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NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗

NASA NTRS2017

Snow Accumulation Variability on a West Antarctic Ice Stream Observed with GPS Reflectometry, 2007-2017

Land ice loss from Antarctica is a significant and accelerating contribution to global sea level rise; however, Antarctic mass balance estimates are complicated by insufficient knowledge of surface mass balance processes such as snow accumulation. Snow accumulation is challenging to observe on a continental scale and in situ data are sparse, so we largely rely on estimates from atmospheric models. Here we employ a novel technique, GPS interferometric reflectometry (GPS-IR), to measure upper (less than 2 m) firn column thickness changes across a 23-station GPS array in West Antarctica. We compare the results with antenna heights measured in situ to establish the method's daily uncertainty (0.06 m) and with output from two atmospheric reanalysis products to categorize spatial and temporal variability of net snow accumulation. GPS-IR is an effective technique for monitoring surface mass balance processes that can be applied to both historic GPS data sets and future experiments to provide critical in situ observations of processes driving surface height evolution.

snow↗

DOE OSTI2022

Stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases in a mountainous watershed (Crested Butte, Colorado)

Temperature and precipitation variability in response to climate change affects water cycling of watersheds and can potentially impact water quality, water availability, elemental and molecular fluxes, and biogeochemical processes. Here we report the measurements of light stable isotopes and greenhouse gases analysis to a large multidisciplinary project addressing watershed function. Water hydrogen (H) and oxygen (O) stable isotopes of snow and water saturated soil were measured to constrain source contributions to groundwater and streamflow. In an effort to better constrain existing nitrogen and carbon cycling models in high-altitude ecosystems, carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) greenhouse gases concentrations and stable isotope signatures of CO2, dissolved inorganic carbon (DIC), and water were measured. This dataset includes one CSV file and one Excel file containing data including stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases. The CSV and Excel files contain the same information/data but have included both versions for accessibility.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Leveraging Groundwater Dynamics to Improve Predictions of Summer Low-Flow Discharges

Summer streamflow predictions are critical for managing water resources; however, warming-induced shifts from snow to rain regimes impact low-flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. Here, we quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low-flow prediction compared to snow metrics at a rain-dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep-storage-dominated systems. Overall, including RLGW improved low-flow prediction across diverse watersheds.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Blowing Snow Sublimation and Transport over Antarctica from 11 Years of CALIPSO Observations

Blowing snow processes commonly occur over the earth's ice sheets when the 10 mile wind speed exceeds a threshold value. These processes play a key role in the sublimation and redistribution of snow thereby influencing the surface mass balance. Prior field studies and modeling results have shown the importance of blowing snow sublimation and transport on the surface mass budget and hydrological cycle of high-latitude regions. For the first time, we present continent-wide estimates of blowing snow sublimation and transport over Antarctica for the period 2006-2016 based on direct observation of blowing snow events. We use an improved version of the blowing snow detection algorithm developed for previous work that uses atmospheric backscatter measurements obtained from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The blowing snow events identified by CALIPSO and meteorological fields from MERRA-2 are used to compute the blowing snow sublimation and transport rates. Our results show that maximum sublimation occurs along and slightly inland of the coastline. This is contrary to the observed maximum blowing snow frequency which occurs over the interior. The associated temperature and moisture reanalysis fields likely contribute to the spatial distribution of the maximum sublimation values. However, the spatial pattern of the sublimation rate over Antarctica is consistent with modeling studies and precipitation estimates. Overall, our results show that the 2006-2016 Antarctica average integrated blowing snow sublimation is about 393 +/- 196 Gt yr(exp -1), which is considerably larger than previous model-derived estimates. We find maximum blowing snow transport amount of 5 Mt km-1 yr(exp -1) over parts of East Antarctica and estimate that the average snow transport from continent to ocean is about 3.7 Gt yr(exp -1). These continent-wide estimates are the first of their kind and can be used to help model and constrain the surface mass budget over Antarctica.

Antarctica Sublimation Ice-sheet↗

NASA NTRSDate not supplied

The Surface Albedo of Sea Ice in CMIP6 and the Implications for the Surface Albedo Feedback

The Arctic has experienced rapid sea ice loss and a substantial decline in surface albedo, significantly impacting its radiation budget. Climate models from the Coupled Model Intercomparison Project (CMIP6) reproduce these changes. However, inconsistencies remain among models regarding the magnitude, spatial distribution, and seasonal patterns of Arctic surface albedo evolution. This study investigates these discrepancies by comparing model outputs with observation from the Clouds and the Earth's Radiant Energy System (CERES). We develop a decomposition method to assess the contributions of sea ice albedo, sea ice concentration, and sea ice extent to Arctic surface albedo. Over land, differences in snow cover account for the substantial inter-model spread in surface albedo, while over the ocean, sea ice albedo, concentration, and extent all contribute. Comparisons between CMIP6 and the Atmospheric Model Intercomparison Project (AMIP) simulations, which use identically prescribed sea ice concentrations, reveals considerable inter-model spread in Arctic Ocean surface albedo due to differences in sea ice albedo. Applying the decomposition method to projections shows that models predicting larger decreases in sea ice concentration and extent, especially in the Central Arctic, exhibit lower surface albedo and stronger sea ice albedo feedback. Beyond 2050, Arctic Ocean surface albedo decline is mainly influenced by sea ice extent indicating that the retreat of the ice edge is the most important process to constrain the surface albedo feedback. This study provides insights into factors contributing to the spread and changes in Arctic surface albedo and the associated sea ice albedo feedback.

Patrick C Taylor↗

DOE OSTI2022

Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic

In this study, we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007–2010. For this purpose, both approaches, i.e., the assessments of the surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the southeastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes between 66 and 81°N. Excellent agreement (difference less than 1%) in the Arctic-averaged annual snowfall rate between HIRHAM5 and CloudSat is found, whereas ERA-Interim reanalysis shows an underestimation of 45% and significant deficits in the representation of the snowfall rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic variations as well as an underestimation in the northern North Atlantic Ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (PAMTRA) forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72–73°N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model, most likely due to the small sizes of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes, as illustrated by comparison of different schemes available for HIRHAM5.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Chemical Composition and Mixing State of Wintertime Aerosol from the European Arctic Site of Ny-Ålesund, Svalbard

The Arctic is rapidly warming, and aerosols play an increasingly important role by scattering and absorbing sunlight and by participating in cloud formation. Their optical and cloud-forming properties depend on the mixing state and chemical composition, but observations of these features remain limited. This study comprehensively characterizes 25,254 individual particles collected at Ny-Ålesund, Svalbard (November −December 2020), using microspectroscopy techniques to investigate their size, morphology, mixing state, and chemical composition. Fresh sea salt aerosols (SSA) were identified as the most abundant (∼85%), of the total observed aerosol population, with potential sources from sea spray and blowing snow. Air masses originating from the Arctic Ocean surrounding Svalbard likely contribute to increased concentrations of sub-micrometer “Fresh SSA” particles. “Aged SSA” particles (7.4%) are enriched in sulfur and nitrogen, compared to “Fresh SSA”. These elevated ratios may result from various atmospheric aging processes including the uptake of sulfuric and nitric acids. Here, our results suggest that aged SSA, with sizes larger than 300 nm, likely underwent chlorine depletion by sulfuric and nitric acids during transport. Additionally, elemental analysis reveals that both fresh and aged SSA can mix with dust particles, regardless of the SSA size (49.9% in sub-micrometer size and 50.1% in super-micrometer size, respectively). Dust particles are efficient ice-nucleating particles (INPs), and SSA is known to act as cloud condensation nuclei (CCN), and therefore, their mixtures may inherit both properties. The non-negligible number (4.4%) of SSA-dust mixtures underscores the importance of these particles as potential sources of CCN and INP in the Arctic atmosphere.

Arctic↗

NASA NTRS2011

Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Jin, Zhonghai↗

NASA NTRS2006

Percentage Contributions from Atmospheric and Surface Features to Computed Brightness Temperatures

Over the past few years, a few solid precipitation detection and retrieval algorithms have been developed and shown to be applicable for snowing clouds and blizzards. Current precipitating snow retrieval algorithms require the use of millimeter-wave radiometer observations. The millimeter-wave frequencies are especially sensitive to the scattering and emission properties of frozen particles due to the ice particle refractive index. These channels can also be used to discern information about the frozen particles above the melting layer. Passive radiometric channels respond to both the integrated particle mass throughout the volume and field of view, and to the amount, location, and size distribution of the frozen (and liquid) particles with the sensitivity varying for different frequencies and hydrometeor types. This work will show the percentage of the brightness temperature resulting from the liquid hydrometeor, frozen hydrometeor, relative humidity, and surface contributions. The focus will be on precipitating snow events and millimeter-wave frequencies however, other events and frequencies will be included in the analysis.

SkofronickJackson, Gail↗

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↗

NASA NTRS1981

Stellar winds and mass-loss rates from Be stars

An analysis for the effects of stellar winds concerning Copernicus scans of Si III and Si IV resonance lines in the spectra of 22 early B stars, of which 19 have Be characteristics, yields line asymmetries in all but one of the stars to which theoretical profiles were fitted. The resulting wind parameters were used to calculate mass-loss rates. Most of the silicon in the winds of the cooler stars is in the form of Si III and Si IV, leading to a mass loss rate value ranging from 10 to the -11th to 3 x 10 to the -9th solar masses/year. Equatorial gravity darkening is seen in the relative velocity widths of ultraviolet and visible photospheric lines. The measured photospheric Si III and Si IV resonance-line equivalent widths do not agree with those predicted by current stellar atmosphere calculations. Be star mass loss rates are probably insufficient to affect either their own evolution or that of the galaxy, since their winds do not significantly contribute to the enrichment of the interstellar medium.

Snow, T. P., Jr.↗

NASA NTRS1980

The contribution of space observations to water resources management; Proceedings of the Symposium, Bangalore, India, May 29-June 9, 1979

The symposium focused on hydrology, soil moisture estimation and ground water exploration, wetlands monitoring and water quality estimation, hydrometeorology, snow and ice monitoring, and evapotranspiration estimation. Other problems discussed include surface water and flood mapping, watershed runoff estimation and prediction, and new space systems contributing to water resources management.

Salomonson, V. V.↗

NASA NTRS2022

A New Snow Module Improves Predictions of the Isotope-Enabled MAIDENiso Forest Growth Model

The representation of snow processes in forest growth models is necessary to accurately predict the hydrological cycle in boreal ecosystems and the isotopic signature of soil water extracted by trees, photosynthates and tree-ring cellulose. Yet, most process-based models do not include a snow module; consequently, their simulations may be biased in cold environments. Here, we modified the MAIDENiso model to incorporate a new snow module that simulates snow accumulation, melting and sublimation, as well as thermal exchanges driving freezing and thawing of the snow and the soil. We tested these implementations in two sites in eastern and western Canada for black spruce (Picea mariana (Mill.) B.S.P.) and white spruce (Picea glauca (Moench) Voss) forests, respectively. The new snow module improves the skills of the model to predict components of the hydrological cycle. The MAIDENiso model is now able to reproduce the spring discharge peak and to simulate stable oxygen isotopes in tree-ring cellulose more realistically than in the original snow-free version of the model. The new implementation also results in simulations with a higher contribution from the source water on the oxygen isotopic composition of the simulated cellulose, leading to more accurate estimates of cellulose isotopic composition. Future work may include the development of inverse modelling with this new version of MAIDENiso to produce robust reconstructions of the hydrological cycle and isotope processes in cold environments.

Snow processes↗

NASA NTRS2021

Atmospheric Blocking Drives Recent Albedo Change Across the Western Greenland Ice Sheet Percolation Zone

Greenland Ice Sheet (GrIS) albedo has decreased over recent decades, contributing to enhanced surface melt and mass loss. However, it remains unclear whether GrIS darkening is due to snow grain size increases, higher concentrations of light-absorbing impurities (LAIs), or a combination. Here, we assess albedo controls in the western GrIS percolation zone using in situ albedo, LAI, and grain size measurements. We find a significant correlation between albedo and snow grain size (p < 0.01), but not with LAIs. Modeling corroborates that LAI concentrations are too low to significantly reduce albedo, but larger grain sizes could reduce albedo by at least ∼3%. Strong atmospheric blocking increases grain sizes and reduces albedo through increased surface temperature, fewer storms, and higher incoming shortwave radiation. These findings clarify the mechanisms by which anomalously strong blocking contributed to recent GrIS albedo decline and mass loss, highlighting the importance of improving projections of future blocking.

Gabriel Lewis↗

NASA NTRS2012

Integrated Instrument Simulator Suites for Earth Science

The NASA Earth Observing System Simulators Suite (NEOS3) is a modular framework of forward simulations tools for remote sensing of Earth's Atmosphere from space. It was initiated as the Instrument Simulator Suite for Atmospheric Remote Sensing (ISSARS) under the NASA Advanced Information Systems Technology (AIST) program of the Earth Science Technology Office (ESTO) to enable science users to perform simulations based on advanced atmospheric and simple land surface models, and to rapidly integrate in a broad framework any experimental or innovative tools that they may have developed in this context. The name was changed to NEOS3 when the project was expanded to include more advanced modeling tools for the surface contributions, accounting for scattering and emission properties of layered surface (e.g., soil moisture, vegetation, snow and ice, subsurface layers). NEOS3 relies on a web-based graphic user interface, and a three-stage processing strategy to generate simulated measurements. The user has full control over a wide range of customizations both in terms of a priori assumptions and in terms of specific solvers or models used to calculate the measured signals.This presentation will demonstrate the general architecture, the configuration procedures and illustrate some sample products and the fundamental interface requirements for modules candidate for integration.

emission↗

NASA NTRS2012

Integrated Instrument Simulator Suites for Earth Science

radiative transfer↗

NASA NTRS1986

Variations in brightness temperature over cold first-year sea ice near Tuktoyaktuk, Northwest Territories

Microwave radiometric temperature T(B) profiles of first-year sea ice were obtained along 70- to 100-m traverses, with sled-mounted radiometers at 10, 18.7, 33.6, and 37 GHz and an effective spot size of 30 cm. Measurements of T(B) as a function of nadir angle were obtained at selected sites along the traverses. Snow and ice properties were recorded and correlated with the T(B) measurements to infer the effect of snow cover and ice conditions on the radiometric temperature. T(B) correlated positively with the brine volume profile in the ice at several sites, suggesting that brine volume has a strong effect on T(B) under these conditions. An overall statistical comparison of snow thickness with T(B), when compared with previously published models, suggests that the effect of snow cover on the microwave transmission coefficient of the snow/ice interface may be an important contribution to the radiometric temperature at these frequencies. A model is proposed to explain the data.

Lohanick, A. W.↗