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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range

Changes in plant phenology associated with climate change have been observed globally. What is poorly known is whether and how phenological responses to climate warming will differ from year to year, season to season, habitat to habitat, or species to species. Here, we present 5 y of phenological responses to experimental warming for 10 subboreal tree species. Research took place in the open-air B4WarmED experiment in Minnesota. The design is a two habitat (understory and open) × three warming treatments (ambient, +1.7 °C, +3.4 °C) factorial at two sites. Phenology was measured twice weekly during the growing seasons of 2009 through 2013. We found significant interannual variation in the effect of warming and differences among species in response to warming that relate to geographic origin and plant functional group. Moreover, responses to experimental temperature variation were similar to responses to natural temperature variation. Warming advanced the date of budburst more in early compared to late springs, suggesting that to simulate interannual variability in climate sensitivity of phenology, models should employ process-based or continuous development approaches. Differences among species in timing of budburst were also greater in early compared to late springs. Our results suggest that climate change—which will make most springs relatively “early”—could lead to a future with more variable phenology among years and among species, with consequences including greater risk of inappropriately early leafing and altered interactions among species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Global climate mode resonance due to rapidly intensifying El Niño-Southern Oscillation

The El Niño-Southern Oscillation (ENSO) influences climate variability globally, encompassing various other modes of variability, and thus represents a key predictable climate signal on seasonal timescales. Yet, its response to greenhouse warming remains uncertain, with models projecting a range of outcomes. Here, we demonstrate that in response to warming, a state-of-the-art high-resolution climate model simulates a rapid transition from a moderate-amplitude irregular regime, as observed in the current climate, to a highly regular oscillation with intensifying amplitude. This behaviour can be attributed to increasing air-sea feedbacks, which approach criticality in the second half of this century, and growing atmospheric noise. As ENSO intensifies in this model, it synchronizes with other prominent climate modes, such as the North Atlantic Oscillation and the Indian Ocean Dipole, thereby imprinting its regular, predictable variability on them. If realized, this global climate mode resonance would have wide-ranging whiplash impacts on regional hydroclimates.

Atmospheric dynamics↗
DOE OSTI2023

Regime-specific Cloud Vertical Overlap Characteristics from Radar and Lidar Observations at the ARM sites

Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid-scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground-based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. Here, the cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites amongst other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DOE Cold Climate Heat Pump Challenge: Development, Metrics, and Early Field Observations

Space heating in residential buildings is a major contributor of Greenhouse Gas (GHG) emissions in the United States. New, advanced electric heat pumps are poised to provide a low carbon alternative to traditional fossil-based heating, especially in colder climates. Widespread deployment of cold climate heat pumps could help address the significant portion of building emissions and primary energy used in American households, but these gains will require broader acceptance from consumers and decision makers. As part of the “Energy, Emissions, and Equity” (E3) initiative, the U.S. Department of Energy launched the Cold Climate Heat Pump Challenge (CCHP) in 2021 to accelerate the deployment of the next generation of air source heat pumps. The Challenge is currently focused on residential, centrally ducted, electric heat pumps, with a nominal cooling capacity greater than or equal to 24,000 Btu/h (2 tons) and less than or equal to 65,000 Btu/h (5 tons). The Challenge specifications represent a best-in-class heat pump product that provides high-efficiency heating performance in cold climates, employs environmentally friendly low-Global Warming Potential (GWP) refrigerants, and is designed to be grid interactive. Spearheaded by DOE in partnership with the US Environmental Protection Agency (EPA) and Natural Resources Canada (NRCan), the CCHP Challenge brings together numerous major heat pump manufacturers and key stakeholders including utilities and state agencies across the country. In addition to performance testing in a laboratory environment, a key component of this research is evaluating the in-field performance of the prototype heat pumps developed as part of the Challenge. This paper will discuss the development of the CCHP Challenge, key performance specifications, and the energy and non-energy metrics that will be evaluated through the field study. The types of data that are being collected from the units installed in homes across North America as part of the field validation will be described, including details about the sensors and data acquisition system. Data cleaning and analysis methodologies will be discussed, along with early observations from the winter 2022-2023, spring and summer performance periods, and an assessment of non-energy metrics through pre- and post-installation homeowner surveys. Challenges uncovered and lessons learned throughout the process will also be discussed. Finally, the paper will discuss next steps for the second winter assessment period, areas needing additional research, and potential applications of the data collected and analyzed through this work.

Mendon, Vrushali V.↗
DOE OSTI2023

AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics

Abstract. African and South American (ASA) wildfires account for more than 70 % of global burned areas and have strong connection to local climate for sub-seasonal to seasonal wildfire dynamics. However, representation of the wildfire–climate relationship remains challenging due to spatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire–climate relationships in historical and future predictions.

58 GEOSCIENCES↗
DOE OSTI2021

How Snow Aggregate Ellipsoid Shape and Orientation Variability Affects Fall Speed and Self-Aggregation Rates

Snow aggregate shapes and orientations have long been known to exhibit substantial variability. Despite this observed variability, most weather and climate prediction models use fixed power-law functions that deterministically map particle size to mass and fall speed. As such, integrated quantities like precipitation and self-aggregation rates currently ignore nonlinear effects resulting from variation in shape and orientation for aggregates of the same size. This study therefore develops an analytic framework that couples an empirically based bivariate distribution of ellipsoid shapes to classical hydrodynamic theory so as to capture an appropriate dispersion of masses, projected areas, and fall speeds for an assumed size distribution. For a fixed aggregate size, shape variations produce approximately ±0.13 m s -1 standard deviation of fall speed which increases the mass flux fall speed dispersion by more than 100% over traditional microphysics models. This increased fall speed dispersion results predominantly from shape-induced mass dispersion whereas orientation and drag dispersion play a lesser role. Shape variations can increase mass- and reflectivity-weighted fall speeds by up to 60% of traditional models whereas self-aggregation rates can increase by a factor of 100 for very small slope parameters. This implies that aggregate shape variations effectively forestall the theorized onset of fall speed distribution narrowing and subsequent quenching of the aggregation process. As a result, it is likely that secondary ice formation is necessary to prevent an ever decreasing slope parameter. The mathematical theory presented in this study is used to develop simple correction factors for snow forecast and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Nonlocal, Pattern-Aware Response and Feedback Framework for Regional Climate Response

We devise a pattern-aware feedback framework for representing the forced climate response using a suite of Green’s function experiments with solar radiation perturbations. By considering the column energy balance, a comprehensive linear response function (CLRF) for important climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, and lapse rate is learned from Green’s function data. The learned CLRF delineates the effects of the energy diffusion in both the ocean and atmosphere and the pattern-aware feedbacks from the aforementioned radiatively active processes. The CLRF can then be decomposed into forcing–response mode pairs, which are in turn used to construct a reduced-order model describing the dominant dynamics of climate responses. These mode pairs capture nonlocal effects and teleconnections in the climate and thus make the reduced-order model apt for capturing regional features of climate response. A key observation is that the CLRF captures the polar-amplified response as the most excitable mode of the climate system, and this mode is explainable in the data-learned pattern-aware feedback framework. The reduced-order model can be used for predicting the response for a given forcing and for reconstructing the forcing from a given response; we demonstrate these capabilities for multiple independent forcing scenarios.

Feedback↗
DOE OSTI2021

Climate Intervention Assessment and Attribution

Focal Area(s): To ensure the US is positioned to scientifically attribute noncooperative climate interventions, we propose developing a foundational analytical framework for increased confidence in the assessment and attribution of observed changes in the climate. This framework will enable probabilistic attribution of prospective climate interventions. The target is to glean insight from complex Earth system data (Focal Area 3) by using AI to develop a prediction system comprised of a hierarchy of models (Focal Area 2).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Enhanced interactions of Kuroshio Extension with tropical Pacific in a changing climate

Quasi-decadal climate of the Kuroshio Extension (KE) is pivotal to understanding the North Pacific coupled ocean–atmosphere dynamics and their predictability. Recent observational studies suggest that extratropical-tropical coupling between the KE and the central tropical Pacific El Niño Southern Oscillation (CP-ENSO) leads to the observed preferred decadal time-scale of Pacific climate variability. By combining reanalysis data with numerical simulations from a high-resolution climate model and a linear inverse model (LIM), we confirm that KE and CP-ENSO dynamics are linked through extratropical-tropical teleconnections. Specifically, the atmospheric response to the KE excites Meridional Modes that energize the CP-ENSO (extratropicstropics), and in turn, CP-ENSO teleconnections energize the extratropical atmospheric forcing of the KE (tropicsextratropics). However, both observations and the model show that the KE/CP-ENSO coupling is non-stationary and has intensified in recent decades after the mid-1980. Given the short length of the observational and climate model record, it is difficult to attribute this shift to anthropogenic forcing. However, using a large-ensemble of the LIM we show that the intensification in the KE/CP-ENSO coupling after the mid-1980 is significant and linked to changes in the KE atmospheric downstream response, which exhibit a stronger imprint on the subtropical winds that excite the Pacific Meridional modes and CP-ENSO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Air‐Ice‐Ocean Coupling During a Strong Mid‐Winter Cyclone: Observing Coupled Dynamic Interactions Across Scales

Abstract Arctic cyclones are key drivers of sea ice and ocean variability. During the 2019–2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, joint observations of the coupled air‐ice‐ocean system were collected at multiple spatial scales. Here, we present observations of a strong mid‐winter cyclone that impacted the MOSAiC site as it drifted in the central Arctic pack ice. The sea ice dynamical response showed spatial structure at the scale of the evolving and translating cyclonic wind field. Internal ice stress and ocean stress play significant roles, resulting in timing offsets between the atmospheric forcing and the ice response and post‐cyclone inertial ringing in the ice and ocean. Ice motion in response to the wind field then forces the upper ocean currents through frictional drag. The strongest impacts to the sea ice and ocean from the passing cyclone occur as a result of the surface impacts of a strong atmospheric low‐level jet (LLJ) behind the trailing cold front and changing wind directions between the warm‐sector LLJ and post cold‐frontal LLJ. Impacts of the cyclone are prolonged through the coupled ice‐ocean inertial response. Local impacts of the approximately 120 km wide LLJ occur over a 12 hr period or less and at scales of a kilometer to a few tens of kilometers, meaning that these impacts occur at combined smaller spatial scales and faster time scales than most satellite observations and coupled Earth system models can resolve.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Coastal Sea Level Observations Record the Twentieth-Century Enhancement of Decadal Climate Variability

Changes in the amplitude of decadal climate variability over the twentieth century have been noted, with most evidence derived from tropical Pacific sea surface temperature records. However, the length, spatial coverage, and stability of most instrumental records are insufficient to robustly identify such nonstationarity, or resolve its global spatial structure. Here, it is found that the long-term, stable, observing platform provided by tide gauges reveals a dramatic increase in the amplitude and spatial coherence of decadal (11–14-yr period) coastal sea level (ζ) variability between 1960 and 2000. During this epoch, western North American ζ was approximately out of phase with ζ in Sydney, Australia, and led northeastern U.S. ζ by approximately 1–2 years. The amplitude and timing of changes in decadal ζ variability in these regions are consistent with changes in atmospheric variability. Specifically, central equatorial Pacific wind stress and Labrador Sea heat flux are highly coherent and exhibit contemporaneous, order-of-magnitude increases in decadal power. These statistical relationships have a mechanistic underpinning: Along the western North American coastline, equatorial winds are known to drive rapidly propagating ζ signals along equatorial and coastal waveguides, while a 1–2-yr lag between Labrador Sea heat fluxes and northeastern United States ζ is consistent with a remotely forced, buoyancy-driven, mechanism. Tide gauges thus provide strong independent support for an increase in interbasin coherence on decadal time scales over the second half of the twentieth century, with implications for both the interpretation and prediction of climate and sea level variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Remote sensing from unoccupied aerial systems: Opportunities to enhance Arctic plant ecology in a changing climate

The Arctic is warming at a faster rate than any other biome on Earth, resulting in widespread changes in vegetation composition, structure, and function that have important feedbacks to the global climate system. The heterogeneous nature of arctic landscapes creates challenges for monitoring and improving understanding of these ecosystems, as current efforts typically rely on ground, airborne, or satellite-based observations that are limited in space, time, or pixel resolution. The use of remote sensing instruments on small Unoccupied Aerial Systems (UASs) has emerged as an important tool to bridge the gap between detailed, but spatially limited ground-level measurements, and lower resolution, but spatially extensive high-altitude airborne and satellite observations. UASs allow researchers to view, describe and quantify vegetation dynamics at fine spatial scales (1-10 cm) over areas much larger than typical field plots. UASs can be deployed with a high degree of temporal flexibility, enabling observation across diurnal, seasonal, and annual timescales. In this work, we review how established and emerging UAS remote sensing technologies can enhance arctic plant ecological research by quantifying fine-scale vegetation patterns and processes, and by enhancing the ability to link ground-based measurements with broader-scale information obtained from airborne and satellite platforms. Synthesis: Improved ecological understanding and model representation of arctic vegetation is needed to forecast the fate of the Arctic in a rapidly changing climate. Observations from UASs provide an approach to address this need, however, the use of this technology in the Arctic currently remains limited. Here we share recommendations to better enable and encourage the use of UASs to improve the description, scaling, and model representation of arctic vegetation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Toward a Climate OSSE Framework for Satellite Mission Design

The rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is triggered by the need to quantify the value of a particular measurement for reducing the uncertainty in climate predictions, which differ from numerical weather predictions in that they depend on future atmospheric composition rather than the current state of the weather. However, both weather and climate modeling communities share a need for motivating major observing system investments. Here, we outline a new framework for climate OSSEs that leverages the use of machine learning to calibrate climate model physics against existing satellite data. We demonstrate its application using NASA’s GISS-E3 model to objectively quantify the value of potential future improvements in spaceborne measurements of Earth’s planetary boundary layer. A mature climate OSSE framework should be able to quantitatively compare the ability of proposed observing system architectures to answer a climate-related question, thus offering added value throughout the mission design process, which is subject to increasingly rapid advances in instrument and satellite technology. Technical considerations include selection of observational benchmarks and climate projection metrics, approaches to pinpoint the sources of model physics uncertainty that dominate uncertainty in projections, and the use of instrument simulators. Community and policy-making considerations include the potential to interface with an established culture of model intercomparison projects and a growing need to economically assess the value-driven efficiency of social spending on Earth observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Harvest legacies and climate change interact to shape forest structure and biomass through time

Forest structural change driven by climate trends has been observed worldwide and is expected to increase in the future. Management of forest structure has been an important tool for mitigating the impacts of climate change but forest structure may shift independently of management goals as it interacts with climate change. Here, we investigated the long-term impacts of harvest-based management strategies on structure and resistance to climate-induced biomass loss using a process-based ecosystem model for a midwestern USA hardwood forest. We identified aboveground biomass loss events and compared the cumulative number of these events following a five-year period of active management under four management strategies and two climate change scenarios. Management legacy had the clearest impact on climate-driven biomass loss over the mid-term (∼25 years) with the shelterwood scenario experiencing no loss events during this period. However, by the end of the century legacy effects faded and climate change severity became the driver of differences as greater warming scenarios experienced twice the loss events, and end-of-century loss events were 3–10 times more frequent than mid-century events. We found that while structure was distinct among harvest scenarios through the mid-century, differences were negligible by the end-of-century; identical to when management correlated with loss event frequency. Here, we found that loss events were preceded by a drop in precipitation two years prior, while no specific forest structure preceded a loss event. However, the structures preceding a biomass loss event were distinct across different legacies, implying additional influences of past management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Climate Forcing & Feedbacks by Fires, Forests, Fossil Energy & Food Production: Observations are Key to Predictions [Slides]

Human emitted GHGs (long lived, cumulative) that trap more heat (IR) and aerosols (short lived, steady) that reflect sunlight (Vis) have altered Earth’s radiation budget and warmed our climate measurably and predictably. Our complex dynamic climate system has trapped 400 ZettaJ (10 21 ) and has many positive feedbacks. Regions are changing at unprecedented rates and crossing thresholds as BAU will lock us into 8.5 W m -2 in 2100.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗