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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 OSTI2021

Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability

Focal Area: This white paper responds to Focal Area 3. We seek to gain insight into decadal-scale climate predictability by applying novel manifold-finding probabilistic AI techniques to the complex data produced by Earth System models (ESMs) such as E3SM. The associated portfolio of research activities leverages DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise. Science Challenge: Climate and climate models are dynamical systems exhibiting properties that are interpretable through chaos theory. The theory contains an important concept that is relevant to multi-decade-scale climate prediction: a chaotic attractor. While the space containing all the possible states of the Earth’s atmosphere and ocean, the possible weather, is large, the realized states tend to stay near the smaller-dimensioned attractor. This behavior is responsible for the “order behind the irregularity” [1] of climate phenomena. Climate change can be thought of as a change in the properties of the attractor, and predicting the climate over years to decades is equivalent to predicting how those properties will change. To date, the attractor has been a useful conceptual tool, but has not been amenable to direct characterization. A new development is the advent of efficient high-dimensional manifold-finding probabilistic AI techniques, which permit a data-driven characterization of the ESM attractor and its probability distribution over weather states. Such a characterization would result in a natural dimensional reduction — a “non-linear Principal Components Analysis (PCA) adapted to climate simulation data” — leading to important advances in scenario-based long-term climate prediction, long-term prediction of water cycle extremes, ESM verification, inter-model comparison, and process model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Opportunities in multiscale modeling of mosquito-borne flaviviruses

Mosquito-borne flaviviruses, such as Zika, dengue, West Nile, and yellow fever virus, represent a growing public health concern due to their widespread distribution and the severe diseases they cause. These viruses are difficult to control as climate change and urbanization help mosquitoes expand into new areas, increasing the risk of outbreaks. Mathematical models play a key role in understanding their spread, providing insights at every level—from how the virus multiplies inside cells to how it circulates through entire populations. This review examines various approaches used in modeling arboviruses, including microscale models that focus on cellular and molecular dynamics, mesoscale models that address within-host processes, and macroscale models that capture population-level transmission. We briefly summarize the methodology used for models at each scale, which primarily consists of sets of differential equations with parameters that represent physical rates of change for different subprocesses. We particularly highlight how temperature affects virus transmission, which is key to understanding the impact of climate change. We also show how multiscale models can connect viral replication, immune response, and the spread of infection at a larger scale. This is essential for developing better vaccines and treatments, evaluating disease control measures, predicting the impact of climate change, and improving public health responses to outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Indicator Patterns of Forced Change Learned by an Artificial Neural Network

Abstract Many problems in climate science require the identification of signals obscured by both the “noise” of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to predict the year of a given map of annual‐mean temperature (or precipitation) from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as “reliable indicators” of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal‐to‐noise ratios and multilinear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predicting spring green-up across diverse North American grasslands

Vegetation phenology influences many ecosystem and climate processes, such as carbon uptake and energy and water cycles. Thus, understanding drivers of vegetation phenology is crucial for predicting current and future impacts of climate change on ecological systems. Existing models can accurately predict the date of spring green-up in temperate forests but tend to perform poorly in grassland systems. We hypothesize this is because most do not incorporate water availability, a primary limiting factor for grassland plants. In this study, we used long-term datasets of digital imagery from the PhenoCam Network of 43 diverse North American grassland sites (195 site-years) to test existing spring phenology models, as well as develop several new models that incorporate precipitation or soil moisture (53 models). As a result, most of the new models performed substantially better, with the best model requiring sufficient accumulated precipitation followed by warm temperatures to trigger spring onset (root mean square error, RMSE, between predicted and observed dates = 16.0 days). Importantly, the best model performed well across all grassland types using a single set of parameters, from temperate to arid grasslands. Since plants are adapted to their local climates, model performance was further improved when parameters were independently optimized for four separate climate regions (RMSE = 10.4 days). Therefore, both sufficient precipitation and temperature are required for grassland green-up, but optimal thresholds vary by region. Running the top model with projected climate data (representative concentration pathway 8.5) suggests that, depending on the climate region, spring onset will occur up to 12 days earlier within 100 years in temperature-limited sites, but the trend is unclear for precipitation-limited sites (3.5 ± 8.0 days later). This new phenology model improves our ability to understand and predict grassland dynamics, with implications for both current and future ecosystem processes related to carbon and water cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect

Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.

Rugenstein, Maria [Colorado State University Fort ↗
DOE OSTI2024

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

58 GEOSCIENCES↗
DOE OSTI2024

Physiological Responses of C4 Perennial Bioenergy Grasses to Climate Change: Causes, Consequences, and Constraints

C 4 perennial bioenergy grasses are an economically and ecologically important group whose responses to climate change will be important to the future bioeconomy. These grasses are highly productive and frequently possess large geographic ranges and broad environmental tolerances, which may contribute to the evolution of ecotypes that differ in physiological acclimation capacity and the evolution of distinct functional strategies. C 4 perennial bioenergy grasses are predicted to thrive under climate change—C 4 photosynthesis likely evolved to enhance photosynthetic efficiency under stressful conditions of low [CO 2 ], high temperature, and drought—although few studies have examined how these species will respond to combined stresses or to extremes of temperature and precipitation. Important targets for C 4 perennial bioenergy production in a changing world, such as sustainability and resilience, can benefit from combining knowledge of C 4 physiology with recent advances in crop improvement, especially genomic selection.

Plant Sciences↗
DOE OSTI2023

How to engineer the unknown: Advancing a quantitative and predictive understanding of plant and soil biology to address climate change

Our basic understanding of carbon cycling in the biosphere remains qualitative and incomplete, precluding our ability to effectively engineer novel solutions to climate change. How can we attempt to engineer the unknown? This challenge has been faced before in plant biology, providing a roadmap to guide future efforts. We use examples from over a century of photosynthesis research to illustrate the key principles that will set future plant engineering on a solid footing, namely, an effort to identify the key control variables, quantify the effects of systematically tuning these variables, and use theory to account for these observations. The main contributions of plant synthetic biology will stem not from delivering desired genotypes but from enabling the kind of predictive understanding necessary to rationally design these genotypes in the first place. Only then will synthetic plant biology be able to live up to its promise.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Exploring prokaryotic diversity in permafrost-affected soils of Ladakh’s Changthang region and its geochemical drivers

Global warming due to climate change has substantial impact on high-altitude permafrost affected soils. This raises a serious concern that the microbial degradation of sequestered carbon can result in alteration of the biogeochemical cycles. Therefore, the characterization of permafrost affected soil microbiomes, especially of unexplored high-altitude, low oxygen arid region, is important for predicting their response to climate change. This study presents the first report of the bacterial diversity of permafrost-affected soils in the Changthang region of Ladakh. The relationship between soil pH, organic carbon, electrical conductivity, and available micronutrients with the microbial diversity was investigated. Amplicon sequencing of permafrost affected soil samples from Jukti and Tsokar showed that Proteobacteria and Actinobacteria were the dominant phyla in all samples. The genera Brevitalea, Chthoniobacter, Sphingomonas, Hydrogenispora, Clostridium, Gaiella, Gemmatimonas were relatively abundant in the Jukti samples whereas the genera Thiocapsa, Actinotalea, Syntrophotalea, Antracticibcterium, Luteolibacter, Nitrospirillum dominated the Tsokar sample. Correlation analyses highlighted the influence of soil geochemical parameters on the bacterial community structure. PCoA analyses showed that the bacterial beta diversity varied significantly between the sampling locations (PERMANOVA test (F-value: 2.3316; R 2 = 0.466, p = 0.001) and similar results were also obtained while comparing genus abundance data using the ANOSIM test (R = 0.345, p = 0.007).

16S rRNA↗
DOE OSTI2023

Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model

In the last decades, the Arctic climate has changed dramatically, with the loss of multiyear sea ice one of the clearest consequences. These changes have occurred on relatively rapid timescales, and both accurate short-term Arctic prediction (e.g., 10 days to three months) and climate projection of future Arctic scenarios present ongoing challenges. Here we describe a representation of the Arctic ocean and sea ice in a ultrahigh resolution simulation in which the horizontal grid mesh reduces from 8 km at the equator to 2 km at the poles (UH8to2) for the years 2017-2020. We find the simulation reproduces observed distributions of seasonal sea-ice thickness and concentration realistically, although concentration is biased low in the spring and summer and low biases in thickness are found in the central and eastern basins in the fall. Volume, fresh water, and heat transports through key passages are realistic, lying within observationally determined ranges. Climatological comparisons reveal that the UH8to2 Atlantic Water is shallower, warmer, and saltier than the World Ocean Atlas 2018 climatology for 2005-2017 in the eastern basin. Our analysis suggests that these biases, combined with a lack of stratification in the upper 100 m of the simulated ocean, contribute to the winter biases in modeled sea ice thickness. This relationship between biases in the sea ice and ocean points to a potential positive feedback within the model, illuminating challenges for long term model predictive power in a changing Arctic climate.

58 GEOSCIENCES↗
DOE OSTI2025

Water Temperature, Prey Concentration and Salmonid Density Influence Daily Growth of Wild Juvenile Salmonids in Tributaries of the Upper Salmon River, Idaho ( USA )

ABSTRACT Theory, experiments and field studies indicate that the somatic growth rate of freshwater consumers is shaped by the individual, additive and multiplicative effects of multiple factors, including consumer size and condition, temperature, prey resources and biotic interactions. While our understanding of how these factors affect wild populations of freshwater consumers is improving, the topic remains poorly studied, especially with respect to mobile species. Here, we report on an 8‐year, seven‐stream ( n = 49 stream‐year combinations) observational study examining the individual and interactive effects of invertebrate prey concentration (F, mg/m 3 ), mean daily water temperature (T, °C) and juvenile Chinook salmon ( Oncorhynchus tshawytscha ) density (D, fish/100 m 2 ) on summer daily growth rates (%/d) of mobile, anadromous, juvenile Chinook salmon (age‐0+, n = 382) and sub‐yearling (age‐0+, n = 61) and yearling (age‐1+) steelhead trout ( O. mykiss , n = 70) rearing in cold (mean daily summer: 12.1°C, range: 4.2°C–16.7°C) mountain tributaries of the Salmon River basin in central Idaho (USA). AIC c model selection indicated that daily juvenile salmonid growth positively correlated with water temperature, prey biomass concentration, local juvenile Chinook density and the interaction between water temperature and food but with species and age‐specific differences. Water temperature was a covariate in all top‐ranked models, with daily growth (%/day) rate increasing (0.05%–0.23%/d) linearly with mean daily summer water temperature. In addition to a direct positive relationship with daily growth rate, there was evidence that prey concentration positively interacted with water temperature to accelerate daily growth (F × T). The positive relationship between juvenile salmonid daily growth rate and juvenile Chinook density is difficult to explain and could result from confounding factors. The individual success observed in these streams may contribute to population‐level benefits for the focal consumers, as prey‐rich, warm summers may result in larger individuals with higher energy reserves at the end of the summer/autumn growing season, contributing to improved overwinter survival. Our results, taken in combination with evidence from models, experiments and observational studies, have climate change implications. Current and predicted increases in water temperature will necessitate higher rates of prey consumption by aquatic ectothermic consumers to offset accelerated metabolic demands. Thus, to improve the resilience of mobile freshwater consumers in a warming climate, we suggest that natural resource managers not only consider physical and chemical habitat conditions but also biotic conditions, including the spatiotemporal quantity and quality of prey resources.

Kiffney, Peter. M. [Fish Ecology, Northwest Fisher↗
DOE OSTI2026

The effect of relative humidity and temperature on the response of stomatal conductance to vapor pressure deficit in tropical trees

Understanding how leaf gas exchange responds to changes in vapor pressure deficit (VPD) is key to predicting tropical forest resilience to climate change. Stomata regulate leaf water and CO2 diffusion, and respond to changes in temperature and relative humidity (RH), two drivers of VPD. At high temperatures, the cuticular pathway may also become significant and participate in the overall leaf conductance. Here, we measured gas exchange under light and dark conditions to investigate the stomatal and cuticular responses to temperature and RH on detached branches in five tropical tree species. Leaf conductance in the dark, when stomata are essentially closed, was not markedly impacted by temperature and RH, suggesting a minimal response of the cuticular pathway to these conditions. We compared six steady-state conductance models incorporating different effects of photosynthesis and evaporative demand on stomatal control. All models performed well (residual standard deviation, σ, < 0.025 mol m-2 s-1), but the best-fitting model (σ = 0.017 mol m-2 s-1) used a nonlinear relationship between photosynthesis and stomatal conductance and incorporated RH rather than vapor pressure difference. All models overestimated the steady-state conductance at high RH. Furthermore, leaf conductance immediately increased after a decrease in RH (wrong-way response), but not after an increase in leaf temperature. This suggests that the mechanisms underlying stomatal response to VPD need further investigation. The non-linear coupling between photosynthetic rate and stomatal conductance indicates a sharper physiological response than previously acknowledged.

Lamour, Julien↗
DOE OSTI2020

Responses of tundra plant community carbon flux to experimental warming, dominant species removal and elevation

1. Rising temperatures can influence ecosystem processes both directly and indirectly, through effects on plant species and communities. An improved understanding of direct versus indirect effects of warming on ecosystem processes is needed for robust predictions of the impacts of climate change on terrestrial ecosystem carbon (C) dynamics. 2. To explore potential direct and indirect effects of warming on C dynamics in arctic tundra heath, we established a warming (open top chambers) and dominant plant species (Empetrum hermaphroditum Hagerup) removal experiment at a high and low elevation site. We measured the individual and interactive effects of warming, dominant species removal and elevation on plant species cover, the normalized difference vegetation index (NDVI), leaf area index (LAI), temperature, soil moisture and instantaneous net ecosystem CO 2 exchange. 3. We hypothesized that ecosystems would be stronger CO 2 sinks at the low elevation site, and that warming and species removal would weaken the CO 2 sink because warming should increase ecosystem respiration (ER) and species removal should reduce gross primary productivity (GPP). Furthermore, we hypothesized that warming and species removal would have the greatest impact on processes at the high elevation where site temperature should be most limiting and dominant species may buffer the overall community to environmental stress more compared to the low elevation site where plants are more likely to compete with the dominant species. 4. The instantaneous CO 2 flux, which reflected a weak CO 2 sink, was similar at both elevations. Neither experimental warming nor dominant species removal significantly changed GPP or instantaneous net ecosystem CO 2 exchange even though species removal significantly reduced ER, NDVI and LAI. 5. Our results show that even the loss of dominant plant species may not result in significant landscape-scale responses of net ecosystem CO 2 exchange to warming. They also show that NDVI and LAI may be limited in their ability to predict changes in GPP in these tundra heaths systems. Our study highlights the need for more detailed vegetation analyses and ground-truthed measurements in order to accurately predict direct and indirect impacts of climatic change on ecosystem C dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Shrubs Strongly Influence Snow Properties in Two Subarctic Watersheds

Understanding changes in snow distribution in permafrost ecosystems is fundamental to predicting their response to future climate change. The expansion of tall shrubs into tundra ecosystems can trap snow and insulate permafrost ecosystems during the winter, but the overall insulation effect is dependent upon many ecosystem properties. To study shrub–snow–ground interactions, small temperature sensors were deployed at two research sites on the Seward Peninsula of Alaska, USA, during the 2019–2020 winter. Snow temperatures were used to extrapolate multiple metrics, including freezing n-factors, the snow insulation effect, snow cover duration, and the length of the snowmelt period. Statistical and spatial analysis showed that shrub patches were a dominant control on all snow metrics. Within shrub patches, average ground temperatures were 2.1°C warmer, snow persisted 50 days longer, snow insulation was double, and a longer, later spring snowmelt period occurred compared to nonshrubby areas. Site-level differences contributed relatively little to variation in snow metrics, indicating that shrub presence is an overarching driver of snow–ground interactions at the locations examined. Shrub expansion, which is anticipated under climate change, will strongly impact future permafrost distribution and Arctic energy, water, and carbon cycles through snow–shrub–ground feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Abstract Tropical forests face increasing climate risk 1,2 , yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, $$\varPsi $$ Ψ 50 ) and hydraulic safety margins (for example, HSM 50 ) are important predictors of drought-induced mortality risk 3–5 , little is known about how these vary across Earth’s largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters $$\varPsi $$ Ψ 50 and HSM 50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both $$\varPsi $$ Ψ 50 and HSM 50 influence the biogeographical distribution of Amazon tree species. However, HSM 50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM 50 are gaining more biomass than are low HSM 50 forests. We propose that this may be associated with a growth–mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM 50 in the Amazon 6,7 , with strong implications for the Amazon carbon sink.

Science & Technology - Other Topics↗