Snow Surface Research Articles

Chloromonas rubrosalmonea sp. nov. (Chlorophyta) causes blooms of salmon-red snow due to high astaxanthin and low chlorophyll content

ABSTRACT Melting snowfields support microbial communities, including blooms of phototrophic psychrophiles. “Watermelon snow” and its flagship species Sanguina nivaloides are mostly found at exposed, alpine or polar sites. Here, we investigated an alga that is morphologically similar but likely less common, thriving in exposed or semi-exposed snowpacks at lower altitudes frequently close to the tree line, and causing characteristic, intensely salmon-red snow surface spots in Central European and Norwegian mountains. Our aim was to illustrate the taxonomic heterogeneity of red snow by performing a cellular and molecular characterization of this species. The phylogenetic analysis placed the alga into the genus Chloromonas, clade “group C”. Single-cell Sanger sequencing enabled us to link the cell morphotype to its genotype. In addition to light microscopy of the snow samples, applying 18S rRNA gene and ITS2 metabarcoding confirmed that this alga dominated the bloom communities. The cysts had multi-layered smooth cell walls, and had a broadly oval to fully spherical shape, in striking contrast to most other Chloromonas species found on snow surfaces. Another remarkable feature was a pronounced astaxanthin accumulation in relation to a low proportion of chlorophylls, occasionally accompanied by a significant reduction of thylakoid membranes. This likely caused the photosynthetic dormancy of mature cysts found in some samples. Based on these data, we describe the snow alga as Chloromonas rubrosalmonea, sp. nov.

Habitat selection by Snow Buntings Plectrophenax nivalis feeding nestlings, and their use of snow patches, in an edge-of-range population

ABSTRACT Snow Buntings provisioning their nestlings in the Cairngorm mountains of Scotland flew up to 520 m to forage in localized habitats. Invertebrates on snow patch margins in two habitats were exploited, but those trapped on the snow surface were not. The birds’ use of habitats on snow margins was probably related to weather conditions. There was little evidence of specialization in food resources available on snow patches.

Local summer temperature changes over the past 440 ka revealed by the total air content in the Antarctic EPICA Dome C ice core

Abstract. Seasonal temperature reconstructions from ice cores are missing over glacial–interglacial timescales, preventing a good understanding of the driving factors of Antarctic past climate changes. Here the total air content (TAC) record from the Antarctic EPICA Dome C (EDC) ice core is analyzed over the last 440 ka (thousand years). While the water isotopic record, a tracer for annual mean surface temperature, exhibits a dominant ∼100 kyr cyclicity, the TAC record is associated with a dominant ∼40 kyr cyclicity. Our results show that the TAC record is anti-correlated with the mean insolation over the local astronomical summer half-year. They also show for the first time that it is highly anti-correlated with local summer temperature simulated with an Earth system model of intermediate complexity. We propose that (1) the local summer insolation controls the local summer temperature; (2) the latter, through the development of temperature gradients at the near-surface of the ice sheet (<2 m), is affecting the surface snow structure; and (3) those snow structure changes propagating down to the bottom of the firn through densification are eventually controlling the pore volume at the bubble close-off and consequently the TAC. Hence, our results suggest that the EDC TAC record could be used as a proxy for local summer temperature changes. Also, our new simulations show that the mean insolation over the local astronomical summer half-year is the primary driver of Antarctic summer surface temperature variations, while changes in atmospheric greenhouse gas (GHG) concentrations and Northern Hemisphere (NH) ice sheet configurations play a more important role in Antarctic annual surface temperature changes.

Distinct Central and Eastern Pacific El Niño Influence on Antarctic Surface Mass Balance

AbstractThe El Niño‐Southern Oscillation causes anomalous atmospheric circulation, temperature and precipitation across southern polar latitudes, but the influence of Central and Eastern Pacific El Niño events on Antarctic surface mass balance and snow accumulation has not yet been assessed. Here, we use reanalysis and reanalysis‐forced regional climate model output and find that Central Pacific El Niño results in significantly increased snow accumulation in the western Ross Sea sector and significantly decreased snow accumulation in the Amundsen Sea sector. Eastern Pacific El Niño is associated with similar but weaker patterns, with some regional exceptions. In some areas, like Dronning Maud Land, or the Wilkes Subglacial Basin, the effect of El Niño on snow accumulation changes from increased to reduced accumulation depending on the type of El Niño. Our results show that projecting El Niño types is important for constraining future changes in Antarctic surface mass balance.

Rapid Ice‐Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff

AbstractThicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. In this study, we show that a snow fence experiment on High‐Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo‐erosion gully in the landscape. We estimate that 1.1–3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilized aquatic carbon. Our results show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss, highlighting the need for improved model representation.

Measured black carbon deposition over the central Himalayan glaciers: Concentrations in surface snow and impact on snow albedo reduction

Deposition of ambient black carbon (BC) aerosols over snow-covered areas reduces surface albedo and accelerates snowmelt. Based on in-situ atmospheric BC data and the WRF-Chem model, we estimated the dry and wet deposition of BC over the Yala glacier of the central Himalayan region in Nepal during 2016–2018. The maximum and minimum BC dry deposition was reported in pre- and post-monsoon respectively. Approximately 50% of annual dry deposition occurred in the pre-monsoon season (March to May) and 27% of the annual dry deposition occurred in April. The total dry BC deposition rate was estimated as ∼4.6 μg m−2 day−1 providing a total deposition of 531 μg m−2 during the pre-monsoon season. The contribution of biomass burning and fossil fuel sources to BC deposition on an annual basis was 28% and 72% respectively. The annual accumulated wet deposition of BC was 196 times higher than the annual dry deposition. The ten months of observed dry deposition of BC (October 1, 2016 to August 31, 2017 – except December 2016) was ∼39% lower than that of WRF-Chem's estimated annual dry deposition from September 1, 2016 to August 31, 2017 partially due to model bias. The deposited content of BC over the snow surface has an important role in albedo reduction, therefore snow samples were collected from the surface of the Yala Glacier and the surrounding region in April 2016, 2017, and 2018. Samples were analyzed for BC mass concentration through the thermal optical analysis and single particle soot photometer method. The BC calculated via the thermal optical method was in the range of 352–854 ng g−1, higher than the BC calculated through the particle soot photometer method and estimated BC in 2 cm surface snow (imperial equation). The maximum surface snow albedo reduction due to BC was 8.8%, estimated by a widely used snow radiative transfer model and a linear regression equation.

Sublimation of Snow

Abstract Snow is a vital part of water resources, and sublimation may remove 10%–90% of snowfall from the system. To improve our understanding of the physics that govern sublimation rates, as well as how those rates might change with the climate, we deployed an array of four towers with over 100 instruments from NCAR’s Integrated Surface Flux System from November 2022 to June 2023 in the East River watershed, Colorado, in conjunction with the U.S. Department of Energy’s Surface Atmosphere Integrated Field Laboratory (SAIL) and the National Oceanic and Atmospheric Administration (NOAA)’s Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaigns. Mass balance observations, snow pits, particle flux sensors, and terrestrial lidar scans of the evolving snowfield demonstrated how blowing snow influences sublimation rates, which we quantified with latent heat fluxes measured by eddy-covariance systems at heights 1–20 m above the snow surface. Detailed temperature profiles at finer resolutions highlighted the role of the stable boundary layer. Four-stream radiometers indicated the important role of changing albedo in the energy balance and its relationship to water vapor losses. Collectively, these observations span scales from seconds to seasons, from boundary layer turbulence to valley circulation to mesoscale meteorology. We describe the field campaign, highlights in the observations, and outreach and education products we are creating to facilitate cross-disciplinary dialogue and convey relevant findings to those seeking to better understand Colorado River snow and streamflow.

A Drifting and Blowing Snow Scheme in the Weather Research and Forecasting Model

AbstractWind‐driven redistribution of surface snow is one of the key factors leading to heterogeneous accumulation of snow at small scales. Understanding the processes that lead to this heterogeneous accumulation is, therefore, of great importance to many glaciological and hydrological questions. High‐quality information on the wind field is necessary to realistically represent drifting snow. Here, we introduce a novel, intermediate‐complexity drifting and blowing snow module for the Weather Research and Forecasting (WRF) model that integrates seamlessly into the standard WRF infrastructure. The module also accounts for snow particle sublimation and considers the thermodynamic feedback on the atmospheric fields. In an idealized model environment the module was tested for physical consistency. Sensitivity experiments showed that drifting snow sublimation has on the one hand local effects on the erosion and deposition patterns and on the other hand non‐local effects on the larger‐scale atmosphere. The simple and computationally efficient implementation allows this module to be used for small‐scale and large‐scale simulations in polar and glaciated regions.

Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models

Abstract. Differential penetration of green laser light into snow and ice has long been considered a possible cause of range and thus elevation bias in laser altimeters. Over snow, ice, and water, green photons can penetrate the surface and experience multiple scattering events in the subsurface volume before being scattered back to the surface and subsequently the instrument's detector, therefore biasing the range of the measurement. Newly formed sea ice adjacent to open-water leads provides an opportunity to identify differential penetration without the need for an absolute reference surface or dual-color lidar data. We use co-located, coincident high-resolution natural-color imagery and airborne lidar data to identify surface and ice types and evaluate elevation differences between those surfaces. The lidar data reveals that apparent elevations of thin ice and finger-rafted thin ice can be several tens of centimeters below the water surface of surrounding leads, but not over dry snow. These lower elevations coincide with broadening of the laser pulse, suggesting that subsurface volume scattering is causing the pulse broadening and elevation shift. To complement our analysis of pulse shapes and help interpret the physical mechanism behind the observed elevation biases, we match the waveform shapes with a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse, the surface roughness, and the optical scattering properties of the medium. We parameterize the scattering in our model based on the scattering length Lscat, the mean distance a photon travels between isotropic scattering events. The largest scattering lengths are found for thin ice that exhibits the largest negative elevation biases, where scattering lengths of several centimeters allow photons to build up considerable range biases over multiple scattering events, indicating that biased elevations exist in lower-level Airborne Topographic Mapper (ATM) data products. Preliminary analysis of ICESat-2 ATL10 data shows that a similar relationship between subsurface elevations (restored negative freeboard) and “pulse width” is present in ICESat-2 data over sea ice, suggesting that biased elevations caused by differential penetration likely also exist in lower-level ICESat-2 data products. The spatial correlation of observed differential penetration in ATM data with surface and ice type suggests that elevation biases could also have a seasonal component, increasing the challenge of applying a simple bias correction.

Optical effect of Andean mineral dust onto snow surface spectral albedo

Light absorbing particles (LAPs) present high absorbance and contribute to reducing the snow albedo when deposited on snow surfaces. This deposition can be caused by aerosols transported from natural or anthropogenic, either distant or nearby sources. In this study, snow was artificially contaminated with soil samples collected in the Central Andes (near El Yeso dam) to simulate the most common nearby source of Mineral Dust (MD) deposition onto snow surface. Andean soil samples previously conditioned were characterized through Single Particle Optical Sizing (SPOS), X-ray diffraction (XRD) analysis, and Scanning Electron Microscope (SEM) for the determination of optical properties. Spectral snow albedo was measured in situ with a spectroradiometric system. To evaluate the heterogeneity of the particle distribution over the snow surface, aerial photographs were taken with a drone to apply a visual color segmentation of the surface and to determine the equivalent MD concentration. Experimental snow albedo was compared with theoretical values obtained with the OptiPar radiative transfer model. Inputs for the model were: the MD refractive index (calculated from the mineralogical composition and morphology of MD) and particle size, cloudiness, snow density, surface roughness, snow grain size, and LAPs concentration (obtained from the snow samples collected during the experiments and analyzed in the laboratory). Small black carbon concentrations were found in natural snow and considered in the simulations. Spectral albedo measurements showed high albedo reductions in the UV and VIS range (300–800 nm), being less significant in the NIR range (800–1700 nm). A nonlinear behavior was observed in broadband albedo when increasing MD concentration. For lower values of MD concentration (lower than 1500 mg⋅kg−1), a significant albedo reduction rate of 0.1 units per 1000 mg⋅kg−1 was found, while at higher concentrations (¿ 3500 mg⋅kg−1), such reduction tends to the minimum. Simulated values with OptiPar are in agreement with measured albedo, but some differences are observed, probably due to the refractive index considered, the snow surface roughness, and the non-uniform MD concentration in snow.

Mapping surface hoar from near-infrared texture in a laboratory

Abstract. Surface hoar crystals are snow grains that form when water vapor deposits on the snow surface. Once buried, surface hoar creates a weak layer in the snowpack that can later cause large avalanches to occur. The formation and persistence of surface hoar are highly spatiotemporally variable, making its detection difficult. Remote-sensing technology capable of detecting the presence and spatial distribution of surface hoar would be beneficial for avalanche forecasting, but this capability has yet to be developed. Here, we hypothesize that near-infrared (NIR) texture, defined as the spatial variability of reflectance magnitude, may produce an optical signature unique to surface hoar due to the distinct shape and orientation of the grains. We tested this hypothesis by performing reflectance experiments in a controlled cold laboratory environment to evaluate the potential and accuracy of surface hoar mapping from NIR texture using a near-infrared hyperspectral imager (NIR-HSI) and a lidar operating at 1064 nm. We analyzed 41 snow samples, three of which were surface hoar and 38 of which consisted of other grain morphologies. When using NIR-HSI under direct and diffuse illumination, we found that surface hoar displayed higher NIR texture relative to all other grain shapes across numerous spectral bands and a wide range of spatial resolutions (0.5–50 mm). Due to the large number of spectral- and spatial-resolution combinations, we conducted a detailed samplewise case study at 1324 nm spectral and 10 mm spatial resolution. The case study resulted in the median texture of surface hoar being 1.3 to 8.6 times greater than that of the 38 other samples under direct and diffuse illumination (p < 0.05 in all cases). Using lidar, surface hoar also exhibited significantly increased NIR texture in 30 out of 38 samples, but only at select (5–25 mm) spatial resolutions. Leveraging these results, we propose a simple binary classification algorithm to map the extent of surface hoar on a pixelwise basis using both the NIR-HSI and lidar instruments. The NIR-HSI under direct and diffuse illumination performed best, with a median accuracy of 96.91 % and 97.37 %, respectively. Conversely, the median classification accuracy achieved with lidar was only 66.99 %. Further, to assess the repeatability of our method and demonstrate its mapping capacity, we ran the algorithm on a new sample with mixed microstructures, with an accuracy of 99.61 % and 96.15 % achieved using NIR-HSI under direct and diffuse illumination, respectively. As NIR-HSI detectors become increasingly available, our findings demonstrate the potential of a new tool for avalanche forecasters to remotely assess the spatiotemporal variability of surface hoar, which would improve avalanche forecasts and potentially save lives.

Surface snow bromide and nitrate at Eureka, Canada, in early spring and implications for polar boundary layer chemistry

Abstract. This study explores the role of snowpack in polar boundary layer chemistry, especially as a direct source of reactive bromine (BrOx = BrO + Br) and nitrogen (NOx = NO + NO2) in the Arctic springtime. Surface snow samples were collected daily from a Canadian high Arctic location at Eureka, Nunavut (80° N, 86° W) from the end of February to the end of March in 2018 and 2019. The snow was sampled at several sites representing distinct environments: sea ice, inland close to sea level, and a hilltop ∼ 600 m above sea level (a.s.l.). At the inland sites, surface snow salinity has a double-peak distribution with the first and lowest peak at 0.001–0.002 practical salinity unit (psu), which corresponds to the precipitation effect, and the second peak at 0.01–0.04 psu, which is likely related to the salt accumulation effect (due to loss of water vapour by sublimation). Snow salinity on sea ice has a triple-peak distribution; its first and second peaks overlap with the inland peaks, and the third peak at 0.2–0.4 psu is likely due to the sea water effect (a result of upward migration of brine). At all sites, snow sodium and chloride concentrations increase by almost 10-fold from the top 0.2 to ∼ 1.5 cm. Surface snow bromide at sea level is significantly enriched, indicating a net sink of atmospheric bromine. Moreover, surface snow bromide at sea level has an increasing trend over the measurement period, with mean slopes of 0.024 µM d−1 in the 0–0.2 cm layer and 0.016 µM d−1 in the 0.2–0.5 cm layer. Surface snow nitrate at sea level also shows a significant increasing trend, with mean slopes of 0.27, 0.20, and 0.07 µM d−1 in the top 0.2, 0.2–0.5, and 0.5–1.5 cm layers, respectively. Using these trends, an integrated net deposition flux of bromide of (1.01 ± 0.48) × 107 molec.cm-2s-1 and an integrated net deposition flux of nitrate of (2.6 ± 0.37) × 108 molec.cm-2s-1 were derived. In addition, the surface snow nitrate and bromide at inland sites were found to be significantly correlated (R = 0.48–0.76) with the [NO3-]/[Br-] ratio of 4–7 indicating a possible acceleration effect of reactive bromine in atmospheric NOx-to-nitrate conversion. This is the first time such an effect has been seen in snow chemistry data obtained with a sampling frequency as short as 1 d. BrO partial column (0–4 km) data measured by MAX-DOAS show a decreasing trend in early spring, which generally agrees with the derived surface snow bromide deposition flux indicating that bromine in Eureka atmosphere and surface snow did not reach a photochemical equilibrium state. Through mass balance analysis, we conclude that the average release flux of reactive bromine from snow over the campaign period must be smaller than the derived bromide deposition flux of ∼ 1 × 107 molec.cm-2s-1. Note that the net mean fluxes observed do not completely rule out larger bidirectional fluxes over shorter timescales.

Uncovering sources, distribution, and seasonal patterns of trace element deposition: the elemental puzzle of the western Himalayas.

The transport and deposition of atmospheric pollutants in the Himalayas have a adverse impact on the climate, cryosphere, ecosystem, and monsoon patterns. Unfortunately, there is a insufficiency of data on trace element concentrations and behaviors in the high-altitude Himalayan region, leading to limited research in this area. This study presents a comprehensive and detailed comprehension of trace element deposition, its spatial distribution, seasonal variations, and anthropogenic signals in the high-altitude Kashmir region of the Western Himalayas. Our investigation involved the analysis of 10 trace elements (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in glacier ice, snow pits, surface snow, and rainwater collected at various sites including Kolahoi, Thajwas, Pahalgam (Greater Himalayan ranges), and Kongdori and Shopian (Pir Panjal Ranges) during 2021.The study reveals distinct ranges of concentrations for the trace elements at different sampling sites. Our analysis of trace element concentration depth profiles in snow pits reveals seasonal fluctuations during the deposition year. The highest concentrations were found in the autumn(below 20cm) and summer (top layer), compared to the winter concentration (10-20cm). The high enrichment factors (EFs) suggest the severity of human-induced trace metal deposition in the western Himalayan region, relative to surrounding regions. Surprisingly, the concentrations and EFs of trace elements showed seasonal contradictions, with lower concentration values and higher EFs during the non-monsoon season and vice versa. A source apportionment analysis using the positive matrix factorization (PMF) technique identified five sources of trace element deposition in the region, including crustal sources (32.33%), coal combustion (15.62%), biomass burning (17.63%), traffic emission (18.8%), and industrial sources (15.6%). Additionally, the study incorporated backward trajectories coupled with δ18O using the NOAA HYSPLIT model to estimate moisture sources in the region, which suggests atmospheric pollutants predominately deposited from the large-scale atmospheric circulation from westerlies (75%) during non-monsoon season. These findings underscore the urgent need for enhanced monitoring and research efforts in the future.

Consistent Ground Surface Temperature Climatology Over China: 1956–2022

AbstractThe ground surface represents the land‐atmosphere interface and plays a crucial role in exchanging energy, matter, and biochemical fluxes. The ground surface temperature (Ts) is hence widely investigated as an indicator to understand the thermal state of soil in a warming world. However, regular and continuous Ts measurements are rare worldwide, and the early Ts records were derived from snow surface measurements and are not comparable with the measurements of modern automatic systems. In this study, we reconstruct the Ts records of the China Meteorological Administration (CMA) for 1956–2022 by numerical simulation. The results show that the mean annual ground surface temperature (MAGST) during 1981–2010 ranged from 0.4 to 30.8°C at 632 stations. The MAGST was mainly controlled by air temperature and refined by snow depth. The overall MAGST increased by 0.20 ± 0.02°C dec−1 across China during 1956–2022 and showed pronounced interdecadal variability corresponding to the surface air temperature (0.23 ± 0.03°C dec−1). At the snow‐covered sites, the Ts warming was amplified by increased snow depth, leading to about 0.24°C dec−1 (or 70.6%) faster warming for Ts in winter compared to that of surface air temperature. Many of the early reported ground surface temperature studies based on CMA measurements did not consider the measurement inconsistency and likely overestimated the warming trend of ground surface temperature over China.

Sources and distribution of trace elements in surface snow from coastal Zhongshan Station to Dome A (East Antarctica)

The spatial distribution and possible sources of Al, As, Cd, Fe, Mn, Pb, Sb, Ti, V, and Zn were investigated in 120 samples of surface snow collected along a ∼1260 km long traverse from coastal Zhongshan Station to the Antarctic ice sheet summit (Dome A or Dome Argus, at 4093 m a.s.l.). The chemical composition of snow samples was analyzed using microwave-assisted acid treatment with HNO3/HF to determine element concentrations, reflecting their abundance in the Earth's crust. The observed relative concentration pattern of elements in snow suggests a predominant contribution from soil and rock dust particles. The spatial distribution of total element concentrations along the traverse revealed higher values in inland areas compared to coastal areas. The annual deposition flux of elements in each sampling site indicates a significant decreasing trend in Al, Ti, Zn, Fe, Cd, As, and Pb from the coast to inland areas. Significant variations in element concentrations and fluxes observed along the Antarctic traverse suggest a notable influence of distance from the coast on snow deposition patterns. Soil dust from coastal ice-free areas and human activities in scientific stations are the primary sources of analyzed elements in coastal snow, with marine biogenic emissions playing a marginal role, likely contributing to As and Cd. The contribution of sea salt spray to surface snow element concentrations in the East Antarctic traverse is negligible. An analysis of backward trajectories confirmed that long-distance transport from the South American continent and local Antarctic research stations potentially contributes to Antarctic snow elements.

Micrometeorite collections: a review and their current status.

Micrometeorites are estimated to represent the main part of the present flux of extraterrestrial matter found on the Earth's surface and provide valuable samples to probe the interplanetary medium. Here, we describe large and representative collections of micrometeorites currently available to the scientific community. These include Antarctic collections from surface ice and snow, as well as glacial sediments from the eroded top of nunataks-summits outcropping from the icesheet-and moraines. Collections extracted from deep-sea sediments (DSS) produced a large number of micrometeorites, in particular, iron-rich cosmic spherules that are rarer in other collections. Collections from the old and stable surface of the Atacama Desert show that finding large numbers of micrometeorites is not restricted to polar regions or DSS. The advent of rooftop collections marks an important step into involving citizen science in the study of micrometeorites, as well as providing potential sampling locations over all latitudes to explore the modern flux. We explore their strengths of the collections to address specific scientific questions and their potential weaknesses. The future of micrometeorite research will involve the finding of large fossil micrometeorite collections and benefit from recent advances in sampling cosmic dust directly from the air. This article is part of the theme issue 'Dust in the Solar System and beyond'.

Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica

Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's ability to capture snowfall compared to other satellite products over snow and ice surfaces. We addressed limitations of CloudSat, such as poor spatiotemporal sampling, noise, and incomplete coverage near the poles. We utilized the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) to guide development, particularly during the period when the TIROS Operational Vertical Sounder (TOVS) was contributing. We adjusted monthly and seasonal TOVS and Atmospheric Infrared Sounder – Infrared (AIRS-IR) precipitation biases over Antarctica at the pixel level, as these two products are the primary inputs for the Global Precipitation Climatology Project (GPCP) in high latitudes. We assessed the adjusted TOVS and AIRS-IR through analyses of geographical maps and time series of monthly and seasonal mean precipitation rates. The results are encouraging, indicating that the proposed approach could replace the current approach used in the GPCP for adjusting precipitation estimates from AIRS-IR and TOVS over Antarctica. Adjusting precipitation estimates from TOVS and AIRS-IR using the proposed approach improves the Kling-Gupta efficiency (KGE) over their entire period by 162% and 147%, respectively. Moreover, the proposed approach can be applied to adjust other precipitation products over Antarctica at the pixel level, including satellites and reanalysis products.

Dust storms from the Taklamakan Desert significantly darken snow surface on surrounding mountains

Abstract. The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening.

Investigating Road Ice Formation Mechanisms Using Road Weather Information System (RWIS) Observations

Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors of road ice formation and its mechanisms. First, road ice is formed only when the road pavement surface temperature is equal to or below the freezing point (i.e., 32 °F (i.e., 0 °C)), while the corresponding 2 m air temperature could be above 32 °F. Nevertheless, when the road pavement was below 32 °F ice often did not form on the roads. Therefore, one challenge is to know under what conditions road ice forms. Second, the pavement surface temperature is critical for road ice formation. The clear road (i.e., with no ice or snow) surface pavement temperature is generally warmer than the air temperature during both day and night. This feature is different from a natural land surface, where the land skin temperature is lower than the air temperature on cloud-free nights due to radiative cooling. Third, subsurface temperature, measured using a RWIS subsurface sensor below a road surface, did not vary as much as the pavement temperature and, thus, may not be a good index for road ice formation. Fourth, urban heat island effects lead to black ice formation more frequently than roads located in other regions. Fifth, evaporative cooling from the water surface near a road segment further reduces the outlying air temperature, a mechanism that increases heat loss for bridges or lake-side roads in addition to radiative cooling. Additionally, mechanical lifting via mountains and hills is also an efficient mechanism that makes the air condense and, consequently, form ice on the roads. Forecasting road ice formation is in high demand for road safety. These observed features may help to develop a road ice physical model consisting of functions of hyper-local weather conditions, local domain knowledge, the road texture, and geographical environment.

The genome of the Arctic snow alga Limnomonas spitsbergensis (Chlamydomonadales).

Snow algae are a diverse group of extremophilic microeukaryotes found on melting polar and alpine snowfields. They play an important role in the microbial ecology of the cryosphere, and their propagation on snow and ice surfaces may in part accelerate climate-induced melting of these systems. High-quality snow algae genomes are needed for studies on their unique physiology, adaptive mechanisms, and genome evolution under multiple forms of stress, including cold temperatures and intense sunlight. Here, we assembled and annotated the genome of Limnomonas spitsbergensis, a cryophilic biciliate green alga originally isolated from melting snow on Svalbard, in the Arctic. The L. spitsbergensis genome assembly is based primarily on the use of PacBio long reads and secondly Illumina short reads, with an assembly size of 260.248 Mb in 124 contigs. A combination of 3 alternative annotation strategies was used including protein homology, RNA-seq evidence, and PacBio full-length transcript isoforms. The best merged set of annotations identified 18,277 protein-coding genes, which were 95.2% complete based on Benchmarking Universal Single-Copy Orthologs analysis. We also provide the annotated mitogenome, which is a relatively large 77.942 kb circular mapping sequence containing extensive repeats. The L. spitsbergensis genome will provide a new resource for research on snow algae adaptation, behavior, and natural selection in unique, low-temperature terrestrial environments that are under threat from climate change.