Periodic Reporting for period 4 - COLD (Climate Sensitivity of Glacial Landscape Dynamics)
Periodo di rendicontazione: 2022-07-01 al 2023-12-31
The climate sensitivity of mountain glaciers is influenced by debris on their surface that impedes ice melting. Theoretical models of frost-related bedrock fracturing predict that rates of debris production are temperature-sensitive and that its supply to mountain glaciers increases during warming periods. Thus, a previously unrecognized negative feedback emerges that lowers ice melt rates and potentially buffers part of the ice retreat due to warming. However, the temperature-sensitivity of debris production in glacial landscapes is poorly understood. Specifically, we lack robust erosion rate estimates for these landscapes, which are key for testing models of frost-related bedrock fracturing.
Here, I propose an innovative combination of new tools that capitalize on recent developments in cosmogenic nuclide geochemistry, landscape evolution modelling, and planetary-scale remote sensing analysis. I will use these tools to quantify headwall erosion rates in mountainous glacial landscapes and to gauge the sensitivity of mountain glaciers to variations in debris supply. Expected results will provide a basis for assessing the impacts of global warming, for improved predictions of valley glacier evolution, and for palaeoclimate interpretations of glacial landforms. COLD will focus on glacial landscapes, but the inverse modelling approach I will develop is applicable to any landscape on Earth and has the potential to fundamentally transform how we use cosmogenic nuclides to constrain Earth surface dynamics.
The main goals of work package 2 were to develop a coupled ice and landscape evolution model that includes the production of debris on headwalls, its transport to and by the glacier, and its effect on glacial mass balances, and to apply this model to invert cosmogenic nuclide data. We implemented the described modeling approach using the existing numerical model iSOSIA and included a Lagrangian particle tracing module (Scherler and Egholm, 2020). Based on cosmogenic nuclide data from the Chhota Shigri Glacier, we showed that rather large changes in 10Be concentrations along medial moraines are difficult to explain with actual changes in denudation rates. Instead, a significant change in the contributing source area, for example by deglaciation, appears more plausible to lead to these changes. We currently work with another ice model that is orders of magnitudes faster than previous ice flow models, as it employs deep learning and can be parallelized on graphic processing units. Together with our colleague G. Jouvet, a similar particle tracking as in iSOSIA has been implemented and we plan to apply this model to our cosmogenic nuclide data sets.
In work package 3 we used the cloud computing platform Google Earth Engine to develop algorithms that allowed us to map supraglacial debris cover at planetary scale based on Landsat and Sentinel 2 imagery (Scherler et al., 2018). The results provide the first global assessment of supraglacial debris cover extents and the provided data sets are widely used by other researchers. We further explored thermal imagery, which is equally sensitive to debris cover on ice surfaces, but in different ways than optical imagery. To assess its potential and limitations, we equipped a consumer-grade drone with a light-weight thermal sensor and mapped parts of the debris cover of the Tsijiore Nouve Glacier (CH). When using satellite-derived thermal images however, the accuracy of the thermal data improves, but at the cost of spatial resolution (Scherler et al., 2023). We thus focused on exploiting the long time series of thermal imagery offered by the Landsat satellites to derived trends of land surface temperatures across the European Alps (Gök et al., submitted). The obtained trend values allow to decipher areas of exceptional surface warming in terrain that is prone to catastrophic failure and difficult to access.
• Using cosmogenic nuclide geochemistry in order to quantify headwall erosion rates in glacial landscapes is not entirely new, but it has not been frequently applied, and the sampling strategy implemented in this project has not been performed in the same detail by any other study. Similarly, measuring cosmogenic 3He and 14C measured in quartz grains is not novel, but combining these three nuclides the way we do it is new. Our work demonstrated additional applications for these techniques, contributing to a more thorough understanding of both the methodology as well as its applications and limitations.
• In the field of coupled ice and landscape evolution modeling, we have developed a Lagrangian particle tracing approach for simulating the production and transport of glacial debris and tracking the production and decay of cosmogenic nuclides. This is the first time that rock particles are treated as distinct elements in a numerical ice-flow model, with properties that evolve through time. The model provides a robust basis for interpreting cosmogenic nuclide concentrations in samples collected from glacier surfaces.