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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Economic losses from extreme weather in the U.S. Gulf Coast region: spatially differential contributions of climate hazard and socioeconomic exposure and vulnerability

Worldwide economic losses from extreme weather events (EWE) have increased over recent decades, with significant geographic heterogeneity in damages. The IPCC defines the risk from EWE as a function of the climate hazard, socioeconomic exposure, and vulnerability. Although these three drivers vary at fine spatial scales, spatial variability largely has been overlooked in assessments of the drivers of economic loss from EWE. Using cluster analysis, we developed a novel socio-climate hazard typology (SCT) that integrates locally defined climate hazard and socioeconomic exposure and social vulnerability typologies. The results identified 838 unique SCT types impacted by EWE across the Gulf Coastal United States during 1981–2010. We regressed the SCT types and their constituent hazard and socioeconomic components against the cumulative economic loss (1981–2010) from EWE for each SCT type. Across the landscape, economic damages of SCT types were determined by unique, spatially explicit combinations of different risk factors, even in explaining the same level of economic loss. For example, multi-billion-dollar damages in the central Gulf Coast and peninsular Florida were explained by different drivers of risk, with damages in the former explained by additive interaction between climate hazard and multiplicative interaction between climate hazard and socioeconomic exposure and vulnerability, and in the latter explained by socioeconomic exposure and vulnerability. These results highlight the need to diagnose additive and multiplicative interactions among drivers of EWE risk in a spatially explicit context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Incorporating the Impacts of Climate Change on Hydrology in a Performance Assessment Model - 20403

The evidence of climate change is increasingly well-documented and impacts should be incorporated in performance assessment studies. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. Probabilistic modeling is a core requirement for quantifying uncertainty and evaluating its impacts. Not evaluating future climate states in a performance assessment because of the existence of uncertainty is contradictory to good modeling practices - the most uncertain issues and parameters require the most attention in effective probabilistic modeling. Excluding climate change limits development of modeling information that could aid in effective decision making. In this work we develop methods to use the output from hydrologic models and analysis of historical aerial imagery to quantify and implement the impacts of climate change on hydrologic processes at a nuclear waste site in West Valley, New York. Specifically, we used the HELP (Hydraulic Performance of Landfill Performance) model to characterize key hydrologic processes under both current and future climate conditions to assess the impacts of changing climate on hydrology. A suite of previous climatic models were reviewed and synthesized to produce a cohesive representation of the current state of knowledge of the impact of climate change on important model inputs such as precipitation. Output from the HELP simulations was coupled to the GoldSim model that was used to develop the Probabilistic Performance Assessment (PPA) approach through the application of a novel 'nearest neighbor' technique. First, several thousand realizations were generated from the HELP model using a Latin Hypercube experimental design to ensure adequate coverage of the parameter space of explanatory variables used to drive HELP. For example, porosity is a physical parameter that is used as an input to both HELP and the GoldSim PA model. We then ran sensitivity analysis (SA) algorithms on the output of HELP for each of the responses of interest. For each predictor, each time we build an SA model we get a different value for the sensitivity index (SI). From the collection of all the SA models, the average was computed among all of the SIs to represent the predictor within the context of the nearest neighbor approach. That is, we conduct SA on each HELP outcome for each scenario. This gives us parameter sensitivity indices for the outcomes. We average the parameter sensitivity indices across the outcomes to get the average SI for a scenario. For each realization that is generated from the Goldsim PA model, Goldsim generates random values for physical/empirical parameters that HELP uses as well. For each vector of physical/empirical parameters that Goldsim generates, the vector from the 5,000 HELP runs that is most 'similar' to the Goldsim vector is computed using the nearest neighbor approach. In this context 'similar' means minimization of the SA-weighted sum of the absolute differences among the 5,000 values computed for this statistic, where each value corresponds to a different HELP realization. In order to account for the impacts of climate change, this process was repeated using the spatially downscaled future climate projections. For each of the key parameters of interest, it was assumed that a linear change depicted the relationship between the values for the present day and those for 2100. In this way, the climatically-driven changes in key parameters used to inform the GoldSim model are quantified and incorporated into the PA model output for the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Editorial: Future perspectives on separation technologies

Sustainable separation process development is crucial in promoting economic and environmental sustainability. Separation processes can be energy-intensive, and early-stage or emerging separation technologies can also be costly. For instance, separations in biomass-based fuels and chemicals production can account for up to 50% of the production cost, while industrial separations can consume up to 15% of the total energy used in the United States. Separations are essential to many industrial processes and play a crucial role in helping industry achieve several United Nations sustainable development goals (SDG), such as clean energy, responsible production, and climate change mitigation. Therefore, it is imperative to have a comprehensive discussion within the scientific community to identify innovative approaches for sustainable separation processes. This editorial highlights the key insights and research findings presented in this research topic titled "Future Perspectives on Separation Technologies."

42 ENGINEERING↗
DOE OSTI2026

Evaluation of daily gridded climate products using in situ FLUXNET data and tree growth modeling

Gridded climate data products have facilitated research in climate and ecology by providing meteorological data continuously across large spatial scales. However, the sensitivity of scientific outcomes to dataset choice remains poorly understood, and evaluation using station-based records can favor datasets built heavily on weather stations. Here, we evaluate seven high-resolution daily gridded datasets covering the contiguous United States using independent meteorology from the FLUXNET2015 dataset, with a focus on the implications of dataset choice for process-based tree growth modeling. We find that gridded products tend to capture temperature accurately while consistently overestimating the magnitude and frequency of precipitation and its extremes. Moreover, datasets vary in how they define a ‘day,’ which significantly affects temporal alignment with FLUXNET2015 observations. Despite differences among the datasets, the interannual variability in tree ring simulations is insensitive to dataset choice, likely because daily-scale biases are averaged out through accumulated growth across several months. However, inaccuracies in temperature and precipitation can significantly bias modeled xylem cell production, with systematically higher annual precipitation in the gridded datasets leading to greater xylem production compared to simulations using in situ data. Our results suggest that model applications, especially those that integrate to time scales longer than one day, are likely insensitive to climate dataset choice, but applications that are sensitive to daily climate variations or to absolute climate values need to carefully consider biases in gridded climate products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Fresh Take: Seasonal Changes in Terrestrial Freshwater Inputs Impact Salt Marsh Hydrology and Vegetation Dynamics

Abstract Salt marshes exist at the terrestrial-marine interface, providing important ecosystem services such as nutrient cycling and carbon sequestration. Tidal inputs play a dominant role in salt marsh porewater mixing, and terrestrially derived freshwater inputs are increasingly recognized as important sources of water and solutes to intertidal wetlands. However, there remains a critical gap in understanding the role of freshwater inputs on salt marsh hydrology, and how this may impact marsh subsurface salinity and plant productivity. Here, we address this knowledge gap by examining the hydrologic behavior, porewater salinity, and pickleweed ( Sarcocornia pacifica also known as Salicornia pacifica) plant productivity along a salt marsh transect in an estuary along the central coast of California. Through the installation of a suite of hydrometric sensors and routine porewater sampling and vegetation surveys, we sought to understand how seasonal changes in terrestrial freshwater inputs impact salt marsh ecohydrologic processes. We found that salt marsh porewater salinity, shallow subsurface saturation, and pickleweed productivity are closely coupled with elevated upland water level during the winter and spring, and more influenced by tidal inputs during the summer and fall. This seasonal response indicates a switch in salt marsh hydrologic connectivity with the terrestrial upland that impacts ecosystem functioning. Through elucidating the interannual impacts of drought on salt marsh hydrology, we found that the severity of drought and historical precipitation can impact contemporary hydrologic behavior and the duration and timing of the upland-marsh hydrologic connectivity. This implies that the sensitivity of salt marshes to climate change involves a complex interaction between sea level rise and freshwater inputs that vary at seasonal to interannual timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying uncertainty in aggregated climate change risk assessments

High-level assessments of climate change impacts aggregate multiple perils into a common framework. This requires incorporating multiple dimensions of uncertainty. Here we propose a methodology to transparently assess these uncertainties within the ‘Reasons for Concern’ framework, using extreme heat as a case study. We quantitatively discriminate multiple dimensions of uncertainty, including future vulnerability and exposure to changing climate hazards. High risks from extreme heat materialise after 1.5–2 °C and very high risks between 2–3.5 °C of warming. Risks emerge earlier if global assessments were based on national risk thresholds, underscoring the need for stringent mitigation to limit future extreme heat risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output

Emulation through pattern scaling is a well-established method of rapidly producing climate fields (like temperature or precipitation) from existing Earth System Model (ESM) output that, while inaccurate, is often useful for a variety of downstream purposes. Conducting pattern scaling has historically been a laborious process, in large part due to the increasing volume of ESM output data that has often required downloading and storing locally to train on. Here we describe the Pangeo-Enabled ESM Pattern Scaling (PEEPS) dataset, a repository of trained annual and monthly patterns from CMIP6 outputs. This manuscript describes and validates these updated patterns so that users can save effort calculating and reporting error statistics in manuscripts focused on the use of patterns. The trained patterns are available as NetCDF files on Zenodo for ease of use in the impact community, and are reproducible with the code provided via GitHub in both Jupyter notebook and Python script formats. Because all training data for the PEEPS data set is cloud-based, users do not need to download and house the ESM output data to reproduce the patterns in the zenodo archive, should that be more efficient. Validating the PEEPS data set on the CMIP6 archive for annual and monthly temperature, precipitation, and near-surface relative humidity, pattern scaling performs well over a variety of future scenarios except for regions in which there are strong, potentially nonlinear climate feedbacks. Although pattern scaling is normally conducted on annual mean ESM output data, it works equally well on monthly mean ESM output data. We identify several downstream applications of the PEEPS data set, including impacts assessment and evaluating certain types of Earth system uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Reservoir Computing as a Tool for Climate Predictability Studies

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC-based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Continental United States climate projections based on thermodynamic modification of historical weather

Abstract Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980–2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020–2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Microbial Interactions Related to N 2 O Emissions and Temperature Sensitivity from Rice Paddy Fields

The soil microbiome is a driver of nitrous oxide (N 2 O) emissions in terrestrial ecosystems. Identifying the core microbiome of N 2 O emissions and its temperature sensitivity from trillions of soil microorganisms is a great challenge and is essential to improving the predictability of soil-climate feedback related to increasing temperature. Here, the integrated soil microbiome covering archaeal, bacterial, fungal, algal, and microfaunal communities was studied to disengage the potential linkage with its N 2 O emissions and its temperature sensitivity in paddy fields by hunting for core species pairs. The results showed that between-group interactions of core bacterial and archaeal members and the within-group interactions of core bacterial members jointly contributed to the N 2 O emissions and its temperature sensitivity. The contribution of between-group interactions (32 to 33%) was greater than that of within groups (10 to 18%). These results suggested that N 2 O emissions and their fluctuations related to climate warming are affected by the within- and between-group interactions of the soil microbiome. Our results help advance the knowledge on the importance of microbial keystone species and network associations in controlling N 2 O production and their responses to increasing temperature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Grand Challenges of Hydrologic Modeling for Food-Energy-Water Nexus Security in High Mountain Asia

Climate-influenced changes in hydrology affect water-food-energy security that may impact up to two billion people downstream of the High Mountain Asia (HMA) region. Changes in water supply affect energy, industry, transportation, and ecosystems (agriculture, fisheries) and as a result, also affect the region's social, environmental, and economic fabrics. Sustaining the highly interconnected food-energy-water nexus (FEWN) will be a fundamental and increasing challenge under a changing climate regime. High variability in topography and distribution of glaciated and snow-covered areas in the HMA region, and scarcity of high resolution (in-situ) data make it difficult to model and project climate change impacts on individual watersheds. We lack basic understanding of the spatial and temporal variations in climate, surface impurities in snow and ice such as black carbon and dust that alter surface albedo, and glacier mass balance and dynamics. These knowledge gaps create challenges in predicting where and when the impact of changes in river flow will be the most significant economically and ecologically. In response to these challenges, the United States National Aeronautics and Space Administration (NASA) established the High Mountain Asia Team (HiMAT) in 2016 to conduct research to address knowledge gaps. This paper summarizes some of the advances HiMAT made over the past 5 years, highlights the scientific challenges in improving our understanding of the hydrology of the HMA region, and introduces an integrated assessment framework to assess the impacts of climate changes on the FEWN for the HMA region. The framework, developed under a NASA HMA project, links climate models, hydrology, hydropower, fish biology, and economic analysis. The framework could be applied to develop scientific understanding of spatio-temporal variability in water availability and the resultant downstream impacts on the FEWN to support water resource management under a changing climate regime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs

Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end‐users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi‐model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post‐downscaling. We recommend that subset‐selection methods account for this possibility and that decision‐relevant downscaled climate projections provide proper descriptions of fitness‐for‐purpose and essential caveats, so that non‐specialists can interpret the results with an appropriate level of confidence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating the efficacy and equity of environmental stopgap measures

Contemporary environmental policy is rife with measures that do not fully resolve a problem, but instead are proposed to “buy time” for the development and future implementation of more durable solutions. In this perspective, we define such measures as “stopgap measures,” and examine examples from wildfire risk management, hydrochlorofluorocarbon regulation, and Colorado River water management. We introduce an analytical framework to assess stopgaps, and apply this framework to solar geoengineering, a controversial stopgap for climate action. Studying stopgaps as a novel category of policy and management measures can help us understand the why stopgaps emerge, and weigh the equity and efficacy of stopgaps against other policy proposals

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ENSO-Induced Teleconnection: Process-Oriented Diagnostics to Assess Rossby Wave Sources and Ambient Flow Properties in Climate Models

Climate model fidelity in representing ENSO-induced teleconnection is assessed with process-oriented diagnostics that examine a chain of processes, from equatorial Pacific precipitation to the midlatitude circulation pattern over the Pacific–North American regions. Such processes are rarely addressed during model development. Using an upper-tropospheric divergent level, local vorticity gradient of the ambient zonal flow (∂ 2 U¯/∂y 2 ) and a restoring force for Rossby waves (β * ) are estimated, the equivalent barotropic vorticity equation is solved, and an anomalous Rossby wave source (RWS') quantified. The analysis is applied to AMIP5 and AMIP6 simulations. For a realistic circulation response representation, the hypothesis that models accurately represent the strength and location of RWS', and spatial variations in β * is tested. Compared to AMIP5, in AMIP6 there are clear improvements in representing RWS' and β * . To validate the hypothesis, the analysis identifies two metrics: spatially coherent RWS' in the subtropical North Pacific, and longitudes of negative β * over the western-central North Pacific. By projecting these metrics in two and three-dimensional views, improvements or degradations in model versions are apparent. If a model’s fidelity in representing ∂ 2 U¯/∂y 2 and RWS' are compromised, then radiated Rossby waves are reflected more equatorward, resulting in zonally elongated circulation anomalies over the central North Pacific. Thus, during climate model development, applying this analysis frequently will keep a regular check on the fidelity of the modeled response to anomalous El Niño convection in conjunction with changing model ambient flow dependencies. As a result, this analysis is intended to form a process-oriented diagnostics package, a community contribution to the NOAA Model Diagnostics Task Force.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2022

Overcoming the disconnect between energy system and climate modeling

Energy system models underpin decisions by energy system planners and operators. Energy system modeling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modeling has started to form that aims to better integrate energy system models with weather and climate models. In this work, we evaluate the disconnects between the energy system and climate modeling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modeling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision-making.

17 WIND ENERGY↗
DOE OSTI2023

Quantitative Systems Modeling for Critical Infrastructure Predictions in Climate Change: A National Defense Framework

Any national defense is dependent on the efficacy of the available physical infrastructure. Whatever degrades infrastructure, structurally, physically, architecturally, or esthetically, is of interest to the nation. Climate change is now a major significant factor of interest impinging on national critical infrastructure. The devastating effects of climate change have increasing pervasiveness throughout the world. The impact on critical infrastructure is of particular interest to researchers. In consonance with ASCE-ASME's special issue on critical infrastructure protection and resilience, this paper presents a systems-modeling approach for critical infrastructure and predictions in relation to climate change agreements in COP26. The paper addresses high-level critical systems-based assessment of the social, legal, economic, and technical nuances impinging on the viability of COP26 agreements. The paper was written using a multinational collaboration approach. In conclusion, the specific focus of national defense is used as the backdrop for the methodology of the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Shift in Peaks of PAH-Associated Health Risks From East Asia to South Asia and Africa in the Future

Lung cancer risk from exposure to ambient polycyclic aromatic hydrocarbons (PAHs) is expected to change significantly by 2050 compared to 2008 due to changes in climate and emissions. Integrating a global atmospheric chemistry model, a lung cancer risk model, and plausible future emissions trajectories of PAHs, we assess how global PAHs and their associated lung cancer risk will likely change in the future. Benzo(a)pyrene (BaP) is used as an indicator of cancer risk from PAH mixtures. From 2008 to 2050, the population-weighted global average BaP concentrations under all RCPs consistently exceeded the WHO-recommended limits, primarily attributed to residential biofuel use. Peaks in PAH-associated incremental lifetime cancer risk shift from East Asia (4 × 10 –5 ) in 2008 to South Asia (mostly India, 2–4 × 10 –5 ) and Africa (1–2 × 10 –5 ) by 2050. In the developing regions of Africa and South Asia, PAH-associated lung cancer risk increased by 30–64% from 2008 to 2050, due to increasing residential energy demand in households for cooking, heating, and lighting, the continued use of traditional biomass use, increases in agricultural waste burning, and forest fires, accompanied by rapid population growth in these regions. Due to more stringent air quality policies in developed countries, their PAH lung cancer risk substantially decreased by ~80% from 2008 to 2050. Climate change is likely to have minor effects on PAH lung cancer risk compared to the impact of emissions. Future policies, therefore, need to consider efficient combustion technologies that reduce air pollutant emissions, including incomplete combustion products such as PAH.

54 ENVIRONMENTAL SCIENCES↗