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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Global Warming Induced Changes in Rainfall Characteristics in IPCC AR5 Models

Changes in rainfall characteristic induced by global warming are examined from outputs of IPCC AR5 models. Different scenarios of climate warming including a high emissions scenario (RCP 8.5), a medium mitigation scenario (RCP 4.5), and 1% per year CO2 increase are compared to 20th century simulations (historical). Results show that even though the spatial distribution of monthly rainfall anomalies vary greatly among models, the ensemble mean from a sizable sample (about 10) of AR5 models show a robust signal attributable to GHG warming featuring a shift in the global rainfall probability distribution function (PDF) with significant increase (>100%) in very heavy rain, reduction (10-20% ) in moderate rain and increase in light to very light rains. Changes in extreme rainfall as a function of seasons and latitudes are also examined, and are similar to the non-seasonal stratified data, but with more specific spatial dependence. These results are consistent from TRMM and GPCP rainfall observations suggesting that extreme rainfall events are occurring more frequently with wet areas getting wetter and dry-area-getting drier in a GHG induced warmer climate.

Lau, William K. M.↗
DOE OSTI2011

Invasion triangle: an organizational framework for species invasion: Invasion Triangle

Species invasion is a complex, multifactor process. To encapsulate this complexity into an intuitively appealing, simple, and straightforward manner, we present an organizational framework in the form of an invasion triangle. The invasion triangle is an adaptation of the disease triangle used by plant pathologists to help envision and evaluate interactions among a host, a pathogen, and an environment. Our modification of this framework for invasive species incorporates the major processes that result in invasion as the three sides of the triangle: (1) attributes of the potential invader; (2) biotic characteristics of a potentially invaded site; and (3) environmental conditions of the site. The invasion triangle also includes the impact of external influences on each side of the triangle, such as climate and land use change. This paper introduces the invasion triangle, discusses how accepted invasion hypotheses are integrated in this framework, describes how the invasion triangle can be used to focus research and management, and provides examples of application. The framework provided by the invasion triangle is easy to use by both researchers and managers and also applicable at any level of data intensity, from expert opinion to highly controlled experiments. The organizational framework provided by the invasion triangle is beneficial for understanding and predicting why species are invasive in specific environments, for identifying knowledge gaps, for facilitating communication, and for directing management in regard to invasive species.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The amplifying influence of increased ocean stratification on a future year without a summer

In 1816, the coldest summer of the past two centuries was observed over northeastern North America and western Europe. This so-called Year Without a Summer (YWAS) has been widely attributed to the 1815 eruption of Indonesia’s Mt. Tambora and was concurrent with agricultural failures and famines worldwide. To understand the potential impacts of a similar future eruption, a thorough physical understanding of the YWAS is crucial. Climate model simulations of both the 1815 Tambora eruption and a hypothetical analogous future eruption are examined, the latter occurring in 2085 assuming a business-as-usual climate scenario. Here, we show that the 1815 eruption drove strong responses in both the ocean and cryosphere that were fundamental to driving the YWAS. Through modulation of ocean stratification and near-surface winds, global warming contributes to an amplified surface climate response. Limitations in using major volcanic eruptions as a constraint on cloud feedbacks are also found.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The 1996 Mid-Atlantic Winter Flood: Exploring Climate Risk through a Storyline Approach

Abstract This article explores the application of thermodynamic perturbations to a historical midlatitude, wintertime, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14-km variable-resolution simulations completed with the U.S. Department of Energy’s global Energy Exascale Earth System Model (E3SM). We show that the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under preindustrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks from around +1 to +2 K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential nonlinearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

High-Resolution Meteorology with Climate Change Impacts from Global Climate Model Data Using Generative Machine Learning

As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

climate change↗
DOE OSTI2020

GCAM-Demeter-LU

This GCAM-Demeter-LU is made available under the Open Data Commons Attribution License:http://opendatacommons.org/licenses/by/1.0/. The dataset includes the projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). The data has been stored in self-describing NetCDF format. The dataset also includes the mean and standard deviation of the results driven by the five GCMs. More specifically, the data in each year includes grid-explicit fraction (in percent) of each of the 32 plant functional types that are widely used in current Earth system models. The NetCDF files are named as “GCAM_Demeter_LU_SSP_RCP_Model_Year.nc”, where “SSP” and “RCP” denote the SSP and RCP scenarios, including 'ssp1_rcp26', 'ssp1_rcp45', 'ssp1_rcp60', 'ssp2_rcp26', 'ssp2_rcp45', 'ssp2_rcp60', 'ssp3_rcp45', 'ssp3_rcp60', 'ssp4_rcp26', 'ssp4_rcp45', 'ssp4_rcp60', 'ssp5_rcp26', 'ssp5_rcp45', 'ssp5_rcp60', and 'ssp5_rcp85'. “Year” denotes the year of the land use data, and “GCM” denotes the source driving forcing data from five global climate models (gfdl, hadgem, ipsl, miroc, and noresm), or the mean (“modelmean”) and the standard deviation (“modelstd”) of the results from the five GCMs.See https://github.com/JGCRI/chen_et_al_2020a for details on how to reproduce this data. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

All the light we cannot see: Climate manipulations leave short and long‐term imprints in spectral reflectance of trees

Abstract Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant‐environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open‐air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal‐temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400–2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid‐season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long‐ and short‐time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near‐infrared and short‐wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant‐environment relationships, which is particularly important in times of unprecedented climate change.

Stefanski, Artur [Department of Forest Resources U↗
DOE OSTI2025

Impacts of Resolution on Heavy‐Precipitating Storms in Climate Model Hindcasts

The present study investigates the impact of horizontal resolutions on heavy‐precipitating storms using the Energy Exascale Earth System Model version2 (E3SMv2) at low (∼100 km, LR) and high (∼25 km, HR) resolutions through short‐range hindcasts. The short‐range hindcast approach ensures a faithful comparison of model resolution in simulating the same storm events under a controlled large‐scale environment. Using a phenomenon‐based framework, we attribute precipitation to specific storm types: tropical cyclones (TCs), extratropical cyclones, atmospheric rivers, and mesoscale convective systems (MCSs). Our findings show that E3SM hindcasts with both HR and LR configurations significantly underestimate storm‐associated precipitation intensity but overestimate precipitation from other sources. Furthermore, both HR and LR hindcasts face significant challenges in accurately simulating extreme precipitation events, particularly over MCS hotspots. Nevertheless, HR simulations capture more detailed and intense precipitation patterns with an improved representation of storm dynamics. HR hindcasts produce 16% more storm precipitation compared to LR. For precipitation extremes, HR simulates a 33% higher 99th percentile precipitation magnitudes compared to LR, and most of the increment comes from these four heavy‐precipitating storm types. The increase in precipitation mainly comes from stratiform precipitation rather than convective precipitation. The improvement in HR simulations varies across different storm types with TC showing the largest improvement. The phenomenon‐based approach provides important insights into precipitation simulations especially for extremes. Our results emphasize the need for further refinement in high‐resolution models to improve the accuracy of precipitation predictions, which is crucial for better understanding and mitigating climate change impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

climextremes v0.2.1

Software package for R and Python for analysis of extremes in climate data. Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models.

Krishnan, Harinarayan↗
NASA NTRS2013

Desert Dust Properties, Modelling, and Monitoring

This paper is just the three-page introduction to a Special Issue of Advances in Meteorology focusing on desert dust. It provides a paragraph each on 13 accepted papers, most relating to the used of satellite data to assess attributes or distribution of airborne desert dust. As guest Associate Editors of this issue, we organized the papers into a systematic whole, beginning with large-scale transport and seasonal behavior, then to regional dust transport, transport history, and climate impacts, first in the Mediterranean region, then India and central Asia, and finally focusing on transport model assessment and the use of lidar as a technique to constrain dust spatial-temporal distribution.

Kaskaoutis, Dimitris G.↗
DOE OSTI2016

An Assessment of Causes of PEV Success Across U.S. Metro Areas

Using 2014 U.S. light duty PEV registrations by make and model, this paper assesses the causes of Plug-in Electric Vehicle (PEV) success across U.S. states focusing on areas that had incentive and utility programs supporting PEV adoption. Market segments investigated were (1) luxury/performance (>$60,000 list price), (2) mid-market ($40-50,000), (3) mass market (<$40.000), and (4) total. States with either exclusive or preferential BEV incentives are a focus. BEV-biased incentives proved to be very effective in promoting mass market BEVs. After controlling for these attributes, case studies for several utility service areas were conducted to gauge whether PEVs were more or less successfully implemented via outreach by those utilities. These investigations suggest ways that utilities can successfully increase PEV registrations, after taking local climate and state incentives into consideration.

Zhou, Y.↗
DOE OSTI2014

A spectral transform dynamical core option within the Community Atmosphere Model (CAM4)

A spectral transform dynamical core with an 85 spectral truncation resolution (T85) within the Community Atmosphere Model (CAM), version 4, is evaluated within the recently released Community Earth System Model, version 1.0 (CESM) global climate model. The spectral dynamical core option provides a well-known base within the climate model community from which to assess climate behavior and statistics, and its relative computational efficiency for smaller computing platforms allows it to be extended to perform climate length simulations using high-resolution configurations in the near term. To establish the characteristics of the CAM4 T85, an ensemble of simulations covering the present day observational period using forced sea surface temperatures and prescribed sea-ice extent are evaluated. Overall, the T85 ensemble attributes and biases are similar to a companion ensemble of simulations using the one degree finite volume (FV1) dynamical core, relative to observed and model derived datasets. Notable improvements with T85 compared to FV1 include the representation of wintertime Arctic sea level pressure and summer precipitation over the Western Indian subcontinent. The mean and spatial patterns of the land surface temperature trends over the AMIP period are generally well simulated with the T85 ensemble relative to observations, however the model is not able to capture the extent nor magnitude of changes in temperature extremes over the boreal summer, where the changes are most dramatic. Biases in the wintertime Arctic surface temperature and annual mean surface stress fields persist with T85 as with the CAM3 version of T85.

atmosphere model analysis↗
DOE OSTI2019

Optical Properties of Laboratory & Ambient Biomass Burning Aerosols: Elucidating Black, Brown, & Organic Carbon Components & Mixing Regimes

Biomass burning (BB) is an important global source of aerosol and trace gases that degrade air quality, decrease visibility, and impact climate and human health. Refractory black carbon (rBC), brown carbon (BrC), and organic aerosol (OA) are major components of BB emissions. BB aerosol composition is highly variable at the source and depends on fuel composition and combustion phase. Atmospheric aging alters fresh BB aerosol through processes that are complex and dynamic. To better understand the variability in optical properties, we report fresh aerosol laboratory measurements from burning southwestern U.S. fuels and compare them to aged ambient BB aerosol from wildfires over a range of atmospheric timescales. Our BB aerosol analysis uses the relationship between the absorption Ångström exponent (AAE) and single scattering albedo (SSA) to identify rBC, BrC, and OA–dominated regimes that are defined using Mie theory. This model framework is used to interpret the large variability in optical properties measured in laboratory burns. In contrast, we find the observed AAE–SSA relationship for ambient BB aerosol to be less variable and more clustered together with increased atmospheric aging. This transition from fresh to aged behavior is attributed to the homogenization of the BB aerosol from mixing and aging over several hours. As a result, BB aerosol in ambient fire plumes that have aged for several hours exhibit larger SSAs than laboratory flaming burns. We conclude that BrC/OC mixtures play a larger role than rBC in the positive climate forcing of BB aerosol than what would be projected from laboratory results.

09 BIOMASS FUELS↗
DOE OSTI2023

Closure of Earth’s Global Seasonal Cycle of Energy Storage

Abstract The global seasonal cycle of energy in Earth’s climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean’s thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Controls of Net Ecosystem Exchange at an Old Field, a Pine Plantation, & a Hardwood Forest under Identical Climatic & Edaphic Conditions (Final Report)

The project yielded papers on fluxes (energy, water and carbon dioxide)between each ecosystem and the atmosphere, and explained the temporal dynamics of fluxes based on intrinsic (physiology, canopy leaf area and structure) and extrinsic (atmospheric and edaphic conditions). Comparisons between any two of the ecosystems, and among all three followed, attributing differences in behavior to different patterns of phenology and differential sensitivities to soil and atmospheric humidity. Finally, data from one-to-three of the ecosystems (incorporated into FluxNet data archive) were used in syntheses across AmeriFlux sites and even more broadly across FluxNet sites.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

A numerical model of nonmigrating diurnal tides between the surface and 65 km

Observations of surface pressure and middle atmosphere temperatures and winds indicate that a substantial nonmigrating component is present in the diurnal tide. The nonmigrating tides, which propagate with a zonal phase speed that is different from the earth's rotation, are attributed to the diurnal heating of geographically fixed sources. In this study we utilize a classical tidal model to examine the propagation characteristics of diurnal tides. The global fields of tropospheric sensible, radiative, and latent heating used to drive the model are supplied from summer and winter diurnal climatologies of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2). A novel aspect of this study is the focus on the relative importance of the nonmigrating components. The classical model successfully reproduces many observed features of the low-latitude diurnal surface pressure tides. In the middle atmosphere, the simulated migrating (or sun-synchronous) tide shows qualitative agreement with November-March Limb Infrared Monitor of the Stratosphere (LIMS) observations. Tropospheric solar heating is clearly the dominant driving force for the migrating tide, with secondary contributions from boundary-layer sensible heating and tropospheric latent heat release. The leading modes of the zonal mean tide are also driven chiefly by tropospheric solar heating. The higher-order modes of the zonal mean and eastward propagating tides may be attributed to the joint effects of tropospheric solar heating, sensible heating, and latent heat release. The LIMS and other data reveal features that cannot be explained or examined within the context of the classical model used in the present study. These include upward phase propagation, vertical attenuation, and temporal variations in the migrating diurnal tide.

Lieberman, Ruth S.↗
NASA NTRS2016

LS3MIP (v1.0) Contribution to CMIP6: The Land Surface, Snow and Soil Moisture Model Intercomparison Project Aims, Setup and Expected Outcome.

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems).The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (LMIP, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (LFMIP, building upon the GLACE-CMIP blueprint).

atmospheric temperature↗