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International audience; Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9 mm to global mean sea level, with the rate of mass loss rising from 105 Gt yr−1 between 1992 and 1996 to 372 Gt yr−1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9 Gt yr−1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86 Gt yr−1 in 2017 to 444 Gt yr−1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9 Gt yr−1) and, to a lesser extent, from the Antarctic Peninsula (13±5 Gt yr−1). East Antarctica remains close to a state of balance, with a small gain of 3±15 Gt yr−1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).

With the development of highly sensitive bioanalytical techniques, the volume of samples necessary for accurate analysis has reduced. Microsampling, the process of obtaining small amounts of blood, has thus gained popularity as it offers minimal-invasiveness, reduced logistical costs and biohazard risks while simultaneously showing increased sample stability and a potential for the decentralization of the approach and at-home self-sampling. Although the benefits of microsampling have been recognised, its adoption in clinical practice has been slow. Several microsampling technologies and devices are currently available and employed in research studies for various biomedical applications. This review provides an overview of the state-of-the-art in microsampling technology with a focus on the latest developments and advancements in the field of microsampling. Research published in the year 2022, including studies (i) developing strategies for the quantitation of analytes in microsamples and (ii) bridging and comparing the interchangeability between matrices and choice of technology for a given application, is reviewed to assess the advantages, challenges and limitations of the current state of microsampling. Successful implementation of microsampling in routine clinical care requires continued efforts for standardization and harmonization. Microsampling has been shown to facilitate data-rich studies and a patient-centric approach to healthcare and is foreseen to play a central role in the future digital revolution of healthcare through continuous monitoring to improve the quality of life.

Ice shelf instability is a key uncertainty in future sea level rise projections, as several small-scale processes leading to ice shelf collapse remain poorly quantified. Historical large scale ice shelf collapses, like the Conger Ice Shelf collapse in March 2022, therefore, provide unique insights in the processes leading to ice shelf instability.In this study, we assess the long- and short-term changes on Conger Ice Shelf in historical satellite records (Landsat, Sentinel, MODIS, ICESat) and model output of ocean and climate conditions (HYCOM, RACMO, IMAU-FDM and ERA-5). Based on both observations and model output we determine the role of known ice shelf instability processes like hydrofracturing, basal melting and damage changes. Moreover, we evaluate the role of extreme weather and ocean conditions in the sudden Conger Ice Shelf collapse.The longer satellite record shows that Conger Ice Shelf has been weakening for years and then collapsed in two abrupt events (2 and 15 of March 2022). The long-term weakening is the result of damage processes and calving events due to extreme ocean/weather conditions that gradually abate the ice shelf. The abrupt Conger Ice Shelf collapse, however, coincides with extreme atmospheric and ocean conditions (e.g., ocean slope and wave conditions) that trigger the weakened ice shelf into a sudden collapse. Our results show that the known ice shelf instability processes like hydrofracturing and basal melting do not play a key role in the abrupt Conger Ice Shelf collapse, but that gradual weakening followed by extreme weather and ocean conditions triggered the ice shelf collapse.Our results stress the importance of separating ice shelf weakening from ice shelf collapse in studies of ice shelf stability. Moreover, they imply that extreme weather and oceanic conditions need to be to considered when assessing the future vulnerability of Antarctic ice shelves to collapse.

Although recent mass loss of the Antarctic ice sheet (AIS) is predominantly driven by ice shelf thinning and increased solid ice discharge, surface processes also directly affect mass changes. Snowfall fluctuations control the variability in surface mass balance (SMB) of the grounded AIS, while meltwater ponding threatens the viability of floating ice shelves. Surface processes are thus essential to quantify present and project future AIS mass loss, but remain poorly represented in climate models running at 25-100 km spatial resolution. Here we present new, daily Antarctic SMB products at 2 km resolution, statistically downscaled from the output of RACMO2.3p2 at 27 km resolution, for the contemporary climate (1979-2021) and a low, moderate and high-end warming scenario until 2100. We show that statistical downscaling to 2 km resolution modestly enhances contemporary SMB (+8%) but strongly increases melt (+50%), notably in the vicinity of the grounding line, in better agreement with both in situ and remote sensing records. The melt increase in the downscaled products persists in the future projections irrespective of the scenario, suggesting a systematic underestimate in low-resolution (regional) climate models.

Small-scale, subgrid processes on the ice sheets, such as localized surface melt, remain unnoticed by current coarse-resolution Regional Climate Models (RCMs), leading to uncertainties in climate reanalyses and projections. Deep learning allows us to enhance the spatial resolution of RCMs but requires sophisticated model development. Earlier studies have shown that rudimental techniques, such as single-image super-resolution, have failed to capture Antarctic surface melt patterns accurately, because the spatial transferability of these models is low. In this study, we add remote sensing data to a super-resolution model: daily observations of surface albedo from MODIS are used to guide the downscaling of low-resolution surface melt (RACMO2, 27 km) to a high-resolution version (RACMO2, 5.5 km) for a 20-year period, between 2001-2019. We extend a conventional SRResNet and add the MODIS data in different configurations (i.e., spatial-channel communication, content communication, and empirical-physical activation). The models are trained over the Antarctic Peninsula, for which RACMO2 simulations are available at 5.5 km resolution (Van Wessem et al., 2016). We verify the performance of the models with three independent datasets to inspect (1) the overall performance (using QuickSCAT); (2) spatial patterns (using Sentinel-1); and (3) temporal patterns (using automatic weather stations). Our work shows the potential of adding remote sensing data to deep learning-based downscaling models, leading to improved spatial transferability compared to single-image downscaling models.

In the past decade, localized in-situ observation and radar measurements have revealed the presence of firn aquifers on the Antarctic Ice Sheet. They are believed to be an important component of the hydrological system of the ice sheet, but currently no large-scale observational studies exist, hampering a detailed assessment of their contribution to runoff and sea-level rise.We present a probability map of firn-aquifer presence around the Antarctic Ice Sheet, based on a combination of remote sensing and climate modeling data. C-band radar imagery from the Sentinel-1 and Advanced Scatterometer (ASCAT) missions, together with climate data from the regional atmospheric climate model RACMO2.3p2, are combined to map the probability of detecting seasonal and perennial firn aquifers at 5.5x5.5 km2 resolution in the period 2017 to 2020.Our method is based on Monte Carlo simulations: its algorithm predicts the probability of aquifer presence based on a set of fixed inputs, to which dedicated thresholds and weights are assigned.In agreement with observation from previous studies, we find a high probability of firn aquifer presence in the north and northwest of the Antarctic Peninsula, and on the Wilkins and George VI ice shelves. A low probability is found in the higher elevation areas of the central Antarctic Peninsula, where the presence of aquifers is not expected due to the absence of surface melt. Thanks to the large spatio-temporal availability of the input datasets, the presence of aquifers in different regions of Antarctica has been estimated. The methodology has been validated in selected regions of Greenland, where the presence of aquifers has been observed using in-situ and remotely sensed data.

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 reports of the Intergovernmental Panel on Climate Change (IPCC), 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 and temporal limitations and technical challenges of geodetic methods. Towards IPCC SROCC and AR6, 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 digital elevation models (e.g., from ASTER or TanDEM-X). At the same time, new spaceborne altimetry (CryoSat-2, 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 the spread of different results as well as new questions with regard to best practises for data processing chains and for related uncertainty assessments.In this presentation, we introduce the Glacier Mass Balance Intercomparison Exercise (GlaMBIE) project of the European Space Agency, which is building on existing activities and the network of the International Association of Cryospheric Sciences (IACS) working group on Regional Assessments of Glacier Mass Change (RAGMAC) to tackle these challenges in a community effort. We will present our approach to develop a common framework for regional-scale glacier mass-change estimates towards a new data-driven consensus estimate of regional and global mass changes from glaciological, DEM-differencing, altimetric, and gravimetric methods.

Glaciers distinct from the ice sheets melt quickly around the world. Rounce et al., 2023, recently projected (28±9) % glacier mass will be lost in 2100 compared to 2015 in the low-emission scenario SSP1-2.6. In our study, we focus on the observation of recent glacier mass change on Novaya Zemlya, Russian Arctic, using the SARIn altimeter CryoSat-2. Taking advantage of CryoSat-2's spatiotemporal resolution and a novel processor optimized for mountain glaciers, we show a prompt response to the surface temperatures modeled by the regional atmosphere model MAR. Prominent warm and high-melt years were 2013, 2016, and 2020, while 2014 featured lower surface temperature and melt than average. We discuss the potential drivers for those extreme years and furthermore exploit surface mass balance estimates to analyze the relative contribution of ice dynamics and surface processes to the observed mass loss acceleration, and regional variations therein.

Inversion methods play an important role in glacier models, both to calibrate and estimate parameters of interest (e.g. Glen's coefficients). However, inversions are usually made for each glacier individually, without using any global information, i.e. without deriving general laws governing the spatiotemporal variability of those parameters. The reason behind this limitation is twofold: the statistical challenge of making constrained inferences with multiple glaciers, and the computational limitation of processing massive glacier datasets. Machine learning powered with differential programming is a tool that can address both limitations. We introduce a statistical framework for functional inversion of physical processes governing global-scale glacier changes. We apply this framework to invert a prescribed function describing the spatial variability of Glen’s coefficient (A). Instead of estimating a single parameter per glacier, we learn the parameters of a regressor (i.e. a neural network) that encodes information related to each glacier (i.e. long-term air temperature) to the parameter of interest. The inversion is done by embedding a neural network inside the Shallow Ice Approximation PDE - resulting in a Universal Differential Equation - with the goal of minimizing the error on the simulated ice surface velocities. We previously had shown that this hybrid model training is possible thanks to the use of differential programming, enabling differentiation of a PDE, a numerical solver and a neural network simultaneously. In this work we upscale this approach to include larger datasets and with the goal of learning real empirical laws from observations. This framework is built inside ODINN.jl, an open-source package in the Julia programming language for global glacier evolution modelling using Universal Differential Equations. ODINN exploits the latest generation of ice surface velocities and geodetic mass balance remote sensing products, as well as many preprocessing tools from the Open Global Glacier Model (OGGM).

The intrusion of warm circumpolar deep water causes ice shelves in the Amundsen and Bellinghausen Sea Embayments of Antarctica to melt from below, thereby potentially putting their stability at risk. Earlier studies have shown how digital elevation models can be used to obtain high-resolution ice shelf basal melt rates. However, there has been limited availability of high-resolution elevation data, a gap the Reference Elevation Model of Antarctica (REMA) has filled. In this study we use a novel combination of REMA and CryoSat-2 elevation data to obtain high-resolution basal melt rates of the Dotson Ice Shelf in a Lagrangian framework, at a 50 m spatial resolution on a 3-yearly temporal resolution. We present a novel Basal melt rates Using REMA and Google Earth Engine (BURGEE) method, which high resolution is supported through a sensitivity study assuming that the quality of the Lagrangian displacement is the main error source. The high-resolution basal melt rates show a good agreement with an earlier 500-m resolution basal melt product based on CryoSat-2, with a wide melt channel extending from the grounding line to the ice shelf front. Our high-resolution product indicates that the pathway and spatial variability of the main channel is influenced by a pinning point on the ice shelf. Additionally, it reveals a narrower coastal channel running along the western margin, which was not identified in the lower-resolution melt product, but does show up in a recent ocean modeling study. This emphasizes the importance of high-resolution basal melt rates to expand our understanding of channel formation and melt patterns. BURGEE can be expanded to a Pan-Antarctic study of high-resolution basal melt rates. This will provide a better picture of the (in)stability of Antarctic ice shelves.

High-throughput analysis in fields such as industrial biotechnology, combinatorial chemistry, and life sciences is becoming increasingly important. Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique providing exhaustive molecular information on complex samples. Flow NMR in particular is a cost and time-efficient method for large screenings. In this study, we have developed a novel 3.0 mm inner diameter polychlorotrifluoroethylene (PCTFE) flow cell for a segmented-flow analysis (SFA) - NMR automated platform. The platform uses FC-72 fluorinated oil and fluoropolymer components to achieve a fully fluorinated flow path. Samples were repeatably transferred from 96-deepwell plates to the flow cell by displacing a fixed volume of oil, with a transfer time of 42 s. 1H spectra were acquired fully automated with 500 and 600 MHz NMR spectrometers. The spectral performance of the novel PCTFE cell was equal to that of commercial glass cells. Peak area repeatability was excellent with a relative standard deviation of 0.1-0.5% for standard samples, and carryover was below 0.2% without intermediate washing. The sample temperature was conditioned by using a thermostated transfer line in order to reduce the equilibration time in the probe and increase the throughput. Finally, analysis of urine samples demonstrated the applicability of this platform for screening complex matrices.

In recent years, the existence of firn aquifers in the Antarctic Peninsula (AP) has been confirmed by in-situ observations. Given their importance for understanding the hydrology of the Antarctic ice sheet, a more spatially comprehensive assessment of AP firn aquifers is desirable. The purpose of this study is to map firn aquifers in the AP from space using C-band Synthetic Aperture Radar imagery from ESA’s Sentinel-1 mission. These observations enable the detection of firn aquifers at 1 × 1 km2 resolution. The method presented here is based on quantifying the characteristic, gradual backscatter increase during the (partial) refreezing of the liquid water in the firn layer after the peak melt season. When applied to the available time series, it detects perennial aquifers (existing year-round) for the period 2017 to 2020, as well as seasonal aquifers which do not persist through winter. We acknowledge that the backscatter signature in any given year is indistinguishable for seasonal and perennial aquifers. We detect seasonal firn aquifers in the north and northwest of the AP, as well as on the Wilkins and George VI ice shelves. Only in the north and northwest of the AP, aquifers are detected each year in the observation period, here taken as a proxy for perennial firn aquifers. Both distributions agree with model simulations. Further in situ and modelling studies and longer time series of satellite observations are needed to validate the results of this study.

Ice losses from the Greenland and Antarctic Ice Sheets have accelerated since the 1990s, accounting for a significant increase in global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume and in Earth’s gravity field. Between 1992 and 2020, the ice sheets contributed 21.0 ± 1.9 mm to global mean sea-level, with the rate of mass loss rising from 105 Gt yr-1 between 1992 and 1996 to 372 Gt yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169 ± 9 Gt yr-1 between 1992 and 2020 but there are large inter-annual variations in mass balance with mass loss ranging from 86 Gt yr-1 in 2017 to 444 Gt yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (-82 ± 9 Gt yr-1) and to a lesser extent from the Antarctic Peninsula (-13 ± 5 Gt yr-1). East Antarctica remains close to a state of balance (3 ± 15 Gt yr-1), but is the most uncertain component of Antarctica’s mass balance.

Like most of the ice shelves in the Antarctic Amundsen Sea Embayment, intrusion of warm circumpolar deep water onto the continental shelf causes basal thinning to the Dotson Ice Shelf (DIS). Studies on other ice shelves have shown how Digital Elevation Models (DEM) of high spatial resolution can reveal basal melt patterns that are crucial in understanding the dynamics of basal melting and the underlying ocean circulation. In this study we aim to achieve high spatial and temporal resolution basal melt rates of the DIS to try to uncover new basal melt patterns which products of coarser resolution do not capture. This will be done by using the high spatial resolution Reference Elevation Model of Antarctica (REMA) and a method based on the Google Earth Engine (GEE). This allows for fast co-registration and subsequent thinning and basal melt rate analysis of the 2-m resolution REMA strips from 2010-2017. Ice shelf thinning is calculated both in a Eulerian and Lagrangian framework, the latter providing information to the basal melt rate analysis. In agreement with other studies of the DIS a melt channel is found on the western side of the ice shelf. Furthermore, our study indicates a second smaller channel, which has not been revealed by existing altimetry studies. This suggests that high-resolution basal melt rate products could be of great importance. Furthermore, it enlightens the difficulties in coupling ocean and ice models, since such models often run on a coarser grid and therefore, they will not capture the small-scale variabilities.

The USGS digitized many historical photos of Antarctica which could provide useful insights into this region from before the satellite era. However, these images are merely scanned and do not contain semantic information, which makes it difficult to use or search this archive (for example to filter for cloudless images). Even though there are countless semantic segmentation methods, they are not working properly with these images. The images are only grayscale, have often a poor image quality (low contrast or newton’s rings) and do not have very distinct classes, for example snow/clouds (both white pixels) or rocks/water (both black pixels). Furthermore, especially for this archive, these images are not only top-down but can also be oblique.We are training a machine-learning based network to apply semantic segmentation on these images even under these challenging conditions. The pixels of each image will be labelled into one of the six different classes: ice, snow, water, rocks, sky and clouds. No training data was available for these images, so that we needed to create it ourselves. The amount of training data is therefore limited due to the extensive amount of time required for labelling. With this training data, a U-Net was trained, which is a fully convolutional network that can work especially with fewer training images and still give precise results.In its current state, this model is trained with 67 images, split in 80% training and 20% validation images. After around 6000 epochs (approx. 30h of training) the model converges and training is stopped. The model is evaluated on 8 randomly selected images that were not used during training or validation. These images contain all different classes and are challenging to segment due to quality flaws and similar looking classes. The model is able to segment the images with an accuracy of around 75%. Whereas some classes, like snow, sky, rocks and water can be recognized consistently, the classes ice and clouds are often confused with snow. However, the general semantic structure of the images can be recognized.In order to improve the semantic segmentation, more training imagery is required to increase the variability of each class and prepare the model for more challenging scenes. This new training data will include both labelled images from the TMA archive and from other historical archives in order to increase the variability of classes even more. It should be checked if the quality of the model can be further improved by including metadata of the images as additional data sources.

Almost ten years ago, the first elevation estimates based on swath processing of interferometric CryoSat-2 altimeter observations were published, mapping the surface of Devon ice cap. The new method holds a great potential to provide dense data coverage, in space and time. Indeed, ESA recently started releasing digital elevation models at a 2 by 2 km resolution for a rolling 3 month data aggregation cycle. Such spatiotemporal resolutions are especially valuable in versatile and dynamic regions as mountain glaciers. In this presentation, we describe systematic errors on the order of 10 m with about yearly periodicity that arise in the proximity of hills and valleys. One error is caused by the superposition of multiple signals, the other is caused by the Fourier-transformation in the SAR beam-forming process. Both are intrinsic to the measuring concept, but their effect can potentially be limited by data filtering strategies. We report the influence of the commonly used coherence and power threshold based filtering on derived elevation change rates. For data users, awareness of these issues is especially important to interpret the observations correctly and to understand that there is a large systematic part in the overall uncertainty.

A large amount of velocity data is now becoming available through portals, pipelines and repositories. Typically the error characterisation for these individual velocity fields or mosaics is done through sampling statistics, resulting in a proxy of precision for the whole dataset. However even within a scene pair, the appearance can change considerably, or be stable at nearby locations. For example, think of regions close to the transient snowline, or an elongated moraine band, a crevasse train after a bump or a shear zone. Here the precision of localising an exact image match is clearly anisotropic. If such anisotropic precision estimates are taken into account, it is possible to provide a more correct error-propagation. The merit of velocity data can be found in the help for inversion for thickness estimates (as it is related to the fourth power), or shear and strain rates. Here we introduce a simple and fast methodology to generate an individual dispersion estimate, based upon the similarity surface of an image match. A linear least squares adjustment of the neighbouring similarity scores is sufficient to fit an oriented gaussian peak. This setup makes the computation fast and is easy to implement into already available processing pipelines. We demonstrate its effectiveness on two glaciers, Sermeq Kujalleq, a large outlet glacier of the Greenland icesheet, with strong shear margins and Malaspina Glacier a piedmont glacier with looped moraines. We find directionality within an image subset to be the dominant factor influencing the correlation dispersion. This stems from crevasses and moraine bands within the imagery, while a relation to differential flow, such as shear, is less pronounced. It is our hope, this methodology will narrow the integration gap between models and measurements.

Surface meltwater is becoming an increasing driver for ice shelf disintegration and consequent mass loss from the AIS. In this regard, monitoring surface melt over Antarctic ice shelves can enhance our understanding of their stability. Earth Observation (EO) satellites provide decadal records of land dynamics over Antarctica, and have been applied in surface melt monitoring. Thereby, they hold a potential to monitor the spatiotemporal evolution of surface melt over the entire AIS. Among the wealth of EO satellites, scatterometer and radiometer observations are most frequently used for surface melt detection, followed by SAR and optical data. Most studies used observations from a single satellite to study surface melt, while specific sensor characteristics (e.g., spatial resolution, overpass time, penetration depth) largely influence the potential for detecting surface melt. Therefore, we compare differences in melt detection between radiometer, scatterometer, SAR and optical sensors to assess the opportunities and challenges in observing surface melt for different EO satellites. We apply state-of-the-art melt detection algorithms to radiometer (Special Sensor Microwave Imager/Sounder, SMMIS), scatterometer (Advanced Scatterometer, ASCAT), Synthetic Aperture Radar (SAR; Sentinel-1), and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) data over the Larsen B+C and Amery Ice Shelves for the 2015-2020 melt seasons. We construct melt timeseries and spatial maps using the melt detection algorithms. and intercompare the spatiotemporal patterns of detected melt. Finally, we compare areas with different melt patterns with auxiliary data sets (i.e., Regional Atmospheric Climate Model (RACMO2), Digital Elevation Models (DEM), high resolution optical imagery). Our results show that the largest differences in detected melt between the EO satellites can be linked to physical properties of the surface, sensor properties and atmospheric conditions. Over the blue ice areas, MODIS indicates more surface melt than the other sensors, as they miss blue ice areas due to either a coarse spatial resolution or the applied detection algorithms. However, over the ice shelves, MODIS detects significantly less surface melt, which can be attributed to the very high cloud obstruction frequency over AIS. Based on this intercomparison, we discuss the opportunities and challenges for melt detection across the AIS regarding the choice of different sensors and the chosen melt detection algorithms. We conclude that merging observations from different satellites (e.g. using machine learning) would further strengthen our knowledge on the presence of surface melt across the AIS, since this combines the strengths of specific sensors based on their sensor characteristics and the area of interest.

Various techniques exist to model the evolution from glaciers at regional to global scales. Whereas pioneering efforts typically relied on volume-area scaling approximations or parameterizations based on observed glacier changes (retreat parameterization), more recent approaches now also explicitly incorporate ice-dynamical processes. In these latter studies, glaciers are typically represented through central flowlines. Such flowline approaches are particularly suited for mountain glaciers that span over a large elevation range, i.e. valley-glaciers with an elongated shape. However, flowline approaches are not ideal to represent the geometry of ice caps (large glaciers) that generally have a dome-shaped geometry. For ice caps, a model representation that explicitly accounts for the glacier’s 3D geometry and that allows for the glacier to lose and gain mass in all directions, both through mass balance and ice dynamic processes, is needed.Here we present simulations performed with a coupled surface mass balance – ice flow model that explicitly accounts for the 3D geometry of individual glaciers. The model, written in Python, relies on the shallow ice approximation to describe ice flow, allowing to run large ensembles of simulations. The goal is to simulate the temporal evolution of glaciers with distinct shapes and situated in various climatic regimes, i.e. having a model that allows for an automated intialization and that is suited for regional to global-scale applications.In this contribution, we present simulations performed with this new large-scale model for regions with mountain glaciers (e.g. European Alps and Scandinavia), as well as regions with large ice caps (e.g. Iceland). Through this, we highlight various challenges that relate to model initialization or the choice of model settings, for instance. We also explore how simulated glacier evolutions compare to those simulated with a retreat parameterzation and through flowline modelling, thereby shedding light on the need for a 3D modelling approach.

Sample preparation is a challenge for high-throughput analysis, especially for volume-limited samples with low-abundant analytes. Ideally, sample preparation enriches the analytes of interest while removing the interferents to reduce the matrix effect and improve both sensitivity and quantification. In this study, a three-phase electroextraction (EE) method hyphenated to fast online liquid chromatography -mass spectrometry (LC-MS) was developed. Four model acidic drugs of relevance for drug monitoring in plasma, i.e. naproxen, fenoprofen, flurbiprofen, and ibuprofen, were utilized for the optimization and evaluation of the method. A Design of Experiment approach (Box-Behnken design) was used to optimize the critical parameters of the method, i.e., the type of organic solvent, pH of the sample and acceptor phase, and the extraction voltage and time. Good fitness (P < 0.02, R2 > 0.95) was observed for the developed quadratic model. Extraction could be achieved in less than 2 min (115 s) with enrichment factors (EF) up to 190 and extraction recoveries (ER) up to 38% for academic samples. Additionally, the optimized three-phase EE method was successfully applied to spiked plasma samples with lowabundant (50 ng mL-1) analytes and a low sample volume of 15 mL plasma in 10-fold diluted samples. Finally, two crucial contributors to the matrix effect of three-phase EE application on plasma samples were determined. Specifically, the ion-suppression effect in the MS source was reduced by the fast LC separation, and the matrix effect during extraction was negligible for the diluted protein-precipitated plasma samples. The developed three-phase EE method is easy to operate and provides fast and online extraction of trace-level acidic analytes from volume-limited biological samples. Therefore, this method can provide a potential solution for sample-preparation bottlenecks in high-throughput bioanalysis workflows. (c) 2021 Published by Elsevier B.V.

Surface melt is an important driver of ice shelf disintegration and its consequent mass loss over the Antarctic Ice Sheet. Monitoring surface melt using satellite remote sensing can enhance our understanding of ice shelf stability. However, the sensors do not measure the actual physical process of surface melt, but rather observe the presence of liquid water. Moreover, the sensor observations are influenced by the sensor characteristics and surface properties. Therefore, large inconsistencies can exist in the derived melt estimates from different sensors. In this study, we apply state-of-the-art melt detection algorithms to four frequently used remote sensing sensors, i.e., two active microwave sensors, which are Advanced Scatterometer (ASCAT) and Sentinel-1, a passive microwave sensor, i.e., Special Sensor Microwave Imager/Sounder (SSMIS), and an optical sensor, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS). We intercompare the melt detection results over the entire Antarctic Ice Sheet and four selected study regions for the melt seasons 2015-2020. Our results show large spatiotemporal differences in detected melt between the sensors, with particular disagreement in blue ice areas, in aquifer regions, and during wintertime surface melt. We discuss that discrepancies between sensors are mainly due to cloud obstruction and polar darkness, frequency-dependent penetration of satellite signals, temporal resolution, and spatial resolution, as well as the applied melt detection methods. Nevertheless, we argue that different sensors can complement each other, enabling improved detection of surface melt over the Antarctic Ice Sheet.

Sample preparation is a labor-intensive and time-consuming procedure, especially for the bioanalysis of small-volume samples with low-abundant analytes. To minimize losses and dilution, sample preparation should ideally be hyphenated to downstream on-line analysis such as liquid chromatography-mass spectrometry (LC-MS). In this study, an automated three-phase electro-extraction (EE) method coupled to machine vision was developed, integrated with a robotic autosampler hyphenated to LC-MS. Eight model compounds, i.e. amitriptyline, clemastine, clomipramine, haloperidol, loperamide, propranolol, oxeladin, and verapamil were utilized for the optimization and evaluation of the automated EE setup. The stability of automated EE was evaluated by monitoring the acceptor droplet size by machine vision and recording the current during EE. A Design of Experiment approach (Box-Behnken design) was utilized to optimize the critical parameters of the EE method, i. e., the ratio of formic acid in the sample to acceptor phase, extraction voltage, and extraction time. The developed quadratic models showed good fitness (p < 0.001, R-2 > 0.95). Automated EE could be achieved in less than 2 min with enrichment factors (EF) up to 387 and extraction recoveries (ER) up to 97% for academic samples. Finally, the optimized EE method was successfully applied to both spiked human urine and plasma samples with low-concentration (50 ng mL(-1)) analytes and a low starting sample volume of 20 mu L of plasma and urine in 10-fold diluted samples. The developed automated EE setup is easy to operate, provides a fast extraction method for analytes from volume-limited biological samples, and is hyphenated with on-line LC-MS analysis. Therefore, this method can provide fast and automated sample preparation to solve bottlenecks in high-throughput bioanalysis workflows.

In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on fast processing of large data volumes. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality not isotropic and constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image content and the non-uniform flow of the ice. Precise dispersion estimates for each individual velocity measurement can be important for inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and errors. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focussing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq, Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced.

Cast shadow from mountain ridges can be tracked over time. Through simple trigonometry this can give a clue about relative elevation change. These shadow ridges can be easily identified in multi -spectral satellite imagery as they are strong illumination changes in all spectral bands. Following shadow cast can be more complicated, due to different sun angles and changes in terrain slope. This results in a deformed appearance, which complicate tracking when simple pattern matching techniques are used. In this contribution we explore two affine matching methods in both the spatial and frequency domain, to increase the matching success of shadow cast on mountain glaciers.