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DOE OSTI2020

Permafrost thermal conditions are sensitive to shifts in snow timing

Abstract Changes in snow precipitation at high latitudes can significantly affect permafrost thermal conditions and thaw depth, potentially exposing more carbon-laden soil to microbial decomposition. A fully coupled process-based surface/subsurface thermal hydrology model with surface energy balance is used to analyze the impact of intra-annual variability in snow on permafrost thermal regime and the active layer thickness. In the four numerical scenarios considered, simulations were forced by the same meteorological data, except the snow precipitation, which was systematically altered to change timing of snowfall. The scenarios represent subtle shifts in snow timing, but the snow onset/melt days, the end of winter snowpack depth, and total annual snow precipitation are unchanged among scenarios. The simulations show that small shifts in the timing of snow accumulation can have significant effects on subsurface thermal conditions leading to active layer deepening and even talik formation when snowfall arrives earlier in the winter. The shifts in snow timing have a stronger impact on wetter regions, especially soil underneath small ponds, as compared to drained regions. This study highlights the importance of understanding potential changes in winter precipitation patterns for reliable projections of active-layer thickness in a changing Arctic climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Determination of Ground Subsidence Around Snow Fences in the Arctic Region

In this study, we analyzed the effects of snow cover changes caused by snow fences (SFs) installed in 2017 in the Alaskan tundra to examine ground subsidence. Digital surface model data obtained through LiDAR-based remote sensing in 2019 and 2022, combined with a field survey in 2021, revealed approximately 0.2 m of ground subsidence around the SF. To investigate the relationship between SF-induced snow cover changes and ground subsidence, geophysical methods, electrical resistivity tomography (ERT) and ground-penetrating radar (GPR), were applied in 2023 to analyze subsurface characteristics. The increased snow cover due to the SF-enhanced insulation, delaying the penetration of winter cold into the subsurface. This delay caused subsurface temperatures to decrease more slowly, melting the upper permafrost and increasing the thickness of the active layer. ERT and GPR surveys well delineated the boundary between the active layer and permafrost, confirming that the increased snow cover thickened the active layer. This thickening led to the melting of pore ice, causing water runoff and ground compaction, which resulted in subsidence. The runoff also formed channels flowing eastward over the SF. This study highlights how changes in snow cover can influence active layer properties, leading to localized environmental changes and ground subsidence.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Ice sheet surface conditions and their subsequent impact on surface mass balance play an important role in ice sheet dynamics and ice sheet interaction with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) ecosystem of models and reanalyses model ice sheet and glacier surface mass balance with a moderately complex snow and ice module, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. With this configuration, Greenland near surface air temperatures and ice sheet melt are well represented in MERRA-2, GMAO’s current atmospheric reanalysis; however, the similarity to observations in some regions is due, in part, to compensating biases in surface energy budget. Recent improvements to GEOS’ cloud microphysics and longwave radiation parameterizations reduced biases in surface net longwave radiation and exacerbated surface net shortwave radiation biases, effectively increasing near surface temperatures and melting, particularly during the summer months. Here, we demonstrate the Earth system response to an improved representation of the ice sheet surface focused on minimizing the surface energy biases and increasing spatial and temporal representativeness. New changes to the surface albedo parameterization, including the introduction of a prognostic aerosol darkening scheme, help mitigate shortwave biases, while modifications to surface roughness characteristics improve near surface sensible heat fluxes. Focusing on the Arctic and Greenland, we examine the combined impact on these changes on regional weather conditions and ice sheet surface mass balance using a high resolution atmospheric only experiment, and we use a coupled model configuration to assess the impact on oceanic conditions and subseasonal-to-seasonal prediction. Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet mass balance in GMAO’s future reanalyses and forecasting systems.

Lauren C Andrews↗

NASA NTRSDate not supplied

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Lauren C. Andrews↗

DOE OSTI2024

Surface elevation, snow depth, vegetation height, and color imagery from multiple UAV surveys from 2018 to 2023 across a watershed near Teller road mile marker 27, Seward Peninsula, Alaska

Multiple photogrammetric surveys using an unoccupied aerial system (UAS) were performed across a watershed along Teller road on the Seward Peninsula (Alaska) to investigate terrain, vegetation and snowpack properties. This work was led by the environmental geophysics team from Lawrence Berkeley National Laboratory as part of the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic). The multiple photogrammetric surveys used mostly a DJI Matrice 210 UAS with a DJI X5S camera, Ground Control Points (GCPs) surveyed with a Real Time Kinematic (RTK)-GPS, and a structure from motion (SfM) technique for photogrammetric reconstruction. Digital Surface elevation Models (DSMs) were inferred at various times of year, including near peak in plant and leaf density (July 19 2017), close to peak in snow depth (April 1 2019 and April 11 2022), and at low plant and leaf density of tall after the first bare-ground date (June 9 2019). Snow and canopy height were obtained by subtracting DSMs from a Digital Terrain Model (DTM) proxy inferred from the June 9 2019 survey. The derived products include ortho-mosaiced RGB map, DSMs, a DTM proxy, snow depth (April 2019 and 2022) and canopy height (July 2017). Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.csv), image (*.jpg), and GeoTiff (*.tif) formats. This metadata document contains a description of the survey and processing steps and the inferred products.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Arctic warming by abundant fine sea salt aerosols from blowing snow

Abstract The Arctic warms nearly four times faster than the global average, and aerosols play an increasingly important role in Arctic climate change. In the Arctic, sea salt is a major aerosol component in terms of mass concentration during winter and spring. However, the mechanisms of sea salt aerosol production remain unclear. Sea salt aerosols are typically thought to be relatively large in size but low in number concentration, implying that their influence on cloud condensation nuclei population and cloud properties is generally minor. Here we present observational evidence of abundant sea salt aerosol production from blowing snow in the central Arctic. Blowing snow was observed more than 20% of the time from November to April. The sublimation of blowing snow generates high concentrations of fine-mode sea salt aerosol (diameter below 300 nm), enhancing cloud condensation nuclei concentrations up to tenfold above background levels. Using a global chemical transport model, we estimate that from November to April north of 70° N, sea salt aerosol produced from blowing snow accounts for about 27.6% of the total particle number, and the sea salt aerosol increases the longwave emissivity of clouds, leading to a calculated surface warming of +2.30 W m −2 under cloudy sky conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1997

Interferometric SAR for observation of glacier motion and firn penetration

A digital elevation model (DEM) for a swath in west Greenland above Jakobshavn Isbrae derived from a number ERS-1 interferograms combined so as to reduce phase errors and other problems is presented. The DEM shows a wealth of kilometer-scale, dynamically supported topography, which arises from ice sheet flow over the rough bed. A correlation is shown between topography and interferometric phase due solely to ice sheet motion, which clearly shows the translations of scatterers in the surface up and downslope in the topography. Finally, the low correlation in interferograms of ice sheet dry snow zones motivates investigation on the depth-locus of backscattering. A scattering model is presented, including realistic firn grain size distributions and layering, which shows that layering helps to localize backscattering from dry firn to shallower depths than would otherwise be expected.

Winebrenner, Dale P.↗

NASA NTRS1980

Microwave radiometric observations of snowpacks

Models for the microwave emission from snowpacks were generated on the basis of radiometric observations made at 10.7 GHz, 37 HGz, and 94 GHz at a test site near Steamboat Springs, Colorado. In addition to conducting measurements on an approximately daily basis over a six week observation period, measurements were made over several diurnal cycles during which the change in snow wetness was tracked by the microwave radiometers. Also, the variation in emissivity with snow water equivalent was examined, as was the sensitivity to changes in snow surface geometry. The microwave emissivity was observed to (1) decrease exponentially with snow water equivalent and (2) increase with snow wetness. Thus, the emission behavior is the reverse of the backscattering behavior observed by the radar. By fitting the models to the measured data, the variation of the optical depth with snow wetness was estimated.

Ulaby, F. T.↗

DOE OSTI2023

A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Concentrating Antarctic Meteorites on Blue ice Fields: The Frontier Mountain Meteorite Trap

The collection of meteorites in Antarctica has greatly stimulated advancement in the field of meteoritics by providing the community with significant numbers of rare and unique meteorites types and by yielding large numbers of meteorites that sample older infall epochs (Grady et al., 1998). The majority of Antarctic meteorites are found on blue ice fields, where they are thought to be concentrated by wind and glacial drift (cf. Cassidy et al., 1992). The basic "ice flow model" describes the concentration of meteorites by the stagnation or slowing of ice as it moves against a barrier located in a zone with low snow accumulation. However, our limited knowledge of the details of the actual concentration mechanisms prevents establishing firm conclusions concerning the past meteorite flux from the Antarctic record (Zolensky, 1998). The terrestrial ages of Antarctic meteorites indicate that their concentration occurs on time scales of tens to hundreds of thousands of years (Nishiizumi et al., 1989). It is a challenge to measure a mechanism that operates so slowly, and since such time scales can span more than one glacial epoch one cannot assume that the snow accumulation rates, ice velocities and directions, etc. that are measured today are representative of those extant over the age of the trap. Testing the basic "ice flow model" therefore requires the careful measurement of meteorite locations, glacialogical ice flow data, ice thicknesses, bedrock and surface topology, ice ablation and snow accumulation rates, and mass transport by wind over an extended period of time in a location where these quantities can be interpreted in the context of past glacialogical history.

Sandford, Scott A.↗

DOE OSTI2025

Fine-scale vegetation composition and structure shape spatiotemporal variation in surface albedo across a low Arctic tundra landscape

The unprecedented rate of warming in the Arctic is driving changes in the structure and composition of tundra vegetation. Increases in deciduous tall shrub cover, height, and density are of particular concern, as these changes alter local surface albedo in ways that could amplify effects on the regional surface energy budget (SEB). Despite this importance, significant uncertainties remain in understanding the interplay between fine-scale vegetation patterns and emergent albedo dynamics across space and time. Here, we address these uncertainties by (1) quantifying spatiotemporal variation in surface shortwave albedo and (2) determining the relative influence of fine-scale vegetation composition, structure, and environmental conditions on albedo across a representative low-Arctic tundra landscape on Alaska’s Seward Peninsula. To do this, we synthesized multi-scale, multi-platform remote sensing observations, including a novel Landsat-derived albedo time series, a fine-scale map of Arctic plant functional type (PFT) fractional cover, and airborne LiDAR estimates of canopy height and topography. We show that there are substantial reductions in winter albedo for pixels dominated by tall, woody PFTs (28.13%) relative to pixels dominated by non-woody vegetation, but almost no change in summer albedo (3% increase). Further, we identified a unimodal trend in the relationship between canopy height and the timing of the springtime transition from high (snowy) to low (leafy) albedo (peak at 5.5 m), possibly because of competing ‘snow-fence’ and ‘protrusion’ snow-shrub interactions. To explore the primary drivers of albedo, we constructed a random forest model and found that canopy height and the fractional cover of woody PFTs were as- or more important predictors of winter albedo than topographic features. These findings provide strong evidence for the impacts of local vegetation characteristics on regional surface albedo, highlighting the need for better quantification of snow-shrub interactions to accurately predict the Arctic’s SEB under future environmental change.

Arctic↗

NASA NTRS1987

Generation of liquid water on Mars through the melting of a dusty snowpack

An optical/thermal model for dusty snowpacks at temperate Martian latitudes is used to investigate the possibility of valley network formation by liquid water that was provided by snowmelts, assuming insolation absorption under clear-sky conditions. The mean-annual surface temperatures for snow and the atmospheric exchange terms of the surface energy balance are constrained by global climate model results. Under favorable conditions, liquid water is generated at atmospheric pressures as low as 30-100 mbar, provided that the substrate is composed of regolith; this condition is in keeping with the cratered terrain expected in an ancient Martian surface.

Clow, Gary D.↗

NASA NTRS1989

LIMEX '87 ice surface characteristics - Implications for C-band SAR backscatter signatures

Ice surface characterization data collected in 1987, during the Labrador Sea Ice Margin Experiment, are analyzed to estimate the changes in snow and ice properties at the onset of melt. Surface measurements were made from an ice research vessel on several days (some of which had coincident remote-sensing flights) at a number of locations in the marginal ice zone. These data are used as input parameters in a simple scattering model to simulate the effects of variations in material properties upon C-band scattering signatures. Snow moisture and large-scale surface roughness are demonstrated to have the largest effect upon HH polarization scattering cross-section sigma(HH)(O) and large differences are predicted between undeformed floe surfaces and deformed or undulating rough ice surfaces. Using a parametric approach, an approximate picture of rough and smooth ice signatures and their relative contrast are provided. The models reproduce a trend observed in synthetic aperture radar (SAR) images of increasing backscatter contrast between deformed and underformed ice over an early period of warning: such observations are consistent with the results of analysis of SAR images to date. The model also reproduces a calibrated SAR-derived signature with a reasonable degree of accuracy.

Drinkwater, Mark R.↗

NASA NTRS1981

Applications systems verification and transfer project. Volume 6: Operational applications of satellite snow-cover observations NOAA/NESS support study

Geostationary and polar orbiting satellite data from the National Oceanic and Atmospheric Administration were used to operationally provide field hydrologists with basin snowcover percentages for inclusion in runoff models. Data reduction is accomplished thru the use of optical rectification devices and electronic color density slicers. Over two thousand satellite-derived snow maps covering 30 different basins in the western United States were provided to users. Plans for improving snowmapping techniques on computer interactive systems and by all-digital analysis are presented. A description of the newest generation of NOAA polar orbiters, TIROS-N, and its potential for snowmapping is reviewed. Snowcover percentages for all basins determined between November 1974 and July 1978 are presented in tabular format.

Schneider, S. R.↗

NASA NTRS1993

Improvement and further development of SSM/I overland parameter algorithms using the WetNet workstation

Since the launch of the DMSP Special Sensor Microwave/Imager (SSM/I), several algorithms have been developed to retrieve overland parameters. These include the present operational algorithms resulting from the Navy calibration/validation effort such as land surface type (Neale et al. 1990), land surface temperature (McFarland et al. 1990), surface moisture (McFarland and Neale, 1991) and snow parameters (McFarland and Neale, 1991). In addition, other work has been done including the classification of snow cover and precipitation using the SSM/I (Grody, 1991). Due to the empirical nature of most of the above mentioned algorithms, further research is warranted and improvements can probably be obtained through a combination of radiative transfer modelling to study the physical processes governing the microwave emissions at the SSM/I frequencies, and the incorporation of additional ground truth data and special cases into the regression data sets. We have proposed specifically to improve the retrieval of surface moisture and snow parameters using the WetNet SSM/I data sets along with ground truth information namely climatic variables from the NOAA cooperative network of weather stations as well as imagery from other satellite sensors such as the AVHRR and Thematic Mapper. In the case of surface moisture retrievals the characterization of vegetation density is of primary concern. The higher spatial resolution satellite imagery collected at concurrent periods will be used to characterize vegetation types and amounts which, along with radiative transfer modelling should lead to more physically based retrievals. Snow parameter retrieval algorithm improvement will initially concentrate on the classification of snowpacks (dry snow, wet snow, refrozen snow) and later on specific products such as snow water equivalent. Significant accomplishments in the past year are presented.

Neale, Christopher M. U.↗

DOE OSTI2021

Triple-frequency radar retrieval of microphysical properties of snow

Abstract. An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log 10IWC and log 10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1974

Microwave signatures of snow, ice and soil at several wavelengths

Analyses of data obtained from aircraft-borne radiometers have shown that the microwave signatures of various parts of the terrain depend on both the volume scattering cross-section and the dielectric loss in the medium. In soil, it has been found that experimental data fit a model in which the scattering cross section is negligible compared to the dielectric loss. On the other hand, the volume scattering cross-section in snow and continental ice was found, from analyzing data obtained with aircraft- and spacecraft-borne radiometers, to be more important than the dielectric loss or surface reflectivity in determining the observed microwave emissivity. A model which assumes Mie scattering of ice particles of various sizes was found to be the dominant volume scattering mechanism in these media. Both spectral variation in the microwave signatures of snow and ice fields, as well as the variation in the emissivity of continental ice sheets such as those covering Greenland and Antarctica appear to be consistent with this model.

Gloersen, P.↗

DOE OSTI2026

Estimating Fine-Resolution Shortwave Broadband Albedo of Croplands from Harmonized Landsat and Sentinel-2 Data

Altered surface albedo due to land-cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine-resolution (10–30 m) instantaneous albedo and coarse-resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo information at 500-m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine-resolution satellite data. Here, to address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results [root-mean-square error (RMSE)] around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine-resolution satellite data is promising. To facilitate the use of fine-resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias.”

Harmonized Landsat and Sentinel-2↗