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1. Ambiguous stability of glaciers at bed peaks

Author

Alexander A. Robel, Samuel S. Pegler, Ginny Catania, Denis Felikson, and Lauren M. Simkins

Subjects
Ice dynamics, ice-sheet modeling, subglacial processes, Environmental sciences, GE1-350, Meteorology. Climatology, QC851-999
Abstract

Increasing ice flux from glaciers retreating over deepening (retrograde) bed topography has been implicated in the recent acceleration of mass loss from the Greenland and Antarctic ice sheets. We show in observations that some glaciers have remained at peaks in bed topography without retreating despite enduring significant changes in climate. Observations also indicate that some glaciers which persist at bed peaks undergo sudden retreat years or decades after the onset of local ocean or atmospheric warming. Using model simulations, we show that persistence of a glacier at a bed peak is caused by ice slowing as it flows up a reverse-sloping bed to the peak. Persistence at bed peaks may lead to two very different future behaviors for a glacier: one where it persists at a bed peak indefinitely, and another where it retreats from the bed peak after potentially long delays following climate forcing. However, it is nearly impossible to distinguish which of these two future behaviors will occur from current observations. We conclude that inferring glacier stability from observations of persistence obscures our true commitment to future sea-level rise under climate change. We recommend that further research is needed on seemingly stable glaciers to determine their likely future.

Published
2022
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2. Helheim Glacier ice velocity variability responds to runoff and terminus position change at different timescales

Author

Lizz Ultee, Denis Felikson, Brent Minchew, Leigh A. Stearns, and Bryan Riel

Subjects
Science
Abstract

Factors driving ice flow variability in Greenland vary by timescale. At seasonal scale, Helheim Glacier ice velocity responds most strongly to meltwater runoff. Glacier terminus position drives velocity variability at longer time scales.

Published
2022
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3. Topographic modulation of outlet glaciers in Greenland: a review

Author

Ginny Catania and Denis Felikson

Subjects
Arctic glaciology, glacier flow, glacial geology, subglacial processes, processes and landforms of glacial erosion, Meteorology. Climatology, QC851-999
Abstract

Bed topography is a critical parameter for determining the modern-day and future dynamics of ice sheets and their outlet glaciers. This is because the topography controls the state of stress for glaciers. At glacier termini, topography can influence the timing of terminus retreat by controlling access to warm ocean waters and/or by influencing the ability of a glacier terminus to retreat over bed bumps (moraines). Inland from the terminus, the topography can also influence where glacier retreat and thinning can stabilize. In part, this is because of knickpoints in bed topography created through glacial erosion that may influence the extent to which thinning can diffuse inland for an individual glacier and thus, the timing and magnitude of long-term mass loss. Here we provide a review of the current literature on these topics. While much of the reviewed literature assumes that topography is stable on relevant timescales to humans, new research suggests that topography may change much faster than previously thought and this further complicates our ability to project future outlet glacier change.

Published
2022
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4. Future Sea Level Change Under Coupled Model Intercomparison Project Phase 5 and Phase 6 Scenarios From the Greenland and Antarctic Ice Sheets

Author

Antony J. Payne, Sophie Nowicki, Ayako Abe‐Ouchi, Cécile Agosta, Patrick Alexander, Torsten Albrecht, Xylar Asay‐Davis, Andy Aschwanden, Alice Barthel, Thomas J. Bracegirdle, Reinhard Calov, Christopher Chambers, Youngmin Choi, Richard Cullather, Joshua Cuzzone, Christophe Dumas, Tamsin L. Edwards, Denis Felikson, Xavier Fettweis, Benjamin K. Galton‐Fenzi, Heiko Goelzer, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Peter Kuipers Munneke, Eric Larour, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, William H. Lipscomb, Christopher M. Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Martin Rückamp, Nicole‐Jeanne Schlegel, Hélène Seroussi, Andrew Shepherd, Erika Simon, Donald Slater, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Lev Tarasov, Luke D. Trusel, Jonas Van Breedam, Roderik van deWal, Michiel van denBroeke, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger

Subjects
Antarctica, Greenland, ice sheet, sea level, Geophysics. Cosmic physics, QC801-809
Abstract

Abstract Projections of the sea level contribution from the Greenland and Antarctic ice sheets (GrIS and AIS) rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous Coupled Model Intercomparison Project phase 5 (CMIP5) effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Published
2021
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5. Contrasts in the response of adjacent fjords and glaciers to ice-sheet surface melt in West Greenland

Author

Timothy C. Bartholomaus, Leigh A. Stearns, David A. Sutherland, Emily L. Shroyer, Jonathan D. Nash, Ryan T. Walker, Ginny Catania, Denis Felikson, Dustin Carroll, Mason J. Fried, Brice P. Y. Noël, and Michiel R. Van Den Broeke

Subjects
atmosphere/ice/ocean interactions, ice velocity, ice/ocean interactions, iceberg calving, subglacial processes, Meteorology. Climatology, QC851-999
Abstract

Neighboring tidewater glaciers often exhibit asynchronous dynamic behavior, despite relatively uniform regional atmospheric and oceanic forcings. This variability may be controlled by a combination of local factors, including glacier and fjord geometry, fjord heat content and circulation, and glacier surface melt. In order to characterize and understand contrasts in adjacent tidewater glacier and fjord dynamics, we made coincident ice-ocean-atmosphere observations at high temporal resolution (minutes to weeks) within a 10 000 km2 area near Uummannaq, Greenland. Water column velocity, temperature and salinity measurements reveal systematic differences in neighboring fjords that imply contrasting circulation patterns. The observed ocean velocity and hydrography, combined with numerical modeling, suggest that subglacial discharge plays a major role in setting fjord conditions. In addition, satellite remote sensing of seasonal ice flow speed and terminus position reveal both speedup and slow-down in response to melt, as well as differences in calving style among the neighboring glaciers. Glacier force budgets and modeling also point toward subglacial discharge as a key factor in glacier behavior. For the studied region, individual glacier and fjord geometry modulate subglacial discharge, which leads to contrasts in both fjord and glacier dynamics.

Published
2016
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6. Modelling the Greenland Ice Sheet's Committed Contribution to Sea Level During the 21st Century

Author

Isabel J. Nias, Sophie Nowicki, Denis Felikson, and Bryant Loomis

Subjects
Geophysics
Abstract

Mass loss from the Greenland Ice Sheet can be partitioned between surface mass balance and discharge due to ice dynamics through its marine-terminating outlet glaciers. A perturbation to a glacier terminus (e.g., a calving event) results in both an instantaneous response in velocity and mass loss and a diffusive response due to the evolution of ice thickness over time. This diffusive response means the total impact of a retreat event can take decades to be fully realized. Here we model the committed response of the Greenland Ice Sheet by applying perturbations to the marine-terminating glacier termini that represent recent observed changes, and simulating the response over the 21st century, while holding the climate forcing constant. The sensitivity of the ice sheet response to model parameter uncertainty is explored within an ensemble framework, and Gravity Recovery and Climate Experiment data is used to constrain the results using a Bayesian calibration approach. We find that the Greenland Ice Sheet's committed contribution to 21st century sea level rise is at least 33.5 [17.5 52.4] mm (25th and 75th percentiles in brackets), with at least 6 mm being attributable directly to terminus retreat that occurred between 2007 and 2015. The spread in our projections is driven by uncertainty in the basal friction coefficient. Our results complement the ISMIP6 Greenland projections, which report the ice sheet response to future forcing, excluding the background response. In this way, we can obtain estimates of Greenland's total contribution to sea level rise in 2100.

Published
2023
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7. Historical Context of a Recent Warming Event over Wilkes Land, Antarctica from Numerical Analyses

Author

Richard Cullather, Lauren Andrews, Michael Croteau, Denis Felikson, and Sophie Nowicki

Subjects
Meteorology and Climatology
Abstract

Recent melt events over polar ice sheets are of interest for identifying regions of vulnerability in a warming climate. On 11-March 2022 a ridging pattern formed in the jet stream near Enderby Land, Antarctica and progressed eastward. The ridge deepened markedly into a classic omega-shaped blocking pattern (Fig. 1), extending into the plateau by 14-March. The blocking pattern is remarkable for the circulation strength – which forced warm air and moisture into the high interior of the continent, its poleward extent, and its persistence. Here, we use GMAO FP, MERRA-2 analyses, conventional observations, and AIRS/Aqua L3 data to characterize the event. We wish to examine, (1) How unusual was the event relative to available analysis records, (2) What were the effects of the event on surface mass balance (SMB)? and (3) Is there a relation to general circulation trends that led to this event?

Published
2022

8. GBaTSv2: a revised synthesis of the likely basal thermal state of the Greenland Ice Sheet

Author

Joseph A. MacGregor, Winnie Chu, William T. Colgan, Mark A. Fahnestock, Denis Felikson, Nanna B. Karlsson, Sophie M. J. Nowicki, and Michael Studinger

Subjects
Geosciences (General), Earth Resources And Remote Sensing
Abstract

The basal thermal state (frozen or thawed) of the Greenland Ice Sheet is under-constrained due to few direct measurements, yet knowledge of this state is becoming increasingly important to interpret modern changes in ice flow. The first synthesis of this state relied on inferences from widespread airborne and satellite observations and numerical models, for which most of the underlying datasets have since been updated. Further, new and independent constraints on the basal thermal state have been developed from analysis of basal and englacial reflections observed by airborne radar sounding. Here we synthesize constraints on the Greenland Ice Sheet's basal thermal state from boreholes, thermomechanical ice-flow models that participated in the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6; Coupled Model Intercomparison Project Phase 6), IceBridge BedMachine Greenland v4 bed topography, Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Multi-Year Greenland Ice Sheet Velocity Mosaic v1 and multiple inferences of a thawed bed from airborne radar sounding. Most constraints can only identify where the bed is likely thawed rather than where it is frozen. This revised synthesis of the Greenland likely Basal Thermal State version 2 (GBaTSv2) indicates that 33 % of the ice sheet's bed is likely thawed, 40 % is likely frozen and the remainder (28 %) is too uncertain to specify. The spatial pattern of GBaTSv2 is broadly similar to the previous synthesis, including a scalloped frozen core and thawed outlet-glacier systems. Although the likely basal thermal state of nearly half (46 %) of the ice sheet changed designation, the assigned state changed from likely frozen to likely thawed (or vice versa) for less than 6 % of the ice sheet. This revised synthesis suggests that more of northern Greenland is likely thawed at its bed and conversely that more of southern Greenland is likely frozen, both of which influence interpretation of the ice sheet's present subglacial hydrology and models of its future evolution. The GBaTSv2 dataset, including both code that performed the analysis and the resulting datasets, is freely available at https://doi.org/10.5281/zenodo.6759384 (MacGregor, 2022).

Published
2022
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9. Brief Communication: Preliminary ICESat-2 (Ice, Cloud and Land Elevation Satellite-2) Measurements of Outlet Glaciers Reveal Heterogeneous Patterns of Seasonal Dynamic Thickness Change

Author

Christian J Taubenberger, Denis Felikson, and Thomas Neumann

Subjects
Earth Resources And Remote Sensing
Abstract

Dynamic changes of marine-terminating outlet glaciers are projected to be responsible for about half of future ice loss from the Greenland Ice Sheet. However, we lack a unified, process-based understanding that can explain the observed dynamic changes of all outlet glaciers. Many glaciers undergo seasonal dynamic thickness changes, and classifying the patterns of seasonal thickness change can improve our understanding of the processes that drive glacier behavior. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) provides space-based, seasonally repeating altimetry measurements of the ice sheets, allowing us to quantify near-termini seasonal dynamic thickness patterns of 37 outlet glaciers around the Greenland Ice Sheet. We classify the glaciers into seven common patterns of seasonal thickness change over a 2-year period from 2019 to 2020. We find small groupings of neighboring glaciers with similar patterns of seasonal thickness change, but, within larger sectors of the ice sheet, patterns of seasonal thickness change are mostly heterogeneous. Future studies can build upon our results by extending these time series, comparing seasonal dynamic thickness changes with external forcings, such as ocean temperature and meltwater runoff, and with other dynamic variables such as seasonal glacier velocity and terminus position changes.

Published
2022
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10. High-Spatial-Resolution Mass Rates From GRACE and GRACE-FO: Global and Ice Sheet Analyses

Author

Bryant D. Loomis, Denis Felikson, Terence J. Sabaka, and Brooke Medley

Subjects
Geosciences (General)
Abstract

GRACE and GRACE-FO gravimetry data are unique in its ability to observe global water mass variability and has frequently been applied for the determination of global mass rates to quantify the impacts of climate change and human activity on global freshwater availability and land ice evolution. However, its relatively coarse spatial resolution of ∼300-500 km at monthly intervals precludes its direct use to measure mass changes in smaller drainage basins and individual glacier systems. We present a new global high resolution mascon product that improves spatial resolution and signal recovery by estimating trends directly from more than 16 years of Level 1B GRACE and GRACE-FO data. We compare this product to independent gravimetry spherical harmonic trend estimates and to ice sheet mass rates derived from ICESat and ICESat-2 laser altimetry. We demonstrate enhanced global signal recovery and improved agreement with ICESat/ ICESat-2 and discuss likely causes for the remaining discrepancies between the gravimetry and laser altimetry solutions.

Published
2021
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11. Projected land ice contributions to twenty-first-century sea level rise

Author

Tamsin L. Edwards, Sophie Nowicki, Ben Marzeion, Regine Hock, Heiko Goelzer, Hélène Seroussi, Nicolas C. Jourdain, Donald A. Slater, Fiona E. Turner, Christopher J. Smith, Christine M. McKenna, Erika Simon, Ayako Abe-Ouchi, Jonathan M Gregory, Eric Larour, William H. Lipscomb, Antony J. Payne, Andrew Shepherd, Cécile Agosta, Patrick Alexander, Torsten Albrecht, Brian Anderson, Xylar Asay-Davis, Andreas Aschwanden, Alice Barthel, Andrew Bliss, Reinhard Calov, Christopher Chambers, Nicolas Champollion, Youngmin Choi, Richard Cullather, Joshua Cuzzone, Christophe Dumas, Denis Felikson, Xavier Fettweis, Koji Fujita, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicolas R. Gollege, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Matthias Huss, Philippe Huybrechts, Walter Immerzeel, Thomas Kleiner, Philip Kraaijenbrink, Sebastien Le clec'h, Victoria Lee, Gunter R. Leguy, Christopher M. Little, Daniel P. Lowry, Jan-Hendrik Malles, Daniel F. Martin, Fabien Maussion, Mathieu Morlighem, James F. O’Neill, Isabel Nias, Frank Pattyn, Tyler Pelle, Stephen F Price, Aurélien Quiquet, Valentina Radić, Ronja Reese, David R. Rounce, Martin Rückamp, Akiko Sakai, Courtney Shafer, Nicole-Jeanne Schlegel, Sarah Shannon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Lev Tarasov, Luke D. Trusel, Jonas Van Breedam, Roderik van de Wal, Michiel van den Broeke, Ricarda Winkelmann, Harry Zekollari, Chen Zhao, Tong Zhang, and Thomas Zwinger

Subjects
Earth Resources And Remote Sensing, Numerical Analysis
Abstract

The land ice contribution to global mean sea level rise has not yet been predicted1 using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models2,3,4,5,6,7,8, but primarily used previous-generation scenarios9 and climate models10, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios11,12 using statistical emulation of the ice sheet and glacier models. We find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.

Published
2021
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13. Steep Glacier Bed Knickpoints Mitigate Inland Thinning in Greenland

Author

Denis Felikson, Ginny Catania, Timothy C Bartholomaus, Mathieu Morlighem, and Brice P Y Noel

Subjects
Meteorology And Climatology, Geophysics
Abstract

Greenland’s outlet glaciers have been a leading source of mass loss and accompanying sea-level rise from the Greenland Ice Sheet (GrIS) over the last 25 years. The dynamic component of outlet glacier mass loss depends on both the ice flux through the terminus and the inland extent of glacier thinning, initiated at the ice-ocean interface. Here, we find limits to the inland spread of thinning that initiates at glacier termini for 141 ocean-terminating outlet glaciers around the GrIS. Inland diffusion of thinning is limited by steep reaches of bed topography that we call “knickpoints.” We show that knickpoints exist beneath the majority of outlet glaciers but they are less steep in regions of gentle bed topography, giving glaciers in gentle bed topography the potential to contribute to ongoing and future mass loss from the GrIS by allowing the diffusion of thinning far into the ice sheet interior.

Published
2020
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14. Predicting Glacier Terminus Retreat Using Machine Learning

Author

Kevin Shionalyn, Ginny Catania, Daniel Trugman, Denis Felikson, and Leigh Stearns

Abstract

While a majority of mass loss from the Greenland Ice Shelf is attributed to glacial terminus retreat via calving, the superimposed force factors of the ice-ocean interface create a challenge for physically modeling terminus change. Here we use time series of environmental and glacial data, input as features into a machine learning regression model, to forecast terminus retreat for marine-terminating glaciers in Greenland. We then identify the critical features that most impact a glacier’s likelihood of retreat using feature importance analysis. We further analyze the heterogeneous outcomes for individual glaciers to classify them by their terminus change profile. By better understanding the parameters impacting glacial retreat, we inform physical models to reduce uncertainty in mass change projections.

Published
2023

16. CryoCloud community onboarding training (Dec 2, 2022)

Author

Tasha Snow, Joanna Millstein, Wei Ji Leong, Jessica Scheick, Wilson Sauthoff, James Colliander, James Munroe, Fernando Perez, Yuvi Panda, Denis Felikson, Tyler Sutterley, and Matthew Siegfried

Subjects
cloud-computing, CryoCloud, open science, Transform to Open Science, NASA
Abstract

Slides for the onboarding training for users of the CryoCloud JupyterHub. This presentation was recorded in a video (https://youtu.be/QNrDlwiomgc) that provides information about the CryoCloud project, how to contribute to the community, a demo for accessing and using the JupyterHub, and a demo for accessing NASA data in the cloud. CryoCloud (cryointhecloud.com) is maintained through a partnership between 2i2c (2i2c.org) and NASA Cryosphere research communities. This hub work made possible through funding from the NASA Transform to Open Science program, NASA Cryosphere program, and NASA ICESat-2 Science Team., This work was funded by the NASA Transform to Open Science program (grant #80NSSC23K0002), and the NASA Cryosphere Program and ICESat-2 Science Team (grant #80NSSC22K1877).

Published
2022
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18. NASA’s Earth Information System: Sea-Level Change

Author

Denis Felikson, Ian Fenty, Ben Hamlington, Alexey Shiklomanov, Carmen Blackwood, Mark Carroll, Michael Croteau, Cedric David, Kyla Drushka, Daniel Duffy, J. Thomas Farrar, Gael Forget, Severine Fournier, Thomas Frederikse, Ichiro Fukumori, Alexandre Guillaume, Patrick Heimbach, Thomas Huang, Eric Larour, Kyo Lee, Jian Li, Brooke Medley, Sophie Nowicki, Justin Quinn, Ramon Ramirez-Linan, Richard Ray, and Mike Rilee

Published
2022

19. A vast preglacial valley network beneath the Greenland Ice Sheet

Author

Joseph MacGregor, Winnie Chu, William Colgan, Beáta Csathó, Mark Fahnestock, Denis Felikson, Nanna Karlsson, Emma MacKie, Mathieu Morlighem, Guy Paxman, Kirsty Tinto, and Lijing Wang

Abstract

Combining airborne radar-sounding measurements of ice thickness and satellite observations of surface velocity using the principle of mass conservation has revolutionized mapping of subglacial valleys around the periphery of the Greenland Ice Sheet. However, this method cannot presently be applied to the ice sheet’s vast, slow-flowing interior. Instead, ordinary kriging or streamline diffusion are applied to sparser surveys of the interior, despite satellite imagery indicating complex subglacial topography there. This approach aliases that complex topography and limits our ability to interpret the subglacial environment of 75% of the ice sheet. Here we map >1000 prominent lineations in along-flow subaerial surface slope using ArcticDEM. Many lineations are >100 km long and several cross the central ice divide. NASA airborne surveys crossed these lineations thousands of times, and the large majority overlie prominent subglacial valleys, with the remainder overlying ridges. This result is consistent with previous regional studies in both Greenland and Antarctica but its validity is demonstrated here across an entire ice sheet. We explore relations between the geographic distribution of these valleys, their cross-sectional morphology, radar inferences of basal water and theoretical predictions of the transmission of subglacial perturbations to the subaerial ice surface. Our mapping reveals multiple regional networks of dozens of subparallel subglacial valleys of likely preglacial origin, several of which appear to originate from a large but poorly surveyed ice-covered alpine range in eastern Greenland. These patterns indicate that regional geologic fabric is a primary control on subglacial drainage patterns throughout Greenland and they are consistent with the existing hypothesis that the ice sheet’s Pleistocene inception occurred in eastern Greenland. This dataset could be used to validate new methods for mapping subglacial topography that also maintain fidelity to subaerial relief.

Published
2022

20. Brief communication: ICESat-2 reveals seasonal thickness change patterns of Greenland Ice Sheet outlet glaciers for the first time

Author

Christian J. Taubenberger, Thomas Neumann, and Denis Felikson

Subjects
Sea surface temperature, geography, geography.geographical_feature_category, Ice stream, Climatology, Elevation, Greenland ice sheet, Glacier, Altimeter, Ice sheet, Meltwater, Geology
Abstract

Dynamic changes of marine-terminating outlet glaciers are projected to be responsible for about half of future ice loss from the Greenland Ice Sheet. However, we lack a unified, process-based understanding that can explain the observed dynamic changes of all outlet glaciers. Many glaciers undergo seasonal dynamic thickness changes and classifying the patterns of seasonal thickness change can improve our understanding of the processes that drive glacier behavior. The Ice, Cloud and land Elevation Satellite (ICESat-2) provides the first space-based, seasonally repeating altimetry measurements of the ice sheets, allowing us to quantify near-termini seasonal dynamic thickness patterns of 34 outlet glaciers around the Greenland Ice Sheet. We classify the glaciers into seven common patterns of seasonal thickness change over a two-year period from 2019 to 2020. We find small groupings of neighboring glaciers with similar seasonal thickness change patterns but, within larger sectors of the ice sheet, seasonal thickness change patterns are heterogeneous. Comparing the seasonal thickness changes to average glacier ice flow speeds, we find that faster glaciers typically undergo patterns of spring and summer dynamic thickening, while slower glaciers exhibit a variety of thickness change patterns. Future studies can build upon our results by comparing seasonal dynamic thickness changes with external forcings, such as ocean temperature and meltwater runoff, and with other dynamic variables such as seasonal glacier velocity and terminus position changes.

Published
2021

22. Projected land ice contributions to twenty-first-century sea level rise

Author

Luke D. Trusel, Brian Anderson, Youngmin Choi, Michiel R. van den Broeke, Courtney Shafer, Cheng Zhao, William H. Lipscomb, Erika Simon, Sébastien Le clec'h, Victoria Lee, Thomas Kleiner, Donald Slater, Tore Hattermann, Matthias Huss, James F. O’Neill, Sainan Sun, Philip Kraaijenbrink, Benjamin K. Galton-Fenzi, Ayako Abe-Ouchi, Richard I. Cullather, Christophe Dumas, Christopher J. Smith, Nicolas C. Jourdain, Eric Larour, Rupert Gladstone, Jonas Van Breedam, Xavier Fettweis, Christine M. McKenna, Fiona Turner, Nicole Schlegel, Patrick Alexander, Walter W. Immerzeel, Gunter R. Leguy, Torsten Albrecht, Nicholas R. Golledge, Fabien Maussion, Fiammetta Straneo, Valentina Radić, Antony J. Payne, Robin S. Smith, Andrew Bliss, Heiko Goelzer, Andrew Shepherd, Frank Pattyn, Lev Tarasov, Mathieu Morlighem, Christopher Chambers, Tamsin L. Edwards, Reinhard Calov, Koji Fujita, Harry Zekollari, Nicolas Champollion, Akiko Sakai, Sarah Shannon, Ralf Greve, Stephen Price, Isabel Nias, Ricarda Winkelmann, David R. Rounce, Tong Zhang, Jan Hendrik Malles, Ben Marzeion, Roderik S. W. van de Wal, Helene Seroussi, Christopher M. Little, Cécile Agosta, Martin Rückamp, Philippe Huybrechts, Regine Hock, Angelika Humbert, Xylar Asay-Davis, Denis Felikson, Aurélien Quiquet, Daniel F. Martin, Andy Aschwanden, Matthew J. Hoffman, Tyler Pelle, Ronja Reese, Jonathan M. Gregory, Thomas Zwinger, Alice Barthel, Sophie Nowicki, J. K. Cuzzone, Daniel P. Lowry, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Glaces et Continents, Climats et Isotopes Stables (GLACCIOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation du climat (CLIM), Fonds De La Recherche Scientifique - FNRS, FNRS Horizon 2020: 820829 Australian Government: ASCI000002 Horizon 2020 Framework Programme, H2020: 869304 U.S. Department of Energy, USDOE O0100718F British Antarctic Survey, BAS Bundesministerium für Bildung und Forschung, BMBF: 01LP1504D, FKZ 01LP1502C Ministerie van Onderwijs, Cultuur en Wetenschap, OCW: 024.002.001 Natural Environment Research Council, NERC: NE/T007443/1 Deutsche Forschungsgemeinschaft, DFG: WI4556/2-1, 01LP1925D, WI4556/4-1 Norges Forskningsråd: 270061, 295046 American Research Center in Sofia, ARCS National Science Foundation, NSF: 1852977 National Center for Atmospheric Research, NCAR Horizon 2020 Framework Programme, H2020: NNX17AG65G, 820575 U.S. Department of Energy, USDOE Japan Society for the Promotion of Science, KAKEN: JP16H02224, JP17H06104, JP17H06323 Ministerie van Onderwijs, Cultuur en Wetenschap, OCW Office of Science, SC Suomen Akatemia: 286587, 322430 National Centre for Atmospheric Science, NCAS National Aeronautics and Space Administration, NASA Netherlands Earth System Science Centre, NESSC European Space Agency, ESA U.S. Department of Energy, USDOE Belgian Federal Science Policy Office, BELSPO: SR/00/336 National Science Foundation, NSF: PLR-1644277, PLR-1603799 Universiteit Utrecht, UU European Regional Development Fund, ERDF Bundesministerium für Bildung und Forschung, BMBF: WI4556/3-1 Ministry of Education, Culture, Sports, Science and Technology, Monbusho National Aeronautics and Space Administration, NASA Natural Environment Research Council, NERC Natural Environment Research Council, NERC Ministry for Business Innovation and Employment, MBIE: RTUV1705, ANTA1801 International Institute for Applied Systems Analysis, IIASA: NE/T009381/1 Biological and Environmental Research, BER: DE-AC02-05CH11231 Australian Research Council, ARC: SR140300001 European Commission, EC Office of Science, SC: DE-SC0020073 Ministry of Education, Culture, Sports, Science and Technology, Monbusho: JPMXD1420318865, JPMXD1300000000 Advanced Scientific Computing Research, ASCR, Acknowledgements We thank J. Rougier for providing advice and support throughout, and writing the original random effects model. We also thank B. Fox-Kemper, H. Hewitt, R. Kopp, S. Drijfhout and J. Rohmer for discussions, suggestions and support. We thank N. Barrand, W. Chang, V. Volodina and D. Williamson for their thorough and constructive comments, which greatly improved the manuscript. We thank the Climate and Cryosphere (CliC) Project, which provided support for ISMIP6 and GlacierMIP through sponsoring of workshops, hosting the websites and ISMIP6 wiki, and promotion. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and the ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6. This is ISMIP6 contribution no. 13. This publication was supported by PROTECT, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869304. This is PROTECT contribution number 12. T.L.E. was supported by PROTECT and the UK Natural Environment Research Council grant NE/T007443/1. F.T. was supported by PROTECT. J.F.O’N. was supported by the UK Natural Environment Research Council London Doctoral Training Partnership. R. Gladstone’s contribution was supported by Academy of Finland grants 286587 and 322430, and T. Zwinger’s by grant 322430. W.H.L. and G.R.L. were supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement no. 1852977. Computing and data storage resources for CISM simulations, including the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. Support for X.A.-D., M.J.H., S.F.P. and T. Zhang was provided through the Scientific Discovery through Advanced Computing (SciDAC) programme funded by the US Department of Energy (DOE), Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research programmes. N.R.G., D.P.L. and B.A. were supported by New Zealand Ministry for Business, Innovation and Employment contracts RTUV1705 (‘NZSeaRise’) and ANTA1801 (‘Antarctic Science Platform’). J.M.G. and R.S.S. were supported by the National Centre for Atmospheric Science, funded by the UK National Environment Research Council. R. Calov was funded by the PalMod project of the Bundesministerium für Bildung und Forschung (BMBF) with the grants FKZ 01LP1502C and 01LP1504D. D.F.M. and C.S. were supported by the Director, Office of Science, Offices of Advanced Scientific Computing Research (ASCR) and Biological and Environmental Research (BER), of the US Department of Energy under contract no. DE-AC02-05CH11231, as a part of the ProSPect SciDAC Partnership. BISICLES simulations used resources of the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility operated under contract no. DE-AC02-05CH11231. C.Z. and B.K.G.-F. were supported under the Australian Research Council’s Special Research Initiative for Antarctic Gateway Partnership (project ID SR140300001) and received grant funding from the Australian Government for the Australian Antarctic Program Partnership (project ID ASCI000002). Work was performed by E.L., N.-J.S. and H.S. at the California Institute of Technology’s Jet Propulsion Laboratory under a contract with the National Aeronautics and Space Administration, support was provided by grants from NASA’s Cryospheric Science, Sea Level Change Team, and Modeling, Analysis and Prediction (MAP) programmes. They acknowledge computational resources and support from the NASA Advanced Supercomputing Division. The CMIP5 and CMIP6 projection data were processed by C.M.M. with funding from the European Union’s CONSTRAIN project as part of the Horizon 2020 Research and Innovation Programme under grant agreement number 820829. A. Barthel was supported by the DOE Office of Science HiLAT-RASM project and Early Career Research programme. T.A. and R.W. are supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority programme ‘Antarctic research with comparative investigations in Arctic ice areas’ by grants WI4556/2-1 and WI4556/4-1, and within the framework of the PalMod project (FKZ: 01LP1925D) supported by the German Federal Ministry of Education and Research (BMBF) as a Research for Sustainability initiative (FONA). R.R. is supported by the Deutsche Forschungsgemeinschaft (DFG) by grant WI4556/3-1 and through the TiPACCs project that receives funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no. 820575. Development of PISM is supported by NASA grant NNX17AG65G and NSF grants PLR-1603799 and PLR-1644277. The authors gratefully acknowledge the European Regional Development Fund (ERDF), the German Federal Ministry of Education and Research and the Land Brandenburg for supporting this project by providing resources for the high-performance computer system at the Potsdam Institute for Climate Impact Research. Computer resources for this project have also been provided by the Gauss Centre for Supercomputing, Leibniz Supercomputing Centre (http://www.lrz.de, last access: 16 July 2020) under project IDs pr94ga and pn69ru. R. Greve and C.C. were supported by Japan Society for the Promotion of Science (JSPS) KAKENHI grant nos JP16H02224 and JP17H06323. R. Greve was supported by JSPS KAKENHI grant no. JP17H06104, by a Leadership Research Grant of Hokkaido University’s Institute of Low Temperature Science (ILTS), and by the Arctic Challenge for Sustainability (ArCS, ArCS II) project of the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) (programme grant nos JPMXD1300000000, JPMXD1420318865). F.P. and S. Sun were supported by the MIMO project within the STEREO III programme of the Belgian Science Policy Office, contract SR/00/336 and the Fonds de la Recherche Scientifique (FNRS) and the Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO) under the EOS project no. O0100718F. A. Shepherd was supported by the UK Natural Environment Research Council in partnership with the Centre for Polar Observation and Modelling and the British Antarctic Survey and by the European Space Agency Climate Change Initiative. D.F. was supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by Universities Space Research Association under contract with NASA. R.v.d.W. acknowledges the support of the Future Deltas programme of Utrecht University. C.J.S. was supported by a NERC/IIASA Collaborative Research Fellowship (NE/T009381/1). H.G. has received funding from the programme of the Netherlands Earth System Science Centre (NESSC), financially supported by the Dutch Ministry of Education, Culture and Science (OCW) under grant no. 024.002.001 and from the Research Council of Norway under projects INES (270061) and KeyClim (295046). F.S. acknowledges support from DOE Office of Science grant no. DE-SC0020073. High-performance computing and storage resources were provided by the Norwegian Infrastructure for Computational Science through projects NN9560K, NN9252K, NS9560K, NS9252K and NS5011K., University of St Andrews. School of Geography & Sustainable Development, University of St Andrews. Environmental Change Research Group, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Sub Dynamics Meteorology, Hydrologie, Landscape functioning, Geocomputation and Hydrology, Proceskunde, Sub Algemeen Marine & Atmospheric Res, Earth System Sciences, Geography, Physical Geography, and Faculty of Sciences and Bioengineering Sciences

Subjects
010504 meteorology & atmospheric sciences, General Science & Technology, [SDV]Life Sciences [q-bio], 01 natural sciences, 010104 statistics & probability, Taverne, G1, SDG 13 - Climate Action, 0101 mathematics, General, Coastal flood, Sea level, 0105 earth and related environmental sciences, geography, Multidisciplinary, geography.geographical_feature_category, Global warming, G Geography (General), DAS, Glacier, Glaciologie, Snow, Climate Action, Current (stream), Sea level rise, 13. Climate action, Climatology, Environmental science, Ice sheet, Sciences exactes et naturelles
Abstract

The land ice contribution to global mean sea level rise has not yet been predicted 1 using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models 2–8, but primarily used previous-generation scenarios 9 and climate models 10, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios 11,12 using statistical emulation of the ice sheet and glacier models. We find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.

Published
2021
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23. Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets

Author

Antony J Payne, Sophie Nowicki, Ayako Abe-Ouchi, Cécile Agosta, Patrick M. Alexander, Torsten Albrecht, Xylar S Asay-Davis, Andy Aschwanden, Alice Barthel, Thomas J. Bracegirdle, Reinhard Calov, Christopher Chambers, Youngmin Choi, Richard I. Cullather, Joshua K Cuzzone, Christophe dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Benjamin Keith Galton-Fenzi, Heiko Goelzer, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C Jourdain, Thomas Kleiner, Peter Kuipers Munneke, Eric Yves Larour, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, William H. Lipscomb, Christopher M Little, Daniel P Lowry, Mathieu Morlighem, Isabel Nias, Frank Pattyn, Tyler Pelle, Stephen Price, Aurelien Quiquet, Ronja Reese, Martin Rückamp, Nicole -J. Schlegel, Helene Seroussi, Andrew Shepherd, Erika Simon, Donald A Slater, Robin Smith, Fiamma Straneo, Sainan Sun, Lev Tarasov, Luke Trusel, Jonas Van Breedam, Roderik S. W. van de Wal, Michiel R. van den Broeke, Ricarda Winkelmann, Chen Zhao, Tong Zhang, Thomas Zwinger, University of Bristol [Bristol], University at Buffalo [SUNY] (SUNY Buffalo), State University of New York (SUNY), The University of Tokyo (UTokyo), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Glaces et Continents, Climats et Isotopes Stables (GLACCIOS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Columbia University [New York], Potsdam Institute for Climate Impact Research (PIK), Los Alamos National Laboratory (LANL), University of Alaska [Fairbanks] (UAF), British Antarctic Survey (BAS), Natural Environment Research Council (NERC), Hokkaido University Hospital [Sapporo], University of California [Irvine] (UCI), University of California, NASA Goddard Space Flight Center (GSFC), California Institute of Technology (CALTECH), Modélisation du climat (CLIM), King‘s College London, Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Université libre de Bruxelles (ULB), This project received funding from the European Union's Horizon 2020 research and innovation program under Grant agree-ment No 869304, PROTECT contribu-tion number 4., Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of California [Irvine] (UC Irvine), University of California (UC), and 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 )

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 13. Climate action, Glaciologie, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, Sciences exactes et naturelles
Abstract

Projections of the sea level contribution from the Greenland and Antarctic ice sheets (GrIS and AIS) rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous Coupled Model Intercomparison Project phase 5 (CMIP5) effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Published
2021

24. Seasonal tidewater glacier terminus oscillations bias multi-decadal projections of ice mass change

Author

Mathieu Morlighem, Helene Seroussi, Isabel Nias, Denis Felikson, and Sophie Nowicki

Subjects
Climatology, Tidewater glacier cycle, Physics::Atmospheric and Oceanic Physics, Geology, Physics::Geophysics
Abstract

Numerical, process-based simulations of tidewater glacier evolution are necessary to project future sea-level change under various climate scenarios. Previous work has shown that nonlinearities in ...

Published
2021

25. Recent retreat of Greenland's marine terminating glaciers has a lasting impact on velocity and mass loss during the 21st Century

Author

Isabel Nias, Denis Felikson, and Sophie Nowicki

Subjects
geography, geography.geographical_feature_category, Glacier, Physical geography, Geology
Abstract

Mass loss from the Greenland Ice Sheet (GrIS) can be partitioned between surface mass balance (SMB) and discharge due to ice dynamics through its marine-terminating outlet glaciers. A perturbation to a glacier terminus (e.g. a calving event) results in an instantaneous response in velocity and mass loss, but also a diffusive response due to the evolution of ice thickness over time. This diffusive response means the total impact of a retreat event can take decades to be fully realised. Here we model the committed response of the GrIS to recent observed changes in terminus position, neglecting any future climate perturbations. Our simulations quantify the sea level contribution that is locked in due to the slow dynamic response of the ice. Using the Ice Sheet System Model (ISSM), we run forward simulations starting from an initial state representative of the 2007 ice sheet. We apply perturbations to the marine-terminating glacier termini that represent recent observed changes, and simulate the response over the 21st Century, holding the climate forcing constant. The sensitivity of the ice sheet response to model parameter uncertainty is explored with in an ensemble framework, and GRACE data is used to constrain the results. We find that terminus retreat observed between 2007 and 2015 results in approximately 6 mm of sea level rise by 2100, with retreat having a lasting impact on velocity and mass loss. Our results complement the ISMIP6 projections, which report the ice sheet response to future forcing, excluding the background committed response. In this way, we can obtain estimates of Greenland’s total contribution to sea level rise by 2100.

Published
2021

26. Steep Glacier Bed Knickpoints Mitigate Inland Thinning in Greenland

Author

Ginny A. Catania, Mathieu Morlighem, Brice Noël, Timothy C. Bartholomaus, and Denis Felikson

Subjects
Informatics, 010504 meteorology & atmospheric sciences, Glaciology, Greenland ice sheet, 010502 geochemistry & geophysics, 01 natural sciences, Remote Sensing, Snow and Ice, Snow, Meteorology & Atmospheric Sciences, Remote Sensing and Electromagnetic Processes, Ionospheric Propagation, geography.geographical_feature_category, Fourier Analysis, Nonlinear Geophysics, Dynamics, Oceanography: General, Geophysics, Shock Waves, Spatial Modeling, Cryospheric Change, Cryosphere, Mathematical Geophysics, Geology, Ice Sheets, Earth and Planetary Sciences(all), Wavelet Transform, Spatial Analysis and Representation, Radio Science, Solitons and Solitary Waves, Research Letter, Global Change, Ionosphere, Geomorphology, 0105 earth and related environmental sciences, Spatial Analysis, geography, Thinning, Ice, Electromagnetics, Glacier, Climate Action, Spectral Analysis, Nonlinear Waves, Shock Waves, Solitons, Space Plasma Physics, General Earth and Planetary Sciences, Computational Geophysics, Wave Propagation, Hydrology, Ice sheet, human activities, Natural Hazards
Abstract

Greenland’s outlet glaciers have been a leading source of mass loss and accompanying sea‐level rise from the Greenland Ice Sheet (GrIS) over the last 25 years. The dynamic component of outlet glacier mass loss depends on both the ice flux through the terminus and the inland extent of glacier thinning, initiated at the ice‐ocean interface. Here, we find limits to the inland spread of thinning that initiates at glacier termini for 141 ocean‐terminating outlet glaciers around the GrIS. Inland diffusion of thinning is limited by steep reaches of bed topography that we call “knickpoints.” We show that knickpoints exist beneath the majority of outlet glaciers but they are less steep in regions of gentle bed topography, giving glaciers in gentle bed topography the potential to contribute to ongoing and future mass loss from the GrIS by allowing the diffusion of thinning far into the ice sheet interior., Key Points Greenland glaciers with high potential to contribute to mass loss identified using novel metricThe majority of thinning limits are set by a knickpoint, a steep drop in bed topographyGlaciers that transmit thinning far inland are preferentially in regions with gentle bed topography

Published
2021

27. Future Sea Level Change Under Coupled Model Intercomparison Project Phase 5 and Phase 6 Scenarios From the Greenland and Antarctic Ice Sheets

Author

Richard I. Cullather, Sainan Sun, Robin S. Smith, Martin Rückamp, Philippe Huybrechts, Thomas J. Bracegirdle, Christopher M. Little, Daniel P. Lowry, Fiammetta Straneo, Jonas Van Breedam, Xavier Fettweis, Nicolas C. Jourdain, Ricarda Winkelmann, Gunter R. Leguy, Isabel Nias, Erika Simon, Eric Larour, Sophie Nowicki, J. K. Cuzzone, Patrick Alexander, Cheng Zhao, Tore Hattermann, Heiko Goelzer, Christophe Dumas, Alice Barthel, Torsten Albrecht, Peter Kuipers Munneke, Helene Seroussi, Christopher Chambers, Andrew Shepherd, Frank Pattyn, Rupert Gladstone, Nicole Schlegel, Xylar Asay-Davis, Denis Felikson, Michiel R. van den Broeke, Mathieu Morlighem, Victoria Lee, Ayako Abe-Ouchi, Andy Aschwanden, Ralf Greve, Tamsin L. Edwards, Reinhard Calov, Thomas Kleiner, Donald Slater, Tong Zhang, Antony J. Payne, Benjamin K. Galton-Fenzi, Cécile Agosta, Luke D. Trusel, Lev Tarasov, Aurélien Quiquet, Nicholas R. Golledge, Youngmin Choi, William H. Lipscomb, Stephen Price, Angelika Humbert, Sébastien Le clec'h, Roderik S. W. van de Wal, Matthew J. Hoffman, Tyler Pelle, Jonathan M. Gregory, Thomas Zwinger, Ronja Reese, Earth System Sciences, Geography, Physical Geography, Marine and Atmospheric Research, Sub Dynamics Meteorology, Proceskunde, and Sub Algemeen Marine & Atmospheric Res

Subjects
010504 meteorology & atmospheric sciences, Effects of global warming on oceans, Greenland, Antarctic ice sheet, F700, Earth and Planetary Sciences(all), F800, sea level, 010502 geochemistry & geophysics, 7. Clean energy, 01 natural sciences, Sea level, 0105 earth and related environmental sciences, Coupled model intercomparison project, geography, geography.geographical_feature_category, Future sea level, ice sheet, Ice-sheet model, Geophysics, 13. Climate action, Climatology, General Earth and Planetary Sciences, Environmental science, Antarctica, Climate model, Ice sheet
Abstract

Projections of the sea level contribution from the Greenland and Antarctic ice sheets (GrIS and AIS) rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous Coupled Model Intercomparison Project phase 5 (CMIP5) effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Published
2021
Full Text
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28. Experimental protocol for sealevel projections from ISMIP6 standalone ice sheet models

Author

Thomas J. Bracegirdle, Fiammetta Straneo, Peter Kuipers Munneke, Cécile Agosta, Luke D. Trusel, Isabel Nias, Patrick Alexander, Eric Larour, Jonathan M. Gregory, Richard I. Cullather, Erika Simon, Christopher M. Little, Robin S. Smith, Xavier Fettweis, Michiel R. van den Broeke, Mathieu Morlinghem, A. J. Payne, Donald Slater, Sophie Nowicki, Roderik S. W. van de Wal, Xylar Asay-Davis, Heiko Goelzer, Andrew Shepherd, Denis Felikson, Nicolas C. Jourdain, Alice Barthel, Helene Seroussi, Tore Hatterman, Ayako Abe-Ouchi, and William H. Lipscomb

Subjects
0303 health sciences, Coupled model intercomparison project, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Antarctic ice sheet, Context (language use), Forcing (mathematics), 01 natural sciences, Ice-sheet model, 03 medical and health sciences, 13. Climate action, Phase (matter), Climatology, Ice sheet, Sea level, Geology, 030304 developmental biology, 0105 earth and related environmental sciences
Abstract

Projection of the contribution of ice sheets to sea-level change as part of the Coupled Model Intercomparison Project – phase 6 (CMIP6) takes the form of simulations from coupled ice-sheet-climate models and standalone ice sheet models, overseen by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). This paper describes the experimental setup for process-based sea-level change projections to be performed with standalone Greenland and Antarctic ice sheet models in the context of ISMIP6. The ISMIP6 protocol relies on a suite of polar atmospheric and oceanic CMIP-based forcing for ice sheet models, in order to explore the uncertainty in projected sea-level change due to future emissions scenarios, CMIP models, ice sheet models, and parameterizations for ice-ocean interactions. We describe here the approach taken for defining the suite of ISMIP6 standalone ice sheet simulations, document the experimental framework and implementation, as well as present an overview of the ISMIP6 forcing to be used by participating ice sheet modeling groups.

Published
2020

29. Subsurface iceberg melt key to Greenland fjord freshwater budget

Author

Laura M. Kehrl, David A. Sutherland, Twila Moon, Dustin Carroll, Fiammetta Straneo, and Denis Felikson

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Biosphere, Greenland ice sheet, Fjord, 010502 geochemistry & geophysics, 01 natural sciences, Iceberg, Oceanography, Flux (metallurgy), General Earth and Planetary Sciences, Seawater, Geology, 0105 earth and related environmental sciences, Tidewater
Abstract

Liquid freshwater fluxes from the Greenland ice sheet affect ocean water properties and circulation on local, regional and basin-wide scales, with associated biosphere effects. The exact impact, however, depends on the volume, timing and location of freshwater releases, which are poorly known. In particular, the transformation of icebergs, which make up roughly 30–50% of the loss of the ice-sheet mass to liquid freshwater, is not well understood. Here we estimate the spatial and temporal distribution of the freshwater flux for the Helheim–Sermilik glacier–fjord system in southeast Greenland using an iceberg-melt model that resolves the subsurface iceberg melt. By estimating seasonal variations in all the freshwater sources, we confirm quantitatively that iceberg melt is the largest annual freshwater source in this system type. We also show that 68–78% of the iceberg melt is released below a depth of 20 m and, seasonally, about 40–100% of that melt is likely to remain at depth, in contrast with the usual model assumptions. Iceberg melt also peaks two months after all the other freshwater sources peak. Our methods provide a framework to assess individual freshwater sources in any tidewater system, and our results are particularly applicable to coastal regions with a high solid-ice discharge in Greenland. Iceberg melt is the largest annual freshwater source in a south Greenland fjord, with release largely below 20 m depth, according to iceberg-model simulations. Furthermore, iceberg melt peaks later in the year than other sources of freshwater.

Published
2017

30. Mass balance of the Antarctic Ice Sheet from 1992 to 2017

Author

Ted Scambos, Richard I. Cullather, Helmut Rott, David N. Wiese, Valentina R. Barletta, Isabella Velicogna, Brice Noël, Jeremie Mouginot, Edward Hanna, Melchior van Wessem, W. Richard Peltier, Thomas Nagler, Alejandro Blazquez, Eric Rignot, Jennifer Bonin, Nadege Pie, Veit Helm, Bernd Scheuchl, Louise Sandberg-Sørensen, Brian Gunter, Ines Otosaka, Ben Smith, Denis Felikson, Benoit S. Lecavalier, Bryant D. Loomis, Cécile Agosta, Peter L. Langen, Wouter van der Wal, Christopher Harig, René Forsberg, Philip Moore, Giorgio Spada, Ernst Schrama, Alex S. Gardner, T. C. Sutterley, Matthieu Talpe, Daniele Melini, Xavier Fettweis, Andreas Groh, Gerhard Krinner, Bert Wouters, Sebastian H. Mernild, Kate Briggs, Andreas P. Ahlstrøm, Erik R. Ivins, Shfaqat Abbas Khan, Johan Nilsson, Hannes Konrad, Nicole Schlegel, Sebastian B. Simonsen, Kristian K. Kjeldsen, Greg Babonis, Malcolm McMillan, Pippa L. Whitehouse, Ingo Sasgen, Lev Tarasov, Ki-Weon Seo, Lin Gilbert, Geruo A, Yara Mohajerani, Scott B. Luthcke, Gorka Moyano, Andrew Shepherd, Thomas Slater, Michiel R. van den Broeke, Bramha Dutt Vishwakarma, Roelof Rietbroek, Alexander Horvath, Hubert Gallée, Tony Payne, Willem Jan van de Berg, Martin Horwath, Alan Muir, Ian Joughin, Beata Csatho, Himanshu Save, Mark E. Pattle, Sophie Nowicki, Ludwig Schröder, Grace A. Nield, Institut des Géosciences de l’Environnement (IGE), 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]), Andrew Shepherd, Erik Ivin, Eric Rignot, Ben Smith, Michiel VDBroeke, Isabella Velicogna, Pippa Whitehouse, Kate Brigg, Ian Joughin, Gerhard Krinner, Sophie Nowicki, Tony Payne, Ted Scambo, Nicole Schlegel, Geruo A, Cécile Agosta, Andreas Ahlstrøm, Greg Baboni, Valentina Barletta, Alejandro Blazquez, Jennifer Bonin, Beata Csatho, Richard Cullather, Denis Felikson, Xavier Fettwei, Rene Forsberg, Hubert Gallee, Alex Gardner, Lin Gilbert, Andreas Groh, Brian Gunter, Edward Hanna, Christopher Harig, Veit Helm, Alexander Horvath, Martin Horwath, Shfaqat Khan, Kristian Kjeldsen, Hannes Konrad, Peter Langen, Benoit Lecavalier, Bryant Loomi, Scott Luthcke, Malcolm McMillan, Daniele Melini, Sebastian Mernild, Yara Mohajerani, Philip Moore, Jeremie Mouginot, Gorka Moyano, Alan Muir, Thomas Nagler, Grace Nield, Johan Nilsson, Brice Noel, Ines Otosaka, Mark Pattle, William Peltier, Nadege Pie, Roelof Rietbroek, Helmut Rott, LouiseSandberg Sørensen, Ingo Sasgen, Himanshu Save, Bernd Scheuchl, Ernst Schrama, Ludwig Schröder, KiWeon Seo, Sebastian Simonsen, Tom Slater, Giorgio Spada, Tyler Sutterley, Matthieu Talpe, Lev Tarasov, Willem JVdeBerg, Wouter vanderWal, Melchior van Wessem, BramhaDutt Vishwakarma, David Wiese, Bert Wouters, Centre National de la Recherche Scientifique (CNRS), University of California [Irvine] (UCI), University of California, 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), and 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)

Subjects
010504 meteorology & atmospheric sciences, [SDE.MCG]Environmental Sciences/Global Changes, Climate change, Antarctic ice sheet, NN, 010502 geochemistry & geophysics, 01 natural sciences, Glacier mass balance, Peninsula, Taverne, SDG 13 - Climate Action, F890 Geographical and Environmental Sciences not elsewhere classified, Sea level, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, geography, Multidisciplinary, geography.geographical_feature_category, Post-glacial rebound, Balance (accounting), 13. Climate action, [SDE]Environmental Sciences, Environmental science, Physical geography, Tonne
Abstract

The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.

Published
2018

31. Comparison of Elevation Change Detection Methods From ICESat Altimetry Over the Greenland Ice Sheet

Author

Brian Gunter, Hamish D. Pritchard, Denis Felikson, Nadege Pie, Robert Harpold, Bob E. Schutz, and Timothy J. Urban

Subjects
010504 meteorology & atmospheric sciences, Meteorology, 0211 other engineering and technologies, Elevation, Greenland ice sheet, 02 engineering and technology, 01 natural sciences, Lidar, 13. Climate action, Climatology, Drainage system (geomorphology), Range (statistics), General Earth and Planetary Sciences, Satellite, Altimeter, Electrical and Electronic Engineering, Change detection, Geology, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA’s Ice, Cloud, and land Elevation Satellite (ICESat) created altimetric surveys of GrIS surface elevations over the 2003–2009 operational period of the mission. This paper compares four change detection methods using Release 634 ICESat laser altimetry data: repeat tracks (RTs), crossovers (XOs), overlapping footprints (OFPs), and triangulated irregular networks (TINs). All four methods begin with a consistently edited data set and yield estimates of volumetric loss of ice from the GrIS ranging from −193 to −269 km3/yr. Using a uniform approach for quantifying uncertainties, we find that volume change rates at the drainage system scale from the four methods can be reconciled within 1- $\sigma $ uncertainties in just 5 of 19 drainage systems. Ice-sheet-wide volume change estimates from the four methods cannot be reconciled within 1- $\sigma $ uncertainties. Our volume change estimates lie within the range of previously published estimates, highlighting that the choice of method plays a dominant role in the scatter of volume change estimates. We find that for much of the GrIS, the OFP and TIN methods yield the lowest volume change uncertainties because of their superior spatial distribution of elevation change rate estimates. However, the RT and XO methods offer inherent advantages, and the future work to combine the elevation change detection methods to produce better estimates is warranted.

Published
2017

34. Past and future response of Greenland's tidewater glaciers to submarine melting

Author

Donald Slater, James Holte, Christopher M. Little, Xavier Fettweis, Heiko Goelzer, Denis Felikson, and Fiamma Straneo

Subjects
geography, education.field_of_study, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Population, Tidewater glacier cycle, Greenland ice sheet, Submarine, Glacier, 010502 geochemistry & geophysics, 01 natural sciences, Physical geography, Ice sheet, education, Sea level, Geology, 0105 earth and related environmental sciences, Tidewater
Abstract

The effect of the North Atlantic Ocean on the Greenland Ice Sheet through submarine melting of Greenland's tidewater glacier calving fronts is thought to be a key driver of widespread glacier retreat, dynamic mass loss and sea level contribution from the ice sheet. Despite its critical importance, problems of process complexity and scale hinder efforts to represent the influence of submarine melting in ice sheet-scale models. Here we propose parameterizing tidewater glacier terminus position as a simple linear function of submarine melting, with submarine melting in turn estimated as a function of subglacial runoff and ocean temperature. The relationship is tested, calibrated and validated using datasets of terminus position, runoff and ocean temperature covering the full ice sheet and surrounding ocean from the period 1960–present. We demonstrate a statistically significant link between multi-decadal tidewater glacier terminus position and submarine melting and show that the proposed parameterisation has predictive power when considering a population of glaciers. An illustrative 21st century projection is considered suggesting that tidewater glaciers in Greenland will undergo little further retreat in a low emissions RCP2.6 scenario. In contrast, a high emissions RCP8.5 scenario results in a median retreat of ∼6 km, with 35 % of glaciers experiencing retreat exceeding 10 km. Our study provides a long-term and ice sheet-wide assessment of the sensitivity of tidewater glaciers to submarine melting and proposes a practical and empirically validated means of incorporating ocean forcing into models of the Greenland ice sheet.

Published
2019

35. Bed geometry controls timing and magnitude of sea-level rise from Greenland's outlet glaciers

Author

Timothy C. Bartholomaus, Ginny A. Catania, Mathieu Morlighem, and Denis Felikson

Subjects
geography, geography.geographical_feature_category, Sea level rise, Magnitude (mathematics), Greenland ice sheet, Glacier, Mathematical geophysics, Physical geography, Geology
Abstract

The projected contribution to sea-level rise from the Greenland Ice Sheet currently has a large spread in literature, ranging from about 14 to 255 mm by the year 2100. Part of this spread is due to...

Published
2019

36. ESA ice sheet CCI: derivation of the optimal method for surface elevation change detection of the Greenland ice sheet – round robin results

Author

Roderik Lindenbergh, K. Khvorostovsky, Francesca Ticconi, René Forsberg, Alan Muir, Thomas Flament, Nadege Pie, R. T. W. L. Hurkmans, Brian Gunter, Andrew Shepherd, Joanna Fredenslund Levinsen, Marcel Kleinherenbrink, Geir Moholdt, Louise Sandberg Sørensen, and Denis Felikson

Subjects
geography, geography.geographical_feature_category, Drainage basin, Elevation, Greenland ice sheet, law.invention, Data set, law, General Earth and Planetary Sciences, Satellite, Radar, Ice sheet, Geology, Change detection, Remote sensing
Abstract

For more than two decades, radar altimetry missions have provided continuous elevation estimates of the Greenland ice sheet (GrIS). Here, we propose a method for using such data to estimate ice-sheet-wide surface elevation changes (SECs). The final data set will be based on observations acquired from the European Space Agency’s Environmental Satellite (ENVISAT), European Remote Sensing (ERS)-1 and -2, CryoSat-2, and, in the longer term, Sentinel-3 satellites. In order to find the best-performing method, an intercomparison exercise has been carried out in which the scientific community was asked to provide their best SEC estimates as well as feedback sheets describing the applied method. Due to the hitherto few radar-based SEC analyses as well as the higher accuracy of laser data, the participants were asked to use either ENVISAT radar or ICESat (Ice, Cloud, and land Elevation Satellite) laser altimetry over the Jakobshavn Isbræ drainage basin. The submissions were validated against airborne laser-scanner data, and intercomparisons were carried out to analyse the potential of the applied methods and to find whether the two altimeters were capable of resolving the same signal. The analyses found great potential of the applied repeat-track and cross-over techniques, and, for the first time over Greenland, that repeat-track analyses from radar altimetry agreed well with laser data. Since topography-related errors can be neglected in cross-over analyses, it is expected that the most accurate, ice-sheet-wide SEC estimates are obtained by combining the cross-over and repeat-track techniques. It is thus possible to exploit the high accuracy of the former and the large spatial data coverage of the latter. Based on CryoSat’s different operation modes, and the increased spatial and temporal data coverage, this shows good potential for a future inclusion of CryoSat-2 and Sentinel-3 data to continuously obtain accurate SEC estimates both in the interior and margin ice sheet.

Published
2015

37. ESA's Ice Sheets CCI: validation and inter-comparison of surface elevation changes derived from laser and radar altimetry over Jakobshavn Isbræ, Greenland – Round Robin results

Author

Andrew Shepherd, Marcel Kleinherenbrink, Louise Sandberg Sørensen, Roderik Lindenbergh, Nadege Pie, Alan Muir, Denis Felikson, Brian Gunter, Francesca Ticconi, Thomas Flament, René Forsberg, R. T. W. L. Hurkmans, Joanna Fredenslund Levinsen, Geir Moholdt, and K. Khvorostovsky

Subjects
geography, geography.geographical_feature_category, Elevation, Climate change, Greenland ice sheet, Laser, law.invention, law, SDG 13 - Climate Action, Altimeter, Radar, Ice sheet, Geology, Radar altimetry, Remote sensing
Abstract

In order to increase the understanding of the changing climate, the European Space Agency has launched the Climate Change Initiative (ESA CCI), a program which joins scientists and space agencies into 13 projects either affecting or affected by the concurrent changes. This work is part of the Ice Sheets CCI and four parameters are to be determined for the Greenland Ice Sheet (GrIS), each resulting in a dataset made available to the public: Surface Elevation Changes (SEC), surface velocities, grounding line locations, and calving front locations. All CCI projects have completed a so-called Round Robin exercise in which the scientific community was asked to provide their best estimate of the sought parameters as well as a feedback sheet describing their work. By inter-comparing and validating the results, obtained from research institutions world-wide, it is possible to develop the most optimal method for determining each parameter. This work describes the SEC Round Robin and the subsequent conclusions leading to the creation of a method for determining GrIS SEC values. The participants used either Envisat radar or ICESat laser altimetry over Jakobshavn Isbræ drainage basin, and the submissions led to inter-comparisons of radar vs. altimetry as well as cross-over vs. repeat-track analyses. Due to the high accuracy of the former and the high spatial resolution of the latter, a method, which combines the two techniques will provide the most accurate SEC estimates. The data supporting the final GrIS analysis stem from the radar altimeters on-board Envisat, ERS-1 and ERS-2. The accuracy of laser data exceeds that of radar altimetry; the Round Robin analysis has, however, proven the latter equally capable of dealing with surface topography thereby making such data applicable in SEC analyses extending all the way from the interior ice sheet to margin regions. This shows good potential for a~future inclusion of ESA CryoSat-2 and Sentinel-3 radar data in the analysis, and thus for obtaining reliable SEC estimates throughout the entire GrIS.

Published
2013

38. On-Orbit Solar Dynamics Observatory (SDO) Star Tracker Warm Pixel Analysis

Author

Melissa F. Vess, F. Matthew Ekinci, Joseph A. Hashmall, and Denis Felikson

Subjects
Physics, Spacecraft, Pixel, Physics::Instrumentation and Detectors, business.industry, Geosynchronous orbit, Field of view, Star (graph theory), Star tracker, Stars, Physics::Space Physics, Orbit (dynamics), Astrophysics::Solar and Stellar Astrophysics, Astrophysics::Earth and Planetary Astrophysics, business, Astrophysics::Galaxy Astrophysics, Remote sensing
Abstract

This paper describes the process of identification and analysis of warm pixels in two autonomous star trackers on the Solar Dynamics Observatory (SDO) mission. A brief description of the mission orbit and attitude regimes is discussed and pertinent star tracker hardware specifications are given. Warm pixels are defined and the Quality Index parameter is introduced, which can be explained qualitatively as a manifestation of a possible warm pixel event. A description of the algorithm used to identify warm pixel candidates is given. Finally, analysis of dumps of on-orbit star tracker charge coupled devices (CCD) images is presented and an operational plan going forward is discussed. SDO, launched on February 11, 2010, is operated from the NASA Goddard Space Flight Center (GSFC). SDO is in a geosynchronous orbit with a 28.5 inclination. The nominal mission attitude points the spacecraft X-axis at the Sun, with the spacecraft Z-axis roughly aligned with the Solar North Pole. The spacecraft Y-axis completes the triad. In attitude, SDO moves approximately 0.04 per hour, mostly about the spacecraft Z-axis. The SDO star trackers, manufactured by Galileo Avionica, project the images of stars in their 16.4deg x 16.4deg fields-of-view onto CCD detectors consisting of 512 x 512 pixels. The trackers autonomously identify the star patterns and provide an attitude estimate. Each unit is able to track up to 9 stars. Additionally, each tracker calculates a parameter called the Quality Index, which is a measure of the quality of the attitude solution. Each pixel in the CCD measures the intensity of light and a warns pixel is defined as having a measurement consistently and significantly higher than the mean background intensity level. A warns pixel should also have lower intensity than a pixel containing a star image and will not move across the field of view as the attitude changes (as would a dim star image). It should be noted that the maximum error introduced in the star tracker attitude solution during suspected warm pixel corruptions is within the specified 36 attitude error budget requirement of [35, 70, 70] arcseconds. Thus, the star trackers provided attitude accuracy within the specification for SDO. The star tracker images are intentionally defocused so each star image is detected in more than one CCD pixel. The position of each star is calculated as an intensity-weighted average of the illuminated pixels. The exact method of finding the positions is proprietary to the tracker manufacturer. When a warm pixel happens to be in the vicinity of a star, it can corrupt the calculation of the position of that particular star, thereby corrupting the estimate of the attitude.

Published
2011

39. 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

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