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1. How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

Author

Farinotti, Daniel, Brinkerhoff, Douglas J, Clarke, Garry KC, Fürst, Johannes J, Frey, Holger, Gantayat, Prateek, Gillet-Chaulet, Fabien, Girard, Claire, Huss, Matthias, Leclercq, Paul W, Linsbauer, Andreas, Machguth, Horst, Martin, Carlos, Maussion, Fabien, Morlighem, Mathieu, Mosbeux, Cyrille, Pandit, Ankur, Portmann, Andrea, Rabatel, Antoine, Ramsankaran, RAAJ, Reerink, Thomas J, Sanchez, Olivier, Stentoft, Peter A, Kumari, Sangita Singh, van Pelt, Ward JJ, Anderson, Brian, Benham, Toby, Binder, Daniel, Dowdeswell, Julian A, Fischer, Andrea, Helfricht, Kay, Kutuzov, Stanislav, Lavrentiev, Ivan, McNabb, Robert, Gudmundsson, G Hilmar, Li, Huilin, and Andreassen, Liss M

Subjects
Earth Sciences, Physical Geography and Environmental Geoscience, Oceanography, Meteorology & Atmospheric Sciences, Physical geography and environmental geoscience
Abstract

Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably - locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24% of the mean ice thickness (1σ estimate). Models relying on multiple data sets - such as surface ice velocity fields, surface mass balance, or rates of ice thickness change - showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.

Published
2017

3. Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation

Author

Research Council of Norway, Centre National D'Etudes Spatiales (France), Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), Alonso-González, Esteban [0000-0002-1883-3823], López-Moreno, Juan I. [0000-0002-7270-9313], Alonso-González, Esteban, Aalstad, Kristoffer, Pirk, Norbert, Mazzolini, Marco, Treichler, Désirée, Leclercq, Paul, Westermann, Sebastian, López-Moreno, Juan I., Gascoin, Simon, Research Council of Norway, Centre National D'Etudes Spatiales (France), Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), Alonso-González, Esteban [0000-0002-1883-3823], López-Moreno, Juan I. [0000-0002-7270-9313], Alonso-González, Esteban, Aalstad, Kristoffer, Pirk, Norbert, Mazzolini, Marco, Treichler, Désirée, Leclercq, Paul, Westermann, Sebastian, López-Moreno, Juan I., and Gascoin, Simon

Abstract

Data assimilation techniques that integrate available observations with snow models have been proposed as a viable option to simultaneously help constrain model uncertainty and add value to observations by improving estimates of the snowpack state. However, the propagation of information from spatially sparse observations in high-resolution simulations remains an under-explored topic. To remedy this, the development of data assimilation techniques that can spread information in space is a crucial step. Herein, we examine the potential of spatio-temporal data assimilation for integrating sparse snow depth observations with hyper-resolution (5 m) snow simulations in the Izas central Pyrenean experimental catchment (Spain). Our experiments were developed using the Multiple Snow Data Assimilation System (MuSA) with new improvements to tackle the spatio-temporal data assimilation. Therein, we used a deterministic ensemble smoother with multiple data assimilation (DES-MDA) with domain localization. Three different experiments were performed to showcase the capabilities of spatio-temporal information transfer in hyper-resolution snow simulations. Experiment I employed the conventional geographical Euclidean distance to map the similarity between cells. Experiment II utilized the Mahalanobis distance in a multi-dimensional topographic space using terrain parameters extracted from a digital elevation model. Experiment III utilized a more direct mapping of snowpack similarity from a single complete snow depth map together with the easting and northing coordinates. Although all experiments showed a noticeable improvement in the snow patterns in the catchment compared with the deterministic open loop in terms of correlation (r=0.13) and root mean square error (RMSE = 1.11 m), the use of topographical dimensions (Experiment II, r=0.63 and RMSE = 0.89 m) and observations (Experiments III, r=0.92 and RMSE = 0.44 m) largely outperform the simulated patterns in Experiment I (r=0.3

Published
2023

4. Global clustering of recent glacier surges from radar backscatter data, 2017–2022.

Author

Kääb, Andreas, Bazilova, Varvara, Leclercq, Paul Willem, Mannerfelt, Erik Schytt, and Strozzi, Tazio

Subjects
BACKSCATTERING, GLACIERS, RADAR, ALPINE glaciers, OPTICAL images
Abstract

Using global Sentinel-1 radar backscatter data, we systematically map the locations of glaciers with surge-type activity during 2017–22. Patterns of pronounced increases or decreases in the strongest backscatter between two winter seasons often indicate large changes in glacier crevassing, which we treat here as a sign of surge-type activity. Validations against velocity time series, terminus advances and crevassing found in optical satellite images confirm the robustness of this approach. We find 115 surge-type events globally between 2017 and 2022, around 100 of which on glaciers already know as surge-type. Our data reveal a pronounced spatial clustering in three regions, (i) Karakoram, Pamirs and Western Kunlun Shan (~50 surges), (ii) Svalbard (~25) and (iii) Yukon/Alaska (~9), with only a few other scattered surges elsewhere. This spatial clustering is significantly more pronounced than the overall global clustering of known surge-type glaciers. The 2017–22 clustering may point to climatic forcing of surge initiation. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation.

Author

Alonso-González, Esteban, Aalstad, Kristoffer, Pirk, Norbert, Mazzolini, Marco, Treichler, Désirée, Leclercq, Paul, Westermann, Sebastian, López-Moreno, Juan Ignacio, and Gascoin, Simon

Subjects
SNOW accumulation, SATELLITE-based remote sensing, KALMAN filtering, DIGITAL elevation models, EUCLIDEAN distance, SURFACE temperature, SURFACE area
Abstract

Monitoring the snowpack remains challenging in part due to the limited availability of observations. On the one hand, the deployment of dense ground-based monitoring networks is hampered by logistical hurdles. On the other hand, satellite-based remote sensing products provide only partial information about the snowpack, often limited to snow-covered area or surface temperature. Numerical models are a valuable tool to help fill the gaps in snowpack monitoring. Model performance is nonetheless contingent upon the quality of meteorological forcing, which is often highly uncertain especially in complex terrain. To address these limitations, data assimilation techniques that integrate available observations with snow models have been proposed as a viable option to simultaneously help constrain model uncertainty and add value to observations by improving estimates of the snowpack state. However, the propagation of information from spatially sparse observations in high resolution simulations remains an under-explored topic. To remedy this, the development of data assimilation techniques that can spread information in space is a crucial step. Herein, we examine the potential of spatio-temporal data assimilation for integrating sparse snow depth observations with hyper-resolution (5 m) snow simulations in the Izas central Pyrenean experimental catchment (Spain). Our experiments were developed using the Multiple Snow Data Assimilation System (MuSA) with new improvements to tackle the spatio-temporal data assimilation. Therein, we used a Deterministic Ensemble Smoother with Multiple Data Assimilation (DES-MDA) with domain localization. Three different experiments were performed to showcase capabilities of spatio-temporal information transfer in hyper-resolution snow simulations. Experiment I employed the conventional geographical Euclidean distance to map the similarity between cells. Experiment II utilized the Mahalanobis distance in a multi-dimensional topographic space using terrain parameters extracted from a digital elevation model. Experiment III utilized a more direct mapping of snowpack similarity from a single complete snow depth map together with the easting and northing coordinates. Although all experiments showed a noticeable improvement in the snow patterns in the catchment compared with the deterministic open loop in terms of correlation (r = 0.13) and root-mean-square error (RMSE = 1.11 m), the use of topographical dimensions (Experiment II, r = 0.63 and RMSE = 0.89 m) and observations (Experiments III, r = 0.92 and RMSE = 0.44 m) largely outperform the simulated patterns in Experiment I (r = 0.38 and RMSE = 1.16 m). At the same time, Experiments II & III are considerably more challenging to set up. The results of these experiments can help pave the way for the creation of snow reanalysis and forecasting tools that can seamlessly integrate sparse information from national monitoring networks and high-resolution satellite information. [ABSTRACT FROM AUTHOR]

Published
2023
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6. Brief communication: Detection of glacier surge activity using cloud computing of Sentinel-1 radar data

Author

Leclercq, Paul, Kääb, Andreas, Altena, Bas, Sub Dynamics Meteorology, Marine and Atmospheric Research, Sub Dynamics Meteorology, and Marine and Atmospheric Research

Subjects
010504 meteorology & atmospheric sciences, Backscatter, 0211 other engineering and technologies, Climate change, Cloud computing, 02 engineering and technology, 01 natural sciences, law.invention, law, GE1-350, Surge, Radar, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Earth-Surface Processes, Water Science and Technology, geography, QE1-996.5, geography.geographical_feature_category, business.industry, Global Map, Glacier, Geology, Snow, Environmental sciences, Climatology, business
Abstract

For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges, and some other special events such as large rock and snow avalanches.

Published
2021

7. L’évaporation sous vide : un procédé qui sait se réinventer.

Author

Leclercq, Paul

Subjects
INDUSTRIALISTS, WASTE management, SEWAGE, ENERGY consumption, INDUSTRIES
Abstract

Vacuum evaporation provides answers to industrial issues that are more relevant than ever, such as controlling energy costs, treating effluents, and reducing the volume of waste. Thanks to proven results over recent decades, and to the innovations that allow it to improve, it still constitutes an essential solution for manufacturers in different sectors. [ABSTRACT FROM AUTHOR]

Published
2024

12. Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2)

Author

Farinotti, Daniel, Brinkerhoff, Douglas J., Fuerst, Johannes J., Gantayat, Prateek, Gillet-Chaulet, Fabien, Huss, Matthias, Leclercq, Paul W., Maurer, Hansruedi, Morlighem, Mathieu, Pandit, Ankur, Rabatel, Antoine, Ramsankaran, R. A. A. J., Reerink, Thomas J., Robo, Ellen, Rouges, Emmanuel, Tamre, Erik, van Pelt, Ward J. J., Werder, Mauro A., Azam, Mohod Farooq, Li, Huilin, Andreassen, Liss M., Farinotti, Daniel, Brinkerhoff, Douglas J., Fuerst, Johannes J., Gantayat, Prateek, Gillet-Chaulet, Fabien, Huss, Matthias, Leclercq, Paul W., Maurer, Hansruedi, Morlighem, Mathieu, Pandit, Ankur, Rabatel, Antoine, Ramsankaran, R. A. A. J., Reerink, Thomas J., Robo, Ellen, Rouges, Emmanuel, Tamre, Erik, van Pelt, Ward J. J., Werder, Mauro A., Azam, Mohod Farooq, Li, Huilin, and Andreassen, Liss M.

Abstract

Knowing the ice thickness distribution of a glacier is of fundamental importance for a number of applications, ranging from the planning of glaciological fieldwork to the assessments of future sea-level change. Across spatial scales, however, this knowledge is limited by the paucity and discrete character of available thickness observations. To obtain a spatially coherent distribution of the glacier ice thickness, interpolation or numerical models have to be used. Whilst the first phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX) focused on approaches that estimate such spatial information from characteristics of the glacier surface alone, ITMIX2 sought insights for the capability of the models to extract information from a limited number of thickness observations. The analyses were designed around 23 test cases comprising both real-world and synthetic glaciers, with each test case comprising a set of 16 different experiments mimicking possible scenarios of data availability. A total of 13 models participated in the experiments. The results show that the inter-model variability in the calculated local thickness is high, and that for unmeasured locations, deviations of 16% of the mean glacier thickness are typical (median estimate, three-quarters of the deviations within 37% of the mean glacier thickness). This notwithstanding, limited sets of ice thickness observations are shown to be effective in constraining the mean glacier thickness, demonstrating the value of even partial surveys. Whilst the results are only weakly affected by the spatial distribution of the observations, surveys that preferentially sample the lowest glacier elevations are found to cause a systematic underestimation of the thickness in several models. Conversely, a preferential sampling of the thickest glacier parts proves effective in reducing the deviations. The response to the availability of ice thickness observations is characteristic to each approach and varies across m

Published
2021
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16. Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2)

Author

Farinotti, Daniel, primary, Brinkerhoff, Douglas J., additional, Fürst, Johannes J., additional, Gantayat, Prateek, additional, Gillet-Chaulet, Fabien, additional, Huss, Matthias, additional, Leclercq, Paul W., additional, Maurer, Hansruedi, additional, Morlighem, Mathieu, additional, Pandit, Ankur, additional, Rabatel, Antoine, additional, Ramsankaran, RAAJ, additional, Reerink, Thomas J., additional, Robo, Ellen, additional, Rouges, Emmanuel, additional, Tamre, Erik, additional, van Pelt, Ward J. J., additional, Werder, Mauro A., additional, Azam, Mohod Farooq, additional, Li, Huilin, additional, and Andreassen, Liss M., additional

Published
2021
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17. Global sea-level budget 1993–present

Author

Group, WCRP Global Sea Level Budget, Cazenave, Anny, Meyssignac, Benoit, Ablain, Michael, Balmaseda, Magdalena, Bamber, Jonathan, Barletta, Valentina Roberta, Beckley, Brian, Benveniste, Jérôme, Berthier, Etienne, Blazquez, Alejandro, Boyer , Tim, Caceres , Denise, Chambers, Don P., Champollion, Nicolas, Chao , Ben, Chen , Jianli, Cheng , Lijing, Church , John A., Chuter, Stephen, Cogley , J. Graham, Dangendorf , Soenke, Desbruyères , Damien, Döll , Petra, Domingues, Catia, Falk , Ulrike, Famiglietti , James, Fenoglio-Marc, Luciana, Forsberg, René, Galassi , Gaia, Gardner, Alex, Groh, Andreas, Hamlington , Benjamin, Hogg, Anna, Horwath, Martin, Humphrey , Vincent, Husson , Laurent, Ishii , Masayoshi, Jaeggi , Adrian, Jevrejeva , Svetlana, Johnson , Gregory, Kolodziejczyk , Nicolas, Kusche , Jürgen, Lambeck , Kurt, Landerer , Felix, Leclercq , Paul, Legresy , Benoit, Leuliette , Eric, Llovel, William, Longuevergne , Laurent, Loomis , Bryant D., Luthcke, Scott B, Marcos, Marta, Marzeion , Ben, Merchant , Chris, Merrifield , Mark, Milne, Glenn, Mitchum , Gary, Mohajerani, Yara, Monier , Maeva, Monselesan , Didier, Nerem , Steve, Palanisamy , Hindumathi, Paul, Frank, Pérez, Begona, Piecuch , Christopher G., Ponte , Rui M., Purkey , Sarah G., Reager , John T., Rietbroek , Roelof, Rignot, Eric, Riva, Riccardo, Roemmich , Dean H., Sørensen, Louise Sandberg, Sasgen, Ingo, Schram, E.J.O., Seneviratne , Sonia I., Shum, C.K., Spada, Giorgio, Stammer, Detlef, van de Wal , Roderic, Velicogna, Isabella, von Schuckmann, Karina, Wada, Yoshihide, Wang , Yiguo, Watson, Christopher S., Wiese, David, Wijffels , Susan, Westaway , Richard, Woppelmann, Guy, Wouters, Bert, Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), International Space Science Institute [Bern] (ISSI), Géosciences Rennes (GR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Collecte Localisation Satellites (CLS), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National d'Études Spatiales [Toulouse] (CNES), European Centre for Medium-Range Weather Forecasts (ECMWF), University of New South Wales [Sydney] (UNSW), Universität Siegen [Siegen], Institut Français de Recherche pour l'Exploitation de la Mer - Brest (IFREMER Centre de Bretagne), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), National Oceanographic Centre [Liverpool] (NOC ), Australian National University (ANU), CSIRO Marine and Atmosphere Research [Hobart], Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut Mediterrani d'Estudis Avancats (IMEDEA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universidad de las Islas Baleares (UIB), University of Tasmania [Hobart, Australia] (UTAS), LIttoral ENvironnement et Sociétés - UMRi 7266 (LIENSs), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS), Hubrecht Institute [Utrecht, Netherlands], University Medical Center [Utrecht]-Royal Netherlands Academy of Arts and Sciences (KNAW), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Cazenave, Anny, Meyssignac, Benoit, Ablain, Michael, Balmaseda, Magdalena, Bamber, Jonathan, Barletta, Valentina, Beckley, Brian, Benveniste, Jérôme, Berthier, Etienne, Blazquez, Alejandro, Boyer, Tim, Caceres, Denise, Chambers, Don, Champollion, Nicola, Chao, Ben, Chen, Jianli, Cheng, Lijing, Church, John A., Chuter, S., Cogley, J., Dangendorf, Soenke, Desbruyères, Damien, Döll, Petra, Domingues, Catia, Falk, Ulrike, Famiglietti, Jame, Fenoglio-Marc, Luciana, Forsberg, Rene, Galassi, Gaia, Gardner, Alex, Groh, Andrea, Hamlington, Benjamin, Hogg, Anna, Horwath, Martin, Humphrey, Vincent, Husson, Laurent, Ishii, Masayoshi, Jaeggi, Adrian, Jevrejeva, Svetlana, Johnson, Gregory, Kolodziejczyk, Nicola, Kusche, Jürgen, Lambeck, Kurt, Landerer, Felix, Leclercq, Paul, Legresy, Benoit, Leuliette, Eric, Llovel, William, Longuevergne, Laurent, Loomis, Bryant D., Luthcke, Scott B., Marcos, Marta, Marzeion, Ben, Merchant, Chri, Merrifield, Mark, Milne, Glenn, Mitchum, Gary, Mohajerani, Yara, Monier, Maeva, Monselesan, Didier, Nerem, Steve, Palanisamy, Hindumathi, Paul, Frank, Perez, Begoña, Piecuch, Christopher G., Ponte, Rui M., Purkey, Sarah G., Reager, John T., Rietbroek, Roelof, Rignot, Eric, Riva, Riccardo, Roemmich, Dean H., Sandberg Sørensen, Louise, Sasgen, Ingo, Schrama, E. J. O., Seneviratne, Sonia I., Shum, C. K., Spada, Giorgio, Stammer, Detlef, van de Wal, Roderic, Velicogna, Isabella, von Schuckmann, Karina, Wada, Yoshihide, Wang, Yiguo, Watson, Christopher, Wiese, David, Wijffels, Susan, Westaway, Richard, Woppelmann, Guy, and Wouters, Bert

Subjects
010504 meteorology & atmospheric sciences, Climate change, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Context (language use), sea level, 010502 geochemistry & geophysics, 01 natural sciences, Deep sea, SDG 13 - Climate Action, sea level, sea level change, gauge records, 14. Life underwater, Altimeter, Sea level, ComputingMilieux_MISCELLANEOUS, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, geography, geography.geographical_feature_category, 520 Astronomy, Global warming, lcsh:QE1-996.5, Glacier, lcsh:Geology, 13. Climate action, Climatology, gauge records, General Earth and Planetary Sciences, Environmental science, GlobalMass, Ice sheet, sea level change
Abstract

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitledRegional Sea Level and Coastal Impacts, an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017–2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 ± 0.3 mm yr−1and acceleration of 0.1 mm yr−2over 1993–present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42 %, 21 %, 15 % and 8 % to the global mean sea level over the 1993–present period. We also study the sea-level budget over 2005–present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3 mm yr−1(1σ). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level.

Published
2018

18. Global sea-level budget 1993-present

Author

Cazenave, Anny, Meyssignac, Benoit, Ablain, Michael, Balmaseda, Magdalena, Bamber, Jonathan, Barletta, Valentina, Beckley, Brian, Benveniste, Jerome, Berthier, Etienne, Blazquez, Alejandro, Boyer, Tim, Caceres, Denise, Chambers, Don, Champollion, Nicolas, Chao, Ben, Chen, Jianli, Cheng, Lijing, Church, John A., Chuter, Stephen, Cogley, J. Graham, Dangendorf, Soenke, Desbruyeres, Damien, Doell, Petra, Domingues, Catia, Falk, Ulrike, Famiglietti, James, Fenoglio-marc, Luciana, Forsberg, Rene, Galassi, Gaia, Gardner, Alex, Groh, Andreas, Hamlington, Benjamin, Hogg, Anna, Horwath, Martin, Humphrey, Vincent, Husson, Laurent, Ishii, Masayoshi, Jaeggi, Adrian, Jevrejeva, Svetlana, Johnson, Gregory, Kolodziejczyk, Nicolas, Kusche, Juergen, Lambeck, Kurt, Landerer, Felix, Leclercq, Paul, Legresy, Benoit, Leuliette, Eric, Llovel, William, Longuevergne, Laurent, Loomis, Bryant D., Luthcke, Scott B., Marcos, Marta, Marzeion, Ben, Merchant, Chris, Merrifield, Mark, Milne, Glenn, Mitchum, Gary, Mohajerani, Yara, Monier, Maeva, Monselesan, Didier, Nerem, Steve, Palanisamy, Hindumathi, Paul, Frank, Perez, Begona, Piecuch, Christopher G., Ponte, Rui M., Purkey, Sarah G., Reager, John T., Rietbroek, Roelof, Rignot, Eric, Riva, Riccardo, Roemmich, Dean H., Sorensen, Louise Sandberg, Sasgen, Ingo, Schrama, E. J. O., Seneviratne, Sonia I., Shum, C. K., Spada, Giorgio, Stammer, Detlef, Van De Wal, Roderic, Velicogna, Isabella, Von Schuckmann, Karina, Wada, Yoshihide, Wang, Yiguo, Watson, Christopher, Wiese, David, Wijffels, Susan, Westaway, Richard, Woppelmann, Guy, Wouters, Bert, Cazenave, Anny, Meyssignac, Benoit, Ablain, Michael, Balmaseda, Magdalena, Bamber, Jonathan, Barletta, Valentina, Beckley, Brian, Benveniste, Jerome, Berthier, Etienne, Blazquez, Alejandro, Boyer, Tim, Caceres, Denise, Chambers, Don, Champollion, Nicolas, Chao, Ben, Chen, Jianli, Cheng, Lijing, Church, John A., Chuter, Stephen, Cogley, J. Graham, Dangendorf, Soenke, Desbruyeres, Damien, Doell, Petra, Domingues, Catia, Falk, Ulrike, Famiglietti, James, Fenoglio-marc, Luciana, Forsberg, Rene, Galassi, Gaia, Gardner, Alex, Groh, Andreas, Hamlington, Benjamin, Hogg, Anna, Horwath, Martin, Humphrey, Vincent, Husson, Laurent, Ishii, Masayoshi, Jaeggi, Adrian, Jevrejeva, Svetlana, Johnson, Gregory, Kolodziejczyk, Nicolas, Kusche, Juergen, Lambeck, Kurt, Landerer, Felix, Leclercq, Paul, Legresy, Benoit, Leuliette, Eric, Llovel, William, Longuevergne, Laurent, Loomis, Bryant D., Luthcke, Scott B., Marcos, Marta, Marzeion, Ben, Merchant, Chris, Merrifield, Mark, Milne, Glenn, Mitchum, Gary, Mohajerani, Yara, Monier, Maeva, Monselesan, Didier, Nerem, Steve, Palanisamy, Hindumathi, Paul, Frank, Perez, Begona, Piecuch, Christopher G., Ponte, Rui M., Purkey, Sarah G., Reager, John T., Rietbroek, Roelof, Rignot, Eric, Riva, Riccardo, Roemmich, Dean H., Sorensen, Louise Sandberg, Sasgen, Ingo, Schrama, E. J. O., Seneviratne, Sonia I., Shum, C. K., Spada, Giorgio, Stammer, Detlef, Van De Wal, Roderic, Velicogna, Isabella, Von Schuckmann, Karina, Wada, Yoshihide, Wang, Yiguo, Watson, Christopher, Wiese, David, Wijffels, Susan, Westaway, Richard, Woppelmann, Guy, and Wouters, Bert

Abstract

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g.,acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled "Regional Sea Level and Coastal Impacts", an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3mm yr(-1) and acceleration of 0.1 mm yr(-2) over 1993-present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present period. We also study t

Published
2018
Full Text
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20. Supplementary material to "How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment"

Author

Farinotti, Daniel, primary, Brinkerhoff, Douglas, additional, Clarke, Garry K.C., additional, Fürst, Johannes J., additional, Frey, Holger, additional, Gantayat, Prateek, additional, Gillet-Chaulet, Fabien, additional, Girard, Claire, additional, Huss, Matthias, additional, Leclercq, Paul W., additional, Linsbauer, Andreas, additional, Machguth, Horst, additional, Martin, Carlos, additional, Maussion, Fabien, additional, Morlighem, Mathieu, additional, Mosbeux, Cyrille, additional, Pandit, Ankur, additional, Portmann, Andrea, additional, Rabatel, Antoine, additional, Ramsankaran, RAAJ, additional, Reerink, Thomas J., additional, Sanchez, Olivier, additional, Stentoft, Peter A., additional, Singh Kumari, Sangita, additional, van Pelt, Ward J.J., additional, Anderson, Brian, additional, Benham, Toby, additional, Binder, Daniel, additional, Dowdeswell, Julian A., additional, Fischer, Andrea, additional, Helfricht, Kay, additional, Kutuzov, Stanislav, additional, Lavrentiev, Ivan, additional, McNabb, Robert, additional, Gudmundsson, G. Hilmar, additional, Li, Huilin, additional, and Andreassen, Liss M., additional

Published
2016
Full Text
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21. How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

Author

Farinotti, Daniel, primary, Brinkerhoff, Douglas, additional, Clarke, Garry K.C., additional, Fürst, Johannes J., additional, Frey, Holger, additional, Gantayat, Prateek, additional, Gillet-Chaulet, Fabien, additional, Girard, Claire, additional, Huss, Matthias, additional, Leclercq, Paul W., additional, Linsbauer, Andreas, additional, Machguth, Horst, additional, Martin, Carlos, additional, Maussion, Fabien, additional, Morlighem, Mathieu, additional, Mosbeux, Cyrille, additional, Pandit, Ankur, additional, Portmann, Andrea, additional, Rabatel, Antoine, additional, Ramsankaran, RAAJ, additional, Reerink, Thomas J., additional, Sanchez, Olivier, additional, Stentoft, Peter A., additional, Singh Kumari, Sangita, additional, van Pelt, Ward J.J., additional, Anderson, Brian, additional, Benham, Toby, additional, Binder, Daniel, additional, Dowdeswell, Julian A., additional, Fischer, Andrea, additional, Helfricht, Kay, additional, Kutuzov, Stanislav, additional, Lavrentiev, Ivan, additional, McNabb, Robert, additional, Gudmundsson, G. Hilmar, additional, Li, Huilin, additional, and Andreassen, Liss M., additional

Published
2016
Full Text
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24. Ibis / Paul Leclercq,...

Author

Leclercq, Paul (1872-1956). Auteur du texte and Leclercq, Paul (1872-1956). Auteur du texte

Abstract

Avec mode texte

Published
1893

27. Global sea-level budget 1993-present

Author

Cazenave, Anny, Meyssignac, Benoit, Ablain, Michael, Balmaseda, Magdalena, Bamber, Jonathan, Barletta, Valentina, Beckley, Brian, Benveniste, Jerome, Berthier, Etienne, Blazquez, Alejandro, Boyer, Tim, Cáceres, Denise, Chambers, Don, Champollion, Nicolas, Chao, Ben, Chen, Jianli, Cheng, Lijing, Church, John A., Chuter, Stephen, Cogley, J. Graham, Dangendorf, Soenke, Desbruyères, Damien, Döll, Petra, Domingues, Catia, Falk, Ulrike, Famiglietti, James, Fenoglio-Marc, Luciana, Forsberg, Rene, Galassi, Gaia, Gardner, Alex, Groh, Andreas, Hamlington, Benjamin, Hogg, Anna, Horwath, Martin, Humphrey, Vincent, Husson, Laurent, Ishii, Masayoshi, Jäggi, Adrian, Jevrejeva, Svetlana, Johnson, Gregory C., Kolodziejczyk, Nicolas, Kusche, Jürgen, Lambeck, Kurt, Landerer, Felix, Leclercq, Paul W., Legresy, Benoit, Leuliette, Eric, Llovel, William, Longuevergne, Laurent, Loomis, Bryant D., Luthcke, Scott B., Marcos, Marta, Marzeion, Ben, Merchant, Chris, Merrifield, Mark, Milne, Glenn, Mitchum, Gary, Mohajerani, Yara, Monier, Maeva, Monselesan, Didier, Nerem, Steve, Palanisamy, Hindumathi, Paul, Frank, Perez, Begoña, Piecuch, Christopher G., Ponte, Rui M., Purkey, Sarah G., Reager, John T., Rietbroek, Roelof, Rignot, Eric, Riva, Riccardo, Roemmich, Dean, Sørensen, Louise S., Sasgen, Ingo, Schrama, E.J.O., Seneviratne, Sonia I., Shum, C.K., Spada, Giorgio, Stammer, Detlef, Velicogna, Isabella, von Schuckmann, Karina, Wada, Yoshihide, Wang, Yiguo, Watson, Christopher, Wiese, David, Wijffels, Susan, Westaway, Richard, Woppelmann, Guy, and Wouters, Bert

Subjects
13. Climate action, 14. Life underwater
Abstract

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled "Regional Sea Level and Coastal Impacts", an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017–2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1±0.3mmyr−1 and acceleration of 0.1mmyr−2 over 1993–present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42%, 21%, 15% and 8% to the global mean sea level over the 1993–present period. We also study the sea-level budget over 2005–present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3mmyr−1 (1σ). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level., Earth System Science Data, 10 (3), ISSN:1866-3516, ISSN:1866-3508

28. How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

Author

Farinotti, Daniel, Brinkerhoff, Douglas J., Clarke, Garry K.C., Fürst, Johannes J., Frey, Holger, Gantayat, Prateek, Gillet-Chaulet, Fabien, Girard, Claire, Huss, Matthias, Leclercq, Paul W., Linsbauer, Andreas, Machguth, Horst, Martin, Carlos, Maussion, Fabian, Morlighem, Mathieu, Mosbeux, Cyrille, Pandit, Ankur, Portmann, Andrea, Rabatel, Antoine, Ramsankaran, Raaj, Reerink, Thomas J., Sanchez, Olivier, Stentoft, Peter A., Kumari, Sangita Singh, van Pelt, Ward J.J., Anderson, Brian, Benham, Toby, Binder, Daniel, Dowdeswell, Julian A., Fischer, Andrea, Helfricht, Kay, Kutuzov, Stanislav, Lavrentiev, Ivan, McNabb, Robert, Gudmundsson, Gudmundur H., Li, Huilin, and Andreassen, Liss M.

Subjects
13. Climate action
Abstract

The Cryosphere Discussions, ISSN:1994-0432, ISSN:1994-0440

29. How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

Author

Farinotti, Daniel, Brinkerhoff, Douglas J., Clarke, Garry K.C., Fürst, Johannes J., Frey, Holger, Gantayat, Prateek, Gillet-Chaulet, Fabien, Girard, Claire, Huss, Matthias, Leclercq, Paul W., Linsbauer, Andreas, Machguth, Horst, Martin, Carlos, Maussion, Fabien, Morlighem, Mathieu, Mosbeux, Cyrille, Pandit, Ankur, Portmann, Andrea, Antoine Rabatel, Ramsankaran, Raaj, Reerink, Thomas J., Sanchez, Olivier, Stentoft, Peter A., Kumari, Sangita Singh, van Pelt, Ward J.J., Anderson, Brian, Benham, Toby, Binder, Daniel, Dowdeswell, Julian A., Fischer, Andrea, Helfricht, Kay, Kutuzov, Stanislav, Lavrentiev, Ivan, McNabb, Robert, Gudmundsson, Gudmundur H., Li, Huilin, and Andreassen, Liss M.

Subjects
13. Climate action
Abstract

Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches., The Cryosphere, 11 (2), ISSN:1994-0416, ISSN:1994-0424

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