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4. A Reconciled Estimate of Ice-Sheet Mass Balance

Author

Shepherd, Andrew, Ivins, Erik R, A, Geruo, Barletta, Valentina R, Bentley, Mike J, Bettadpur, Srinivas, Briggs, Kate H, Bromwich, David H, Forsberg, René, Galin, Natalia, Horwath, Martin, Jacobs, Stan, Joughin, Ian, King, Matt A, Lenaerts, Jan TM, Li, Jilu, Ligtenberg, Stefan RM, Luckman, Adrian, Luthcke, Scott B, McMillan, Malcolm, Meister, Rakia, Milne, Glenn, Mouginot, Jeremie, Muir, Alan, Nicolas, Julien P, Paden, John, Payne, Antony J, Pritchard, Hamish, Rignot, Eric, Rott, Helmut, Sørensen, Louise Sandberg, Scambos, Ted A, Scheuchl, Bernd, Schrama, Ernst JO, Smith, Ben, Sundal, Aud V, van Angelen, Jan H, van de Berg, Willem J, van den Broeke, Michiel R, Vaughan, David G, Velicogna, Isabella, Wahr, John, Whitehouse, Pippa L, Wingham, Duncan J, Yi, Donghui, Young, Duncan, and Zwally, H Jay

Subjects
Climate Action, Antarctic Regions, Climate Change, Geographic Information Systems, Greenland, Ice Cover, General Science & Technology
Abstract

We combined an ensemble of satellite altimetry, interferometry, and gravimetry data sets using common geographical regions, time intervals, and models of surface mass balance and glacial isostatic adjustment to estimate the mass balance of Earth's polar ice sheets. We find that there is good agreement between different satellite methods--especially in Greenland and West Antarctica--and that combining satellite data sets leads to greater certainty. Between 1992 and 2011, the ice sheets of Greenland, East Antarctica, West Antarctica, and the Antarctic Peninsula changed in mass by -142 ± 49, +14 ± 43, -65 ± 26, and -20 ± 14 gigatonnes year(-1), respectively. Since 1992, the polar ice sheets have contributed, on average, 0.59 ± 0.20 millimeter year(-1) to the rate of global sea-level rise.

Published
2012

7. Quality in the GLIMS Glacier Database

Author

Raup, Bruce H., Khalsa, Siri Jodha S., Armstrong, Richard L., Sneed, William A., Hamilton, Gordon S., Paul, Frank, Cawkwell, Fiona, Beedle, Matthew J., Menounos, Brian P., Wheate, Roger D., Rott, Helmut, Shiyin, Liu, Xin, Li, Donghui, Shangguan, Guodong, Cheng, Kargel, Jeffrey S., Larsen, Chris F., Molnia, Bruce F., Kincaid, Joni L., Klein, Andrew, Konovalov, Vladimir, Kargel, Jeffrey S., editor, Leonard, Gregory J., editor, Bishop, Michael P., editor, Kääb, Andreas, editor, and Raup, Bruce H., editor

Published
2014
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8. European Alps

Author

Paul, Frank, Arnaud, Yves, Ranzi, Roberto, Rott, Helmut, Kargel, Jeffrey S., editor, Leonard, Gregory J., editor, Bishop, Michael P., editor, Kääb, Andreas, editor, and Raup, Bruce H., editor

Published
2014
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9. Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover

Author

Lemmetyinen, Juha, primary, Cohen, Juval, additional, Kontu, Anna, additional, Vehviläinen, Juho, additional, Hannula, Henna-Reetta, additional, Merkouriadi, Ioanna, additional, Scheiblauer, Stefan, additional, Rott, Helmut, additional, Nagler, Thomas, additional, Ripper, Elisabeth, additional, Elder, Kelly, additional, Marshall, Hans-Peter, additional, Fromm, Reinhard, additional, Adams, Marc, additional, Derksen, Chris, additional, King, Joshua, additional, Meta, Adriano, additional, Coccia, Alex, additional, Rutter, Nick, additional, Sandells, Melody, additional, Macelloni, Giovanni, additional, Santi, Emanuele, additional, Leduc-Leballeur, Marion, additional, Essery, Richard, additional, Menard, Cecile, additional, and Kern, Michael, additional

Published
2022
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13. EO Small Satellite Missions and Formation Flying

Author

Sephton, Tony, Wishart, Alex, Rott, Helmut, Nagler, Thomas, Grafmueller, Bernhard, Hall, David, Robert, Alice, Claessens, Marline, de Negueruela Alemán, Cristina, Strauch, Karsten, Gantois, Kristof, Sandau, Rainer, editor, Roeser, Hans-Peter, editor, and Valenzuela, Arnoldo, editor

Published
2010
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14. The Ka-Band Interferometric Radar Mission Proposal for Cold Environments - SKADI

Author

Hajnsek, Irena, Aðalgeirsdóttir, Guðfinna, Bartsch, Annett, Rodriguez Cassola, Marc, Fischer, Georg, Gierlich, Roland, Grosse, Guido, Haas, Christian, Huber, Sigurd, Jesswein, Katarina, Kääb, Andreas, Kim, Junsu, Krieger, Gerhard, Mak, Karen, Mössinger, Alexander, Montpetit, Benoit, Münzenmayer, Ralf, Otto, Tobias, Papathanassiou, Konstantinos, Queiroz de Almeida, Felipe, Rott, Helmut, Strozzi, Tazio, Tesmer, Volker, Villano, Michelangelo, Westermann, Sebastian, Younis, Marwan, and Zonno, Mariantonietta

Subjects
ESA Earth Explorer missions, permafrost areas, SKADI, ice, glaciers, cold environments, cryosphere
Published
2022

15. Disaster Prevention for Alpine Routes

Author

Eisl, Markus M., Granica, Klaus, Nagler, Thomas, Böhm, Florian, Rott, Helmut, Proske, Herwig, Schardt, Mathias, van Oosterom, Peter, editor, Zlatanova, Siyka, editor, and Fendel, Elfriede M., editor

Published
2005
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18. Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands

Author

Santi, Emanuele, primary, Brogioni, Marco, additional, Leduc-Leballeur, Marion, additional, Macelloni, Giovanni, additional, Montomoli, Francesco, additional, Pampaloni, Paolo, additional, Lemmetyinen, Juha, additional, Cohen, Juval, additional, Rott, Helmut, additional, Nagler, Thomas, additional, Derksen, Chris, additional, King, Joshua, additional, Rutter, Nick, additional, Essery, Richard, additional, Menard, Cecile, additional, Sandells, Melody, additional, and Kern, Michael, additional

Published
2022
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21. Airborne SnowSAR data at X- and Ku- bands over boreal forest, alpine and tundra snow cover

Author

Lemmetyinen, Juha, primary, Cohen, Juval, additional, Kontu, Anna, additional, Vehviläinen, Juho, additional, Hannula, Henna-Reetta, additional, Merkouriadi, Ioanna, additional, Scheiblauer, Stefan, additional, Rott, Helmut, additional, Nagler, Thomas, additional, Ripper, Elisabeth, additional, Elder, Kelly, additional, Marshall, Hans-Peter, additional, Fromm, Reinhard, additional, Adams, Marc, additional, Derksen, Chris, additional, King, Joshua, additional, Meta, Adriano, additional, Coccia, Alex, additional, Rutter, Nick, additional, Sandells, Melody, additional, Macelloni, Giovanni, additional, Santi, Emanuele, additional, Leduc-Leballeur, Marion, additional, Essery, Richard, additional, Menard, Cecile, additional, and Kern, Michael, additional

Published
2021
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28. Mass balance of the Greenland Ice Sheet from 1992 to 2018

Author

Shepherd, Andrew, Ivins, Erik, Rignot, Eric, Smith, Ben, van den Broeke, Michiel, Velicogna, Isabella, Whitehouse, Pippa, Briggs, Kate, Joughin, Ian, Krinner, Gerhard, Nowicki, Sophie, Payne, Tony, Scambos, Ted, Schlegel, Nicole, Geruo, A., Agosta, Cécile, Ahlstrøm, Andreas, Babonis, Greg, Barletta, Valentina R., Bjørk, Anders A., Blazquez, Alejandro, Bonin, Jennifer, Colgan, William, Csatho, Beata, Cullather, Richard, Engdahl, Marcus E., Felikson, Denis, Fettweis, Xavier, Forsberg, Rene, Hogg, Anna E., Gallee, Hubert, Gardner, Alex, Gilbert, Lin, Gourmelen, Noel, Groh, Andreas, Gunter, Brian, Hanna, Edward, Harig, Christopher, Helm, Veit, Horvath, Alexander, Horwath, Martin, Khan, Shfaqat, Kjeldsen, Kristian K., Konrad, Hannes, Langen, Peter L., Lecavalier, Benoit, Loomis, Bryant, Luthcke, Scott, McMillan, Malcolm, Melini, Daniele, Mernild, Sebastian, Mohajerani, Yara, Moore, Philip, Mottram, Ruth, Mouginot, Jeremie, Moyano, Gorka, Muir, Alan, Nagler, Thomas, Nield, Grace, Nilsson, Johan, Noël, Brice, Otosaka, Ines, Pattle, Mark E., Peltier, W. Richard, Pie, Nadège, Rietbroek, Roelof, Rott, Helmut, Sørensen, Louise Sandberg, Sasgen, Ingo, Save, Himanshu, Scheuchl, Bernd, Schrama, Ernst, Schröder, Ludwig, Seo, Ki-Weon, Simonsen, Sebastian B., Slater, Thomas, Spada, Giorgio, Sutterley, Tyler, Talpe, Matthieu, Tarasov, Lev, Jan van de Berg, Willem, van der Wal, Wouter, van Wessem, Melchior, Vishwakarma, Bramha Dutt, Wiese, David, Wilton, David, Wagner, Thomas, Wouters, Bert, Wuite, Jan, Team, The IMBIE, Marine and Atmospheric Research, Sub Dynamics Meteorology, Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Andrew Shepherd, Erik Ivin, Eric Rignot, Ben Smith, Michiel van den Broeke, Isabella Velicogna, Pippa Whitehouse, Kate Brigg, Ian Joughin, Gerhard Krinner, Sophie Nowicki, Tony Payne, Ted Scambo, Nicole Schlegel, A Geruo, Cécile Agosta, Andreas Ahlstrøm, Greg Baboni, Valentina R. Barletta, Anders A. Bjørk, Alejandro Blazquez, Jennifer Bonin, William Colgan, Beata Csatho, Richard Cullather, Marcus E. Engdahl, Denis Felikson, Xavier Fettwei, Rene Forsberg, Anna E. Hogg, Hubert Gallee, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian Gunter, Edward Hanna, Christopher Harig, Veit Helm, Alexander Horvath, Martin Horwath, Shfaqat Khan, Kristian K. Kjeldsen, Hannes Konrad, Peter L. Langen, Benoit Lecavalier, Bryant Loomi, Scott Luthcke, Malcolm McMillan, Daniele Melini, Sebastian Mernild, Yara Mohajerani, Philip Moore, Ruth Mottram, Jeremie Mouginot, Gorka Moyano, Alan Muir, Thomas Nagler, Grace Nield, Johan Nilsson, Brice Noël, Ines Otosaka, Mark E. Pattle, W. Richard Peltier, Nadège Pie, Roelof Rietbroek, Helmut Rott, Louise Sandberg Sørensen, Ingo Sasgen, Himanshu Save, Bernd Scheuchl, Ernst Schrama, Ludwig Schröder, Ki-Weon Seo, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler Sutterley, Matthieu Talpe, Lev Tarasov, Willem Jan van de Berg, Wouter van der Wal, Melchior van Wessem, Bramha Dutt Vishwakarma, David Wiese, David Wilton, Thomas Wagner, Bert Wouter, Jan Wuite, Marine and Atmospheric Research, and Sub Dynamics Meteorology

Subjects
geography, Multidisciplinary, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Global warming, Greenland ice sheet, Climate change, Glacier, GLACIAL ISOSTATIC-ADJUSTMENT, RELATIVE SEA-LEVEL PETERMANN GLACIER, ELEVATION CHANGE, SURFACE, GRACE, CLIMATE, MODEL, ACCELERATION, ANTARCTICA, 010502 geochemistry & geophysics, Atmospheric sciences, 01 natural sciences, Glacier mass balance, 13. Climate action, Taverne, [SDE]Environmental Sciences, SDG 13 - Climate Action, Environmental science, Climate model, Ice sheet, F840 Physical Geography, Meltwater, 0105 earth and related environmental sciences
Abstract

ArticlePublished: 10 December 2019This is an unedited manuscript that has been accepted for publication. Nature Research are providing this early version of the manuscript as a service to our customers. The manuscript will undergo copyediting, typesetting and a proof review before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.Mass balance of the Greenland Ice Sheet from 1992 to 2018The IMBIE TeamNature (2019)Cite this article6914 Accesses1410 AltmetricMetricsdetailsAbstractIn recent decades, the Greenland Ice Sheet has been a major contributor to global sea-level rise1,2, and it is expected to be so in the future3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the degree and trajectory of today’s imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. Although the ice sheet was close to a state of balance in the 1990s, annual losses have risen since then, peaking at 335 ± 62 billion tonnes per year in 2011. In all, Greenland lost 3,800 ± 339 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.6 ± 0.9 millimetres. Using three regional climate models, we show that reduced surface mass balance has driven 1,971 ± 555 billion tonnes (52%) of the ice loss owing to increased meltwater runoff. The remaining 1,827 ± 538 billion tonnes (48%) of ice loss was due to increased glacier discharge, which rose from 41 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. Between 2013 and 2017, the total rate of ice loss slowed to 217 ± 32 billion tonnes per year, on average, as atmospheric circulation favoured cooler conditions15 and as ocean temperatures fell at the terminus of Jakobshavn Isbræ16. Cumulative ice losses from Greenland as a whole have been close to the IPCC’s predicted rates for their high-end climate warming scenario17, which forecast an additional 50 to 120 millimetres of global sea-level rise by 2100 when compared to their central estimate.

Published
2020

29. EO Small Satellite Missions and Formation Flying

Author

Sephton, Tony, primary, Wishart, Alex, additional, Rott, Helmut, additional, Nagler, Thomas, additional, Grafmueller, Bernhard, additional, Hall, David, additional, Robert, Alice, additional, Claessens, Marline, additional, de Negueruela Alemán, Cristina, additional, Strauch, Karsten, additional, and Gantois, Kristof, additional

Published
2009
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30. Seasonal and short-term variability of multifrequency, polarimetric radar backscatter of alpine terrain from SIR-C/X-SAR and AIRSAR data

Author

Floricioiu, Dana and Rott, Helmut

Subjects
Backscattering -- Models, Glaciers -- Measurement, Synthetic aperture radar -- Usage, Polariscope -- Usage, Business, Earth sciences, Electronics and electrical industries
Abstract

Signatures of glaciated and ice free areas were analyzed from polarimetric SAR data at C-, L-, and P-band and single polarization X-band data. The data base includes an AIRSAR scene from June 25, 1991, and SIR-C/X-SAR images from April and October 1994 (SRL-1 and SRL-2), acquired over the high alpine test site Otztal in Austria. The environmental conditions were different at the time of the three experiments. Ground measurements, meteorological observations, and backscattering modeling are the basis for interpreting the backscattering signatures. Seasonal differences are due mainly to the presence or absence of snow and due to changes of its properties. Short term variations of snow conditions can be monitored at C- and X-band. For unglaciated areas, the surface roughness has a dominant influence on backscattering in all seasons. The dependence of the mean backscattering and correlation coefficients on the incidence angle was analyzed. Spectral and depolarization ratios and the magnitude of the HHVV correlation coefficient were selected as components of the multidimensional feature vector for studying the target separability. Good separability was found between the accumulation and ablation areas on the glaciers, whereas on ice-free areas, the dominance of surface roughness limits the discrimination of different surface types. Short-term variations of backscattering have significant impact for the classification of accumulation and ablation areas on glaciers, as verified by comparisons with field data. Index Terms--AIRSAR, backscatter modeling, glacier, signatures, SIR-C/X-SAR, snow, synthetic aperture radar (SAR) polarimetry.

Published
2001

34. Airborne SnowSAR data at X- and Ku- bands over boreal forest, alpine and tundra snow cover.

Author

Lemmetyinen, Juha, Cohen, Juval, Kontu, Anna, Vehviläinen, Juho, Hannula, Henna-Reetta, Merkouriadi, Ioanna, Scheiblauer, Stefan, Rott, Helmut, Nagler, Thomas, Ripper, Elisabeth, Elder, Kelly, Marshall, Hans-Peter, Fromm, Reinhard, Adams, Marc, Derksen, Chris, King, Joshua, Meta, Adriano, Coccia, Alex, Rutter, Nick, and Sandells, Melody

Subjects
TUNDRAS, TAIGAS, SNOW cover, SYNTHETIC aperture radar, LAND cover, ALTITUDES
Abstract

The European Space Agency SnowSAR instrument is a side looking, dual polarized (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from a small aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps, and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow -covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland for a single flight mission in March 2011 and twelve missions at two sites (tundra and boreal forest) in the winter of 2011-2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones, representing a montane valley, Alpine tundra, and a glacier environment. In Canada, a total of two missions were flown in March and April 2013, over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data, as well as coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record the datasets described here are deposited in a permanent data repository (https://doi.pangaea.de/ 10.1594/PANGAEA.933255; Lemmetyinen et al., 2021). A temporary link to access the data without login information is provided for reviewers of this manuscript: https://www.pangaea.de/tok/e8c562c3c8a15ac34daa83d00 c76fcb347330884. [ABSTRACT FROM AUTHOR]

Published
2021
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36. Snow mapping in alpine regions with synthetic aperture radar

Author

Shi, Jiancheng, Dozier, Jeff, and Rott, Helmut

Subjects
Synthetic aperture radar -- Usage, Alpine regions -- Portrayals, Digital mapping -- Analysis, Business, Earth sciences, Electronics and electrical industries
Abstract

For climatological and hydrological investigations, the areas covered by snow and glacial ice are important parameters. Active microwave sensors can discriminate snow from other surfaces in all weather conditions, and their spatial resolution is compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR in the Otztal Alps in 1989 and 1991, we examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. By comparing polarimetric SAR data to images from the Landsat Thematic Mapper obtained under clear conditions one week after the SAR flight, we find that SAR data at 5.3 GHz (C-band) can discriminate between areas covered by snow from those that are ice-free. However, they are less suited to discrimination of glacier ice from snow and rock. The overall pixel-by-pixel accuracies - 74% from VV polarization alone with topographic information, 76% from polarimetric SAR without any topographic information, and 79% from polarimetric SAR with topographic information - are high enough to justify the use of SAR as the data source in areas that are too cloud-covered to obtain data from the Thematic Mapper. This is especially true for snow discrimination, where accuracies exceed 80%, because mapping of a transient snow cover during a cloudy melt season is often difficult with an optical sensor. The AIRSAR survey was carried out in summer during a heavy rainstorm, when the snow surfaces were unusually rough. Even better results for snow discrimination can be expected for mapping in the spring, when snow is usually smoother. Glacier boundaries vary over longer time scales and can be mapped easily with an optical sensor, because only one good image per year is needed, so the SAR's poorer accuracy for glacier/rock discrimination is not as important.

Published
1994

38. ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks

Author

Krinner, Gerhard, Derksen, Chris, Essery, Richard, Flanner, Mark, Hagemann, Stefan, Clark, Martyn, Hall, Alex, Rott, Helmut, Brutel-Vuilmet, Claire, Kim, Hyungjun, Ménard, Cécile B., Mudryk, Lawrence, Thackeray, Chad, Wang, Libo, Arduini, Gabriele, Balsamo, Gianpaolo, Bartlett, Paul, Boike, Julia, Boone, Aaron, Chéruy, Frédérique, Colin, Jeanne, Cuntz, Matthias, Dai, Yongjiu, Decharme, Bertrand, Derry, Jeff, Ducharne, Agnès, Dutra, Emanuel, Fang, Xing, Fierz, Charles, Ghattas, Josephine, Gusev, Yeugeniy, Haverd, Vanessa, Kontu, Anna, Lafaysse, Matthieu, Law, Rachel, Lawrence, Dave, Li, Weiping, Marke, Thomas, Marks, Danny, Ménégoz, Martin, Nasonova, Olga, Nitta, Tomoko, Niwano, Masashi, Pomeroy, John, Raleigh, Mark S., Schaedler, Gerd, Semenov, Vladimir, Smirnova, Tanya G., Stacke, Tobias, Strasser, Ulrich, Svenson, Sean, Turkov, Dmitry, Wang, Tao, Wever, Nander, Yuan, Hua, Zhou, Wenyan, Zhu, Dan, SILVA (SILVA), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-AgroParisTech, World Climate Research Programme's Climate and Cryosphere (CliC) core project NERC Natural Environment Research CouncilNE/P011926/1European Space Agency Russian Science Foundation (RSF)16-17-10039Russian Foundation for Basic Research (RFBR)18-05-60216APPLICATE project European Union (EU)727862Swiss National Science Foundation (SNSF)200021E-160667European Union (EU)641816Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science16H06291National Science Foundation (NSF)1543268, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Climate Research Division [Toronto], Environment and Climate Change Canada, School of Geosciences Grant Institute, University of Edinburgh, Department of Climate and Space Sciences and Engineering (CLaSP), University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, Research Applications Laboratory [Boulder] (RAL), National Center for Atmospheric Research [Boulder] (NCAR), Environmental Earth Observation IT GmbH (ENVEO), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institute of Industrial Science (IIS), The University of Tokyo, Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École pratique des hautes études (EPHE)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), The University of Tokyo (UTokyo), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.EE]Life Sciences [q-bio]/Ecology, environment, thermal-conductivity, arctic amplification, latitude hydrological processes, torne-kalix basin, water equivalent, Modélisation et simulation, land-surface model, intercomparison project, Earth sciences, Modeling and Simulation, [SDE]Environmental Sciences, ddc:550, albedo feedback, pilps phase-2(e), [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Milieux et Changements globaux, sensitivity-analysis
Abstract

Krinner, Gerhard Derksen, Chris Essery, Richard Flanner, Mark Hagemann, Stefan Clark, Martyn Hall, Alex Rott, Helmut Brutel-Vuilmet, Claire Kim, Hyungjun Menard, Cecile B. Mudryk, Lawrence Thackeray, Chad Wang, Libo Arduini, Gabriele Balsamo, Gianpaolo Bartlett, Paul Boike, Julia Boone, Aaron Cheruy, Frederique Colin, Jeanne Cuntz, Matthias Dai, Yongjiu Decharme, Bertrand Derry, Jeff Ducharne, Agnes Dutra, Emanuel Fang, Xing Fierz, Charles Ghattas, Josephine Gusev, Yeugeniy Haverd, Vanessa Kontu, Anna Lafaysse, Matthieu Law, Rachel Lawrence, Dave Li, Weiping Marke, Thomas Marks, Danny Menegoz, Martin Nasonova, Olga Nitta, Tomoko Niwano, Masashi Pomeroy, John Raleigh, Mark S. Schaedler, Gerd Semenov, Vladimir Smirnova, Tanya G. Stacke, Tobias Strasser, Ulrich Svenson, Sean Turkov, Dmitry Wang, Tao Wever, Nander Yuan, Hua Zhou, Wenyan Zhu, Dan Menard, Cecile/F-7860-2014; Raleigh, Mark S/M-7687-2015; Krinner, Gerhard/A-6450-2011; Balsamo, Gianpaolo/M-5734-2019; Flanner, Mark/C-6139-2011; Nasonova, Olga/B-6093-2014; KIM, HYUNGJUN/I-5099-2014; Dutra, Emanuel/A-3774-2010; Turkov, Dmitry/Y-3186-2018; gusev, yeugeniy/G-4711-2014; Kontu, Anna/O-8886-2014; Zhu, Dan/J-4450-2019; Balsamo, Gianpaolo/I-3362-2013; Niwano, Masashi/N-6723-2016 Menard, Cecile/0000-0003-2166-9523; Raleigh, Mark S/0000-0002-1303-3472; Krinner, Gerhard/0000-0002-2959-5920; Balsamo, Gianpaolo/0000-0002-1745-3634; Flanner, Mark/0000-0003-4012-174X; KIM, HYUNGJUN/0000-0003-1083-8416; Dutra, Emanuel/0000-0002-0643-2643; Turkov, Dmitry/0000-0002-1813-757X; gusev, yeugeniy/0000-0003-3886-2143; Kontu, Anna/0000-0001-6880-6260; Zhu, Dan/0000-0002-5857-1899; Balsamo, Gianpaolo/0000-0002-1745-3634; Fang, Xing/0000-0002-4333-4815; Decharme, Bertrand/0000-0002-8661-1464; Niwano, Masashi/0000-0003-3121-3802 1991-9603; International audience; This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local-and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercom-parison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).

Published
2018

40. Changing pattern of ice flow and mass balance for glaciers discharging into the Larsen A and B embayments, Antarctic Peninsula, 2011 to 2016

Author

Rott, Helmut, Abdel Jaber, Wael, Wuite, Jan, Scheiblauer, Stefan, Floricioiu, Dana, Van Wessem, Jan Melchior, Nagler, Thomas, Miranda, Nuno, Van Den Broeke, Michiel R., Sub Dynamics Meteorology, Marine and Atmospheric Research, Sub Dynamics Meteorology, and Marine and Atmospheric Research

Subjects
Glacier ice accumulation, 010504 meteorology & atmospheric sciences, Ice stream, 0211 other engineering and technologies, 02 engineering and technology, Antarctic sea ice, 01 natural sciences, Ice shelf, Antarctic Peninsula, Sea ice, Cryosphere, Geomorphology, lcsh:Environmental sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Water Science and Technology, Earth-Surface Processes, lcsh:GE1-350, geography, geography.geographical_feature_category, lcsh:QE1-996.5, calving flux, Glacier morphology, lcsh:Geology, SAR-Signalverarbeitung, Physical geography, Ice sheet, TanDEM-X, Geology, mass balance
Abstract

We analysed volume change and mass balance of outlet glaciers on the northern Antarctic Peninsula over the periods 2011 to 2013 and 2013 to 2016, using high-resolution topographic data from the bistatic interferometric radar satellite mission TanDEM-X. Complementary to the geodetic method that applies DEM differencing, we computed the net mass balance of the main outlet glaciers using the mass budget method, accounting for the difference between the surface mass balance (SMB) and the discharge of ice into an ocean or ice shelf. The SMB values are based on output of the regional climate model RACMO version 2.3p2. To study glacier flow and retrieve ice discharge we generated time series of ice velocity from data from different satellite radar sensors, with radar images of the satellites TerraSAR-X and TanDEM-X as the main source. The study area comprises tributaries to the Larsen A, Larsen Inlet and Prince Gustav Channel embayments (region A), the glaciers calving into the Larsen B embayment (region B) and the glaciers draining into the remnant part of the Larsen B ice shelf in Scar Inlet (region C). The glaciers of region A, where the buttressing ice shelf disintegrated in 1995, and of region B (ice shelf break-up in 2002) show continuing losses in ice mass, with significant reduction of losses after 2013. The mass balance numbers for the grounded glacier area of region A are −3.98 ± 0.33 Gt a−1 from 2011 to 2013 and −2.38 ± 0.18 Gt a−1 from 2013 to 2016. The corresponding numbers for region B are −5.75 ± 0.45 and −2.32 ± 0.25 Gt a−1. The mass balance in region C during the two periods was slightly negative, at −0.54 ± 0.38 Gt a−1 and −0.58 ± 0.25 Gt a−1. The main share in the overall mass losses of the region was contributed by two glaciers: Drygalski Glacier contributing 61 % to the mass deficit of region A, and Hektoria and Green glaciers accounting for 67 % to the mass deficit of region B. Hektoria and Green glaciers accelerated significantly in 2010–2011, triggering elevation losses up to 19.5 m a−1 on the lower terminus during the period 2011 to 2013 and resulting in a mass balance of −3.88 Gt a−1. Slowdown of calving velocities and reduced calving fluxes in 2013 to 2016 coincided with years in which ice mélange and sea ice cover persisted in proglacial fjords and bays during summer.

Published
2017

44. Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data

Author

Rizzoli, Paola, Martone, Michele, Rott, Helmut, and Moreira, Alberto

Subjects
volume decorrelation, Ice-sheet dynamics, 010504 meteorology & atmospheric sciences, Greenland, 0211 other engineering and technologies, Greenland ice sheet, 02 engineering and technology, 01 natural sciences, Physics::Geophysics, Interferometric synthetic aperture radar, snow facies, Ice Sheet, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, geography, geography.geographical_feature_category, TanDEM-X, SAR, interferometric coherence, Firn, Snow, Interferometry, Facies, General Earth and Planetary Sciences, Astrophysics::Earth and Planetary Astrophysics, Ice sheet, Geology
Abstract

This paper presents for the first time a detailed study on information content of X-band single-pass interferometric spaceborne SAR data with respect to snow facies characterization. An approach for classifying different snow facies of the Greenland Ice Sheet by exploiting X-band TanDEM-X interferometric synthetic aperture radar acquisitions is firstly detailed. Large-scale mosaics of radar backscatter and volume correlation factor, derived from quicklook images of the interferometric coherence, represent the starting point for applying an unsupervised classification method based on the c-means fuzzy clustering algorithm. The data was acquired during winter 2010/2011. A partition of four different snow facies was chosen and interpreted using reference melt data, snow density, and in situ measurements. The variations in the stratification and micro-structure of firn, such as the variations of density with depth and the presence of percolation features, are identified as relevant parameters for explaining the significant differences in the observed interferometric signatures among different snow facies. Moreover, a statistical analysis of backscatter and volume correlation factor provided useful parameters for characterizing the snow facies behavior and analyzing their dependency on the acquisition geometry. Finally, knowing the location and characterization of the different snow facies, the two-way X-band penetration depth over the whole Ice Sheet was estimated. The obtained mean values vary from 2.3 m for the outer snow facies up to 4.18 m for the inner one. The presented approach represents a starting point for a long-term monitoring of ice sheet dynamics, by acquiring time-series, and is of high relevance for the design of future SAR missions as well.

Published
2017
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47. An improved algorithm for retrieval of snow wetness using C-band AIRSAR

Author

Shi, Jiancheng, Dozier, Jeff, and Rott, Helmut

Subjects
Earth Resources And Remote Sensing
Abstract

This study shows recent results of our efforts to develop and verify an algorithm for snow wetness retrieval from a polarimetric SAR (Synthetic Aperture Radar). Our algorithm is based on the first-order scattering model with consideration of both surface and volume scattering. It operates at C-band and requires only rough information about the ice volume fraction in snowpack. Comparing ground measurements and inferred from JPL AIRSAR data, the results showed that the relative error inferred from SAR imagery was within 25 percent. The inferred snow wetness from different looking geometries (two flight passes) provided consistent results within 2 percent. Both regional and point measurement comparisons between the ground and SAR derived snow wetness indicates that the inversion algorithm performs well using AIRSAR (Airborne Synthetic Aperture Radar) data and should prove useful for routine and large-area snow wetness (in top layer of a snowpack) measurements.

Published
1993

48. High Resolution Wide Swath SAR Applications Study - Final Report: Conclusions and Recommendations

Author

Sanjuan Ferrer, Maria Jose, Lopez Dekker, Paco, Hajnsek, Irena, Younis, Marwan, Bordoni, Federica, Prats, Pau, Adamiuk, Grzegorz, Zonno, Mariantonietta, Hanssen, Ramon, van Leijen, Freek, Skriver, Henning, Danielson, Rick, Nagler, Thomas, Rott, Helmut, Pedersen, Leif T., Lang, Oliver, Borla Tridon, Daniela, and Börner, Thomas

Subjects
performance model, system requirements, observation concept, SAR applications, mission analysis, observation plan, performance analysis, SAR, High Resolution Wide Swath
Published
2016

49. Polarimetric and multifrequency SAR signatures of wet snow

Author

Rott, Helmut, Davis, Robert E, and Dozier, Jeff

Subjects
Earth Resources And Remote Sensing
Abstract

C-, L-, and P-band polarimetric signatures of wet snow surfaces have been analyzed, based on airborne synthetic aperture radar (AIRSAR) surveys of an Alpine test site. The importance of surface roughness is evident in the C- and L-band signatures, whereas the diffuse scattering contribution by internal inhomogeneities in the snowpack increases from the C- to the P-band at incidence angles below 50 deg due to increasing penetration.

Published
1992

50. Snow and glacier mapping with polarimetric SAR

Author

Shi, Jiancheng, Dozier, Jeff, Rott, Helmut, and Davis, Robert E

Subjects
Earth Resources And Remote Sensing
Abstract

The objective of this study was to examine the capability of mapping snow and glaciers in alpine regions using synthetic aperture radar (SAR) imagery when topographic information is not available. The topographic effects on the received power for a resolution cell can be explained by the change in illumination area and incidence angle in a slant-rante representation of SAR imagery. The specific polarization signatures and phase difference between HH and VV components are relatively independent of the illuminated are, and the incidence angle has only a small effect on these parameters. They provide a suitable measurement data set for snow and glacier mapping in a high-relief area. The results show that the C-band images of the enhancement factor, the phase difference between HH and VV scattering components, and the normalized cross product of VV scattering elements provide the capability to discriminate among snow with different wetnesses, glaciers, and rocky regions.

Published
1991

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