Search

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]

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]

In Antarctica, Global Positioning System (GPS) vertical time series exhibit non‐linear signals over a wide range of temporal scales. To explain these non‐linearities, a number of hypotheses have been proposed, among them the short‐term rapid solid Earth response to contemporaneous ice mass change. Here we use GPS vertical time series to reveal the solid Earth response to variations in surface mass balance (SMB) in the Southern Antarctic Peninsula (SAP). At four locations in the SAP we show that interannual variations of SMB anomalies cause measurable elastic deformation. We use regional climate model SMB products to calculate the induced displacement assuming a perfectly elastic Earth. Our results show a reduction of the misfit when fitting a linear trend to GPS time series corrected for the elastic response to SMB variations. Our results imply that, for a better understanding of the glacial isostatic adjustment signal in Antarctica, SMB variability must be considered. Plain Language Summary: The Global Positioning System (GPS) allows us to measure the changing shape of the Earth's surface with high accuracy. These changes reflect multiple processes operating both within the Earth and at the surface. Using continuously recording GPS instruments in the Antarctic Peninsula, we study the response of the solid Earth to seasonal and annual variations in snow and ice accumulation over the past decade. We show that such loading triggers a measurable response that varies between years, complicating efforts to calculate the long‐term Earth response to past ice sheet change. We discuss the importance of our findings for understanding feedbacks between the solid Earth and the Antarctic ice sheet. Key Points: Global Positioning System time series of vertical deformation in the Southern Antarctic Peninsula show transient signalsModeled elastic deformation due to surface mass balance (SMB) variation can largely explain these signalsConsidering SMB elastic deformation improves estimates of linear vertical velocities [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]

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA's ICESat‐2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat‐2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat‐2 and two from coincident Landsat‐8 and Sentinel‐2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat‐2 produced the most accurate depths; the image‐based algorithms underestimated depths (by 30%–70%). This implies that ICESat‐2 depths can be used to tune image‐based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland. Plain Language Summary: Summer surface melting on Antarctica's ice shelves is a small component of overall ice sheet mass loss but can be important for individual ice shelves and may increase as the climate warms. However, the volume of meltwater has been difficult to monitor because depth estimates are challenging. NASA's ICESat‐2 laser altimetry mission brings a new capability to this problem. ICESat‐2 532 nm photons (green light) are able to pass through water and reflect from both the water surface and the underlying ice surface; the difference in elevation provides meltwater depth estimates. In this pilot study, we compared depths from eight algorithms (six ICESat‐2 and two image based) over four Amery Ice Shelf meltwater lakes for an ICESat‐2 pass in early January 2019. The ICESat‐2 algorithms all produced more reliable depth estimates, and the image‐based algorithms underestimated the depths. This implies that ICESat‐2 water depths can be used to tune image‐based depth retrieval algorithms, enabling improved performance and allowing us to estimate more accurately how much surface melt is stored in melt ponds on the ice sheets each summer. Key Points: ICESat‐2 photons penetrate surface melt lakes and reflect from both the water surface and the underlying ice, providing depth estimatesWe compared depths from eight algorithms (six ICESat‐2 and two image‐based) for four lakes present on Amery Ice Shelf in January 2019Depths from ICESat‐2 were more accurate than from imagery (30%–70% too low); merging these data will improve estimates ice‐sheet wide [ABSTRACT FROM AUTHOR]

Surface mass balance (SMB) provides mass input to the surface of the Antarctic and Greenland Ice Sheets and therefore comprises an important control on ice sheet mass balance and resulting contribution to global sea level change. As ice sheet SMB varies highly across multiple scales of space (meters to hundreds of kilometers) and time (hourly to decadal), it is notoriously challenging to observe and represent in models. In addition, SMB consists of multiple components, all of which depend on complex interactions between the atmosphere and the snow/ice surface, large‐scale atmospheric circulation and ocean conditions, and ice sheet topography. In this review, we present the state‐of‐the‐art knowledge and recent advances in ice sheet SMB observations and models, highlight current shortcomings, and propose future directions. Novel observational methods allow mapping SMB across larger areas, longer time periods, and/or at very high (subdaily) temporal frequency. As a recent observational breakthrough, cosmic ray counters provide direct estimates of SMB, circumventing the need for accurate snow density observations upon which many other techniques rely. Regional atmospheric climate models have drastically improved their simulation of ice sheet SMB in the last decade, thanks to the inclusion or improved representation of essential processes (e.g., clouds, blowing snow, and snow albedo), and by enhancing horizontal resolution (5–30 km). Future modeling efforts are required in improving Earth system models to match regional atmospheric climate model performance in simulating ice sheet SMB, and in reinforcing the efforts in developing statistical and dynamic downscaling to represent smaller‐scale SMB processes. Plain Language Summary: Ice sheets, the largest class of glaciers, contain the majority of ice on Earth. The amount of ice contained in ice sheets changes constantly with the addition of new snow and ice, and melting taking place at the surface, base, and terminus of ice sheets. The balance between these inputs and outputs is known as the "mass balance." Processes affecting the addition and removal of snow on top of the ice sheet are termed the "surface mass balance" and include rainfall, moisture evaporation, snow‐transporting winds, and melting due to temperature changes. Scientists can now monitor these processes with tools on‐site, such as automated weather stations, Global Positioning Systems, and sensors that record high‐energy radiation (cosmic rays) originating outside the Earth's atmosphere. Several methods are also available where Earth‐orbiting satellites measure how ice is changing. Data collected in these ways have revealed how the surface mass balance varies over time and space. A better understanding of these processes is critical to predicting future behavior of ice sheets and their effect on sea level. Improvements to regional‐scale models in the past decade have allowed good simulations of surface mass balance, and the next step is to build models that work at a global scale. Key Points: Emerging (remote) observational techniques provide enhanced insights in spatial and temporal variability of ice sheet surface mass balance (SMB)Regional climate models can be used to assess ice sheet SMB, although deficiencies remain in representing subgrid processesIn the near future, Earth System Models can be used to assess internal variability, forced change, and positive feedbacks on ice sheet SMB [ABSTRACT FROM AUTHOR]

The Canadian Arctic Archipelago comprises multiple small glaciers and ice caps, mostly concentrated on Ellesmere and Baffin Islands in the northern (NCAA, Northern Canadian Arctic Archipelago) and southern parts (SCAA, Southern Canadian Arctic Archipelago) of the archipelago, respectively. Because these glaciers are small and show complex geometries, current regional climate models, using 5‐ to 20‐km horizontal resolution, do not properly resolve surface mass balance patterns. Here we present a 58‐year (1958–2015) reconstruction of daily surface mass balance of the Canadian Arctic Archipelago, statistically downscaled to 1 km from the output of the regional climate model RACMO2.3 at 11 km. By correcting for biases in elevation and ice albedo, the downscaling method significantly improves runoff estimates over narrow outlet glaciers and isolated ice fields. Since the last two decades, NCAA and SCAA glaciers have experienced warmer conditions (+1.1°C) resulting in continued mass loss of 28.2 ± 11.5 and 22.0 ± 4.5 Gt/year, respectively, more than doubling (11.9 Gt/year) and doubling (11.9 Gt/year) the pre‐1996 average. While the interior of NCAA ice caps can still buffer most of the additional melt, the lack of a perennial firn area over low‐lying SCAA glaciers has caused uninterrupted mass loss since the 1980s. In the absence of significant refreezing capacity, this indicates inevitable disappearance of these highly sensitive glaciers. [ABSTRACT FROM AUTHOR]

This study discusses the design aspects for the construction of a microfluidic device for comprehensive spatial two-dimensional liquid chromatography. In spatial two-dimensional liquid chromatography each peak is characterized by its coordinates in the plane. After completing the first-dimension separation all fractions are analyzed in parallel second-dimension separations. Hence, spatial two-dimensional liquid chromatography potentially provides much higher peak-production rates than a coupled column multi-dimensional liquid chromatography approach in which the second-dimension analyses are performed sequentially. A chip for spatial two-dimensional liquid chromatography has been manufactured from cyclic olefin copolymer and features a first-dimension separation channel and 21 parallel second-dimension separation channels oriented perpendicularly to the former. Compartmentalization of first- and second-dimension developments by physical barriers allowed for a preferential flow path with a minimal dispersion into the second-dimension separation channels. To generate a homogenous flow across all the parallel second-dimension channels, a radially interconnected flow distributor containing two zones of diamond-shaped pillars was integrated on-chip. A methacrylate ester based monolithic stationary phase with optimized macroporous structure was created in situ in the confines of the microfluidic chip. In addition, the use of a photomask was explored to localize monolith formation in the parallel second-dimension channels. Finally, to connect the spatial chip to the liquid chromatography instrument, connector ports were integrated allowing the use of Viper fittings. As an alternative, a chip holder with adjustable clasp locks was designed that allows the clamping force to be adjusted. [ABSTRACT FROM AUTHOR]

Thermal analysis and SEM were employed to gain insights in the different stages of morphology development and the thermal properties of polymer-monolithic stationary phases. The studied system was a thermally initiated free-radical copolymerization reaction at 70°C of styrene and divinylbenzene in the presence of tetrahydrofuran and 1-decanol. The key events in the early stages of morphology development are initiation, chain growth, branching, and cyclization, leading to microgel particles. Interparticle reactions through pendant vinyl groups lead to the formation of microgel clusters. The rapid increase in molecular weight and cross-link density of the microgel clusters causes a reaction-induced phase separation, and the formation of a macroscopic network of interconnected globules was observed (macrogelation) at around 45 min. After 3 h or 65% conversion, a space-filling macroporous monolithic network was observed. Afterwards, mainly growth of existing globules takes place, reducing the macropore size. The porogen ratio affects the timing of the reaction-induced phase separation, strongly influencing the morphology of the polymer material. The use of a mixture of divinylbenzene isomers yielded a monolithic material that is less cross-linked at the surface compared to the central part of the polymer backbone due to copolymerization-composition drift. The less cross-linked outer layer starts devitrifying at 100°C. [ABSTRACT FROM AUTHOR]