Articles | Volume 9, issue 6
https://doi.org/10.5194/tc-9-2429-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-9-2429-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| Highlight paper
| 
21 Dec 2015
Research article | Highlight paper |
| 21 Dec 2015
Committed retreat of Smith, Pope, and Kohler Glaciers over the next 30 years inferred by transient model calibration
Univ. of Edinburgh, School of GeoSciences, Edinburgh, UK
P. Heimbach
University of Texas, Institute for Computational Engineering and Sciences/Institute for Geophysics, Austin, Texas, USA
I. Joughin
Applied Physics Laboratory, University of Washington, Seattle, USA
Applied Physics Laboratory, University of Washington, Seattle, USA
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Jowan M. Barnes, Thiago Dias dos Santos, Daniel Goldberg, G. Hilmar Gudmundsson, Mathieu Morlighem, and Jan De Rydt
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Bryan Riel, Brent Minchew, and Ian Joughin
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Andrew O. Hoffman, Knut Christianson, Daniel Shapero, Benjamin E. Smith, and Ian Joughin
The Cryosphere, 14, 4603–4609, https://doi.org/10.5194/tc-14-4603-2020, https://doi.org/10.5194/tc-14-4603-2020, 2020
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Stephen L. Cornford, Helene Seroussi, Xylar S. Asay-Davis, G. Hilmar Gudmundsson, Rob Arthern, Chris Borstad, Julia Christmann, Thiago Dias dos Santos, Johannes Feldmann, Daniel Goldberg, Matthew J. Hoffman, Angelika Humbert, Thomas Kleiner, Gunter Leguy, William H. Lipscomb, Nacho Merino, Gaël Durand, Mathieu Morlighem, David Pollard, Martin Rückamp, C. Rosie Williams, and Hongju Yu
The Cryosphere, 14, 2283–2301, https://doi.org/10.5194/tc-14-2283-2020, https://doi.org/10.5194/tc-14-2283-2020, 2020
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Ian Joughin, David E. Shean, Benjamin E. Smith, and Dana Floricioiu
The Cryosphere, 14, 211–227, https://doi.org/10.5194/tc-14-211-2020, https://doi.org/10.5194/tc-14-211-2020, 2020
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David A. Lilien, Ian Joughin, Benjamin Smith, and Noel Gourmelen
The Cryosphere, 13, 2817–2834, https://doi.org/10.5194/tc-13-2817-2019, https://doi.org/10.5194/tc-13-2817-2019, 2019
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David E. Shean, Ian R. Joughin, Pierre Dutrieux, Benjamin E. Smith, and Etienne Berthier
The Cryosphere, 13, 2633–2656, https://doi.org/10.5194/tc-13-2633-2019, https://doi.org/10.5194/tc-13-2633-2019, 2019
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We produced an 8-year, high-resolution DEM record for Pine Island Glacier (PIG), a site of substantial Antarctic mass loss in recent decades. We developed methods to study the spatiotemporal evolution of ice shelf basal melting, which is responsible for ~ 60 % of PIG mass loss. We present shelf-wide basal melt rates and document relative melt rates for kilometer-scale basal channels and keels, offering new indirect observations of ice–ocean interaction beneath a vulnerable ice shelf.
Ian M. Howat, Claire Porter, Benjamin E. Smith, Myoung-Jong Noh, and Paul Morin
The Cryosphere, 13, 665–674, https://doi.org/10.5194/tc-13-665-2019, https://doi.org/10.5194/tc-13-665-2019, 2019
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Ian Joughin, Ben E. Smith, and Ian Howat
The Cryosphere, 12, 2211–2227, https://doi.org/10.5194/tc-12-2211-2018, https://doi.org/10.5194/tc-12-2211-2018, 2018
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Adriano Lemos, Andrew Shepherd, Malcolm McMillan, Anna E. Hogg, Emma Hatton, and Ian Joughin
The Cryosphere, 12, 2087–2097, https://doi.org/10.5194/tc-12-2087-2018, https://doi.org/10.5194/tc-12-2087-2018, 2018
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David A. Lilien, Ian Joughin, Benjamin Smith, and David E. Shean
The Cryosphere, 12, 1415–1431, https://doi.org/10.5194/tc-12-1415-2018, https://doi.org/10.5194/tc-12-1415-2018, 2018
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David E. Shean, Knut Christianson, Kristine M. Larson, Stefan R. M. Ligtenberg, Ian R. Joughin, Ben E. Smith, C. Max Stevens, Mitchell Bushuk, and David M. Holland
The Cryosphere, 11, 2655–2674, https://doi.org/10.5194/tc-11-2655-2017, https://doi.org/10.5194/tc-11-2655-2017, 2017
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We used long-term GPS data and interferometric reflectometry (GPS-IR) to measure velocity, strain rate and surface elevation for the PIG ice shelf – a site of significant mass loss in recent decades. We combined these observations with high-res DEMs and firn model output to constrain surface mass balance and basal melt rates. We document notable spatial variability in basal melt rates but limited temporal variability from 2012 to 2014 despite significant changes in sub-shelf ocean heat content.
Sophie Clayton, Stephanie Dutkiewicz, Oliver Jahn, Christopher Hill, Patrick Heimbach, and Michael J. Follows
Biogeosciences, 14, 2877–2889, https://doi.org/10.5194/bg-14-2877-2017, https://doi.org/10.5194/bg-14-2877-2017, 2017
Benjamin E. Smith, Noel Gourmelen, Alexander Huth, and Ian Joughin
The Cryosphere, 11, 451–467, https://doi.org/10.5194/tc-11-451-2017, https://doi.org/10.5194/tc-11-451-2017, 2017
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In this paper we investigate elevation changes of Thwaites Glacier, West Antarctica, one of the main sources of excess ice discharge into the ocean. We find that in early 2013, four subglacial lakes separated by 100 km drained suddenly, discharging more than 3 km3 of water under the fastest part of the glacier in less than 6 months. Concurrent ice-speed measurements show only minor changes, suggesting that ice dynamics are not strongly sensitive to changes in water flow.
Stephen M. Griffies, Gokhan Danabasoglu, Paul J. Durack, Alistair J. Adcroft, V. Balaji, Claus W. Böning, Eric P. Chassignet, Enrique Curchitser, Julie Deshayes, Helge Drange, Baylor Fox-Kemper, Peter J. Gleckler, Jonathan M. Gregory, Helmuth Haak, Robert W. Hallberg, Patrick Heimbach, Helene T. Hewitt, David M. Holland, Tatiana Ilyina, Johann H. Jungclaus, Yoshiki Komuro, John P. Krasting, William G. Large, Simon J. Marsland, Simona Masina, Trevor J. McDougall, A. J. George Nurser, James C. Orr, Anna Pirani, Fangli Qiao, Ronald J. Stouffer, Karl E. Taylor, Anne Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Maria Valdivieso, Qiang Wang, Michael Winton, and Stephen G. Yeager
Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, https://doi.org/10.5194/gmd-9-3231-2016, 2016
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The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This document defines OMIP and details a protocol both for simulating global ocean/sea-ice models and for analysing their output.
Daniel N. Goldberg, Sri Hari Krishna Narayanan, Laurent Hascoet, and Jean Utke
Geosci. Model Dev., 9, 1891–1904, https://doi.org/10.5194/gmd-9-1891-2016, https://doi.org/10.5194/gmd-9-1891-2016, 2016
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Geophysical adjoint models are powerful tools, allowing sensitivity studies that are not possible otherwise, and enabling optimized fit of models to observing data sets. The complexity involved requires the use of algorithmic differentiation (AD) software, but AD adjoint calculation for ice models can be slow, with prohibitive memory requirements. In this paper, we present a method to improve the performance of ice model adjoint generation, in terms of timing, memory load, and accuracy.
I. M. Howat, C. Porter, M. J. Noh, B. E. Smith, and S. Jeong
The Cryosphere, 9, 103–108, https://doi.org/10.5194/tc-9-103-2015, https://doi.org/10.5194/tc-9-103-2015, 2015
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In the summer of 2011, a large crater appeared in the surface of the Greenland Ice Sheet. It formed when a subglacial lake, equivalent to 10,000 swimming pools, catastrophically drained in less than 14 days. This is the first direct evidence that surface meltwater that drains through cracks to the bed of the ice sheet can build up in subglacial lakes over long periods of time. The sudden drainage may have been due to more surface melting and an increase in meltwater reaching the bed.
R. T. W. L. Hurkmans, J. L. Bamber, C. H. Davis, I. R. Joughin, K. S. Khvorostovsky, B. S. Smith, and N. Schoen
The Cryosphere, 8, 1725–1740, https://doi.org/10.5194/tc-8-1725-2014, https://doi.org/10.5194/tc-8-1725-2014, 2014
I. M. Howat, A. Negrete, and B. E. Smith
The Cryosphere, 8, 1509–1518, https://doi.org/10.5194/tc-8-1509-2014, https://doi.org/10.5194/tc-8-1509-2014, 2014
B. Medley, I. Joughin, B. E. Smith, S. B. Das, E. J. Steig, H. Conway, S. Gogineni, C. Lewis, A. S. Criscitiello, J. R. McConnell, M. R. van den Broeke, J. T. M. Lenaerts, D. H. Bromwich, J. P. Nicolas, and C. Leuschen
The Cryosphere, 8, 1375–1392, https://doi.org/10.5194/tc-8-1375-2014, https://doi.org/10.5194/tc-8-1375-2014, 2014
D. Callens, K. Matsuoka, D. Steinhage, B. Smith, E. Witrant, and F. Pattyn
The Cryosphere, 8, 867–875, https://doi.org/10.5194/tc-8-867-2014, https://doi.org/10.5194/tc-8-867-2014, 2014
I. Joughin, B. E. Smith, D. E. Shean, and D. Floricioiu
The Cryosphere, 8, 209–214, https://doi.org/10.5194/tc-8-209-2014, https://doi.org/10.5194/tc-8-209-2014, 2014
M. J. Siegert, N. Ross, H. Corr, B. Smith, T. Jordan, R. G. Bingham, F. Ferraccioli, D. M. Rippin, and A. Le Brocq
The Cryosphere, 8, 15–24, https://doi.org/10.5194/tc-8-15-2014, https://doi.org/10.5194/tc-8-15-2014, 2014
D. N. Goldberg and P. Heimbach
The Cryosphere, 7, 1659–1678, https://doi.org/10.5194/tc-7-1659-2013, https://doi.org/10.5194/tc-7-1659-2013, 2013
P. Dutrieux, D. G. Vaughan, H. F. J. Corr, A. Jenkins, P. R. Holland, I. Joughin, and A. H. Fleming
The Cryosphere, 7, 1543–1555, https://doi.org/10.5194/tc-7-1543-2013, https://doi.org/10.5194/tc-7-1543-2013, 2013
A. P. Ahlstrøm, S. B. Andersen, M. L. Andersen, H. Machguth, F. M. Nick, I. Joughin, C. H. Reijmer, R. S. W. van de Wal, J. P. Merryman Boncori, J. E. Box, M. Citterio, D. van As, R. S. Fausto, and A. Hubbard
Earth Syst. Sci. Data, 5, 277–287, https://doi.org/10.5194/essd-5-277-2013, https://doi.org/10.5194/essd-5-277-2013, 2013
I. Joughin, S. B. Das, G. E. Flowers, M. D. Behn, R. B. Alley, M. A. King, B. E. Smith, J. L. Bamber, M. R. van den Broeke, and J. H. van Angelen
The Cryosphere, 7, 1185–1192, https://doi.org/10.5194/tc-7-1185-2013, https://doi.org/10.5194/tc-7-1185-2013, 2013
Related subject area
Data Assimilation
Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model
Impact of time-dependent data assimilation on ice flow model initialization and projections: a case study of Kjer Glacier, Greenland
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model
A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams
Towards improving short-term sea ice predictability using deformation observations
Bounded and categorized: targeting data assimilation for sea ice fractional coverage and non-negative quantities in a single column multi-category sea ice model
Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates
Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system
Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles
Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)
A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations
Estimating parameters in a sea ice model using an ensemble Kalman filter
DeepBedMap: a deep neural network for resolving the bed topography of Antarctica
Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter
Two-dimensional inversion of wideband spectral data from the capacitively coupled resistivity method – first applications in periglacial environments
Brief communication: Evaluation and inter-comparisons of Qinghai–Tibet Plateau permafrost maps based on a new inventory of field evidence
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system
Estimation of sea ice parameters from sea ice model with assimilated ice concentration and SST
Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment
Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra
On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model
Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation
Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis
Adjoint accuracy for the full Stokes ice flow model: limits to the transmission of basal friction variability to the surface
Changing basal conditions during the speed-up of Jakobshavn Isbræ, Greenland
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin
The Cryosphere, 18, 1597–1619, https://doi.org/10.5194/tc-18-1597-2024, https://doi.org/10.5194/tc-18-1597-2024, 2024
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Individual satellite passes instead of daily means of sea ice concentration are used to correct the sea ice model forecast in the Barents Sea. The use of passes provides a significantly larger improvement of the forecasts even after a 7 d period due to the more precise information on temporal and spatial variability contained in the passes. One major advantage of the use of satellite passes is that there is no need to wait for the daily mean availability in order to update the forecast.
Youngmin Choi, Helene Seroussi, Mathieu Morlighem, Nicole-Jeanne Schlegel, and Alex Gardner
The Cryosphere, 17, 5499–5517, https://doi.org/10.5194/tc-17-5499-2023, https://doi.org/10.5194/tc-17-5499-2023, 2023
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Ice sheet models are often initialized using snapshot observations of present-day conditions, but this approach has limitations in capturing the transient evolution of the system. To more accurately represent the accelerating changes in glaciers, we employed time-dependent data assimilation. We found that models calibrated with the transient data better capture past trends and more accurately reproduce changes after the calibration period, even with limited observations.
Keguang Wang, Alfatih Ali, and Caixin Wang
The Cryosphere, 17, 4487–4510, https://doi.org/10.5194/tc-17-4487-2023, https://doi.org/10.5194/tc-17-4487-2023, 2023
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A simple, efficient. and accurate data assimilation method, local analytical optimal nudging (LAON), is introduced to assimilate high-resolution sea ice concentration in a pan-Arctic high-resolution coupled ocean and sea ice model. The method provides a new vision by nudging the model evolution to the optimal estimate forwardly, continuously, and smoothly. This method is applicable to the general nudging theory and applications in physics, Earth science, psychology, and behavior sciences.
Beatriz Recinos, Daniel Goldberg, James R. Maddison, and Joe Todd
The Cryosphere, 17, 4241–4266, https://doi.org/10.5194/tc-17-4241-2023, https://doi.org/10.5194/tc-17-4241-2023, 2023
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Ice sheet models generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, capturing the complete range of possible projections of mass loss is of critical societal importance. Here we add to data assimilation techniques commonly used in ice sheet modelling (a Bayesian inference approach) and fully characterize calibration uncertainty. We successfully propagate this type of error onto sea level rise projections of three ice streams in West Antarctica.
Anton Korosov, Pierre Rampal, Yue Ying, Einar Ólason, and Timothy Williams
The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, https://doi.org/10.5194/tc-17-4223-2023, 2023
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It is possible to compute sea ice motion from satellite observations and detect areas where ice converges (moves together), forms ice ridges or diverges (moves apart) and opens leads. However, it is difficult to predict the exact motion of sea ice and position of ice ridges or leads using numerical models. We propose a new method to initialise a numerical model from satellite observations to improve the accuracy of the forecasted position of leads and ridges for safer navigation.
Molly Wieringa, Christopher Riedel, Jeffrey Anderson, and Cecilia Bitz
EGUsphere, https://doi.org/10.5194/egusphere-2023-2016, https://doi.org/10.5194/egusphere-2023-2016, 2023
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Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for realistic forecasting efforts.
Imke Sievers, Till A. S. Rasmussen, and Lars Stenseng
The Cryosphere, 17, 3721–3738, https://doi.org/10.5194/tc-17-3721-2023, https://doi.org/10.5194/tc-17-3721-2023, 2023
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The satellite CryoSat-2 measures freeboard (FB), which is used to derive sea ice thickness (SIT) under the assumption of hydrostatic balance. This SIT comes with large uncertainties due to errors in the observed FB, sea ice density, snow density and snow thickness. This study presents a new method to derive SIT by assimilating the FB into the sea ice model, evaluates the resulting SIT against in situ observations and compares the results to the CryoSat-2-derived SIT without FB assimilation.
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
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Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
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Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
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This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Jean Odry, Marie-Amélie Boucher, Simon Lachance-Cloutier, Richard Turcotte, and Pierre-Yves St-Louis
The Cryosphere, 16, 3489–3506, https://doi.org/10.5194/tc-16-3489-2022, https://doi.org/10.5194/tc-16-3489-2022, 2022
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The research deals with the assimilation of in-situ local snow observations in a large-scale spatialized snow modeling framework over the province of Quebec (eastern Canada). The methodology is based on proposing multiple spatialized snow scenarios using the snow model and weighting them according to the available observations. The paper especially focuses on the spatial coherence of the snow scenario proposed in the framework.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, https://doi.org/10.5194/tc-16-61-2022, 2022
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Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, https://doi.org/10.5194/tc-15-2857-2021, 2021
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Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.
Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth
The Cryosphere, 15, 1277–1284, https://doi.org/10.5194/tc-15-1277-2021, https://doi.org/10.5194/tc-15-1277-2021, 2021
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Sea ice models suffer from large uncertainties arising from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
Wei Ji Leong and Huw Joseph Horgan
The Cryosphere, 14, 3687–3705, https://doi.org/10.5194/tc-14-3687-2020, https://doi.org/10.5194/tc-14-3687-2020, 2020
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A machine learning technique similar to the one used to enhance everyday photographs is applied to the problem of getting a better picture of Antarctica's bed – the part which is hidden beneath the ice. By taking hints from what satellites can observe at the ice surface, the novel method learns to generate a rougher bed topography that complements existing approaches, with a result that is able to be used by scientists running fine-scale ice sheet models relevant to predicting future sea levels.
Fabien Gillet-Chaulet
The Cryosphere, 14, 811–832, https://doi.org/10.5194/tc-14-811-2020, https://doi.org/10.5194/tc-14-811-2020, 2020
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Marine-based sectors of the Antarctic Ice Sheet are increasingly contributing to sea-level rise. The basal conditions exert an important control on the ice dynamics. For obvious reasons of inaccessibility, they are an important source of uncertainties in numerical ice flow models used for sea-level projections. Here we assess the performance of an ensemble Kalman filter for the assimilation of transient observations of surface elevation and velocities in a marine ice sheet model.
Jan Mudler, Andreas Hördt, Anita Przyklenk, Gianluca Fiandaca, Pradip Kumar Maurya, and Christian Hauck
The Cryosphere, 13, 2439–2456, https://doi.org/10.5194/tc-13-2439-2019, https://doi.org/10.5194/tc-13-2439-2019, 2019
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The capacitively coupled resistivity (CCR) method enables the determination of frequency-dependent electrical parameters of the subsurface. CCR is well suited for application in cryospheric areas because it provides logistical advantages regarding coupling on hard surfaces and highly resistive grounds. With our new spectral two-dimensional inversion, we can identify subsurface structures based on full spectral information. We show the first results of the inversion method on the field scale.
Bin Cao, Tingjun Zhang, Qingbai Wu, Yu Sheng, Lin Zhao, and Defu Zou
The Cryosphere, 13, 511–519, https://doi.org/10.5194/tc-13-511-2019, https://doi.org/10.5194/tc-13-511-2019, 2019
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Many maps have been produced to estimate permafrost distribution over the Qinghai–Tibet Plateau. However the evaluation and inter-comparisons of them are poorly understood due to limited in situ measurements. We provided an in situ inventory of evidence of permafrost presence or absence, with 1475 sites over the Qinghai–Tibet Plateau. Based on the in situ measurements, our evaluation results showed a wide range of map performance, and the estimated permafrost region and area are extremely large.
Sindre Fritzner, Rune Graversen, Kai H. Christensen, Philip Rostosky, and Keguang Wang
The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, https://doi.org/10.5194/tc-13-491-2019, 2019
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In this work, a coupled ocean and sea-ice ensemble-based assimilation system is used to assess the impact of different observations on the assimilation system. The focus of this study is on sea-ice observations, including the use of satellite observations of sea-ice concentration, sea-ice thickness and snow depth for assimilation. The study showed that assimilation of sea-ice thickness in addition to sea-ice concentration has a large positive impact on the coupled model.
Siva Prasad, Igor Zakharov, Peter McGuire, Desmond Power, and Martin Richard
The Cryosphere, 12, 3949–3965, https://doi.org/10.5194/tc-12-3949-2018, https://doi.org/10.5194/tc-12-3949-2018, 2018
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A numerical sea ice model, CICE, was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay, Hudson Bay and Labrador Sea. The modelled ice parameters were compared with parameters estimated from remote-sensing data. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty of the observations except during March.
Jiping Xie, François Counillon, and Laurent Bertino
The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, https://doi.org/10.5194/tc-12-3671-2018, 2018
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We use the winter sea-ice thickness dataset CS2SMOS merged from two satellites SMOS and CryoSat-2 for assimilation into an ice–ocean reanalysis of the Arctic, complemented by several other ocean and sea-ice measurements, using an Ensemble Kalman Filter. The errors of sea-ice thickness are reduced by 28% and the improvements persists through the summer when observations are unavailable. Improvements of ice drift are however limited to the Central Arctic.
Gaia Piazzi, Guillaume Thirel, Lorenzo Campo, and Simone Gabellani
The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, https://doi.org/10.5194/tc-12-2287-2018, 2018
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The study focuses on the development of a multivariate particle filtering data assimilation scheme into a point-scale snow model. One of the main challenging issues concerns the impoverishment of the particle sample, which is addressed by jointly perturbing meteorological data and model parameters. An additional snow density model is introduced to reduce sensitivity to the availability of snow mass-related observations. In this configuration, the system reveals a satisfying performance.
Anh Phuong Tran, Baptiste Dafflon, and Susan S. Hubbard
The Cryosphere, 11, 2089–2109, https://doi.org/10.5194/tc-11-2089-2017, https://doi.org/10.5194/tc-11-2089-2017, 2017
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Soil organics carbon (SOC) and its influence on terrestrial ecosystem feedbacks to global warming in permafrost regions are particularly important for the prediction of future climate variation. Our study proposes a new surface–subsurface, joint deterministic–stochastic hydrological–thermal–geophysical inversion approach and documents the benefit of including multiple types of data to estimate the vertical profile of SOC content and its influence on hydrological–thermal dynamics.
Luc Charrois, Emmanuel Cosme, Marie Dumont, Matthieu Lafaysse, Samuel Morin, Quentin Libois, and Ghislain Picard
The Cryosphere, 10, 1021–1038, https://doi.org/10.5194/tc-10-1021-2016, https://doi.org/10.5194/tc-10-1021-2016, 2016
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This study investigates the assimilation of optical reflectances, snowdepth data and both combined into a multilayer snowpack model. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter. Experiments assimilating only synthetic data are conducted at one point in the French Alps, the Col du Lautaret, over five hydrological years. Results of the assimilation experiments show improvements of the snowpack bulk variables estimates.
Qinghua Yang, Martin Losch, Svetlana N. Losa, Thomas Jung, Lars Nerger, and Thomas Lavergne
The Cryosphere, 10, 761–774, https://doi.org/10.5194/tc-10-761-2016, https://doi.org/10.5194/tc-10-761-2016, 2016
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We assimilate the summer SICCI sea ice concentration data with an ensemble-based Kalman Filter. Comparing with the approach using a constant data uncertainty, the sea ice concentration estimates are further improved when the SICCI-provided uncertainty are taken into account, but the sea ice thickness cannot be improved. We find the data assimilation system cannot give a reasonable ensemble spread of sea ice concentration and thickness if the provided uncertainty are directly used.
D. R. Harp, A. L. Atchley, S. L. Painter, E. T. Coon, C. J. Wilson, V. E. Romanovsky, and J. C. Rowland
The Cryosphere, 10, 341–358, https://doi.org/10.5194/tc-10-341-2016, https://doi.org/10.5194/tc-10-341-2016, 2016
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This paper investigates the uncertainty associated with permafrost thaw projections at an intensively monitored site. Permafrost thaw projections are simulated using a thermal hydrology model forced by a worst-case carbon emission scenario. The uncertainties associated with active layer depth, saturation state, thermal regime, and thaw duration are quantified and compared with the effects of climate model uncertainty on permafrost thaw projections.
N. Martin and J. Monnier
The Cryosphere, 8, 721–741, https://doi.org/10.5194/tc-8-721-2014, https://doi.org/10.5194/tc-8-721-2014, 2014
M. Habermann, M. Truffer, and D. Maxwell
The Cryosphere, 7, 1679–1692, https://doi.org/10.5194/tc-7-1679-2013, https://doi.org/10.5194/tc-7-1679-2013, 2013
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Short summary
We calibrate a time-dependent ice model through optimal fit to transient observations of surface elevation and velocity, a novel procedure in glaciology and in particular for an ice stream with a dynamic grounding line. We show this procedure gives a level of confidence in model projections that cannot be achieved through more commonly used glaciological data assimilation methods. We show that Smith Glacier is in a state of retreat regardless of climatic forcing for the next several decades.
We calibrate a time-dependent ice model through optimal fit to transient observations of surface...
