Displays

Retreating and thinning glaciers are icons of climate change and impact the local hazard situation, regional runoff as well as global sea level. For past IPCC reports, regional glacier change assessments were challenged by the small number and heterogeneous spatio-temporal distribution of in situ measurement series and uncertain representativeness for the respective mountain range as well as by spatial limitations of current satellite altimetry (only point data) and gravimetry (coarse resolution). Towards IPCC SROCC, there have been considerable improvements with respect to available geodetic datasets. Geodetic volume change assessments for entire mountain ranges have become possible thanks to recently available and comparably accurate DEMs. At the same time, advances have been made in processing methods for radar altimetry (CryoSat-2 swath processing), as well as new spaceborne laser altimetry (ICESat-2) and gravimetry (GRACE-FO) missions are in orbit and about to release data products to the science community. This opens new opportunities for regional evaluations of results from different methods as well as for truly global assessments of glacier mass changes and related contributions to sea-level rise. At the same time, the glacier research and monitoring community is facing new challenges related to data size, formats, and availability as well as new questions with regard to best practises for data processing chains and for related uncertainty assessments.

In this PICO presentation, we introduce a new working group of the International Association of Cryospheric Sciences (IACS) on Regional Assessments of Glacier Mass Change (RAGMAC; https://cryosphericsciences.org/activities/wg-ragmac/). The overall goal of this working group (WG) is bringing together the research community that is assessing regional glacier mass changes from various observation technologies and to come up with a new consensus estimate of global glacier mass changes and related uncertainties. The WG is organized in three work packages, two related to different remote sensing technologies (WG1: glaciological and geodetic DEM-differencing methods, WG2: altimetry and gravimetry) and a third that aims at regional comparisons of corresponding results. Participation is open to everybody who is willing to actively contribute to one or several of these work packages.

How to cite: Zemp, M., Braun, M. H., Gardner, A. S., Wouters, B., Moholdt, G., and Hock, R.: A new working group on the Regional Assessments of Glacier MAss Change (RAGMAC), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2642, https://doi.org/10.5194/egusphere-egu2020-2642, 2020.

Glacier mass loss is recognized as a significant contributor to current sea-level rise. However, large uncertainties remain in projections of glacier mass loss on global and regional scales. We present an ensemble of 279 global-scale glacier mass and area change projections for the 21st century based on eleven glacier models using up to ten General Circulation Models (GCMs) and four Representative Concentration Pathways (RCPs) as boundary conditions. We partition the total uncertainty into the individual contributions caused by glacier models, GCMs, RCPs, and natural variability. We find that emission scenario uncertainty is growing throughout the 21st century, and is the largest source of uncertainty by 2100. The relative importance of glacier model uncertainty decreases over time, but it is the greatest source of uncertainty until the middle of this century. The projection uncertainty associated with natural variability is small on the global scale but has strong effects on small regional scales. The projected global mass loss by 2100 relative to 2015 (75±64 mm sea-level equivalent (SLE) for RCP2.6, 165±98 mm SLE for RCP8.5) is lower than, but within the uncertainty range of previous projections.

How to cite: Marzeion, B., Hock, R., Anderson, B., Bliss, A., Champollion, N., Fujita, K., Huss, M., Immerzeel, W., Kraaijenbrink, P., Malles, J.-H., Maussion, F., Radic, V., Rounce, D., Sakai, A., Shannon, S., van de Wal, R., and Zekollari, H.: Partitioning the Uncertainty of Ensemble Projections of Global Glacier Mass Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5579, https://doi.org/10.5194/egusphere-egu2020-5579, 2020.

Glaciers outside the two major ice sheets are key contributors to sea level rise, act as important sources of freshwater, and have great touristic value. To simulate the temporal evolution of these ice masses at regional- to global scale, simplified models are typically used that rely on volume scaling approximations or parameterizations based on observed glacier changes. These approaches rely on minimal data and are fast, but they do not account for mass redistribution through ice flow. More recently, efforts have been undertaken to represent ice dynamical processes in flowline models that can be applied at large spatial scales. These flowline approaches represent the mass transfer within a glacier in a more realistic way, but fail at reproducing the evolution of large glaciers, which are typically not confined by the local topography and do not have a pronounced elongated shape as represented in flowline models.

Here we present our first efforts to develop a 3D coupled surface mass balance – ice flow model that can be used to model the temporal evolution of an ensemble of glaciers. The main goal of such a model is to be able to simulate the temporal evolution of glaciers with distinct shapes and situated in various climatic regimes in an automated way. By relying on a 3D model architecture we aim to better represent processes crucial for glacier evolution, such as glacier calving and convergent flow from several tributaries. Here, we will present first tests with a prototype version of the model by reproducing steady state geometries of selected glaciers, and by simulating the evolution of these ice bodies under idealised forcing scenarios.

How to cite: Zekollari, H., Goelzer, H., Pattyn, F., Wouters, B., and Lhermitte, S.: Towards a 3-D model for large-scale glacier simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10947, https://doi.org/10.5194/egusphere-egu2020-10947, 2020.

Glaciers in High Mountain Asia have experienced intense scientific scrutiny in the past decade due to their hydrological and societal importance. The explosion of freely-available satellite observations has greatly advanced our understanding of their thinning, motion, and overall mass losses, and it has become clear that they exhibit both local and regional variations due to debris cover, surging and climatic regime. However, our understanding of glacier accumulation and ablation rates is limited to a few individual sites, and altitudinal surface mass balance is essentially unknown across the vast region.

Here we combine recent assessments of ice thickness and surface velocity to correct observed glacier thinning rates for mass redistribution in a flowband framework to derive the first estimates of altitudinal glacier surface mass balance across the region. We first evaluate our results at the glacier scale with all available glaciological field measurements (27 glaciers), then analyze 4665 glaciers (we exclude surging and other anomalous glaciers) comprising 43% of area and 36% of mass for glaciers larger than 2 km² in the region. The surface mass balance results allow us to determine the equilibrium line altitude for each glacier for the period 2000-2016.  We then aggregate our altitudinal and hypsometric surface mass balance results to produce idealised profiles for distinct subregions, enabling us to consider the subregional heterogeneity of mass balance and the importance of debris-covered ice for the region’s overall ablation.

We find clear patterns of ELA variability across the region.  9% of glaciers accumulate mass over less than 10% of their area on average for the study period. These doomed  glaciers are concentrated in Nyainqentanglha, which also has the most negative mass balance of the subregions, whereas accumulation area ratios of 0.7-0.9 are common for glaciers in the neutral-balance Karakoram and Kunlun Shan. We find that surface debris extent is negatively correlated with ELA, explaining up to 1000 m of variability across the region and reflecting the importance of avalanching as a mass input for debris-covered glaciers at lower elevations. However, in contrast with studies of thinning rates alone, we find a clear melt reduction for low-elevation debris-covered glacier areas, consistent across regions, largely resolving the ‘debris cover anomaly’.  

Our results provide a comprehensive baseline for the health of the High Asian ice reservoirs in the early 21^st Century. The estimates of altitudinal surface mass balance and ELAs will additionally enable novel strategies for the calibration of glacier and hydrological models. Finally, our results emphasize the potential of combined remote-sensing observations to understand the environmental factors and physical processes responsible for High Asia’s heterogeneous patterns of recent glacier evolution.

How to cite: Miles, E., McCarthy, M., Dehecq, A., Kneib, M., Fugger, S., and Pellicciotti, F.: Early 21st-century glacier surface mass balance across High Mountain Asia derived from remote sensing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9330, https://doi.org/10.5194/egusphere-egu2020-9330, 2020.

We demonstrate modelling of regional- and global-scale volume changes in glaciers over the last millennium with the Open Global Glacier Model (OGGM) - a glacier geometry and surface mass balance model in active development - using reconstructed climate data timeseries from a set of 6 GCMs. The goals are: 1) to better understand how well different longer-term (extending back to the pre-industrial period) climate datasets perform specifically in terms of their impact on glaciers; 2) to analyse the ability of OGGM to model glaciers over longer timescales while still capturing observed changes over the period of instrumental record; and 3) to determine which regions are better or worse suited to this type of modelling on large scales. A secondary goal is to understand the relative impact of precipitation and temperature - the two primary climate variables used to drive OGGM - on regional glacier volume over this time period, using synthetic climate inputs which isolate long-term trends from each variable individually. Modelling over this last millennium timescale is important due to the preponderance of available instrumental data being much more recent, with glacier models developed and calibrated using data that are mostly recorded in a period of pronounced global glacier retreat. Modelling periods that include both recent warming (and associated observed glacier retreat) and the preceding period that is without such globally coherent changes in climate provides a valuable test of glacier models, to ensure they can generate both relative stability in glacier geometry in stable climates with realistic variability and subsequent reduction in ice mass where appropriate in response to clearer recent temperature trends.

How to cite: Parkes, D. and Goosse, H.: Modelling regional- and global-scale glacier volume changes over the last millennium, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4262, https://doi.org/10.5194/egusphere-egu2020-4262, 2020.

Thick debris cover, greater than about 5 cm, insulates ice and reduces melt rates. Despite this melt-suppressing effect, glaciers often thin rapidly under thick debris cover. In High Mountain Asia, the European Alps, and Alaska many debris-covered and debris-free glacier tongues are thinning at similar rates (e.g., Kääb et al., 2012). This apparent paradox is known as the ‘debris-cover anomaly’ (Pellicciotti et al., 2015). Two mechanisms have been proposed to explain this behavior, which are not mutually exclusive. First, glacier thinning under thick debris is enhanced by melt hotspots (lakes, ice cliffs, and streams) within otherwise continuous debris cover. Second, the decline in ice flow from upglacier leads to thinning under thick debris (e.g., Vincent et al., 2016).

We propose a new mechanism to explain why thinning amplifies under thick debris. It appears that debris cover—through its affect on the melt pattern—controls glacier geometry (i.e., patterns of ice thickness and surface slope). A characteristic debris-perturbed driving stress pattern results which in turn controls where dynamical thinning amplifies, often in the upper reaches of debris-covered tongues. Our explanation is supported with data from a suite of glaciers in the Himalaya and with simulations from a numerical debris-covered glacier model responding to climate change (Anderson and Anderson, 2016).

In all numerical simulations, the zone of maximum glacier thinning initially occurs upglacier from the debris cover. This zone of maximum thinning then propagates downglacier into the debris-covered portion. We explain how this zone of maximum thinning can be spatially pinned and amplified at different locations relative to the terminus depending on debris thickness, bed slope, glacier size, and glacier topology. This seemingly paradoxical mechanism in which debris itself controls thinning under thick debris is further supported by an analysis of published thinning data from glaciers across High Mountain Asia.

How to cite: Anderson, L. and Scherler, D.: Thick debris paradoxically controls the ‘anomalous’ thinning of debris-covered glaciers in High Mountain Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19296, https://doi.org/10.5194/egusphere-egu2020-19296, 2020.

In order to assess glacier mass balance on large temporal and/or spatial scales, numerical modelling is an essential tool, complementing ground observations and remote sensing methods. For a reliable simulation of a glacier’s development over time, knowledge of its initial state is fundamental. Attaining this information entirely through empirical evidence is impossible due to a lack of data, hence the need for alternative, numerical methods. In this study, three methods of varying complexity are applied to initialize the Open Global Glacier Model (OGGM) for 254 glaciers. These glaciers have a minimum of 5 years of in-situ mass balance observations, allowing for direct comparison with modelled values. The initialization methods comprise, in brief, i) a basic spin-up, starting from present-day conditions, running the model for 200 years with a random climate, representative of the period 1900-2000 ii) a cold climate spin-up, allowing the glacier to grow and create a more representative initial condition for e.g. the year 1901 and iii) a synthetic experiment based on present day glacier observations and past climate information, used to generate a large set of physically plausible initial states, which are then evaluated. Using each method, we reconstruct the glaciers’ initial states and set up a forward run from which to extract mass balance values over the time period 1970-2014, used for validation purposes. The overall aim is to identify an initialization approach that can be successfully applied to our current set of 254 glaciers, as well as areas with even sparser data available, expanding the range of scale for glacier modelling.

How to cite: van der Laan, L., Eis, J., Förster, K., and Marzeion, B.: Comparison of methods for initialization of the Open Global Glacier Model (OGGM), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4977, https://doi.org/10.5194/egusphere-egu2020-4977, 2020.

            The most significant quantity characterizing current state of a mountain glacier is its surface mass balance (SMB). SMB responds to changing climatic conditions and therefore determines present and future behavior of the glacier. Formulation of SMB in terms of a mathematical model allows better understanding complex processes of the atmospheric impact on glacier dynamics. After several decades of development, common universal modeling principles and approaches have been elaborated. At present, most of the newly developed models are quite similar with only varying details mostly concerning parameterization of heat fluxes.

SMB is an interplay between positive (accumulation) and negative (ablation) components. Ablation is formulated either using temperature-index (positive degree day) approach or surface energy balance calculation (or combination of both). Both these approaches are based on genuine physical principles and that is why they can be easily transformed into computational algorithms. Results of ablation model calculations are relatively easily constrained by observations. In contrast, evaluation of accumulation is much more dependent on poorly constrained factors such as local atmospheric circulation, snow-storm transport (including post-depositional) and avalanche feeding.

Our approach to simulate components of SMB is based on energy balance approach and emulation of meteorological conditions using a simple stochastic weather generator. To validate the model, we use observed SMB data from several mountain glaciers in different environmental conditions – Djankuat (Central Caucasus), Tuyuksu (Zailiyski Alatau), Sary-Tor and Karabatkak (Inner Tien Shan). Suggested approach allows to easily construct an ensemble of numerical experiments and implement Monte Carlo method for the SMB evaluation. This possibility is especially significant for simulation of future states of glaciers according to one or another climatic scenario on a coupled ice flow-SMB model.

The reported study was funded by RFBR, project number 20-05-00681 (“Evolution of glaciation of Inner Tien Shan under climate change and technogenic influence”)

How to cite: Rybak, O., Rybak, E., Popovnin, V., Gubanov, A., Satylkanov, R., Shahgedanova, M., and Kapitsa, V.: Surface mass balance modeling of mountain glaciers in the Caucasus and in the High Mountain Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4257, https://doi.org/10.5194/egusphere-egu2020-4257, 2020.

We present an approach to calibrate a regional glacier model based on the well observed period since the mid-19^th century LIA maximum.
We chose 30 glaciers distributed across the entire European Alps with frequent length change observations in that period. These glaciers account for 25% of today's total glacier area in the Alps. We run simulations with the Open Global Glacier Model (OGGM, https://oggm.org) driven by HISTALP (http://www.zamg.ac.at/histalp) gridded climate data. To calibrate the glaciers individually, we vary three model parameters within a reasonable range: (i) a precipitation scaling factor governing average mass-turnover and mass-balance profiles, (ii) the ice creep parameter governing basal sheer stress and the dynamics of ice flow, and (iii) a constant mass balance perturbation applied to the yearly mass-balance. This results in  1365 unique parameter combinations which were tested for all glaciers. We chose individual parameter subsets for every glacier based on objective criteria minimizing the difference between modeled and observed length changes.

We find that there is no unique parameter combination satisfying our criteria for all glaciers. It is also challenging to identify an ideal parameter combination for each individual glacier, since there is a trade-off between reproducing variability (useful for paleo-climate interpretations) and reproducing observed length change (useful for projections and planing).
Furthermore, model and input data uncertainties are variable in time, leading to non-unique optimal parameter sets. Therefore, we rely on an ensemble of simulations consisting of the best runs with respect to multiple statistical measures. Together with a cross-validation procedure, the ensemble produces a probabilistic uncertainty range which can be applied to Holocene glacier reconstructions and future evolution scenarios.

How to cite: Dusch, M., Nicolussi, K., and Maussion, F.: Calibrating a regional glacier model using post-LIA glacier length changes in the Alps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9589, https://doi.org/10.5194/egusphere-egu2020-9589, 2020.

With the widespread retreat of glaciers, concerns emerge for the availability of water resources. These concerns are largest for future dry spells, when runoff from other sources is low. In this context, mass balance estimates for time horizons from days to weeks might help to better manage water resources in alpine regions. Here, we obtain such estimates from a combined modelling and data assimilation approach. Starting with three glaciers with detailed monitoring in Switzerland, we extrapolate our signal to other unmeasured glaciers in the country.

For the mass balance modeling, an ensemble of four melt models is tuned to match semi-annual in-situ observations from the Glacier Monitoring Switzerland (GLAMOS) program. With this ensemble, we then infer mass balance for the observed glaciers. Three of the glaciers (Rhonegletscher, Findelgletscher and Glacier de la Plaine Morte) were equipped with on-ice cameras between mid-June and early October 2019. The cameras transmitted 352 daily point mass balance observations which we assimilate into our model results by employing a particle filter.

To transfer the mass balance information of the three well-observed glaciers to other glaciers in Switzerland, we make use of the strong spatial correlation of cumulative melt. In a workflow here termed “percentile extrapolation method”, first, all glaciers without direct mass balance measurements are calibrated based on geodetic mass balances covering the 1980-2010 period. To reduce the large uncertainty in calibration on geodetic mass changes, we first predict average mass balance model parameters for each glacier with a random forest regressor. Then, we tune these parameters to match the geodetic mass balance in a least squares minimization. As soon as a mass balance climatology for the past has been calculated with this calibration, we determine with which percentiles of this climatology the current year’s mass balance ensemble estimate overlaps at the well-observed glaciers. These percentiles are then extrapolated in space using inverse distance weighting and they are applied to the climatology of unmeasured glaciers. The procedure yields a mass balance estimate at every single day of a year for every Swiss glacier taking into account specific glacier characteristics.

We compare the assimilated camera mass balances with interpolated measurements from the GLAMOS program. First results indicate that for the annual mass balance, the camera data lower the mean absolute error to 0.19 m water equivalent (w.e.), from 0.36 m w.e for a model prediction without data assimilation. The standard deviation of the prediction ensemble is reduced by 0.37 m w.e. on average. A cross-validation using percentile extrapolation between the glaciers equipped with a camera shows that annual mass balance can be predicted within 0.27 m w.e.. The summer (May to September) melt of other glaciers in the GLAMOS program can be predicted with an absolute error of 0.07m w.e. (model: 0.27 m w.e). Our results indicate that the continuous monitoring of a few selected sites has the potential of strongly improving daily near real-time mass balance estimates at the regional scale.

How to cite: Landmann, J., Ogier, C., Huss, M., and Farinotti, D.: Predicting glacier mass balance by data assimilation from on-ice cameras, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17711, https://doi.org/10.5194/egusphere-egu2020-17711, 2020.

Significant seasonal changes in ice flow have been reported for outlet glaciers in Greenland. Understanding the mechanisms that control these rapid intra-annual changes in dynamics could potentially help to clarify Greenland's long-term evolution and climate change response.

In this study, we investigate seasonal changes in ice flow velocity in order to better understand the processes controlling them. We focus on 3 Greenlandic glaciers of different types: Russell which is a land-terminating glacier with speed ranging from 50 to 350 m/yr,  Upernavik Isstrøm which is a marine-terminating tidewater glacier with speeds up to 4 km/yr, and Petermann Gletscher that has a large ice shelf and with speed at the order of 1 km/yr. Since 2014, the number of spaceborne observations over the ice sheet has increased dramatically with the launch of Landsat-8, Sentinel-1 and -2, providing almost continuous monitoring of glacier dynamics.

Here, we develop an automatic processing chain to derive dense time series of surface ice flow from radar sensors, Sentinel -1a/b, and optical sensors, Landsat-7/8 and Sentinel-2, using speckle or feature tracking algorithms. We construct a post-processing analysis based on local polynomial regression to filter our multi-sensor time series and create a velocity database with high temporal resolution and reduced noise. The database allows us to reconstruct the continuous evolution of surface ice velocity with frequency intervals ranging from monthly for the entire glacial basin to weekly for the downstream parts. 

Using this methodology, we obtain velocity fields for 4 years between 2015 and 2019 of the entire basins of Russell, Upernavik and Petermann glaciers. Our results clearly show the seasonal variations in flow to which these glaciers are subjected. We analyze the average seasonal fluctuations during the 4 years, as well as particular behavior in different years. These results are then compared and discussed in relation to potential external forcings such as subglacial hydrology (change in basal friction), fluctuations in the ice front or grounding line positions (change in buttressing) and the presence of sea ice or ice melange in front of the glaciers. 

Finally, we conclude on the benefits of our post-processing approach for the analysis of dense ice flow time series and provide first insights on the causes of seasonal variations observed on these 3 glaciers.

How to cite: Derkacheva, A., Mouginot, J., Millan, R., and Gillet-Chaulet, F.: Processing and analysis of dense ice velocity time series to reconstruct seasonal fluctuations of 3 greenlandic glaciers : Russell Gletscher, Upernavik Isstrom, Petermann Gletscher., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-307, https://doi.org/10.5194/egusphere-egu2020-307, 2020.

Glaciers in the Tien Shan (Central-Asia) mountains contribute a considerable part of the freshwater used for irrigation and households in the dry lowland areas of Kyrgyzstan and its neighbouring countries. Since the Little Ice Age, the total ice mass in this mountain range has been decreasing significantly. However, accurate measurements of the current ice volume and ice thickness distribution in the Tien Shan remain scarce, and accurate data is largely lacking at the local scale. In 2016, 2017 and 2019, we organized 1-month field campaigns in Central-Asia to sound the ice thickness of four different glaciers in the Tien Shan using a Narod ground penetrating radar (GPR) system.

Here, we present and discuss our in-situ ice thickness measurements of the four glaciers. We performed in total more than 1000 GPR soundings. We found a maximum ice thickness of 200 meters in the central part of the southern facing Ashuu-Tor glacier. On both Bordu and Golubina, we measured ice thicknesses up to 140 meters. Kara-Batkak was found to have the thinnest ice which is in agreement to the large average slope of this glacier. We extended all the ice thickness measurements to the entire glacier surfaces using three different methods based on the assumption of plastic flow (method 1) and the principle of mass conservation (method 2 & 3) and assessed their differences.

In this research, we show a detailed ice thickness distribution of Ashuu-Tor, Bordu, Golubina and Kara-Batkak glaciers. This can be used for glaciological modelling and assessing ice and water storage. We also point out the locations of potential lake formation in bedrock overdeepenings as a succession of glacier retreat.

How to cite: Van Tricht, L., Huybrechts, P., Van Breedam, J., Fuerst, J., Rybak, O., Satylkanov, R., Ermenbaev, B., Popovnin, V., and Paice, C. M.: Ice thickness, volume and subglacial relief of Ashuu-Tor, Bordu, Kara-Batkak and Golubina glaciers (Central-Asia) derived from GPR measurements and different approaching methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1649, https://doi.org/10.5194/egusphere-egu2020-1649, 2020.