Periodic Reporting for period 4 - COLD (Climate Sensitivity of Glacial Landscape Dynamics)
Berichtszeitraum: 2022-07-01 bis 2023-12-31
How do erosion rates in glacial landscapes vary with climate change and how do such changes affect the dynamics of mountain glaciers? Providing quantitative constraints towards this question is the main objective of COLD. These constraints are so important because mountain glaciers are sensitive to climate change and their deposits provide a unique history of Earths terrestrial climate that allows reconstructing leads and lags in the climate system.
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 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 1 were the quantification of denudation rates at steep rock walls in the European Alps, assessing how they vary in space and time, and test if denudation rates are linked to temperature-sensitive processes. To reach these goals, we conducted several field expeditions during which we collected different types of samples for analysis of cosmogenic nuclides. Comparison of rock wall denudation rates with predictions based on frost-cracking models showed limited convergence, leading us to suggest that frost-cracking may not be the key process controlling denudation at these sites. Instead, permafrost thaw and associated recent acceleration of denudation rates may play a more important role. A recent acceleration of denudation rates is supported by results from our medial moraine samples that provide archives of denudation rates in source areas over time periods of decades to centuries (Wetterauer et al., 2022). Comparing denudation rates from multiple glaciers, we find faster denudation at steeper, north-facing and presumably colder rock walls (Wetterauer and Scherler, 2023). However, we also observed that temporal variations in 10Be and 14C are complicated by stochastic erosion processes (Dennis and Scherler, 2022), and can be strongly influenced by recent glacier retreat in various ways (Wetterauer, 2023).
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.
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.
• Within the COLD project, we developed codes for planetary-scale mapping of supraglacial debris cover using Google’s Earth Engine and used this method to generate the first global assessment of supraglacial debris cover extents. We expanded these techniques to thermal band imagery, allowing us to derive decadal trends of land surface temperatures in alpine terrain at high resolution. These data are important for assessing the thermal evolution of the subsurface and the associated thaw of permafrost and destabilization of hillslopes.
• 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.
• 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.