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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Substantial contribution of iodine to Arctic ozone destruction

Unlike bromine, the effect of iodine chemistry on the Arctic surface ozone budget is poorly constrained. We present ship-based measurements of halogen oxides in the high Arctic boundary layer from the sunlit period of March to October 2020 and show that iodine enhances springtime tropospheric ozone depletion. We find that chemical reactions between iodine and ozone are the second highest contributor to ozone loss over the study period, after ozone photolysis-initiated loss and ahead of bromine.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Arctic Ocean annual high in ${{\boldsymbol{p}}}_{{{\bf{CO}}}_{{\bf{2}}}}$ could shift from winter to summer

Long-term stress on marine organisms from ocean acidification will differ between seasons. As atmospheric carbon dioxide (CO 2 ) increases, so do seasonal variations of ocean CO 2 partial pressure (p co 2 ), causing summer and winter long-term trends to diverge. Trends may be further influenced by an unexplored factor—changes in the seasonal timing of p co 2 . In Arctic Ocean surface waters, the observed timing is typified by a winter high and summer low because biological effects dominate thermal effects. Here we show that 27 Earth system models simulate similar timing under historical forcing but generally project that the summer low, relative to the annual mean, eventually becomes a high across much of the Arctic Ocean under mid-to-high-level CO 2 emissions scenarios. Often the greater increase in summer p co 2 , although gradual, abruptly inverses the chronological order of the annual high and low, a phenomenon not previously seen in climate-related variables. The main cause is the large summer sea surface warming from earlier retreat of seasonal sea ice. Warming and changes in other drivers enhance this century’s increase in extreme summer p co 2 by 29 ± 9 per cent compared with no change in driver seasonalities. Thus the timing change worsens summer ocean acidification, which in turn may lower the tolerance of endemic marine organisms to increasing summer temperatures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Profile observations of the Arctic atmospheric boundary layer with the BELUGA tethered balloon during MOSAiC

During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, the Balloon-bornE moduLar Utility for profilinG the lower Atmosphere (BELUGA) was deployed from an ice floe drifting in the Fram Strait from 29 June to 27 July 2020. The BELUGA observations aimed to characterize the cloudy Arctic atmospheric boundary layer above the sea ice using a modular setup of five instrument packages. The in situ measurements included atmospheric thermodynamic and dynamic state parameters (air temperature, humidity, pressure, and three-dimensional wind), broadband solar and terrestrial irradiance, aerosol particle microphysical properties, and cloud particle images. In total, 66 profile observations were collected during 33 balloon flights from the surface to maximum altitudes of 0.3 to 1.5 km. The profiles feature a high vertical resolution of 0.01 m to 1 m, including measurements below, inside, and above frequently occurring low-level clouds. This publication describes the balloon operations, instruments, and the obtained data set. We invite the scientific community for joint analysis and model application of the freely available data on PANGAEA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

From peak to plummet: impending decline of the warm Arctic-cold continents phenomenon

Abstract This study assesses the projected trajectory of Warm Arctic-Cold Continent (WACC) events in East Asia and North America through large ensemble simulation for the upcoming decades. It analyses the evolution of the geographical boundaries of threshold cold temperatures, revealing a significant northward shift as a sign of global warming’s impact on the southward advancement of Arctic cold air. While validating the intensification of the WACC phenomenon until the 2020 s, echoing earlier studies, the findings indicate a marked decrease beginning in the 2030s. If warming is not curbed, this shift suggests a critical modification in the WACC pattern, prompting a re-evaluation of existing theories and models for extreme winter weather events. The results have major implications, spurring communities impacted by WACC to anticipate future changes and encouraging the climate forecasting community to update conceptual models for improved adaptation and mitigation approaches.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Salinity tolerance mechanisms of an Arctic Pelagophyte using comparative transcriptomic and gene expression analysis

Abstract Little is known at the transcriptional level about microbial eukaryotic adaptations to short-term salinity change. Arctic microalgae are exposed to low salinity due to sea-ice melt and higher salinity with brine channel formation during freeze-up. Here, we investigate the transcriptional response of an ice-associated microalgae over salinities from 45 to 8. Our results show a bracketed response of differential gene expression when the cultures were exposed to progressively decreasing salinity. Key genes associated with salinity changes were involved in specific metabolic pathways, transcription factors and regulators, protein kinases, carbohydrate active enzymes, and inorganic ion transporters. The pelagophyte seemed to use a strategy involving overexpression of Na + -H + antiporters and Na + -Pi symporters as salinity decreases, but the K + channel complex at higher salinities. Specific adaptation to cold saline arctic conditions was seen with differential expression of several antifreeze proteins, an ice-binding protein and an acyl-esterase involved in cold adaptation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Summertime low clouds mediate the impact of the large-scale circulation on Arctic sea ice

The rapid Arctic sea ice retreat in the early 21 st century is believed to be driven by several dynamic and thermodynamic feedbacks, such as ice-albedo feedback and water vapor feedback. However, the role of clouds in these feedbacks remains unclear since the causality between clouds and these processes is complex. Here, we use NASA CERES satellite products and NCAR CESM model simulations to suggest that summertime low clouds have played an important role in driving sea ice melt by amplifying the adiabatic warming induced by a stronger anticyclonic circulation aloft. The upper-level high pressure regulates low clouds through stronger downward motion and increasing lower troposphere relative humidity. The increased low clouds favor more sea ice melt via emitting stronger longwave radiation. Then decreased surface albedo triggers a positive ice-albedo feedback, which further enhances sea ice melt. Considering the importance of summertime low clouds, accurate simulation of this process is a prerequisite for climate models to produce reliable future projections of Arctic sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Nitrogen fixing shrubs advance the pace of tall-shrub expansion in low-Arctic tundra

Abstract Tall deciduous shrubs are critically important to carbon and nutrient cycling in high-latitude ecosystems. As Arctic regions warm, shrubs expand heterogeneously across their ranges, including within unburned terrain experiencing isometric gradients of warming. To constrain the effects of widespread shrub expansion in terrestrial and Earth System Models, improved knowledge of local-to-regional scale patterns, rates, and controls on decadal shrub expansion is required. Using fine-scale remote sensing, we modeled the drivers of patch-scale tall-shrub expansion over 68 years across the central Seward Peninsula of Alaska. Models show the heterogeneous patterns of tall-shrub expansion are not only predictable but have an upper limit defined by permafrost, climate, and edaphic gradients, two-thirds of which have yet to be colonized. These observations suggest that increased nitrogen inputs from nitrogen-fixing alders contributed to a positive feedback that advanced overall tall-shrub expansion. These findings will be useful for constraining and projecting vegetation-climate feedbacks in the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Anaerobic respiration pathways and response to increased substrate availability of Arctic wetland soils

The availability of labile carbon (C) compounds in Arctic wetland soils is expected to increase due to thawing permafrost and increased fermentation as a result of decomposition of organic matter with warming. How microbial communities respond to this change will affect the balance of CO 2 and CH 4 emitted during anaerobic organic matter decomposition, and ultimately the net radiative forcing of greenhouse gas emissions from these soils. While soil water content limits aerobic respiration, the factors controlling methanogenesis and anaerobic respiration are poorly defined in suboxic Arctic soils. In this study, we conducted incubation experiments on two tundra soils from field sites on the Seward Peninsula, Alaska, with contrasting pH and geochemistry to determine the pathways of anaerobic microbial respiration and changes with increasing substrate availability upon warming. In incubation of soils from the circumneutral Teller site, the ratio of CO 2 to CH 4 dropped from 10 to <2 after 60 days, indicating rapid depletion of alternative terminal electron acceptors (TEAs). Addition of acetate stimulated production of CO 2 and CH 4 in a nearly 1:1 ratio, consistent with methanogenesis, and the composition of the microbial community shifted to favor clades capable of utilizing the added acetate such as the Fe(III)-reducing Geobacter and the methanogenic archaea Methanosarcina. In contrast, both CO 2 and CH 4 production declined with acetate addition during incubation of soils from the more acidic Council site, and fermentative microorganisms increased in abundance despite the high availability of fermentation products. These results demonstrate that the degree to which increasing substrate availability stimulates greenhouse gas production in tundra wetlands will vary widely depending on soil pH and geochemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Iron and iron-bound phosphate accumulate in surface soils of ice-wedge polygons in arctic tundra

Phosphorus (P) is a limiting or co-limiting nutrient to plants and microorganisms in diverse ecosystems that include the arctic tundra. Certain soil minerals can adsorb or co-precipitate with phosphate, and this mineral-bound P provides a potentially large P reservoir in soils. Iron (Fe) oxyhydroxides have a high capacity to adsorb phosphate; however, the ability of Fe oxyhydroxides to adsorb phosphate and limit P bioavailability in organic tundra soils is not known. In this study, we examined the depth distribution of soil Fe and P species in the active layer (<30 cm) of low-centered and high-centered ice-wedge polygons at the Barrow Environmental Observatory on the Alaska North Slope. Soil reservoirs of Fe and P in bulk horizons and in narrower depth increments were characterized using sequential chemical extractions and synchrotron-based X-ray absorption spectroscopy (XAS). Organic horizons across all polygon features ( e.g. , trough, ridge, and center) were enriched in extractable Fe and P relative to mineral horizons. Soil Fe was dominated by organic-bound Fe and short-range ordered Fe oxyhydroxides, while soil P was primarily associated with oxides and organic matter in organic horizons but apatite and/or calcareous minerals in mineral horizons. Iron oxyhydroxides and Fe-bound inorganic P (P i ) were most enriched at the soil surface and decreased gradually with depth, and Fe-bound P i was >4× greater than water-soluble P i . These results demonstrate that Fe-bound P i is a large and ecologically important reservoir of phosphate. We contend that Fe oxyhydroxides and other minerals may regulate P i solubility under fluctuating redox conditions in organic surface soils on the arctic tundra.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Range shifts in a foundation sedge potentially induce large Arctic ecosystem carbon losses and gains

Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge (Eriophorum vaginatum) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ~0.97 tussocks per m2 over the past ~38 years on Alaska's North Slope from ~1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. -0.8 tussocks per m2 and -85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m2 and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlight the need for better representation of this species in TBMs..

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating the impact of peat soils and snow schemes on simulated active layer thickness at pan-Arctic permafrost sites

Abstract Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO 2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impacts of benchmarking choices on inferred model skill of the Arctic–Boreal terrestrial carbon cycle

Abstract Land surface models require continuous validation against observations to improve and reduce simulation uncertainty. However, inferred model performance can be heavily influenced by subjective choices made in the selection and application of observational data products. A key area often misrepresented by models is the Arctic–Boreal region, which is a potential tipping point region in Earth’s climate system due to large permafrost carbon stocks that are vulnerable to release with climate warming. We use the International Land Model Benchmarking (ILAMB) framework to evaluate how the model skill of TRENDY-v9 models varies based on the choice of observational-based benchmark and how benchmarks are applied in model evaluation. This analysis uses global datasets integrated into ILAMB and new, regionally-specific observational products from the Arctic–Boreal Vulnerability Experiment. Our results cover the overall time period of 1979–2019 and show that model scores can vary substantially depending on the data product applied, with higher model scores indicating better model performance against observations. The lowest model scores occur when benchmarked against regional, compared to global, datasets. We also evaluate observed and modeled functional relationships between ecosystem respiration and air temperature and between gross primary production and precipitation. Here, we find that the magnitude and shape of the responses are strongly impacted by the choice of observational dataset and the approach used to construct the functional relationship benchmark. These results suggest that model evaluation studies could conclude a false sense of model skill if only using a single benchmark data product or if not applying regional data products when performing a regional model analysis. Collectively, our findings highlight the influence of benchmarking choices on model evaluation and point to the need for benchmarking guidelines when assessing model skill.

Poe, Jeralyn (ORCID:0000000318495278)↗
DOE OSTI2024

Distinct and rich assemblages of giant viruses in Arctic and Antarctic lakes

Giant viruses (GVs) are key players in ecosystem functioning, biogeochemistry, and eukaryotic genome evolution. GV diversity and abundance in aquatic systems can exceed that of prokaryotes, but their diversity and ecology in lakes, especially polar ones, remain poorly understood. We conducted a comprehensive survey and meta-analysis of GV diversity across 20 lakes, spanning polar to temperate regions, combining our extensive lake metagenome database from the Canadian Arctic and subarctic with publicly available datasets. Leveraging a novel GV genome identification tool, we identified 3304 GV metagenome-assembled genomes, revealing lakes as untapped GV reservoirs. Phylogenomic analysis highlighted their dispersion across all Nucleocytoviricota orders. Strong GV population endemism emerged between lakes from similar regions and biomes (Antarctic and Arctic), but a polar/temperate barrier in lacustrine GV populations and differences in their gene content could be observed. Our study establishes a robust genomic reference for future investigations into lacustrine GV ecology in fast changing polar environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Summer thaw duration is a strong predictor of the soil microbiome and its response to permafrost thaw in arctic tundra

Abstract Climate warming has increased permafrost thaw in arctic tundra and extended the duration of annual thaw (number of thaw days in summer) along soil profiles. Predicting the microbial response to permafrost thaw depends largely on knowing how increased thaw duration affects the composition of the soil microbiome. Here, we determined soil microbiome composition from the annually thawed surface active layer down through permafrost from two tundra types at each of three sites on the North Slope of Alaska, USA. Variations in soil microbial taxa were found between sites up to ~90 km apart, between tundra types, and between soil depths. Microbiome differences at a site were greatest across transitions from thawed to permafrost depths. Results from correlation analysis based on multi‐decadal thaw surveys show that differences in thaw duration by depth were significantly, positively correlated with the abundance of dominant taxa in the active layer and negatively correlated with dominant taxa in the permafrost. Microbiome composition within the transition zone was statistically similar to that in the permafrost, indicating that recent decades of intermittent thaw have not yet induced a shift from permafrost to active‐layer microbes. We suggest that thaw duration rather than thaw frequency has a greater impact on the composition of microbial taxa within arctic soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Genomic insights into redox-driven microbial processes for carbon decomposition in thawing Arctic soils and permafrost

Climate change is rapidly transforming Arctic landscapes where increasing soil temperatures speed up permafrost thaw. This exposes large carbon stocks to microbial decomposition, possibly worsening climate change by releasing more greenhouse gases. Understanding how microbes break down soil carbon, especially under the anaerobic conditions of thawing permafrost, is important to determine future changes. Here, we studied the microbial community dynamics and soil carbon decomposition potential in permafrost and active layer soils under anaerobic laboratory conditions that simulated an Arctic summer thaw. The microbial and viral compositions in the samples were analyzed based on metagenomes, metagenome-assembled genomes, and metagenomic viral contigs (mVCs). Following the thawing of permafrost, there was a notable shift in microbial community structure, with fermentative Firmicutes and Bacteroidota taking over from Actinobacteria and Proteobacteria over the 60-day incubation period. The increase in iron and sulfate-reducing microbes had a significant role in limiting methane production from thawed permafrost, underscoring the competition within microbial communities. We explored the growth strategies of microbial communities and found that slow growth was the major strategy in both the active layer and permafrost. Our findings challenge the assumption that fast-growing microbes mainly respond to environmental changes like permafrost thaw. Instead, they indicate a common strategy of slow growth among microbial communities, likely due to the thermodynamic constraints of soil substrates and electron acceptors, and the need for microbes to adjust to post-thaw conditions. The mVCs harbored a wide range of auxiliary metabolic genes that may support cell protection from ice formation in virus-infected cells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Capacity of Arctic fjord sediments to degrade carbohydrates from permafrost active layer

The degradation of organic matter (OM) by microorganisms in thawing permafrost produces greenhouse gases. Terrestrial OM is transported into fjords through hydrological runoff, but it is unclear whether the microbial mechanisms of OM degradation on land persist after soils enter marine environments, which differ greatly in conditions and microbial communities. This question is particularly relevant for low-OM soils, which dominate Arctic landscapes and are more exposed to oxidants. Here, we compared OM-degrading capacity in permafrost-affected active layer soils and adjacent fjord sediments from Kongsfjorden, Svalbard, focusing on carbohydrate-active enzymes (CAZymes), which target some of the most abundant types of organic matter in soils. Using multi-omics approaches—metagenomics, metagenome-assembled genomes (MAGs), metabolomics, metatranscriptomics, and metaproteomics—we examined CAZyme presence, distribution, and activity. Despite environmental differences, both soils and sediments harbored diverse glycoside hydrolases and polysaccharide lyases, most of which showed evidence of activity. Verrucomicrobia expressed the highest number of CAZyme transcripts, indicating that they dominated active carbohydrate degradation in fjord sediments, while Acidobacteria and Actinobacteria were more active in soils. Notably, CAZymes in fjord sediments targeted primarily soil-derived OM, and the proportions of enzymes degrading terrestrial OM, marine OM, and microbial necromass—remnants of dead microbial cells were similar across both environments. These results suggest that microbial communities in both soils and fjord sediments are equipped to degrade carbohydrates, and that burial of terrestrial-derived OM in fjord sediments may not protect it from microbial breakdown under Arctic warming.

Chukwufumnanya, Abuah [University of Tennessee, Kn↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗