Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2)

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.

Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

“…Van Pelt and others, 2013). Although ITMIX2 (Farinotti and others, 2021) does not provide a direct answer in our case, no significant differences in performance or biases regarding computed ice thickness between models of different types were found. Overall, ITMIX2 revealed no drift in the modelderived ice thicknesses when restricting the input to fewer observational data.…”

“…to which we refer to hereafter as MEAN. It has been concluded from the Ice Thickness Model Intercomparison eXperiments -ITMIX (Farinotti and others, 2017) and ITMIX2 (Farinotti and others, 2021), that model ensembles have a higher skill in predicting ice thickness than any individual model, provided all models perform similarly well. In ITMIX2, GlaTE and ITVEO both showed a similarly good performance.…”

“…For estimating u + int (x, y), a similar experiment is performed as the one presented by Farinotti and others (2021), which is a form of the bootstrap technique (Efron, 1979). The model-based ice thickness interpolation is repeated 30 times with GlaTE and ITVEO based on randomly selected subsets (20, 50 and 80%, 10 times each) of the total GPR data.…”

“…The deviations between modeled and measured ice thickness at locations of skipped data were then analyzed with respect to the distance d to the closest retained point with measured ice thickness. In contrast to Farinotti and others (2021), points at the glacier boundary, where ice thickness is known to be zero, are also regarded as points with known ice thickness. Model misfits Δh are defined as deviations of the modeled ice thickness from the GPR-ice thickness plus/minus the GPR-measurement uncertainty, and normalized by the modeled mean ice thickness of the corresponding glacier, h i .…”

“…Ice thickness estimates obtained from modeling exhibit substantial uncertainties unless they are calibrated to ground-truth data (Farinotti and others, 2021). For the Antarctic and Greenlandic ice sheets, measured ice thickness data have been acquired systematically with a comprehensive coverage (e.g.…”