4 ). J. Glaciol. 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. 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. 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. This allows us to assess the MB models responses at a regional scale to changes in individual predictors (Fig. In summary, the linear approximations used by the Lasso manage to correctly fit the main cluster of average values but perform poorly for extreme values31. The Open Global Glacier Model (OGGM) v1.1. The cumulative positive degree days (CPDD), snowfall and rainfall dl, are at the glaciers annually evolving centroids. A glacier flows naturally like a river, only much more slowly. Simulations were then performed by averaging the outputs of each one of the 60 ensemble members. Recent efforts have been made to improve the representation of ice flow dynamics in these models, replacing empirical parametrizations with simplified physical models9,10. Ice melt sensitivity to PDDs strongly decreases with increasing summer temperatures, whereas snow melt sensitivity changes at a smaller rate34. Partitioning the uncertainty of ensemble projections of global glacier mass change. b, c, d and f, g, h annual glacier-wide MB probability distribution functions for all n scenarios in each RCP. Alternatively, the Lasso model used here includes 13 DDFs: one for the annual CPDDs and 12 for each month of the hydrological year. Glacier ice thickness observations are available for four different glaciers in the regions, which were compared to the estimates used in this model. In our model, we specifically computed this parameterized function for each individual glacier larger than 0.5km2, representing 80% of the total glacierized area in 2015, using two DEMs covering the whole French Alps: a photogrammetric one in 1979 and a SPOT-5 one in 2011. J. Clim. Differences in projected glacier changes become more pronounced from the second half of the century, when about half of the initial 2015 ice volume has already been lost independent of the considered scenario. Ser. This implies that specific climatic differences between massifs can be better captured by ALPGM than GloGEMflow. 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. J. Glaciol. Bolibar, J. ALPGM (ALpine Parameterized Glacier Model) v1.1. By the end of the century, we predict a glacier volume loss between 75 and 88%. ice caps) that are found in other glacierized regions such as the Arctic, where the largest volumes of glacier ice (other than the ice sheets) are stored32, cannot retreat to higher elevations. 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. 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. creates a Nisqually Glacier response similar to those seen from its historical waves, suggesting that there are other factors contributing to kinematic wave formation, and 4) the Nisqually . 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. 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. Each one of these cross-validations served to evaluate the model performance for the spatial, temporal and both dimensions, respectively. Hastie, T., Tibshirani, R. & Friedman, J. Nonetheless, these differences have been shown to be rather small, having a lower impact on results than climate forcings or the initial glacier ice thickness10. By Carol Rasmussen,NASA's Earth Science News Team. 4e and 5). Several differences are present between ALPGM, the model used in this study, and GloGEMflow (TableS2), which hinder a direct meaningful comparison between both. 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. "It has been pretty much doing this nonstop since the mid-1800s." The Nisqually Glacier is losing nearly a quarter of a mile in length a year, Kennard added. The projections without glacier geometry adjustment explore the behaviour of glaciers which cannot retreat to higher elevations (i.e. 31, n/an/a (2004). Since the neural network used here virtually behaves like a black box, an alternative way is needed to understand the models behaviour. 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. 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. volume13, Articlenumber:409 (2022) Front. 1). Bolibar, J., Rabatel, A., Gouttevin, I. et al. To obtain 3). This method has the advantage of including glacier-specific dynamics in the model, encompassing a wide range of different glacier behaviours. The anomaly in snowfall was evenly distributed for every month in the accumulation (October 1April 31) and ablation seasons, respectively. The high spatial resolution enables a detailed representation of mountain weather patterns, which are often undermined by coarser resolution climate datasets. For this, a newly-developed state-of-the-art modelling framework based on a deep learning mass balance component and glacier-specific parametrizations of glacier changes is used. Bolibar, J., Rabatel, A., Gouttevin, I. Therefore, an alternative nonlinear parameterization for the reduction in MB sensitivity under increasing air temperatures would be useful. (Springer, New York, 2009). deep artificial neural networks) glacier evolution projections by modelling the regional evolution of French alpine glaciers through the 21st century. Due to the statistical nature of the Lasso model, the response to snowfall anomalies is also highly influenced by variations in PDDs (Fig. S4). By performing glacier projections both with mountain glaciers in the French Alps and a synthetic experiment reproducing ice cap-like behaviour, we argue that the limitations identified here for linear models will also have implications for many other glacierized regions in the world. Six, D. & Vincent, C. Sensitivity of mass balance and equilibrium-line altitude to climate change in the French Alps. (2019) https://doi.org/10.18750/MASSBALANCE.2019.R2019. Hock, R. et al. The climatic forcing comes from high-resolution climate ensemble projections from 29 combinations of global climate models (GCMs) and regional climate models (RCMs) adjusted for mountain regions for three Representative Concentration Pathway (RCP) scenarios: 2.6, 4.5, and 8.525. 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. In the meantime, to ensure continued support, we are displaying the site without styles Alternatively, flatter glaciers (i.e. 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. This behaviour is not observed with the nonlinear model, hinting at a positive bias of linear MB models under RCP 2.6. All climate anomalies are computed with respect to the 19672015 mean values. This ensures that the model is capable of reproducing MB rates for unseen glaciers and years. The scheme simulates the mass balance as well as changes of the areal . 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. The dataset of initial glacier ice thickness, available for the year 2003, determines the starting point of our simulations. C.G. When using the linear MB model (Lasso), glaciers are close to reaching an equilibrium with the climate in the last decades of the century, which is not the case for the nonlinear MB model (deep learning). 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. S5h, j, l). Google Scholar. 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. Roe, G. H. Orographic precipitation. Years in white in c-e indicate the disappearance of all glaciers in a given massif. Alternatively, the comparisons against an independent large-scale glacier evolution model were less straightforward to achieve. On top of that, they happen to be among the glacierized regions with the largest projected uncertainties8. Since these two glaciers are expected to be some of the few large glaciers that will survive the 21st century climate, an accurate representation of their initial ice thickness has an important effect on the estimates of remaining ice. 33, 645671 (2005). Braithwaite, R. J. "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. 4a, b) and negative (Fig. Conversely, for RCP 8.5, annual glacier-wide MB are estimated to become increasingly negative by the second half of the century, with average MB almost twice as negative as todays average values (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.

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