Share sensitive information only on official, secure websites.. The Elements of Statistical Learning. acknowledges the funding received from a EU Horizon 2020 Marie Skodowska-Curie Individual Fellowship (grant no. Roberts, D. R. et al. The authors declare no competing interests. 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. 33, 645671 (2005). This translates into a more linear response to air temperature changes compared to the ablation season (Fig. This means that these differences linked to MB nonlinearities observed in this experiment could be even greater for such ice caps. 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. Hydrol. Thank you for visiting nature.com. To obtain Google Scholar. 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. a Projected mean glacier altitude evolution between 2015 and 2100. "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. Bolibar, J. ALPGM (ALpine Parameterized Glacier Model) v1.1. Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps). Huss, M., Jouvet, G., Farinotti, D. & Bauder, A. 4), as the linear model tends to over-estimate positive MB rates both from air temperature and snowfall (Fig. 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. I.G. 4e) MB rates. 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. 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). We perform, to the best of our knowledge, the first-ever deep learning (i.e. Provided by the Springer Nature SharedIt content-sharing initiative. Data 12, 18051821 (2020). Alternatively, the Lasso model used here includes 13 DDFs: one for the annual CPDDs and 12 for each month of the hydrological year. Correspondence to Appl. These conclusions drawn from these synthetic experiments could have large implications given the important sea-level contribution from ice cap-like ice bodies8. Recent efforts have been made to improve the representation of ice flow dynamics in these models, replacing empirical parametrizations with simplified physical models9,10. S10). 1). Geophys. & Zumbhl, H. J. Future projections of glacier-wide MB evolution were performed using climate projections from ADAMONT25. regularized multilinear regression. However, many glacierized regions in the world present different topographical setups, with flatter glaciers, commonly referred to as ice caps, covering the underlying terrain39. 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. "Seeing the rapid and devastating collapse of this incredible and critical salmon in the Nisqually River is heartbreaking," said Troutt. 2008. This behaviour is particularly clear for summer snowfall, for which the differences are the largest (Fig. 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. https://doi.org/10.1016/B978-0-12-821575-3.00009-8. 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. Under warmer conditions (RCP 8.5), the differences between the linear and nonlinear MB model become smaller, as the topographical feedback from glacier retreat compensates for an important fraction of the losses induced by the late century warmer climate (Fig. Nisqually Glacier in Mount Rainier National Park, Wash., covers 2.5 square miles (6.5 square kilometers) (1961) and extends from an altitude of about 14,300 feet (4,400 meters) near the top of Mount Rainier down to 4,700 feet (1,400 meters), in a horizontal distance of 4.1 miles (6.6 kilometers). Front. A similar trend is under way. ISSN 2041-1723 (online). Simulations for projections in this study were made by generating an ensemble of 60 cross-validated models based on LSYGO. In that study, a temperature-index model with a separate degree-day factor (DDF) for snow and ice is used, resulting in piecewise linear functions able to partially reproduce nonlinear MB dynamics. Clarke, G. K. C., Berthier, E., Schoof, C. G. & Jarosch, A. H. Neural networks applied to estimating subglacial topography and glacier volume. The Lasso30, used for the linear mass balance model, is a linear regression analysis method which shrinks model parameters, thus performing both variable selection and regularization. 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. This experiment enabled the exploration of the response to specific climate forcings of a wide range of glaciers of different topographical characteristics in a wide range of different climatic setups, determined by all meteorological conditions from the years 19672015 (Fig. Glaciers in the European Alps have been monitored for several decades, resulting in the longest observational series in the world23,24. Earth Sci. 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. 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. performed simulations with another glacier model, provided results for comparison, and contributed to the glaciological analyses. S6). Google Scholar. 4e and 5). CAS Roe, G. H. Orographic precipitation. This creates a total of 34 input predictors for each year (7 topographical, 3 seasonal climate, and 24 monthly climate predictors). 3). This ensures that the model is capable of reproducing MB rates for unseen glaciers and years. The initial glacier ice thickness data for the year 2003 also differs slightly between both models. 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. Simulating these processes at a large geographical scale is challenging, with models requiring several parametrizations and simplifications to operate. 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. 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. Therefore, linear MB models present more limitations for projections of ice caps, showing a tendency to negative MB biases. The Karakoram and the Himalayan mountain range accommodate a large number of glaciers and are the major source of several perennial rivers downstream. 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. As the Earth heats up due to climate change, glaciers are melting. 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. GLAMOS. This approach is known as a cross-validation ensemble49. Braithwaite, R. J. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning. A comprehensive bibliography of scientific publications relating to the glacier is included. (b) Climate predictors are based on climatic anomalies computed at the glaciers mean altitude with respect to the 19672015 reference period mean values. Fr Hydrobiol. Deep learning applied to glacier evolution modelling. Zemp, M. et al. A glacier flows naturally like a river, only much more slowly. Nonetheless, since the main GCM-RCM climate signal is the same, the main large-scale long-term trends are quite similar. J. R. Stat. This removes the topographical feedback typical from mountain glaciers, and reproduces the more extreme climate conditions that ice caps are likely to endure through the 21st century40. Reanalysis of 47 Years of Climate in the French Alps (19582005): Climatology and Trends for Snow Cover. Park, and S. Beason. Therefore, an alternative nonlinear parameterization for the reduction in MB sensitivity under increasing air temperatures would be useful. Through these surveys "bulges" have been tracked as they travel down the glacier (c). 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. S4). For such cases, we assumed that ice dynamics no longer play an important role, and the mass changes were applied equally throughout the glacier. 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 This translates into more frequent extreme negative MB rates, and therefore greater differences due to nonlinearities for the vast majority of future climate scenarios (Fig. Our projections highlight the almost complete disappearance of all glaciers outside the Mont-Blanc and Pelvoux (Ecrins region) massifs under RCP 4.5 (Fig. ADS Secure .gov websites use HTTPS A lock ( ) or https:// means you've safely connected to the .gov website. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Geophys. contributed to the climate analyses. Lett. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The high spatial resolution enables a detailed representation of mountain weather patterns, which are often undermined by coarser resolution climate datasets. Therefore, their sensitivities to the projected 21st century increase in PDDs are linear. a1 and an r2 of 0.3531. 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. Differences for individual glaciers can be much more pronounced, as large and flat glaciers will have topoclimatic configurations that produce more extreme MB rates than small and steep glaciers with a short response time. In the meantime, to ensure continued support, we are displaying the site without styles You are using a browser version with limited support for CSS. Interestingly, this matches the nonlinear, less sensitive response to summer snowfall in the ablation season of our deep learning model (Fig. 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. ArXiv200104385 Cs Math Q-Bio Stat (2020). Since 2005, study finds that surface melt off glaciers in the North has risen by 900%. Nature 577, 364369 (2020). B Methodol. Pellicciotti, F. et al. The Cryosphere 13, 11251146 (2019). longwave radiation budget, turbulent fluxes), in comparison with a future warmer climate. Spandre, P. et al. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Canada's glaciers and ice caps are now a major contributor to sea level change, a new UCI study shows. b, c, d and f, g, h annual glacier-wide MB probability distribution functions for all n scenarios in each RCP. 3a). Hastie, T., Tibshirani, R. & Friedman, J. 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. (Zenodo, 2020). With this setup, we reproduced the ice cap-like behaviour with a lack of topographical adjustment to higher elevations. Lett. 14, 815829 (2010). Fundam. 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. 31, n/an/a (2004). A global synthesis of biodiversity responses to glacier retreat. Studies have warned about the use of temperature-index models for snow and ice projections under climate change for decades34,35,36. Gaining a better understanding of how warming ocean water affects these glaciers will help improve predictions of their fate. This allows us to assess the MB models responses at a regional scale to changes in individual predictors (Fig. Earths Future https://doi.org/10.1029/2019EF001470 (2020). "The Patagonia Icefields are dominated by so-called 'calving' glaciers," Rignot said. The two recent iterations of the Glacier Model Intercomparison Project (GlacierMIP7,8) have proved a remarkable effort to aggregate, compare and understand global glacier evolution estimates and their associated uncertainties. Glaciers are experiencing important changes throughout the world as a consequence of anthropogenic climate change1. MathSciNet 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. 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. Alternatively, flatter glaciers (i.e. 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. 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. Front.
6x6 Tattoo Size, Articles N