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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗
DOE OSTI2022

Soil moisture thresholds explain a shift from light-limited to water-limited sap velocity in the Central Amazon during the 2015–16 El Niño drought

Transpiration is often considered to be light- but not water-limited in humid tropical rainforests due to abundant soil water, even during the dry seasons. The record-breaking 2015–16 El Niño drought provided a unique opportunity to examine whether transpiration is constrained by water under severe lack of rainfall. We measured sap velocity, soil water content, and meteorological variables in an old-growth upland forest in the Central Amazon throughout the 2015–16 drought. We found a rapid decline in sap velocity (-38 ± 21%, mean ± SD.) and in its temporal variability (-88%) during the drought compared to the wet season. Such changes were accompanied by a marked decline in soil moisture and an increase in temperature and vapor pressure deficit. Sap velocity was largely limited by net radiation during the wet and normal dry seasons; however, it shifted to be primarily limited by soil moisture during the drought. The threshold in which sap velocity became dominated by soil moisture was at 0.33 m 3 m -3 (around -150 kPa in soil matric potential), below which sap velocity dropped steeply. Our study provides evidence for a soil water threshold on transpiration in a moist tropical forest, suggesting a shift from light limitation to water limitation under future climate characterized by increased temperature and an increased frequency, intensity, duration and extent of extreme drought events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Natural Gas Community of the Future (Final Report)

Nicor Gas, a subsidiary of Southern Company Gas and its parent company Southern Company, is developing the concept of the Carbon-neutral, Affordable, Resilient, and Equitable Community ("CARE Community") to address the challenges of accessibility and affordability of grid-interactive efficient buildings in historically disadvantaged communities. The CARE Community is a new construction net-zero energy and carbon neutral residential neighborhood consisting of 50 homes with electricity and natural gas services in South Suburban Chicago, built as low-income affordable housing. The National Renewable Energy Laboratory (NREL) is assisting Southern Company to explore how energy efficiency, renewable technologies, and the reliability and resilience of natural gas can be combined and optimized in an islandable energy system as part of a broader effort to understand the role natural gas plays in future communities integrated with renewable energy and improved energy efficiency. The CARE Community will demonstrate how energy efficiency, distributed energy resources (DERs), and advanced controls can be combined with existing natural gas infrastructure to serve historically disadvantaged communities in a cold climate with low energy cost for affordable housing programs and enhanced resilience to withstand extreme weather. Homes will be a part of an islandable energy system, integrating multiple DER technologies such as solar panels, natural gas fuel cells, reciprocating natural gas engines, and battery storage. The CARE Community will provide a replicable template that affordable housing developers and energy companies can adopt throughout the United States. In this report, we present the design and modeling of the community and potential approaches for integrating DERs. We analyze and discuss annual simulations under normal operating conditions and week-long simulations under electric power grid outage conditions.

03 NATURAL GAS↗
DOE OSTI2022

Longleaf Pine Seedlings Are Extremely Resilient to the Combined Effects of Experimental Fire and Drought

The longleaf pine ecosystem is dependent on frequent fire. Climate change is expected to influence moisture availability and it is unclear how drought conditions may interact with prescribed fire to influence management objectives associated with maintaining longleaf pine ecosystems. This study aimed to understand the impacts of drought, fire intensity and their interaction on P. palustris grass-stage seedlings. We used droughted and well-watered P. palustris seedlings burned at two different fire intensity levels at an indoor combustion facility. Needle fuel moisture content of burned seedlings was not different between droughted and well-watered groups. Mortality and resprouting only occurred at fire intensity levels exceeding 3.5 MJ m−2 in combination with drought that resulted in predawn water potentials more negative than −1.7 MPa. Our observations of minimal mortality after exposing P. palustris seedlings to a range of fire intensities in a burn lab contrast the higher mortality observed in field studies for the species. Compared to seedlings and saplings of Western US Pinus species, this study demonstrates that P. palustris is considerably more resistant to the combined effects of high surface fire intensity and drought.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Understanding Diverse Model Projections of Future Extreme El Niño

The majority of future projections in the Coupled Model Intercomparison Project (CMIP5) show more frequent exceedances of the 5 mm day -1 rainfall threshold in the eastern equatorial Pacific rainfall during El Niño, previously described in the literature as an increase in “extreme El Niño events”; however, these exceedance frequencies vary widely across models, and in some projections actually decrease. Here we combine single-model large ensemble simulations with phase 5 of the Coupled Model Intercomparison Project (CMIP5) to diagnose the mechanisms for these differences. The sensitivity of precipitation to local SST anomalies increases consistently across CMIP-class models, tending to amplify extreme El Niño occurrence; however, changes to the magnitude of ENSO-related SST variability can drastically influence the results, indicating that understanding changes to SST variability remains imperative. Future El Niño rainfall intensifies most in models with 1) larger historical cold SST biases in the central equatorial Pacific, which inhibit future increases in local convective cloud shading, enabling more local warming; and 2) smaller historical warm SST biases in the far eastern equatorial Pacific, which enhance future reductions in stratus cloud, enabling more local warming. Additionally, these competing mechanisms complicate efforts to determine whether CMIP5 models under- or overestimate the future impacts of climate change on El Niño rainfall and its global impacts. However, the relation between future projections and historical biases suggests the possibility of using observable metrics as “emergent constraints” on future extreme El Niño, and a proof of concept using SSTA variance, precipitation sensitivity to SST, and regional SST trends is presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Environmental Co‐Benefits of Maintaining Native Vegetation With Solar Photovoltaic Infrastructure

Abstract Co‐locating solar photovoltaics with vegetation could provide a sustainable solution to meeting growing food and energy demands. However, studies quantifying multiple co‐benefits resulting from maintaining vegetation at utility‐scale solar power plants are limited. We monitored the microclimate, soil moisture, panel temperature, electricity generation and soil properties at a utility‐scale solar facility in a continental climate with different site management practices. The compounding effect of photovoltaic arrays and vegetation may homogenize soil moisture distribution and provide greater soil temperature buffer against extreme temperatures. The vegetated solar areas had significantly higher soil moisture, carbon, and other nutrients compared to bare solar areas. Agrivoltaics in agricultural areas with carbon debt can be an effective climate mitigation strategy along with revitalizing agricultural soils, generating income streams from fallow land, and providing pollinator habitats. However, the benefits of vegetation cooling effects on electricity generation are rather site‐specific and depend on the background climate and soil properties. Overall, our findings provide foundational data for site preservation along with targeting site‐specific co‐benefits, and for developing climate resilient and resource conserving agrivoltaic systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Rapidly Changing Range Limits in a Warming World: Critical Data Limitations and Knowledge Gaps for Advancing Understanding of Mangrove Range Dynamics in the Southeastern USA

Climate change is altering species’ range limits and transforming ecosystems. For example, warming temperatures are leading to the range expansion of tropical, cold-sensitive species at the expense of their cold-tolerant counterparts. In some temperate and subtropical coastal wetlands, warming winters are enabling mangrove forest encroachment into salt marsh, which is a major regime shift that has significant ecological and societal ramifications. Here, we synthesized existing data and expert knowledge to assess the distribution of mangroves near rapidly changing range limits in the southeastern USA. We used expert elicitation to identify data limitations and highlight knowledge gaps for advancing understanding of past, current, and future range dynamics. Mangroves near poleward range limits are often shorter, wider, and more shrublike compared to their tropical counterparts that grow as tall forests in freeze-free, resource-rich environments. The northern range limits of mangroves in the southeastern USA are particularly dynamic and climate sensitive due to abundance of suitable coastal wetland habitat and the exposure of mangroves to winter temperature extremes that are much colder than comparable range limits on other continents. Thus, there is need for methodological refinements and improved spatiotemporal data regarding changes in mangrove structure and abundance near northern range limits in the southeastern USA. Advancing understanding of rapidly changing range limits is critical for foundation plant species such as mangroves, as it provides a basis for anticipating and preparing for the cascading effects of climate-induced species redistribution on ecosystems and the human communities that depend on their ecosystem services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Global Microphysical Sensitivity of Superparameterized Precipitation Extremes

Abstract A recent study found statistically significant differences in extreme precipitation distributions over the contiguous United States (CONUS) when changing the microphysics scheme in a superparameterized global climate model. Here, we repeat the analysis globally and similarly find that differences are widespread when varying the number of predicted moments in the microphysics parameterization, but not when comparing variants of the double‐moment scheme. However, contrary to the previous study in which differences largely disappeared over CONUS when 5‐day simulations were conducted, we found that the signal in these shorter integrations remains within the tropics, implying a direct local effect of microphysics on precipitation extremes in these regions. The effect on precipitation is traced back to changes in vertical velocity profiles changes that are then amplified in the climatological simulations compared to the 5‐day ones. Finally, the superparameterized extremes, regardless of the microphysics scheme, are shown to be larger than those from the Global Precipitation Climatology Project One‐Degree Daily data set and generally smaller than those from the Tropical Rainfall Measuring Mission 3B42 data set.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluation of Extreme Soil Moisture Conditions During the 2020 Sahel Floods and Implications for Disease Outbreaks

The June-October 2020 growing season was characterized by sustained and extreme flooding across the African Sahel. One consequence of flooding events such as this is outbreaks of vector borne diseases (VBDs) which are often associated with climate anomalies. In this study, data from the soil moisture active passive (SMAP) mission is used with other soil moisture and precipitation data to show that the 2020 Sahelian growing season was the most extreme over the past four decades, ranking first in seasonally accumulated precipitation, which on average exceeded the climatology by around 300 mm. VBD outbreaks of Rift Valley fever and Chikungunya followed in Mauritania/Senegal and Chad, respectively. In some cases, soil moisture is a better indicator of VBD outbreak risk than precipitation, which has so far been more commonly used in studies of VBD outbreaks. It is expected that this finding will inform future monitoring and prediction efforts for VBD risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bayesian weighting of climate models based on climate sensitivity

Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO 2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 °C for a low emissions scenario (SSP1-2.6) and 5 °C for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

On the relationship between precipitation extreme and local temperature over eastern China based on convection permitting simulations: roles of different moisture processes and precipitation types

The Clausius–Clapeyron (CC) scaling, which indicates a roughly 7% increase in saturated water vapor per 1 °C increase in temperature, can serve as a strong constraint linking the intensity of precipitation extremes and local temperature. However, the relationship between precipitation extreme and local temperature (referred to as the PE-T relationship) does not always follow the CC scaling and is highly dependent on climate regimes. In this study, we investigated the impacts of different moisture processes and precipitation types on the PE-T relationship over eastern China during the summertime based on convection-permitting model simulations. Consistent with observations, the simulated intensity of precipitation extremes increases with temperature at a rate close to CC (double-CC) scaling below (above) 20 °C. When the temperature exceeds 25 °C, precipitation intensity starts to drop. Precipitation extremes are mainly contributed by the stratiform, MCS (i.e., mesoscale convective system) convective, and non-MCS convective precipitation at low (< 20 °C), medium (20–25 °C), and high (> 25 °C) temperatures, respectively, suggesting that the double-CC scaling occurs when convective types become dominant, while the negative scaling at high temperatures is attributed to the reduced horizontal scale of convection. Corresponding to the reduced intensity of precipitation at high temperatures, there are stronger divergence and subsidence in the low-level atmosphere, which is probably caused by the net cooling associated with the enhanced melting and evaporation of falling hydrometeors due to the lower relative humidity in the low-level atmosphere. Overall, our findings contribute to a deeper understanding of the temperature dependence of precipitation extremes in eastern China.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Coordination of rooting, xylem, and stomatal strategies explains the response of conifer forest stands to multi-year drought in the southern Sierra Nevada of California

Abstract. Extreme droughts are a major determinant of ecosystem disturbance that impacts plant communities and feeds back into climate change through changes in plant functioning. However, the complex relationships between aboveground and belowground plant hydraulic traits and their role in governing plant responses to drought are not fully understood. In this study, we use a model, the Functionally Assembled Terrestrial Ecosystem Simulator in a configuration that includes plant hydraulics (FATES-Hydro), to investigate ecosystem responses to the 2012–2015 California drought in comparison with observations at a site in the southern Sierra Nevada that experienced widespread tree mortality during this drought. We conduct a sensitivity analysis to explore how different plant water sourcing and hydraulic strategies lead to differential responses during normal and drought conditions. The analysis shows the following. Deep roots that sustain productivity through the dry season are needed for the model to capture observed seasonal cycles of evapotranspiration (ET) and gross primary productivity (GPP) in normal years, and deep-rooted strategies are nonetheless subject to large reductions in ET and GPP when the deep soil reservoir is depleted during extreme droughts, in agreement with observations. Risky stomatal strategies lead to greater productivity during normal years as compared to safer stomatal control, but they also lead to a high risk of xylem embolism during the 2012–2015 drought. For a given stand density, stomatal and xylem traits have a stronger impact on plant water status than on ecosystem-level fluxes. Our study highlights the significance of resolving plant water sourcing strategies to represent drought impacts on plants and consequent feedbacks in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing–informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Science & Technology - Other Topics↗
DOE OSTI2024

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest

Atmospheric nanoparticles can significantly influence Earth’s climate if they grow to sizes large enough to nucleate cloud droplets. While the contribution of extremely low volatility vapors to nanoparticle growth has been discussed extensively, the role of semivolatile organics has been largely overlooked. Here we examine the growth and impacts of air pollution nanoparticles from an isolated metropolis amidst the Amazon rainforest. Although extremely low volatility organics are necessary for the initial growth of nanoparticles, model analysis suggests that dynamic gas-particle partitioning of semivolatile oxidation products of anthropogenic and natural hydrocarbons is predominantly responsible for the observed rapid growth to 50 nm. Furthermore, cloud-resolving simulations demonstrate that the grown particles appreciably modify shallow cloud droplet size distribution, suppress precipitation, and enhance the transition to deep clouds. With condensable semivolatile organics typically formed in greater proportion, similar nanoparticle growth and impacts on clouds could likely occur in heavily urbanized forested regions globally.

Zaveri, Rahul A↗