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. 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. Our results also highlight the important role played by glacier geometry adjustment under changing climatic conditions, which is typical of mountain glaciers38. A glacier flows naturally like a river, only much more slowly. 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. Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2. 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. 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. on various mass balance and radiation components) are opening the door for updated and better constrained projections. 60, 11401154 (2014). Map-based methods for estimating glacier equilibrium-line altitudes 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. Uncertainties of existing projections of future glacier evolution are particularly large for the second half of the 21st century due to a large uncertainty on future climatic conditions. Temperature-index models are known to be over-sensitive to temperature changes, mainly due to important differences in the processes contributing to future warming. McKinley, Alaska, change in response to the local climate. Internet Explorer). The authors declare no competing interests. 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. 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. https://doi.org/10.5281/zenodo.3609136. Average ice velocities on the Nisqually Glacier were previously measured at approximately 200 mm/day (8 in) (Hodge 1974). The Nature of Kinematic Waves in Glaciers and their Application to Loss of glaciers contributes to sea-level rise, creates environmental hazards and can alter aquatic habitats. Contrasting glacier responses to recent climate change in high-mountain S5b). 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. 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. 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. 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. Article As Arctic warms, Canada's glaciers playing major role in sea - CBC Nonlinear deep learning response and linear Lasso response to a Cumulative positive degree days (CPDD) anomalies, b winter snowfall, and c summer snowfall. 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. Tibshirani, R. Regression Shrinkage and Selection via the Lasso. Consortium, R. G. I. Randolph Glacier Inventory 6.0 (2017) https://doi.org/10.7265/N5-RGI-60. 14, 815829 (2010). 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. S5cf), except for the largest glaciers (e.g. Both the Lasso and the temperature-index MB model rely on linear relationships between PDDs, solid precipitation and MB. 36, L23501 (2009). We previously demonstrated that this period is long enough to represent the secular trend of glacier dynamics in the region31. H.Z. 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. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. 0.78m.w.e. Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. The record, which was started in 1931, shows the glacier's dramatic responses to about half a century of small but significant climatic variations. In recent years, shrinking glaciers have contributed to about 30% of global sea level rise 1. 22, 21462160 (2009). (Springer, New York, 2009). Earth Syst. 3). Glacier-wide MB is simulated annually for individual glaciers using deep learning (i.e. The Cryosphere 13, 13251347 (2019). Taking into account that for several regions in the world about half of the glacierized volume will be lost during this first half of the 21st century, glacier models play a major role in the correct assessment of future glacier evolution. Predicting future glacier evolution is of paramount importance in order to correctly anticipate and mitigate the resulting environmental and social impacts. 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. Nisqually Glacier - glaciers.pdx.edu 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. 10, 42574283 (2017). Lett. CAS Nisqually Glacier - Wikipedia 282, 104115 (2003). Res. how climate change and glacier retreat are reshaping whole aquatic ecosystems, there is a need to develop an integrated understanding spanning multiple taxonomic groups and trophic levels in glacier-fed rivers (e.g., bacteria, protists, fungi, algae, diatoms, invertebrates, mammals, amphibians, and fish; Clitherow et al. S8 and Fig. J.B. was supported by a NWO VIDI grant 016.Vidi.171.063. To obtain 58, 267288 (1996). Share sensitive information only on official, secure websites.. 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. 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. a1) over the French Alps. 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. Get the most important science stories of the day, free in your inbox. 3). J. Glaciol. All climate anomalies are computed with respect to the 19672015 mean values. GlaciersUnderstanding Climate Drivers | U.S. Geological Survey Rainier, Washington. provided glacier mass balance data and performed the glaciological analyses. Bolibar, J., Rabatel, A., Gouttevin, I. Landscape response to climate change and its role in infrastructure Annual glacier-wide mass balance (MB) is estimated to remain stable at around 1.2m.w.e. regularized multilinear regression. All values correspond to ensemble means under RCP 4.5. Res. 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. By Carol Rasmussen,NASA's Earth Science News Team. As such, these values reflect both the climatic forcing and the changing glacier geometry. 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. 2) and RCP 8.5 by the end of the century. 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. Advances occurred from 1963-68 and from 1974-79. April 17, 2019. GLAMOS. 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. Steiner, D., Walter, A. The Karakoram and the Himalayan mountain range accommodate a large number of glaciers and are the major source of several perennial rivers downstream. Future high-mountain hydrology: a new parameterization of glacier retreat. 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). These results revealed that the main uncertainties on glacier simulations arise from the initial ice thickness used to initialize the model. Geosci. 1d, g). Across the globe, glaciers are decreasing in volume and number in response to climate change. (b) Climate predictors are based on climatic anomalies computed at the glaciers mean altitude with respect to the 19672015 reference period mean values. 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. Model Dev. Vis. Despite these differences, the average altitude difference of the glaciers between both models is never greater than 50m (Fig. 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). (a) Topographical predictors were computed based on the glaciers annually updated digital elevation model (DEM). 4), as the linear model tends to over-estimate positive MB rates both from air temperature and snowfall (Fig. Since these flatter glaciers are more likely to go through extreme negative MB rates, nonlinear responses to future warming play a more important role, producing cumulative MB differences of up to 20% by the end of the century (Fig. The dataset of initial glacier ice thickness, available for the year 2003, determines the starting point of our simulations. The training was performed with an RMSprop optimizer, batch normalization46, and we used both dropout and Gaussian noise in order to regularize it. An accurate prediction of future glacier evolution will be crucial to successfully adapt socioeconomic models and preserve biodiversity. S5h, j, l). a1 and an r2 of 0.3531. Photographs taken by Simo Rsnen (Bossons glacier, European Alps, CC BY-SA 3.0) and Doug Hardy (Quelccaya ice cap, Andes, CC BY-SA 4.0). Res. A similar behaviour is observed when comparing temperature-index models to more complex models (e.g. Nonetheless, since the main GCM-RCM climate signal is the same, the main large-scale long-term trends are quite similar. C.G. 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. The application of a non-linear back-propagation neural network to study the mass balance of Grosse Aletschgletscher, Switzerland. The rest of the story appears to lie primarily in the unique dynamic response of the region's glaciers to climate change. 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. Glob. In order to investigate the effects of MB nonlinearities on ice caps, we performed the same type of comparison between simulations, but the glacier geometry update module described in the Glacier geometry evolution section was deactivated. See how Mount Rainier glaciers have vanished over time, with this eye Lett. PDF Centennial glacier retreat as categorical evidence of regional climate Solved Activity 13.3 Nisqually Glacier Response to Climate - Chegg Nisqually Glacier is the lengthiest of any made in North America. Tour. Nisqually Glacier | glacier, Washington, United States Conversely, the linear MB model appears to be over-sensitive to extreme positive and negative snowfall anomalies. 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. Global-scale hydrological response to future glacier mass loss 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. As previously mentioned, here these differences are computed at regional level for a wide variety of glaciers. The lower fraction of variance explained by linear models is present under all climate scenarios. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Alternatively, the Lasso model used here includes 13 DDFs: one for the annual CPDDs and 12 for each month of the hydrological year. Multiple copies of this dataset were created, and for each individual copy a single predictor (i.e. Climate Change Indicators: Glaciers | US EPA The Cryosphere 12, 13671386 (2018). 3a). This ensures that the model is capable of reproducing MB rates for unseen glaciers and years. 4a). Another source of discrepancy between both models comes from the different MB data used to calibrate or train the MB models. Google Scholar. Ioffe, S. & Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015). Analyses were made of the annual photographs . 799904) and from the Fonds de la Recherche Scientifique FNRS (postdoctoral grant charg de recherches). An increase in the thickness of ice in the higher portion of the Nisqually Glacier was first observed by Arthur Johnson Reference Johnson 1 about ten years ago, and the progress of this "wave" of increased ice thickness has been measured by Johnson each year since that time. energy balance), with differences increasing when the conditions considerably differ from the calibration period33. Previous studies on 21st century large-scale glacier evolution projections have covered the French Alps7,8. Magnin, F., Haeberli, W., Linsbauer, A., Deline, P. & Ravanel, L. Estimating glacier-bed overdeepenings as possible sites of future lakes in the de-glaciating Mont Blanc massif (Western European Alps). This synthetic experiment is an approximation of what might occur in other glacierized regions with ice caps. Earth Planet. Since the neural network used here virtually behaves like a black box, an alternative way is needed to understand the models behaviour. snowfall, avalanches and refreezing) and the mass lost via different processes of ablation (e.g. S5 and S6). Mt. 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.
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