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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

NASA Initiatives to Support Renewable Energy and Sustainable Infrastructure

Initiatives to build and manage climate-resilient infrastructure require spatially explicit environmental and climate information. NASA’s vantage point of Earth from space provides valuable observations that, in concert with additional information, can be used to inform both the design and operation of infrastructure that builds resilience to climate stressors. In this presentation, we provide an overview of how NASA Earth science is used to support decision-making associated with climate adaptation and mitigation, with a particular focus on renewable energy and sustainable infrastructure. Much of this work is currently delivered through NASA’s Prediction of Worldwide Energy Resources (POWER) project, which generates high quality solar and meteorological data products that are customized to the needs of user communities. Importantly, these products are delivered through a variety of web-based applications that make it easier for users to access relevant and timely information. We will highlight several examples of how the impact of POWER is scaled via a robust user community and discuss further plans to expand impact. Additionally, this presentation will highlight efforts of the NASA Earth Science Division’s Energy & Infrastructure Applications program to expand collaborative activities with researchers and practitioners that work on renewable energy and sustainable infrastructure.

Allison K Leidner↗
DOE OSTI2022

Processes and mechanisms of coastal woody-plant mortality

Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO 2 , and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO 2 , drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.

salinity↗
NASA NTRS1999

Global Warming: Discussion for EOS Science Writers Workshop

The existence of global warming this century is no longer an issue of scientific debate. But there are many important questions about the nature and causes of long-term climate change, th roles of nature and human-made climate forcings and unforced (chaotic) climate variability, the practical impacts of climate change, and what, if anything, should be done to reduce global warming, Global warming is not a uniform increase of temperature, but rather involves at complex geographically varying climate change. Understanding of global warming will require improved observations of climate change itself and the forcing factors that can lead to climate change. The NASA Terra mission and other NASA Earth Science missions will provide key measurement of climate change and climate forcings. The strategy to develop an understanding of the causes and predictability of long-term climate change must be based on combination of observations with models and analysis. The upcoming NASA missions will make important contributions to the required observations.

Hansen, James E↗
NASA NTRS2018

Lessons Learned During the Transition of SORCE Science Operations to Daylight Only Operations

In July 2013, NASA's Solar Radiation and Climate Experiment experienced a battery anomaly which placed it into safemode halting all science observations. Initial attempts to recover the spacecraft to an operational configuration failed due to the reduced capacity of the battery. As the keystone mission for measuring total solar irradiance, and the cornerstone mission for measuring the solar spectral irradiance there was a strong motivation for developing a new operations concept that would allow SORCE to resume daily measurements of the Sun. The operations team faced many challenges over the next several months. For a five-day period in late 2013 the operations team was able resume science observations to cross-calibrate SORCE data with a new instrument launched in November 2013. After the cross-calibration campaign was completed a new operations concept was deployed which allowed SORCE to perform daylight only operations. In this mode of operations all non-essential components are powered off at each eclipse entry and then turned back on at sunrise. In March 2014 SORCE resumed making daily measurements of the Sun. This paper will review the events and lessons learned from the six-month recovery effort.

Spacecraft Anomaly↗
DOE OSTI2021

Combining artificial intelligence, Earth observations, and climate models to improve predictability of ice-biogeochemistry interactions

Focal Area: Predictive Modeling. We describe how artificial intelligence (AI) can be combined with state-of-the-science Earth system models to better predict future regional climate responses. To demonstrate, we describe a case study in biogeochemical interactions with sea ice. Science Challenge: Biogeochemical models are poorly constrained for high latitude systems. Machine learning methods and edge computing can be combined with Earth system models, such as the Energy Exascale Earth System Model (E3SM), to gain insight into ice-biogeochemical interactions and improve sea ice extent prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mechanistic Simulations Suggest Riparian Restoration Can Partly Counteract Climate Impacts to Juvenile Salmon

Climate change is reducing summertime water availability and elevating water temperature, placing human consumptive needs in competition with needs of coldwater fishes. Here we worked with natural resource managers in the Snoqualmie River (Washington, USA) to develop riparian management scenarios, and used a process-based modeling system to examine how a threatened population of Chinook salmon (Oncorhynchus tschawytcha) may respond to climate change and whether riparian restoration could reduce climate effects. Linking models of global climate, regional hydrology and water temperature, and fish, we projected that streams would become warmer year-round and drier during summer, further stressing salmon. Climate change accelerated egg emergence, increased juvenile growth and survival, and accelerated outmigration of sub-yearling migrants. Growth was depressed for salmon remaining instream during summer (potential yearling migrants). Riparian restoration counteracted ~10% of summer increases in water temperature, and affected salmon similarly regardless of whether riparian buffers were partially or fully restored, whereas riparian degradation further warmed streams. Riparian restoration fully mitigated climate change effects on potential yearling migrant size, but only minimally affected sub-yearling migrants (assessment metrics changed <2%). Our results will be useful for watershed managers in aligning priorities for fish and humans and our framework can be applied elsewhere.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

The Earth System Grid Federation : an Open Infrastructure for Access to Distributed Geospatial Data

The Earth System Grid Federation (ESGF) is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. The ESGF's architecture employs a system of geographically distributed peer nodes, which are independently administered yet united by the adoption of common federation protocols and application programming interfaces (APIs). The cornerstones of its interoperability are the peer-to-peer messaging that is continuously exchanged among all nodes in the federation; a shared architecture and API for search and discovery; and a security infrastructure based on industry standards (OpenID, SSL, GSI and SAML). The ESGF software is developed collaboratively across institutional boundaries and made available to the community as open source. It has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the entire model output used for the next international assessment report on climate change (IPCC-AR5) and a suite of satellite observations (obs4MIPs) and reanalysis data sets (ANA4MIPs).

search,↗
NASA NTRS2010

NASA Tools for Climate Impacts on Water Resources

Climate and environmental change are expected to fundamentally alter the nation's hydrological cycle and water availability. Satellites provide global or near-global coverage using instruments, allowing for consistent, well-calibrated, and equivalent-quality data of the Earth system. A major goal for NASA climate and environmental change research is to create multi-instrument data sets to span the multi-decadal time scales of climate change and to combine these data with those from modeling and surface-based observing systems to improve process understanding and predictions. NASA and Earth science data and analyses will ultimately enable more accurate climate prediction, and characterization of uncertainties. NASA's Applied Sciences Program works with other groups, including other federal agencies, to transition demonstrated observational capabilities to operational capabilities. A summary of some of NASA tools for improved water resources management will be presented.

Toll, David↗
DOE OSTI2020

Development of Future Heatwaves for Different Hazard Thresholds

In 2018 and 2019, heatwaves set all-time temperature records around the world and caused adverse effects on human health, agriculture, natural ecosystems, and infrastructure. Often, severe impacts relate to the joint spatial and temporal extent of the heatwaves, but most research so far focuses either on spatial or temporal attributes of heatwaves. Furthermore, sensitivity of heatwaves characteristics to the choice of the heatwave thresholds in a warming climate are rarely discussed. Here, we analyze the largest spatiotemporal moderate heatwaves—that is, three-dimensional (space-time) clusters of hot days—in simulations of global climate models. We use three different hazard thresholds to define a hot day: fixed thresholds (time-invariant climatological thresholds), seasonally moving thresholds based on changes in the summer means, and fully moving thresholds (hot days defined relative to the future climatology). We find a substantial increase of spatiotemporally contiguous moderate heatwaves with global warming using fixed thresholds, whereas changes for the other two hazard thresholds are much less pronounced. In particular, no or very little changes in the overall magnitude, spatial extent, and duration are detected when heatwaves are defined relative to the future climatology using a temporally fully moving threshold. This suggests a dominant contribution of thermodynamic compared to dynamic effects in global climate model simulations. The similarity between seasonally moving and fully moving thresholds indicates that seasonal mean warming alone can explain large parts of the warming of extremes. The strong sensitivity of simulated future heatwaves to hazard thresholds should be considered in the projections of potential future heat-related impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understanding Recent Global Hydroclimate Change Using Multivariate Detection and Attribution Techniques and GCM Experiments

The observed slowdown in the rate of global average surface temperature (T) warming during the post-1998 decade (referred to at the time as the “global warming hiatus”) posed a challenge for climate researchers because it was not captured in global climate models projection. Observational and modeling evidence hinted that that leveling of the T trend was linked with the simultaneous appearance of cold surface temperature in the eastern equatorial Pacific that formed a protracted La Niña-like state, a phenomenon known as the Inter-decadal Pacific Oscillation (IPO). It was however not clear whether the concurrent equatorial Pacific cooling was a regional response to the rise in atmospheric greenhouse gas levels or anthropogenic aerosols that is not captured by the climate models or whether it was an unrelated, serendipitous manifestation of a natural phenomenon, internal to the climate system. To resolve this issue, we proposed in this project to develop and apply fingerprinting methods to differentiate between the observed climate response to various external forcing agents and the manifestations of internal climate variability. The project also sought to find out if climate models are able to reproduce the change signals detected in key observational phenomena and if they can be used to investigate the underlying processes. As such we examined changes in global hydroclimate variability and in an outstanding example of regional hydroclimate change, namely the multi-decadal 20 th century change in Sahel rainfall.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Optimization of a Parallel Ocean General Circulation Model

Global climate modeling is one of the grand chalenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting the future climate change.

Optimization Ocean Modeling Parallel Computations↗
DOE OSTI2024

Wildfire smoke impacts lake ecosystems

Abstract Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under‐recognized. We introduce the concept of the lake smoke‐day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke‐day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke‐days a year, with 89.6% of lakes receiving over 30 lake smoke‐days, and lakes in some regions experiencing up to 4 months of cumulative smoke‐days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Climate Model Evaluation using New Datasets from the Clouds and the Earth's Radiant Energy System (CERES)

There are some in the science community who believe that the response of the climate system to anthropogenic radiative forcing is unpredictable and we should therefore call off the quest . The key limitation in climate predictability is associated with cloud feedback. Narrowing the uncertainty in cloud feedback (and therefore climate sensitivity) requires optimal use of the best available observations to evaluate and improve climate model processes and constrain climate model simulations over longer time scales. The Clouds and the Earth s Radiant Energy System (CERES) is a satellite-based program that provides global cloud, aerosol and radiative flux observations for improving our understanding of cloud-aerosol-radiation feedbacks in the Earth s climate system. CERES is the successor to the Earth Radiation Budget Experiment (ERBE), which has widely been used to evaluate climate models both at short time scales (e.g., process studies) and at decadal time scales. A CERES instrument flew on the TRMM satellite and captured the dramatic 1998 El Nino, and four other CERES instruments are currently flying aboard the Terra and Aqua platforms. Plans are underway to fly the remaining copy of CERES on the upcoming NPP spacecraft (mid-2010 launch date). Every aspect of CERES represents a significant improvement over ERBE. While both CERES and ERBE measure broadband radiation, CERES calibration is a factor of 2 better than ERBE. In order to improve the characterization of clouds and aerosols within a CERES footprint, we use coincident higher-resolution imager observations (VIRS, MODIS or VIIRS) to provide a consistent cloud-aerosol-radiation dataset at climate accuracy. Improved radiative fluxes are obtained by using new CERES-derived Angular Distribution Models (ADMs) for converting measured radiances to fluxes. CERES radiative fluxes are a factor of 2 more accurate than ERBE overall, but the improvement by cloud type and at high latitudes can be as high as a factor of 5. Diurnal cycles are explicitly resolved by merging geostationary satellite observations with CERES and MODIS. Atmospheric state data are provided from a frozen version of the Global Modeling and Assimilation Office- Data Assimilation System at the NASA Goddard Space Flight Center. In addition to improving the accuracy of top-of-atmosphere (TOA) radiative fluxes, CERES also produces radiative fluxes at the surface and at several levels in the atmosphere using radiative transfer modeling, constrained at the TOA by CERES (ERBE was limited to the TOA). In all, CERES uses 11 instruments on 7 spacecraft all integrated to obtain climate accuracy in TOA to surface fluxes. This presentation will provide an overview of several new CERES datasets of interest to the climate community (including a new adjusted TOA flux dataset constrained by estimates of heat storage in the Earth system), show direct comparisons between CERES ad ERBE, and provide a detailed error analysis of CERES fluxes at various time and space scales. We discuss how observations can be used to reduce uncertainties in cloud feedback and climate sensitivity and strongly argue why we should NOT "call off the quest".

Loeb, Norman G.↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Viral infections likely mediate microbial controls on ecosystem responses to global warming

Abstract Climate change is affecting how energy and matter flow through ecosystems, thereby altering global carbon and nutrient cycles. Microorganisms play a fundamental role in carbon and nutrient cycling and are thus an integral link between ecosystems and climate. Here, we highlight a major black box hindering our ability to anticipate ecosystem climate responses: viral infections within complex microbial food webs. We show how understanding and predicting ecosystem responses to warming could be challenging—if not impossible—without accounting for the direct and indirect effects of viral infections on different microbes (bacteria, archaea, fungi, protists) that together perform diverse ecosystem functions. Importantly, understanding how rising temperatures associated with climate change influence viruses and virus-host dynamics is crucial to this task, yet is severely understudied. In this perspective, we (i) synthesize existing knowledge about virus-microbe-temperature interactions and (ii) identify important gaps to guide future investigations regarding how climate change might alter microbial food web effects on ecosystem functioning. To provide real-world context, we consider how these processes may operate in peatlands—globally significant carbon sinks that are threatened by climate change. We stress that understanding how warming affects biogeochemical cycles in any ecosystem hinges on disentangling complex interactions and temperature responses within microbial food webs.

54 ENVIRONMENTAL SCIENCES↗