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DOE OSTI2023

SPRUCE Manual Phenology Observations and Photographs Beginning in 2011

This dataset contains periodic (most often weekly during the active season) observations of the phenology of vegetation bud swell, leaf out, leaf off, flowering, fruiting and the nature of snow cover and ice presence from the 2011-2023 growing seasons. These observations are supplemented by the collection of images from periodic photographs of the experimental plots (e.g., panoramic photographs) or images within plots (e.g., pictures of vegetation development or community status within defined ground footprints such as flux collars). A summary of vegetation phenology and snow and ice presence form 2011-2016 is also provided. Related data sets are also available that summarize site phenology (see Related Datasets). This dataset contains 336 phenology notes files in *.pdf format, 2284 phenology photos in *.jpg format, and three companion files containing phenology summaries, methodological, other contextual information. Early observations were conducted throughout the S1-Bog with an emphasis on the southern end of the bog. Observations collected since the spring of 2013 have focused on the southern end of the S1-Bog and the plots 2 through 21 of the SPRUCE experiment. Beginning in 2015 those observations included assessments within the enclosure space surrounding plots 4, 6, 8, 10, 11, 13, 16, 17, 19 and 20. These observations are ongoing, and this dataset will be appended annually.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Atmospheric River Frequency-Category Characteristics Shape U.S. West Coast Runoff

Abstractrunoff response to atmospheric rivers (ARs) over the U.S. West Coast. We focused on runoff time series variations impacted by AR characteristics (e.g., category and frequency) and land preconditions during Northern Hemisphere cool seasons in the period of 1940–2023. Results show that high-category ARs significantly increase local runoff with higher hourly precipitation rates leading to a greater incremental rate and peak runoff. Extreme runoff increases greatly with the AR category with an increase rate up to 12.5 times stronger than non-extreme runoff. Besides the AR category, land preconditions such as soil moisture and snowpack also play crucial roles in modulating runoff response. We found that runoff induced by weak-category ARs is more sensitive to land preconditions than high-category ARs, with high peak runoff occurring when soil is nearly saturated. Additionally, more than 50% of high-peak-runoff events in snow-covered grid cells are associated with rain-on-snow events particularly for the events associated with weaker ARs. Regression analysis reveals that AR precipitation and land preconditions jointly influence runoff, emphasizing the importance of including soil moisture and snowpack levels in AR impact assessments. The study also highlights the intensified runoff response to back-to-back ARs with short intervals, which may become more frequent with climate warming, posing increased flood risks via facilitating wet soil conditions. Our findings have significant implications for AR risk predictions and the development of prediction models for AR-induced runoff.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024

A new version of this dataset is available at doi:10.15485/3001338 and is the first citation in the 'Related References' section. It is expands on this dataset by appending another water year of data collection and additional logger sites.This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 12 August 2024. Instruments were deployed at 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 shows smaller-scale snow patterns across a nearby meadow.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.Dataset update 2025-11-18: Modified abstract and related references sections to include new version of dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Spatial patterns of snow distribution in the sub-Arctic

Abstract. The spatial distribution of snow plays a vital role in sub-Arctic and Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019; ∼ 2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22 000 data points), we developed simple models of the spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was random forest, using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, TPI), while slope, wind, and fine microtopography factors were less important. The characterization of the SWE spatial distribution patterns will be used to validate and improve snow distribution modeling in the Department of Energy's Earth system model and for improved understanding of hydrology, topography, and vegetation dynamics in the sub-Arctic and Arctic regions of the globe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 8 August 2025. Instruments were deployed at 19 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 (Avery Picnic) provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 (Emerald Lake) shows smaller-scale snow patterns across a nearby meadow. Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Sensitivity of Simulated Polar Climate to Improved Partitioning of Spectral Solar Irradiance Between Visible and Near‐Infrared Bands

The solar radiative processes that contribute to Earth's surface and atmospheric energy budgets are strongly dependent on wavelength. For example, snow and water vapor become more absorptive as incident radiation shifts from visible (VIS) to near-infrared (NIR) wavelengths. Some earth system models (ESMs) aggregate solar radiation into just two bands (VIS and NIR) partitioned at 0.7 μm for transmission between the atmospheric and surface components. In the widely used radiative transfer model RRTMG_SW this partition is near the center of the overlap spectral band spanning 0.625–0.778 μm, whose flux is often approximated as being evenly divided between the VIS and NIR surface bands. Using a hyperspectral radiative transfer model, we show that the fractional downwelling surface flux within the overlap band is usually distributed about 55.5%:44.5% VIS:NIR. This improved approximation shifts as much as 4.27 W m −2 from the NIR to the VIS band, leading to an instantaneous decrease in surface absorption of up to 0.91 W m −2 over snow-covered surfaces. Century-long fully coupled ESM simulations show surface absorption over snow decreases by over 1.6 W m −2 . The coupled response in sea ice regions amplifies the initial forcing ten-fold, and increases seasonal sea ice area by up to 4.9%. These results highlight the importance of accurately representing the spectral distribution of solar radiation in the cryosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values d max were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Snow Albedo Feedbacks Enhance Snow Impurity-Induced Radiative Forcing in the Sierra Nevada

This study employs a fully coupled meteorology-chemistry-snow model to investigate the impacts of light-absorbing particles (LAPs) on snow darkening in the Sierra Nevada. After comprehensive evaluation with spatially and temporally complete satellite retrievals, the model shows that LAPs in snow reduce snow albedo by 0.013 (0-0.045) in the Sierra Nevada during the ablation season (April-July), producing a midday mean radiative forcing (RF) of 4.5 W m -2 which increases to 15-22 W m -2 in July. LAPs in snow accelerate snow aging processes and reduce snow cover fraction, which doubles the albedo change and radiative forcing caused by LAPs. The impurity-induced snow darkening effects decrease snow water equivalent and snow depth by 20 mm and 70 mm in June in Sierra Nevada bighorn sheep habitat. The earlier snowmelt reduces root zone soil moisture by 20%, deteriorating the forage productivity and playing a negative role in the survival of bighorn sheep.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Brown Carbon Fuel and Emission Source Attributions to Global Snow Darkening Effect

Snow and ice albedo reduction due to deposition of absorbing particles (i.e., snow darkening effect (SDE)) warms the Earth system and is largely attributed to black carbon (BC) and dust. Absorbing organic aerosol (BrC) also contributes to SDE but has received less attention due to uncertainty and challenges in model representation. This work incorporates the SDE of absorbing organic aerosol (BrC) from biomass burning and biofuel sources into the Snow Ice and Aerosol Radiative (SNICAR) model within a variant of the Community Earth System Model (CESM). Additionally, 12 different emission regions of BrC and BC from biomass burning and biofuel sources are tagged to quantify the relative contribution to global and regional SDE. BrC global SDE (0.021–0.056 Wm -2 ) is larger than other model estimates, corresponding to 37%–98% of the SDE from BC. When compared to observations, BrC simulations have a range in median bias (-2.5% to +21%), with better agreement in the simulations that include BrC photochemical bleaching. The largest relative contributions to global BrC SDE are traced to Northern Asia (23%–31%), Southeast Asia (16%–21%), and South Africa (13%–17%). Transport from Southeast Asia contributes nearly half of the regional BrC SDE in Antarctica (0.084–0.3 Wm -2 ), which is the largest regional input to global BrC SDE. Lower latitude BrC SDE is correlated with snowmelt, in-snow BrC concentrations, and snow cover fraction, while polar BrC SDE is correlated with surface insolation and snowmelt. This indicates the importance of in-snow processes and snow feedbacks on modeled BrC SDE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Shrubs Strongly Influence Snow Properties in Two Subarctic Watersheds

Understanding changes in snow distribution in permafrost ecosystems is fundamental to predicting their response to future climate change. The expansion of tall shrubs into tundra ecosystems can trap snow and insulate permafrost ecosystems during the winter, but the overall insulation effect is dependent upon many ecosystem properties. To study shrub–snow–ground interactions, small temperature sensors were deployed at two research sites on the Seward Peninsula of Alaska, USA, during the 2019–2020 winter. Snow temperatures were used to extrapolate multiple metrics, including freezing n-factors, the snow insulation effect, snow cover duration, and the length of the snowmelt period. Statistical and spatial analysis showed that shrub patches were a dominant control on all snow metrics. Within shrub patches, average ground temperatures were 2.1°C warmer, snow persisted 50 days longer, snow insulation was double, and a longer, later spring snowmelt period occurred compared to nonshrubby areas. Site-level differences contributed relatively little to variation in snow metrics, indicating that shrub presence is an overarching driver of snow–ground interactions at the locations examined. Shrub expansion, which is anticipated under climate change, will strongly impact future permafrost distribution and Arctic energy, water, and carbon cycles through snow–shrub–ground feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

National Center for Airborne Laser Mapping (NCALM) LiDAR and DEM data from two NGEE Arctic Sites, Seward Peninsula, Alaska, Winter 2022

From April 3 through April 6 of 2022, airborne remote sensing data was collected by the National Center for Airborne Laser Mapping (NCALM) in collaboration with NGEE Arctic scientists. Snow-on data was collected around two NGEE Arctic study sites on the Seward Peninsula of Alaska: Teller mm 27 and Kougarok mm 64. A Robinson R44 II helicopter with a RIEGL VQ-580 II airborne laser scanner was used to collect the LiDAR point cloud data for each study site. This survey was conducted during expected peak snow cover at the end of the winter on the Seward Peninsula, and can be paired with data collected in 2021 during the snow-off campaign "National Center for Airborne Laser Mapping (NCALM) LiDAR, Imagery, and DEM data from five NGEE Arctic Sites, Seward Peninsula, Alaska, August 2021" (Singhania et al., 2023) (NGA270). This data package contains LiDAR point clouds (.las), Digital Elevation Models (.tif), and shapefiles of the .las tiling system (.shp). A project report detailing data collection details, GNSS stations and GNSS corrections, and processing steps is also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort 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 OSTI2020

Dust dominates high-altitude snow darkening and melt over high-mountain Asia

Westerly-driven, long-range transportation of dust particles in elevated aerosol layers (EALs) is a persistent phenomenon during spring and summer over the Indian subcontinent. During the snow accumulation season, EALs transport substantial dust to the snow-covered slopes of high-mountain Asia (HMA). Here we use multiple satellite-based estimates to demonstrate a robust physical association between the EALs and dust-induced snow darkening over HMA. Results from a fully-coupled atmosphere-chemistry-snow model support these observations, revealing a signature of increasing dust-induced snow darkening with surface elevation over HMA that peaks near 4500 m. Moreover, the influence of dust on snow darkening is greater than that of black carbon above 4000 m. Our findings suggest a discernable role of dust in the observed spatial heterogeneity of snowmelt and snowline trends over HMA and highlight an increasing contribution of dust to snowmelt as the snowline rises with warming.

Elevated aerosol layers (EAL), Light absorbing par↗
DOE OSTI2024

iButton and Tinytag snow/ground interface temperature measurements at Teller 27 and Kougarok 64 from 2022-2023, Seward Peninsula, Alaska

Snow/ground interface temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula, Alaska. Data were collected between October 1, 2022 to September 18, 2023 using iButton Link DS1921G-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours, while Tinytag data were collected every 30 minutes. In total, data were collected from 196 iButtons and 26 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and two *.kml files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain.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 OSTI2024

iButton and Tinytag snow temperature measurements at Teller 27 and Kougarok 64, Seward Peninsula, Alaska, 2021-2022

Snow temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula. Data were collected from October 1, 2021 to August 16, 2022 using iButton Link DS1926-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours beginning on October 1, 2021. Tinytag data collection began between October 9 and October 12, 2021 depending on sensor installation date. Tinytag data were collected every 30 minutes. In total, data were collected from 236 iButtons and 30 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and four *.shp files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort 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 OSTI2024

iButton snow temperature measurements at Teller 27 and Kougarok 64, Seward Peninsula, Alaska, 2019-2020

Snow temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula. Data were collected from October 1, 2021 to August 16, 2022 using iButton Link DS1926-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours beginning on October 1, 2021. Tinytag data collection began between October 9, 2021 and October 12, 2021 depending on the specific Tinytag and data were collected every 30 minutes. In total, data were collected from 236 iButtons and 30 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and four *.shp files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain.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 OSTI2024

iButton snow-ground interface temperature measurements in Los Alamos, New Mexico from 2023-2024

Snow/ground interface temperature measurements were collected at two sites in Los Alamos, New Mexico. Data were collected from November 29, 2023 to April 8, 2024 using iButton Link DS1921G-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g). These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 3 hours from a total of 19 iButtons. iButtons were placed in pairs, with one iButton placed at the ground surface and another buried 1 - 5 cm below the ground surface. One buried iButton did not successfully collect data, and therefore was excluded from this dataset. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Specifically, this dataset was used as a validation source for a novel machine learning approach to estimating snow depth (see related publication). Sensors were placed in areas with bare ground or minimal grass coverage, located away from any large vegetation. At Site A (TA51), manual snow depths were collected as validation data. These measurements were taken next to iButtons periodically throughout the winter, and notes on other precipitation types were also recorded. At Site B (TA6 Meteorological Station), a nearby sensor collected snow depths throughout the winter. This dataset contains one *.csv file of snow/ground interface temperatures at two sites, one *.csv file of manually collected snow depths, and one *.kml file of sensor locations. 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 OSTI2024

Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region

In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km 2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Snow thermal conductivity and conductive flux in the Central Arctic: Estimates from observations and implications for models

During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m −1 K −1 to 0.47 W m −1 K −1 with a median from all data of 0.39 W m −1 K −1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m −2 , but the average residual during winter is −6 W m −2 , which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.

54 ENVIRONMENTAL SCIENCES↗