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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Length Scales of Hydrological Variability as Inferred from SMAP Soil Moisture Retrievals

Hydrological variability at a given location is characterized in part by a horizontal length scale – a measure of how far one can travel from that location and still see similar time variations of a hydrological variable of interest. Here, using Level-2 soil moisture retrievals produced by the NASA Soil Moisture Active Passive (SMAP) mission, we compute global distributions of these length scales for the Northern Hemisphere warm and cold seasons (May-September and November-March, respectively). The length scales show significant spatial and seasonal variability, with, as expected, much larger values (e-folding scales of greater than 500 km) often seen in the cold season, when convective rainfall is less prominent. The SMAP-derived length scales are found to be largely consistent with those derived directly, where possible, from precipitation measurements. This suggests a unique value of the retrievals: outside of well-instrumented areas, satellite-based soil moisture datasets have the potential to provide otherwise unattainable estimates of the horizontal length scales of hydrological variability.

Hydrology↗
NASA NTRS2021

Geomagnetically Induced Currents at Middle Latitudes: 1. Quiet-Time Variability

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long-period and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of thermosphere-ionosphere-electrodynamics general circulation model (TIE- GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

Adam C Kellerman↗
NASA NTRS1994

Rainfall variability over the tropical Pacific from July 1987 through December 1991 as inferred via monthly estimates from SSM/I

Estimates of monthly rainfall have been computed over the tropical Pacific using passive microwave satellite observations from the Special Sensor Microwave/Imager (SSM/I) for the preiod from July 1987 through December 1991. The monthly estimates were calibrated using measurements from a network of Pacific atoll rain gauges and compared to other satellite-based rainfall estimation techniques. Based on these monthly estimates, an analysis of the variability of large-scale features over intraseasonal to interannual timescales has been performed. While the major precipitation features as well as the seasonal variability distributions show good agreement with expected values, the presence of a moderately intense El Nino during 1986-87 and an intense La Nina during 1988-89 highlights this time period.

Berg, Wesley↗
NASA NTRS2005

Time Series Vegetation Aerodynamic Roughness Fields Estimated from MODIS Observations

Most land surface models used today require estimates of aerodynamic roughness length in order to characterize momentum transfer between the surface and atmosphere. The most common method of prescribing roughness is through the use of empirical look-up tables based solely on land cover class. Theoretical approaches that employ satellite-based estimates of canopy density present an attractive alternative to current look-up table approaches based on vegetation cover type that do not account for within-class variability and are oftentimes simplistic with respect to temporal variability. The current research applies Raupach s formulation of momentum aerodynamic roughness to MODIS data on a regional scale in order to estimate seasonally variable roughness and zero-plane displacement height fields using bulk land cover parameters estimated by [Jasinski, M.F., Borak, J., Crago, R., 2005. Bulk surface momentum parameters for satellite-derived vegetation fields. Agric. For. Meteorol. 133, 55-68]. Results indicate promising advances over look-up approaches with respect to characterization of vegetation roughness variability in land surface and atmospheric circulation models.

Borak, Jordan S.↗
NASA NTRS2019

Diurnal Cycle of IMERG V06 Precipitation

This study demonstrates the maturing ability of the half-hourly precipitation estimates from the Integrated Multi-satellitE Retrievals for GPM (IMERG) for use in global analyses of the diurnal cycle. The refined intercalibration and interpolation between the sensors in V06 leads to greater consistency in the precipitation retrievals over different hours of the day. Evaluation against ground measurements suggests a slight lag in the diurnal phase of only +0.59 h. We demonstrate the diurnal cycle over different regions around the globe, including the Maritime Continent, where accurate representation of precipitation variability in global models remains a challenge. Using examples over Singapore, Bangladesh, and Lake Victoria, we reveal the intricate interplay between diurnal and seasonal variability. This study demonstrates the unprecedented capability of IMERG in capturing the diurnal cycle of precipitation globally, potentially advancing our understanding in regions of sparse ground measurements and supporting improvements in its representation in global models.

Jackson Tan↗
DOE OSTI2021

Sensitivity of Carbonaceous Aerosol Properties to the Implementation of a Dynamic Aging Parameterization in the Regional Climate Model RegCM

Freshly emitted soot is hydrophobic, but condensation of secondary aerosols and coagulation with other particles modify its hygroscopic optical properties. This conversion is referred to as “aerosol aging.” Many climate models represent this aging process with a fixed aging time scale, whereas in reality, it is a dynamic process that depends on environmental conditions. Here, we implement a dynamic aging parameterization scheme in the regional climate model RegCM4 in place of the fixed aging timescale of 1.15 days (~27.6 h) and examine its impact on the aerosol life cycle over the Indian subcontinent. The conversion from hydrophobic to hydrophilic aerosol is usually lower than 27.6 h over the entire landmass and lower than 10 h over the polluted Indo-Gangetic Basin (IGB), with seasonal variability. Due to the implementation of the dynamic aging scheme, the column burden and surface mass concentration of carbonaceous aerosols increase during the drier season (December–February) when washout is negligible. The burden is reduced during the wet season (June–September) due to a more efficient washout except over the IGB, where a reduction in precipitation as a result of radiative feedbacks increases the aerosol concentrations. Over the polluted IGB, surface dimming increases due to the dynamic aging scheme, with the top of the atmosphere forcing remaining mostly unchanged. As a result, atmospheric heating increases by at least 1.2 W/m 2 . Our results suggest that climate models should incorporate dynamic aging for a more realistic representation of aerosol simulations, especially in highly polluted regions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2012

Influence of Global Vegetation on Mid-Tropospheric CO2 Early Results

AIRS Mid-Tropospheric CO2 shows a high degree of horizontal variability. Ongoing efforts show AIRS data influenced by global circulation patterns including El Nino Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) What is the influence of global vegetation cycle on CO2 seasonal behavior? (1) Can we correlate mid-trop CO2 seasonal variability with global vegetation for different regions? (2) For now: First look at zonal averages and Land Vegetation (ocean biomass later) (3) Goal: Sanity Check on AIRS Data Seasonal Cycle, Solicit interest by carbon cycle community

climatology↗
NASA NTRS1991

General circulation of the South Atlantic between 5 deg N and 35 deg S

The TOPEX/POSEIDON altimeter will provide the temporal mean seal level. So, secondly, we propose to compute the difference between these two surfaces (mean sea level minus general circulation dynamic topography). The result will be an estimate of the marine geoid, which is time invariant for the 5-year period under consideration. If this geoid is precise enough, it will permit a description of seasonal variability of the large-scale surface circulation. If there happens to be enough float data, it may be possible to infer the first vertical modes of this variability. Thus the main goal of our investigation is to determine the 3-D general circulation of the South Atlantic and the large-scale seasonal fluctuations. This last objective, however, may be restricted to the western part of the South Atlantic because float deployments have been scheduled only in the Brasil basin.

Ollitrault, Michel↗
NASA NTRS2021

Revisiting the Global Patterns of Seasonal Cycle in Sea Surface Salinity

Argo profiling floats and L-band passive microwave remote sensing have significantly improved the global sampling of sea surface salinity (SSS) in the past 15 years, allowing the study of the range of SSS seasonal variability using concurrent satellite and in situ platforms. Here, harmonic analysis was applied to four 0.25° satellite products and two 1° in situ products between 2016 and 2018 to determine seasonal harmonic patterns. The 0.25° World Ocean Atlas (WOA) version 2018 was referenced to help assess the harmonic patterns from a long-term perspective based on the 3-year period. The results show that annual harmonic is the most characteristic signal of the seasonal cycle, and semiannual harmonic is important in regions influenced by monsoon and major rivers. The percentage of the observed variance that can be explained by harmonic modes varies with products, with values ranging between 50% and 72% for annual harmonic and between 15% and 19% for semiannual harmonic. The large spread in the explained variance by the annual harmonic reflects the large disparity in nonseasonal variance (or noise) in the different products. Satellite products are capable of capturing sharp SSS features on meso- and frontal scales and the patterns agree well with the WOA 2018. These products are, however, subject to the impacts of radiometric noises and are algorithm dependent. The coarser-resolution in situ products may underrepresent the full range of high-frequency small scale SSS variability when data record is short, which may have enlarged the explained SSS variance by the annual harmonic.

L-band↗
NASA NTRS1999

The Variability and Predictability of Precipitation over Southeast Asian

The large scale atmospheric moisture budget, including precipitation, moisture flux convergence, and evaporation is described over southeast Asian. ne results are from the Goddard Earth Observing System (GEOS-1) data assimilation system for the period March 1981 - February 1995. On average, in the late spring and early summer season, precipitation is largely balanced by the atmospheric moisture flux convergence over southeast Asian; evaporation is also an important contributor. The seasonal variability of precipitation is best related to variations in the moisture flux convergence. On the monthly time scales, both the precipitable water and precipitation anomalies are most closely related to moisture flux convergence anomalies and less closely related to evaporation anomalies. The impact of the low-level jet (LLJ) on the strong low-level moisture flux convergence will be discussed. An ensemble of GEOS-2 atmospheric General Circulation Model (GCM) seasonal forecasts and long-term simulation are analyzed to assess the controlling influences of boundary forcing and memory of initial conditions. We also assess the variability and potential predictability from these experiments.

Chang, Yehui↗
NASA NTRS1991

Vertical column abundances and seasonal cycle of acetylene, C2H2, above the Jungfraujoch station, derived from IR solar observations

Results derived from a spectroscopic analysis of the nu5 band R19 transition of C2H2 observed in solar spectra recorded at the Jungfraujoch station, Switzerland, between June 1986 and April 1991 are reported. A least-squares sine fit to the data reveals a seasonal variability with an amplitude of about +/-40 percent of the mean; the maximum occurs during mid-winter and the minimum in summer. In general, the observed seasonal observations are comparable with those derived from airborne in-situ measurements and those reported from ground-based stations.

Zander, R.↗
NASA NTRS2019

Modeling Phenological Controls on Carbon Dynamics in Dryland Sagebrush Ecosystems

Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbonfluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle,we must also be able to understand and model its effects on dryland vegetation. Dynamic Global VegetationModels (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduceseasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to bothtotal productivity and competitive interactions with other plants, errors in how process-based models representphenology hinder our ability to predict climate change impacts. This may be particularly problematic in drylandecosystems where many species have developed a complex phenology in response to seasonal variability in bothmoisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure ofexisting phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) andgross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized modelparameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, wemodified the model to include a semi-deciduous phenology type and added flexibility to the representation ofgrass phenology. While optimizing parameters reduced model bias, the largest gains in model performance wereassociated with the development of our new representation of phenology. This modified model was able to bettercapture seasonal patterns of both leaf area index (R2=0.75) and gross primary productivity (R2=0.84), thoughits ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new modelalso resulted in a more realistic outcome of modeled competition between grass and shrubs. These findingsdemonstrate the importance of improving how DGVMs represent phenology in order to accurately forecastclimate change impacts in dryland ecosystems.

Ecosystem model Phenology Parameter optimization E↗
NASA NTRSDate not supplied

NO2 Anomalies - Economy Attribution and Rapid Climate Response

Using principal component (PC) analysis of 16 years of monthly series of NO2 from OMI/Aura, we show that it is the third PC (PC3) from the full hierarchy of principal modes that is best coupled with the economic indicators. This coupling is positive, i.e. PC3 and economic indicators manifest positive covariance. However, the economic variability can explain only 40% of the information in PC3. Furthermore, this mode by itself explains only 3% of the total deseasonalized NO2 variability. We thus conclude that, while having an unambiguous impact, the economy can be awarded at best third order of importance in driving the deseasonalized NO2 series, i.e. after seasonal variability is removed from the series. Once we identified PC3 as the NO2 mode that is coupled with economic variability, we use this mode as an indicator and look for rapid climate adjustments to that part of NO2 variability that we are confident is coupled with the economic variability. We focus on observational data from the Atmospheric Infrared Sounder (AIRS) on board of NASA Aqua satellite, decompose series of surface skin temperature and clear-sky outgoing longwave radiances (OLR) into principal components, and identify potential impacts of NO2 PC3 on these climate variables.

NO2↗
NASA NTRSDate not supplied

NO2 Anomalies - Economy Attribution and Rapid Climate Response

Using principal component (PC) analysis of 16 years of monthly series of NO2 from OMI/Aura, we show that it is the third PC (PC3) from the full hierarchy of principal modes that is best coupled with the economic indicators. This coupling is positive, i.e. PC3 and economic indicators manifest positive covariance. However, the economic variability can explain only 40%of the information in PC3. Furthermore, this mode by itself explains only 3% of the total deseasonalized NO2 variability. We thus conclude that, while having an unambiguous impact, the economy can be awarded at best third order of importance in driving the deseasonalized NO2 series, i.e. after seasonal variability is removed from the series. Once we identified PC3 as the NO2 mode that is coupled with economic variability, we use this mode as an indicator and look for rapid climate adjustments to that part of NO2 variability that we are confident is coupled with the economic variability. We focus on observational data from the Atmospheric Infrared Sounder (AIRS) on board of NASA Aqua satellite, decompose series of surface skin temperature and clear-sky outgoing longwave radiances (OLR) into principal components, and identify potential impacts of NO2 PC3 on these climate variables.

NO2↗
NASA NTRS1986

Nonmethane hydrocarbons in the troposphere - Impact on the odd hydrogen and odd nitrogen chemistry

A one-dimensional model is utilized to study the role of nonmethane hydrocarbons (NMHCs) in tropospheric chemistry. The effects of NMHCs on tropospheric peroxyacetyl nitrate (PAN) formation are analyzed; the global distribution and seasonal variability of PAN in the troposphere of the Northern and Southern Hemispheres are examined. The NMHC-PAN measurements obtained by model simulations are compared to the photochemical theory. The seasonal cycles in NMHC abundances are predicted, and their effects on other tropospheric species are investigated.

Kasting, James F.↗
DOE OSTI2020

Quantifying the drivers and predictability of seasonal changes in African fire

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2019

Modeling Phenological Controls on Carbon Dynamics in Dryland Sagebrush Ecosystems

Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbon fluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle, we must also be able to understand and model its effects on dryland vegetation. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduce seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology hinder our ability to predict climate change impacts. This may be particularly problematic in dryland ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R(exp 2) = 0.75) and gross primary productivity (R(exp 2) = 0.84), though its ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new model also resulted in a more realistic outcome of modeled competition between grass and shrubs. These findings demonstrate the importance of improving how DGVMs represent phenology in order to accurately forecast climate change impacts in dryland ecosystems.

Ecosystem model↗