MAMMAMIA – Multi-scAle-Multi-Method Analysis of Mechanisms causing Ice Acceleration
Sliding of ice is a major player in controlling the glacier contribution to sea-level rise, yet it is poorly understood. In the research project MAMMAMIA we will conduct a Multi-method-multi-scale analysis of mechanisms causing ice acceleration.
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About the project
MAMMAMIA focuses on the interface dynamics at the base of polar glaciers to determine how thermal conditions, subglacial friction and fine scale ice dynamics respond to temporal changes in meltwater supply. MAMMAMIA collects synchronous, high-resolution records of ice velocity along with associated cryo-seismicity.
In a multiple model approach, we simulate glacier hydraulics, basal friction and ice flow acting in concert to interpret the observations in terms of current theory, as well as to explore the conditions under which local perturbations can cause a glacier-wide acceleration.
Our findings will advance process understanding and generate the parameterizations required for including dynamic responses of glaciers in more realistic assessments of future sea-level rise.
Avoiding limitations of single-disciplinary approaches, MAMMAMIA will scrutinize mechanisms of ice acceleration by adopting a multi-method approach to exploit the supplementary information gained by combining several observational techniques and state-of-the-art modeling.
1) To collect a comprehensive dataset of glacier motion covering multiple spatial and temporal scales, along with complementary variables to define the glacio-hydraulic context. New smart-sensor technology on drifting platforms collects information along the flowpath and will be used to better constrain hydraulic conditions within the ice.
2) In a multiple model approach, we will simulate glacier hydraulics, basal friction and ice flow acting in concert to interpret the observations in terms of current theory, as well as to explore the conditions under which local perturbations can propagate to cause a glacier-wide acceleration.
MAMMAMIA targets the dynamic discharge component of mass loss from land-based ice, which is the major uncertainty in sea-level rise projections. The strategy of MAMMAMIA to fuse geophysical and remote sensing based data with glacier dynamic modelling will not only produce more precise datasets but also foster multidisciplinary contributions in theory, methodology and outreach, acting as a catalyst for research innovation and projects. Through dedicated field experiments, remote sensing and advanced modelling, MAMMAMIA discoveries will advance process understanding required for improved assessments of future glacier contributions to sea-level rise.
By scrutinizing the relationships between hydraulics, friction and ice acceleration, MAMMAMIA will not only generate knowledge required for more accurate assessments of climate change impacts, but also lay the foundation for further development through contributing open-access data and open-source modeling tools.
Transfer of land-based ice masses into the oceans is a strong contributor to ongoing sea level rise; both, melt-water runoff, as well as ice discharge into the oceans are expected to increase with continued climate warming. Dynamic instabilities allow for larger, more rapid ice mass loss than surface melt, and Earth history has experienced several episodes of rapid ice sheet decay with severe impact on sea level, climate and ecology.
There is considerable variability in the way glaciers respond to climate change; some glaciers become dynamically inactive and exhibit moderate rates of mass loss, whereas others feature dynamic instabilities and discharge large amounts of ice. For instance, the drastic acceleration of a single basin of the Austfonna ice cap since 2012 doubled sea-level contribution from the entire Svalbard archipelago. The discovery of widespread acceleration of the Greenland Ice Sheet in the early 2000s sparked intense research activity on the hydraulic lubrication of glacier beds. Since then, a wealth of new observations in unprecedented quality, detail and coverage suggest the existence of additional, hitherto neglected, cryo-hydrological feedbacks. This incomplete process understanding gives rise to considerable uncertainties about future evolution of sea level, as acknowledged by the Intergovernmental Panel on Climate Change. MAMMAMIA addresses these crucial knowledge gaps to ultimately facilitate improved assessments of the dynamic stability of polar ice masses.
The full name of the project MAMMAMIA is 'Multi-scAle-Multi-Method Analysis of Mechanisms causing Ice Acceleration'. This project is funded by The Research Council of Norway in the FRIPRO-programme. The NFR-project number is 301837.
The project MAMMAMIA started up in 2020, and will be finished in 2024.
The project is based on a collaboration with researchers from the University in Oslo and researchers from several different research institutions in Norway and internationally. The project team contains of collaborating researchers from:
- Department of Geosciences, Department of Physics, Universitetet i Oslo (UiO), Norway: www.uio.no
- The University Centre in Svalbard (UNIS), Svalbard, Norway: www.unis.no
- Norwegian Polar Institute: Glaciology, Norway: www.npolar.no
- NORSAR, Norway: www.norsar.no
- Department of Geosciences, University of Bergen, Norway: www.uib.no/geo
- Institute of Low Temperature Science, Hokkaido University, Japan: www.lowtem.hokudai.ac.jp/en/
- Centre for Biorobotics – Tallinn University of Technology, Estonia: www.biorobotics.ttu.ee
- Institut für Geowissenschaften, University of Kiel, Germany: www.ifg.uni-kiel.de/
- Institute of Environmental Geosciences, Grenoble, France: www.ige-grenoble.fr/
- Klimahuset, Naturhistorisk Museum, UiO, Oslo, Norway: www.nhm.uio.no/klimahuset/index.html
Bouchayer, Coline Lili Mathy; Aiken, J.M.; Thøgersen, Kjetil; Renard, Francois & Schuler, Thomas (2022). A Machine learning framework to automate the classification of surge-type glaciers in Svalbard. Journal of Geophysical Research (JGR): Earth Surface. ISSN 2169-9003. 127. doi: 10.1029/2022JF006597.