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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 OSTI2023

Field retrieved photovoltaic backsheet survey from diverse climate zones: Analysis of degradation patterns and phenomena

Understanding the impact of climate stressors on photovoltaic (PV) backsheet degradation in real-use conditions is critical to improve the accelerated testing exposures, extend the backsheet lifetime, and increase the confidence in PV reliability. Here, in this work, a total of 33 PV module backsheets were retrieved from six climatic zones worldwide with 2 - 28 years of exposure. These modules included five types of backsheet air-side materials (or outer layer): poly(vinylidene fluoride) (PVDF), poly(tetrafluoroethylene-co-hexafluoropropylene-co-vinylidene fluoride) (THV), poly(vinyl fluoride) (PVF), poly(ethylene terephthalate) (PET), and polyamide (PA). Attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) was used to identify air-side materials. The degradation induced color change, gloss loss, and chemical material changes analyzed using optical microscopy, differential scanning calorimetry (DSC), scanning electron microscopy (SEM), colorimetry (yellowness index (YI)), and gloss measurements. PVDF, THV, and PVF air-side layer backsheets, in particular PVF, had minimal degradation in the air-side layer appearance and chemical structures after exposure in different climatic zones. The PET air-side backsheets exhibited obvious color increase (22.55 YI units after about 9 years exposure) and the PA/PA/PA backsheets showed large gloss loss (up to 76.4 %) relative to the unexposed backsheets. Severe cracks between cells that penetrated through the entire thickness of backsheets are observed on PA/PA/PA backsheets after 4-6 years of exposure in 6 climatic zones. The current indoor exposure standards were not sufficient to identify this degradation type. However, fluoropolymer based PV backsheets showed lower levels of degradation predictors and increased climatic resistance. Specific samples (PVF) showed little change from baseline after 28 years of outdoor exposure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Effects of Climate Change on Capacity Expansion Decisions of an Electricity Generation Fleet in the Southeast U.S.

The electric power sector in the United States faces many challenges related to climate change. On the demand side, climate change could shift demand patterns due to increased air temperatures. On the supply side, climate change could lead to deratings of thermal units due to changes in air temperature, water temperature, and water availability. Past studies have typically analyzed these risks separately. Here, we developed an integrated, multimodel framework to analyze how compounding risks of climate-change impacts on demand and supply affect long-term planning decisions in the power system. In the southeast U.S., we found that compounding climate-change impacts could result in a 35% increase in installed capacity by 2050 relative to the reference case. Participation of renewables, particularly solar, in the fleet increased, driven mostly by the expected increase in summertime peak demand. Such capacity requirements would increase investment costs by approximately 31 billion (USD 2015) over the next 30 years, compared to the reference case. These changes in investment decisions align with carbon emission mitigation strategies, highlighting how adaptation and mitigation strategies can converge.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Detecting Signals of Large–Scale Climate Phenomena in Discharge and Nutrient Loads in the Mississippi–Atchafalaya River Basin

Agricultural runoff from the Mississippi–Atchafalaya River Basin delivers nitrogen (N) and phosphorus (P) to the Gulf of Mexico, causing hypoxia, and climate drives interannual variation in nutrient loads. Climate phenomena such as El Niño–Southern Oscillation may influence nutrient export through effects on river flow, nutrient uptake, or biogeochemical transformation, but landscape variation at smaller spatial scales can mask climate signals in load or discharge time series within large river networks. Here, we used multivariate autoregressive state–space modeling to investigate climate signals in the long–term record (1979–2014) of discharge, N, P, and SiO 2 loads at three nested spatial scales within the Mississippi–Atchafalaya River Basin. We detected significant signals of El Niño–Southern Oscillation and land–surface temperature anomalies in N loads but not discharge, SiO 2 , or P, suggesting that large–scale climate phenomena contribute to interannual variation in nutrient loads through biogeochemical mechanisms beyond simple discharge–load relationships.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Modeling Climate Change Impacts on an Arctic Polygonal Tundra: 2. Changes in CO 2 and CH 4 Exchange Depend on Rates of Permafrost Thaw as Affected by Changes in Vegetation and Drainage

Model projections of future CO 2 and CH 4 exchange in Arctic tundra diverge widely. Here we used ecosys to examine how climate change will affect CO 2 and CH 4 exchange in troughs, rims, and centers of a coastal polygonal tundra landscape at Barrow, AK. The model was shown to simulate diurnal and seasonal variation in CO 2 and CH 4 fluxes associated with those in air and soil temperatures ( T a and T s ) and soil water contents (θ) under current climate in 2014 and 2015. During RCP 8.5 climate change from 2015 to 2085, rising T a , atmospheric CO 2 concentrations ( C a ), and precipitation ( P ) increased net primary productivity (NPP) from 50–150 g C m -2 y -1 , consistent with current biometric estimates, to 200–250 g C m -2 y -1 . Concurrent increases in heterotrophic respiration ( R h ) were slightly smaller, so that net CO 2 exchange rose from values of -25 (net emission) to +50 (net uptake) g C m -2 y -1 to ones of -10 to +65 g C m -2 y -1 . Increases in net CO 2 uptake were largely offset by increases in CH 4 emissions from 0–6 to 1–20 g C m -2 y -1 , reducing gains in net ecosystem productivity. As a result, these increases in net CO 2 uptake and CH 4 emissions were modeled with hydrological boundary conditions that were assumed not to change with climate. Both these increases were smaller if boundary conditions were gradually altered to increase landscape drainage during model runs with climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Lagrangian perspective on tropical anvil cloud lifecycle in present and future climate

The evolution of tropical anvil clouds from their origin in deep convective cores to their slow decay determines the climatic effects of clouds in tropical convective regions. Despite the relevance of anvil clouds for climate and responses of clouds to global warming, processes dominating their evolution are not well understood. Currently available observational data reveal instantaneous snapshots of anvil cloud properties, but cannot provide a process-based perspective on anvil evolution. We therefore conduct simulations with the high resolution version of the Exascale Earth System Model in which we track mesoscale convective systems over the Tropical Western Pacific and compute trajectories that follow air parcels detrained from peaks of convective activity. With this approach we gain new insight into the anvil cloud evolution both in present day and future climate. Comparison with geostationary satellite data shows that the model is able to simulate maritime mesoscale convective systems reasonably well. Trajectory results indicate that anvil cloud lifetime is about 15 hours with no significant change in a warmer climate. The anvil ice mixing ratio is larger in a warmer climate due to a larger source of ice by detrainment and larger depositional growth leading to a more negative net cloud radiative effect along detrained trajectories. However, the increases in sources are counteracted by increases in sinks of ice, particularly snow formation and sedimentation. Furthermore, we find that the mean anvil cloud feedback along trajectories is positive and consistent with results from more traditional cloud feedback calculation methods.

climate, weather, mesocale cellular convection↗
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 OSTI2022

Forest Canopy Density Effects on Snowpack Across the Climate Gradients of the Western United States Mountain Ranges

Limitations inherent to field studies such as limited spatial and temporal coverage has made it very challenging, if not impossible, to explicitly disentangle the effect of canopy density from climate on snowpack dynamics. Hence, our understanding of how variations in canopy density can affect snow processes under diverse climate conditions has been limited. To address this knowledge gap, this study uses a physics-based modeling approach to represent most key processes that drive snow accumulation and melt in the open and forest with varied forest density, across the climate gradients of the Western U.S. mountain ranges, as represented by 228 Snow Telemetry (SNOTEL) locations. Simulations suggest that, under most winter climate conditions, canopy density exerts a strong influence on under-canopy snowpack dynamics and their controlling processes. Changing canopy density can shift the direction of the modeled differential peak snow accumulation and snow disappearance date (SDD) between the open and forest. For example, while simulated snowpack lasts longer in the open than the high-density forest at most wet/warm and dry/cold locations by up to 55 days, about 90% of all locations show a longer snow duration in the low-density forest by up to 27 days as compared to the open. Interannual variability in climate can also change the magnitude and direction of canopy impact on snow processes. By comparing model simulations to published field observations, and we also identify conditions that require additional field observations to improve process-scale understanding of forest-snow interaction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Agreement of Analytical and Simulation-Based Estimates of the Required Land Depth in Climate Models

Previous analytical and simulation-based analyses suggest that deeper land surface models are needed to realistically simulate the terrestrial thermal state in climate models, with implications for land-atmosphere interactions. Analytical approaches mainly focused on the subsurface propagation of harmonics such as the annual temperature signal, and a direct comparison with climate-change model output has been elusive. Here, this study addresses the propagation of a harmonic pulse fitted to represent the timescale and amplitude of anthropogenic warming. Its comparison to land model simulations with stepwise increased bottom boundary depth leads to an agreement between the simulation-based and analytical frameworks for long-term climate trends. Any depth increase gradually decreases the relative error in the subsurface thermodynamics, and a minimum depth of 170 m is recommended to simulate the ground climate adequately. The approach provides an accurate estimate of the required land-model depth for climate-change simulations and assesses the relative bias in insufficiently deep land models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adaptation Strategies Strongly Reduce the Future Impacts of Climate Change on Simulated Crop Yields

Abstract Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Exploring Climate-Disease Connections in Geopolitical Versus Ecological Regions: The Case of West Nile Virus in the United States

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A meta-analysis of global fungal distribution reveals climate-driven patterns

The evolutionary and environmental factors that shape fungal biogeography are incompletely understood. Here, we assemble a large dataset consisting of previously generated mycobiome data linked to specific geographical locations across the world. We use this dataset to describe the distribution of fungal taxa and to look for correlations with different environmental factors such as climate, soil and vegetation variables. Our meta-study identifies climate as an important driver of different aspects of fungal biogeography, including the global distribution of common fungi as well as the composition and diversity of fungal communities. In our analysis, fungal diversity is concentrated at high latitudes, in contrast with the opposite pattern previously shown for plants and other organisms. Mycorrhizal fungi appear to have narrower climatic tolerances than pathogenic fungi. We speculate that climate change could affect ecosystem functioning because of the narrow climatic tolerances of key fungal taxa.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Aerosols overtake greenhouse gases causing a warmer climate and more weather extremes toward carbon neutrality

To mitigate climate warming, many countries have committed to achieve carbon neutrality in the mid-21 st century. Here, we assess the global impacts of changing greenhouse gases (GHGs), aerosols, and tropospheric ozone (O 3 ) following a carbon neutrality pathway on climate and extreme weather events individually using the Community Earth System Model version 1 (CESM1). The results suggest that the future aerosol reductions significantly contribute to climate warming and increase the frequency and intensity of extreme weathers toward carbon neutrality and aerosol impacts far outweigh those of GHGs and tropospheric O 3 . It reverses the knowledge that the changing GHGs dominate the future climate changes as predicted in the middle of the road pathway. Therefore, substantial reductions in GHGs and tropospheric O 3 are necessary to reach the 1.5 °C warming target and mitigate the harmful effects of concomitant aerosol reductions on climate and extreme weather events under carbon neutrality in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate change drives reduced biocontrol of the invasive spongy moth

The effects of climate change on forest-defoliating insects are poorly understood, but could severely reduce forest productivity, biodiversity and timber production. For decades following its introduction in 1869, the spongy moth ( Lymantria dispar ) severely defoliated North American forests, but the introduction of the fungal pathogen Entomophaga maimaiga in 1989 suppressed spongy moth defoliation for 27 years. E. maimaiga , however, needs cool, moist conditions, whereas climate change is bringing hot, dry conditions to the range of the insect. Here, in this work, we use an empirically verified eco-climate model to project that climate change will sharply reduce E. maimaiga infection rates, thereby increasing spongy moth defoliation. Recent rebounds in defoliation are consistent with our projections. Our work demonstrates that the effects of climate change on species interactions can have important consequences for natural ecosystems.

climate-change ecology↗
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 OSTI2017

Crop yield response to climate change varies with crop spatial distribution pattern

The linkage between crop yield and climate variability has been confirmed in numerous studies using statistical approaches. A crucial assumption in these studies is that crop spatial distribution pattern is constant over time. Here, we explore how changes in county-level corn spatial distribution pattern modulate the response of its yields to climate change at the state level over the Contiguous United States. Our results show that corn yield response to climate change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Corn yield is predicted to decrease by 20~40% by 2050s when considering crop spatial distribution pattern changes, which is 6~12% less than the estimates with fixed cropping pattern. The beneficial effects are mainly achieved by reducing the negative impacts of daily maximum temperature and strengthening the positive impacts of precipitation. Our results indicate that previous empirical studies could be biased in assessing climate change impacts by ignoring the changes in crop spatial distribution pattern. As a result, this has great implications for understanding the increasing debates on whether climate change will be a net gain or loss for regional agriculture.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Climate responses in China to domestic and foreign aerosol changes due to clean air actions during 2013–2019

In recent years, to improve air quality, significant efforts have been made to reduce regional aerosols including China, Europe and North America, which have potential impacts on climate. In this study, fast and slow climate responses in China to changes in domestic and foreign anthropogenic emissions of aerosols and precursors from 2013 to 2019 are investigated using the Community Earth System Model version 1 (CESM1). Aerosol emissions changes, especially reductions in China, North America and Europe, during 2013–2019 resulted in surface air temperature increases across the Northern Hemisphere. Global aerosol changes induce a regional warming of 0.2 °C in China, equally contributed by domestic and foreign emissions changes. China’s domestic emission reductions lead to a regional average temperature rise of 0.1°C, primarily driven by rapid atmospheric adjustments over eastern China. Foreign aerosol changes, particularly reductions in North America and Europe, also contributed to a 0.1°C warming in China through slow oceanic processes. This warming in China induced by foreign aerosol changes is due to a teleconnection between the aerosol-induced anomalous regional warming in Eastern U.S.-North Atlantic Ocean-Europe and the downstream East Asian climate through anomalous wave trains propagation. The comparable influence of domestic and foreign aerosol changes on climate in China underscores the importance of international collaboration in climate mitigation endeavors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multigenic resistance to Xylella fastidiosa in wild grapes ( Vitis sps.) and its implications within a changing climate

Xylella fastidiosa is a bacterium that infects crops like grapevines, coffee, almonds, citrus and olives. There is little understanding of the genes that contribute to plant resistance, the genomic architecture of resistance, and the potential role of climate in shaping resistance, in part because major crops like grapevines (Vitis vinifera) are not resistant to the bacterium. Here we study a wild grapevine species, V. arizonica, that segregates for resistance. Using genome-wide association, we identify candidate resistance genes. Resistance-associated kmers are shared with a sister species of V. arizonica but not with more distant species, suggesting that resistance evolved more than once. Finally, resistance is climate dependent, because individuals from low (< 10 °C) temperature locations in the wettest quarter were typically susceptible to infection, likely reflecting a lack of pathogen pressure in colder climates. In fact, climate is as effective a predictor of resistance phenotypes as some genetic markers. We extend our climate observations to additional crops, predicting that increased pathogen pressure is more likely for grapevines and almonds than some other susceptible crops.

60 APPLIED LIFE SCIENCES↗