643 research outputs found
Construction and operation of the new Belgian research station, Dronning Maud Land, Antarctica
No full textFinal Comprehensive Environmental Evaluation Report (CEE). F. Pattyn is one of the principal contributors to this reportinfo:eu-repo/semantics/publishe
Antarctic subglacial conditions inferred from combined interferometric velocity data and ice sheet modeling
No full textAntarctic subglacial conditions can be elucidated through several techniques. However, since direct measurements are only limited to a few deep drillings to the bed, there is always a substantial amount of ice sheet and thermodynamical modeling involved. This can either be done based on a fully coupled thermomechanical ice sheet model, or a thermodynamical model coupled to present-day ice sheet geometry and environmental conditions. The latter technique was recently employed by Pattyn (2010) in an attempt to determine the likelihood of basal temperate conditions of the Antarctic ice sheet using a series of existing datasets on mass balance and geothermal heat flux. Here, we made an update of this estimate using new data on bedrock elevation and ice thickness (ALBMAP; Le Brocq et al. 2010) and observed surface velocities obtained from interferometric analysis (Rignot et al. 2011). The latter were further constrained by a hybrid ice sheet/ice shelf model to correct for the interior ice flow (where observations are lacking) and for correcting the ice flow across subglacial lakes. The new estimates are compared to the initial basal temperature calculation.Le Brocq, A. Payne, A. Vieli, A. 2010, An improved Antarctic dataset for high resolution numerical ice sheet models (ALBMAP V1), Earth Syst. Sci. Data Discuss. 3, 195-230.Pattyn, F. 2010, Antarctic subglacial conditions inferred from a hybrid ice sheet/ice stream model, Earth Planet. Sci. Lett. 295, 451-461.Rignot, E. Mouginot, J. Scheuchl, B. 2011, Ice Flow of the Antarctic Ice Sheet, Science 333, 1427-1429.info:eu-repo/semantics/nonPublishe
Modelling historical and recent mass loss of McCall Glacier, Alaska, USA
No full textVolume loss of valley glaciers is now considered to be a significant contribution to sea level rise. Understanding and identifying the processes involved in accelerated mass loss are necessary to determine their impact on the global system. Here we present results from a series of model experiments with a higher-order thermomechanically coupled flowline model (Pattyn, 2002). Boundary conditions to the model are parameterizations of surface mass balance, geothermal heating, observed surface and 10 m ice depth temperatures. The time-dependent experiments aim at simulating the glacier retreat from its LIA expansion to present according to different scenarios and model parameters. Model output was validated against measurements of ice velocity, ice surface elevation and terminus position at different stages. Results demonstrate that a key factor in determining the glacier retreat history is the importance of internal accumulation (>50%) in the total mass balance. The persistence of a basal temperate zone characteristic for this polythermal glacier depends largely on its contribution. Accelerated glacier retreat since the early nineties seems directly related to the increase in ELA and the sudden reduction in AAR due to the fact that a large lower elevation cirque – previously an important accumulation area – became part of the ablation zone. © Author(s) 2008.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
A revised evaluation of Antarctic subglacial conditions and the contribution of basal melt to present day sea-level rise.
No full textAntarctic subglacial conditions can be elucidated through several techniques. However, since direct measurements are only limited to a few deep drillings to the bed, there is always a substantial amount of ice sheet and thermodynamical modeling involved. This can either be done based on a fully coupled thermomechanical ice sheet model, or a thermodynamical model coupled to present-day ice sheet geometry and environmental conditions. The latter technique was recently employed by Pattyn (2010) in an attempt to determine the likelihood of basal temperate conditions of the Antarctic ice sheet using a series of existing datasets on mass balance and geothermal heat flux. Here, we made an update of this estimate using new data on bedrock elevation and ice thickness (ALBMAP; Le Brocq et Al. 2010) and observed surface velocities obtained from interferometric analysis (Rignot et Al. 2011). The latter were further constrained by a hybrid ice sheet/ice shelf model to correct for the interior ice flow (where error of observations are to high) and for correcting the ice flow across subglacial lakes. We coupled the model with a new lake inventory from Wright et Al. (in review) to improve the contribution of the geothermal heat flux to the temperature. This revised calculation of the temperature allows us to improve our knowledge of basal melting and its contribution to present-day sea-level rise.Le Brocq, A. Payne, A. Vieli, A. 2010, An improved Antarctic dataset for high resolution numerical ice sheet models (ALBMAP V1), Earth Syst. Sci. Data Discuss. 3, 195-230.Pattyn, F. 2010, Antarctic subglacial conditions inferred from a hybrid ice sheet/ice stream model, Earth Planet. Sci. Lett. 295, 451-461.Rignot, E. Mouginot, J. Scheuchl, B. 2011, Ice Flow of the Antarctic Ice Sheet, Science 333, 1427-1429.Wright, A. and Siegert, M. (in review), A fourth inventory of Antarctic subglacial lakes.info:eu-repo/semantics/nonPublishe
Using ice-flow models to evaluate potential sites of million year-old ice in Antarctica
No full textFinding suitable potential sites for an undisturbed record of million-year old ice in Antarctica requires slow-moving ice (preferably an ice divide) and basal conditions that are not disturbed by large topographic variations. Furthermore, ice should be thick and cold basal conditions should prevail, since basal melting would destroy the bottom layers. However, thick ice (needed to resolve the signal at sufficient high resolution) increases basal temperatures, which is a conflicting condition for finding a suitable drill site. In addition, slow moving areas in the center of ice sheets are also low-accumulation areas, and low accumulation reduces potential cooling of the ice through vertical advection. While boundary conditions such as ice thickness and accumulation rates are relatively well constrained, the major uncertainty in determining basal thermal conditions resides in the geothermal heat flow (GHF) underneath the ice sheet. We explore uncertainties in existing GHF data sets and their effect on basal temperatures of the Antarctic Ice Sheet, and propose an updated method based on Pattyn (2010) to improve existing GHF data sets in agreement with known basal temperatures and their gradients to reduce this uncertainty. Both complementary methods lead to a better comprehension of basal temperature sensitivity and a characterization of potential ice coring sites within these uncertainties. The combination of both modeling approaches show that the most likely oldest ice sites are situated near the divide areas (close to existing deep drilling sites, but in areas of smaller ice thickness) and across the Gamburtsev Subglacial Mountains.info:eu-repo/semantics/publishe
www.the-cryosphere.net/2/23/2008/ © Author(s) 2008. This work is distributed under the Creative Commons Attribution 3.0 License.
No full textAbstract. Volume loss of valley glaciers is now consid-ered to be a significant contribution to sea level rise. Un-derstanding and identifying the processes involved in accel-erated mass loss are necessary to determine their impact on the global system. Here we present results from a series of model experiments with a higher-order thermomechani-cally coupled flowline model (Pattyn, 2002). Boundary con-ditions to the model are parameterizations of surface mass balance, geothermal heating, observed surface and 10 m ice depth temperatures. The time-dependent experiments aim at simulating the glacier retreat from its LIA expansion to present according to different scenarios and model parame-ters. Model output was validated against measurements of ice velocity, ice surface elevation and terminus position at different stages. Results demonstrate that a key factor in de-termining the glacier retreat history is the importance of in-ternal accumulation (>50%) in the total mass balance. The persistence of a basal temperate zone characteristic for this polythermal glacier depends largely on its contribution. Ac-celerated glacier retreat since the early nineties seems di-rectly related to the increase in ELA and the sudden reduction in AAR due to the fact that a large lower elevation cirque – previously an important accumulation area – became part of the ablation zone
Scripts and data for the PARASO Geoscientific Model Development paper figures
No full textScripts and data for reproducing all figures from the PARASO model description paper.
*.tex sources generate Tikz pdf figures calling pdflatex . they require a working LaTeX installation with some standard libraries (e.g. Tikz and others).
*.py are python scripts generating figures. They require standard python libraires such as matplotlib, numpy, cartopy...
Model description: Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully-coupled ice-sheet - ocean - sea-ice - atmosphere - land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-315, in review, 2021.
Source code (no COSMO): Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. https://doi.org/10.5281/zenodo.5576201
Input data: Pelletier, Charles, Klein, François, Zipf, Lars, Vanden Broucke, Sam, Haubner, Konstanze, & Helsen, Samuel. (2021). Input data for PARASO, a circum-Antarctic fully-coupled 5-component model (v1.4.3) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5588468
Forcings: Pelletier, Charles, & Helsen, Samuel. (2021). PARASO ERA5 forcings (1.4.3) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5590053
Acknowledgements
The ERA5 data (Hersbach, 2019) was downloaded on 01-09-2019 from the Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.
Reference: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 01-SEP-2019), https://doi.org/10.24381/cds.adbb2d47.
The sea-ice index (NSIDC-G02315) was downloaded on 01-09-2019 from the National Snow & Ice Data Center.
Reference: Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. Daily Antarctic. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. https://doi.org/10.7265/N5K072F8. Accessed on 01-SEP-2019.
The World Ocean Atlas 2018 (WOA18):
Reference: Boyer, Tim P.; Garcia, Hernan E.; Locarnini, Ricardo A.; Zweng, Melissa M.; Mishonov, Alexey V.; Reagan, James R.; Weathers, Katharine A.; Baranova, Olga K.; Seidov, Dan; Smolyar, Igor V. (2018). World Ocean Atlas 2018. Statistical means of temperature and salinity on grid.. NOAA National Centers for Environmental Information. Dataset. https://accession.nodc.noaa.gov/NCEI-WOA18. Accessed 01-SEP-2020.
Ice-shelf melt rates observations:
Reference: Rignot, E., Jacobs, S., Mouginot, J., & Scheuchl, B. (2013). Ice-shelf melting around Antarctica (Supplementary Material) Science, 341(6143), 266-270. https://doi.org/10.1126/science.1235798.
Reference: Adusumilli, Susheel; Fricker, Helen A.; Medley, Brooke C.; Padman, Laurie; Siegfried, Matthew R. (2020). Data from: Interannual variations in meltwater input to the Southern Ocean from Antarctic ice shelves. UC San Diego Library Digital Collections. https://doi.org/10.6075/J04Q7SHT
JRA-55 reanalysis:
Reference: Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi , 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5-48, https://doi.org/10.2151/jmsj.2015-001
BedMachine Antarctic topography:
Reference: Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nature Geoscience 13, 132–137 (2020). https://doi.org/10.1038/s41561-019-0510-8
Reference: Morlighem, M. 2020. MEaSUREs BedMachine Antarctica, Version 2. Ice-sheet thickness, surface elevation and mask. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/E1QL9HFQ7A8M (accessed 01-FEB-2020).Developed within the framework of the PARAMOUR project, Decadal predictability and variability of polar climate: the role of atmosphere-ocean-cryosphere multiscale interactions.
Fonds de la Recherche Scientifique–FNRS Grant number O0100718F (EOS ID 30454083)
Learned Symptom-Specific Fear Toward a Visceral Sensation and Its Impact on Perceptual Habituation
No full textObjective Impaired habituation of bodily sensations has been suggested as a contributing factor to chronic pain. We examined in healthy volunteers the influence of fear learning toward a nonpainful sensation in the esophagus on the perceptual habituation of this sensation. Methods In a homoreflexive fear learning paradigm, nonpainful electrical sensations in the esophagus were used as a conditioned stimulus (CS). This sensation was presented 42 times before, during, and after fear learning. In the fear learning group (n = 41), the CS was paired with a painful electrical sensation in the esophagus (unconditioned stimulus [US]). In the control group (n = 41), the CS was not paired with the US. Ratings for CS intensity, US expectancy, startle electromyogram (EMG), skin conductance responses (SCR), and event-related potentials (ERPs) to the CS were assessed. Results Compared to the control group, fear learning was observed in the fear learning group as evidenced by potentiated startle responses after the CS relative to ITI (t(1327) = 3.231, p = .001) and higher US expectancy ratings (t(196) = 3.17, p = .002). SCRs did not differ between groups (F-1,F-817 = 1.241, p = .33). Despite successful fear learning, the fear learning group did not show a distinct pattern of habituation to the visceral CS relative to the control group (intensity ratings: F-1,F-77.731 = 0.532, p = .47; ERPs: F-1,F-520.78 = 0.059, p = .94). Conclusion Acquired fear to nonpainful esophageal sensations does not affect their perceptual habituation patterns.Research Foundation Flanders (FWO) [G071918N, 11G1320N]; FWO [I011320N]
RIMBAY — a multi-approximation 3D ice-dynamics model for comprehensive applications: model description and examples
No full textGlaciers and ice caps exhibit currently the largest cryospheric contributions to sea level rise. Modelling the dynamics and mass balance of the major ice sheets is therefore an important issue to investigate the current state and the future response of the cryosphere in response to changing environmental conditions, namely global warming. This requires a powerful, easy-to-use, versatile multi-approximation ice dynamics model. Based on the well-known and established ice sheet model of Pattyn (2003) we develop the modular multi-approximation thermomechanic ice model RIMBAY, in which we improve the original version in several aspects like a shallow ice–shallow shelf coupler and a full 3D-grounding-line migration scheme based on Schoof's (2007) heuristic analytical approach. We summarise the full Stokes equations and several approximations implemented within this model and we describe the different numerical discretisations. The results are cross-validated against previous publications dealing with ice modelling, and some additional artificial set-ups demonstrate the robustness of the different solvers and their internal coupling. RIMBAY is designed for an easy adaption to new scientific issues. Hence, we demonstrate in very different set-ups the applicability and functionality of RIMBAY in Earth system science in general and ice modelling in particular
PARASO source code (no COSMO)
No full textSource code for the PARASO Antarctic configuration, except the COSMO model (atmosphere), which is only accessible to CLM-Community members (free membership charge for all research applications). A full version of these sources, including the COSMO part, has been uploaded to the CLM-Community RedC under "Downloads" -> "COSMO-CLM".
Hence, these sources are provided for dicdactical purposes, not for running the full model (which requires COSMO).
Model described in:
Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully-coupled ice-sheet - ocean - sea-ice - atmosphere - land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-315, in review, 2021.Developed within the framework of the PARAMOUR project, Decadal predictability and variability of polar climate: the role of atmosphere-ocean-cryosphere multiscale interactions.
Fonds de la Recherche Scientifique–FNRS Grant number O0100718F (EOS ID 30454083)
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