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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Large Divergence in Tropical Hydrological Projections Caused by Model Spread in Vegetation Responses to Elevated CO 2

Increasing atmospheric CO 2 and associated global warming are expected to alter the global hydrological cycle, thereby posing widespread threats to freshwater availability. However, future hydrological projections differ greatly between models, particularly over the tropical regions. The large difference between model projections directly limits policy planning efforts, and the responsible modeling processes remain unclear. Here, we identify the primary processes accounting for model differences in tropical hydrological changes using multiple CO 2 sensitivity experiments in the Coupled Model Intercomparison Project. We show that differences in projected changes to tropical evapotranspiration, precipitation, and surface water availability mainly arise from model representations of vegetation cover and stomatal conductance responses to elevated CO 2 and associated changes in atmospheric moisture and circulation. Atmospheric responses to sea surface warming contribute additionally to the divergence in hydrological projections. Given the importance of vegetation responses to elevated CO 2 and associated atmosphere feedbacks, our results underscore the need to improve representations of the vegetation physiological response to rising CO 2 and its coupling to the atmosphere, to provide reliable tropical hydrological projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Human Mobility to Parks Under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States

In 2020, people's health suffered a great crisis under the dual effects of the COVID-19 pandemic and the extensive, severe wildfires in the western and central United States. Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemic. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and mobility to parks. In this study, we employed the geographically and temporally weighted regression (GTWR) Models to investigate how COVID-19 and wildfires jointly shaped human mobility to parks, regarding the number of visits per capita, dwell time, and travel distance to parks, during June - September 2020. We detected strong correlations between visitations and COVID-19 incidence in southern Montana, western Wyoming, Colorado, and Utah before August. However, the pattern was weakened over time, indicating the decreasing trend of the degree of concern regarding the pandemic. Moreover, more park visits and lower dwell time were found in parks further away from wildfires and less air pollution in Washington, Oregon, California, Colorado, and New Mexico, during the wildfire season, suggesting the potential avoidance of wildfires when visiting parks. This study provides important insights on people's responses in recreation and social behaviors when facing multiple severe crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying Carbon Cycle Extremes and Attributing Their Causes Under Climate and Land Use and Land Cover Change From 1850 to 2300

The increasing atmospheric carbon dioxide (CO 2 ) mole fraction affects global climate through radiative (trapping longwave radiation) and physiological effects (reduction of plant transpiration). We use the simulations of the Community Earth System Model (CESM1-BGC) forced with Representative Concentration Pathway 8.5 to investigate climate-vegetation feedbacks from 1850 to the year 2300. Human-induced land use and land cover change (LULCC), through biogeochemical and biogeophysical processes, alter the climate and modify photosynthetic activity. The changing characteristics of extreme anomalies in photosynthesis, referred to as carbon cycle extremes, increase the uncertainty of terrestrial ecosystems to act as a net carbon sink. However, the role of LULCC in altering carbon cycle extremes under business-as-usual (continuously rising) CO 2 emissions is unknown. Here we show that LULCC magnifies the intensity, frequency, and extent of carbon cycle extremes, resulting in a net reduction in expected photosynthetic activity in the future. We found that large temporally contiguous negative carbon cycle extremes are due to a persistent decrease in soil moisture, which is triggered by declines in precipitation. With LULCC and global warming, vegetation exhibits increased vulnerability to hot and dry environmental conditions, increasing the frequency of fire events and resulting in considerable losses in photosynthetic activity. While most regions show strengthening of negative carbon cycle extremes, a few locations show a weakening effect driven by declining vegetation cover or benign climate conditions for photosynthesis. Increasing hot, dry, and fire-driven carbon cycle extremes are essential for improving carbon cycle modeling and estimation of ecosystem responses to LULCC and rising CO 2 mole fractions. Moreover, large aberrations in vegetation productivity represent potential and growing threats to human lives, wildlife, and food security.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

PreMevE Update: Forecasting Ultra-Relativistic Electrons Inside Earth's Outer Radiation Belt

Energetic electrons inside Earth's Van Allen belts pose a major radiation threat to spaceborne electronics that often play vital roles in modern society. Ultra-relativistic electrons with energies greater than or equal to two megaelectron-volt (MeV) are of particular interest, and thus forecasting these ≥2 MeV electrons has a significant meaning to all space sectors. Here, we update the latest development of the predictive model for MeV electrons in the outer radiation belt. The new version, called PREdictive MEV Electron (PreMevE)-2E, forecasts ultra-relativistic electron flux distributions across the outer belt, with no need for in situ measurements of the trapped MeV electron population except at the geosynchronous orbit (GEO). Model inputs include precipitating electrons observed in low-Earthorbits by NOAA satellites, upstream solar wind speeds and densities from solar wind monitors, as well as ultra-relativistic electrons measured by one Los Alamos GEO satellite. We evaluated 32 supervised machine learning models that fall into four different classes of linear and neural network architectures, and successfully tested ensemble forecasting by using groups of top-performing models. All models are individually trained, validated, and tested by in situ electron data from NASA's Van Allen Probes mission. It is shown that the final ensemble model outperforms individual models at most L-shells, and this PreMevE-2E model can provide 25-h (~1-day) and 50-h (~2-day) forecasts with high mean performance efficiency and correlation values. Our results also suggest that this new model is dominated by nonlinear components at L-shells <~4 for ultra-relativistic electrons, different from the dominance of linear components for 1 MeV electrons as previously discovered.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Modeling the Water Systems of the Western US to Support Climate‐Resilient Electricity System Planning

Abstract Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS‐wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water‐related electricity use, which can be linked with a grid expansion model to support climate‐resilient electricity planning. The water system model combines climatically‐driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water‐related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy‐intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid‐century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water‐related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water‐for‐energy and energy‐for‐water to inform planning for grid reliability and decarbonization goals.

13 HYDRO ENERGY↗
DOE OSTI2022

Increasing Hurricane Intensification Rate Near the US Atlantic Coast

Hurricanes often cause severe damage and loss of life, and storms that intensify close to the coast pose a particularly serious threat. While changes in hurricane intensification and environment have been examined at basin scales previously, near-coastal changes have not been adequately explored. In this study, we address this using a suite of observations and climate model simulations. Over the 40-year period of 1979–2018, the mean 24-hr hurricane intensification rate increased by ~1.2 kt 6-hr –1 near the US Atlantic coast. However, a significant increase in intensification did not occur near the Gulf coast over the same period. The enhanced hurricane intensification along the Atlantic coast is consistent with an increasingly favorable dynamic and thermodynamic environment there, which is well simulated by climate models over the historical period. Further, multi-model projections suggest a continued enhancement of the storm environment and hurricane intensification near the Atlantic coast in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Consistent Timing of Arctic Permafrost Loss Across the CESM1 Large Ensemble

Abstract The Arctic is especially vulnerable to climate change and is warming faster than the global average. Changes in this region pose a heightened threat due to the immense amount of carbon frozen as organic material in the soil. When permafrost thaws, organic material decays and releases as greenhouse gases into the atmosphere, contributing to further warming. A better understanding of the processes that influence permafrost degradation is needed to inform climate adaptation and mitigation planning. This study assesses changes to Arctic permafrost across 35 ensemble member simulations from the Community Earth System Model 1 Large Ensemble Project, under the Representative Concentration Pathway 8.5 21st century scenario. Most Arctic near‐surface permafrost is lost by 2100, but timing varies across regions and with soil depth. Internal climate variability, represented by differences between ensemble members, has a constrained influence on degradation timing due to relative consistency of summer temperature increases and insulation by winter snow cover.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014

Abstract Low snow levels over the past few decades and predictions of a low‐to‐no snow future have spurred research into snow droughts, which pose a threat to water security and management. Systematic data‐model comparisons of snow drought have been lacking, hindering our understanding of the drivers of snow drought in the past. To address this gap, we analyzed snow drought events using standardized snow water equivalent index derived from monthly results of four numerical experiments using the E3SM Land Model (ELM) and ERA5‐Land data during the period of 1980–2014. Additionally, we compared snow drought duration calculated from models with those from the ERA5‐Land data during selected El Niño‐Southern Oscillation (ENSO) years. The numerical experiments were conducted with ELM driven by two prescribed atmospheric forcings, and with the coupled land‐atmosphere configuration of E3SM with and without plant hydraulics scheme feedback. Analysis reveals that 20%–30% of snow droughts occur due to factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., especially in high latitudes (50° North). Furthermore, our study highlights the exacerbating effect of ENSO events on snow drought conditions in various regions, despite some discrepancies between model and ERA5‐Land results. We also identified limitations of the coupled land‐atmosphere models in our current configuration in capturing the spatial patterns of snow droughts. This study underscores the challenge of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impacts of Sea‐Level Rise on Coastal Groundwater Table Simulated by an Earth System Model With a Land‐Ocean Coupling Scheme

Abstract Sea‐level rise (SLR) poses a severe threat to the coastal environment through seawater intrusion into freshwater aquifers. The rising groundwater table also exacerbates the risk of pluvial, fluvial, and groundwater flooding in coastal regions. However, current Earth system models (ESMs) commonly ignore the exchanges of water at the land‐ocean interface. To address this gap, we developed a novel land‐ocean hydrologic coupling scheme in a state‐of‐the‐science ESM, the Energy Exascale Earth System Model version 2 (E3SMv2). The new scheme includes the lateral exchange between seawater and groundwater and the vertical infiltration of seawater driven by the SLR‐induced inundation. Simulations were performed with the updated E3SMv2 for the global land‐ocean interface to assess the impacts of SLR on coastal groundwater under a high CO 2 emission scenario. By the middle of this century, seawater infiltration on the inundated areas will be the dominant component in the land‐ocean coupling process, while the lateral subsurface flow exchange will be much smaller. The SLR‐induced seawater infiltration will raise the groundwater levels, enhance evapotranspiration, and increase runoff with distinct spatial patterns globally in the future. Although the coupling process is induced by SLR, we found topography and warming temperature have more control on the coupling impacts, probably due to the relatively modest magnitude of SLR during the selected future period. Overall, our study suggests significant groundwater and seawater exchange at the land‐ocean interface, which needs to be considered in ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Global Warming Amplifies Outdoor Extreme Moist Heat During the Indian Summer Monsoon

Because of the climatological prevalence of hot, humid conditions, moist heat extremes are a significant challenge to the health and wellbeing of the people in India. While research has demonstrated the importance of summer monsoon to moist heat in India, impact of monsoon–break and warm spells in modulating extreme moist heat regionally has not been fully investigated. Here we investigate moist heat extremes, as measured by the Wet–Bulb Globe Temperature (WBGT) metric, specifically during monsoon and monsoon–break periods and find that they pose a major threat to physical labor and health relative to other seasons. During the 1951–2020 break period, an increase in area exposed (~42.76 million km 2 ), representing at least 670 million people, to extreme and detrimental WBGT values >31°C occur. Our results imply that future studies on extreme moist heat must pay close attention to the variation of weather systems on synoptic to subseasonal time scales that are superimposed on the seasonal monsoon migration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Increasing Mosquito Abundance Under Global Warming

Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence‐based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short‐Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5‐8.5, respectively. We expect that this model can serve as a valuable tool and inform science‐based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites

Abstract Ultra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The P132H mutation in the main protease of Omicron SARS-CoV-2 decreases thermal stability without compromising catalysis or small-molecule drug inhibition

The ongoing SARS-CoV-2 pandemic continues to be a significant threat to global health. First reported in November 2021, the Omicron variant (B.1.1.529) is more transmissible and can evade immunity better than previous SARS-CoV-2 variants, fueling an unprecedented surge in cases. To produce functional proteins from its polyprotein, SARS-CoV-2 relies on the cysteine proteases Nsp3/papain-like protease (PL pro ) and Nsp5/main protease (M pro )/3C-like protease to cleave at three and more than 11 sites, respectively. Therefore, M pro and PL pro inhibitors are considered to be one of the most promising SARS-CoV-2 antivirals. On December 22, 2021, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for PAXLOVID, a ritonavir-boosted formulation of nirmatrelvir. Nirmatrelvir is a first-in-class orally bioavailable SARS-CoV-2 M pro inhibitor. Thus, the scientific community must vigilantly monitor potential mechanisms of drug resistance, especially because SARS-CoV-2 is naïve to M pro inhibitors. Mutations have been well identified in variants to this point. Notably, Omicron M pro (OM pro ) harbors a single mutation—P132H. Here, we characterized the enzymatic activity, drug inhibition, and structure of OM pro while evaluating the past and future implications of M pro mutations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Fecal microbiota transplant rescues mice from human pathogen mediated sepsis by restoring systemic immunity

Death due to sepsis remains a persistent threat to critically ill patients confined to the intensive care unit and is characterized by colonization with multi-drug-resistant healthcare-associated pathogens. Here we report that sepsis in mice caused by a defined four-member pathogen community isolated from a patient with lethal sepsis is associated with the systemic suppression of key elements of the host transcriptome required for pathogen clearance and decreased butyrate expression. More specifically, these pathogens directly suppress interferon regulatory factor 3. Fecal microbiota transplant (FMT) reverses the course of otherwise lethal sepsis by enhancing pathogen clearance via the restoration of host immunity in an interferon regulatory factor 3-dependent manner. This protective effect is linked to the expansion of butyrate-producing Bacteroidetes. Taken together these results suggest that fecal microbiota transplantation may be a treatment option in sepsis associated with immunosuppression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Wildfires increasingly impact western US fluvial networks

Wildfires are increasing globally in frequency, severity, and extent, but their impact on fluvial networks, and the resources they provide, remains unclear. We combine remote sensing of burn perimeter and severity, in-situ water quality monitoring, and longitudinal modeling to create the first large-scale, long-term estimates of stream+river length impacted by wildfire for the western US. We find that wildfires directly impact ~6% of the total stream+river length between 1984 and 2014, increasing at a rate of 342 km/year. When longitudinal propagation of water quality impacts is included, we estimate that wildfires affect ~11% of the total stream+river length. Our results indicate that wildfire activity is one of the largest drivers of aquatic impairment, though it is not routinely reported by regulatory agencies, as wildfire impacts on fluvial networks remain unconstrained. We identify key actions to address this knowledge gap and better understand the growing threat to fluvial networks, water security, and public health risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Estimating the strength of selection for new SARS-CoV-2 variants

Abstract Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present two models for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals that uncertainty is large very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A modular vaccine platform enabled by decoration of bacterial outer membrane vesicles with biotinylated antigens

Engineered outer membrane vesicles (OMVs) derived from Gram-negative bacteria are a promising technology for the creation of non-infectious, nanoparticle vaccines against diverse pathogens. However, antigen display on OMVs can be difficult to control and highly variable due to bottlenecks in protein expression and localization to the outer membrane of the host cell, especially for bulky and/or complex antigens. Here, we describe a universal approach for avidin-based vaccine antigen crosslinking (AvidVax) whereby biotinylated antigens are linked to the exterior of OMVs whose surfaces are remodeled with multiple copies of a synthetic antigen-binding protein (SNAP) comprised of an outer membrane scaffold protein fused to a biotin-binding protein. We show that SNAP-OMVs can be readily decorated with a molecularly diverse array of biotinylated subunit antigens, including globular and membrane proteins, glycans and glycoconjugates, haptens, lipids, and short peptides. When the resulting OMV formulations are injected in mice, strong antigen-specific antibody responses are observed that depend on the physical coupling between the antigen and SNAP-OMV delivery vehicle. Overall, these results demonstrate AvidVax as a modular platform that enables rapid and simplified assembly of antigen-studded OMVs for application as vaccines against pathogenic threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Structural insights into the mechanism of overcoming Erm-mediated resistance by macrolides acting together with hygromycin-A

The ever-growing rise of antibiotic resistance among bacterial pathogens is one of the top healthcare threats today. Although combination antibiotic therapies represent a potential approach to more efficiently combat infections caused by susceptible and drug-resistant bacteria, only a few known drug pairs exhibit synergy/cooperativity in killing bacteria. Here, we discover that well-known ribosomal antibiotics, hygromycin A (HygA) and macrolides, which target peptidyl transferase center and peptide exit tunnel, respectively, can act cooperatively against susceptible and drug-resistant bacteria. Remarkably, HygA slows down macrolide dissociation from the ribosome by 60-fold and enhances the otherwise weak antimicrobial activity of the newest-generation macrolide drugs known as ketolides against macrolide-resistant bacteria. By determining a set of high-resolution X-ray crystal structures of drug-sensitive wild-type and macrolide-resistant Erm-methylated 70S ribosomes in complex with three HygA-macrolide pairs, we provide a structural rationale for the binding cooperativity of these drugs and also uncover the molecular mechanism of overcoming Erm-type resistance by macrolides acting together with hygromycin A. Altogether our structural, biochemical, and microbiological findings lay the foundation for the subsequent development of synergistic antibiotic tandems with improved bactericidal properties against drug-resistant pathogens, including those expressing erm genes.

59 BASIC BIOLOGICAL SCIENCES↗