ISSN 2041-1723 (online). 21, 229246 (2021). melt and sublimation of ice, firn and snow; or calving)9; and (2) ice flow dynamics, characterized by the downward movement of ice due to the effects of gravity in the form of deformation of ice and basal sliding. Nonetheless, since they are both linear, their calibrated parameters establishing the sensitivity of melt and glacier-wide MB to temperature variations remain constant over time. We previously demonstrated that this period is long enough to represent the secular trend of glacier dynamics in the region31. 14, 815829 (2010). Sci. performed simulations with another glacier model, provided results for comparison, and contributed to the glaciological analyses. These are among the cascading effects linked to glacier loss which impact ecosystems and . Several aquatic and terrestrial ecosystems depend on these water resources as well, which ensure a base runoff during the warmest or driest months of the year6. We compare model runs using a nonlinear deep learning MB model (the reference approach in our study) against a simplified linear machine learning MB model based on the Lasso30, i.e. Both machine learning MB models were trained with exactly the same data coming from the 1048 annual glacier-wide MB values, and both were cross-validated using LSYGO. The increase in glacier altitude also causes the solid to liquid precipitation ratio to remain relatively constant. By unravelling nonlinear relationships between climate and glacier MB, we have demonstrated the limitations of linear statistical MB models to represent extreme MB rates in long-term projections. A small ablation increase may cause . GLAMOS. Years in white in c-e indicate the disappearance of all glaciers in a given massif. Our results suggest that, except for the lowest emissions climate scenarios and for large glaciers with long response times, MB models with linear relationships for PDDs and precipitation are suitable for mountain glaciers with a marked topographical feedback. This suggests that linear MB models are adequate tools for simulating MB of mountain glaciers with important topographical adjustment, with the only exception being the most optimistic climate scenarios and glaciers with long response times. The linear Lasso MB model suggests a stabilization of glacier evolution, reaching neutral MB rates by the end of the century. J.B. developed the main glacier model, performed the simulations, analysed the results, and wrote the paper. 5). Partitioning the uncertainty of ensemble projections of global glacier mass change. Geophys. Google Scholar. Paul, F. et al. At present, using complex surface energy balance models for large-scale glacier projections is not feasible yet, mainly due to the lack of input data. This enables the recalculation of every topographical predictor used for the MB model, thus updating the mean glacier altitude at which climate data for each glacier are retrieved. We ran glacier evolution projections for both the deep learning and Lasso MB models, but we kept the glacier geometry constant, thus preserving the glacier centroid where the climate data is computed constant through time. The Open Global Glacier Model (OGGM) v1.1. Our analyses suggest that these limitations can also be translated to temperature-index MB models, as they share linear relationships between PDDs and melt, as well as precipitation and accumulation. Grenoble Alpes, CNRS, G-INP, Laboratoire Jean Kuntzmann, Grenoble, France, You can also search for this author in The Nisqually Glacier is one of the larger glaciers on the southwestern face of Mount Rainier in the U.S. state of Washington.The glacier is one of the most easily viewed on the mountain, and is accessible from the Paradise visitor facilities in Mount Rainier National Park.The glacier has had periods of advance and retreat since 1850 when it was much more extensive. Through synthetic experiments, we showed that the associated uncertainties are likely to be even more pronounced for ice caps, which host the largest reserves of ice outside the two main ice sheets32. With a secondary role, glacier model uncertainty decreases over time, but it represents the greatest source of uncertainty until the middle of the century8. Several differences are present between ALPGM, the model used in this study, and GloGEMflow (TableS2), which hinder a direct meaningful comparison between both. Geophys. Additionally, the specific responses of the deep learning and Lasso MB models to air temperature and snowfall were extracted by performing a model sensitivity analysis. Reanalysis of 47 Years of Climate in the French Alps (19582005): Climatology and Trends for Snow Cover. Landscape response to climate change and its role in infrastructure Additionally, glacier surface area was found to be a minor predictor in our MB models31. Our results show that the mean elevation is far more variable than the kinematic ELA ( Fig. 4). All these glacier models, independently from their approach, need to resolve the two main processes that determine glacier evolution: (1) glacier mass balance, as the difference between the mass gained via accumulation (e.g. Nisqually Glacier is well known for its kinematic waves ( Meier, 1962 ), but its mass balance has never been measured due to the difficulty of the glacier terrain. The mountain has three major peaks: Liberty Cap, Point Success, and Columbia Crest (the latter is the summit, located on the rim of the caldera). Lett. Preliminary results suggest winter accumulation in 2018 was slightly above the 2003-2017 average for the Emmons & Nisqually. The Nature of Kinematic Waves in Glaciers and their Application to This translates into a more linear response to air temperature changes compared to the ablation season (Fig. This oversensitivity directly results from the fact that temperature-index models rely on linear relationships between PDDs and melt and that these models are calibrated with past MB and climate data. Glacier variations in response to climate change from 1972 to 2007 in B Methodol. Despite these differences, the average altitude difference of the glaciers between both models is never greater than 50m (Fig. Despite the existence of slightly different trends during the first half of the century, both the Lasso and the temperature-index model react similarly under RCP 4.5 and 8.5 during the second half of the century, compared to the deep learning model. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Immerzeel, W. W. et al. Tibshirani, R. Regression Shrinkage and Selection via the Lasso. J.B. was supported by a NWO VIDI grant 016.Vidi.171.063.
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