We acknowledge the more than 50 years of glaciological monitoring performed by the GLACIOCLIM French National Observatory (https://glacioclim.osug.fr), which provided essential observations for our modelling study. ice cap-like behaviour). J.B. developed the main glacier model, performed the simulations, analysed the results, and wrote the paper. a1 throughout the whole century under RCP 4.5, with glacier retreat to higher elevations (positive effect on MB) compensating for the warmer climate (negative effect on MB). Our results point out that this lack of topographical feedback leads to an increased frequency of extreme negative MB rates and to more pronounced differences between the nonlinear and linear MB models (Figs. Nisqually Glacier is perhaps the most visited, best-surveyed glacier on Mount Rainier. 58, 267288 (1996). Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. Model Dev. The machine learning models used in this study are useful to highlight and quantify how nonlinearities in MB affect climate-glacier interactions, but are limited in terms of process understanding. These differences in the received climate signal are explained by the retreat of glaciers to higher altitudes, which keep up with the warming climate in RCP 4.5 but are outpaced by it under RCP 8.5. a Glacier-wide annual MB, b Ice volume, c Glacier area. These conclusions drawn from these synthetic experiments could have large implications given the important sea-level contribution from ice cap-like ice bodies8. a1 and an r2 of 0.3531. snowfall, avalanches and refreezing) and the mass lost via different processes of ablation (e.g. 2) and RCP 8.5 by the end of the century. Google Scholar. The same was done with winter snowfall anomalies, ranging between 1500mm and +1500mm in steps of 100mm, and summer snowfall anomalies, ranging between 1000mm and +1000mm in steps of 100mm. A glacier flows naturally like a river, only much more slowly. Spandre, P. et al. Clarke, G. K. C., Berthier, E., Schoof, C. G. & Jarosch, A. H. Neural networks applied to estimating subglacial topography and glacier volume. Gardent, M., Rabatel, A., Dedieu, J.-P. & Deline, P. Multitemporal glacier inventory of the French Alps from the late 1960s to the late 2000s. Glacier topography is a crucial driver of future glacier projections and is expected to play an important role in determining the magnitude that nonlinearities will have on the mass balance. Winter tourism under climate change in the Pyrenees and the French Alps: relevance of snowmaking as a technical adaptation. "Their numbers have gone from regularly exceeding 50,000 adult salmon in the Nisqually to about 5,000 last year." The Nisqually River near its glacial origins. In this study, we investigate the future evolution of glaciers in the French Alps and their nonlinear response to multiple climate scenarios. Lett. Indeed, the projected 21st century warming will lead to increasing incoming longwave radiation and turbulent fluxes, with no marked future trends in the evolution of shortwave radiation37. Grenoble Alpes, Universit de Toulouse, Mto-France, CNRS, CNRM, Centre dtudes de la Neige, Grenoble, France, Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands, Laboratoire de Glaciologie, Universit Libre de Bruxelles, Brussels, Belgium, Univ. Long-term historical interactions between French society and glaciers have developed a dependency of society on them for water resources, agriculture, tourism18particularly the ski business19and hydropower generation. Univ. However, the use of ANNs remains largely unexplored in glaciology for regression problems, with only a few studies using shallow ANNs for predicting the ice thickness14 or mass balance13 of a single glacier. Sci. This creates an interesting dilemma, with more complex temperature-index MB models generally outperforming simpler models for more climatically homogeneous past periods but introducing important biases for future projections under climate change. This reanalysis is specifically designed to represent meteorological conditions over complex mountain terrain, being divided by mountain massif, aspect and elevation bands of 300m. Winter climate data are computed between October 1 and March 31, and summer data between April 1 and September 30. In order to do so, we applied a deterministic sampling process as a sensitivity analysis to both the deep learning and the Lasso MB models. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Gosciences de lEnvironnement, Grenoble, France, INRAE, UR RiverLy, Lyon-Villeurbanne, France, Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, Netherlands, Univ. Conversely, during the accumulation season, glaciers are mostly covered by snow, with a much higher albedo and a reduced role of shortwave radiation in the MB that will persist even under climate change. In order to investigate the effects of MB nonlinearities on flatter glaciers, we conducted a synthetic experiment using the French Alps dataset. Nature Communications (Nat Commun) Deep artificial neural networks (ANNs) are nonlinear models that offer an alternative approach to these classic methods. Grenoble Alpes, CNRS, G-INP, Laboratoire Jean Kuntzmann, Grenoble, France, You can also search for this author in Glacier topography is a crucial driver of future glacier projections and is expected to play an important role in determining the magnitude that nonlinearities will have on the mass balance signal: ice caps and large flatter glaciers are expected to be more influenced by these nonlinear sensitivities than steep mountain glaciers in a warming climate. Nisqually Glacier is the lengthiest of any made in North America. Canada's glaciers and ice caps are now a major contributor to sea level change, a new UCI study shows. 799904) and from the Fonds de la Recherche Scientifique FNRS (postdoctoral grant charg de recherches). When working with spatiotemporal data, it is imperative to respect spatial and temporal data structures during cross-validation in order to correctly assess an accurate model performance48. J. Hosp. Carlson, B. Lett. This creates a total of 34 input predictors for each year (7 topographical, 3 seasonal climate, and 24 monthly climate predictors). In order to overcome these differences, some adaptations were performed to the GloGEMflow output, accompanied with some hypotheses to ensure a realistic comparison. 1). By 2100, under RCP 4.5, these two high-altitude massifs are predicted to retain on average 26% and 13% of their 2015 volume, respectively, with most of the ice concentrated in a few larger glaciers (>1km2, Fig. GloGEMflow10 is a state-of-the-art global glacier evolution model used in a wide range of studies, including the second phase of GlacierMIP7,8. In Climate Change 157176 (Elsevier, 2021). As the Earth heats up due to climate change, glaciers are melting. H.Z. 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. 185, 235246 (2014). This behaviour is expected for mountain glaciers, as they are capable of retreating to higher altitudes, thus producing a positive impact on their glacier-wide MB (Fig. Such ice caps cannot retreat to higher elevations in a warming climate, which inhibits this positive impact on MB40 (Fig. This allows us to assess the MB models responses at a regional scale to changes in individual predictors (Fig. Our results also highlight the important role played by glacier geometry adjustment under changing climatic conditions, which is typical of mountain glaciers38. Earth Syst. 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. Here, with our newly presented approach, we were able for the first time to quantify the effect that stationary parameters in temperature-index mass balance models have on transient glacier evolution. As previously mentioned, here these differences are computed at regional level for a wide variety of glaciers. 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 perform, to the best of our knowledge, the first-ever deep learning (i.e. This ensures that the model is capable of reproducing MB rates for unseen glaciers and years. Earth Syst. The main uncertainties in future glacier estimates stem from future climate projections and levels of greenhouse gas emissions (differences between RCPs, GCMs, and RCMs), whose relative importance progressively increases throughout the 21st century. Thank you for visiting nature.com. Nonlinear deep learning response and linear Lasso response to a Cumulative positive degree days (CPDD) anomalies, b winter snowfall, and c summer snowfall. longwave radiation budget, turbulent fluxes), in comparison with a future warmer climate. J. Glaciol. Therefore, their sensitivities to the projected 21st century increase in PDDs are linear. However, the impact of different climate configurations, such as a more continental and drier climate or a more oceanic and humid climate, would certainly have an impact on the results, albeit a much less important one than the lack of topographical feedback explored here. The linear Lasso MB model suggests a stabilization of glacier evolution, reaching neutral MB rates by the end of the century. Alternatively, the Lasso MB model displayed an RMSE of 0.85m.w.e. 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. Cauvy-Frauni, S. & Dangles, O. deep artificial neural networks) glacier evolution projections by modelling the regional evolution of French alpine glaciers through the 21st century. If material is not included in the articles Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. However, as shown in our previous work and confirmed here, the accuracy of linear models drastically drops as soon as the input climate data diverges from the mean cluster of values used for training. Article During the last decade, various global glacier evolution models have been used to provide estimates on the future sea-level contribution from glaciers7,8. Researchers analyzed almost 2 million satellite images of the glaciers and found that 94 . Water resources provided by glaciers sustain around 10% of the worlds population living near mountains and the contiguous plains4, depending on them for agriculture, hydropower generation5, industry or domestic use. The vertical blue and red lines indicate the distribution of extreme (top 5%) values for all 21st century projected climate scenarios, with the mean value in the center and 1 indicated by dashed lines. Therefore, we were capable of isolating the different behaviours of the nonlinear deep learning model and a linear machine learning model based on the Lasso30. A global synthesis of biodiversity responses to glacier retreat. Hugonnet, R. et al. Arch. Overall, the evolving glaciers are expected to undergo rather stable climate conditions under RCP 4.5, but increasingly higher temperatures and rainfall under RCP 8.5 (Fig. 1). 6 (2018). Both DEMs were resampled and aligned at a common spatial resolution of 25m. For each glacier, an individual parameterized function was computed representing the differences in glacier surface elevation with respect to the glaciers altitude within the 19792011 period. S4). 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. 3). 4a). We previously demonstrated that this period is long enough to represent the secular trend of glacier dynamics in the region31. This adjustment represents a major improvement over most climate data used to force regional and global glacier models. Years in white in c-e indicate the disappearance of all glaciers in a given massif. 4e and 5). The maximum downvalley position of the glacier is marked by either a The source code of the glacier model can be freely accessed in the following repository: https://github.com/JordiBolibar/ALPGM. 51, 313323 (2005). Fr Hydrobiol. J. Glaciol. We performed a validation simulation for the 20032015 period by running our model through this period and comparing the simulated glacier surface area of each of the 32 glaciers with MB to observations from the 2015 glacier inventory16,52. Overall, this results in linear MB models overestimating both extreme positive (Fig. Millan, R., Mouginot, J., Rabatel, A., & Morlighem, M. Ice velocity and thickness of the worlds glaciers.
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