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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Ozone Sensitivity to Varying Greenhouse Gases and Ozone-Depleting Substances in CCMI-1 Simulations

Ozone fields simulated for the first phase of the Chemistry-Climate Model Initiative (CCMI-1) will be used as forcing data in the 6th Coupled Model Intercomparison Project. Here we assess, using reference and sensitivity simulations produced for CCMI-1, the suitability of CCMI-1 model results for this process, investigating the degree of consistency amongst models regarding their responses to variations in individual forcings. We consider the influences of methane, nitrous oxide, a combination of chlorinated or brominated ozone-depleting substances, and a combination of carbon dioxide and other greenhouse gases. We find varying degrees of consistency in the models' responses in ozone to these individual forcings, including some considerable disagreement. In particular, the response of total-column ozone to these forcings is less consistent across the multi-model ensemble than profile comparisons. We analyse how stratospheric age of air, a commonly used diagnostic of stratospheric transport, responds to the forcings. For this diagnostic we find some salient differences in model behaviour, which may explain some of the findings for ozone. The findings imply that the ozone fields derived from CCMI-1 are subject to considerable uncertainties regarding the impacts of these anthropogenic forcings. We offer some thoughts on how to best approach the problem of generating a consensus ozone database from a multi-model ensemble such as CCMI-1.

CCMI↗
NASA NTRS1972

Post-workshop models of Jupiter's radiation belts

Models for the charged particle populations of Jupiter's trapped radiation belts were derived at the Jupiter Radiation Belt Workshop on the basis of several assumptions which represented a consensus of opinion. It was possible to improve the models on the basis of work performed after the workshop concluded. These improvements affect the models in two ways. The effects of special relativity on the particle energy and flux dependences in the magnetosphere were included in a derivation based on L-shell diffusion with conservation of the magnetic moment. Quantitative conclusions are available for the limit which ion cyclotron instability places on the proton population. A set of models which incorporates these developments in a way consistent with the original workshop assumptions and conclusions is described.

Divine, N.↗
NASA NTRS1992

Consensus theoretic classification methods

Consensus theory is adopted as a means of classifying geographic data from multiple sources. The foundations and usefulness of different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-Gaussian data is investigated. The application of consensus theory in pattern recognition is tested on two data sets: 1) multisource remote sensing and geographic data and 2) very-high-dimensional remote sensing data. The results obtained using consensus theoretic methods are found to compare favorably with those obtained using well-known pattern recognition methods. The consensus theoretic methods can be applied in cases where the Gaussian maximum likelihood method cannot. Also, the consensus theoretic methods are computationally less demanding than the Gaussian maximum likelihood method and provide a means for weighting data sources differently.

Benediktsson, Jon A.↗
NASA NTRS1988

An illustration of the complexity of continent formation

It was pointed out that a consensus may be emerging in crustal growth models, considering the clustering of most growth curves and their uncertainties. Curves most distant from this clustering represent models involving extensive recycling of continental material back into the mantle, but the author wondered if geochemical signatures for this would be recognizable considering the lack of evidence from seismic tomography for discrete mantle reservoirs, and the likelihood of core-mantle interaction based on recent high pressure experiments. Unreactivated Archean rocks represent only 2 percent of present continental area, and the author was uncomfortable about basing inferences on what the early Earth was like on such a small amount of information. He feels that the hypothesis of continental assembly that needs testing is that of banging together of island arcs, such as in Indonesia today. As an example of how complex this process can be, the author described the geology of the Caribbean arc system, which shows evidence for reversals of subduction polarity, numerous collisional events, and substantial strike-slip movements. It seemed unlikely to the author that Archean examples would have been less complicated.

Burke, Kevin↗
NASA NTRS2014

Comparison of Climatological Planetary Boundary Layer Depth Estimates Using the GEOS-5 AGCM

Planetary boundary layer (PBL) processes, including those influencing the PBL depth, control many aspects of weather and climate and accurate models of these processes are important for forecasting changes in the future. However, evaluation of model estimates of PBL depth are difficult because no consensus on PBL depth definition currently exists and various methods for estimating this parameter can give results that differ by hundreds of meters or more. In order to facilitate comparisons between the Goddard Earth Observation System (GEOS-5) and other modeling and observational systems, seven PBL depth estimation methods are used to produce PBL depth climatologies and are evaluated and compared here. All seven methods evaluate the same atmosphere so all differences are related solely to the definition chosen. These methods depend on the scalar diffusivity, bulk and local Richardson numbers, and the diagnosed horizontal turbulent kinetic energy (TKE). Results are aggregated by climate class in order to allow broad generalizations. The various PBL depth estimations give similar midday results with some exceptions. One method based on horizontal turbulent kinetic energy produces deeper PBL depths in the winter associated with winter storms. In warm, moist conditions, the method based on a bulk Richardson number gives results that are shallower than those given by the methods based on the scalar diffusivity. The impact of turbulence driven by radiative cooling at cloud top is most significant during the evening transition and along several regions across the oceans and methods sensitive to this cooling produce deeper PBL depths where it is most active. Additionally, Richardson number-based methods collapse better at night than methods that depend on the scalar diffusivity. This feature potentially affects tracer transport.

Planetary Boundary Layer Depth↗
NASA NTRS1991

The study of PDF turbulence models in combustion

The accurate prediction of turbulent combustion is still beyond reach for today's computation techniques. It is the consensus of the combustion profession that the predictions of chemically reacting flow were poor if conventional turbulence models were used. The main difficulty lies in the fact that the reaction rate is highly nonlinear, and the use of averaged temperature, pressure, and density produces excessively large errors. The probability density function (PDF) method is the only alternative at the present time that uses local instant values of the temperature, density, etc. in predicting chemical reaction rate, and thus it is the only viable approach for turbulent combustion calculations.

Hsu, Andrew T.↗
NASA NTRS1992

Software reliability through fault-avoidance and fault-tolerance

Accomplishments in the following research areas are summarized: structure based testing, reliability growth, and design testability with risk evaluation; reliability growth models and software risk management; and evaluation of consensus voting, consensus recovery block, and acceptance voting. Four papers generated during the reporting period are included as appendices.

Vouk, Mladen A.↗
NASA NTRS2013

Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains

Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions.

Remotely sensed soil moisture validation↗
NASA NTRS2018

Remote Sensing of Evapotranspiration over the Central Arizona Irrigation and Drainage District, USA

Knowledge of baseline water use for irrigated crops in the U.S. Southwest is important for understanding how much water is consumed under normal farm management and to help manage scarce resources. Remote sensing of evapotranspiration (ET) is an effective way to gain that knowledge: multispectral data can provide synoptic and time-repetitive estimates of crop-specific water use, and could be especially useful for this arid region because of dominantly clear skies and minimal precipitation. Although multiple remote sensing ET approaches have been developed and tested, there is not consensus on which of them should be preferred because there are still few intercomparison studies within this environment. To help build the experience needed to gain consensus, a remote sensing study using three ET models was conducted over the Central Arizona Irrigation and Drainage District (CAIDD). Aggregated ET was assessed for 137 wheat plots (winter/spring crop), 183 cotton plots (summer crop), and 225 alfalfa plots (year-round). The employed models were the Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), the Two Source Energy Balance (TSEB), and Vegetation Index ET for the US Southwest (VISW). Remote sensing data were principally Landsat 5, supplemented by Landsat 7, MODIS Terra, MODIS Aqua, and ASTER. Using district-wide model averages, seasonal use (excluding surface evaporation) was 742 mm (millimeters) for wheat, 983 mm for cotton, and 1427 mm for alfalfa. All three models produced similar daily ET for wheat, with 6-8 mm per day mid-season. Model estimates diverged for cotton and alfalfa sites. Considering ET over cotton, TSEB estimates were 9.5 mm per day, METRIC 6 mm per day, and VISW 8 mm per day. For alfalfa, the ET values from TSEB were 8.0 mm per day, METRIC 5 mm per day, and VISW 6 mm per day. Lack of local validation information unfortunately made it impossible to rank model performance. However, by averaging results from all of them, ET model outliers could be identified. They ranged from minus 10 percent to plus 18 percent, values that represent expected ET modeling discrepancies. Relative to the model average, standardized ET-estimators - potential ET (ET (sub 0)), FAO-56 ET, and USDA-SW gravimetric-ET - showed still greater deviations, up to 35 percent of annual crop water use for summer and year-round crops, suggesting that remote sensing of actual ET could lead to significantly improved estimates of crop water use. Results from this study highlight the need for conducting multi-model experiments during summer-months over sites with independent ground validation.

Alfalfa↗
NASA NTRS2018

NASA's SnowEx Campaign and Measuring Global Snow from Space

Snow blankets 30% of Earth's land surface (60% of northern hemisphere land) in midwinter, dramatically changing our planet's land surface and affecting our weather for months. Seasonal snow is critically important to society for the management of water resources, natural hazards, water security, and in many economic sectors. The only practical way to estimate the quantity of snow on a global scale is through satellites. Despite 4 decades of satellite observations, the highly variable nature of snow still presents significant challenges toward achieving this goal. For example, current space-based techniques underestimate snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly versus estimates based on remotely-sensed observations. Snow community consensus is that a multi-sensor approach is needed to adequately address global snow, combined with modeling and data assimilation to fill the gaps in space and time. What remains, then, is how best to combine and use the various sensors under different types of snow conditions and confounding factors. NASA's multi-year SnowEx airborne campaign is designed to collect measurements needed to enable algorithm development and to guide those mission trade studies. Year 1 (2017) focused on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. This paper will discuss the various remote sensing options for snow, the challenging factors, and describe the recently-completed first year of SnowEx in Colorado, USA. Ground-based remote sensing and in situ data collection involved nearly 100 participants over three weeks. The airborne campaign included nine sensors on five aircraft. We will conclude by discussing options for a future snow satellite mission.

campaign↗
NASA NTRS2009

V3885 Sagittarius: A Comparison With a Range of Standard Model Accretion Disks

A chi-squared analysis of standard model accretion disk synthetic spectrum fits to combined Far Ultraviolet Spectroscopic Explorer and Space Telescope Imaging Spectrograph spectra of V3885 Sagittarius, on an absolute flux basis, selects a model that accurately represents the observed spectral energy distribution. Calculation of the synthetic spectrum requires the following system parameters. The cataclysmic variable secondary star period-mass relation calibrated by Knigge in 2006 and 2007 sets the secondary component mass. A mean white dwarf (WD) mass from the same study, which is consistent with an observationally determined mass ratio, sets the adopted WD mass of 0.7M(solar mass), and the WD radius follows from standard theoretical models. The adopted inclination, i = 65 deg, is a literature consensus, and is subsequently supported by chi-squared analysis. The mass transfer rate is the remaining parameter to set the accretion disk T(sub eff) profile, and the Hipparcos parallax constrains that parameter to mas transfer = (5.0 +/- 2.0) x 10(exp -9) M(solar mass)/yr by a comparison with observed spectra. The fit to the observed spectra adopts the contribution of a 57,000 +/- 5000 K WD. The model thus provides realistic constraints on mass transfer and T(sub eff) for a large mass transfer system above the period gap.

MASS TRANSFER RATE↗
NASA NTRS1990

Multiversion software reliability through fault-avoidance and fault-tolerance

In this project we have proposed to investigate a number of experimental and theoretical issues associated with the practical use of multi-version software in providing dependable software through fault-avoidance and fault-elimination, as well as run-time tolerance of software faults. In the period reported here we have working on the following: We have continued collection of data on the relationships between software faults and reliability, and the coverage provided by the testing process as measured by different metrics (including data flow metrics). We continued work on software reliability estimation methods based on non-random sampling, and the relationship between software reliability and code coverage provided through testing. We have continued studying back-to-back testing as an efficient mechanism for removal of uncorrelated faults, and common-cause faults of variable span. We have also been studying back-to-back testing as a tool for improvement of the software change process, including regression testing. We continued investigating existing, and worked on formulation of new fault-tolerance models. In particular, we have partly finished evaluation of Consensus Voting in the presence of correlated failures, and are in the process of finishing evaluation of Consensus Recovery Block (CRB) under failure correlation. We find both approaches far superior to commonly employed fixed agreement number voting (usually majority voting). We have also finished a cost analysis of the CRB approach.

Vouk, Mladen A.↗
NASA NTRS2011

Evaluating the Application of Multi-Satellite Observations in Hydrologic Modeling

When monitoring local or regional hydrosphere dynamics for applications such as agricultural productivity or drought and flooding events, it is necessary to have accurate, high-resolution estimates of terrestrial water and energy storages. Though in-situ observations provide reliable estimates of hydrologic states and fluxes, they are only capable of accurately capturing the dynamics at relatively discrete points in space and time, which makes them inadequate for characterizing the variability of the water budget across scales. In contrast, satellite-based remote sensing is ideal for providing observations of hydrological states and fluxes because it provides spatially-distributed observations at spatial and temporal scales required for regional land surface process modeling. Due to the continued progress in algorithm development and emerging satellite technology, we now have near-real time monitoring of several components of the water cycle including precipitation, soil moisture, lake and river height, terrestrial water storage, snow cover, and evapotranspiration. As these data become more readily available, their application to hydrologic modeling is becoming more common, however there remains little consensus on the most appropriate method for optimal integration and evaluation in regard to hydrological applications. Here we present two case studies operationally applying several remotely sensed products from AMSR-E, GRACE, and MODIS and discuss assimilation strategies, ease of integration and interpretation, and methods for quantifying the success of the application methodology.

Bolten, John↗
NASA NTRS2022

Process-Oriented Analysis of Dominant Sources of Uncertainty in the Land Carbon Sink

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO 2 and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO 2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

Attribution↗
NASA NTRS2016

Connecting Paleo and Modern Oceanographic Data to Understand Atlantic Meridional Overturning Circulation Over Decades to Centuries

Modeling is an important tool for understanding AMOC on all timescales. Mechanistic studies of modern AMOC variability have been hampered by a lack of consistency between free-running models and the sensitivity of AMOC to resolution and parameterization. Recent work within the framework of the phase two Coordinated Ocean- Reference Experiments (CORE-II) addresses this issue head on, looking at model differences of AMOC mean state and interannual variability. One consistent feature across the models is that AMOC mean transport is related to mixed layer depths and Labrador Sea salt content, whereas interannual variability is primarily associated with Labrador Sea temperature anomalies. This is consistent with the hypothesized importance of salt balance for AMOC variability on geological timescales. The simulated relationships between AMOC and subsurface temperature anomalies in fully coupled climate models reveal subsurface AMOC fingerprints that could be used to reconstruct historical AMOC variations at low frequency.With the lack of long-term AMOC observations, models of ocean state that assimilate observational data have been explored as a way to reconstruct AMOC, but comparisons between models indicate they are quite variable in their AMOC representations. Karspeck et al. (2015) found that historical reconstructions of AMOC in such models are sensitive to the details of the data assimilation procedure. The ocean data assimilation community continues to address these issues through improved models and methods for estimating and representing error information.Two objectives of paleoclimate modeling are 1) to provide mechanistic information for interpretation of paleoclimate observations, and 2) to test the ability of predictive models to simulate Earth's climate under different background forcing states. In a good example of the first objective, Schmittner and Lund (2015) and Menviel et al. (2014) provided key information about the proxy signals expected under freshwater disturbance of AMOC, which were used to support the paleoclimate observations made by Henry et al. (2016). In an example of the second objective, Muglia and Schmittner (2015) analyzed Third Paleoclimate Modeling Intercomparison Project (PMIP3) models of the Last Glacial Maximum (LGM) and found consistently more intense and deeper AMOC transports relative to preindustrial simulations, counter to the paleoclimate consensus of LGM conditions, indicating that some processes are not well represented in the PMIP3 models. One challenge is to find adequate paleo observations against which to test these models. PMIP is now in phase 4 (part of CMIP6), which includes experiments covering five periods in Earth's history: the last millennium, last glacial maximum, last interglacial, and the mid-Pliocene. Newly compiled paleoclimate datasets from the PAGES2k project, more transient simulations, and participation of isotope enabled models planned for CMIP6PMIP4 will enable richer paleo data-model comparisons in the near future.

Kilbourne, Hali↗
NASA NTRS2017

Network Analysis of Rodent Transcriptomes in Spaceflight

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

genomics↗
NASA NTRS2020

Strong Sensitivity of the Isotopic Composition of Methane to the Plausible Range of Tropospheric Chlorine

The C-13 isotopic ratio of methane, δC-13 of CH4, provides additional constraints on the CH4 budget to complement the constraints from CH4 observations. The interpretation of δC-13 observations is complicated, however, by uncertainties in the methane sink. The reaction of CH4 with Cl is highly fractionating, increasing the relative abundance of 13CH4, but there is currently no consensus on the strength of the tropospheric Cl sink. Global model simulations of halogen chemistry differ strongly from one another in terms of both the magnitude of tropospheric Cl and its geographic distribution. This study explores the impact of the inter-model diversity in Cl fields on the simulated δC-13 of CH4. We use a set of GEOS global model simulations with different predicted Cl fields to test the sensitivity of the δC-13 of CH4 to the diversity of Cl output from chemical transport models. We find that δC-13 is highly sensitive to both the amount and geographic distribution of Cl. Simulations with Cl providing 0.28% or 0.66% of the total CH4 loss bracket the δC-13 observations for a fixed set of emissions. Thus, even when Cl provides only a small fraction of the total CH4 loss and has a small impact on total CH4, it provides a strong lever on δC-13. Consequently, it is possible to achieve a good representation of total CH4 using widely different Cl concentrations, but the partitioning of CH4 loss between the OH and Cl reactions leads to strong differences in isotopic composition depending on which model’s Cl field is used. Comparing multiple simulations, we find that altering the tropospheric Cl field leads to approximately a 0.5‰ increase in δ(13)CH4for each percent increase in how much CH4 is oxidized by Cl. The geographic distribution and seasonal cycle of Cl also impacts the hemispheric gradient and seasonal cycle of δC-13. The large effect of Cl on δC-13 compared to total CH4 broadens the range of CH4 source mixtures that can be reconciled with δC-13 observations. Stronger constraints on tropospheric Cl are necessary to improve estimates of CH4 sources from δC-13 observations.

Global model simulations of halogen chemistry↗
NASA NTRSDate not supplied

Beyond Fair: Engagement, Data Usability, and Open Community Productivity through the NASA Open Science Data Repository

The FAIR principle (findable, accessible, interoperable, and reusable) governs the storage and sharing of NASA space biology and health data[1]. These guiding principles maximize reuse of data and the reproducibility of scientific findings. The NASA Open Science Data Repository (OSDR; an expansion of NASA GeneLab) was built on the FAIR principles and houses over 500 studies and close to 1000 datasets from decades of space life sciences experiments. OSDR embodies the FAIR principles through data governance that includes mediated, embargoed, and fully open access data. The FAIR data governance principles were recently proposed to be expanded to encompass a FAIREST framework for assessing research data repositories (FAIR + Engagement, Social connections, and Trust)[2]. FAIREST emphasizes the importance of data repositories engaging with the scientific community and gaining the trust of researchers regarding data quality. Trust also refers to the TRUST principles developed for assessment of digital repositories: Transparency, Responsibility, User Focus, Sustainability, Technology[3]. We present the “Open Science for Life in Space” Analysis Working Groups (AWGs) as evidence regarding the power of engagement, social connections, and trust which has enhanced OSDR’s capabilities and productivity. AWG members engage in two main activities. One, members provide feedback on OSDR scientific standards for data ingestion, curation, and reuse (study, subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability). Two, AWG members collaborate to mine-reuse OSDR data to conduct scientific analysis. With nearly 800 active members, the AWGs have resulted in 32 publications re-using OSDR data and contributed many papers in two major special issues in Cell (2020) and Nature (2024). AWGs also serve as networking groups, facilitate social connections between researchers at all levels of experience, and also have a social online ‘Forum’ used to keep members informed on projects and opportunities. This community-centric, productive, and trustworthy data culture has resulted in a broader effect with international space agencies, academics, and the commercial space sector wanting to submit their data to OSDR. Ten studies of Inspiration 4 data were recently publicly released by OSDR, as were some JAXA human data. Coming up soon in OSDR are data submissions from the European Space Agency, Virgin Galactic PIs, and SpaceX Polaris Dawn. A major benefit of OSDR is the array of standardized and uniformly formatted data (which was developed through AWG member consensus), from which visualization tools, analysis tools, and machine learning models can be built or trained. This talk will cover the Multi-Study Visualization Tool, the Environmental Data Application, RadLab, and a UCSF-NSF funded knowledge graph biomedical health discovery tool ‘SPOKE’ currently being integrated with OSDR. OSDR also provides training programs in bioinformatics and machine learning to improve the scientific community’s awareness of data availability and to boost their ability to perform data analysis. The increasing engagement of the scientific community and the public with technologies powered by artificial intelligence (AI) heightens the need for data analysis to be transparent. The AI for Life in Space initiative leverages the data products provided in OSDR to train AI models, with an emphasis on explainable and trustworthy AI, which would not be possible without FAIR data and metadata. Overall, here we will demonstrate the importance for NASA life sciences data repositories to adhere to the FAIREST framework, by providing examples and success stories from different aspects of OSDR.

data↗