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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Predictability in a solvable stochastic climate model

A highly idealized atmospheric model is presented for the purpose of examining the limits of predictability for the large scales of the temperature field. The model is of the semiempirical type introduced by Budyko (1968, 1969) and Sellers (1969), but forced by a white noise heating term. The advantage of the considered model is its simplicity and the fact that analytical methods can be used throughout so that each assumption and simplification can be examined explicitly. On the other hand, the model lacks many features expected to be important in the real geophysical system. The predictability problem is illustrated by considering first a simple model for the global temperature. The characteristic time for the decay of a global temperature anomaly is determined by the ratio of the associated heat storage to the radiative loss rate.

North, G. R.↗
NASA NTRS2018

Emerging Trends in Global Freshwater Availability

Freshwater availability is changing worldwide. Here we quantify 34 trends in terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during 2002-2016 and categorize their drivers as natural interannual variability, unsustainable groundwater consumption, or climate change. Several of these trends had been lacking thorough investigation and attribution, including massive changes in northwestern China and the Okavango delta. Others are consistent with climate model predictions. This observation-based assessment of how the world's water landscape is responding to human impacts and climate variations provides a blueprint for evaluating and predicting emerging threats to water and food security.

Okavango delta↗
DOE OSTI2024

Upland Yedoma taliks are an unpredicted source of atmospheric methane

Landscape drying associated with permafrost thaw is expected to enhance microbial methane oxidation in arctic soils. Here we show that ice-rich, Yedoma permafrost deposits, comprising a disproportionately large fraction of pan-arctic soil carbon, present an alternate trajectory. Field and laboratory observations indicate that talik (perennially thawed soils in permafrost) development in unsaturated Yedoma uplands leads to unexpectedly large methane emissions (35–78 mg m -2 d -1 summer, 150–180 mg m -2 d -1 winter). Upland Yedoma talik emissions were nearly three times higher annually than northern-wetland emissions on an areal basis. Approximately 70% emissions occurred in winter, when surface-soil freezing abated methanotrophy, enhancing methane escape from the talik. Remote sensing and numerical modeling indicate the potential for widespread upland talik formation across the pan-arctic Yedoma domain during the 21 st and 22 nd centuries. Contrary to current climate model predictions, these findings imply a positive and much larger permafrost-methane-climate feedback for upland Yedoma.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Coastal-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) Science Plan

Understanding the mechanisms governing the urban atmospheric environment is critical for informing urban populations regarding the impacts of climate change and associated mitigation and adaptation measures. Earth system (climate and weather) models have not yet been adapted to provide accurate predictions of climate and weather variability within cities, nor do they provide well-tested representations of the impacts of urban systems on the atmospheric environment. These limitations are largely due to limited field data available for testing and development of these models. We will deploy the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s first Mobile Facility (AMF1) to the mid-Atlantic region surrounding the city of Baltimore for the Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE). This deployment will create a four-node regional atmospheric observatory network including Baltimore and its three primary surrounding environments – rural, urban, and bay. CoURAGE investigators will study the interactions among the Earth’s surface, the atmospheric boundary layer, aerosols and atmospheric composition, clouds, radiation, and precipitation at each site, and examine how the spatial gradients across the region interact to create the climate conditions in Baltimore. This study will determine the degree to which Baltimore’s atmospheric environment depends on interactive feedbacks in the atmospheric system and the degree to which conditions in Baltimore depend on the surrounding environment. Some topics of interest include how urban land management exacerbates heat waves, the impact of regional mesoscale winds (nocturnal jet, bay breeze) on urban air pollution and cloud cover, and the impact of the urban heat island and aerosol production on heavy precipitation events. Understanding this integrated coast-urban-rural system quantitatively and with good accuracy and precision is critical to informing climate adaptation and mitigation efforts in the city of Baltimore. The understanding gained should be applicable to many similar coastal, mid-latitude urban centers. Another important objective of CoURAGE is to improve the representation of the climate of coastal cities in Earth systems models (ESMs). CoURAGE investigators will use the observations to test current ESMs, identify weaknesses and work towards improved simulations of this complex environment. The ARM core facility will be deployed in the city of Baltimore, complementing the Baltimore Social-Environmental Collaborative (BSEC), a DOE urban integrated field laboratory (UIFL). Ancillary sites will be deployed to rural Maryland northwest of Baltimore, and to the southern end of Kent Island within Chesapeake Bay. The fourth node will be a long-term atmospheric observatory operated in Beltsville, Maryland by Howard University and the Maryland Department of the Environment. Measurements will be conducted for one year, starting in December of 2024. There will be two intensive operational periods (IOPs), one in summer and one in winter, when the ancillary sites will be enhanced with additional balloon launches, tethered balloon system (TBS) operation, and added atmospheric composition measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A full year of aerosol size distribution data from the central Arctic under an extreme positive Arctic Oscillation: insights from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition

Abstract. The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Global Warming in the 21st Century: An Alternate Scenario

Evidence on a broad range of time scales, from Proterozoic to the most recent periods, shows that the Earth's climate responds sensitively to global forcings. In the past few decades the Earth's surface has warmed rapidly, apparently in response to increasing anthropogenic greenhouse gases in the atmosphere. The conventional view is that the current global warming rate will continue or accelerate in the 21st century. I will describe an alternate scenario that would slow the rate of global warming and reduce the danger of dramatic climate change. But reliable prediction of future climate change requires improved knowledge of the carbon cycle and global observations that allow interpretation of ongoing climate change.

Hansen, James E.↗
NASA NTRSDate not supplied

Clouds and the Earth's Radiant Energy System

The Clouds and the Earth's Radiant Energy System (CERES) is a key component of the Earth Observing System (EOS) program. The CERES instrument provides radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999, and two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4,2002. [Mission Objectives] The scientific justification for the CERES measurements can be summarized by three assertions: (1) changes in the radiative energy balance of the Earth-atmosphere system can cause long-term climate changes (e.g., carbon dioxide inducing global warming); (2) besides the systematic diurnal and seasonal cycles of incoming solar energy, changes in cloud properties (amount, height, optical thickness) cause the largest changes of the Earth's radiative energy balance; and (3) cloud physics is one of the weakest components of current climate models used to predict potential global climate change. CERES has four main objectives: 1) For climate change analysis, provide a continuation of the ERBE record of radiative fluxes at the top of the atmosphere (TOA), analyzed using the same algorithms that produced the ERBE data. 2) Double the accuracy of estimates of radiative fluxes at TOA and the Earth's surface. 3) Provide the first long-term global estimates of the radiative fluxes within the Earth's atmosphere. 4) Provide cloud property estimates that are consistent with the radiative fluxes from surface to TOA. [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

Wielicki, Bruce A.↗
NASA NTRS2007

Contrasts Between Precipitation over Mediterranean Sea and Adjacent Continental Areas Based on Decadal Scale Satellite Estimates

Most knowledge concerning the last century's climatology and climate dynamics of precipitation over the Mediterranean Sea basin is based on observations taken from rain gauges surrounding the sea itself. In turn, most of the observations come from Southern Europe, with many fewer measurements taken from widely scattered sites situated over North Africa, the Middle East, and the Balkans. This aspect of research on the Mediterranean Sea basin is apparent in a recent compilation of studies presented in book form concerning climate variability of the Mediterranean region [Lionello, P., P. Malanotte-Rizzoli, and R. Boscolo (eds.), 2006: Mediterranean Climate Variability. Elsevier, Amsterdam, 9 chapters.] In light of this missing link to over-water observations, this study (in conjunction with four companion studies by Z. Haddad, A. Mugnai, T. Nakazawa, and G. Stephens) will contrast the nature of precipitation variability directly over the Mediterranean Sea to precipitation variability over the surrounding land areas based on three decades of satellite-based precipitation estimates which have stood up well to validation scrutiny. The satellite observations are drawn from the Global Precipitation Climatology Project (GPCP) dataset extending back to 1979 and the TRMM Merged Algorithm 3b42 dataset extending back to 1998. Both datasets are mostly produced from microwave measurements, excepting the period from 1979 to mid-1987 when only infrared satellite measurements were available for the GPCP estimates. The purpose of this study is to emphasize how the salient properties of precipitation variability over land and sea across a hierarchy of space and time scales, and the salient differences in these properties, might be used in guiding short-term climate models to better predictions of future climate states under different regional temperature-change scenarios.

Smith, Eric A.↗
DOE OSTI2024

Ventenata dubia projected to expand in the western United States despite future novel conditions

Abstract Distributions of both native and invasive species are expected to shift under future climate. Species distribution models (SDMs) are often used to explore future habitats, but sources of uncertainty including novel climate conditions may reduce the reliability of future projections. We explore the potential spread of the invasive annual grass ventenata ( Ventenata dubia ) in the western United States under both current and future climate scenarios using boosted regression tree models and 30 global climate models (GCMs). We quantify novel climate conditions, prediction variability arising from both the SDMs and GCMs, and the agreement among GCMs. Results demonstrate that currently suitable habitat is concentrated inside the invaded range of the northwest, but substantial habitat exists outside the invaded range in the Southern Rockies and southwestern US mountains. Future suitability projections vary greatly among GCMs, but GCMs commonly projected decreased suitability in the invaded range and increased suitability along higher elevations of interior mountainous areas. Climate novelty did not appear to undermine the prediction reliability in many cases where the climate–species relationship was fully represented by the occurrence data. GCM‐derived variability resulting from variation in future cool season precipitation and temperature seasonality was greatest in the Rocky Mountains. SDM‐derived variability was higher in currently suitable habitat, and few GCMs projections agreed that these areas would contain future suitable habitat. However, while prediction variability was high, many GCM projections agreed that parts of the Rocky, Wasatch, and Uinta Mountains would contain highly suitable habitat in the future. As disturbances in the interior mountains occur in coming decades, reducing some natural barriers to invasion, land managers, and conservationists will need to monitor for ventenata in post‐disturbance environments. Changes to invasion potential may not play out for several decades, but results related to current potential may have applications for early detection and rapid response planning.

Environmental Sciences & Ecology↗
NASA NTRS2002

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 km or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed-shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗
NASA NTRS2002

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 kin or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed- shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗
DOE OSTI2024

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Rahimi, Stefan (ORCID:0000000331884462)↗
DOE OSTI2024

Physics-Reinforced Machine Learning Algorithms for Multiscale Closure Model Discovery

The central objective of this project was to address the challenge of modeling and simulating complex multiscale turbulence phenomena by leveraging physics-guided machine learning (PGML) and hybrid modeling approaches. By integrating physics-based methods with data-driven models, the research focused on achieving robust and scalable solutions for geophysical turbulence, enhancing numerical weather prediction and climate research tools. The project resulted in significant advancements in computational modeling paradigms, predictive tools for reduced-order modeling, and innovative algorithms for fluid dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Task Parallelism to Optimize Performance of Environmental Modeling Software

Climate modeling is an integral part of environmental research, from studying rare phenomena to predicting future climate trends. The need for more accurate models is only growing, but as climate modeling capabilities advance, existing workflows require optimization to recoup performance. A solution comes in the form of task parallelism, a novel programming capability that provides an opportunity for optimization at execution time by allowing tasks to be executed in parallel, reducing runtime significantly. Using Parsl, an intuitive and scalable parallel scripting library for Python, we implement task parallelism within support software to aid in the continuous advancement of climate modeling technology.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

It's a Sooty Problem: Black Carbon and Aerosols from Space

Our knowledge of atmospheric aerosols (smoke, pollution, dust or sea salt particles, small enough to be suspended in the air), their evolution, composition, variability in space and time and interaction with solar radiation, clouds and precipitation is lacking despite decades of research. Just recently we recognized that understanding the global aerosol system is fundamental for progress in climate change and hydrological cycle research. While a single instrument was used to demonstrate 50 yrs ago that the global CO2 levels are rising, posing thread to our climate, we need an may of satellites, surface networks of radiometers, elaborated laboratory and field experiments coupled with chemical transport models to understand the global aerosol system. This complexity of the aerosol problem results from their short lifetime (1 week), variability of the chemical composition and complex chemical and physical processes in the atmosphere. The result is a heterogeneous distribution of aerosol and their properties. The new generation of satellites and surface networks of radiometers provides exciting opportunities to measure the aerosol properties and their interaction with clouds and climate. However farther development in the satellite capability, aerosol chemical models and climate models is needed to fully decipher the aerosol secrets with accuracy required to predict future climates.

Kaufman, Yoram J.↗
NASA NTRS2016

Health and Climate Impacts of Ocean-Going Vessels in East Asia

East Asia has the most rapidly growing shipping emissions of both CO2 and traditional air pollutants, but the least in-depth analysis. Full evaluation of all pollutants is needed to assess the impacts of shipping emissions. Here, using an advanced method based on detailed dynamic ship activity data, we show that shipping emissions in East Asia accounted for 16% of global shipping CO2 in 2013, compared to only 4-7% in 2002-2005. Increased emissions lead to large adverse health impacts, with 14,500-37,500 premature deaths per year. Global mean radiative forcing from East Asian shipping is initially negative, but would become positive after approximately eight years for constant current emissions. As a large fraction of vessels are registered elsewhere, joint efforts are necessary to reduce emissions and mitigate the climate and health impacts of shipping in the region.

climate change impacts↗
NASA NTRS2023

Importance of Internal Variability for Climate Model Assessment

Benchmarking climate model simulations against observations of the climate is core to the process of building realistic climate models and developing accurate future projections. However, in many cases, models do not match historical observations, particularly on regional scales. If there is a mismatch between modeled and observed climate features, should we necessarily conclude that our models are deficient? Using several illustrative examples, we emphasize that internal variability can easily lead to marked differences between the basic features of the model and observed climate, even when decades of model and observed data are available. This can appear as an apparent failure of models to capture regional trends or changes in global teleconnections, or simulation of extreme events. Despite a large body of literature on the impact of internal variability on climate, this acknowledgment has not yet penetrated many model evaluation activities, particularly for regional climate. We emphasize that using a single or small ensemble of simulations to conclude that a climate model is in error can lead to premature conclusions on model fidelity. A large ensemble of multidecadal simulations is therefore needed to properly sample internal climate variability in order to robustly identify model deficiencies and convincingly demonstrate progress between generations of climate models.

Climate and Earth system modelling↗
DOE OSTI2023

Accelerating Informed Local Climate Action by Improving Access to DOE Predictive Modeling and Science-Based Tools

The threat of climate change is becoming increasingly urgent, and local governments across the United States are working to develop and implement climate action plans to reduce greenhouse gas emissions and mitigate the impacts of climate change. At the same time, the U.S. Department of Energy's Office of Science, Earth and Environmental Systems Sciences Division (EESSD) has been developing a range of tools and technologies to help predict the potential multi-faceted impacts from climate change given varying environmental and socioeconomic scenarios. However, local governments have been slow to engage with these scientific assets due to a lack of understanding and awareness. The DOE researchers interviewed highlighted three primary types of resources that could benefit local governments and climate practitioners: climate models, modeled datasets, and scientific journal articles. Although many of these resources are open source and available to the public, there were several challenges identified in translating these resources and their research insights directly to decision-makers.

54 ENVIRONMENTAL SCIENCES↗