Luminescence rock surface dating of englacial transported debris from Mer de Glace glacier, French Alps

Significant mass loss and increased rock debris cover have been observed across many mountain glaciers due to climate change. However, the dynamics of sediment transport through alpine glaciers are not fully understood and should be investigated to better constrain the future evolution of mountain glaciers under a changing climate. Englacial sediment transport is difficult to observe and to that end, we quantify the englacial transport time of debris within a glacier using a novel method combining luminescence rock surface burial dating and ice-flow modelling. Our study focuses on Mer de Glace, Mont Blanc Massif, French Alps, where supraglacial debris has expanded over the past 20 years.

We collected near-surface rock debris (4–22 cm in diameter) of granite from the ablation area of Mer de Glace that we expect to have experienced different englacial transport durations. Under subdued red light, we cored the samples perpendicular to their surfaces and sliced the cores into ~1 mm discs for luminescence dating. We first evaluated whether the luminescence signals had been well bleached prior to deposition by measuring the evolution of luminescence signals with depth throughout the core (i.e. measurement of the bleaching plateau). We used a protocol comprising infra-red stimulation at 50 °C and 225 °C, followed by blue stimulation at 125 °C to explore the signals of different minerals with different luminescence properties. Of the 29 samples investigated, 20 were well bleached, exhibiting a clear plateau in luminescence signals with depth (following the approach of Rades et al., 2018). We are currently using a single-aliquot regenerative dose protocol to date the rock surfaces of these samples to obtain englacial transport durations. In the next step, we will contrast the englacial transport durations measured using luminescence with those predicted using the ice-flow model IGM (Jouvet et al., 2022), allowing us to better understand the dynamics of mountain glaciers over centennial to millennial time scales.

 

References

Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., & Aschwanden, A. (2022). Deep learning speeds up ice flow modelling by several orders of magnitude. Journal of Glaciology, 68(270), 651-664.

Rades, E. F., Sohbati, R., Lüthgens, C., Jain, M., & Murray, A. S. (2018). First luminescence-depth profiles from boulders from moraine deposits: Insights into glaciation chronology and transport dynamics in Malta valley, Austria. Radiation Measurements, 120, 281-289.