Since both MB models also include monthly temperature data as predictors, this CPDD anomaly was distributed evenly between the ablation season (April 1September 30), following the expected increase in mostly summer temperatures instead of winter temperatures in the future (Fig. 3). The French Alps, located in the westernmost part of the European Alps, experience some of the strongest glacier retreat in the world15,16,17. Cauvy-Frauni, S. & Dangles, O. Conversely, the linear MB model appears to be over-sensitive to extreme positive and negative snowfall anomalies. 2015 IEEE Int. Hydrol. An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier dArolla, Switzerland. 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. Nonetheless, a close inspection of the annual glacier-wide MB rates from both models reveals similar patterns to those found when comparing deep learning and Lasso approaches (Figs. This parametrization reproduces in an empirical manner the changes in glacier geometry due to the combined effects of ice dynamics and MB. With this setup, we reproduced the ice cap-like behaviour with a lack of topographical adjustment to higher elevations. Google Scholar. In order to avoid overfitting, MB models were thoroughly cross-validated using all data for the 19672015 period in order to ensure a correct out-of-sample performance. Together with recent findings by another study41 highlighting the increased uncertainties in ice thickness distribution estimates of ice caps compared to mountain glaciers, our results raise further awareness on the important uncertainties in glacier projections for ice caps. For that, a dataset of input predictors covering all the glaciers in the French Alps for the 19672015 period was generated from a past MB reconstruction study15. 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. A.R. Our projections highlight the almost complete disappearance of all glaciers outside the Mont-Blanc and Pelvoux (Ecrins region) massifs under RCP 4.5 (Fig. J. Glaciol. Peer reviewer reports are available. Lett. Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps). Model Dev. Vertical axes are different for the two analyses. Google Scholar. ICCV (2015) https://doi.org/10.1109/iccv.2015.123. regularized multilinear regression. Average ice velocities on the Nisqually Glacier were previously measured at approximately 200 mm/day (8 in) (Hodge 1974). The rest of the story appears to lie primarily in the unique dynamic response of the region's glaciers to climate change. This dataset applies a statistical adjustment specific to French mountain regions based on the SAFRAN dataset, to EURO-CORDEX26 GCM-RCM-RCP members, covering a total of 29 different future climate scenarios for the 20052100 period. 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. Each one of these models was created by training a deep learning model with the full dataset except all data from a random glacier and year, and evaluating the performance on these hidden values. To interactively describe to response of glaciers to climate change, a glacier parameterization scheme has been developed and implemented into the regional climate model REMO. A comparison between the two MB models shows that a nonlinear response to climate forcings is captured by the deep learning MB model, allowing a better representation of glacier mass changes, including significantly reduced biases for extreme values (see Methods). Ice thickness data for Argentire glacier (12.27km2 in 2015) was taken from a combination of field observations (seismic, ground-penetrating radar or hot-water drilling53) and simulations32. Zekollari, H., Huss, M. & Farinotti, D. On the Imbalance and Response Time of Glaciers in the European Alps. Data 12, 19731983 (2020). 4). Finally, there are differences as well in the glacier dynamics of both models, with ALPGM using a glacier-specific parameterized approach and GloGEMflow explicitly reproducing the ice flow dynamics. Some of these models use a single DDF, while others have separate DDFs for snow and ice, producing a piecewise function composed of two linear sub-functions that can partially account for nonlinear MB dynamics depending on the snowpack. As the Earth heats up due to climate change, glaciers are melting. With this study, we provide new predictions of glacier evolution in a highly populated mountain region, while investigating the role of nonlinearities in the response of glaciers to multiple future climate forcings. A sensitivity analysis of both MB models revealed nonlinear relationships between PDDs, snowfall (in winter and summer) and glacier-wide MB, which the linear model was only able to approximate (r2=0.41 for the Lasso vs. r2=0.76 for deep learning in cross-validation31; Fig. Grenoble Alpes, CNRS, G-INP, Laboratoire Jean Kuntzmann, Grenoble, France, You can also search for this author in 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. Glaciers are important for agriculture, hydropower, recreation, tourism, and biological communities. 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. Both models agree around the average values seen during training (i.e. 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. We reduced these differences by running simulations with GloGEMflow using exactly the same 29 climate members used by ALPGM in this study (TableS1). A recent study he did found that 80 percent of the glaciers in Alberta and British Columbia could melt in the next 50 years. Earth Sci. glacier length12,14. Each one of these cross-validations served to evaluate the model performance for the spatial, temporal and both dimensions, respectively. The two models with linear MB responses to PDDs and accumulation simulate more positive MB rates under RCP 2.6, highlighting their over-sensitivity to negative air temperature anomalies and positive snowfall anomalies (Fig. 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). Res. Ice melt sensitivity to PDDs strongly decreases with increasing summer temperatures, whereas snow melt sensitivity changes at a smaller rate34. Despite marked differences among regions, the generalized retreat of glaciers is expected to have major environmental and social impacts2,3. These conclusions drawn from these synthetic experiments could have large implications given the important sea-level contribution from ice cap-like ice bodies8. A globally complete, spatially and temporally resolved estimate of glacier mass change: 2000 to 2019. https://meetingorganizer.copernicus.org/EGU2020/EGU2020-20908.html (2020) https://doi.org/10.5194/egusphere-egu2020-20908. Sci. In order to improve the comparability between both models, a MB bias correction was applied to GloGEMflows simulated MB, based on the average annual MB difference between both models for the 20032015 period (0.4m.w.e. 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. ice cap-like behaviour). For intermediate and pessimistic climate scenarios, no significant differences were found (Fig. Huss, M., Jouvet, G., Farinotti, D. & Bauder, A. Additionally, glacier surface area was found to be a minor predictor in our MB models31. This rapid glacier retreat is already having an environmental impact on natural hazards20, mountain ecosystems21, and biodiversity6. By the end of the century, we predict a glacier volume loss between 75 and 88%. However, many glacierized regions in the world present different topographical setups, with flatter glaciers, commonly referred to as ice caps, covering the underlying terrain39. All climate anomalies are computed with respect to the 19672015 mean values. April 17, 2019. Then in 1884, Allen Mason photographed the glacier for the first time . 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. Nature 577, 364369 (2020). Our analysis suggests that due to this positive impact on the MB signal, only relevant differences are observed between nonlinear and linear MB models for the lowest emission climate scenarios (Fig. Studies have warned about the use of temperature-index models for snow and ice projections under climate change for decades34,35,36. Carlson, B. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. S10). Braithwaite, R. J. Rabatel, A., Sanchez, O., Vincent, C. & Six, D. Estimation of glacier thickness from surface mass balance and ice flow velocities: a case study on Argentire Glacier, France. "Such glaciers spawn icebergs into the ocean or lakes and have different dynamics from glaciers that end on land and melt at their front ends. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. (a) Topographical predictors were computed based on the glaciers annually updated digital elevation model (DEM). Huss, M. & Hock, R. A new model for global glacier change and sea-level rise. Our results also highlight the important role played by glacier geometry adjustment under changing climatic conditions, which is typical of mountain glaciers38. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning. The maximum advance of Nisqually Glacier in the last thousand years was located, and retreat from this point is believed to have started about 1840. 49, 26652683 (2017). https://doi.org/10.1038/s41467-022-28033-0, DOI: https://doi.org/10.1038/s41467-022-28033-0. 4 vs.S5). Massifs without glaciers by 2100 are marked with a cross, b Glacier ice volume distribution per massif, with its remaining fraction by 2100 (with respect to 2015), c Annual glacier-wide MB per massif, d Annual snowfall per massif, e Annual cumulative positive degree-days (CPDD) per massif. Nature 568, 382386 (2019). Climatol. Maussion, F. et al. Climate Change 2013: The Physical Science Basis. An analysis of the climate signal at the glaciers mean altitude throughout the century reveals that air temperature, particularly in summer, is expected to be the main driver of glacier mass change in the region (Fig. The vast majority of glaciers in the French Alps are very small glaciers (<0.01km2), that are mainly remnants from the Little Ice Age, with a strong imbalance with the current climate15. S5cf), except for the largest glaciers (e.g. This results in a higher complexity of the Lasso compared to a temperature-index model. As previously mentioned, here these differences are computed at regional level for a wide variety of glaciers. A glacier flows naturally like a river, only much more slowly. 0.78m.w.e. B Methodol. Res. 2a). Appl. This synthetic setup allowed us to reproduce the climatic conditions to be undergone by most ice caps, with their mean surface altitude hardly evolving through time. Bolibar, J. et al. Then, we ran multiple simulations for this same period by altering the initial ice thickness by 30% and the glacier geometry update parametrizations by 10%, according to the estimated uncertainties of each of the two methods31. This modelling approach was described in detail in a previous publication dedicated to the methods, where the ALpine Parameterized Glacier Model (ALPGM43) was presented31. 4 ). In many aspects, it might be too optimistic, as many ice caps will have a negative impact on MB through thinning, bringing their mean surface elevation to lower altitudes, thus further warming their perceived climate. a Projected mean glacier altitude evolution between 2015 and 2100. 41, 153160 (1995). MATH 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. Rackauckas, C. et al. Particularly in Asia, water demand exceeds supply due to rapid population growth, with glacier . Kinematic waves on glaciers move as several times the speed of the ice as a whole, and are subtle in topographic expression. Hock, R. et al. Activity 13.3 Nisqually Glacier Response to Climate Change Course/Section Date: Name: Nisqually Glacier is a mountain glacier located on the south side of Mt. Moreover these three aspects of glacier behavior are inextricably interwoven: a high sensitivity to climate change goes hand-in-hand with a large natural variability. 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. Geophys. We argue that such models can be suitable for steep mountain glaciers. 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. Such ice caps cannot retreat to higher elevations in a warming climate, which inhibits this positive impact on MB40 (Fig. Rainier is considered by the USGS to be one of the most threatening volcanoes in the Cascade Mountains. Seasonal Arctic sea ice forecasting with probabilistic deep learning, Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016, Two decades of glacier mass loss along the Andes, Centennial response of Greenlands three largest outlet glaciers, Accelerated global glacier mass loss in the early twenty-first century, High Mountain Asian glacier response to climate revealed by multi-temporal satellite observations since the 1960s, Rapid glacier retreat and downwasting throughout the European Alps in the early 21st century, Ice velocity and thickness of the worlds glaciers, Constraining glacier elevation and mass changes in South America, https://meetingorganizer.copernicus.org/EGU2020/EGU2020-20908.html, https://doi.org/10.5194/egusphere-egu2020-20908, https://doi.org/10.18750/MASSBALANCE.2019.R2019, https://doi.org/10.1016/B978-0-12-821575-3.00009-8, https://doi.org/10.1038/s41561-021-00885-z, http://creativecommons.org/licenses/by/4.0/, Unabated wastage of the Muz Taw Glacier in the Sawir Mountains during 19592021. The Karakoram and the Himalayan mountain range accommodate a large number of glaciers and are the major source of several perennial rivers downstream. Ioffe, S. & Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015). This type of model uses a calibrated linear relationship between positive degree-days (PDDs) and the melt of ice or snow11. Such glaciers are often remnants of the Little Ice Age, and mainly lose mass via non-dynamic downwasting51. In order to simulate annual glacier-wide MB values, (a) topographical and (b) climate data for those glaciers and years were compiled for each of the 1048 glacier-year values. Google Scholar. Glaciers smaller than 0.5km2 often display a high climate imbalance, with their equilibrium line being higher than the glaciers maximum altitude. Glacier surface mass changes are commonly modelled by relying on empirical linear relationships between PDDs and snow, firn or ice melt8,9,10,29. b, c, d and f, g, h annual glacier-wide MB probability distribution functions for all n scenarios in each RCP. J. Glaciol. DDFs are known to vary much less with increasing temperatures for intermediate values of albedo (i.e. ALPGM uses a feed-forward fully connected multilayer perceptron, with an architecture (40-20-10-5-1) with Leaky-ReLu44 activation functions and a single linear function at the output. When it was built in the early 1900s, the road into Mount Rainier National Park from the west passed near the foot of the Nisqually Glacier, one of the mountain's longest . Ecol. In order to overcome these differences, some adaptations were performed to the GloGEMflow output, accompanied with some hypotheses to ensure a realistic comparison. and JavaScript. New methods bridging the gap between domain-specific equations and machine learning are starting to arise42, which will play a crucial role in further investigating the physical processes driving these nonlinear climate-glacier interactions. MathSciNet 33, 645671 (2005). Other articles where Nisqually Glacier is discussed: Mount Rainier: from the broad summit, including Nisqually Glacier, whose retreat and advance over the last 150 years has helped scientists determine patterns in the Earth's climate. 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. 2008. We previously demonstrated that this period is long enough to represent the secular trend of glacier dynamics in the region31. contributed to the climate analyses. 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. Robinson, C. T., Thompson, C. & Freestone, M. Ecosystem development of streams lengthened by rapid glacial recession. 799904) and from the Fonds de la Recherche Scientifique FNRS (postdoctoral grant charg de recherches). 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. A recent Northern Hemisphere temperature reconstruction indicates an oscillating temperature drop from A.D. 1000-1850 of about 0.2C with a subsequent and still continuing warming of nearly 0.8C ( 3 ). 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. Bolibar, J., Rabatel, A., Gouttevin, I. S4). 12, 1959 (2020). ADS 3). 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. Earth Syst. S5b). 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. In Climate Change 157176 (Elsevier, 2021). The ice thickness data for two of the largest glaciers in the French Alps were modified in order to improve data quality. The training was performed with an RMSprop optimizer, batch normalization46, and we used both dropout and Gaussian noise in order to regularize it. 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. Nature 575, 341344 (2019). Projected changes in surface solar radiation in CMIP5 global climate models and in EURO-CORDEX regional climate models for Europe. Between 1857 and 1979, Nisqually Glacier receded a total of 1,945 meters and advanced a total of 294 meters. 6 (2018). Nonetheless, since the main GCM-RCM climate signal is the same, the main large-scale long-term trends are quite similar. 185, 235246 (2014). Google Scholar. Nat. Our results show that the mean elevation is far more variable than the kinematic ELA ( Fig. Consequently, a simple MB model with a single DDF (e.g. As for the MB modelling approach, a detailed explanation on this method can be found in a previous dedicated paper on the methods31. Fundam. 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. 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. Alternatively, the comparisons against an independent large-scale glacier evolution model were less straightforward to achieve. Farinotti, D. et al. This behaviour is particularly clear for summer snowfall, for which the differences are the largest (Fig. 5). Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (2018). contributed to the extraction of nonlinear mass balance responses and to the statistical analysis. 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. Zemp, M. et al. CAS Earth Planet. On the one hand, this improves our confidence in long-term MB projections for steep glaciers made by most GlacierMIP models for intermediate and high emissions climate scenarios. Rveillet, M. et al. 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. A well-established parametrization based on empirical functions50 was used in order to redistribute the annually simulated glacier-wide mass changes over each glacier. We perform, to the best of our knowledge, the first-ever deep learning (i.e. S5h, j, l). In recent years, shrinking glaciers have contributed to about 30% of global sea level rise 1. Despite these differences, the average altitude difference of the glaciers between both models is never greater than 50m (Fig. 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. Immerzeel, W. W. et al. 3). These results are in agreement with the main known drivers of glacier mass change in the French Alps28. The Elements of Statistical Learning. This behaviour has already been observed for the European Alps, with a reduction in DDFs for snow during the ablation season of 7% per decade34. Res. From this behavior, inferences of past climate can be drawn. Nature Communications (Nat Commun) A physically-based method for mapping glacial debris-cover thickness from ASTER satellite imagery: development and testing at Miage Glacier, Italian Alps Discovery - the University of Dundee Research Portal Common climatic signal from glaciers in the European Alps over the last 50 years: Common Climatic Signal in the Alps. 2a and S3). 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. 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. Average cumulative MB projections of French Alpine glaciers with a nonlinear deep learning vs. a linear Lasso model for 29 climate scenarios; a with topographical feedback (allowing for glacier retreat) and e without topographical feedback (synthetic experiment with constant mean glacier altitude). 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. The first main difference is related to the climate data used to force the models. During the last decade, various global glacier evolution models have been used to provide estimates on the future sea-level contribution from glaciers7,8. The initial glacier ice thickness data for the year 2003 also differs slightly between both models. Source: Mount Rainier National Park A glacier is a large mass of snow and ice that has accumulated over many years and is present year-round. The linear Lasso MB model suggests a stabilization of glacier evolution, reaching neutral MB rates by the end of the century. Clarke, G. K. C., Berthier, E., Schoof, C. G. & Jarosch, A. H. Neural networks applied to estimating subglacial topography and glacier volume. Through these surveys "bulges" have been tracked as they travel down the glacier (c). For small perturbations, the response time of a glacier to a perturbation in mass balance can be estimated by dividing the maximum thickness of the glacier by the balance rate at the terminus. Geophys. 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. The Cryosphere 14, 565584 (2020). (Zenodo, 2020). ADS 51, 313323 (2005). You are using a browser version with limited support for CSS. Ice-surface altitude changes of as much as 25 meters occurred between 1944 and 1955. Ten . 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 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. Conf. 2) and RCP 8.5 by the end of the century. S1a). By Carol Rasmussen,NASA's Earth Science News Team. 1gi)26 and glaciers shrinking to higher elevations where precipitation rates are higher as a result of orographic precipitation enhancement27. Hugonnet, R. et al. Z. et al. Internet Explorer). The high spatial resolution enables a detailed representation of mountain weather patterns, which are often undermined by coarser resolution climate datasets. CAS A global synthesis of biodiversity responses to glacier retreat. Univ. snowfall, avalanches and refreezing) and the mass lost via different processes of ablation (e.g. Article Hastie, T., Tibshirani, R. & Friedman, J. 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. Both MB models were trained with exactly the same data, and all other glacier model parameters were unchanged in order to allow isolating the effects of the nonlinearities in the MB. 4e). 1d, g). Glaciers are experiencing important changes throughout the world as a consequence of anthropogenic climate change1. However, glacier projections under low-emission scenarios and the behaviour of flatter glaciers and ice caps are likely to be biased by mass balance models with linear sensitivities, introducing long-term biases in sea-level rise and water resources projections. a1 and a r2 of 0.69, explaining 69% of the total MB variance. GlacierMIP A model intercomparison of global-scale glacier mass-balance models and projections. Geosci. S6). 4e and 5). Three different types of cross validation were performed: a Leave-One-Glacier-Out (LOGO), a Leave-One-Year-Out (LOYO) and a Leave-Some-Years-and-Glaciers-Out (LSYGO). Alternatively, the Lasso model used here includes 13 DDFs: one for the annual CPDDs and 12 for each month of the hydrological year. Moreover, these differences between nonlinear and linear models appear to come from an over-sensitivity of linear models to increasing ablation season air temperatures, when ice is exposed in a large fraction of glaciers.
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