Understanding natural climate variability is crucial for assessing the range of plausible future climate trajectories in the next centuries. Natural climate variability will impact future regional climate trends and risks, and is important to consider for climate change attribution and adaptation planning (Laepple et al., 2023). Previous studies suggest that climate models often fail to capture long-term variability recorded in paleoclimate data and that this discrepancy varies depending on the spatial scale (local vs. global) and between regions (Hébert et al., 2022; Laepple et al., 2023).

This project will, for the first time, map the time-scale-dependent temperature variability based on paleoclimate data to investigate the corresponding range of possible future climate trends. Ultimately, the obtained results will help to improve the understanding of the origins of this variability and the representation of variability in climate models.

The interdisciplinary and complementary expertise of the team, which includes specialists in climate variability, the use of paleoclimate data, Bayesian hierarchical modeling, and data science, will provide an optimal supervision basis for the project.


Selection of relevant publications:

Hébert, R., Herzschuh, U. & Laepple, T. (2022). Millennial-scale climate variability over land overprinted by ocean temperature fluctuations. Nature Geoscience, 1–7. https://doi.org/10.1038/s41561-022-01056-4.

Klein, N., Kneib, T. & Lang, S. (2015). Bayesian generalized additive models for location, scale, and shape for zero-inflated and overdispersed count data. Journal of the American Statistical Association, 110(509):405–419. doi:10.1080/01621459.2014.912955.

Laepple, T., Ziegler, E., Weitzel, N., Hébert, R., Ellerhoff, B., Schoch, P., ... & Rehfeld, K. (2023). Regional but not global temperature variability underestimated by climate models at supradecadal timescales. Nature Geoscience, 16(11), 958-966.

Senf, C., Pflugmacher, D., Heurich, M., & Krueger, T. (2017). A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series. Remote Sensing of Environment, 194, 155-160.

Reconstructing Past Vegetation and Fire History in the Landes de Gascogne Area

The department is recruiting a student to undertake a PhD within their team at the Environmental Geography Laboratory - GEODE, based in Toulouse, France. The doctoral candidate will participate in the ANR FIRELANDES research project (https://fire-landes.com/), which aims to improve their ability to project future interactions between climate and fires in the Landes de Gascogne region (SW France) by using paleoecological data (paleo-fires and paleo-vegetation) and models to capture a larger portion of the natural variability of ecosystem flammability. The thesis will focus on reconstructing vegetation (composition, abundance, structure) and fire history in the Landes region, Southwest France.

The objective of this PhD project is to document and quantify the impacts caused by human activities (hydroelectricity, mining, logging, vacationing, tourism) on the human and physical environments of the Manicouagan Lake-reservoir and Dechêne Lake using the analysis of sediment cores collected in the Manic-5 reservoir and profiles of the physicochemical properties of the water column of the reservoir.
This project is part of the multidisciplinary project “Imaging Manicouagan-Uapishka through Territorial Aquatic and Cultural Prospecting (IMPACT)”, funded by the Institut nordique du Québec and the Sentinel North program. The project brings together a university team from five research institutes and partners from the Territories and Resources office of the Innus of Pessamit Council, the Manicouagan-Uapishka World Biosphere Reserve, and the Uapishka Station.
The Innu of Pessamit community, the Ndakina office of the Waban-Aki Nation, Caroline Desbiens and Justine Gagnon of the Canada Research Chair in Indigenous Heritage and Tourism also collaborate to this projet.

Higher severity forest fires lead to greater overall emissions of carbon dioxide (CO2) and have the potential to release “old” carbon previously buried in deeper layers of soil and permafrost. The ability to evaluate boreal fire severity over the recent geological past would provide a much-needed historical context that is currently lacking from modern estimates of burn depth. Through a series of experiments and application using dated lake sediment cores, this PhD project will work on developing and improving geochemical techniques to understand boreal fire severity over time. The results from this research will inform national policies that promote adaptation and resilience in boreal communities under a changing climate.
This research is part of the multidisciplinary project “Development of innovative geochemical tools for understanding historical boreal fire severity” funded by Natural Resources Canada’s (NRCan’s) GEM-GeoNorth program. This project brings together a team of researchers from across the Geological Survey of Canada (NRCan) and INRS.

This studentship has two related projects:

Project 1A: History matching (cf https://www.physics.mun.ca/~lev/revCalG.pdf) of a global coupled ice and climate model for the last glacial cycle. From Last Glacial Maximum onward, glacial geology and relative sea level data provide a strong set of constraints which are a challenge to fit. Conversely, prior to LGM, there are much fewer constraints on ice sheet evolution aside from far-field sea level proxies. However there are many more constraints on regional climate from ice core, terrestrial, and marine core records. This history matching will therefore focus on pre-LGM global ice sheet and climate evolution relying more on paleoclimate constraints.

Project 1B: Using the history-matched parameter vectors from project 1A, examine the stability of select glacial cycles of the last 2 million years especially in the context of explaining the mid-Pleistocene transition from approximately 40 kyr glacial cycles to 100 kyr cycles. This will entail analysis of noise sensitivity (eg from large volcanic events), the impact of changing topography due to subglacial erosion and sediment transport, and the testing of simplified carbon cycle representations. These projects will rely on a hierarchy of coupled ice and climate models including the LCice2.0 coupled glacial system model (GSM) and LOVECLIM EMIC (an earlier version was detailed in Bahadory and Tarasov, GMD, 2018, Bahadory et al, CP 2021) as well as a new version coupled to the Plasim GCM (Andres and Tarasov, CP 2019). A key relevant feature of LCice is the state-of-the-art fully coupled sub-glacial sediment processes model in the GSM (cf Drew and Tarasov, https://egusphere.copernicus.org/preprints/2024/egusphere-2024-620/)