1). With a secondary role, glacier model uncertainty decreases over time, but it represents the greatest source of uncertainty until the middle of the century8. (Springer, New York, 2009). Article By submitting a comment you agree to abide by our Terms and Community Guidelines. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. The processing chain for extracting glacier outlines from images is composed of four steps: (1) calculation of band ratio, (2) selection of threshold value, (3) creation of binary image and (4) manual digitization. Climate variations change a glacier's mass balance by affecting ablation and accumulation amounts. For intermediate and pessimistic climate scenarios, no significant differences were found (Fig. Greenland's melting glaciers, which plunge into Arctic waters via steep-sided inlets, or fjords, are among the main contributors to global sea level rise in response to climate change. Model Dev. Nature 568, 382386 (2019). a1), but when conditions deviate from this mean training data centroid, the Lasso can only linearly approximate the extremes based on the linear trend set on the main cluster of average values (Fig. B Methodol. A well-established parametrization based on empirical functions50 was used in order to redistribute the annually simulated glacier-wide mass changes over each glacier. Our projections show a strong glacier mass loss for all 29 climate members, with average ice volume losses by the end of the century of 75%, 80%, and 88% compared to 2015 under RCP 2.6 (9%, n=3), RCP 4.5 (17% +11%, n=13) and RCP 8.5 (15% +11%, n=13), respectively (Fig. The position of the front of the wave will be defined as the transverse line across the glacier where the flow of . These are among the cascading effects linked to glacier loss which impact ecosystems and . (a) Topographical predictors were computed based on the glaciers annually updated digital elevation model (DEM). For such cases, we assumed that ice dynamics no longer play an important role, and the mass changes were applied equally throughout the glacier. 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. In fact, in many cases the surface lowering into warmer air causes this impact on the MB to be negative, further enhancing extreme negative mass balance rates. Researchers analyzed almost 2 million satellite images of the glaciers and found that 94 . GloGEMflow has been previously applied in a study over the whole European Alps, and its temperature-index model was mainly calibrated with MB data from the Swiss Alps. Strong Alpine glacier melt in the 1940s due to enhanced solar radiation. Interestingly, our analysis indicates that more complex models using separate DDFs for ice, firn and snow might introduce stronger biases than more simple models using a single DDF. However, to further investigate these findings, experiments designed more towards ice caps, and including crucial mechanisms such as ice-ocean interactions and thermodynamics, should be used for this purpose. Ice caps in the Canadian Arctic, the Russian Arctic, Svalbard, and parts of the periphery of Greenland are major reservoirs of ice, as well as some of the biggest expected contributors to sea level rise outside the two polar ice sheets7. The original ice thickness estimates of the methods used by both models are different10,32, and for ALPGM we performed some additional modifications to the two largest glaciers in the French Alps (see Glacier geometry evolution for details). volume13, Articlenumber:409 (2022) Geophys. Provided by the Springer Nature SharedIt content-sharing initiative. 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. An accurate prediction of future glacier evolution will be crucial to successfully adapt socioeconomic models and preserve biodiversity. provided glacier mass balance data and performed the glaciological analyses. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Change 120, 2437 (2014). Our results serve as a strong reminder that the outcomes of existing large-scale glacier simulations should be interpreted with care, and that newly available techniques (such as the nonlinear deep learning approach presented here) and observations (e.g. This synthetic experiment is an approximation of what might occur in other glacierized regions with ice caps. All climate anomalies are computed with respect to the 19672015 mean values. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. 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). 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. and JavaScript. Huss, M., Funk, M. & Ohmura, A. This behaviour is not observed with the nonlinear model, hinting at a positive bias of linear MB models under RCP 2.6. When comparing our deep learning simulations with those from the Lasso, we found average cumulative MB differences of up to 17% by the end of the century (Fig. Preliminary results suggest winter accumulation in 2018 was slightly above the 2003-2017 average for the Emmons & Nisqually. These different behaviours and resulting biases can potentially induce important consequences in long-term glacier evolution projections. 4 ). A consensus estimate for the ice thickness distribution of all glaciers on Earth. 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. Clim. Presentation at 2008 National Hydraulic Engineering . The effect of glaciers shrinking to smaller extents is not captured by these synthetic experiments, but this effect is less important for flat glaciers that are dominated by thinning (Fig. Robinson, C. T., Thompson, C. & Freestone, M. Ecosystem development of streams lengthened by rapid glacial recession. Nat. Fundam. This is well in agreement with the known uncertainties of glacier evolution models, with glacier ice thickness being the second largest uncertainty after the future GCM-RCM-RCP climate members used to force the model29. Through his research in that area, he's seen firsthand the impact of climate change and has been studying the long-term effects of a warming planet. Glaciers are important for agriculture, hydropower, recreation, tourism, and biological communities. (2019) https://doi.org/10.18750/MASSBALANCE.2019.R2019. The source code of the glacier model can be freely accessed in the following repository: https://github.com/JordiBolibar/ALPGM. This approach is known as a cross-validation ensemble49. 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. Glaciers with the greatest degree of seasonality in their flow behavior, such as Nisqually and Shoestring glaciers, responded most rapidly. Farinotti, D. et al. 3). However, both the climate and glacier systems are known to react non-linearly, even to pre-processed forcings like PDDs13, implying that these models can only offer a linearized approximation of climate-glacier relationships. Peer reviewer reports are available. S10). In the past, shortwave radiation represented a more important fraction in the glacier surface energy budget than the energy fluxes directly related to air temperature (e.g. 12, 909931 (2019). Nat Commun 13, 409 (2022). Projected changes in surface solar radiation in CMIP5 global climate models and in EURO-CORDEX regional climate models for Europe. CoRR abs/1505.00853 (2015). Common climatic signal from glaciers in the European Alps over the last 50 years: Common Climatic Signal in the Alps. We previously demonstrated that this period is long enough to represent the secular trend of glacier dynamics in the region31. 12, 1959 (2020). Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. Grenoble Alpes, CNRS, G-INP, Laboratoire Jean Kuntzmann, Grenoble, France, You can also search for this author in 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. Without these cold water resources during the hottest months of the year, many aquatic and terrestrial ecosystems will be impacted due to changes in runoff, water temperature or habitat humidity6,21,22. 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. This is not the case for the nonlinear deep learning MB model, which captures the nonlinear response of melt and MB to increasing air temperatures, thus reducing the MB sensitivity to extreme positive and negative air temperature and summer snowfall anomalies (Fig. Bolibar, J. ALPGM (ALpine Parameterized Glacier Model) v1.1. 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. Since the climate and glacier systems are known to be nonlinear13, we investigate the benefits of using a model treating, among others, PDDs in a nonlinear way in order to simulate annual glacier-wide MB at a regional scale. Article Gardent, M., Rabatel, A., Dedieu, J.-P. & Deline, P. Multitemporal glacier inventory of the French Alps from the late 1960s to the late 2000s. Regarding air temperature, a specific CPDD anomaly ranging from 1500 PDD to +1500 PDD in steps of 100 PDD was prescribed to all glaciers for each dataset copy. Ice melt sensitivity to PDDs strongly decreases with increasing summer temperatures, whereas snow melt sensitivity changes at a smaller rate34. A.R. Glacier landscapes are expected to see important changes throughout the French Alps, with the average glacier altitude becoming 300m (RCP 4.5) and 400m (RCP 8.5) higher than nowadays (Fig. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning, https://doi.org/10.1038/s41467-022-28033-0. We further assessed the effect of MB nonlinearities by comparing our simulated glacier changes with those obtained from other glacier evolution studies from the literature, which rely on temperature-index models for MB modelling. (Zenodo, 2020). 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. Through these surveys "bulges" have been tracked as they travel down the glacier (c). 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. We reduced these differences by running simulations with GloGEMflow using exactly the same 29 climate members used by ALPGM in this study (TableS1). As the Earth heats up due to climate change, glaciers are melting. The model output data generated in this study have been deposited in netCDF and CSV format in a Zenodo repository under accession code Creative Commons Attribution 4.0 International. Despite the existence of a wide variety of different approaches to simulate glacier dynamics, all glacier models in GlacierMIP rely on MB models with linear relationships between PDDs and melt, and precipitation and accumulation. 3c). The 29 RCP-GCM-RCM combinations available, hereafter named climate members, are representative of future climate trajectories with different concentration levels of greenhouse gases (TableS1). 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. contributed to the extraction of nonlinear mass balance responses and to the statistical analysis. Geophys. 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. Glaciers smaller than 0.5km2 often display a high climate imbalance, with their equilibrium line being higher than the glaciers maximum altitude. From this behavior, inferences of past climate can be drawn. Glacier response to climate change Jim Salinger, Trevor Chinn, Andrew Willsman, and how fluctuations in New Zealand glaciers reflect regional climate change. Deep artificial neural networks (ANNs) are nonlinear models that offer an alternative approach to these classic methods. We argue that such models can be suitable for steep mountain glaciers. S1a). Res. On the one hand, MB nonlinearities for mountain glaciers appear to be only relevant for climate scenarios with a reduction in greenhouse gases emissions (Fig. On the other hand, for flatter glaciers large differences between deep learning and Lasso are obtained for almost all climate scenarios (Fig. The cumulative positive degree days (CPDD), snowfall and rainfall dl, are at the glaciers annually evolving centroids. Mt. Meteorol. 31, n/an/a (2004). Verfaillie, D., Dqu, M., Morin, S. & Lafaysse, M. The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models.
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