The Arctic is among the most vulnerable regions to recent warming trends in Earth’s climate. The sparsity of available historical data in the region necessitates the use of numerical models to better understand the effects of climate change on Arctic landscapes and ecosystems. There remains a notable gap, however, between realistically modeling the highly dynamic nature of Arctic landscapes and efficiently scaling to longer time-frames or global studies.
Physics-informed machine learning has the potential to help bridge this gap by providing tools that better leverage a wide variety of available data sources, thereby potentially providing new insights into the processes driving changes in Arctic environments.
This project aims to 1) build a modern, data-driven framework for land surface modeling in the Arctic and 2) apply these tools to better quantify and explain major sources of uncertainty in modeling permafrost processes.
- B. Groenke, M. Langer, G. Gallego, and J. Boike. Learning soil freeze characteristic curves with universal differential equations, (PICO presentation), EGU General Assembly, Online, 19–30 Apr 2021, EGU21-13409. https://doi.org/10.5194/egusphere-egu21-13409
- B. Groenke, M. Langer, G. Gallego and J. Boike. A model-driven approach to quantifying uncertainty in permafrost temperature trends. (Poster presentation), 6th Data Science Symposium, Bremen, Germany, 8-9 Nov 2021.
- B. Groenke, F. Miesner, M. Langer, G. Gallego and J. Boike. An energy conserving method for simulating heat transfer in permafrost with hybrid modeling. (Oral presentation), Climate Informatics 2022, virtual, 9-13 May 2022.
- B. Groenke, M. Langer, G. Gallego and J. Boike. A probabilistic analysis of permafrost temperature trends with ensemble modeling of heat transfer. (PICO presentation),EGU General Assembly, Vienna, Austria, 23–27 May 2022, EGU22-10509, https://doi.org/10.5194/egusphere-egu22-10509
- B. Groenke, M. Langer, J. Nitzbon, S. Westermann, G. Gallego, and J. Boike (2022). Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer.EGUsphere.https://doi.org/10.5194/egusphere-2022-630 [Preprint]