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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Climate Data Initiative: A Geocuration Effort to Support Climate Resilience

Curation is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest and typically occurs in museums, art galleries, and libraries. The task of organizing data around specific topics or themes is a vibrant and growing effort in the biological sciences but to date this effort has not been actively pursued in the Earth sciences. In this paper, we introduce the concept of geocuration and define it as the act of searching, selecting, and synthesizing Earth science data/metadata and information from across disciplines and repositories into a single, cohesive, and useful compendium We present the Climate Data Initiative (CDI) project as an exemplar example. The CDI project is a systematic effort to manually curate and share openly available climate data from various federal agencies. CDI is a broad multi-agency effort of the U.S. government and seeks to leverage the extensive existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship to support national climate-change preparedness. We describe the geocuration process used in CDI project, lessons learned, and suggestions to improve similar geocuration efforts in the future.

virtual collections↗
NASA NTRS2020

Using Climate Models to Learn: Investigating the Phenomenon of Increasing Surface Air Temperatures Using a Global Climate Modeling Approach

Since 1880, Earth’s average global temperature has increased by approximately 1.4 °F (0.8 °C). Approximately two-thirds of the warming has occurred since 1975 (Wuebbles, Easterling, Hayhoe, et al. 2017). Despite consensus about global climate change (GCC) within the scientific community, students struggle to comprehend - why temperatures are increasing globally - at what rate this change is occurring, and - how climate scientists predict Earth’s future climate. These challenges are due, in part, to the abstraction inherent to understanding GCC, a phenomenon that occurs over long periods of time (past and future), and over large regions (regional and global). With the adoption of the Next Generation Science Standards (NGSS Lead States 2013), GCC is emphasized at the secondary school level, with a focus on analyzing and understanding the evidence for GCC (HS-ESS3-1), climate modeling (HS-ESS2-6, HS-ESS3-5), and the impact of humans on the environment (HS-LS2-7, HS-ESS3-6).

climate models↗
DOE OSTI2024

The National Climate Data Base (NCDB): A Bias-Corrected High-Resolution Climate Dataset

Assessing renewable energy resources under future climate scenarios has been highlighted in recent years to analyze and understand potential impacts of future change in renewable generation on the power sector. Solar energy is well-known as the most plentiful among various renewable resources and usually converted to electricity using photovoltaics (PV) technologies, and the global deployment of PV technology has increased rapidly in recent decades. In this study, we develop a statistical technique to downscale the future projection of solar irradiance for PV energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the proposed downscaling method and future extension of this research.

climate data↗
NASA NTRS2017

Selection of a Representative Subset of Global Climate Models that Captures the Profile of Regional Changes for Integrated Climate Impacts Assessment

We present the Representative Temperature and Precipitation (T&P) GCM Subsetting Approach developed within the Agricultural Model Intercomparison and Improvement Project (AgMIP) to select a practical subset of global climate models (GCMs) for regional integrated assessment of climate impacts when resource limitations do not permit the full ensemble of GCMs to be evaluated given the need to also focus on impacts sector and economics models. Subsetting inherently leads to a loss of information but can free up resources to explore important uncertainties in the integrated assessment that would otherwise be prohibitive. The Representative T&P GCM Subsetting Approach identifies five individual GCMs that capture a profile of the full ensemble of temperature and precipitation change within the growing season while maintaining information about the probability that basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry) are projected in the full GCM ensemble. We demonstrate the selection methodology for maize impacts in Ames, Iowa, and discuss limitations and situations when additional information may be required to select representative GCMs. We then classify 29 GCMs over all land areas to identify regions and seasons with characteristic diagonal skewness related to surface moisture as well as extreme skewness connected to snow-albedo feedbacks and GCM uncertainty. Finally, we employ this basic approach to recognize that GCM projections demonstrate coherence across space, time, and greenhouse gas concentration pathway. The Representative T&P GCM Subsetting Approach provides a quantitative basis for the determination of useful GCM subsets, provides a practical and coherent approach where previous assessments selected solely on availability of scenarios, and may be extended for application to a range of scales and sectoral impacts.

Climate Change; GCMs; Climate Model Subset; Repres↗
DOE OSTI2018

Local radiative feedbacks over the Arctic based on observed short-term climate variations

We compare various radiative feedbacks in response to short-term climate variations estimated from reanalysis, satellite, and global climate model datasets using the combined Kernel-Gregory approach over the Arctic (60-90°N). Except for the shortwave cloud feedback, all feedbacks are positive over the Arctic. Lapse rate feedback is comparable with surface albedo feedback. Relative to the tropics (30°S-30°N), the temperature feedback (lapse rate feedback plus Planck feedback deviation from its global mean) is the largest contributor to Arctic amplification among all feedbacks, followed by surface albedo feedback. The choice of meteorological fields has a small impact on the cloud feedback estimate, but different top-of-the-atmosphere clear-sky fluxes datasets can result in a wide spread of estimated cloud feedbacks. The net cloud feedback over the Arctic is positive due to a larger positive in longwave than the negative shortwave feedback, but has the largest uncertainty. Relative to the tropics, the longwave cloud feedback largely contributes to the Arctic amplification.

Zhang, Rudong↗
DOE OSTI2018

Climate sensitive size-dependent survival in tropical trees

Survival rates of large trees determine forest biomass dynamics. Survival rates of small trees are linked to mechanisms that maintain biodiversity across tropical forests. How species survival rates change with size offers insight into the links between biodiversity and ecosystem function across tropical forests. We tested patterns of size-dependent tree survival across the tropics using data from 1781 species and over two million individuals to assess whether tropical forests can be characterized by size-dependent life-history survival strategies. We found species were classifiable into four “survival modes” that explain life-history variation that shapes carbon-cycling and the relative abundances within forests. Frequently collected functional traits, such as wood density, leaf mass per area, and seed mass, were not generally predictive of species’ survival modes. Mean annual temperature and cumulative water deficit predicted the proportion of biomass of survival modes, indicating important links between evolutionary strategies, climate, and carbon cycling. The application of survival modes in demographic simulations predicted biomass change across forest sites. Our results reveal globally identifiable size-dependent survival strategies that differ across diverse systems in a consistent way. The abundance of survival modes and interaction with climate ultimately determine forest structure, carbon storage in biomass, and future forest trajectories.

Johnson, Daniel J.↗
DOE OSTI2020

Quantifying the impacts of climate change and extreme climate events on energy systems

Climate induced extreme weather events and weather variations will affect both the demand of energy and the resilience of energy supply systems. The specific potential impact of extreme events on energy systems has been difficult to quantify due to the unpredictability of future weather events. In this work, we develop a stochastic-robust optimization method to consider both low impact variations and extreme events. Applications of the method to 30 cities in Sweden, by considering 13 climate change scenarios, reveal that uncertainties in renewable energy potential and demand can lead to a significant performance gap (up to 34% for grid integration) brought by future climate variations and a drop in power supply reliability (up to 16%) due to extreme weather events. Finally, appropriate quantification of the climate change impacts will ensure robust operation of the energy systems and enable renewable energy penetration above 30% for a majority of the cities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bayesian weighting of climate models based on climate sensitivity

Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO 2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 °C for a low emissions scenario (SSP1-2.6) and 5 °C for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Lack of clear standards and usable comparisons of downscaled climate projections pose a roadblock for US climate discovery and adaptation

Abstract The release of global climate projections coupled with the demand for local-resolution climate-forced meteorology has prompted many research groups to downscale these projections using various statistical, dynamical, and current machine learning techniques. Such downscaled datasets are being used to plan infrastructure and other community needs over the coming decades. Faced with roughly a dozen available US downscaled datasets, many practitioners ask, ‘What are the relevant differences between datasets?’ This work highlights the difficulty of comparing downscaled datasets and illustrates ways in which datasets differ even when using identical climate model input data. We show that substantial variability in precipitation projections arises from downscaling alone and that the downscaled dataset agreement varies depending on global climate projection. This analysis emphasizes the need for greater coordination and movement toward rigorous benchmarking of downscaling strategies within the downscaling research community, à la the land-modeling community, to better quantify downscaling dataset differences, strengths, and weaknesses for practitioners.

Hartke, Samantha H. (ORCID:0000000202394723)↗
DOE OSTI2024

Improved Decarbonization Planning through Climate Resiliency Modeling

California has set ambitious decarbonization goals concerning both emissions and the portion of energy generated by renewable resources. However, climate change poses considerable uncertainties. Variation in load is driven both by the level of electrification as well as the impact of climate change on weather. Further, climate change stands to make weather more variable, impacting not just load but generation from renewable resources. In this paper, we approach the issue of the impacts of climate change on decarbonization planning from two perspectives. First, we look at the range of decarbonization pathways through 8 pathways that account for differences in socioeconomic development, global emissions, and warming. Second, we develop a more robust way of ensuring the reliability of energy resources planning than the commonly used planning reserve margin. We show that the proposed method can save between 6 and 14 billion dollars in investment and maintenance costs and outline critical policy implications concerning the reliance of power plants for satisfying planning reliability requirements, including the potential retirement of dozens of peaker power plants.

Anderson, Osten P.↗
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 OSTI2023

The Impact of Model Resolution on the Southern Hemisphere in Community Climate System Model Version 4 Idealized Climate Simulations

Model resolution plays a large role in accurately simulating the Southern Hemisphere circulation in both the ocean and atmosphere. Resolving the mesoscale field is important as it has been shown to have a significant impact on the large-scale climate in eddy-rich regions, which are regions of large CO2 absorption. The presence of ocean and atmospheric mesoscale features can affect sea surface temperatures, the strength and location of storm tracks, and many other air-sea processes. Additionally, with an improvement in resolution, the eddy kinetic energy in the ocean can be expected to change considerably. The significance model resolution has on the Southern Hemisphere is examined using the Community Climate System Model, Version 4, eddy-parameterizing and eddy-resolving simulations. The CO2 concentrations and ozone levels are specified independently to better understand how the mesoscale field responds to extreme changes in external forcing and the resulting climate impacts. Overall, in the eddy-parameterizing simulations, the ozone forcing is found to be more important than the changes in CO2 concentrations. However, in the case of the eddy-resolving simulations, the CO2 concentrations are found to be more dominant, especially in eddy-rich regions. These results demonstrate the need for an increase in model resolution for climate prediction.

Daher, Houraa (ORCID:0000000200177346)↗
DOE OSTI2018

Patterns of longer-term climate change effects on CO 2 efflux from biocrusted soils differ from those observed in the short term

Biological soil crusts (biocrusts) are predicted to be sensitive to the increased temperature and altered precipitation associated with climate change. We assessed the effects of these factors on soil carbon dioxide (CO 2 ) balance in biocrusted soils using a sequence of manipulations over a 9-year period. We warmed biocrusted soils by 2 and, later, by 4 °C to better capture updated forecasts of future temperature at a site on the Colorado Plateau, USA. We also watered soils to alter monsoon-season precipitation amount and frequency and had plots that received both warming and altered precipitation treatments. Within treatment plots, we used 20 automated flux chambers to monitor net soil exchange (NSE) of CO 2 hourly, first in 2006–2007 and then again in 2013–2014, for a total of 39 months. Net CO 2 efflux from biocrusted soils in the warming treatment increased a year after the experiment began (2006–2007). However, after 9 years and even greater warming (4 °C), results were more mixed, with a reversal of the increase in 2013 (i.e., controls showed higher net CO 2 efflux than treatment plots) and with similarly high rates in all treatments during 2014, a wet year. Over the longer term, we saw evidence of reduced photosynthetic capacity of the biocrusts in response to both the temperature and altered precipitation treatments. Patterns in biocrusted soil CO 2 exchange under experimentally altered climate suggest that (1) warming stimulation of CO 2 efflux was diminished later in the experiment, even in the face of greater warming; and (2) treatment effects on CO 2 flux patterns were likely driven by changes in biocrust species composition and by changes in root respiration due to vascular plant responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C

A new temperature goal of “holding the increase in global average temperature well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels” has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0°C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106–2115 in warming scenarios of 1.5 and 2.0°C using a method of ensemble simulation with well-validated Model to capture the Crop–Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Correlated k-distribution method for radiative transfer in climate models: Application to effect of cirrus clouds on climate

A radiative transfer method appropriate for use in simple climate models and three dimensional global climate models was developed. It is fully interactive with climate changes, such as in the temperature-pressure profile, cloud distribution, and atmospheric composition, and it is accurate throughout the troposphere and stratosphere. The vertical inhomogeneity of the atmosphere is accounted for by assuming a correlation of gaseous k-distributions of different pressures and temperatures. Line-by-line calculations are made to demonstrate that The method is remarkably accurate. The method is then used in a one-dimensional radiative-convective climate model to study the effect of cirrus clouds on surface temperature. It is shown that an increase in cirrus cloud cover can cause a significant warming of the troposphere and the Earth's surface, by the mechanism of an enhanced green-house effect. The dependence of this phenomenon on cloud optical thickness, altitude, and latitude is investigated.

Lacis, A. A.↗
NASA NTRS1982

Climate studies from satellite observations - Special problems in the verification of earth radiation balance, cloud climatology, and related climate experiments

A body of techniques that have been developed and planned for use during the Earth Radiation Budget Experiment (ERBE), the International Satellite Cloud Climatology Project (ISCCP), and related climate experiments of the 1980's are reviewed. Validation and verification methods must apply for systems of satellites. They include: (1) use of a normalization or intercalibration satellite, (2) special intensive observation areas located over ground-truth sites, and (3) monitoring of sun and earth by several satellites and/or several instruments at the same time. Since each climate application area has a hierarchy of user communities, validation techniques vary from very detailed methods to those that simply assure high relative accuracy in detecting space and time variations for climate studies. It is shown that climate experiments generally require more emphasis on long-term stability and internal consistency of satellite data sets than high absolute accuracy.

Vonder Haar, T. H.↗
NASA NTRS1995

Modeling Climate Change in the Absence of Climate Change Data. Editorial Comment

Practitioners of climate change prediction base many of their future climate scenarios on General Circulation Models (GCM's), each model with differing assumptions and parameter requirements. For representing the atmosphere, GCM's typically contain equations for calculating motion of particles, thermodynamics and radiation, and continuity of water vapor. Hydrology and heat balance are usually included for continents, and sea ice and heat balance are included for oceans. The current issue of this journal contains a paper by Van Blarcum et al. (1995) that predicts runoff from nine high-latitude rivers under a doubled CO2 atmosphere. The paper is important since river flow is an indicator variable for climate change. The authors show that precipitation will increase under the imposed perturbations and that owing to higher temperatures earlier in the year that cause the snow pack to melt sooner, runoff will also increase. They base their simulations on output from a GCM coupled with an interesting water routing scheme they have devised. Climate change models have been linked to other models to predict deforestation.

Skiles, J. W.↗