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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Cross-Scale and Cross-Interface Processes and Arctic Amplification

The Arctic is a dynamic region, demonstrated by its remarkable internal variability and rapid response to anthropogenic climate change over the last 40 years. Actionable predictions and projections hold significant value for managing both natural and human systems. The value of these model outputs only grows in a warmer, less icy Arctic. However, substantial gaps exist in our understanding of cross-scale and cross-interface (air-sea ice-ocean) interactions that limit our predictive capabilities. Studies dating back at least 40 years provide evidence that the evolution of the Arctic climate system is sensitive to these cross-scale and cross-interface interactions. This presentation summarizes our understanding of cross-scale and cross-interface interactions relevant to the Arctic’s response to anthropogenic climate change—Arctic Amplification. The presentation emphasizes the influence of surface-type dependent turbulent flux exchanges of heat and moisture, the rectification of episodic atmospheric heat transport events on time-averaged changes, local and remote feedback interactions, and cross-seasonal energy transfers. The presentation concludes with a summary of knowledge gaps and discusses potential pathways for accelerating our understanding of the Arctic climate system.

Patrick C. Taylor↗
NASA NTRSDate not supplied

The Importance of Cross-Scale and Cross-Interface Processes on Arctic Amplification

The Arctic is a dynamic region, demonstrated by its remarkable internal variability and rapid response to anthropogenic climate change over the last >40 years. Actionable predictions and projections hold significant value for managing both natural and human systems. The value of these model outputs only grows in a warmer, less icy Arctic. However, substantial gaps exist in our understanding of cross-scale and cross-interface (air-sea ice-ocean) interactions that limit our predictive capabilities. Studies dating back at least 40 years provide evidence that the evolution of the Arctic climate system is sensitive to these cross-scale and cross-interface interactions. This presentation summarizes our understanding of cross-scale and cross-interface interactions relevant to the Arctic’s response to anthropogenic climate change—Arctic Amplification. The presentation emphasizes the influence of surface-type dependent turbulent flux exchanges of heat and moisture, the rectification of episodic atmospheric heat transport events on time-averaged changes, local and remote feedback interactions, and cross-seasonal energy transfers. The presentation concludes with a summary of knowledge gaps and discusses potential pathways for accelerating our understanding of the Arctic climate system.

Patrick C Taylor↗
NASA NTRSDate not supplied

Cross-Scale and Cross-Interface Processes and Arctic Amplification

The Arctic is a dynamic region, demonstrated by its remarkable internal variability and rapid response to anthropogenic climate change over the last >40 years. Actionable predictions and projections hold significant value for managing both natural and human systems. The value of these model outputs only grows in a warmer, less icy Arctic. However, substantial gaps exist in our understanding of cross-scale and cross-interface (air-sea ice-ocean) interactions that limit our predictive capabilities. Studies dating back at least 40 years provide evidence that the evolution of the Arctic climate system is sensitive to these cross-scale and cross-interface interactions. This presentation summarizes our understanding of cross-scale and cross-interface interactions relevant to the Arctic’s response to anthropogenic climate change—Arctic Amplification. The presentation emphasizes the influence of surface-type dependent turbulent flux exchanges of heat and moisture, the rectification of episodic atmospheric heat transport events on time-averaged changes, local and remote feedback interactions, and cross-seasonal energy transfers. The presentation concludes with a summary of knowledge gaps and discusses potential pathways for accelerating our understanding of the Arctic climate system.

Patrick C. Taylor↗
DOE OSTI2021

Simulated hydrological dynamics and coupled iron redox cycling impact methane production in an Arctic soil: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "Simulated hydrological dynamics and coupled iron redox cycling impact methane production in an Arctic soil" in the Journal of Geophysical Research-Biogeosciences. We simulated biogeochemical cycling in arctic soils using the PFLOTRAN geochemical model combined with measurements from previous NGEE Arctic incubations of polygonal permafrost soils in northern Alaska (Zheng et al., 2018). Simulated iron cycling, carbon dioxide production, and methane production were compared with incubation measurements and the parameterized model was then used to simulate coupled iron and carbon cycling over repeated oxic-anoxic cycles at different levels of carbon substrate availability and pH. The most recent data version (2.0) in the archive incorporates changes to the model and simulations as suggested by reviewers during the manuscript review process. These changes include an updated parameterization of the model; a new set of simulations omitting the iron cycle for direct evaluation of how iron cycle processes affect modeled outcomes; and a set of simulations testing different scenarios of carbon substrate availability in addition to scenarios of initial soil pH. This archive contains simulation code, model output, and analysis code for PFLOTRAN simulations. All scripts are python except the batch script for submitting multiprocessor jobs. Note that the model also requires compiled versions of the Alquimia interface and the NGEE Arctic fork of the PFLOTRAN geochemical simulator (see the README_INSTALL document for basic instructions). The Output directory contains eight data files in netCDF format generated by the model. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) 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↗
NASA NTRS2023

A Tuned Ocean Color Algorithm for the Arctic Ocean: A Solution for Waters With High CDM Content

The Arctic Ocean (AO) is the most river-influenced ocean. Located at the land-sea interface wherein phytoplankton blooms are common, Arctic coastal waterbodies are among the most affected regions by climate change. Given phytoplankton are critical for energy transfer supporting marine food webs, accurate estimation of chlorophyll a concentration (Chl), which is frequently used as a proxy of phytoplankton biomass, is critical for improving our knowledge of the Arctic marine ecosystem and its response to the ongoing climate change. Due to the unique and complex bio-optical properties of the AO, efforts are still needed to obtain more accurate Chl estimates, especially for coastal waters with high colored detrital material (CDM) content. In this study, we optimized the the Garver-Siegel-Maritorena (GSM) algorithm, using an Arctic bio-optical dataset comprised of seven wavelengths (the original GSM wavelengths plus 625 nm). Results suggested that our tuned algorithm, denoted GSMA, outperformed an alternative AO GSM algorithm denoted AO.GSM, but the accuracy of Chl estimates was only improved by 8%. In addition, GSMA showed appreciable robustness when assessed using a satellite image and two non-Arctic coastal datasets.

Arctic↗
DOE OSTI2022

NGEE Arctic LANL Overview [Slides]

The Next-Generation Ecosystem Experiments (NGEE Arctic) project has a goal to "deliver a process-rich ecosystem model, extending from bedrock to the top of the vegetative canopy/atmospheric interface, in which the evolution of Arctic ecosystems in a changing climate can be modeled at the scale of a high resolution Earth System Model (ESM) grid cell." LANL works across multiple NGEE Arctic science questions, such as: Q1. How does the structure and organization of the landscape control permafrost evolution and associated carbon and nutrient fluxes in a changing climate? Q5. Where, when, and why will the Arctic become wetter or drier, and what are the implications for climate forcing? and Q6. What controls the vulnerability of Arctic ecosystems to disturbance, and how do disturbances alter the structure and function of these ecosystems? This report features LANL's research summaries.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Projected Impact of Climate Change on the Energy Budget of the Arctic Ocean by a Global Climate Model

The annual energy budget of the Arctic Ocean is characterized by a net heat loss at the air-sea interface that is balanced by oceanic heat transport into the Arctic. The energy loss at the air-sea interface is due to the combined effects of radiative, sensible, and latent heat fluxes. The inflow of heat by the ocean can be divided into two components: the transport of water masses of different temperatures between the Arctic and the Atlantic and Pacific Oceans and the export of sea ice, primarily through Fram Strait. Two 150-year simulations (1950-2099) of a global climate model are used to examine how this balance might change if atmospheric greenhouse gases (GHGs) increase. One is a control simulation for the present climate with constant 1950 atmospheric composition, and the other is a transient experiment with observed GHGs from 1950 to 1990 and 0.5% annual compounded increases of CO2 after 1990. For the present climate the model agrees well with observations of radiative fluxes at the top of the atmosphere, atmospheric advective energy transport into the Arctic, and surface air temperature. It also simulates the seasonal cycle and summer increase of cloud cover and the seasonal cycle of sea-ice cover. In addition, the changes in high-latitude surface air temperature and sea-ice cover in the GHG experiment are consistent with observed changes during the last 40 and 20 years, respectively. Relative to the control, the last 50-year period of the GHG experiment indicates that even though the net annual incident solar radiation at the surface decreases by 4.6 W(per square meters) (because of greater cloud cover and increased cloud optical depth), the absorbed solar radiation increases by 2.8 W(per square meters) (because of less sea ice). Increased cloud cover and warmer air also cause increased downward thermal radiation at the surface so that the net radiation into the ocean increases by 5.0 Wm-2. The annual increase in radiation into the ocean, however, is compensated by larger increases in sensible and latent heat fluxes out of the ocean. Although the net energy loss from the ocean surface increases by 0.8 W (per square meters), this is less than the interannual variability, and the increase may not indicate a long-term trend. The seasonal cycle of heat fluxes is significantly enhanced. The downward surface heat flux increases in summer (maximum 2 of 19 W per square meters or 23% in June) while the upward heat flux increases in winter (maximum of 16 W per square meters or 28% in November). The increased downward flux in summer is due to a combination of increases in absorbed solar and thermal radiation and smaller losses of sensible and latent heat. The increased heat loss in winter is due to increased sensible and latent heat fluxes, which in turn are due to reduced sea-ice cover. On the other hand, the seasonal cycle of surface air temperature is damped, as there is a large increase in winter temperature but little change in summer.

Miller, James R.↗
NASA NTRS1995

C-Band Backscatter Measurements of Winter Sea-Ice in the Weddell Sea, Antarctica

During the 1992 Winter Weddell Gyre Study, a C-band scatterometer was used from the German ice-breaker R/V Polarstern to obtain detailed shipborne measurement scans of Antarctic sea-ice. The frequency-modulated continuous-wave (FM-CW) radar operated at 4-3 GHz and acquired like- (VV) and cross polarization (HV) data at a variety of incidence angles (10-75 deg). Calibrated backscatter data were recorded for several ice types as the icebreaker crossed the Weddell Sea and detailed measurements were made of corresponding snow and sea-ice characteristics at each measurement site, together with meteorological information, radiation budget and oceanographic data. The primary scattering contributions under cold winter conditions arise from the air/snow and snow/ice interfaces. Observations indicate so e similarities with Arctic sea-ice scattering signatures, although the main difference is generally lower mean backscattering coefficients in the Weddell Sea. This is due to the younger mean ice age and thickness, and correspondingly higher mean salinities. In particular, smooth white ice found in 1992 in divergent areas within the Weddell Gyre ice pack was generally extremely smooth and undeformed. Comparisons of field scatterometer data with calibrated 20-26 deg incidence ERS-1 radar image data show close correspondence, and indicate that rough Antarctic first-year and older second-year ice forms do not produce as distinctively different scattering signatures as observed in the Arctic. Thick deformed first-year and second-year ice on the other hand are clearly discriminated from younger undeformed ice. thereby allowing successful separation of thick and thin ice. Time-series data also indicate that C-band is sensitive to changes in snow and ice conditions resulting from atmospheric and oceanographic forcing and the local heat flux environment. Variations of several dB in 45 deg incidence backscatter occur in response to a combination of thermally-regulated parameters including sea-ice brine volume, snow and ice complex dielectric properties, and snow physical properties.

Drinkwater, M. R.↗
DOE OSTI2025

Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors

Abstract. Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. The model performed well on Alaska's Seward Peninsula where it was trained and at Arctic evaluation sites (RMSE ≤ 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.

54 ENVIRONMENTAL SCIENCES↗
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↗
NASA NTRS2019

The Arctic-Boreal vulnerability experiment model benchmarking system

NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.

ABoVE↗
NASA NTRS1992

Comparison of radar backscatter from Antarctic and Arctic sea ice

Two ship-based step-frequency radars, one at C-band (5.3 GHz) and one at Ku-band (13.9 GHz), measured backscatter from ice in the Weddell Sea. Most of the backscatter data were from first-year (FY) and second-year (SY) ice at the ice stations where the ship was stationary and detailed snow and ice characterizations were performed. The presence of a slush layer at the snow-ice interface masks the distinction between FY and SY ice in the Weddell Sea, whereas in the Arctic the separation is quite distinct. The effect of snow-covered ice on backscattering coefficients (sigma0) from the Weddell Sea region indicates that surface scattering is the dominant factor. Measured sigma0 values were compared with Kirchhoff and regression-analysis models. The Weibull power-density function was used to fit the measured backscattering coefficients at 45 deg.

Hosseinmostafa, R.↗
DOE OSTI2023

Snow-substrate interface temperature and air temperature for the SALVO 2019 Campaign

The springtime, surface-albedo transition in the Alaskan Arctic and the forcings that determine the duration and nature of that transition are the focus of our Snow ALbedo eVOlution (SALVO I & II) campaign. The SALVO team are assessing the “whys” and “how longs” of the stages of the spring melt during which albedo values drop from 0.8 to 0.1, the largest and most significant change of the year. The near-shore location of the ARM NSA observatory makes this an ideal place to investigate these melt stages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Snow-substrate interface temperature and air temperature for the SALVO 2022 Campaign

The springtime, surface-albedo transition in the Alaskan Arctic and the forcings that determine the duration and nature of that transition are the focus of our Snow ALbedo eVOlution (SALVO I & II) campaign. The SALVO team are assessing the “whys” and “how longs” of the stages of the spring melt during which albedo values drop from 0.8 to 0.1, the largest and most significant change of the year. The near-shore location of the ARM NSA observatory makes this an ideal place to investigate these melt stages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Overview of the MOSAiC expedition: Snow and sea ice

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Overview of the MOSAiC Expedition - Snow and Sea Ice

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the five MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties, and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in-situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models, and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice-ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

snow and sea ice↗