Scientific overview:
This PhD thesis aims at studying the impacts of climate change-induced permafrost thaw in the Arctics, by using advanced thermo-hydro-mechanical modelling capabilities developed in the framework of the PERMACHANGE project. Permafrost is soil permanently frozen in depth, covering a quarter of Northern Hemisphere lands. Due to climate warming, it is experiencing fast and widespread thawing, and this induces essential impacts in the Arctics, both on the environment (e.g., water resources) and on societies (e.g., infrastructure destabilisation). These permafrost thaw impacts are expected to generate significant additional financial costs for maintaining key human activities, up to hundreds of billions of dollars by the end of the century. Moreover, permafrost thaw will likely trigger critical climatic feedback. Thus, anticipating permafrost thaw by numerical simulations is paramount for ensuring the resilience of Arctic environments, societies and activities while controlling the associated costs. Meanwhile, numerical simulations of permafrost dynamics are highly complex and challenging due to the strong non-linearities and couplings involved in the related physics.
The PERMACHANGE project builds on high performance computing (Orgogozo et al., 2023, Xavier et al., 2024) and hydrid modelling (Chinesta et al., 2020; Champaney et al., 2022) for developing a site scale (~10’s of km2) permafrost thermo-hydological hybrid twin, to be coupled with state of the art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani et al., 2024) using symbolic regression approaches (e.g. Guayacán-Carrillo and Sulem, 2024). By doing so, PERMACHANGE will enable unprecedented high fidelity and high efficiency numerical simulations of subterranean heat and water transfers and terrain stability under permafrost thaw.
This PhD thesis aims at applying this advanced modelling chain to three sites of long-term permafrost monitoring in the European Arctics: Abisko in Northern Sweden, Ny-Ålesund in Svalbard and Ilulissat in Greenland. In this way, projections of permafrost-thaw impacts at different time scale will be done, from the seasonal time scale to the century time scale. This will allow us to demonstrate the capabilities of the PERMACHANGE advanced modelling workflow and to provide critical information to local stakeholders as an alert for decision-making regarding emergency actions for short-term permafrost thaw hazards and information for long-term land planning, taking into account climate change. It will also enable establishing a general procedure for the operational application of this advanced permafrost modelling workflow all across the Arctics and contribute to the gathering of the relevant information needed for improving permafrost modelling in Earth System Models used for climate change simulations.Collaborations:
- Processes and Engineering in Mechanics and Materials (PIMM) laboratory
- Navier laboratory, Mechanics and physics of materials, structures and geomaterials
- Alfred Wegener Institute (AWI)
- Swedish Polar Research Secretariat (SPRS)
- Arctic Danish Technical University (Arctic DTU)
Tasks:
For each of the three Arctic study sites, making:
- thermo-hydrological projections at the century-scale by applying the hybrid twin approach, using the climate modelling results of the CMIP6 as input data.
- real-time thermo-hydrological projections at seasonal time scale by applying the hybrid twin approach, using observed and statistically inferred possible extremum weather conditions to come.
- mechanical projections at the century-scale by applying the machine learning based surrogate soil mechanics model, using the results of task 1) as input data.
- real-time mechanical projections at seasonal time scale by applying machine learning based surrogate soil mechanics model, using the results of task 2) as input data.
Needed skills and knowledge:
- Numerical modeling of physical processes (at best with OpenFOAM and/or AI-based modelling)
- Working in linux environment (at best with experience on supercomputers)
- Collaborative work in a large and diverse international team
- Interest for scientific communication and writing
- Although not mandatory, a background in geosciences and/or cryosciences and/or porous media physics would be appreciated.
How to apply:
please send a CV and a motivation letter to the advisor.
Champaney et al., 2022. Int J Mater Form 15, 31. https://doi.org/10.1007/s12289-022-01678-4
Chinesta et al., 2020. Arch Computat Methods Eng 27, 105–134 https://doi.org/10.1007/s11831-018-9301-4
Guayacán-Carrillo and Sulem, 2024. Computers & Geotechnics, 171, 106355. https://doi.org/10.1016/j.compgeo.2024.106355
Orgogozo et al., 2023. Computer Physics Communications, 282, 108541, https://doi.org/10.1016/j.cpc.2022.108541
Richa et al., 2024. In: Geotechnical engineering challenge to meet current and emerging needs of society. Proc. XVIII European Conference on Soil Mechanics and Geotechnical Engineering. Lisbonne, August 2024. https://doi.org/10.1201/9781003431749-359
Tristani et al., 2024. International Journal for Numerical & Analytical Methods in Geomechanics. https://doi.org/10.1002/nag.3889
Xavier et al., 2024. The Cryosphere, 18, 5865–5885, https://doi.org/10.5194/tc-18-5865-2024
