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Data assimilation in high-order ice flow modeling is a challenging and computationally costly task, yet crucial to find ice thickness and ice flow parameter distributions that are consistent with ice flow mechanics and mass balance while best matching observations. Failing to find these distributions that are required as initial conditions leads to a disequilibrium between mass balance and ice flow, resulting in nonphysical transient effects in the prognostic model. Here we tackle this problem by inverting an emulator of the Stokes ice flow model based on deep learning. By substituting the ice flow equations using a convolutional neural network emulator, we simplify, make more robust and dramatically speed up the solving of the underlying optimization problem thanks to automatic differentiation, stochastic gradient methods and implementation of graphics processing unit (GPU). We demonstrate this process by simultaneously inferring the ice thickness distribution, ice flow parametrization and ice surface of ten of the largest glaciers in Switzerland. As a result, we obtain a high degree of assimilation while guaranteeing an equilibrium between mass-balance and ice flow mechanics. The code runs very efficiently (optimizing one large-size glacier at 100 m takes < 1 min on a laptop) while it is open-source and publicly available.

For reconstructing paleoclimate or studying glacial isostatic effects on the Earth’s lithosphere, increasingly more studies focus on modeling the large-scale ice cover in mountainous regions over long time scales. However, balancing model complexity and the spatial extent with computational costs is challenging. Previous studies of large-scale ice cover simulation in mountain areas such as the European Alps, New Zealand, and the Tibetan Plateau, typically used 1-2 km spatial resolution. However, mountains are characterized by high peaks and steep slopes - topographic features that are crucial for glacier mass balance and dynamics, but poorly resolved in coarse resolution topography. The Instructed Glacier Model (IGM) is a novel 3D ice model equipped with a Convolutional Neural Network which is trained from high order ice models to simulate ice flow. This results in a significant acceleration of run times, and thereby opening the possibility of higher spatial resolution runs. We use IGM to model the glaciation of the European Alps with different resolutions (2 km, 1 km and 200 m) over a time period of 160,000 years. We apply a linear cooling rate to today’s climate until 6 °C colder to mimic ice age conditions. Preliminary results indicate systematic, resolution-related differences: At the beginning of cooling the 2 km resolution yields slightly more ice volume. However, this trend reverses after the large valleys are filled with thick ice. When the Alps are fully ice covered, we find up to 15% more ice volume in the higher resolution models., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

For more than a century, a major focus of Quaternary research has been the investigation of glacier advances and retreats during ice age cycles. The cradle for these studies are the European Alps. In recent years, glacier modelling permitted to produce long-term transient simulations of the European Alps glacier evolution. However, only sparse empirical data, e.g. geological reconstructions of glacier margins in the foreland of the Alps of the Last Glacial Maximum, are available to validate these simulations.Speleothems from the Alps are a widespread palaeoclimate archive. They provide stable carbon isotope records, which can potentially inform about soil and vegetation conditions above a cave site but also about the lack of soil during times of glacier coverage. In addition, speleothem growth in cold, high-elevation cave sites during glacials are a strong indicator of temperatures in the soil-karst-cave system above the freezing point, which is only likely to occur if the cave is covered by a temperate glacier. We here utilise existing speleothem data (growth histories and stable carbon isotopes) from Alpine caves to infer glacier coverage and thermo-dynamical state during the last glacial cycle and to assess the compatibility with modelled reconstructions. We compare data from the last glacial cycle from multiple cave sites located at different elevations (785 to 2319 m a.s.l.) in the Alps with simulations obtained with the Parallel Ice Sheet Model (PISM). We find a general agreement between speleothem-derived soil presence or absence and modelled glacier coverage. However, the speleothem data provide evidence of temperate glacier coverage which is not shown by all of the PISM simulations. Our work demonstrates the value of speleothem-based reconstructions from the Alps as proxies for assessing performance of palaeo-ice flow models.

Mountain glaciers are a major source of sea-level rise and also represent an important freshwater resource in many mountainous regions. Thus, accurate estimations of their thickness and, therefore, the total ice volume are important both in predicting and mitigating the global and local effects of climate change. However, to date, only 2% of the world’s glaciers outside the ice sheets have any thickness data, due to the logistical difficulties of obtaining such measurements, creating a large and policy-relevant scientific gap.The recent development of a global-scale ice-velocity dataset, however, provides an ideal opportunity to fill this gap and determine ice thickness across the 98% of glaciers for which no thickness data is available. This can be done by inverting an ice-dynamics model to solve for the ice thickness. For accurate thickness results, this needs to be a full-Stokes model, but such a model is far too computationally cumbersome to apply on a global scale, and simpler, quicker methods usually based on the shallow ice approximation (SIA) are too inaccurate, particularly where sliding dominates glacier motion. The only attempt that has been made to leverage the global velocity dataset to retrieve ice thickness has, though, used the SIA, simply because higher-order approaches are not computationally realistic at this scale. Consequently, most of the widely-used global glacier models have made no concerted attempt to invert for global ice thickness, owing to these limitations.As an additional related problem, failing to fully assimilate ice-velocity data into an ice-flow model necessarily introduces a shock when initialising prognostic glacier simulations, resulting in model glaciers and predictions that may diverge substantially from their real-world counterparts.As a solution to these problems, we present results from a deep-learning-driven inversion model that emulates the performance of state-of-the-art full-Stokes models at a thousandth of the computational cost. This model, by solving an optimisation problem, can fully use and assimilate all available input datasets (surface velocity and topography, ice thickness, etc.) as components of its cost function to simultaneously invert for and optimise multiple control parameters (here, we focus on ice thickness). This approach also gives us the possibility of using the same ice-velocity field for inversion and forward modelling, reducing the magnitude of the shock inherent in traditional modelling approaches. With a view to a large-scale application to all the world’s 200,000 glaciers, we present initial thickness-inversion results for the relatively well-documented European Alps to help constrain model parameters and provide a test bed for extension to other glaciated regions, with initial extension to the Caucasus and the Southern Alps.

Outlet glaciers and ice streams transport ice from the ice sheets to the ocean, where the glaciers lose mass by iceberg calving. Sermeq Kujalleq (SKK, Jakobshavn Isbræ) is one of the largest and most dynamic ice streams of the Greenland Ice Sheet with ice flow velocities up to 40 m/day. With extensive fieldwork and detailed repeated UAV surveys we aim at understanding the complex processes occuring at the ice stream margins and at the calving front of SKK. Such processes are often neclected in numerical models inducing uncertainties in projections of the ice sheet evolution.Within the framework of the COEBELI project we conducted drone photogrammetry surveys in July 2022 at SKK along other field measurements including in-situ GPS, GPRI, seismometers, and time-lapse imagery. Despite challenging weather conditions and constraints due to flying restrictions, we acquired 17 repeated flight surveys over the calving front of SKK during two weeks. As a result, we produced a large imagery data set, which was processed to infer high-resolution ortho-images and digital elevation models (DEM). Comparing the different products enables us to estimate changes in surface topography and ice dynamics. During the observation period several large calving events occurred allowing us to investigate the interaction between frontal processes and ice flow dynamics. With the very detailed data we can study crevasse opening, acceleration at the front, weaknesses in the ice and their origin, and the reaction of the glacier to large calving events.

Outlet glaciers and ice streams are the main channels through which ice sheets transport their mass towards the ocean. One of Greenland’s largest outlet glaciers Sermeq Kujalleq in Kangia (Jakobshavn Isbrae) has been broadly researched after experiencing a rapid retreat of the terminus and accompanying speedup to up to 40 m/day in the early 2000’s. However, such short-term ice dynamic variations remain poorly understood making numerical models difficult to constrain and predictions on future sea-level rise uncertain.The short-term ice dynamics of Sermeq Kujalleq consists in transient states and can only be captured by in-situ measurements of high spatial and temporal resolution. Glacier seismology has proven to be a valuable tool to study these dynamics, it provides data with a high temporal resolution and can provide information on processes happening below the ice surface. Within the COEBELI project we combine passive glacier seismology with global navigation satellite system (GNSS) receivers, long-range drones, time-lapse cameras and terrestrial radar interferometry to capture processes such as calving and basal sliding at their respective timescales.Here, we present results from a multi-array seismic deployment at Sermeq Kujalleq in Summer 2022. From May until September two arrays were deployed in the upstream part of the fast-flowing ice stream (>22 km from calving front) and one array on slower moving ice North of the main trunk. For a 3-week period in July, four more arrays were deployed on the fast-flowing ice stream closer to the calving front (50 Hz) icequakes. By studying these different signals, we are able to better constrain the processes and forces that control fluctuating ice-flow velocity and calving events.

Sermeq Kujalleq in Kangia (Jakobshavn Isbræ), Greenland is one of the most studied glaciers in the world mainly due to its recent retreat associated with extremely fast ice stream flow and high solid ice discharge. However, large limitations remain in the understanding of its short-term ice dynamics as the study of sub-daily variations, generally undetectable in spaceborne observations, requires high-rate field measurements that are challenging to acquire. Here, we present glacier surface velocities determined in Post-Processed Kinematic (PPK) mode from eight autonomous Global Navigation Satellite System (GNSS) stations deployed in July 2022 along the ice stream at a distance of 4 to 30 kilometers from the calving front. During this field campaign, we identified an 8-hour-long glacier speedup which was recorded at all GNSS stations and reached up to 11% of the pre-event velocity, followed by a 12-hour-long slowdown of similar magnitude. We further found the peak velocity was first measured at a GNSS station 16 kilometers away from the calving front, then recorded consecutively at each of the three other downstream GNSS stations with a positive time lag corresponding to a ~3 km/h wave propagation speed. At the station closest to the calving front, the timing of peak velocity corresponded to the occurrence of large-scale calving events. We further present line-of-sight glacier surface velocities measured along three shear margin transects with a terrestrial radar interferometer deployed simultaneously with the GNSS array. Across all profiles, we observed a widespread and simultaneous response of fast- and slow-moving ice suggesting a strong coupling between the main trunk and the shear margins of the ice stream.

We present the Instructed Glacier Model (IGM) -- a new framework to model the evolution of glaciers at any scale by coupling ice dynamics, surface mass balance, and mass conservation. The key novelty of IGM is that it models the ice flow by a Convolutional Neural Network (CNN), which is trained from physical high-order ice flow mechanical models. Doing so has major advantages in both forward and inverse modelling. In forward modelling, the most computationally demanding model component (the ice flow) is substituted by a very cheap CNN emulator. Once trained with representative data, IGM permits to model individual mountain glaciers several orders of magnitude faster than high-order ones on CPU with fidelity levels above 90 \% in terms of ice flow solutions leading to nearly identical transient thickness evolution. Switching to Graphics Processing Unit (GPU) permits additional significant speed-ups, especially when modelling large-scale glacier networks and/or high spatial resolutions. In inverse modelling, the substitution by a CNN emulator does not only speed up but facilitates dramatically the data assimilation step, i.e. the search for optimal ice thickness and ice flow parameter spatial distributions that match spatial observations at best (such as ice flow, surface topography or ice thickness profiles) while being consistent with the high-order ice flow mechanics. The reason is that inverting a CNN can take great benefit from the tools used for its training such as automatic differentiation, stochastic gradient methods, and GPU. IGM is an open-source Python code (https://github.com/jouvetg/igm), which deals with two-dimensional gridded input and output data. Together with a companion library of trained ice flow emulators, IGM permits user-friendly, computationally highly-efficient, easy-to-customize, and mechanically state-of-the-art glacier forward and inverse modelling at any scale. We illustrate its potential by replicating a simulation of the great Aletsch Glacier, Switzerland, from 1880 to 2100, based on a Stokes model.

Glacier modelling of the Alpine region during past ice ages has received increasing attention in the recent years. Considering the complexity of the Alpine topography, high spatial resolution climate information is required for running glacier models over the area. However, continuous climatic signal at resolution higher than 10 km and covering several hundred thousand of years cannot be directly derived using dynamical climate models. Alternative strategies must be considered.Here, a climate emulator providing monthly temperature and precipitation over the Alpine region, with a horizontal resolution of 2x2 km and covering the last 400’000 years at intervals of 100 years, is presented. The emulatorcombines a dynamical modelling chain of Earth System Models (ESMs) and a Regional Climate Model (RCM) with different statistical modelling methods. The dynamical modelling chain delivers climate information at highest accuracy based upon physical prognostic equations for specific time slices. The subsequent statistical modelling uses these time slices as physically consistent boundaries and estimates the climate conditions in between them, thus generatinga long-term climate evolution.A total of 19 climate model experiments are conducted for different time-slices of the considered study period, including also sensitivity tests with changes in the ice sheet height of the Northern Hemisphere and land cover type. Starting from a simple linear regression, a series of different statistical approaches is tested for building the emulator. An evaluation of the different versions is then conducted against one of the RCM time-slice experiments, left out in turns from the training set of the statistical model. Results show robust skills of the emulator in the representation of temperature, whose changes are mainly driven by smooth variations in the seasonal pattern of insolation at different time-steps, already using a simple linear regression. For precipitation, non-linearities associated to changes in the large-scale atmospheric circulation seem to dominate, making the use of more complex statistical approaches more appropriate. Additional evaluation tests conducted using glacier modelling driven by the outputs of the developed emulator confirm its potential for reconstructing the ice extent over the Alpine region during the last ice ages.

Sermeq Kujalleq in Kangia (Jakobshavn Isbræ), Greenland has been extensively investigated over the past decades due to its recent retreat associated with extremely fast ice stream flow and high solid ice discharge. However, its short-term dynamics still remain poorly understood as they consist in transient states that can only be captured by high spatial and temporal in situ measurements. In the new COEBELI project, we aim at combining high resolution field data sets from seismic arrays, global navigation satellite system (GNSS) receivers, long-range uncrewed aerial vehicles and terrestrial radar interferometers (TRI) to achieve a comprehensive and detailed study of the short-term ice stream dynamics. Here, we present TRI and GNSS retrievals of surface velocity and elevation acquired during the first, exploratory field campaign of the COEBELI project in summer 2021. Seven kilometers away from the calving front, we specifically identified a slowdown of 1.12 m d-1 within a single day in the main trunk of the ice stream. While the absolute slowdown is larger in the main trunk than in the outer area of the shear margin (1.12 m d-1 versus 0.75 m d-1), it corresponds to a larger fraction of the pre-slowdown velocity in the latter zone (-4.48% versus -7.94%). We further discuss the challenges associated with the acquisition, processing and analysis of high-resolution data sets for the study of such complex and dynamic environments.

Glacier calving plays a key role in the recently observed dynamic massloss of the Greenland ice sheet. Calving waves, generated by thesudden detachment of ice from the glacier terminus, can reach tens ofmeters of height and have devastating effects upon impact onsurrounding shores. In this study, we describe a new method for thedetection of source location and timing of calving waves, and theanalysis of their magnitude and spreading properties using aterrestrial radar interferometer (TRI). This method was applied to11,500 minute-interval TRI acquisitions from Eqip Sermia, Greenland.More than 2,000 calving waves were detected within sevendays. Quantitative assessment with a Wave Power Index (WPI) showedspatially distinctive patterns: the sector of the calving front endingin deep water shows a higher wave activity (+49%) with highercumulative WPI (+34%) than the shallow sector. In combination witha detection of meltwater plume locations, we highlighted a 2.3 timeshigher occurrence of visible meltwater plumes in the deep sector than theshallow one. We found both the cumulated WPI and the number of wavesto increase by more than 80% in the presence of a meltwater plumein the deep sector while only by 30% in the shallow sector. Wetherefore explain the higher calving activity in the deep sector to be strongly related to a combination of higher occurrence of meltwater plumes and more efficient calving enhancement linked to better connections to deep warm waters.

Crystal orientation fabric (COF) analysis provides information about the c-axis orientation of ice grains and the associated anisotropy and microstructural information about deformation and recrystallisation processes within the glacier. This information can be used to introduce modules that fully describe the microstructural anisotropy or at least direction-dependent enhancement factors for glacier modelling. The COF was studied at an ice core that was obtained from the temperate Rhonegletscher, located in the central Swiss Alps. Seven samples, extracted at depths between 2 and 79 m, were analysed with an automatic fabric analyser. The COF analysis revealed conspicuous four-maxima patterns of the c-axis orientations at all depths. Additional data, such as microstructural images, produced during the ice sample preparation process, were considered to interpret these patterns. Furthermore, repeated high-precision global navigation satellite system (GNSS) surveying allowed the local glacier flow direction to be determined. The relative movements of the individual surveying points indicated longitudinal compressive stresses parallel to the glacier flow. Finally, numerical modelling of the ice flow permitted estimation of the local stress distribution. An integrated analysis of all the data sets provided indications and suggestions for the development of the four-maxima patterns. The centroid of the four-maxima patterns of the individual core samples and the coinciding maximum eigenvector approximately align with the compressive stress directions obtained from numerical modelling with an exception for the deepest sample. The clustering of the c axes in four maxima surrounding the predominant compressive stress direction is most likely the result of a fast migration recrystallisation. This interpretation is supported by air bubble analysis of large-area scanning macroscope (LASM) images. Our results indicate that COF studies, which have so far predominantly been performed on cold ice samples from the polar regions, can also provide valuable insights into the stress and strain rate distribution within temperate glaciers., The Cryosphere, 15 (2), ISSN:1994-0416, ISSN:1994-0424

Future mass loss predictions, and thereby sea level rise predictions, are strongly affected by the representation of iceberg calving in numerical ice sheet models. Despite recent advances, gaps in our understanding of calving mechanisms remain and there exists a lack of data to constrain mechanical properties related to ice fracturing. For instance, observed critical strain rates for crevasse initiation span two orders of magnitude.Bowdoin Glacier in Northwest Greenland provides a unique opportunity to conduct in-situ measurements near the calving front due to its accessibility via a crevasse-free walkable moraine. In July 2019, two major calving events were surveyed by 10 GPS stations installed along the front in close vicinity to the calving events. Measurements show glacier uplift prior to the first calving event and horizontal compression prior to the second major calving event.In contrast to previously observed major events, no precursor such as a large surface crack was visible on the field. Our data suggest a change in calving behaviour from surface crevasses due to hydro-fracturing to basal crevasse formation due to buoyancy, which may be favoured by observed thinning (~4 m yr-1 since 2013).

Retreat of calving glaciers worldwide has contributed substantially to sea-level rise in recent decades. Mass loss by calving contributes significantly to the uncertainty of sea-level rise projections. At Bowdoin Glacier, Northwest Greenland, most calving occurs by a few large events resulting from kilometre-scale fractures forming parallel to the calving front. High-resolution terrestrial radar interferometry data of such an event reveal that crevasse opening is fastest at low tide and accelerates during the final 36 h before calving. Using the ice flow model Elmer/Ice, we identify the crevasse water level as a key driver of modelled opening rates. Sea water-level variations in the range of local tidal amplitude (1 m) can reproduce observed opening rate fluctuations, provided crevasse water level is at least 4 m above the low-tide sea level. The accelerated opening rates within the final 36 h before calving can be modelled by additional meltwater input into the crevasse, enhanced ice cliff undercutting by submarine melt, ice damage increase due to tidal cyclic fatigue, crevasse deepening or a combination of these processes. Our results highlight the influence of surface meltwater and tides on crevasse opening leading to major calving events at grounded tidewater glaciers such as Bowdoin., Journal of Glaciology, 66 (255), ISSN:0022-1430, ISSN:1727-5652

The subglacial hydrology of tidewater glaciers is a key but poorly understood component of the complex ice-ocean system, which affects sea level rise. As it is extremely difficult to access the interior of a glacier, our knowledge relies mostly on the observation of input variables such as air temperature, and output variables such as the ice flow velocities reflecting the englacial water pressure, and the dynamics of plumes reflecting the discharge of meltwater into the ocean. In this study we use a cost-effective Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) to monitor the daily movements of Bowdoin Glacier, north-west Greenland, and the dynamics of its main plume. Using Structure-from-Motion photogrammetry and feature-tracking techniques, we obtained 22 high-resolution ortho-images and 19 velocity fields at the calving front for 12 days in July 2016. Our results show a two-day-long speed-up event (up to 170%) – caused by an increase in buoyant subglacial forces – with a strong spatial variability revealing that enhanced acceleration is an indication of shallow bedrock. Further, we used the Particle Image Velocimetry (PIV) method to analyze water flow from successive UAV images taken while flying over the main plume of the glacier. We found that PIV successfully captures the area of radially diverging flow of the plume, and provides information on spatial and time variability as no other remote sensing technique can. Most interestingly, the active part of the plume features pulsating water jets at the time scale of seconds, and is 1 to 5 times smaller than its visual footprint defined by the iceberg-free area. Combined with an ice flow model or a non-steady plume model, our approach has the potential to generate a novel set of input data to gather information about the depth of the bedrock, the discharge of meltwater, or the subglacial melting rate of tidewater glaciers.

Measuring glacier calving magnitude, frequency and location in high temporal resolution is necessary to understand mass loss mechanisms of ocean-terminating glaciers. We utilized calving-generated tsunami signals recorded with a pressure sensor for estimating the calving flux of Bowdoin Glacier in northwestern Greenland. We find a relationship between calving ice volume and wave amplitude. This relationship was used to compute calving flux variation. The calving flux showed large spatial and temporal fluctuations in July 2015 and in July 2016, with a mean flux of 2.3 ± 0.15 × 105 m3 d−1. Calving flux was greater during periods of fast ice flow, high air temperature, and at low/falling tide, indicating the importance of increased longitudinal strain due to glacier acceleration and/or submarine melting at the calving front. Long-term measurements with the method introduced here are promising for understanding the complex interplay of ice dynamics, melting and calving at glacier fronts., Earth and Planetary Science Letters, 515, ISSN:0012-821X, ISSN:1385-013X

Measuring the ice flow motion accurately is essential to better understand the time evolution of glaciers and ice sheets, and therefore to better anticipate the future consequence of climate change in terms of sea-level rise. Although there exist a variety of remote sensing methods to fill this task, in-situ measurements are always needed for validation or to capture high temporal resolution movements. Yet glaciers are in general hostile environments where the installation of instruments might be tedious and risky when not impossible. Here we report the first-ever in-situ measurements of ice flow motion using a remotely controlled Unmanned Aerial Vehicle (UAV). We used a multicopter UAV to land on a highly crevassed area of Eqip Sermia Glacier, West Greenland, to measure the displacement of the glacial surface with the aid of an on-board differential GNSS receiver. Despite the unfortunate loss of the UAV, we measured approximately 70 cm of displacement over 4.36 hours without setting foot onto the glacier – a result validated by applying UAV photogrammetry and template matching techniques. Our study demonstrates that UAVs are promising instruments for in-situ monitoring, and have a great potential for capturing short-term ice flow variations in inaccessible glaciers – a task that remote sensing techniques can hardly achieve.

The European Alps, the cradle of pioneering glacial studies, are one of the regions where geological markers of past glaciations are most abundant and well-studied. Such conditions make the region ideal for testing numerical glacier models based on simplified ice flow physics against field-based reconstructions and vice versa. Here, we use the Parallel Ice Sheet Model (PISM) to model the entire last glacial cycle (120–0ka) in the Alps, using horizontal resolutions of 2 and 1km. Climate forcing is derived using two sources: present-day climate data from WorldClim and the ERA-Interim reanalysis; time-dependent temperature offsets from multiple palaeo-climate proxies. Among the latter, only the European Project for Ice Coring in Antarctica (EPICA) ice core record yields glaciation during marine oxygen isotope stages 4 (69–62ka) and 2 (34–18ka). This is spatially and temporally consistent with the geological reconstructions, while the other records used result in excessive early glacial cycle ice cover and a late Last Glacial Maximum. Despite the low variability of this Antarctic-based climate forcing, our simulation depicts a highly dynamic ice sheet, showing that Alpine glaciers may have advanced many times over the foreland during the last glacial cycle. Ice flow patterns during peak glaciation are largely governed by subglacial topography but include occasional transfluences through the mountain passes. Modelled maximum ice surface is on average 861m higher than observed trimline elevations in the upper Rhône Valley, yet our simulation predicts little erosion at high elevation due to cold-based ice. Finally, despite the uniform climate forcing, differences in glacier catchment hypsometry produce a time-transgressive Last Glacial Maximum advance, with some glaciers reaching their modelled maximum extent as early as 27ka and others as late as 21ka., The Cryosphere, 12 (10), ISSN:1994-0416, ISSN:1994-0424

Environments along the coast of Greenland are rapidly changing under the influence of a warming climate in the Arctic. To better understand the changes in the coastal environments, we performed researches in the Qaanaaq region in northwestern Greenland as a part of the ArCS (Arctic Challenge for Sustainability) Project. Mass loss of ice caps and marine-terminating outlet glaciers were quantified by field and satellite observations. Measurements and sampling in fjords revealed the important role of glacial meltwater discharge in marine ecosystems. Flooding of a glacial stream in Qaanaaq and landslides in a nearby settlement were investigated to identify the drivers of the incidents. Our study observed rapid changes in the coastal environments, and their critical impact on the society in Qaanaaq. We organized workshops with the residents to absorb local and indigenous knowledge, as well as to share the results and data obtained in the project. Continuous effort towards obtaining long-term observations requiring involvement of local communities is crucial to contribute to a sustainable future in Greenland.

We develop a posteriori ‘mechanical’ error estimators that are able to evaluate the solution discrepancy between two ice flow models. We first reformulate the classical shallow ice flow models by applying simplifications to the weak formulation of the Glen–Stokes model. This approach leads to a unified hierarchical formulation which relates the Glen–Stokes model, the Blatter model, the shallow ice approximation and the shallow shelf approximation. Based on this formulation and on residual techniques commonly used to estimate numerical errors, we derive three a posteriori estimators, each of which compares a pair of models using measures of the velocity field from the simpler (shallower) model. Numerical experiments confirm that these estimators can be used to assess the validity of the shallow ice models that are commonly used in glacier and ice sheet modelling.

During the Last Glacial Maximum (LGM), glaciers in the Alps reached a maximum extent, and broad sections of the foreland were covered by ice. In this study, we simulated the alpine ice cap using a glacier flow model to constrain the prevailing precipitation pattern with a geomorphological reconstruction of ice extent. For this purpose we forced the model using different temperature cooling and precipitation reduction factors. The use of the present-day precipitation pattern led to a systematic overestimation of the ice cover on the northern part of the Alps relative to the southern part. To reproduce the LGM ice cap, a more severe decrease in precipitation in the north than in the south was required. This result supports a southwesterly advection of atmospheric moisture to the Alps, sustained by a southward shift of the North Atlantic storm track during the LGM., Geographica Helvetica, 71 (3), ISSN:0016-7312