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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Aerosol-Ice Formation Closure Pilot Study (AEROICSTUDY) Field Campaign Report

The aerosol-ice formation closure pilot study (AEROICESTUDY) was a field campaign conducted at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility at the Southern Great Plains (SGP) observatory from October 7 to 28, 2019. The purpose of this campaign was to test a field observational approach for conducting an aerosol-ice formation closure study. In other words, the goal was to predict the number of ice nucleating particles (INPs) from the measurements of physical and chemical aerosol properties and compare those to measured INP number concentrations. This project was motivated by the fact that the aerosol community has widely conducted aerosol radiative closure, aerosol-cloud condensation nuclei (CCN), and CCN-droplet closure studies to test the physical models and parameterizations that cloud-resolving and climate models rely on to perform reliable simulations of the Earth system and energy budget. However, very few closure studies related to INPs have been conducted, and to our knowledge, none using robust, size-resolved, ambient aerosol composition measurements as model inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Drought and Extreme Heat Impacts to Data Centers in Northern California

Commonly, data centers are designed and operated on the assumption of stationarity of the climate. Historical weather data informs the basis of their design and operations. However, as a result of climate change, this assumption is not valid and poses a great risk for data centers. In the coming decades, Northern California’s drought and extreme heat risk is projected to increase significantly. This report provides a drought and extreme heat impacts assessment of data centers in this region. Expert solicitation of data center experts enabled a tailored approach to this report. We identify the climate variables and analytics relevant for assessing data center-specific impacts from extreme heat and drought and demonstrate how to access and understand future climate projections from climate models. We emphasize the importance of including future climate projections into data center design and planning. This report is an important first step towards building an effective extreme heat and drought adaptation strategy for data centers in Northern California. While our geographical focus in this report is Northern California, many findings in this report are broadly applicable.

58 GEOSCIENCES↗
DOE OSTI2016

Technical Note: A generic law-of-the-minimum flux limiter for simulating substrate limitation in biogeochemical models

We present a generic flux limiter to account for mass limitations from an arbitrary number of substrates in a biogeochemical reaction network. The flux limiter is based on the observation that substrate (e.g., nitrogen, phosphorus) limitation in biogeochemical models can be represented as to ensure mass conservative and non-negative numerical solutions to the governing ordinary differential equations. Application of the flux limiter includes two steps: (1) formulation of the biogeochemical processes with a matrix of stoichiometric coefficients and (2) application of Liebig's law of the minimum using the dynamic stoichiometric relationship of the reactants. This approach contrasts with the ad hoc down-regulation approaches that are implemented in many existing models (such as CLM4.5 and the ACME (Accelerated Climate Modeling for Energy) Land Model (ALM)) of carbon and nutrient interactions, which are error prone when adding new processes, even for experienced modelers. Through an example implementation with a CENTURY-like decomposition model that includes carbon, nitrogen, and phosphorus, we show that our approach (1) produced almost identical results to that from the ad hoc down-regulation approaches under non-limiting nutrient conditions, (2) properly resolved the negative solutions under substrate-limited conditions where the simple clipping approach failed, (3) successfully avoided the potential conceptual ambiguities that are implied by those ad hoc down-regulation approaches. We expect our approach will make future biogeochemical models easier to improve and more robust.

58 GEOSCIENCES↗
DOE OSTI2018

Mid-Latitude Circulation and Extremes in a Changing Climate. Final Technical Report for DOE Award DE-SC0012599

Mid-latitude extreme weather events are responsible for a large part of climate-related damage. Yet large uncertainties remain in climate model projections of heat waves, droughts, and heavy rain/snow events on regional scales, limiting our ability to effectively use these projections for climate adaptation and mitigation. These uncertainties can be attributed to both the lack of spatial resolution in the models, and to the lack of a dynamical understanding of these extremes. The objectives of this proposal were to first to develop empirical and theoretical understanding of how fine-scale features of the atmospheric circulation that are responsible for extreme events (such as blocking, atmospheric rivers, Rossby wave breaking) are related to more easily quantifiable larger scale circulation patterns, and on the basis of this develop specific metrics that can be applied to a range of atmospheric and coupled atmosphere-ocean models to diagnose their prediction of extremes. These metrics were used to understand what model resolution is necessary to describe the probability of extreme events realistically, and will be applied to CMIP5 models and high resolution time-slice projections of future climate to better understand the potential for the shifts and intensification of extremes over North America in a changing climate. The approach was to relate the fine-scale features to the large scales in current climate simulations, seasonal re-forecasts, and climate change projections in a very wide range of models, including the atmospheric and coupled models of ECMWF over a range of horizontal resolutions (125 to 10 km), aqua-planet configuration of the Model for Prediction Across Scales and High Order Method Modeling Environments (resolutions ranging from 240 km – 7.5 km) with various physics suites, and selected CMIP5 model simulations. The large scale circulation will be quantified both on the basis of the well-tested preferred circulation regime approach, and very recently developed measures, the finite amplitude Wave Activity and its spectrum. The fine scale structures related to extremes were diagnosed following the latest approaches in the literature. The goal is to use the large scale measures as indicators of the probability of occurrence of the finer scale structures, and hence extreme events. These indicators will then be applied to the CMIP5 models and time-slice projections of a future climate. The expected outcomes of this research were: (i) a unified methodology and set of metrics that will enable modelers to assess the probability of extreme events in various current and future climate simulations, (ii) an estimate of the model resolution needed to capture extreme events in a realistic way, and (iii) a better understanding of the extreme events over North America in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Mid-Latitude Circulation and Extremes in a Changing Climate

Mid-latitude extreme weather events are responsible for a large part of climate-related damage. Yet large uncertainties remain in climate model projections of heat waves, droughts, and heavy rain/snow events on regional scales, limiting our ability to effectively use these projections for climate adaptation and mitigation. These uncertainties can be attributed to both the lack of spatial resolution in the models, and to the lack of a dynamical understanding of these extremes. The objectives of this proposal were to first to develop empirical and theoretical understanding of how fine-scale features of the atmospheric circulation that are responsible for extreme events (such as blocking, atmospheric rivers, Rossby wave breaking) are related to more easily quantifiable larger scale circulation patterns, and on the basis of this develop specific metrics that can be applied to a range of atmospheric and coupled atmosphere-ocean models to diagnose their prediction of extremes. These metrics were used to understand what model resolution is necessary to describe the probability of extreme events realistically, and will be applied to CMIP5 models and high resolution time-slice projections of future climate to better understand the potential for the shifts and intensification of extremes over North America in a changing climate. The approach was to relate the fine-scale features to the large scales in current climate simulations, seasonal re-forecasts, and climate change projections in a very wide range of models, including the atmospheric and coupled models of ECMWF over a range of horizontal resolutions (125 to 10 km), aqua-planet configuration of the Model for Prediction Across Scales and High Order Method Modeling Environments (resolutions ranging from 240 km – 7.5 km) with various physics suites, and selected CMIP5 model simulations. The large scale circulation will be quantified both on the basis of the well-tested preferred circulation regime approach, and very recently developed measures, the finite amplitude Wave Activity and its spectrum. The fine scale structures related to extremes were diagnosed following the latest approaches in the literature. The goal is to use the large scale measures as indicators of the probability of occurrence of the finer scale structures, and hence extreme events. These indicators will then be applied to the CMIP5 models and time-slice projections of a future climate. The expected outcomes of this research were: (i) a unified methodology and set of metrics that will enable modelers to assess the probability of extreme events in various current and future climate simulations, (ii) an estimate of the model resolution needed to capture extreme events in a realistic way, and (iii) a better understanding of the extreme events over North America in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Tracking Climate Change through the Spatiotemporal Dynamics of the Teletherms, the Statistically Hottest and Coldest Days of the Year

Instabilities and long term shifts in seasons, whether induced by natural drivers or human activities, pose great disruptive threats to ecological, agricultural, and social systems. Here, we propose, measure, and explore two fundamental markers of location-sensitive seasonal variations: the Summer and Winter Teletherms—the on-average annual dates of the hottest and coldest days of the year. We analyze daily temperature extremes recorded at 1218 stations across the contiguous United States from 1853–2012, and observe large regional variation with the Summer Teletherm falling up to 90 days after the Summer Solstice, and 50 days for the Winter Teletherm after the Winter Solstice. We show that Teletherm temporal dynamics are substantive with clear and in some cases dramatic shifts reflective of system bifurcations. We also compare recorded daily temperature extremes with output from two regional climate models finding considerable though relatively unbiased error. In conclusion, our work demonstrates that Teletherms are an intuitive, powerful, and statistically sound measure of local climate change, and that they pose detailed, stringent challenges for future theoretical and computational models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Vegetation biogeography is a main source of uncertainty in modelling the land carbon cycle

The terrestrial biosphere exchanges a large amount of CO 2 with the atmosphere through photosynthesis and respiration, determining the magnitude of land carbon sink and consequently influencing the rate of global warming. The magnitudes of global photosynthesis and respiration, however, vary widely across models (100-200 PgC/year), constituting a key and persistent source of uncertainty in carbon cycle and climate modelling. Here, we argue that the uncertainty in the land carbon cycle modelling is largely attributable to the uncertainty in biogeography – the distribution of plant functional types (PFTs). Using an ensemble of dynamic global vegetation models (DGVMs), we find a strong dependence of total photosynthesis on total area for each PFT. The dependence allows us to reduce the spread of land carbon cycle estimates by ~75% using remote sensing-based PFT maps. We further find that 56 ± 21% of climate-driven changes in global photosynthesis modelled by DGVMs are caused by changes in PFT distribution in the last two decades. Our study identifies vegetation biogeography as a main controlling factor of uncertainty in land carbon cycle modelling and highlights the importance of biogeography-climate interactions in carbon cycle and climate studies.

Zhao, Ruiying [National Univ. of Singapore (Singap↗
DOE OSTI2017

Methods and Model Dependency of Extreme Event Attribution: The 2015 European Drought

Science on the role of anthropogenic influence on extreme weather events, such as heat-waves or droughts, has evolved rapidly in the past years. The approach of “event attribution” compares the occurrence-probability of an event in the present, factual climate with its probability in a hypothetical,counterfactual climate without human-induced climate change. Several methods can be used for event attribution, based on climate model simulations and observations, and usually researchers only assess a subset of methods and data sources. Here, we explore the role of methodological choices for the attribution of the 2015 meteorological summer drought in Europe. We present contradicting conclusions on the relevance of human influence as a function of the chosen data source and event attribution methodology. Assessments using the maximum number of models and counterfactual climates with pre-industrial greenhouse gas concentrations point to an enhanced drought risk in Europe. However, other evaluations show contradictory evidence. Furthermore, these results highlight the need for a multi-model and multi-method frame-work in event attribution research, especially for events with a low signal-to-noise ratio and high model dependency such as regional droughts.

2015 European drought↗
DOE OSTI2026

Development of a 95-Year Solar Dataset for Resource Adequacy Studies

Long-term high-resolution solar data provides enhanced understanding of variability of solar generation and enhances our ability to develop strategies for a resilient and reliable electric grid under high deployment of solar energy. Therefore, it is important to develop long-term synthetic datasets that can provide multiple occurrences of various severe weather scenarios that are expected to test the limits of resource adequacy under scenarios contain various energy generation sources. Examples of such scenarios could be long periods of high temperatures when demand for electricity is high or periods where high winds could lead to a shut-down of transmission lines for long periods of time to ensure fire safety. NREL has developed the first version of such a dataset covering a 95-year period covering 2006-2100 at a 4km hourly resolution. This dataset contains all variables necessary to calculate solar generation. During development of this dataset, we focused on creating unbiased, high-resolution solar irradiance through statistical downscaling methods, using Regional Climate Model (RCM) simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) as input. The National Solar Radiation Database (NSRDB) containing over 25 years of observations was used to calibrate the statistical downscaling models. This presentation will outline the primary steps in developing this dataset, including (1) regridding RCM data to a common grid at 20-km resolution, (2) correcting RCM biases with NSRDB, (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar and ancillary data. Additionally, we will present an evaluation of the downscaled data against the NSRDB across various zones in the CONUS. Lastly, we will present a user guide for accessing the datasets.

14 SOLAR ENERGY↗
DOE OSTI2016

The link between extreme precipitation and convective organization in a warming climate: Global radiative-convective equilibrium simulations

The rate of increase of extreme precipitation in response to global warming varies dramatically across climate model simulations, particularly over the tropics, for reasons that have yet to be established. Here we propose one potential mechanism: changing organization of convection with climate. We analyze a set of simulations with the Community Atmosphere Model version 5 with an idealized global radiative-convective equilibrium configuration forced by fixed sea surface temperatures varying in 2° increments from 285 to 307 K. In these simulations, convective organization varies from semiorganized in cold simulations, disorganized in warm simulations, and abruptly becomes highly organized at just over 300 K. The change in extreme precipitation with warming also varies across these simulations, including a large increase at the transition from disorganized to organized convection. We develop an extreme precipitation-focused metric for convective organization and use this to explore their connection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Physical and Thermodynamic Properties of Secondary Organic Materials for Modeling (Final Technical Report)

Secondary organic material constitutes more than 50% of the submicron component of atmospheric aerosol particles. This organic material is produced by oxidation of biogenic and anthropogenic volatile organic compounds. The physicochemical properties of these aerosol particles and their variability with atmospheric conditions influence many climate-relevant endpoints, such as rates of chemical reactions, production of secondary organic material, number-diameter distributions, direct radiative forcing, and cloud condensation nuclei. The results of the project research highlight a nonlabile-to-labile transition for material representative of anthropogenic secondary organic particulate matter at a threshold humidity range. This behavior differs markedly from materials representing biogenic aerosol sources, which lack such a transition. More specifically, the mass lability of atmospheric organic particulate matter, meaning its tendency to evaporate, is an important property in the mechanisms governing the climate effects. This property is affected by both the volatility and the molecular diffusivity of the constituent organic species. Via a series of laboratory experiments, the project directly determined the mass labilities of films of secondary organic material representative of anthropogenic and biogenic organic particulate matter (PM). The evaporation rates for anthropogenic organic PM increased above a threshold relative humidity (RH) between 20% and 30%, indicating rapid partitioning above a transition RH. Below the threshold, the characteristic time for equilibration was estimated at up to 1 week for a typically sized particle. In contrast, for films representing biogenic PM, no RH threshold was observed, suggesting equilibrium partitioning is rapidly obtained for all RHs. The effective molecular diffusivity for the biogenic case was at least 1000 times greater than that of the anthropogenic case. The results allow for an improved representation of anthropogenic and biogenic secondary organic aerosol formation in climate models. The results of the project also provide new measurements on how organic aerosol particles take up water and serve as cloud condensation nuclei, thus explaining the observed complex thermodynamic behaviors using a simple thermodynamic model. The project results reconciled the discrepancy between sub-saturation hygroscopicity and super-saturation cloud condensation nuclei activity for organic aerosol particles. The key to the explanation is the phase separation between hydrophobic and hydrophilic organics and the induced surface tension lowering. The results are essential for the assessment of the aerosol indirect effect on climate. In more detail, hygroscopic growth and cloud condensation nuclei activation are key processes for accurately modeling the climate impacts of organic particulate matter. Nevertheless, the microphysical mechanisms of these processes remain unresolved. The project results focus on complex thermodynamic behaviors, including humidity-dependent hygroscopicity, diameter-dependent cloud condensation nuclei activity, and liquid-liquid phase separation in the laboratory for biogenically derived secondary organic material representative of similar atmospheric organic particulate matter. These behaviors can be explained by the non-ideal mixing of water with hydrophobic and hydrophilic organic components. The non-ideality-driven liquid-liquid phase separation further enhances water uptake and induces lowered surface tension at high relative humidity, which results in a lower barrier to cloud condensation nuclei activation. By comparison, secondary organic material representing anthropogenic sources does not exhibit complex thermodynamic behavior. The combined results highlight the importance of detailed thermodynamic representations of the hygroscopicity and cloud condensation nuclei activity in models of the Earth’s climate system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Microphysical-macrophysical interactions in low cloud systems over the Eastern North Atlantic

How low clouds over the oceans respond to changes in atmospheric greenhouse gases and aerosol particles is poorly understood. Climate model disagreement in these responses limits the accuracy of predictions of future climate. Processes governing the behavior of marine low cloud need to be improved in models and comprehensive long-term observations provide important constraints. Cloud structure and impacts on climate are shaped by processes operating on a wide range of time and space scales, which interact to modulate cloud dynamics, boundary layer structure, and precipitation. The Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site at Graciosa Island (Azores, 28°W 39°N) began taking measurements in late 2013 to address the need for comprehensive cloud, aerosol, and meteorological observations in a remote marine low cloud environment. The site straddles the boundary between the subtropical and midlatitude climate zones in the eastern North Atlantic and experiences a great diversity of meteorological and cloud conditions. In addition, the ENA site is downwind of the North American continent and is periodically impacted by continental aerosol particles transported there on the prevailing winds. In response to a 2014 proposal call, our team (ENA Site Science Team) laid out a strategy to accelerate research focused on understanding the multi-scale interactions that shape cloud properties by maximizing the utility of the observational capabilities of the ENA site. The proposed work aligned with the scientific directions of the ASR Working Groups. The team, together with collaborators, conducted a program of research focused upon multi-scale cloud processes. The team worked with scientists both within and outside the Atmospheric System Research (ASR) program, and with the ARM infrastructure to ensure that the full potential of the ENA site is realized. The suite of instruments at the ENA site has been increasing since the site’s inception and is now essentially complete. In 2019, we laid out a two-year extension of the research, and were granted an extension of the Site Science award.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6

This paper documents the methodology and preliminary results from a perturbed parameter ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2–CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks are analyzed. The use of various emulators is explored in the multi-dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties, are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2–CAM6 PPE is publicly available and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g., coupling to the land surface).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1°C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2°C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3°C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23°C(±0.12°C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2015

Campaign datasets for Observations and Modeling of the Green Ocean Amazon (GOAMAZON)

The hydrologic cycle of the Amazon Basin is one of the primary heat engines of the Southern Hemisphere. Any accurate climate model must succeed in a good description of the Basin, both in its natural state and in states perturbed by regional and global human activities. At the present time, however, tropical deep convection in a natural state is poorly understood and modeled, with insufficient observational data sets for model constraint. Furthermore, future climate scenarios resulting from human activities globally show the possible drying and the eventual possible conversion of rain forest to savanna in response to global climate change. Based on our current state of knowledge, the governing conditions of this catastrophic change are not defined. Human activities locally, including the economic development activities that are growing the population and the industry within the Basin, also have the potential to shift regional climate, most immediately by an increment in aerosol number and mass concentrations, and the shift is across the range of values to which cloud properties are most sensitive. The ARM Climate Research Facility in the Amazon Basin seeks to understand aerosol and cloud life cycles, particularly the susceptibility to cloud aerosol precipitation interactions, within the Amazon Basin.

54 ENVIRONMENTAL SCIENCES↗