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NASA NTRS2016

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

NASA NTRS2004

Wave-Particle Interactions As a Driving Mechanism for the Solar Wind

Our research has been focusing on a highly experimentally relevant issue: intermittency of the fluctuating fields in outflowing plasmas. We have contributed to both the theoretical and experimental research of the topic. In particular, we have developed a theoretical model and data analyzing programs to examine the issue of intermittency in space plasma outflows, including the solar wind. As fluctuating electric fields in the solar wind are likely to provide a heating and acceleration mechanism for the ions, our studies of the intermittency in turbulence in space plasma outflows help us toward achieving the goal of comparing major physical mechanisms that contribute to the driving of the fast solar wind. Our new theoretical model extends the utilities of our global hybrid model, which has allowed us to follow the kinetic evolution of the particle distributions along an inhomogeneous field line while the particles are subjected to various physical mechanisms. The physical effects that were considered in the global hybrid model included wave-particle interactions, an ambipolar electric field that was consistent with the particle distributions themselves, and Coulomb collisions. With an earlier version of the global hybrid model, we examined the overall impact on the solar wind flow due to the combination of these physical effects. In particular, we studied the combined effects of two major mechanisms that had been proposed as the drivers of the fast solar wind: (1) velocity filtration effect due to suprathermal electrons; (2) ion cyclotron resonance. Since the approval of this research grant, we have updated the model such that the effects due to these two driving mechanisms can be examined separately, thereby allowing us to compare their contributions to the acceleration of the solar wind. In the next section, we shall demonstrate that the velocity filtration effect is rather insignificant in comparison with that due to ion cyclotron resonance.

Wagner, William J.↗

NASA NTRS1989

Global Reference Atmosphere Model (GRAM)

GRAM series of four-dimensional atmospheric model validated by years of data. GRAM program, still available. More current are Gram 86, which includes atmospheric data from 1986 and runs on DEC VAX, and GRAM 88, which runs on IBM 3084. Program generates altitude profiles of atmospheric parameters along any simulated trajectory through atmosphere, and also useful for global circulation and diffusion studies.

Woodrum, A. W.↗

NASA NTRS1973

Some effects of surface anomalies in a global general circulation model.

The Mintz-Arakawa two-level general circulation model has been used in a series of experiments to compute the response of the atmosphere to certain persistent sea-surface temperature anomalies and to changes in the position of the continental Northern Hemisphere snow line over periods up to 90 days. Results are shown in terms of differences between anomaly and control histories are revealed by global, 30-day mean sea-level pressure maps, and time series of three regional indices of synoptic activity. The experiments show significant interhemispheric effects after about 1 month, phase shifts of 1 to 2 weeks in major cyclone developments, stronger reactions to sea-temperature anomalies in winter than in summer, and marked influence of the snow line on the winter monsoonal pressure difference between the continents and the North Atlantic Ocean.

Spar, J.↗

NASA NTRS1999

Seasonal Water Transport in the Atmosphere of Mars: Applications of a Mars General Circulation Model Using Mars Global Surveyor Data

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. We present below a summary of progress made during the duration of this JRI. The focus of this JRI has been to investigate seasonal water vapor transport in the atmosphere of Mars and its effects on the planet's present climate. To this end, the primary task has been to adapt a new dynamical processor for the adiabatic tendencies of the atmospheric circulation into the NASA Ames Mars general circulation model (MGCM). Using identical boundary and initial conditions, several comparative tests between the new and old MGCMs have been performed and the nature of the simulated circulations have been diagnosed. With confidence that the updated version of the Ames MGCM produces quite similar mean and eddy circulation statistics, the new climate model is well poised as a tool to pursue fundamental questions related to the spatial and seasonal variations of atmospheric water vapor on Mars, and to explore exchanges of water with non-atmospheric reservoirs and transport within its atmosphere. In particular, the role of surface sources and sinks can be explored, the range of water-vapor saturation altitudes can be investigated, and plausible precipitation mechanisms can be studied, for a range of atmospheric dust loadings, such future investigations can contribute to a comprehensive study of surface inventories, exchange mechanisms, and the relative importance of atmospheric transport Mars' water cycle. A listing of presentations made and manuscripts submitted during the course of this project is provided.

Hollingsworth, Jeffery L.↗

NASA NTRS1999

Seasonal Water Transport in the Atmosphere of Mars: Applications of a Mars General Circulation Model Using Mars Global Surveyor Data

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. We present below a summary of progress made during the duration of this JRI. The focus of this JRI has been to investigate seasonal water vapor transport in the atmosphere of Mars and its effects on the planet's present climate. To this end, the primary task has been to adapt a new dynamical processor for the adiabatic tendencies of the atmospheric circulation into the NASA Ames Mars general circulation model (MGCM). Using identical boundary and initial conditions, several comparative tests between the new and old MGCMs have been performed and the nature of the simulated circulations have been diagnosed. With confidence that the updated version of the Ames MGCM produces quite similar mean and eddy circulation statistics, the new climate model is well poised as a tool to pursue fundamental questions related to the spatial and seasonal variations of atmospheric water vapor on Mars, and to explore exchanges of water with non-atmospheric reservoirs and transport within its atmosphere. In particular, the role of surface sources and sinks can be explored, the range of water-vapor saturation altitudes can be investigated, and plausible precipitation mechanisms can be studied, for a range of atmospheric dust loadings. Such future investigations can contribute to a comprehensive study of surface inventories, exchange mechanisms, and the relative importance of atmospheric transport Mars' water cycle. A listing of presentations made and manuscripts submitted during the course of this project is provided.

Hollingsworth, Jeffery L.↗

DOE OSTI2022

Increasing Functional Diversity in a Global Land Surface Model Illustrates Uncertainties Related to Parameter Simplification

Abstract Simulations of the land surface carbon cycle typically compress functional diversity into a small set of plant functional types (PFT), with parameters defined by the average value of measurements of functional traits. In most earth system models, all wild plant life is represented by between five and 14 PFTs and a typical grid cell (≈100 × 100 km) may contain a single PFT. Model logic applied to this coarse representation of ecological functional diversity provides a reasonable proxy for the carbon cycle, but does not capture the non‐linear influence of functional traits on productivity. Here we show through simulations using the Energy Exascale Land Surface Model in 15 diverse terrestrial landscapes, that better accounting for functional diversity markedly alters predicted total carbon uptake. The shift in carbon uptake is as great as 30% and 10% in boreal and tropical regions, respectively, when compared to a single PFT parameterized with the trait means. The traits that best predict gross primary production vary based on vegetation phenology, which broadly determines where traits fall within the global distribution. Carbon uptake is more closely associated with specific leaf area for evergreen PFTs and the leaf carbon to nitrogen ratio in deciduous PFTs.

58 GEOSCIENCES↗

DOE OSTI2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Laura Duncanson↗

DOE OSTI2022

Experiments and modeling to evaluate global reaction kinetics of three-way catalyst light off for net-zero carbon fuels and selected fuel blends

Internal combustion engine exhaust composition can have a significant impact on the light-off characteristics of CO, NO, and organics in the three-way-catalyst during cold-start. The U.S. Department of Energy’s Co-Optimization of Fuels and Engines initiative was aimed at developing new high-performance fuels, which raises the question of how current three-way-catalysts will perform if the exhaust composition is significantly changed. This work is aimed at quantifying the performance of the catalyst under a variety of different fuel components and mixtures. To this end, a reduced-order model is developed wherein the set of organics examined are placed into molecular groups based on their reactivities and to model each organic within that group with a common kinetic expression. In conclusion, this strategy allows for a first approximation to the light-off characteristics of all organics that fall within that group and to make predictions for fuel blends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1991

Empirical models of the latitudinal variations of Te and Ne in the ionosphere at solar maximum

A global spectral model of electron temperature and density is developed in which only the latitudinal variations observed in narrow altitude and local time bands are used. The measurements for this purpose are obtained during intervals that are short as compared to a season. By suppressing all variables except latitude the model can use 17th-order polynomials and generates a latitudinal structure that is similar to that described by the observational data. The treatment is designed to correct for the eccentricity and slow evolution of the data-collection satellite orbit so that small-scale features can be resolved. By comparing the results of a global model and the present latitudinal model to observational measurements of electron temperature and density it is shown that auroral-zone and midlatitude trough structures are not resolved in latitudinal models. The observational data can be used to study the response of electron density and temperature to solar EUV flux variations and geomagnetic activity.

Brace, L. H.↗

DOE OSTI2022

Decarbonization Scenarios in the United States: Comparing Biofuels Growth in Two Models - GCAM and BSM

Scenarios for deep decarbonization rely on biomass for biofuels, biopower, and bioproducts, often including negative emissions via carbon capture and storage or utilization. Despite the prominence of biomass in many deep decarbonization pathways, critical questions remain about biomass allocation, effects of transportation electrification, the pace of growth, and implications for agriculture and land use. We address these questions through a unique comparison of carbon pricing effects on the growth of biomass utilization and its effects on land use in the United States by comparing results from a multisectoral integrated assessment model, the Global Change Analysis Model [GCAM], with results from a biomass-to-biofuels system dynamics model, the Biomass Scenario Model [BSM]. We contribute to model comparison efforts by analyzing the biomass deployment needed for a scenario consistent with a "Middle of the Road" Shared Socioeconomic Pathway [SSP2] and a representative concentration pathway of 2.6 W/m2. The GCAM scenarios solve for global equilibrium conditions that are consistent with this pathway, including demands for biomass across all economic sectors and representing bioenergy with carbon capture and storage as a technology option. The BSM scenarios assess those biomass and biofuel results for the United States and identify challenges associated with that pace and amount of expansion. In the scenario analysis, we harmonize key factors such as carbon price trajectory, domestic ethanol fuel demand, ethanol blending, and arable land availability, and vary them in both models. In GCAM, we vary the carbon price, transportation electrification, ethanol blending constraints, and arable land availability inputs and the value of the carbon in land; in BSM, in addition to directly inputting certain GCAM results, we vary the maximum rate of biorefinery construction, flexibility of feedstock types across conversion processes, and policy incentives such as tax credits and renewable identification number payments. The selected carbon price trajectory results in a rapid increase in biofuel production in the United States, reaching about 9.4 EJ/year in 2060 in the highest scenario analyzed in GCAM. Results differ between the two models in timing and ultimate quantity of biomass and biofuel production. GCAM biofuel quantities generally exceed BSM amounts because CCS is applied to biofuel pathways in GCAM, and because of differences in capacity expansion and related dynamics of land allocation, biomass production, and price dynamics. These dynamics include rapid biorefinery capacity expansion in high demand cases. To satisfy this biomass demand, GCAM rapidly equilibrates land allocation, but the BSM limits the rate at which this re-allocation can occur. A further contrast with the equilibrium approach in GCAM is that the BSM represents a delay between planting and harvesting woody biomass resources. As a result of these model contrasts, feedstock costs in BSM increase more than in GCAM, and the absence of CCS in the BSM also reduces the relative economic attractiveness of biofuels production. The bottlenecks, lags, and price increases also lead to potential for volatility in feedstock price and land allocation to biomass in the BSM. GCAM has more biomass production than BSM in all scenarios, partly because of the broader, economy-wide coverage of GCAM, in contrast to BSM's exclusive focus on biofuels. In both models, trends like those of biofuels production were observed for biomass production: minimal growth without a carbon price and policy incentives, and increases with a carbon price, particularly with carbon capture and storage, because the inputs assume that biopower and biofuels decrease greenhouse gas emissions. In high policy scenarios, biomass demand is high, and the consequent high biomass prices due to the land re-allocation bottleneck in the BSM limit biofuel production even if the biorefinery capacity is expanded. However, because biomass prices do not increase as much in the low policy scenario, growth is slower and the land-reallocation bottleneck no longer dominates, such that the effect of increased capacity can be seen. Across both the models, a change in assumptions from less to more land availability increases biofuel production in both GCAM and BSM, as the upward pressure on feedstock price and volatility are both reduced.

biofuels↗

NASA NTRS1981

Global weather research

Modeling, prediction, and analysis of global meteorological phenomena influencing the large scale behavior of the atmosphere are summarized. Prediction of global weather phenomena based on satellite data is discussed and models of global phenomena developed. The atmospheric general circulation model (AGCE) is reviewed, axisymmetric flow calculated, and axisymmetric states in cylindrical, spherical, three dimensional, and spin up numerical models for AGCE described. The role of latent heat release in baroclinic waves, latent heat and cyclonic systems, and a theoretical study of baroclinic flow related to the AGCE and the flow regime were studied with a simplified general circulation model. AGCE and the geophysical fluid flow cell (GFFC) instrumentation are discussed. Investigation of solar and planetary convection for GFFC is described. The utilization of satellite cloud observations to diagnose the energy state and transformations in extratropical cyclones is reviewed.

Source record↗

NASA NTRS2010

Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

Putman, William M.↗

NASA NTRS2003

Low-shear modeled microgravity: a global environmental regulatory signal affecting bacterial gene expression, physiology, and pathogenesis

Bacteria inhabit an impressive variety of ecological niches and must adapt constantly to changing environmental conditions. While numerous environmental signals have been examined for their effect on bacteria, the effects of mechanical forces such as shear stress and gravity have only been investigated to a limited extent. However, several important studies have demonstrated a key role for the environmental signals of low shear and/or microgravity in the regulation of bacterial gene expression, physiology, and pathogenesis [Chem. Rec. 1 (2001) 333; Appl. Microbiol. Biotechnol. 54 (2000) 33; Appl. Environ. Microbiol. 63 (1997) 4090; J. Ind. Microbiol. 18 (1997) 22; Curr. Microbiol. 34(4) (1997) 199; Appl. Microbiol. Biotechnol. 56(3-4) (2001) 384; Infect Immun. 68(6) (2000) 3147; Cell 109(7) (2002) 913; Appl. Environ. Microbiol. 68(11) (2002) 5408; Proc. Natl. Acad. Sci. U. S. A. 99(21) (2002) 13807]. The response of bacteria to these environmental signals, which are similar to those encountered during prokaryotic life cycles, may provide insight into bacterial adaptations to physiologically relevant conditions. This review focuses on the current and potential future research trends aimed at understanding the effect of the mechanical forces of low shear and microgravity analogues on different bacterial parameters. In addition, this review also discusses the use of microgravity technology to generate physiologically relevant human tissue models for research in bacterial pathogenesis.

NASA Discipline Cell Biology↗

NASA NTRS1993

Simulations of satellite Doppler wind observations

This study will involve two objectives: (1) to develop, through computer simulations, optimal satellite-based sensor scanning techniques for direct measurement of tropospheric winds on the meso- and synoptic scales; and (2) to construct simulations of remotely measured wind fields for assessing impact of such fields on the diagnosis and prognosis of atmospheric phenomena through the use of Observing System Simulation Experiments (OSSE). Using the LAWS Simulation Model (LSM), various global coverage scenarios have been investigated as part of an effort to define the optimal orbit, configuration and sampling strategies for observations of winds for use in global circulation models. Simulated data sets have been provided to GSFC, FSU and several LAWS team members. Particular emphasis has been on providing realistic cloud cover, cirrus backscatter, aerosol distribution and wind variance on scales less than 600 km. Progress is currently being made to incorporate other remote sensors (AIRS/AMSU, STIKSCAT) into the global OSSEs.

Emmitt, George D.↗

DOE OSTI2024

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

A comparison of Type III metric radio bursts and global solar potential field models

Evidence of coronal magnetic fields from polarized metric type III radio bursts is compared with (1) global potential field models, (2) direct averages of the observed photospheric magnetic field, and (3) H-alpha synoptic charts. The comparison clearly indicates both that the principal aspects of type III burst radiation are understood and that global potential field models are a significantly more accurate representation of coronal magnetic field structure than either the large-scale photospheric field or H-alpha synoptic charts.

Jackson, B. V.↗