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From April through June in 2019 and 2022, we monitored snow melt at three sites near Utqiaġvik, Alaska. Along 200‐m lines we measured snow depth, density, stratigraphy, snow‐covered area, and spectral albedo. Site 1 (ARM) was sloped tundra drained by water tracks. Site 2 (BEO) was flat polygonal tundra. Site 3 (ICE) was on undeformed landfast sea ice. All three sites were within a 6 km radius. Despite similar pre‐melt snow distributions and weather, the melt progression differed markedly between sites. In 2019, by mid‐melt, there was 40% less snow‐covered area at ARM versus ICE, and 34% less snow‐covered area at ARM versus BEO. The 2022 melt started 2 weeks later than in 2019 and was rapid, so smaller differences in snow‐covered areas developed. In both years meltout dates varied by up to 25 days between sites, and more than 20 days within sites, with melt rates at locations only meters apart differing by up to a factor of seven. This melt diachroneity led to highly heterogeneous meltout patterns at all three sites. Our measurements and observations indicate that, in addition to reductions in snow reflective properties and wind‐driven heat advection, the fate of meltwater plays a key role in producing melt diachroneity. We identify seven snow‐water mechanisms that can enhance or inhibit melt rates, all largely controlled by the local topography and the nature of the substrate. These mechanisms are important because the most rapid changes in albedo coincide with the peak of water‐snow melt interactions. Plain Language Summary: We observed in detail snow melt on two different tundra locations and one sea ice location in Arctic Alaska in the spring of 2019 and 2022. During the melt season, these landscapes go from reflecting over 80% of the sunlight to less than 20%, a transition that impacts the global climate. Our observations showed that while warming weather is what ultimately controls snowmelt, the re‐freezing and flow of meltwater within the snowpack, plays a crucial role in controlling local melt rates of individual snow patches. These water‐related melt mechanisms, together with wind‐driven laterally advected air and the reflective properties of the snow, largely control the date individual snow patches disappear, and therefore, when the most rapid changes in solar reflectivity will take place. Key Points: Water‐related processes in snow can promote or impede lateral advection of heat and mass, enhancing or inhibiting melt in the ArcticThe composition and topography of the substrate govern the nature of the water‐snow interactions and thereby influence meltout patternsThe most rapid and largest changes in surface albedo happen just prior to the snow‐free date when water‐snow melt mechanisms reach a peak [ABSTRACT FROM AUTHOR]

The taiga snow cover accumulates in relatively stable and windless weather. This should produce a uniform snow cover with continuous, laterally homogeneous stratigraphy and snow properties when the snow is deposited on a level, smooth substrate. However, such substrates are rare, and local variations in vegetation and ground surface topography alter the structure of the snow cover and produce irregular snow layers. In this study, we investigated the effects of vegetation, microtopography, and microclimatic variability on the taiga snow near Fairbanks, Alaska. Through the winter of 2019–2020, in situ measurements were made at three locations with distinctly different local microtopographic features and radically different (but typical) vegetation. One site was an open grassy field, the second a mature spruce forest, and the third a birch forest located on thermokarst terrain with steep microrelief where ice wedges had degraded. Widely different canopy interception processes proved to have the strongest impact on the resulting snow cover heterogeneity by altering the initial deposition, with surface microtopography having the second strongest influence through postdepositional processes. In this article, we suggest a conceptual framework for understanding and modeling taiga snow variability in terms of the vegetation and microtopography that created it. [ABSTRACT FROM AUTHOR]

Ricin is a family member of the lethal ribosome-inactivating proteins (RIP) found in plants. Ricin toxin A-chain (RTA) from castor beans catalyzes the hydrolytic depurination of a single base from a GAGA tetraloop of eukaryotic rRNA to release a single adenine from the sarcin-ricin loop (SRL). Protein synthesis is inhibited by loss of the elongation factor binding site resulting in cell death. We report a sensitive coupled assay for the measurement of adenine released from ribosomes or small stem-loop RNAs by RTA catalysis. Adenine phosphoribosyl transferase (APRTase) and pyruvate orthophosphate dikinase (PPDK) convert adenine to ATP for quantitation by firefly luciferase. The resulting AMP is cycled to ATP to give sustained luminescence proportional to adenine concentration. Subpicomole adenine quantitation permits the action of RTA on eukaryotic ribosomes to be followed in continuous, high-throughput assays. Facile analysis of RIP catalytic activity will have applications in plant toxin detection, inhibitor screens, mechanistic analysis of depurinating agents on oligonucleotides and intact ribosomes, and in cancer immunochemotherapy. Kinetic analysis of the catalytic action of RTA on rabbit reticulocyte 80S ribosomes establishes a catalytic efficiency of 2.6 x [10.sup.8] [M.sup.-1] [s.sup.-1], a diffusion limited reaction indicating catalytic perfection even with large reactants.

A study was conducted to investigate circular DNA and DNA/RNA hybrid GAGA sequence oligonucleotides as minimal substrates and inhibitor scaffolds for Ricin Toxin A-chain (RTA) catalysis. The results revealed that RNA/DNA hybrid oligonucleotide cyclic GdAGA provides a new scaffold for RTA inhibitor design.

Part of the Earth Observing Sysytem Aqua Advanced Microwave Scanning Radiometer (AMSR-E) Arctic sea ice validation campaign in March 2003 was dedicated to the validation of snow depth on sea ice and ice temperature products. The difficulty with validating these two variables is that neither can currently be measured other than in situ. For this reason, two aircraft flights on March 13 and 19, 2003, were dedicated to these products, and flight lines were coordinated with in situ measurements of snow and sea ice physical properties. One flight was in the vicinity of Barrow, AK, covering Elson Lagoon and the adjacent Chukchi and Beaufort Seas. The other flight was farther north in the Beaufort Sea (about 73[degrees]N, 147.5[degrees]W) and was coordinated with a Navy ice camp. The results confirm the AMSR-E snow depth algorithm and its coefficients for first-year ice when it is relatively smooth. For rough first-year ice and for multiyear ice, there is still a relationship between the spectral gradient ratio of 19 and 37 GHz, but a different set of algorithm coefficients is necessary. Comparisons using other AMSR-E channels did not provide a clear signature of sea ice characteristics and, hence, could not provide guidance for the choice of algorithm coefficients. The limited comparison of in situ snow-ice interface and surface temperatures with 6-GHz brightness temperatures, which are used for the retrieval of ice temperature, shows that the 6-GHz temperature is correlated with the snow-ice interface temperature to only a limited extent. For strong temperature gradients within the snow layer, it is clear that the 6-GHz temperature is a weighted average of the entire snow layer. Index Terms--Advanced Microwave Scanning Radiometer (AMSR), passive microwave, sea ice, snow on sea ice, validation.

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over-or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper were assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7, 18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an 'effective' viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors. Index Terms--Passive microwave, remote sensing, sea ice.

The objective of this paper is to characterize spatial properties of snow-depth structures and their role as indicators of sea-ice properties and sea-ice-morphogenetic processes, and to provide quantitative measures of sea-ice properties that may be utilized in analyses of passive-microwave data. Snow-depth data collected near Point Barrow, Alaska, as part of the AMSR-Ice03 Field Validation Campaign for Advanced Microwave Scanning Radiometer (AMSR)-E-Sea-Ice Products from the NASA Earth Observing System Aqua satellite, are analyzed and compared to P-3 Polarimetric Scanning Radiometer (PSR) data, a proxy for AMSR-E brightness temperatures. The approach taken in the analysis is geostatistical characterization. Vario functions of first and second order are calculated for the snow-depth profiles, then geostatistical classification parameters are extracted and combined into feature vectors, on which the characterization is based. The complexity of sea ice requires a generalization of the method by introduction of the hyperparameter concept. Results include a quantitative characterization of sea-ice provinces from field transects in the Beaufort Sea, Chukchi Sea, and Elson Lagoon, which represent a good subset of Arctic sea-ice types, an internal segmentation of the longer profiles, and a derivation of surface-roughness length and of sea-ice-type complexity. PSR data reflect complexity of spatial snow-depth structures as captured in multidimensional feature vectors and, less directly, snow-depth and surface-roughness length. These results indicate that passive-microwave data in general may be affected by spatial snow depth and surface roughness, with a dependence on scale and quantified by geostatistical classification. Index Terms--Classification, morphogenesis, remote sensing, roughness, sea ice, spatial data analysis.

Aircraft-acquired passive microwave data, laser radar height observations, RADARSAT synthetic aperture radar imagery, and in situ measurements obtained during the AMSR-Ice03 experiment are used to investigate relationships between microwave emission and ice characteristics over several space scales. The data fusion allows delineation of the shore-fast ice and pack ice in the Barrow area, AK, into several ice classes. Results show good agreement between observed and Polarimetric Scanning Radiometer (PSR)-derived snow depths over relatively smooth ice, with larger differences over ridged and rubbled ice. The PSR results are consistent with the effects on snow depth of the spatial distribution and nature of ice roughness, ridging, and other factors such as ice age. Apparent relationships exist between ice roughness and the degree of depolarization of emission at 10, 19, and 37 GHz. This depolarization would yield overestimates of total ice concentration using polarization-based algorithms, with indications of this seen when the NT-2 algorithm is applied to the PSR data. Other characteristics of the microwave data, such as effects of grounding of sea ice and large contrast between sea ice and adjacent land, are also apparent in the PSR data. Overall, the results further demonstrate the importance of macroscale ice roughness conditions such as ridging and rubbling on snow depth and microwave emissivity. Index Terms--Passive microwave, roughness, sea ice, shore-fast ice, snow depth.

An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft/surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms. Index Terms--Advanced Microwave Scanning Radiometer for the Earth Observing Mission (AMSR-E), Arctic, sea ice, validation.

In March 2003, a field validation campaign was conducted on the sea ice near Barrow, AK. The goal of this campaign was to produce an extensive dataset of sea ice thickness and snow properties (depth and stratigraphy) against which remote sensing products collected by aircraft and satellite could be compared. Chief among these were products from the Polarimetric Scanning Radiometer (PSR) flown aboard a NASA P-3B aircraft and the Aqua Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The data were collected in four field areas: three on the coastal sea ice near Barrow, AK, and the fourth out on the open ice pack 175 km northeast of Barrow. The snow depth ranged from 9.4-20.8 cm in coastal areas (n = 9881 for three areas) with the thinnest snow on ice that had formed late in the winter. Out in the main pack ice, the snow was 20.6 cm deep (n = 1906). The ice in all four areas ranged from 138-219 cm thick (n = 1952), with the lower value again where the ice had formed late in the winter. Snow layer and grain characteristics observed in 118 snow pits indicated that 44% of observed snow layers were depth hoar; 46% were wind slab. Snow and ice measurements were keyed to photomosaics produced from low-altitude vertical aerial photographs. Using these, and a distinctive three-way relationship between ice roughness, snow surface characteristics, and snow depth, strip maps of snow depth, each about 2 km wide, were produced bracketing the traverse lines. These maps contain an unprecedented level of snow depth detail against which to compare remote sensing products. The maps are used in other papers in this special issue to examine the retrieval of snow properties from the PSR and AMSR-E sensors. Index Terms--Cryosphere, field validation, sea ice, snow.

FIFTY MORE YEARS BELOW ZERO: TRIBUTES AND MEDITATIONS FOR THE NAVAL ARCTIC RESEARCH LABORATORY'S FIRST HALF CENTURY. Edited by DAVID W. NORTON. Calgary, Alberta, and Fairbanks, Alaska: The Arctic Institute [...]

Between 2012 and 2018 we mapped near‐peak seasonal snow depths across two swaths covering 126 km2 in Northern Alaska using aerial structure‐from‐motion photogrammetry and lidar surveys. The surveys were validated by over a hundred thousand ground‐based depth measurements. Using a quantitative method for identifying drift areas, we conducted a snowdrift census that showed on average 18% of the study area is covered by snowdrifts each winter, with 40% of the snow‐water‐equivalent contained in the drifts. Within the census we identified six types of drifts, some of which fill each winter, others which do not. The seasonal drift evolution was distinctly different in the two swaths, a result largely explained by topographic differences. Using four metrics from the field of image quality analysis, we tested the year‐to‐year fidelity of these drift patterns, finding overall high year‐to‐year similarity, but with higher similarity values for filling drifts, and higher similarity values in one swath versus the other, again a function of the topography. These high drift fidelity values are best explained by climatically convergent winter‐cumulative windblown snow fluxes interacting with drift traps to produce the same drifts year after year. However, due to the existence of filling versus nonfilling drifts, and a predicted increasing frequency of rain‐on‐snow events in the Arctic, future snowdrift patterns and drift evolution pathways in the Arctic could diverge from those of today, with direct hydrologic impacts. Key Points: Arctic snowdrifts are landscape features important for hydrology (comprising 18% of the area but 40% of the snow‐water‐equivalent)Novel methods for snowdrift identification (quantitative) and classification (quanitative) are introduced to produce drift censusesArctic snowdrift and scour patterns repeat with exceptional fidelity each winter—but could be disrupted by a changing climate [ABSTRACT FROM AUTHOR]

The catalytic properties of saporin-L1 from Saponaria officinalis (soapwort) leaves are characterized to demonstrate its robust activity against defined nucleic acid substrates and mammalian ribosomes. Transition state analogues of saporin-L1 are found to have potential application in cancer therapy that employs saporin-L1-linked immunotoxins.

An experiment to observe the formation of snow crystals in class room using low cost easy to make equipment is described.

LAII stands for Land-Atmosphere-Ice Interactions. It, along with OAII (Ocean-Atmosphere-Ice Interactions), was one of the first components of the Arctic System Science (ARCSS) program at the National Science Foundation (NSF). [...]

Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied. [ABSTRACT FROM AUTHOR]

Every winter, snowy landscapes are smoothed by snow deposition in calm conditions (no wind). In this study, we investigated how vertically falling snow attenuates topographic relief at horizontal scales less than or approximately equal to snow depth (e.g., 0.1–10 m). In a set of three experiments under natural snowfall, we observed the particle-scale mechanisms by which smoothing is achieved, and we examined the cumulative effect at the snowpack scale. The experiments consisted of (a) a strobe-light box for tracking the trajectories of snowflakes at deposition, (b) allowing snow to fall through a narrow gap (40 mm) and examining snow accumulation above and below the gap, and (c) allowing snow to accumulate over a set of artificial surfaces. At the particle scale, we observed mechanisms enhancing (bouncing, rolling, ejection, breakage, creep, metamorphism) and retarding (interlocking, cohesion, adhesion, sintering) the rate of smoothing. The cumulative effect of these mechanisms is found to be driven by snowpack surface curvature, introducing a directional bias in the lateral transport of snow particles. Our findings suggest that better quantification of the mechanisms behind smoothing by snow could provide insights into the evolution of snow depth variability, and snow-vegetation interactions. [ABSTRACT FROM AUTHOR]

Last September I was asked to give the closing talk at the American Association for the Advancement of Science (AAAS) meeting at Denali Park in Alaska. It was the 50th [...]

An automatic snow depth probe (magnaprobe) patented in 1999 (United States Patent 5,864,059, 1999) and produced commercially by Snow‐Hydro LLC has now been used to obtain more than a million simultaneous snow depths (up to 140 cm) and GPS measurements during a wide range of field validation campaigns. The magnaprobe consists of a ski pole‐like rod housing a magnetostrictive device along which a basket and magnet assembly slides. The rod is inserted to the base of the snow, the basket floats on the snow, and when a button is pushed, the distance between rod tip and basket is measured while a position is acquired. The nature of the substrate beneath the snow controls the snow depth accuracy with errors ranging from near zero for hard bases to +5 cm in soft vegetation. The Wide Area Augmentation System‐enabled GPS provides a position accurate to ±2.5 m. The probe increases the speed with which a depth and position measurement can be obtained by a factor of 10 compared to measuring with a traditional ruler probe and writing down the results. The highest boost in snow depth measurement efficiency occurs when the distance between measuring locations is kept relatively small (<10 m). Magnaprobes have materially improved our ability to evaluate airborne and satellite‐based snow depth products and are likely to see continued heavy use over the next decade as efforts to develop satellite systems for monitoring snow remotely are tested in various field settings. Plain Language Summary: We developed an automatic snow depth probe that can measure both depth and position simultaneously. These probes have been used extensively in snow field campaigns. They are accurate to better than 5 cm in measuring depth, with the poorer accuracy coming when soft ground under the snow is encountered; they are accurate to about ±5 m in measuring position. We expect probes like these will continue to be used in field validation campaigns for the foreseeable future. Key Points: Since 1999 a magnetostrictive device called a magnaprobe has been used to measure simultaneous snow depths and positions (GPS) during dozens of snow research field campaigns worldwide, with more than a million data points collected in this mannerDepths measured in this manner are accurate to better than 5 cm, with the ease penetration of the substrate below the snow determining the over‐probe error; position errors are ±2.5 mThe device, or something similar, is likely to see continued use until airborne and satellite products reach a stand‐alone level of accuracy and reliability [ABSTRACT FROM AUTHOR]