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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Radar and the Detection of Liquid Water on Mars

Detection of the seasonally variable radar reflectivity in the Goldstone Mars data (the Solis Lacus radar anomaly and the proposed interpretation in terms of the near-surface presence of liquid water created a controversy in the planetary science community. Over the past year, skepticism was voiced about the reality of the phenomenon of a seasonally variable radar reflectivity anywhere on Mars. The necessary background information and the pertinent data are reviewed in a format perhaps more convincing than that employed in the original presentation of the discovery. A summary of the results and recommendations for future work are included.

Roth, L. E.↗
NASA NTRSDate not supplied

Expanding the Spatiotemporal Range of Soil Moisture Analysis Utilizing NASA Earth Observations and In Situ Measurements

Drought can cause immense agricultural and ecological damage resulting in high mitigation and compensation costs. Climate variability in future decades is expected to cause severe drought conditions and threaten necessary water resources. Stakeholders seek to implement effective drought assessments in preparation for potential economic and environmental damage invoked by drought. Although in-situ measurements are accurate, the current infrastructure is spatially limited and costly to maintain. A framework was created to compare modeled, satellite and in-situ data in drought monitoring. Here we show that the comparison of in-situ and remotely sensed soil moisture (SM) measurements can increase the spatiotemporal range of SM assessments. Data collected between 2003 and 2021 by NASA’s SPoRT Land Information System (SPoRT-LIS) and Soil Moisture Active Passive (SMAP) mission were standardized and compared with in-situ data provided through the Illinois Climate Network (WARM). Statistical analysis results including the Pearson correlation coefficient (r), root mean squared error, mean absolute error and others were calculated to compare the WARM measurements to the SMAP and SPoRT-LIS products. Results indicate that both satellite products demonstrate seasonally variable bias that is not present in the in-situ measurements. Bias was highest in the winter months and lowest in the late summer and early fall months in both satellite datasets. Overall, WARM-SPoRT comparisons resulted in lower seasonal variability. However, on average, the SMAP comparison demonstrated higher correlation values and lower error values. The WARM-SMAP average correlation (r) was 0.61 compared to the WARM-SPoRT average correlation (r) value of 0.54. Average mean absolute error values calculated for the SMAP and SPoRT comparisons were 0.07 and 0.08 percent soil moisture by volume, respectively. These analyses suggest integrating in-situ measurements and those provided by NASA Earth observations can be utilized in a multi-faceted SM evaluation, a valuable contribution to drought monitoring and water resource decision making.

Emma P Myrick↗
NASA NTRS1999

Role of Ocean Currents and Bottom Pressure Variability on Seasonal Polar Motion

Changes in the ocean angular momentum (OAM) components about the equatorial axes, either due to fluctuations in currents or bottom pressure (mass redistribution), can induce movements of the Earth's pole of rotation, commonly referred to as polar motion or wobble. Output from a 1 deg resolution ocean model is used to calculate the effective equatorial OAM functions chi(sub 1, sup O) and chi(sub 2, sup O), corresponding to polar motion excitation about the equatorial axis pointing to the Greenwich and 90 deg E meridians, respectively. Time series of chi(sup O) are combined with similar atmospheric series chi(sup A), computed from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalyses, to interpret the observed low-frequency polar motion excitation for the period 1985-1996. Results indicate that the oceans are a very important excitation source for the Chandler (approximately 433 days), annual, and semiannual wobbles, providing for much better amplitude and phase agreement with the observed excitation at these periods, in comparison with what is obtained when only the atmosphere is considered. Both oceanic mass and motion terms are found to be important but with mass signals having somewhat larger amplitudes. The role of regional variability in ocean currents and bottom pressure in contributing to chi(sup O) signals is quantified. Midlatitude regions (approximately 30 deg - 70 deg) figure prominently as places of strong local oceanic excitation signals. The North Pacific basin is found to be generally important for chi(sup O) excitation, while the Southern Ocean is important for both chi(sub 1, sup O) and chi(sub 2, sup O). The largest positive covariances of local with global chi(sup O) signals occur in the Kuroshio region near the western boundary of the North Pacific for chi(sub 1, sup O) and southwest of Australia for chi(sub 2, sup O).

Ponte, Rui M.↗
NASA NTRS2006

Bio-Optical Properties and Ocean Color Algorithms for Coastal Waters Influenced by the Mississippi River During a Cold Front Passage

During the passage of a cold front in March 2002, bio-optical properties examined in coastal waters impacted by the Mississippi River indicated westward advective flows and increasing river discharge containing a larger nonalgal particle content contributed significantly to surface optical variability. A comparison of seasonal data from three cruises indicated spectral models of absorption and scattering to be generally consistent with other coastal environments, while their parameterization in terms of chlorophyll a concentration (Chl) showed seasonal variability. The exponential slope of the colored dissolved organic matter (CDOM) averaged 0.0161 plus or minus 0.00054 per nanometer, and for nonalgal absorption it averaged 0.011 per nanometer with deviations from general trends observed due to anomalous water properties. Although the phytoplankton specific absorption coefficients varied over a wide range (0.02 to 0.1 square meters (mg Chl) sup -1)) being higher in offshore surface waters, values of phytoplankton absorption spectra at the SeaWiFS wavebands were highly correlated to modeled values. The normalized scattering spectral shapes and the mean spectrum were in agreement to observations in other coastal waters, while the backscattering ratios were on average lower in phytoplankton dominated surface waters (0.0101 plus or minus 0.002) and higher in near-bottom waters (0.0191 plus or minus 0.0045) with low Chl. Average percent differences in remote sensing reflectance R (sub rs) derived form modeled and in-eater radiometric measurements were highest in the blue wavebands (52%) and at sampling stations with a ore stratified water column. Estimates of Chl and CDOM absorption derived from SeaWiFS images generated using regional empirical algorithms were highly correlated to in situ data.

D'Sa Eurico J.↗
NASA NTRS2020

Detecting Seasonal Ice Dynamics in Satellite Images

Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how a glacier interacts with various forcing mechanisms throughout the year. The task of generating continuous ice velocity time series that resolve seasonal variability is made more difficult by a spotty satellite record that contains no optical observations throughout the dark, polar winters. Furthermore, velocities obtained by feature tracking are marked by high noise when image pairs are separated by short time intervals and contain no direct insights into variability that occurs between images separated by long time intervals. In this paper, we describe a method of analyzing optical or SAR-derived feature-tracked velocities to characterize the magnitude and timing of seasonal ice dynamic variability. Our method is agnostic to data gaps and is able to recover climatological average winter velocities regardless of the availability of direct observations during winter. Using characteristic image acquisition times and error distributions from Antarctic image pairs in the ITS_LIVE dataset, we generate synthetic ice velocity time series, then apply our method to recover imposed magnitudes of seasonal variability within ±1.4 m yr−1. We then validate the techniques by comparing our results to GPS data collected on Russell Glacier in Greenland. The methods presented here may be applied to better understand how ice dynamic signals propagate on seasonal timescales, and what mechanisms control the flow of the world’s ice.

Chad A. Greene↗
DOE OSTI2022

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Performance evaluation of CMIP6 models on the Arctic-Siberian Plain teleconnection affecting the East Asian heat waves

The frequency and intensity of summer heat waves in East Asia have increased sharply in recent decades, significantly impacting public health and the economy. The Arctic-Siberian Plain (ASP) teleconnection pattern has been identified as a key driver, with ASP warming amplifying atmospheric circulation patterns conducive to extreme temperatures. This study evaluates the ability of Coupled Model Inter-comparison Project phase 6 models to simulate the ASP pattern across interannual variability (IAV) and intra-seasonal variability (ISV) timescales using the Common Basis Function method. The multi-model mean shows statistically significant pattern correlations with ERA5 reanalysis, with correlation coefficients of 0.90 and 0.99 for IAV and ISV, respectively. While the ASP pattern is generally well captured, models exhibit substantial inter-model diversity in the intensity and position of anticyclonic anomalies over the ASP and East Asia. Models with ASP pattern variability similar to reanalysis better reproduce extreme East Asian temperatures, whereas those over- or underestimating ASP variability exhibit lower skill. These performance differences are related to differences in simulating key variables associated with the development of the ASP pattern. Our findings highlight the role of the ASP pattern in modulating extreme heat events, as models with improved ASP simulations align more closely with observed temperature extremes. Refining ASP representations in models could enhance seasonal heat wave predictions, improving climate adaptation strategies.

Arctic-Siberian Plain (ASP)↗
NASA NTRS2011

Observational Diagnoses of Extratropical Ozone STE from 2005-2010

The transport of ozone from the stratosphere to the extratropical troposphere is an important boundary condition to tropospheric chemistry. However, previous direct estimates from models and indirect estimates from observations have poorly constrained the magnitude of ozone stratosphere-troposphere exchange (STE). In this study we provide a direct diagnosis of the extratropical ozone STE using data from the Microwave Limb Sounder on Aura and output of the MERRA reanalysis over the time period from 2005 to the present. We find that the mean annual STE is about 275 Tg yr-1 and 205 Tg yr-1 in the NH and SH, respectively. The interannual variability of the magnitude is about twice as great in the NH than the SH. This variability is dominated by the seasonal variability during the late winter and spring. A comparison of the ozone flux to the mass flux reveals that there is not a simple relationship between the two quantities. This presentation will also examine the magnitude and distribution of ozone in the lower stratosphere relative to the years of maximum and minimum ozone STE

Olsen, Mark A.↗
NASA NTRS2011

Observational Diagnoses of Extratropical Ozone STE During the Aura Era

The transport of ozone from the stratosphere to the extratropical troposphere is an important boundary condition to tropospheric chemistry. However, previous direct estimates from models and indirect estimates from observations have poorly constrained the magnitude of ozone stratosphere-troposphere exchange (STE). In this study we provide a direct diagnosis of the extratropical ozone STE using data from the Microwave Limb Sounder on Aura and output of the MERRA reanalysis over the time period from 2005 to the present. We find that the mean annual STE is about 275 Tg/yr and 205 Tg/yr in the NH and SH, respectively. The interannual variability of the magnitude is about twice as great in the NH than the SH. We find that this variability is dominated by the seasonal variability during the late winter and spring. A comparison of the ozone flux to the mass flux reveals that there is not a simple relationship between the two quantities. This presentation will also examine the magnitude and distribution of ozone in the lower stratosphere relative to the years of maximum and minimum ozone STE. Finally, we will examine any possible signature of increased ozone STE in the troposphere using sonde and tropospheric ozone residual (TOR) data, and output from the Global Modeling Initiative Chemistry Transport Model (GMI CTM).

Olsen, Mark A.↗
NASA NTRS2022

Prescribing Stratospheric Chemistry Overestimates Southern Hemisphere Climate Change During Austral Spring in Response to Quadrupled CO2

The interaction of stratospheric chemistry with a changing climate from an abrupt CO2 quadrupling is assessed using the coupled atmosphere–ocean Goddard Earth Observing System Chemistry-Climate Model (GEOSCCM). Two abrupt 4 × CO2 experiments were performed, one with interactive stratospheric chemistry and the other with a prescribed stratospheric chemistry that does not simulate stratospheric ozone response to 4 × CO2. The interactive and prescribed chemistry experiments simulate similar global mean surface temperature change. Nevertheless, interactive chemistry is critical to capture the Southern Hemisphere tropospheric midlatitude jet response to 4 × CO2. When stratospheric ozone response to 4 × CO2 is neglected, GEOSCCM overestimates Southern Hemisphere tropospheric circulation change. This stratospheric chemistry-induced climate impact has large seasonal variability. During the austral spring season September–October–November (SON), prescribed chemistry yields a stronger poleward shift and intensification of the Southern Hemisphere midlatitude tropospheric jet, surface wind stress, and the Southern Ocean meridional overturning circulation than occurs with interactive chemistry. In other seasons interactive and prescribed chemistry have similar effects on the Southern Hemisphere circulation. The seasonality of stratospheric chemistry-induced climate impact is related to the seasonality of Antarctic lower stratospheric ozone response to 4 × CO2. In contrast to this stratospheric ozone response to 4 × CO2, stratospheric ozone recovery from decline of the ozone depleting substances has its largest impact on the Southern Hemisphere tropospheric circulation in austral summer (December–January–February), but no effects in SON. It is found that the different seasonality for these two stratospheric ozone layer change scenarios is related to the different seasonality of tropopause meridional temperature gradient response.

Stratospheric ozone feedback↗
NASA NTRS1973

An investigation to improve the Menhaden fishery prediction and detection model through the application of ERTS-A data

The author has identified the following significant results. Linear regression of secchi disc visibility against number of sets yielded significant results in a number of instances. The variability seen in the slope of the regression lines is due to the nonuniformity of sample size. The longer the period sampled, the larger the total number of attempts. Further, there is no reason to expect either the influence of transparency or of other variables to remain constant throughout the season. However, the fact that the data for the entire season, variable as it is, was significant at the 5% level, suggests its potential utility for predictive modeling. Thus, this regression equation will be considered representative and will be utilized for the first numerical model. Secchi disc visibility was also regressed against number of sets for the three day period September 27-September 29, 1972 to determine if surface truth data supported the intense relationship between ERTS-1 identified turbidity and fishing effort previously discussed. A very negative correlation was found. These relationship lend additional credence to the hypothesis that ERTS imagery, when utilized as a source of visibility (turbidity) data, may be useful as a predictive tool.

Maughan, P. M.↗
DOE OSTI2023

Climate-driven changes in the predictability of seasonal precipitation

Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

stochprop Documentation (Release 1.0)

Simulations of infrasonic propagation in the atmosphere typically utilize a single atmospheric specification describing the acoustic sound speed, ambient winds, and density as a function of altitude. Due to the dynamic and sparsely sampled nature of the atmosphere, there is a notable amount of uncertainty in the atmospheric state at a given location and time so that a more robust analysis of infrasonic propagation requires inclusion of this uncertainty. This Python library, stochprop, has been implemented using methods developed jointly by infrasound scientists at Los Alamos National Laboratory (LANL) and the University of Mississippi’s National Center for Physical Acoustics (NCPA). This software library includes methods to quantify variability in the atmospheric state, identify typical seasonal variability in the atmospheric state and generate suites of representative atmospheric states during a given season, as well as perform uncertainty analysis on a specified atmospheric state given some level of uncertainty. These methods have been designed to interface between propagation modeling capabilities such as InfraGA/GeoAc and NCPAprop and signal analysis methods in the LANL InfraPy tool.

58 GEOSCIENCES↗
DOE OSTI2021

Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

Abstract This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance ( χ a ), the mixing of optically absorbing and nonabsorbing species ( χ o ), and the mixing of hygroscopic and nonhygroscopic species ( χ h ). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χ a , between 38% and 94% for χ o , and between 20% and 87% for χ h with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigating Spatial Variability of Aerosol, Cloud Condensation Nuclei, and Ice Nucleating Particles in Mountainous Terrain Field Campaign Report

The U.S. Department of Energy Atmospheric System Research (ASR)-supported Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the ERW as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the second ARM Mobile Facility (AMF2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small portable optical particle spectrometer (POPS; Gao et al. 2016), cloud condensation nuclei (CNN) using a miniature CCN counter (CloudPuck), and ice nucleating particles (INP) using the time-resolved aerosol filter sampler (TRAPS; Creamean et al. 2018). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al. 2019, Kelly et al. 2021, Asher et al. 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al. 2017, Popoola et al. 2018, Caubel et al. 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al. 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al. 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in the ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies. We met this goal by answering the following science questions: 1. What is the aerosol temporal variability, and how does aerosol inhomogeneity vary seasonally? Is there significant seasonal variability in sources, or are short-term meteorological conditions the most important determining factor in sources for cloud nuclei? 2. What is the aerosol spatial variability? What are the aerosol characteristics at cloud base, presumably the particles most representative of those acting as cloud nuclei? 3. How should measurement networks be designed to capture aerosol-cloud interactions, and what do they need to measure? Can a single measurement site accurately represent aerosol properties in regions of complex terrain? SAIL-Net consisted of six measurement nodes spread across the ERW near Crested Butte, Colorado. The primary objective in site placement was to select locations that captured the vertical variation in aerosol properties while also spanning the domain of the SAIL campaign. The elevation of the sites ranged from roughly 2750 m along the valley floor of the ERW to approximately 3500 m near the top of Crested Butte Mountain, which is one of the taller peaks in the ERW. The farthest distance between sites was 14 km, while the closest two sites were approximately 1 km apart. Two of the sites were collocated with the ARM SAIL sites; our instruments sat on top of one of the trailers at AOS and another one of our sites was located in a meadow just above AMF2.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Simulation of high-frequency dissolved oxygen dynamics in a shallow estuary, the Corsica River, Chesapeake Bay

Understanding shallow water biogeochemical dynamics is a challenge in coastal regions, due to the presence of highly variable land-water interface fluxes, tight coupling with sediment processes, tidal dynamics, and diurnal variability in biogeochemical processes. While the deployment of continuous monitoring devices has improved our understanding of high-frequency (12 - 24 hours) variability and spatial heterogeneity in shallow regions, mechanistic modeling of these dynamics has lagged behind conceptual and empirical models. The inherent complexity of shallow water systems is represented in the Corsica River estuary, a small basin within the Chesapeake Bay ecosystem, where abundant monitoring data have been collected from long-term monitoring stations, continuous monitoring sensors, synoptic sensor surveys, and measurements of sediment-water fluxes. A state-of-the-art modeling system, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), was applied to the Corsica domain with a high-resolution grid and nutrient loads from the most recent version of the Chesapeake Bay watershed model. The Corsica SCHISM model reproduced observed high-frequency variability in dissolved oxygen, as well as seasonal variability in chlorophyll-a and sediment-water fluxes. Time-series signal analyses using Empirical Model Decomposition and spectral analysis revealed that the diurnal and M2 tide frequencies are the dominant high-frequency modes and physical transport contributes a larger share to dissolved oxygen budgets than biogeochemical processes on an hourly time scale. Heterogeneity and patchiness in dissolved oxygen resulting from phytoplankton distributions and geometry-driven eddies amplify the physical transport effect, and on longer time scales oxygen is controlled more by photosynthesis and respiration. Our simulation demonstrates that interactions among physical and biological dynamics generate complex high-frequency variability in water quality and non-linear reposes to nutrient loading and environmental forcing in shallow water systems.

54 ENVIRONMENTAL SCIENCES↗