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Because Antarctic surface melt is mostly driven by local processes, its simulation necessitates high‐resolution regional climate models (RCMs). However, the current horizontal resolution of RCMs (≈25–30 km) is inadequate for capturing small‐scale melt processes. To address this limitation, we present SUPREME (SUPer‐REsolution‐based Melt Estimation over Antarctica), a deep learning method to downscale surface melt to 5.5 km resolution using a physically‐informed super‐resolution model. The physical information integrated into the model originates from observations tied to surface melt, specifically remote sensing‐derived albedo and elevation. These remote sensing data, in addition to a Regional Atmospheric Climate Model (RACMO) run at 27 km resolution, account for the diverse drivers of surface melt across Antarctica, facilitating effective generalization beyond the training region of the Antarctic Peninsula. A comparison of SUPREME with a dynamically downscaled RACMO run at 5.5 km over the Antarctic Peninsula shows high accuracy, with average yearly RMSE and bias of 5.5 mm w.e. yr−1 and 4.5 mm w.e. yr−1, respectively. Validation at five automatic weather stations reveals SUPREME's marked improvement with substantially lower average RMSE (81 mm w.e.) compared to RACMO 27 km (129 mm w.e.). Beyond the training region, SUPREME aligns more closely with remote sensing products associated with surface melt than super‐resolution models lacking physical constraints. While further validation of SUPREME is needed, our study highlights the potential of super‐resolution techniques with physical constraints for high‐resolution surface melt monitoring in Antarctica, providing insights into the impacts of localized melting on processes affecting ice shelf integrity such as hydrofracturing. Plain Language Summary: To improve surface melt monitoring in Antarctica, high‐resolution climate models are essential. Existing models, like the Regional Atmospheric Climate Model (RACMO), do not have a fine enough spatial resolution to capture small‐scale melt processes. To overcome this, we introduce SUPREME (SUPer‐REsolution‐based Melt Estimation over Antarctica), a method that refines surface melt data to a higher resolution of 5.5 km using an advanced super‐resolution model. We enhance this model with physical information derived from observations directly related to surface melt, specifically using albedo and elevation data from remote sensing. By incorporating these observations, along with RACMO data at 27 km resolution, we account for the various triggers of surface melt across Antarctica. SUPREME accurately predicts high‐resolution surface melt beyond the training region of the Antarctic Peninsula, showing promising results compared to existing melt observations. Further validation is needed, but this approach, combining super‐resolution techniques and remote sensing data, holds potential for accurate surface melt monitoring in Antarctica. This may advance our understanding of the impacts of localized features on processes affecting ice shelf integrity such as meltwater‐induced hydrofracturing. Key Points: Our method downscales Antarctic surface melt from a regional climate model, employing a physically‐informed super‐resolution architectureThe super‐resolution model relies on physical information derived from remote sensing data, specifically surface albedo and elevationIncorporating physical information boosts model generalization, enabling accurate high‐resolution surface melt beyond the training region [ABSTRACT FROM AUTHOR]

Geoscientific models are facing increasing challenges to exploit growing datasets coming from remote sensing. Universal differential equations (UDEs), aided by differentiable programming, provide a new scientific modelling paradigm enabling both complex functional inversions to potentially discover new physical laws and data assimilation from heterogeneous and sparse observations. We demonstrate an application of UDEs as a proof of concept to learn the creep component of ice flow, i.e. a nonlinear diffusivity differential equation, of a glacier evolution model. By combining a mechanistic model based on a two-dimensional shallow-ice approximation partial differential equation with an embedded neural network, i.e. a UDE, we can learn parts of an equation as nonlinear functions that then can be translated into mathematical expressions. We implemented this modelling framework as ODINN.jl, a package in the Julia programming language, providing high performance, source-to-source automatic differentiation (AD) and seamless integration with tools and global datasets from the Open Global Glacier Model in Python. We demonstrate this concept for 17 different glaciers around the world, for which we successfully recover a prescribed artificial law describing ice creep variability by solving ∼ 500 000 ordinary differential equations in parallel. Furthermore, we investigate which are the best tools in the scientific machine learning ecosystem in Julia to differentiate and optimize large nonlinear diffusivity UDEs. This study represents a proof of concept for a new modelling framework aiming at discovering empirical laws for large-scale glacier processes, such as the variability in ice creep and basal sliding for ice flow, and new hybrid surface mass balance models. [ABSTRACT FROM AUTHOR]

In recent years, the existence of perennial firn aquifers in the Antarctic Peninsula (AP) has been confirmed by in situ observations. Previous studies have suggested that these subsurface aquifers, together with meltwater ponds at the surface, provide a reservoir of liquid water to feed propagating fractures, promoting hydrofracture-driven ice-shelf disintegration. This study maps perennial firn aquifers in the AP from space using C-band Synthetic Aperture Radar imagery from ESA's Sentinel-1 (S1) mission. With these observations, we detect firn aquifers at 1 km × 1 km resolution, for the period 2017 to 2020. Existing methods, that use S1 data and rely on a backscatter intensity difference threshold approach, are prone to misclassify late-melt events as aquifers, when applied to the AP. Therefore, we have developed and evaluated a new approach that is better suited to the Antarctic environment. The new method exploits the characteristic, gradual backscatter increase during the (partial) refreezing of the liquid water in the firn layer after the peak of the melt season. Most firn aquifers are detected in the north and northwest of the AP, as well as on the Wilkins and George VI ice shelves. Aquifer locations detected with the present methodology agree with in situ observations and with model simulations of firn water content. [ABSTRACT FROM AUTHOR]

The intrusion of Circumpolar Deep Water in the Amundsen and Bellingshausen Sea embayments of Antarctica causes ice shelves in the region to melt from below, potentially putting their stability at risk. Earlier studies have shown how digital elevation models can be used to obtain ice shelf basal melt rates at a high spatial resolution. 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 posting on a 3-yearly temporal resolution. We present a novel method: Basal melt rates Using REMA and Google Earth Engine (BURGEE). The high resolution of BURGEE is supported through a sensitivity study of the Lagrangian displacement. The high-resolution basal melt rates show a good agreement with an earlier basal melt product based on CryoSat-2. Both products show a wide melt channel extending from the grounding line to the ice front, but our high-resolution product indicates that the pathway and spatial variability of this channel is influenced by a pinning point on the ice shelf. This result 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 50m 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. [ABSTRACT FROM AUTHOR]

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 x 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. [ABSTRACT FROM AUTHOR]

Icelandic glaciers have been losing mass since the Little Ice Age in the mid‐to‐late 1800s, with higher mass loss rates in the early 21st century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958–2019) surface mass balance of Icelandic glaciers, we show that the post‐2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high‐end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid‐2050s. High mass loss rates resume thereafter as the regional cooling signal weakens. Plain Language Summary: Icelandic glaciers are currently losing mass, but with a recent slowdown in the last decade. We show that this slowdown coincides with the development of an area of regional cooling in the North Atlantic Ocean to the south of Greenland, called Blue Blob. Cooling in the Blue Blob has been mitigating atmospheric warming in Iceland since 2011, reducing glacier melt. In a future warmer climate, North Atlantic cooling is projected to persist until the mid‐2050s, further slowing down mass loss of Icelandic glaciers. High mass loss resumes thereafter as the regional cooling in the Blue Blob weakens. Key Points: We project daily surface mass balance of Icelandic glaciers at 500 m spatial resolution under a high‐end warming scenario until 2100We correlate the post‐2011 glacier mass loss slowdown with a local cooling in the North Atlantic OceanWe predict that local North Atlantic cooling will continue mitigating Icelandic glaciers mass loss until the mid‐2050s [ABSTRACT FROM AUTHOR]