Abstract
Knowing the ice thickness distribution of a glacier is of fundamental importance for a number of applications, ranging from the planning of glaciological fieldwork to the assessments of future sea-level change. Across spatial scales, however, this knowledge is limited by the paucity and discrete character of available thickness observations. To obtain a spatially coherent distribution of the glacier ice thickness, interpolation or numerical models have to be used. Whilst the first phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX) focused on approaches that estimate such spatial information from characteristics of the glacier surface alone, ITMIX2 sought insights for the capability of the models to extract information from a limited number of thickness observations. The analyses were designed around 23 test cases comprising both real-world and synthetic glaciers, with each test case comprising a set of 16 different experiments mimicking possible scenarios of data availability. A total of 13 models participated in the experiments. The results show that the inter-model variability in the calculated local thickness is high, and that for unmeasured locations, deviations of 16% of the mean glacier thickness are typical (median estimate, three-quarters of the deviations within 37% of the mean glacier thickness). This notwithstanding, limited sets of ice thickness observations are shown to be effective in constraining the mean glacier thickness, demonstrating the value of even partial surveys. Whilst the results are only weakly affected by the spatial distribution of the observations, surveys that preferentially sample the lowest glacier elevations are found to cause a systematic underestimation of the thickness in several models. Conversely, a preferential sampling of the thickest glacier parts proves effective in reducing the deviations. The response to the availability of ice thickness observations is characteristic to each approach and varies across models. On average across models, the deviation between modeled and observed thickness increase by 8.5% of the mean ice thickness every time the distance to the closest observation increases by a factor of 10. No single best model emerges from the analyses, confirming the added value of using model ensembles.
Highlights
The ice thickness distribution of a glacier is one of its fundamental properties
The individual Intercomparison eXperiment Phase 2 (ITMIX2) experiments were addressed by using a calibration procedure similar to the one described in Werder et al (2020): for each experiment, the model is fitted to the available profiles with ice thickness observations and to the distributed surface flow speeds; unlike in the original procedure, the glacier length is not used for fitting
An example for how the characteristics discussed above express themselves in the actual ice thickness distribution is given in Figure 5: two profiles are shown for the compulsory test case Unteraar and the ensemble-approach GilletChaulet, as well as the models Morlighem, and VanPeltLeclerq
Summary
The ice thickness distribution of a glacier is one of its fundamental properties. By defining the glacier’s morphology and total volume, ice thickness controls the ice dynamics, defines the amount of water stored, and determines the glacier’s lifetime in a changing climate. Large-scale ice thickness mapping requires airborne platforms Whilst such platforms have been used for surveying ice sheets and other large, cold ice masses for almost 70 years (for reviews, see, e.g., Plewes and Hubbard, 2001; Schroeder et al, 2020), airborne systems capable of operating in mountain environments have emerged only more recently (Blindow et al, 2012; Rutishauser et al, 2016; Zamora et al, 2017; Langhammer et al, 2019b; Pritchard et al, 2020). ITMIX2 considered 23 test cases with 16 experiments each, and attracted the participation of 13 different approaches that submitted an ensemble of 2,544 solutions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
