., Johannessen, O. M., Wang, H., and Ohmura, A.: Accumulation over the
Greenland Ice Sheet as represented in reanalysis data, Adv. Atmos.
Sci., 28, 1030–1038, https://doi.org/10.1007/s00376-010-0150-9, 2011. a
Chu, V. W.: Greenland ice sheet hydrology, Prog. Phys. Geog., 38, 19–54,
https://doi.org/10.1177/0309133313507075, 2014. a, b
Clason, C. C., Mair, D. W. F., Nienow, P. W., Bartholomew, I. D., Sole, A., Palmer, S., and Schwanghart, W.: Modelling the transfer of supraglacial meltwater to the bed of Leverett Glacier, Southwest Greenland, The Cryosphere, 9, 123–138, https://doi.org/10.5194/tc-9-123-2015, 2015. a
Cooley, S. W. and Christoffersen, P.: Observation Bias Correction Reveals
More Rapidly Draining Lakes on the Greenland Ice Sheet: Bias
in Rapid Lake Drainage Detections, J. Geophys. Res.-Earth., 122,
1867–1881, https://doi.org/10.1002/2017JF004255, 2017. a
Croci-Maspoli, M., Schwierz, C., and Davies, H. C.: A Multifaceted
Climatology of Atmospheric Blocking and Its Recent Linear
Trend, J. Climate, 20, 633–649, https://doi.org/10.1175/JCLI4029.1, 2007. a
Dacre, H. F., Clark, P. A., Martinez-Alvarado, O., Stringer, M. A., and Lavers,
D. A.: How Do Atmospheric Rivers Form?, B. Am. Meteorol. Soc., 96,
1243–1255, https://doi.org/10.1175/BAMS-D-14-00031.1, 2015. a
Delhasse, A., Fettweis, X., Kittel, C., Amory, C., and Agosta, C.: Brief communication: Impact of the recent atmospheric circulation change in summer on the future surface mass balance of the Greenland Ice Sheet, The Cryosphere, 12, 3409–3418, https://doi.org/10.5194/tc-12-3409-2018, 2018. a, b
Delhasse, A., Kittel, C., Amory, C., Hofer, S., van As, D., Fausto, S. R., and Fettweis, X.: Brief communication: Evaluation of the near-surface climate in ERA5 over the Greenland Ice Sheet, The Cryosphere, 14, 957–965, https://doi.org/10.5194/tc-14-957-2020, 2020. a
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, Nature
Geosci., 8, 647–653, https://doi.org/10.1038/ngeo2482, 2015. a, b
Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay. Meteorol., 7,
363–372, https://doi.org/10.1007/BF00240838, 1974. a
Elvidge, A. D. and Renfrew, I. A.: The Causes of Foehn Warming in the
Lee of Mountains, B. Am. Meteorol. Soc., 97,
455–466, https://doi.org/10.1175/BAMS-D-14-00194.1, 2016. a
Elvidge, A. D., Kuipers Munneke, P., King, J. C., Renfrew, I. A., and Gilbert,
E.: Atmospheric Drivers of Melt on Larsen C Ice Shelf: Surface
Energy Budget Regimes and the Impact of Foehn, J. Geophys. Res.-Atmos., 125, e2020JD032463, https://doi.org/10.1029/2020JD032463, 2020. a
Fausto, R. S., van As, D., Box, J. E., Colgan, W., and Langen, P. L.:
Quantifying the Surface Energy Fluxes in South Greenland during the
2012 High Melt Episodes Using In-situ Observations, Front. Earth
Sci., 4, 82, https://doi.org/10.3389/feart.2016.00082, 2016a. a
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, 2016b. a
Fausto, R. S., van As, D., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., Andersen, S. B., Colgan, W., Karlsson, N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., Nielsen, S., Pedersen, A. Ø., Shields, C. L., Solgaard, A. M., and Box, J. E.: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth Syst. Sci. Data, 13, 3819–3845, https://doi.org/10.5194/essd-13-3819-2021, 2021. a, b
Fausto, R. S., Van As, D., and Mankoff, K. D.: AWS one boom tripod Edition 3 (deprecated), V2, GEUS Dataverse [data set], https://doi.org/10.22008/FK2/8SS7EW, 2022. a, b
Fettweis, X.: Reconstruction of the 1979–2006 Greenland ice sheet surface mass balance using the regional climate model MAR, The Cryosphere, 1, 21–40, https://doi.org/10.5194/tc-1-21-2007, 2007. a
Fettweis, X.: Modèle Atmosphérique Régional (MAR) version 3.11 regional climate model output, 1979–2019, Greenland domain, 10 kilometer (km) horizontal resolution, Arctic Data Center [data set], https://doi.org/10.18739/A28G8FJ7F (last access: 12 September 2023), 2022. a
Fettweis, X., Hanna, E., Lang, C., Belleflamme, A., Erpicum, M., and Gallée, H.: Brief communication “Important role of the mid-tropospheric atmospheric circulation in the recent surface melt increase over the Greenland ice sheet”, The Cryosphere, 7, 241–248, https://doi.org/10.5194/tc-7-241-2013, 2013. a, b
Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017. a, b
Fitzpatrick, A. A., Hubbard, A., Joughin, I., Quincey, D. J., As, D. V.,
Mikkelsen, A. P., Doyle, S. H., Hasholt, B., and Jones, G. A.: Ice flow
dynamics and surface meltwater flux at a land-terminating sector of the
Greenland ice sheet, J. Glaciol., 59, 687–696,
https://doi.org/10.3189/2013JoG12J143, 2013. a
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. a
Gallé, 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. a
Gallée, H. and Duynkerke, P. G.: Air-snow interactions and the surface
energy and mass balance over the melting zone of west Greenland during the
Greenland Ice Margin Experiment, J. Geophys. Res.-Atmos., 102,
13813–13824, https://doi.org/10.1029/96JD03358, 1997. a
Goelzer, H., Nowicki, S., Payne, A., Larour, E., Seroussi, H., Lipscomb, W. H., Gregory, J., Abe-Ouchi, A., Shepherd, A., Simon, E., Agosta, C., Alexander, P., Aschwanden, A., Barthel, A., Calov, R., Chambers, C., Choi, Y., Cuzzone, J., Dumas, C., Edwards, T., Felikson, D., Fettweis, X., Golledge, N. R., Greve, R., Humbert, A., Huybrechts, P., Le clec'h, S., Lee, V., Leguy, G., Little, C., Lowry, D. P., Morlighem, M., Nias, I., Quiquet, A., Rückamp, M., Schlegel, N.-J., Slater, D. A., Smith, R. S., Straneo, F., Tarasov, L., van de Wal, R., and van den Broeke, M.: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6, The Cryosphere, 14, 3071–3096, https://doi.org/10.5194/tc-14-3071-2020, 2020. a
Hahn, L. C., Storelvmo, T., Hofer, S., Parfitt, R., and Ummenhofer, C. C.:
Importance of Orography for Greenland Cloud and Melt Response to
Atmospheric Blocking, J. Climate, 33, 4187–4206,
https://doi.org/10.1175/JCLI-D-19-0527.1, 2020. a
Hanna, E.: Runoff and mass balance of the Greenland ice sheet: 1958–2003,
J. Geophys. Res., 110, D13108, https://doi.org/10.1029/2004JD005641, 2005. a
Hay, J. E. and Fitzharris, B. B.: A Comparison of the Energy-Balance and
Bulk-aerodynamic Approaches for Estimating Glacier Melt, J.
Glaciol., 34, 145–153, https://doi.org/10.3189/S0022143000032172, 1988. a
Hermann, M., Papritz, L., and Wernli, H.: A Lagrangian analysis of the dynamical and thermodynamic drivers of large-scale Greenland melt events during 1979–2017, Weather Clim. Dynam., 1, 497–518, https://doi.org/10.5194/wcd-1-497-2020, 2020. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Tépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020. a, b
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 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47 (last access: 12 September 2023), 2023. a, b
Hofer, S., Tedstone, A. J., Fettweis, X., and Bamber, J. L.: Decreasing cloud
cover drives the recent mass loss on the Greenland Ice Sheet, Sci.
Adv., 3, e1700584, https://doi.org/10.1126/sciadv.1700584, 2017. a
Hofer, S., Tedstone, A. J., Fettweis, X., and Bamber, J. L.: Cloud
microphysics and circulation anomalies control differences in future
Greenland melt, Nat. Clim. Change, 9, 523–528,
https://doi.org/10.1038/s41558-019-0507-8, 2019. a
Hoffman, M. J., Catania, G. A., Neumann, T. A., Andrews, L. C., and Rumrill,
J. A.: Links between acceleration, melting, and supraglacial lake drainage of
the western Greenland Ice Sheet, J. Geophys. Res., 116, F04035,
https://doi.org/10.1029/2010JF001934, 2011. a
Holtslag, A. A. M. and De Bruin, H. A. R.: Applied Modeling of the
Nighttime Surface Energy Balance over Land, J. Appl. Meteorol., 27,
689–704, https://doi.org/10.1175/1520-0450(1988)027<0689:AMOTNS>2.0.CO;2, 1988. a
Howat, I. M., Negrete, A., and Smith, B. E.: The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere, 8, 1509–1518, https://doi.org/10.5194/tc-8-1509-2014, 2014. a
Huai, B., van den Broeke, M. R., and Reijmer, C. H.: Long-term surface energy balance of the western Greenland Ice Sheet and the role of large-scale circulation variability, The Cryosphere, 14, 4181–4199, https://doi.org/10.5194/tc-14-4181-2020, 2020. a
Kjeldsen, K., Korsgaard, N., Bjørk, A., Khan, S., Box, J., Funder, S.,
Larsen, N., Bamber, J., Colgan, W., van den Broeke, M., Siggaard-Andersen,
M.-L., Nuth, C., Schomacker, A., Andresen, C., Willerslev, E., and Kjaer, K.:
Spatial and temporal distribution of mass loss from the Greenland Ice
Sheet since AD 1900, Nature, 528, 396–400, https://doi.org/10.1038/nature16183,
2015. a
Kohonen, T.: Essentials of the self-organizing map, Neural Networks, 37,
52–65, https://doi.org/10.1016/j.neunet.2012.09.018, 2013. a
Koziol, C. P. and Arnold, N.: Modelling seasonal meltwater forcing of the velocity of land-terminating margins of the Greenland Ice Sheet, The Cryosphere, 12, 971–991, https://doi.org/10.5194/tc-12-971-2018, 2018. a
Laffin, M. K., Zender, C. S., Singh, S., Van Wessem, J. M., Smeets, C. J.
P. P., and Reijmer, C. H.: Climatology and Evolution of the Antarctic
Peninsula Föhn Wind-Induced Melt Regime From 1979–2018,
J. Geophys. Res.-Atmospheres, 126, https://doi.org/10.1029/2020JD033682, 2021. a
Lavers, D. A., Ralph, F. M., Waliser, D. E., Gershunov, A., and Dettinger,
M. D.: Climate change intensification of horizontal water vapor transport in
CMIP5, Geophys. Res. Lett., 42, 5617–5625, 2015. a
Le clec'h, S., Charbit, S., Quiquet, A., Fettweis, X., Dumas, C., Kageyama, M., Wyard, C., and Ritz, C.: Assessment of the Greenland ice sheet–atmosphere feedbacks for the next century with a regional atmospheric model coupled to an ice sheet model, The Cryosphere, 13, 373–395, https://doi.org/10.5194/tc-13-373-2019, 2019. a
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. a
Liu, Y. and Weisberg, R. H.: A review of self-organizing map applications in
meteorology and oceanography, in: Self Organizing Maps – Applications
and Novel Algorithm Design, edited by: Mwasiagi, J. I., INTECH Open
Access Publisher, London, https://doi.org/10.5772/13146, 2011. a
Mair, D., Willis, I., Fischer, U. H., Hubbard, B., Nienow, P., and Hubbard, A.:
Hydrological controls on patterns of surface, internal and basal motion
during three “spring events”: Haut Glacier d'Arolla,
Switzerland, J. Glaciol., 49, 555–567, https://doi.org/10.3189/172756503781830467,
2003. a
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. a, b
Mattingly, K. S., Mote, T. L., and Fettweis, X.: Atmospheric river impacts
on Greenland Ice Sheet surface mass balance, J. Geophys.
Res.-Atmos., 123, 8538–8560, https://doi.org/10.1029/2018jd028714, 2018. a, b
Mattingly, K. S., Mote, T. L., Fettweis, X., As, D. v., Tricht, K. V.,
Lhermitte, S., Pettersen, C., and Fausto, R. S.: Strong summer
atmospheric rivers trigger Greenland Ice Sheet melt through
spatially varying surface energy balance and cloud regimes, J.
Climate, 33, 6809–6832, https://doi.org/10.1175/jcli-d-19-0835.1, 2020. a, b, c, d, e
Mattingly, K. S., Turton, J. V., Wille, J. D., Noël, B., Fettweis, X.,
Rennermalm, S. K., and Mote, T. L.: Increasing extreme melt in northeast
Greenland linked to foehn winds and atmospheric rivers, Nat. Commun., 14,
1743, https://doi.org/10.1038/s41467-023-37434-8, 2023. a, b
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface
layer of the atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst., 24, 163–187,
1954. a
Neff, W., Compo, G. P., Martin Ralph, F., 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. a
Nghiem, S. V., Hall, D. K., Mote, T. L., Tedesco, M., Albert, M. R., Keegan,
K., Shuman, C. A., DiGirolamo, N. E., and Neumann, G.: The extreme melt
across the Greenland ice sheet in 2012, Geophys. Res. Lett., 39, L20502,
https://doi.org/10.1029/2012gl053611, 2012. a
Nienow, P. W., Sole, A. J., Slater, D. A., and Cowton, T. R.: Recent advances
in our understanding of the role of meltwater in the Greenland
Ice Sheet system, Curr. Clim. Change Rep., 3, 330–344,
https://doi.org/10.1007/s40641-017-0083-9, 2017. a
Preece, J. R., Wachowicz, L. J., Mote, T. L., Tedesco, M., and Fettweis, X.:
Summer Greenland Blocking Diversity and Its Impact on the Surface
Mass Balance of the Greenland Ice Sheet, J. Geophys. Res.-Atmos.,
127, e2021JD035489, https://doi.org/10.1029/2021JD035489, 2022. a
Radić, V., Cannon, A. J., Menounos, B., and Gi, N.: Future changes in
autumn atmospheric river events in British Columbia, Canada, as
projected by CMIP5 global climate models, J. Geophys. Res.-Atmos., 120,
9279–9302, https://doi.org/10.1002/2015jd023279, 2015. a
Rivière, G. and Orlanski, I.: Characteristics of the Atlantic
storm–track eddy activity and its relation with the North
Atlantic oscillation, J. Atmos. Sci., 64, 241–266,
https://doi.org/10.1175/JAS3850.1, 2007. a
Röthlisberger, M. and Papritz, L.: Quantifying the physical processes leading
to atmospheric hot extremes at a global scale, Nat. Geosci., 16, 210–216,
https://doi.org/10.1038/s41561-023-01126-1, 2023. a
Schmid, T.: Influence of synoptic scale weather variability on ice speed-up
events in Southwest Greenland, Master thesis, ETH Zurich, Zurich,
https://doi.org/10.3929/ethz-b-000561596, 2021. a, b
Schoof, C.: Ice-sheet acceleration driven by melt supply variability, Nature,
468, 803–806, https://doi.org/10.1038/nature09618, 2010. a, b, c
Schuenemann, K. C. and Cassano, J. J.: Changes in synoptic weather patterns and
Greenland precipitation in the 20th and 21st centuries: 2. Analysis of
21st century atmospheric changes using self-organizing maps, J. Geophys.
Res., 115, D05108, https://doi.org/10.1029/2009JD011706, 2010. a, b, c, d
Schwierz, C., Croci-Maspoli, M., and Davies, H. C.: Perspicacious indicators of
atmospheric blocking, Geophys. Res. Lett., 31, L06125,
https://doi.org/10.1029/2003GL019341, 2004. a
Selmes, N., Murray, T., and James, T. D.: Fast draining lakes on the
Greenland Ice Sheet, Geophys. Res. Lett., 38, L15501,
https://doi.org/10.1029/2011GL047872, 2011. a, b
Shannon, S. R., Payne, A. J., Bartholomew, I. D., van den Broeke, M. R.,
Edwards, T. L., Fettweis, X., Gagliardini, O., Gillet-Chaulet, F., Goelzer,
H., Hoffman, M. J., Huybrechts, P., Mair, D. W. F., Nienow, P. W., Perego,
M., Price, S. F., Smeets, C. J. P. P., Sole, A. J., van de Wal, R. S. W., and
Zwinger, T.: Enhanced basal lubrication and the contribution of the
Greenland ice sheet to future sea-level rise, P. Natl. Acad. Sci., 110,
14156–14161, https://doi.org/10.1073/pnas.1212647110, 2013. a
Smeets, C. J. P. P. and van den Broeke, M. R.: The Parameterisation of
Scalar Transfer over Rough Ice, Bound.-Lay. Meteorol., 128, 339–355,
https://doi.org/10.1007/s10546-008-9292-z, 2008. a
Smeets, P. C. J. P., Munneke, P. K., As, D. v., Broeke, M. R. v. d., Boot, W.,
Oerlemans, H., Snellen, H., Reijmer, C. H., and Wal, R. S. W. v. d.: The
K-transect in west Greenland: Automatic weather station data
(1993–2016), Arct. Antarct. Alp. Res., 50, S100002,
https://doi.org/10.1080/15230430.2017.1420954, 2018. a
Sodemann, H., Wernli, H., Knippertz, P., Cordeira, J. M., Dominguez, F., Guan,
B., Hu, H., Ralph, F. M., and Stohl, A.: Structure, Process, and
Mechanism, in: Atmospheric Rivers, edited by: Ralph, F. M., Dettinger,
M. D., Rutz, J. J., and Waliser, D. E., Springer International
Publishing, Cham, 15–43, https://doi.org/10.1007/978-3-030-28906-5_2, 2020. a
Sole, A., Nienow, P., Bartholomew, I., Mair, D., Cowton, T., Tedstone, A., and
King, M. A.: Winter motion mediates dynamic response of the Greenland Ice
Sheet to warmer summers, Geophys. Res. Lett., 40, 3940–3944,
https://doi.org/10.1002/grl.50764, 2013. a
Sprenger, M. and Wernli, H.: The LAGRANTO Lagrangian analysis tool – version 2.0, Geosci. Model Dev., 8, 2569–2586, https://doi.org/10.5194/gmd-8-2569-2015, 2015. a
Sprenger, M., Fragkoulidis, G., Binder, H., Croci-Maspoli, M., Graf, P., Grams,
C. M., Knippertz, P., Madonna, E., Schemm, S., Škerlak, B., and Wernli, H.:
Global Climatologies of Eulerian and Lagrangian Flow Features based
on ERA-Interim, B. Am. Meteorol. Soc., 98, 1739–1748,
https://doi.org/10.1175/BAMS-D-15-00299.1, 2017. a
Stevens, L. A., Behn, M. D., Das, S. B., Joughin, I., Noël, B. P. Y.,
Broeke, M. R., and Herring, T.: Greenland Ice Sheet flow response to
runoff variability, Geophys. Res. Lett., 43, 11295–11303,
https://doi.org/10.1002/2016GL070414, 2016. a
Stull, R. B. (Ed.): An Introduction to Boundary Layer Meteorology,
Kluwer Academic Publishers, Dordrecht, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Sutterley, T. C., Velicogna, I., Fettweis, X., Rignot, E., Noël, B., and
Broeke, M.: Evaluation of reconstructions of snow/ice melt in
Greenland by regional atmospheric climate models using laser
altimetry data, Geophys. Res. Lett., 45, 8324–8333,
https://doi.org/10.1029/2018GL078645, 2018. a
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. a
Tedstone, A. and Neinow, P.: Ice motion measurements, south-west Greenland Ice Sheet (version 2), Polar Data Centre; British Antarctic Survey, Natural Environment Research Council, Cambridge, CB3 0ET, UK [data set], https://doi.org/10.5285/1f69fba3-4c62-47ad-8119-08cfeec05e46, 2018. a, b
Tedstone, A. J., Nienow, P. W., Sole, A. J., Mair, D. W. F., Cowton, T. R.,
Bartholomew, I. D., and King, M. A.: Greenland ice sheet motion insensitive
to exceptional meltwater forcing, P. Natl. Acad. Sci. USA, 110, 19719–19724,
https://doi.org/10.1073/pnas.1315843110, 2013. a, b
Tedstone, A. J., Nienow, P. W., Gourmelen, N., Dehecq, A., Goldberg, D., and
Hanna, E.: Decadal slowdown of a land-terminating sector of the Greenland
Ice Sheet despite warming, Nature, 526, 692–695,
https://doi.org/10.1038/nature15722, 2015. a
The IMBIE Team: Mass balance of the Greenland Ice Sheet from 1992 to
2018, Nature, 579, 233–239, https://doi.org/10.1038/s41586-019-1855-2, 2020. a
Turton, J. V., Kirchgaessner, A., Ross, A. N., and King, J. C.: The spatial
distribution and temporal variability of föhn winds over the Larsen C
ice shelf, Antarctica, Q. J. Roy. Meteor. Soc., 144, 1169–1178,
https://doi.org/10.1002/qj.3284, 2018. a
van de Wal, R. S. W., Boot, W., Broeke, M. R. v. d., Smeets, C. J. P. P.,
Reijmer, C. H., Donker, J. J. A., and Oerlemans, J.: Large and rapid
melt-induced velocity changes in the ablation zone of the
Greenland Ice sheet, Science, 321, 111–113,
https://doi.org/10.1126/science.1158540, 2008. a, b, c, d, e, f
van de Wal, R. S. W., Smeets, C. J. P. P., Boot, W., Stoffelen, M., van Kampen, R., Doyle, S. H., Wilhelms, F., van den Broeke, M. R., Reijmer, C. H., Oerlemans, J., and Hubbard, A.: Self-regulation of ice flow varies across the ablation area in south-west Greenland, The Cryosphere, 9, 603–611, https://doi.org/10.5194/tc-9-603-2015, 2015. a, b, c
van den Broeke, M. R., Smeets, C. J. P. P., and van de Wal, R. S. W.: The seasonal cycle and interannual variability of surface energy balance and melt in the ablation zone of the west Greenland ice sheet, The Cryosphere, 5, 377–390, https://doi.org/10.5194/tc-5-377-2011, 2011. a
van Tricht, K., Lhermitte, S., Lenaerts, J. T. M., Gorodetskaya, I. V.,
L'Ecuyer, T. S., Noël, B., Broeke, M. R. v. d., Turner, D. D., and van
Lipzig, N. P. M.: Clouds enhance Greenland ice sheet meltwater runoff,
Nature Commun., 7, 10266, https://doi.org/10.1038/ncomms10266, 2016. a
Wang, W., Zender, C. S., As, D., and Miller, N. B.: Spatial Distribution of
Melt Season Cloud Radiative Effects Over Greenland:
Evaluating Satellite Observations, Reanalyses, and Model
Simulations Against In Situ Measurements, J. Geophys. Res.-Atmos.,
124, 57–71, https://doi.org/10.1029/2018JD028919, 2019. a
Ward, J. L., Flanner, M. G., and Dunn-Sigouin, E.: Impacts of Greenland
Block Location on Clouds and Surface Energy Fluxes Over the
Greenland Ice Sheet, J. Geophys. Res.-Atmos., 125, e2020JD033172,
https://doi.org/10.1029/2020JD033172, 2020. a
Wernli, H. and Davies, H. C.: A Lagrangian-based analysis of extratropical
cyclones. I: The method and some applications, Q. J. Roy. Meteor. Soc.,
123, 467–489, https://doi.org/10.1002/qj.49712353811, 1997. a
Wernli, H. and Papritz, L.: Role of polar anticyclones and mid-latitude
cyclones for Arctic summertime sea-ice melting, Nat. Geosci., 11,
108–113, https://doi.org/10.1038/s41561-017-0041-0, 2018. a
Wernli, H. and Schwierz, C.: Surface Cyclones in the ERA-40 Dataset
(1958–2001). Part I: Novel Identification Method and Global
Climatology, J. Atmos. Sci., 63, 2486–2507, https://doi.org/10.1175/JAS3766.1, 2006.
a
Woollings, T., Barriopedro, D., Methven, J., Son, S.-W., Martius, O., Harvey,
B., Sillmann, J., Lupo, A. R., and Seneviratne, S.: Blocking and its
Response to Climate Change, Curr. Clim. Change Rep., 4, 287–300,
https://doi.org/10.1007/s40641-018-0108-z, 2018. a, b
Zwally, H. J., Abdalati, W., Herring, T., Larson, K., Saba, J., and Steffen,
K.: Surface Melt-Induced Acceleration of Greenland Ice-Sheet
Flow, Science, 297, 218–222, https://doi.org/10.1126/science.1072708, 2002. a, b, c
Co-editor-in-chief
This study demonstrates the connection between two important parts of the climate system: atmospheric conditions over the Greenland Ice Sheet and the seasonal ice flow of glaciers -- specifically a glacier in Southwest Greenland. The authors use GPS measurements to identify more than 40 cases of speed up of the glacier. The majority of the observed speed up can be linked to the melting of the surface of the ice. In particular, the study shows that atmospheric rivers are linked to the strongest speed-up events. The findings have implications for the future dynamics of Greenlandic glaciers as weather patterns change intensity in response to the warming climate.
This study demonstrates the connection between two important parts of the climate system:...
Short summary
The Greenland Ice Sheet contributes strongly to sea level rise in the warming climate. One process that can affect the ice sheet's mass balance is short-term ice speed-up events. These can be caused by high melting or rainfall as the water flows underneath the glacier and allows for faster sliding. In this study we found three main weather patterns that cause such ice speed-up events on the Russell Glacier in southwest Greenland and analyzed how they induce local melting and ice accelerations.
The Greenland Ice Sheet contributes strongly to sea level rise in the warming climate. One...
