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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Implications of Changes in Water Stress and Precipitation Extremes for Cocoa Production in Côte D'ivoire and Ghana

ABSTRACT Climate change induces high variability in drought patterns and extreme precipitation indices in rainfed cocoa farming, impacting cocoa production. This study evaluated water stress, meteorological and agricultural drought conditions, and critical extreme precipitation indices in the world's two largest cocoa‐producing nations from 1981 to 2022. The results revealed a significant reduction in total annual precipitation (PRCPTOT), in the last three decades, with the greatest decline in the 1991–2000 and 2011–2022 periods. Ghana experienced the most significant reduction up to 15% (200 mm/year) in the last decade, attributed to a substantial decrease in wet days number (RR1) up to 25 days per year, a reduction in maximum consecutive wet days (CWD) up to 6 days per year, and an increase in maximum consecutive dry days (CDD) up to 15 days per year. Moreover, there was a notable decline in the Simple Daily Intensity Index (SDII), with reductions of up to 4 mm/day in certain areas, contributing to increased drought frequency, severity, and duration. In the most recent decade (2011–2022), particularly during the extremely dry years of 2013 and 2015, cocoa‐growing regions in Ghana (GHA) and eastern Côte d'Ivoire (CIV) experienced prolonged agricultural drought expressed by soil moisture deficit, typically extending from May to September. Additionally, large portions of central and eastern Ghana, as well as northeastern Côte d'Ivoire experienced sustained water stress, with over three consecutive months of total monthly precipitation falling below 100 mm, negatively impacting cocoa productivity. The decrease in the yield in the range of 2.5% to 37% was noted in the dry years and the following years, varying according to the country depending on the severity of the drought. Sensitivity analysis highlights cocoa yield's responsiveness to drought and water stress, particularly in specific years when water stress occurred, such as 1984,1985, 1989, 1995, 1999, 2000, and 2008. Considering the observed trends in precipitation patterns and their impact on cocoa production, it is crucial to acknowledge the inherent uncertainty of future precipitation patterns due to climate change. To address this challenge effectively, our study underscores the importance of identifying and closely monitoring regions currently facing water stress, as determined by precipitation and drought indicators. Over the analysed period (1981–2022), we have noted shifts in the distribution of water‐stressed areas, highlighting the dynamic nature of this issue. Consequently, we advocate for a targeted approach to implement cocoa supplementary irrigation in consistently water‐stressed regions.

Obahoundje, Salomon [International Water Managemen↗
NASA NTRS1975

Fractionation in the solar nebula - Condensation of yttrium and the rare earth elements

The condensation of Y and the rare earth elements (REE) from the solar nebula may be controlled by thermodynamic equilibrium between gas and condensed solids. Highly fractionated REE patterns may result if condensates are removed from the gas before condensation is complete. It is found that the fractionation is not a smooth function of REE ionic radius but varies in an extremely irregular pattern. Both Yb and Eu are predicted to be extremely depleted in the early condensate without the requirement of condensation in the divalent state. The model is discussed with respect to a highly fractionated pattern observed by Tanaka and Masuda (1973), in a pink Ca-Al-rich inclusion from the Allende meteorite and can account for the abundances of each REE determined. According to the model this inclusion represents a condensate from a previously fractionated gas rather than from a gas of solar composition. Before the condensation of this inclusion, an earlier condensate was formed and was removed from equilibrium with the gas.

Boynton, W. V.↗
DOE OSTI2021

Additive Lithography–Organic Monolayer Patterning Coupled with an Area-Selective Deposition

The combination of area-selective deposition (ASD) with a patternable organic monolayer provides a versatile additive lithography platform, enabling the generation of a variety of nanoscale feature geometries. Stearate hydroxamic acid self-assembled monolayers (SAMs) were patterned with extreme ultraviolet (λ = 13.5 nm) or electron beam irradiation and developed with ASD to achieve line space patterns as small as 50 nm. Density functional theory was employed to aid in the synthesis of hydroxamic acid derivatives with optimized packing density to enhance the imaging contrast and improve dose sensitivity. Near-edge X-ray absorption fine structure spectroscopy and infrared spectroscopy reveal that the imaging mechanism is based on improved deposition inhibition provided by the cross-linking of the SAM to produce a more effective barrier during a subsequent deposition step. With patterned substrates composed of coplanar copper lines and silicon spacers, hydroxamic acids selectively formed monolayers on the metal portions and could undergo a pattern-wise exposure followed by ASD in the first combination of a patternable monolayer with ASD. This material system presents an additional capability compared to traditional ASD approaches that generally reflect a starting patterned surface. Finally, this bottoms-up additive approach to lithography may be a viable alternative to subtractive nanoscale feature generation.

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

A stochastic model of future extreme temperature events for infrastructure analysis

Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%-90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850-1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov-Smirnov -value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67 degrees C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Variational AutoEncoders Reveal Intensifying GPP Extremes in Continental United States based on CESM2 Simulations

Climate extremes significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under SSP5-8.5 scenario. The VAE architecture employs three dense layers and a latent space with input sequence length of 12 months, training on normalized GPP time series to reconstruct the GPP and identify anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE consistently producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. This research demonstrates the potential of deep learning approaches for extremes detection and provides a foundation for improved understanding of future carbon cycle risks under future conditions.

Sharma, Bharat [ORNL] (ORCID:0000000266982487)↗
DOE OSTI2025

Projected evolution of droughts and human exposure in the contiguous United States under SSP5-8.5: a regional downscaling perspective

The increasingly unpredictable and extreme weather patterns under a warming climate underscore the urgency of accurate regional assessments of future drought risk. This study evaluates the projected drought evolution in the contiguous United States under the high-emission shared socioeconomic pathway 5–8.5 climate scenario for the coming decades. Using a multi-model ensemble of six Coupled Model Intercomparison Project Phase 6 global climate models combined with dynamical downscaling techniques, we analyzed near-term (2020–2039) and mid-term (2040–2059) drought patterns using the self-calibrating palmer drought severity index (ScPDSI), the standardized precipitation index (SPI-12), and the Standardized Precipitation-Evapotranspiration Index (SPEI-12). Results reveal a widespread increase in abnormally dry (D0) and moderate drought (D1) conditions, particularly in urban areas, while severe (D2), extreme (D3), and exceptional (D4) droughts are expected to become less common in many regions. Meanwhile, persistent and intensifying droughts are projected in the western and southwestern U.S., driven by long-term soil moisture deficits. The ScPDSI projects that 1.1 million urban residents will be affected by D0 conditions in 2050, while SPI-12 suggests a decrease in the total affected populations after 2040. ScPDSI indicates prolonged droughts in the West, and SPI-12 captures transient variability. Although the total drought-exposed population is expected to decrease, urban areas will continue to bear a greater burden, particularly for mild droughts (D0, D1). These findings highlight a shift toward more frequent mild droughts, fewer severe droughts, and persistent drying in the Southwest, emphasizing the need for region-specific adaptation strategies.

CMIP6↗
DOE OSTI2023

Compound Continental Risk of Multiple Extreme Floods in the United States

Abstract Understanding spatially correlated floods and modeling joint hazard associated with threshold exceedances across multiple locations is crucial for accurate estimation of continental‐scale portfolio risk. This work uses a non‐parametric copula‐based spatial simulator to analyze peak floods across the United States to derive the first‐of‐its‐kind continental portfolio risk estimates at the 10‐ and 100‐year return levels. We find significant interdependence in floods across the nation, revealing the recurring pattern of extreme events affecting the Northeast, Central, West, and Northwest United States in the same year. The stochastic simulator effectively manages high‐dimensional data and offers reliable uncertainty estimates for both spatially dependent floods and the aggregated flood losses at the continental level. El Niño‐Southern Oscillation and Atlantic Multidecadal Oscillation are identified as statistically significant tele‐connectors of aggregate loss. This research aims to advance the understanding of compound continental flood hazard and the potential large‐scale climate teleconnections that lead to such compound floods.

Geology↗
DOE OSTI2019

The east–west division of changing precipitation in Nepal

Abstract Changes to precipitation patterns and extremes over the Nepalese Himalayas were examined using a high‐resolution, station‐based daily dataset, Asian Precipitation‐Highly Resolved Observational Data Integration Towards Evaluation (0.05° × 0.05° APHRODITE) from 1951 to 2007. The annual statistics of extreme precipitation across Nepal show a significant increase since the end of the 20th century. However, seasonal mean precipitation shows a remarkable decrease in western Nepal, particularly since 1980, forming an east–west division in the precipitation change. This decreasing trend of precipitation led to a reduction to the dry‐season stream flow of Karnali River, the major river in western Nepal. At the same time, the increasing extreme precipitation produced greater threat of flash flood in Nepal. This east–west division of the precipitation trend agrees with the second leading mode of the mean precipitation variability, which was traced to the interannual variability of the Indian Ocean sea surface temperature that showed a slowdown of warming. Similar to the APHRODITE trends, precipitation simulated by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models depicted the decreasing historical trend in western Nepal, but future projections reverse that trend towards an all‐Nepal increase. CMIP5 future climate projections depict continual warming in the Indian Ocean, potentially reversing the historical decreasing trends of precipitation in western Nepal.

Pokharel, Binod↗
DOE OSTI2026

Subseasonal Tropical Convection Characteristics in the Energy Exascale Earth System Model Version 2

Accurate simulation of subseasonal tropical moist convection remains a key challenge for Earth system models. The difficulties stem from the reliance of cumulus cloud processes on model parameterizations and the need to represent the multiscale nature of interactions among clouds, radiation, moisture, circulation, and surface energy fluxes. Equatorial convection drives circulation anomalies that can affect weather patterns and extremes globally, motivating efforts to better understand and simulate these tropical disturbances. Here, a detailed review of subseasonal tropical convective behavior as simulated in the Energy Exascale Earth System Model version 2 (E3SMv2) is presented, with comparison to its predecessor version 1 (E3SMv1) and reference data sets. Model structural changes to the deep convective trigger function and surface fluxes, along with parametric tuning of the cloud and microphysics schemes, together result in an improved depiction of organized tropical convection across scales. In particular, E3SMv2 exhibits a more realistic Madden‐Julian oscillation (MJO) and low‐frequency Kelvin waves—owing to a sharper time mean equatorial meridional moisture gradient and improved convection‐circulation coupling —as well as a better depiction of MJO Northern Hemisphere teleconnections. Despite these improvements, subseasonal precipitation variance continues to be strongly underestimated in E3SMv2. Use of a cloud plume model also reveals that the coupling between daily averaged tropical precipitation and lower tropospheric instability in E3SM is inconsistent with observations, a bias that could potentially impact the simulation of intraseasonal disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Power Distribution Designing For Resilience Application

The number of power outages have been on the rise as extreme weather patterns have been occurring more frequently due to climate change. These outages have an economic impact in the billions of dollar. The modernization of the power grid and adoption of high penetration of distributed generation such as wind and solar have the potential to reduce the number and length of time of these power outages. However, planners and operators need a way to value the resilience the distributed assets provide to the grid. PowDDeR is a tool that allows them to evaluate the resilience each asset provides as well as how they provide resilience over the connected network.

Phillips, TylerB↗
DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Global Building Decarbonization Trends and Strategies

It is evident from extreme weather patterns that climate change is impacting the whole world. The consequences in the form of unpredicted severe weather conditions, hurricanes, floods, and wildfires have been disrupting national economies and affecting lives across the globe. As the global economy is recovering from the COVID-19 pandemic, the temporary drop in greenhouse gas (GHG) emissions mainly caused due to travel bans and economic slowdowns is no longer the case and, moving forward, controlling the increase in GHG emissions has emerged as a global challenge. The impact of climate change has enabled an acknowledgement of the challenge and this coincided with the rapid development of clean energy technologies for buildings, transportation, and industrial sectors, while ensuring a significant reduction in costs for their wide implementation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

The HZE radiation problem

Radiation-exposure limits have yet to be established for missions envisioned in the framework of the Space Exploration Initiative. The radiation threat outside the earth's magnetosphere encompasses protons from solar particle events and the highly charged energetic particles constituting galactic cosmic rays; radiation biology entails careful consideration of the extremely nonuniform patterns of such particles' energy deposition. The ability to project such biological consequences of exposure to energetic particles as carcinogenicity currently involves great uncertainties from: (1) different regions of space; (2) the effects of spacecraft structures; and (3) the dose-effect relationships of single traversals of energetic particles.

Schimmerling, Walter↗
NASA NTRS1992

Effects on post-glacial rebound from the hard rheology in the transition zone

The effect of an increase of viscosity in the earth-mantle's transition zone between 420 and 670 km on the geophysical signatures induced by postglacial rebound was analyzed using a self-gravitating model consisting of an elastic lithosphere, a three-layer viscoelastic mantle, and an inviscid core. Results show that the horizontal displacements and velocities as well as the stress pattern are extremely sensitive to the viscosity increase and to the chemical stratification of the transition zone. The findings indicate that geodetic data on intraplate deformation can be used to put constraints on the viscosity increase in the transition zone and on the amount of chemical stratification in the mantle.

Spada, Giorgio↗
NASA NTRS1995

Isentropic mixing in the Arctic stratosphere during the 1992-1993 and 1993-1994 winters

Dynamic isolation of the winter Arctic circumpolar vortex during 1992-1993 and 1993-1994 (the second and third northern hemisphere winters of the UARS mission) is studied using quasi-horizontal isentropic trajectories. Ejection of vortex air and entrainment of mid-latitude air into the vortex are quantified and compared with climatological values obtained from the analysis of 16 Arctic winters. A number of unusual features of both winters are discussed. The most notable features are the anomalous isolation experienced by the vortex during December 1992 and the unusual degree of isolation and persistence of the vortex during February and March of both years. the 1992-1993 winter season is the most consistently isolated vortex on record. Only during January 1993, when entrainment is large, is this pattern of extreme isolation broken.

Dahlberg, Steven P.↗
NASA NTRS1996

High Resolution Spectroscopy of Vega-like Stars: Abundances and Circumstellar Gas

Vega-like stars are main-sequence stars exhibiting excess infrared emission. In an effort to improve the information available on this class of star, 13 stars have been analyzed which have been classed as Vega-like, or have an infra-red excess attributable to dust in their circumstellar environment. In a separate paper stellar properties such as effective temperature and log g have been derived and in this poster we highlight the results of the photospheric abundance analysis also carried out during this work. King recently drew attention to the possible link between Vega-like stars and the photospheric metal-depleted class of A-stars, the Lambda Bootis stars. Since Vega-like stars are thought to have disks of dust, it might be expected that accretion of depleted gas onto the surface of these stars may cause this same phenomenon. In the 6 stars studied for depletions, none showed the extreme underabundance patterns observed in Lambda Bootis stars. However, depletions of silicon and magnesium were found in two of the sample, suggesting that these elements are in silicate dust grains in the circumstellar environment of these stars. Absorption lines attributed to circumstellar gas have been positively identified in three stars in our sample. Individual cases show evidence either of high-velocity outflowing gas, variability in the circumstellar lines observed, or evidence of circumstellar gas in excited lines of Fe II. No previous identification of circumstellar material has been made for two of the stars in question.

Dunkin, S. K.↗