GlacierMIP – A model intercomparison of global-scale glacier mass-balance models and projections

Hock, Regine; Bliss, Andrew; Marzeion, Ben; Giesen, Rianne H.; Hirabayashi, Yukiko; Huss, Matthias; Radić, Valentina; Slangen, Aimée B. A.

doi:10.1017/jog.2019.22

Cited by 222 publications

(226 citation statements)

References 58 publications

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“…The contribution to GMSL from glaciers and ice caps melting generally corroborate the findings by Hock et al (2019), who found that by the end of the 21 st century mountain glaciers and ice caps lose between 11-25 % (RCP2.6) and 25-47 % (RCP8.5) of their volume. For scenario MMCP8.5, our results are below the average estimates at the end of the 21 st century because of 325 the rather low climate sensitivity in LOVECLIM.…”

Section: Discussionsupporting

confidence: 87%

Semi-equilibrated global sea-level change projections for the next 10 000 years

Breedam¹,

Goelzer²,

Huybrechts³

2020

Preprint

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The emphasis for informing policy makers on future sea-level rise has been on projections by the end of the 21 st century. However, due to the long lifetime of atmospheric CO2, the thermal inertia of the climate system and the slow equilibration of the ice sheets, global sea level will continue to rise on a multi-millennial timescale even when anthropogenic CO2 emissions cease completely during the coming decades to centuries. Here we present global sea-level change projections 25 due to melting of land ice combined with steric sea effects during the next 10,000 years calculated in a fully interactive way with the Earth System Model of Intermediate Complexity LOVECLIMv1.3. The climate forcing is based on the Extended Concentration Pathways defined until 2300 AD with no carbon dioxide emissions thereafter and the inclusion of a methaneemission feedback for the highest forcing scenario, equivalent to a cumulative CO2 release of around 460 to 5800 GtC. After 10,000 years, the sea-level change rate drops below 0.05 m per century and a semi-equilibrated state is reached. The Greenland 30 ice sheet is found to nearly disappear for all forcing scenarios. The Antarctic ice sheet contributes only about 1.6 m to sea level for the lowest forcing scenario with a limited retreat of the grounding line in West Antarctica. For the higher forcing scenarios, the marine basins of the East Antarctic ice sheet also become ice free, resulting in a sea-level rise of up to 27 m. The global mean sea-level change after 10,000 years ranges from 9.2 m to more than 37 m. The projections of multi-millennial semiequilibrated sea-level rise for a given CO2 forcing are shown to be in good agreement with geological archives. 35 45 from 10,000 years for accumulation rate changes to up to 100,000 years for temperature changes for the Antarctic ice sheet (Alley and Whillans, 1984).On a multi-millennial timescale, changes in land ice volume (mass contribution) together with ocean density changes from thermal expansion (thermosteric contribution) and haline contraction (halosteric contribution) are the main components contributing to global sea-level change (Miller et al., 2005). The mass contribution comes from melting or growing of the 50 Antarctic ice sheet, the Greenland ice sheet and glaciers and ice caps. The steric contribution is the expansion of ocean water when it gets warmer and less saline or conversely, the contraction when water is cooling and gets more salty (Feistel, 2010).The magnitude of thermal expansion depends on the climatic temperature forcing and on the rate of oceanic heat uptake.Because of the large mass and slow turnover time of the deep ocean, the oceanic heat content (and hence thermal expansion) will continue to rise in the ocean for centuries to millennia, even when surface warming has halted. Haline contraction -or 55 expansion of ocean water due to freshening -is a smaller, though not negligible component of steric sea-level rise. Physicallybased models have been used to study the different components contributing to g...

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Section: Discussionsupporting

confidence: 87%

Semi-equilibrated global sea-level change projections for the next 10 000 years

Breedam¹,

Goelzer²,

Huybrechts³

2020

Preprint

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“…To assess glacier evolution under future climate scenarios, geodetic glacier mass balance observations can be used to calibrate and validate glacier evolution model projections. These models estimate ∼36-87% mass loss for HMA glaciers by 2100, depending on the glacier evolution model, climate forcing, and representative concentration pathway (RCP) scenario (Kraaijenbrink et al, 2017;Hock et al, 2019). This loss will lead to a significant decrease in seasonal glacier meltwater runoff, with important implications for downstream stakeholders (Huss and Hock, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance

et al. 2020

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High-mountain Asia (HMA) constitutes the largest glacierized region outside of the Earth's polar regions. Although available observations are limited, long-term records indicate sustained HMA glacier mass loss since ∼1850, with accelerated loss in recent decades. Recent satellite data capture the spatial variability of this mass loss, but spatial resolution is coarse and some estimates for regional and HMA-wide mass loss disagree. To address these issues, we generated 5,797 high-resolution digital elevation models (DEMs) from available sub-meter commercial stereo imagery (DigitalGlobe WorldView-1/2/3 and GeoEye-1) acquired over HMA glaciers from 2007 to 2018 (primarily 2013-2017). We also reprocessed 28,278 ASTER DEMs over HMA from 2000 to 2018. We combined these observations to generate robust elevation change trend maps and geodetic mass balance estimates for 99% of HMA glaciers between 2000 and 2018. We estimate total HMA glacier mass change of −19.0 ± 2.5 Gt yr −1 (−0.19 ± 0.03 m w.e. yr −1 ). We document the spatial pattern of HMA glacier mass change with unprecedented detail, and present aggregated estimates for HMA glacierized sub-regions and hydrologic basins. Our results offer improved estimates for the HMA contribution to global sea level rise in recent decades with total cumulative sea-level rise contribution of ∼0.7 mm from exorheic basins between 2000 and 2018. We estimate that the range of excess glacier meltwater runoff due to negative glacier mass balance in each basin constitutes ∼12-53% of the total basin-specific glacier meltwater runoff. These results can be used for calibration and validation of glacier mass balance models, satellite gravimetry observations, and hydrologic models needed for present and future water resource management.

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“…Glaciers are arguably one of the most important icons of climate change, being climate proxies which can clearly depict the evolution of climate for the global audience (IPCC, 2018). For the coming decades, mountain glaciers will be some of the most important contributors to sea level rise and will most likely drive important changes in the hydrological regime of glaciarized catchments (Beniston et al, 2018;Vuille et al, 2018;Hock et al, 2019). The reduction in ice volume may produce an array of consequences which requires to be properly predicted.…”

Section: Introductionmentioning

confidence: 99%

Deep learning applied to glacier evolution modelling

Bolíbar¹,

Rabatel²,

Gouttevin³

et al. 2019

Preprint

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Abstract. We present a parameterized glacier evolution model, with a surface mass balance (SMB) component based on a deep artificial neural network (i.e. deep learning). While most glacier models tend to incorporate more and more physical processes, here we take an alternative approach by creating a parameterized model based on data science. Annual glacier-wide SMBs can be simulated using either deep learning or Lasso (regularized multilinear regression), whereas the glacier geometry is updated using a glacier-specific parameterization. We compare and cross-validate our nonlinear deep learning SMB model against other standard linear statistical methods on a dataset of 32 French alpine glaciers. Deep learning is found to outperform linear methods, with improved explained variance (up to +64 % in space and +108 % in time) and accuracy (up to +47 % in space and +58 % in time), resulting in an estimated r2 of 0.77 and RMSE of 0.51 m.w.e. Substantial nonlinear structures are captured by deep learning, with around 35 % of nonlinear behaviour in the temporal dimension. For the glacier geometry evolution, the main uncertainties come from the ice thickness data used to initialize the model. These results should encourage the use of deep learning in glacier modelling as a powerful nonlinear tool, capable of capturing the nonlinearities of the climate and glacier systems, that can serve to reconstruct or simulate SMB time series for individual glaciers at a regional scale for past and future climates.

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GlacierMIP – A model intercomparison of global-scale glacier mass-balance models and projections

Cited by 222 publications

References 58 publications

Semi-equilibrated global sea-level change projections for the next 10 000 years

Semi-equilibrated global sea-level change projections for the next 10 000 years

A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance

Deep learning applied to glacier evolution modelling

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