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1. Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning.

Author

Mohajerani, Yara, Jeong, Seongsu, Scheuchl, Bernd, Velicogna, Isabella, Rignot, Eric, and Milillo, Pietro

Abstract

Delineating the grounding line of marine-terminating glaciers-where ice starts to become afloat in ocean waters-is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mostly-manual digitizations of differential interferometric synthetic-aperture radar interferograms by human experts. This approach is no longer viable with a fast-growing set of satellite observations and the need to establish time series over entire continents with quantified uncertainties. We present a fully-convolutional neural network with parallel atrous convolutional layers and asymmetric encoder/decoder components that automatically delineates grounding lines at a large scale, efficiently, and accompanied by uncertainty estimates. Our procedure detects grounding lines within 232 m in 100-m posting interferograms, which is comparable to the performance achieved by human experts. We also find value in the machine learning approach in situations that even challenge human experts. We use this approach to map the tidal-induced variability in grounding line position around Antarctica in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West Antarctica, we demonstrate that grounding zones are one order magnitude (13.3 ± 3.9) wider than expected from hydrostatic equilibrium, which justifies the need to map grounding lines repeatedly and comprehensively to inform numerical models.

Published
2021

2. Evaluation of Regional Climate Models Using Regionally Optimized GRACE Mascons in the Amery and Getz Ice Shelves Basins, Antarctica

Author

Mohajerani, Yara, Velicogna, Isabella, and Rignot, Eric

Subjects
Climate Action, Meteorology & Atmospheric Sciences
Abstract

We develop regionally optimized Gravity Recovery and Climate Experiment (GRACE) solutions to evaluate the mass balance of the drainage basins of Amery Ice Shelf, East Antarctica, and Getz Ice Shelf, West Antarctica. We find that the Amery region is near balance, while the Getz region is rapidly losing mass. We compare the results with the mass budget method (MBM) combining ice discharge along the periphery with surface mass balance derived from three regional climate models: (1) Regional Atmospheric Climate Model (RACMO) 2.3p1 and (2) 2.3p2, and (3) Modèle Atmosphérique Régional 3.6.41. For Amery, MBM/RACMO2.3p1 agrees with GRACE, while MBM/RACMO2.3p2 and MBM/MAR3.6.41 suggest a positive mass balance. For Getz, all estimates agree with a mass loss and the GRACE results are robust to uncertainties in glacial isostatic adjustment derived from an ensemble 128,000 forward models. Over the period April 2002 to November 2015, the mass loss of the Getz drainage basin is 22.9 ± 10.9 Gt/yr with an acceleration of 1.6 ± 0.9 Gt/yr2.

Published
2019

3. Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study

Author

Mohajerani, Yara, Wood, Michael, Velicogna, Isabella, and Rignot, Eric

Subjects
Climate Action, calving front, image segmentation, U-Net, convolutional neural network, machine learning, Greenland, Classical Physics, Physical Geography and Environmental Geoscience, Geomatic Engineering
Abstract

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set of Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m (1.89 pixels). We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.

Published
2019

4. Mass Loss of Totten and Moscow University Glaciers, East Antarctica, Using Regionally Optimized GRACE Mascons

Author

Mohajerani, Yara, Velicogna, Isabella, and Rignot, Eric

Subjects
Climate Action, Totten glacier, East Antarctica, GRACE, regional climate models, mass budget, mass balance, Meteorology & Atmospheric Sciences
Abstract

Totten and Moscow University glaciers, in the marine-based sector of East Antarctica, contain enough ice to raise sea level by 5 m. Obtaining precise measurements of their mass balance is challenging owing to large area of the basins and the small mass balance signal compared to West Antarctic glaciers. Here we employ a locally optimized processing of Gravity Recovery and Climate Experiment (GRACE) harmonics to evaluate their mass balance at the sub-basin scale and compare the results with mass budget method (MBM) estimates using regional atmospheric climate model version 2.3 (RACMO2.3) or Modèle Atmosphérique Régional version 3.6.4 (MAR3.6.4). The sub-basin mass loss estimate for April 2002 to November 2015 is 14.8 ± 4.3 Gt/yr, which is weakly affected by glacial isostatic adjustment uncertainties (±1.4 Gt/yr). This result agrees with MBM/RACMO2.3 (15.8 ± 2.0 Gt/yr), whereas MBM/MAR3.6.4 underestimates the loss (6.6 ± 1.6 Gt/yr). For the entire drainage, the mass loss for April 2002 to August 2016 is 18.5 ± 6.6 Gt/yr, or 15 ± 4% of its ice flux. These results provide unequivocal evidence for mass loss in this East Antarctic sector.

Published
2018

7. Understanding Regional Ice Sheet Mass Balance: Remote Sensing, Regional Climate Models, and Deep Learning

Author

Mohajerani, Yara

Subjects
Remote sensing, Geophysics, Geographic information science and geodesy, Deep Learning, GRACE, Ice Sheets, Machine Learning, Mass Balance, Regional Climate Models
Abstract

The Antarctic and Greenland ice sheets are experiencing significant mass change with heterogeneous spatial and temporal characteristics and global consequences such as sea level rise affecting millions of people in low-lying coastal areas. Advances in large-scale satellite remote-sensing, modeling, and machine learning have ushered a new era of improved monitoring and understanding of these changes. In this dissertation, we analyze the mass balance of glaciers across the ice sheets at basin and sub-basin scales using satellite gravimetric data from the Gravity Recovery and Climate Experiment (GRACE) mission using a novel regionally-optimized mascon methodology, as well as Mass Budget Method (MBM) estimates from grounding line discharge measurements and surface mass balance from regional climate models. We find that Totten and Moscow University glaciers in the marine sector of East Antarctica, with a total 5-meter sea level rise potential, have been losing mass at a rate of 18.5±6.6 Gt/yr from April 2002 to August 2016. The MBM estimate obtained with RACMO2.3p1 (Regional Atmospheric Climate Model version 2.3 part 1) is in excellent agreement with GRACE at a sub-basin scale, while those obtained with RACMO2.3p2 and MAR (Modèle Atmosphérique Régional) version 3.6.41 show less negative trends. These results are robust with respect to Glacial Isostatic Adjustment (GIA) uncertainty. By extending this methodology to the Amery Ice Shelf drainage basin in East Antarctica, we find this basin is in balance and is also in agreement with MBM/RACMO2.3p1 at a sub-basin scale, while MBM/RACMO2.3p2 and MBM/MAR3.6.41 produce more positive trends. The discrepancies shown by RACMO2.3p2 and MAR3.6.41 in these regions of East Antarctica are attributed to larger mean monthly SMB magnitudes. By adjusting all models to have the same mean magnitude as RACMO2.3p1, all MBM time-series fall into agreement with the independent gravimetric data. Furthermore, we implement the regional optimization approach in the Getz Ice Shelf drainage basin in West Antarctica, where previous studies have shown disagreements between GRACE and MBM estimates, and find that by minimizing leakage in the GRACE estimate, all MBM estimates are in excellent agreement with the gravimetric result. The Getz Ice Shelf basin is found to have a mass loss rate of 22.9±10.9 Gt/yr with an acceleration of 1.6±0.9 Gt/yr2 from April 2002 to November 2015 (the common time-period with the MBM estimates). We use an ensemble of 128,000 GIA forward models to ensure the results are robust with respect to GIA uncertainty. Lastly, we focus on improving the monitoring and understanding of glacier dynamics by implementing a deep Convolutional Neural Network (CNN) to automatically delineate glacier calving fronts from Landsat imagery on the Greenland Ice Sheet. By training the network on Jakobshavn, Sverdrup, and Kangerlussuaq glaciers and testing it on Helheim glacier, we demonstrate that the performance of the network is comparable to that of a human investigator, with a mean CNN error of 1.97 pixels (96.3 meters) compared to a mean human error of 1.89 pixels (92.5 meters) on the same resolution images. Thus, we show that CNNs enable large-scale monitoring of glacier dynamics across the globe, which offers new possibilities for an improved understanding of the processes affecting the mass balance of glaciers. Ultimately, a better understanding of the ice sheets is crucial for a better assessment of the effects of a changing cryosphere and sea level rise around the globe.

Published
2019

11. Update on GRACE/GRACE-FO continuity in mass loss of the glaciers and big ice sheets

Author

Isabella Velicogna, Mohajerani Yara, Enrico Ciraci, Felix Landerer, and David Wiese

Abstract

The GRACE missions have changed the way that we measure mass changes of ice sheets, glaciers and ice caps, with quantied uncertainties that factor processing errors, atmospheric and oceanic corrections, and removal of glacial isostatic adjustment (GIA). We present GRACE/GRACE-FO estimates of mass balance over the Greenland and Antarctic Ice Sheets and the World’s glaciers and ice caps (GIC). The data gap between missions is filled with GRACE-calibrated results from the input-output method for ice sheets and surface mass balance (SMB) reconstructions from regional atmospheric climate models and MERRA-2 reanalysis data for the glaciers and ice caps. Over Greenland, we report low losses during the cold years of 2017-2018 followed by record melt in 2019 and an onset of rapid melt for 2020. As warm air and ocean masses get blocked over Greenland more frequently because of interactions between the wobbling jet stream and topography, we observe more high melt events in this decade than recorded in prior centuries. In Antarctica, the ongoing rapid loss in West Antarctica dominates the mass balance, but we observe a steady increase in snowfall in the Atlantic sector of East Antarctica. The exercise provides a mass balance record that can be continuously improved with better corrections and improved processing, with reduced errors, so that we can provide better constraints for ice sheet models in charge of sea level projections and improve the validation of various Earth system models and global climate models.

Published
2020

13. Continuity of ice sheet mass loss in Greenland and Antarctica from the GRACE and GRACE Follow-Onmissions

Author

Velicogna, Isabella, Mohajerani, Yara, Geruo, A, Landerer, Felix, Mouginot, Jeremie, Noel, Brice, Rignot, Eric, Sutterley, Tyler, van den Broeke, Michiel, van Wessem, J.M., Wiese, David, Sub Dynamics Meteorology, and Marine and Atmospheric Research

Abstract

We examine data continuity between the GRACE and GRACE-FO missions over Greenland and Antarctica using independent data from the mass budget method (MBM) which calculates the difference between ice sheet surface mass balance and ice discharge at the periphery. For both ice sheets, we find consistent GRACE/GRACE-FO time series across the data gap, at the continental and regional scales, and the data gap is confidently filled with MBM data. In Greenland, the GRACE-FO data reveal an exceptional summer loss of 600 Gigatonnes in 2019 following two cold summers. In Antarctica, ongoing high mass losses in the Amundsen Sea Embayment of West Antarctica, the Antarctic Peninsula, and Wilkes Land in East Antarctica cumulate to 2130, 560, and 370 Gigatonnes, respectively, since 2002. A cumulative mass gain of 980 Gigatonnes in Queen Maud Land since 2009, however, led to a pause in the acceleration in mass loss from Antarctica after 2016.

Published
2020

14. 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

19. 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

24. Estimating changes of temperatures and precipitation extremes in India using the Generalized Extreme Value (GEV) distribution.

Author

Pangaluru, Kishore, Velicogna, Isabella, Sutterley, Tyler C., Mohajerani, Yara, Ciraci, Enrico, Sompalli, Jyothi, and Rao Saranga, Vijaya Bhaskara

Abstract

Changes in extreme temperature and precipitation may give some of the largest significant societal and ecological impacts. For changes in the magnitude of extreme temperature and precipitation over India, we used a statistical model of generalized extreme value (GEV) distribution. The GEV statistical distribution is a time-dependent distribution with different time scales of variability bounded by a precipitation, maximum (Tmax), and minimum (Tmin) temperature extremes and also assessed their possibility changes are evaluated and quantified over India is presented. The GEV-based method is applied on both precipitation and temperature extremes over India during the 20th and 21st centuries using multiple coupled climate models taking an interest in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and observational datasets. The regional means of historical warm extreme temperatures are 34.89, 36.42, and 38.14°C for three different (10, 20, and 50-year) periods, respectively; whereas the cold extreme mean temperatures are 7.75, 4.19, and −1.57°C. It indicates that 20th century cold extreme temperatures have relatively larger variations than the warm extremes. As for the future, the CMIP5 models of warm extreme regional mean values increase from 0.33 to 0.75°C in all return periods (10-, 20-, and 50-year periods), while in the case of cold extreme means values vary between 0.58 and 2.29°C. In the future, cold extreme values have a larger increasing rate over the northwest, northeast, some parts of north-central, and Inter Peninsula regions. The CRU precipitation extremes are larger than the historical extreme precipitation in all three (10, 20, and 50-year) return-periods. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
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25. 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

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