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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

James A Franke↗
NASA NTRS2017

Development OF A Multi-Scale Framework for Mapping Global Evapotranspiration

As the worlds water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring.

Drought↗
NASA NTRSDate not supplied

Data Fusion and Mining Techniques to Map Water Use and Drought across Spatial and Temporal Scales

As the world’s water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring. Until recently, ALEXI has been limited to areas with high resolution temporal sampling of geostationary sensors. The use of geostationary sensors makes global mapping a complicated process, especially for real-time applications, as data from as many as five different sensors are required to be ingested and harmonized to create a global mosaic. However, our research team has developed a new and novel method of using twice-daily observations from polar-orbiting sensors such as MODIS and VIIRS to estimate the mid-morning rise in LST that is used to drive the energy balance estimations within ALEXI. This allows the method to be applied globally using a single sensor (in this case, initially MODIS with a planned transition to VIIRS) rather than a global compositing of all available geostationary data. Other advantages of this new method include the higher spatial resolution provided by MODIS and VIIRS and the increased sampling at high latitudes where oblique view angles limit the utility of geostationary sensors. This presentation will focus on global applications for mapping water use and drought using data mining and data fusion across spatial scales extending from 5-km to 30-m “field-scale” estimates.

Christopher Hain↗
DOE OSTI2025

Climate Nowcasting

The climate is changing so rapidly that climatologies based on historical statistics cannot reliably capture the current risk of extreme weather events hazardous to society. Decision relevant projections of weather extreme probability over the next 10–15 years are needed to enable adaptation and resilience in the face of this evolving risk. Current weather forecasts/predictions and long-term climate projections for decades into the future are inadequate for providing this information to stakeholders that need it, targeting forecast horizons either too short or too far into the future. We argue that a new approach is needed: climate nowcasting. Climate nowcasting would focus on user-inspired extreme metrics over the next 10–15 year time frame, targeting specific impacts and locations down to a local scale, by engaging with stakeholders to understand their needs and provide information in a format relevant for decision making. Importantly, climate nowcasting will not consist of a single approach or data set, involving rather data fusion from different sources of information: simulations, observations and data driven methods, likely through different weights depending on the metric of interest. Predictions must be accompanied by serious engagement with stakeholders facing climate risks, and clearly present uncertainties and limitations of any prediction. Such a vision is very different from how typical climate or weather forecasts are applied today.

Gettelman, Andrew↗
NASA NTRS2014

Probabilistic 21st and 22nd Century Sea-Level Projections at a Global Network of Tide-Gauge Sites

Sea-level rise due to both climate change and non-climatic factors threatens coastal settlements, infrastructure, and ecosystems. Projections of mean global sea-level (GSL) rise provide insufficient information to plan adaptive responses; local decisions require local projections that accommodate different risk tolerances and time frames and that can be linked to storm surge projections. Here we present a global set of local sea-level (LSL) projections to inform decisions on timescales ranging from the coming decades through the 22nd century. We provide complete probability distributions, informed by a combination of expert community assessment, expert elicitation, and process modeling. Between the years 2000 and 2100, we project a very likely (90% probability) GSL rise of 0.5–1.2 m under representative concentration pathway (RCP) 8.5, 0.4–0.9 m under RCP 4.5, and 0.3–0.8 m under RCP 2.6. Site-to-site differences in LSL projections are due to varying non-climatic background uplift or subsidence, oceanographic effects, and spatially variable responses of the geoid and the lithosphere to shrinking land ice. The Antarctic ice sheet (AIS) constitutes a growing share of variance in GSL and LSL projections. In the global average and at many locations, it is the dominant source of variance in late 21st century projections, though at some sites oceanographic processes contribute the largest share throughout the century. LSL rise dramatically reshapes flood risk, greatly increasing the expected number of “1-in-10” and “1-in-100” year events.

sea level↗
DOE OSTI2021

A prion-like protein regulator of seed germination undergoes hydration-dependent phase separation

Many organisms evolved strategies to survive desiccation. Plant seeds protect dehydrated embryos from various stressors and can lay dormant for millennia. Hydration is the key trigger to initiate germination, but the mechanism by which seeds sense water remains unresolved. Here we identified an uncharacterized Arabidopsis thaliana prion-like protein we named FLOE1, which phase separates upon hydration and allows the embryo to sense water stress. We demonstrate that biophysical states of FLOE1 condensates modulate its biological function in vivo in suppressing seed germination under unfavorable environments. We find intragenic, intraspecific, and interspecific natural variation in FLOE1 expression and phase separation and show that intragenic variation is associated with adaptive germination strategies in natural populations. This combination of molecular, organismal, and ecological studies uncovers FLOE1 as a tunable environmental sensor with direct implications for the design of drought-resistant crops, in the face of climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Eastward-shifting boreal summer intraseasonal oscillation amplifies North America heatwave and wildfire risks in warming climate

Boreal summer intraseasonal oscillation (BSISO) is a key component of tropical climate variability, characterized by eastward and northeastward propagation of organized convection across the Indo-Pacific region. BSISO influences weather and climate extremes through atmospheric teleconnections, but its future changes and related extremes remain unclear. Here, based on Coupled Model Intercomparison Project Phase 6, we find that under a high-emission scenario, BSISO convection will shift eastward by ~3° (~300 km) in 2065–2099, driven by moisture profile changes associated with sea surface temperature warming over central-to-eastern Pacific. This eastward-shifted BSISO convection, along with a northward expansion of the westerly jet, strengthens the BSISO teleconnections that extend into broader areas of North America. These changes will increase the BSISO-related heatwave risks by 23% and wildfire risks by 3.5 times over northern North America compared to present-day conditions. The findings underscore the amplification of BSISO’s influence on extreme risks of extreme weather, emphasizing the need for improved adaptation and mitigation strategies for future climates.

BSISO teleconnection↗
NASA NTRS2023

How Have Hydrological Extremes Changed Over the Past 20 Years?

Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.

Bailing Li↗
DOE OSTI2022

Genetic diversity loss in the Anthropocene

Anthropogenic habitat loss and climate change are reducing species’ geographic ranges, increasing extinction risk and losses of species’ genetic diversity. Although preserving genetic diversity is key to maintaining species’ adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations’ post-2020 targets for genetic preservation.

Science & Technology - Other Topics↗
NASA NTRS2014

Uncertainty in Agricultural Impact Assessment

This chapter considers issues concerning uncertainty associated with modeling and its use within agricultural impact assessments. Information about uncertainty is important for those who develop assessment methods, since that information indicates the need for, and the possibility of, improvement of the methods and databases. Such information also allows one to compare alternative methods. Information about the sources of uncertainties is an aid in prioritizing further work on the impact assessment method. Uncertainty information is also necessary for those who apply assessment methods, e.g., for projecting climate change impacts on agricultural production and for stakeholders who want to use the results as part of a decision-making process (e.g., for adaptation planning). For them, uncertainty information indicates the degree of confidence they can place in the simulated results. Quantification of uncertainty also provides stakeholders with an important guideline for making decisions that are robust across the known uncertainties. Thus, uncertainty information is important for any decision based on impact assessment. Ultimately, we are interested in knowledge about uncertainty so that information can be used to achieve positive outcomes from agricultural modeling and impact assessment.

damage assessment↗
DOE OSTI2023

Anthropogenic climate change impacts exacerbate summer forest fires in California

Record-breaking summer forest fires have become a regular occurrence in California. Observations indicate a fivefold increase in summer burned area (BA) in forests in northern and central California during 1996 to 2021 relative to 1971 to 1995. While the higher temperature and increased dryness have been suggested to be the leading causes of increased BA, the extent to which BA changes are due to natural variability or anthropogenic climate change remains unresolved. Here, we develop a climate-driven model of summer BA evolution in California and combine it with natural-only and historical climate simulations to assess the importance of anthropogenic climate change on increased BA. Our results indicate that nearly all the observed increase in BA is due to anthropogenic climate change as historical model simulations accounting for anthropogenic forcing yield 172% (range 84 to 310%) more area burned than simulations with natural forcing only. We detect the signal of combined historical forcing on the observed BA emerging in 2001 with no detectable influence of the natural forcing alone. In addition, even when considering fuel limitations from fire-fuel feedbacks, a 3 to 52% increase in BA relative to the last decades is expected in the next decades (2031 to 2050), highlighting the need for proactive adaptations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning for modern power distribution systems: Progress and perspectives

The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Marković, Marija (ORCID:0000000247839837)↗
DOE OSTI2026

A bioinspired approach for adaptive solid-solid phase change material coatings with optimized surface features for passive thermal regulation

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that surface texture, rather than the intrinsic phase transition of the SS PCM, dominates optical control. Relative to a flat SS PCM coating, optimized isotropic random roughness and broader range features yielded the highest passive heating power increase of about 144 % and 319 % respectively suitable for cold climates. Saharan ant-inspired features enhanced passive cooling for hot climates, achieving a 21.8 % improvement. For moderate climates, Butterfly-wing-inspired surface features provided a balanced enhancement of 19 % for heating and 7 % for cooling. Across all cases, the optimized surface features reduced combined heating and cooling energy demand more effectively than the baseline coating, while preserving material thickness. These findings demonstrate that climate-adaptive, optimized bio-inspired surface features can unlock the full potential of SS PCM coatings, providing a versatile pathway to significant energy savings in buildings and other applications. The methodology establishes a framework for designing next-generation adaptive envelopes that leverage natural photonic principles for high-impact, low-cost thermal regulation.

36 MATERIALS SCIENCE↗
NASA NTRS2014

Ten Years of Forest Cover Change in the Sierra Nevada Detected Using Landsat Satellite Image Analysis

The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology was applied to detected changes in forest vegetation cover for areas burned by wildfires in the Sierra Nevada Mountains of California between the periods of 1975- 79 and 1995-1999. Results for areas burned by wildfire between 1995 and 1999 confirmed the importance of regrowing forest vegetation over 17% of the combined burned areas. A notable fraction (12%) of the entire 5-km (unburned) buffer area outside the 1995-199 fires perimeters showed decline in forest cover, and not nearly as many regrowing forest areas, covering only 3% of all the 1995-1999 buffer areas combined. Areas burned by wildfire between 1975 and 1979 confirmed the importance of disturbed (or declining evergreen) vegetation covering 13% of the combined 1975- 1979 burned areas. Based on comparison of these results to ground-based survey data, the LEDAPS methodology should be capable of fulfilling much of the need for consistent, low-cost monitoring of changes due to climate and biological factors in western forest regrowth following stand-replacing disturbances.

Vegetation↗
DOE OSTI2021

Plant Single-Cell Solutions for Energy and the Environment (Second Workshop Report)

Plants are important sources of energy and materials, and they collectively represent a critical component of Earth’s ecosystem. With increasing environmental stresses due to climate change and intensive agricultural practices, the need for resilient plants is greater than ever before. To secure plant resources for bioenergy, biomaterials, food, and ecosystem adaptation, a deeper understanding of the fundamental biology of plants at a cellular level is urgently needed. Plants contain a multitude of specialized cell types that compose tissues and organs. Pathogens often target specific cell types within plants, and the response of one cell to a particular stimulus is likely to be distinct from its neighbor because of underlying molecular and contextual differences. Understanding how these responses are distributed among cells, the main goal of single-cell approaches, will substantially enhance our ability to use targeted engineering for improving plant productivity and resilience. Furthermore, single-cell approaches are necessary to understand the interactions between plants and other ecosystem members such as fungi, bacteria, and archaea. Unlocking these gene-response mechanisms at a cellular level can improve our ability to adapt plants to environmental stresses, increasing their utility as feedstocks for biomaterials and bioenergy. Recent advances in high-throughput sequencing, mass spectrometry, microfluidics and miniaturization, artificial intelligence and machine learning, and bioinformatics have greatly improved our ability to detect and understand processes at a cellular level. In mammalian systems, single-cell transcriptomics has already led to many advances, such as newly identified cell types and cell-targeted treatment of diseases, and mass spectrometry-based single-cell proteomics has recently been demonstrated as a promising emerging technology. However, plant single-cell omics has lagged behind mammalian approaches due to the high cost of the technologies relative to available resources and to the innate biological features of plants, including the complexity of the cell wall and polyploidy. To better understand how single-cell methods could enable plant science, Lawrence Berkeley National Laboratory (Berkeley Lab) hosted a workshop on April 29, 2021, that brought together a diverse group of leaders in plant and/or single-cell biology. Attendees represented federal research programs and domestic and international academic institutions. During the workshop, three presenters described the current state of research in both experimental and computational approaches. While the focus of the workshop was on factors preventing plant biology researchers from fully adopting single-cell methodologies, workshop participants agreed that most barriers could be overcome with focused, strategic investment and coordinated efforts among institutions leading to significant scientific discoveries that would be difficult to obtain using more conventional technologies.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here we apply an Ensemble Kalman Filter DA system as part of the Data Assimilation Research Testbed to a variety of land surface simulations. First, we describe the use of remotely sensed biomass observations to provide improved simulations of plant phenology, carbon and water cycling for regions highly sensitive to climate change (Western US, China, and Arctic). We discuss approaches to account for systemic biases between models and observations, including the use of spatially-varying adaptive ensemble inflation as an alternative approach to re-scaling soil moisture observations. Finally, we discuss a strategy to incorporate complementary observations (snow water equivalent, solar-induced fluorescence) to better constrain the representation of carbon and water cycling across complex terrain.

DART↗
NASA NTRSDate not supplied

Next-generation wildlife tracking devices and integrated sensors for measuring species-environment interactions and advancing conservation

Global land use and climate change have magnified the importance of collecting accurate animal movement and environmental data to improve our ability to model species interactions with the environment and promote effective conservation. Advanced animal tracking devices that couple cutting-edge technologies with our expanding need for ecological information is critical. In addition to further optimizing device size to monitor small, sensitive species, new paradigms for collecting, storing, and transmitting data about animals and their environments will propel wildlife tracking devices forward. Advanced telemetry capabilities will increase our ability to pair location and sensor data from tagged wildlife with remote sensing data. The USGS Western Ecological Research Center, NASA Ames Research Center, and collaborators have been developing new wildlife tracking devices and integrated environmental sensors to address these challenges. We describe a new, miniaturized solar GPS-enabled Globalstar satellite transmitter with accelerometer capability that is 20% lighter and 25% less expensive than existing commercially available tags. We also introduce a novel peer-to-peer, solar-powered network tag that uses long-range, low-power, wireless platform technology. Concurrently, we began adapting a carbon nanotube (CNT) sensor to detect dimethyl sulfide (DMS), a trace gas relevant to marine ecology and climate studies. CNT sensors offer a small, lightweight, and low-power technology that can detect changes in resistance across carbon nanotubes coated with adsorbent selected to specifically bind with a variety of reactive trace gasses. We flew a prototype sensor in a small UAS and sampled an apparent increase in DMS concentration consistent with a well-defined marine frontal boundary. Currently we are integrating project components to create a hybridized architecture of networked peer-to-peer and satellite tags with integrated CNT and additional on-board and remote sensors to advance animal tracking and to provide information for next generation wildlife conservation.

Ian G. Brosnan↗
DOE OSTI2023

Divergent selection and climate adaptation fuel genomic differentiation between sister species of Sphagnum (peat moss)

Abstract Background and Aims New plant species can evolve through the reinforcement of reproductive isolation via local adaptation along habitat gradients. Peat mosses (Sphagnaceae) are an emerging model system for the study of evolutionary genomics and have well-documented niche differentiation among species. Recent molecular studies have demonstrated that the globally distributed species Sphagnum magellanicum is a complex of morphologically cryptic lineages that are phylogenetically and ecologically distinct. Here, we describe the architecture of genomic differentiation between two sister species in this complex known from eastern North America: the northern S. diabolicum and the largely southern S. magniae. Methods We sampled plant populations from across a latitudinal gradient in eastern North America and performed whole genome and restriction-site associated DNA sequencing. These sequencing data were then analyzed computationally. Key Results Using sliding-window population genetic analyses we find that differentiation is concentrated within ‘islands’ of the genome spanning up to 400 kb that are characterized by elevated genetic divergence, suppressed recombination, reduced nucleotide diversity and increased rates of non-synonymous substitution. Sequence variants that are significantly associated with genetic structure and bioclimatic variables occur within genes that have functional enrichment for biological processes including abiotic stress response, photoperiodism and hormone-mediated signalling. Demographic modelling demonstrates that these two species diverged no more than 225 000 generations ago with secondary contact occurring where their ranges overlap. Conclusions We suggest that this heterogeneity of genomic differentiation is a result of linked selection and reflects the role of local adaptation to contrasting climatic zones in driving speciation. This research provides insight into the process of speciation in a group of ecologically important plants and strengthens our predictive understanding of how plant populations will respond as Earth’s climate rapidly changes.

58 GEOSCIENCES↗