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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Contribution of Anthropogenic Warming to California Drought During 2012-2014

A suite of climate data sets and multiple representations of atmospheric moisture demand are used to calculate many estimates of the self-calibrated Palmer Drought Severity Index, a proxy for near-surface soil moisture, across California from 1901 to 2014 at high spatial resolution. Based on the ensemble of calculations, California drought conditions were record breaking in 2014, but probably not record breaking in 2012-2014, contrary to prior findings. Regionally, the 2012-2014 drought was record breaking in the agriculturally important southern Central Valley and highly populated coastal areas. Contributions of individual climate variables to recent drought are also examined, including the temperature component associated with anthropogenic warming. Precipitation is the primary driver of drought variability but anthropogenic warming is estimated to have accounted for 8-27 percent of the observed drought anomaly in 2012-2014 and 5-18 percent in 2014. Although natural variability dominates, anthropogenic warming has substantially increased the overall likelihood of extreme California droughts.

California↗
DOE OSTI2021

Attribution of the role of climate change in the forest fires in Sweden 2018

In this study, we analyse the role of climate change in the forestfires that raged through large parts of Sweden in the summer of 2018 from ameteorological perspective. This is done by studying the Canadian FireWeather Index (FWI) based on sub-daily data, both in reanalysis data sets(ERA-Interim, ERA5, the Japanese 55 year Reanalysis, JRA-55, and Modern-Era Retrospective analysis for Research and Applications version 2, MERRA-2) and three large-ensemble climatemodels (EC-Earth, weather@home, W@H, and Community Earth System Model, CESM) simulations. The FWI, based on reanalysis, correlates well with the observed burnt area in summer (r=0.6 to 0.8). We find that the maximum FWI in July 2018 had return times of ~24 years (90 % CI, confidence interval, > 10 years) for southern and northern Sweden. Furthermore, we find a negative trend of the FWI for southern Sweden over the 1979 to 2017 time period in the reanalyses, yielding a non-significant reduced probability of such an event. However, the short observational record, large uncertainty between the reanalysis products and large natural variability of the FWI give a large confidence interval around this number that easily includes no change, so we cannot draw robust conclusions from reanalysis data. The three large-ensembles with climate models point to a roughly 1.1 (0.9 to 1.4) times increased probability (non-significant) for such events in the current climate relative to preindustrial climate. For a future climate (2 °C warming), we find a roughly 2 (1.5 to 3) times increased probability for such events relative to the preindustrial climate. The increased fire weather risk is mainly attributed to the increase intemperature. The other main factor, i.e. precipitation during summer months, isprojected to increase for northern Sweden and decrease for southern Sweden. We, however, do not find a clear change in prolonged dry periods in summer months that could explain the increased fire weather risk in the climate models. In summary, we find a (non-significant) reduced probability of such eventsbased on reanalyses, a small (non-significant) increased probability due to global warming up to now and a more robust (significant) increase in the risk for such events in the future based on the climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Is There Really an Intermittent Biennial Oscillation in the Great Plains Low-Level Jet Over Texas?

In the 15-year GEOS-1 reanalysis data set, a maximum of interannual variance of low- level meridional flow for the warm season (May through August) occurs over southeast Texas. This variance maximum seems to be dominated by a marked biennial oscillation that occurs only during the first 6 (or possibly 8) years of the reanalysis period (1980-85 or possibly 1980-1987) and then completely disappears by the 9th year. This biennial oscillation seems to be associated with interannual fluctuations in ground wetness, surface temperature and surface pressure gradients over Texas. The periods of drier soil lead to warmer surface temperatures, lower surface pressures, stronger pressure gradients between Texas and the Gulf of Mexico and stronger southerly winds. This intermittent biennial oscillation is also evident in corresponding fields for the the NCEP/NCAR reanalysis data set for the years 1978-1985 (and possibly from 1978- 1987) and 1995-2000, but not during other periods. There are also obvious biennial oscillations evident during these periods in U.S. Climate Division records for the Palmer Drought Severity Index (PDSI) for Texas. Month-by-month correlations of this index with certain el Nino related indices are as high as .45 for the first period and as high as .55 or .6 for the second period for some regions in Texas. The seasonal cycle of the biennial signal in the PDSI and precipitation for the first period suggest that the drought in Texas and Mexico is ended (caused) by a reversal in the sign of anomalies in precipitation rate for the fall/winter season. Analysis of tropical Pacific SST patterns shows a .5 to .75 K biennial oscillation of SSTs along the precipitation-free track to the southwest of the Mexican coast during the fall and winter months of the 1978 to 1985 period that might explain the reversal in precipitation anomalies and hence the entire intermittent biennial oscillation in ground hydrology and low-level flow.

Helfand, H. Mark↗
NASA NTRSDate not supplied

Comparison of Recent Solar Minima Using SABER Data and the Thermosphere Climate Index

The solar minimum at the end of Solar Cycle (SC) 23 was surprisingly long and deep, and it is predicted that the SC 24 minimum will be similar. We present results from analysis of the two most recent solar minima in terms of their duration in time and the degree of cooling that occurs, along with comparisons to the minima of five preceding solar cycles. Measurements of infrared emissions from NO and CO2 made by the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) instrument on the NASA Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite for nearly 19 years allow us to see the effects of solar minima in the thermosphere. These data,and products derived from them, including the Thermosphere Climate Index (TCI), provide direct, quantitative information on the state of the upper atmosphere and enable comparisons of different SCs. The TCI extends the SABER record of nitric oxide infrared emission at 5.3 ìm using a linear multiple regression fit of solar and geomagnetic indices to the daily cooling power derived from SABER NO. Thus the TCI provides a 73-year measure of solar variability in Earth’s atmosphere and permits determination of the relative contribution of solar irradiance and geomagnetic processes at solar minimum and throughout the solar cycle. Results of these calculations will be presented to compare solar minima for the six solar cycles included in the TCI time series.

thermosphere↗
DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Recent streamflow trends across permafrost basins of North America

Introduction Climate change impacts, including changing temperatures, precipitation, and vegetation, are widely anticipated to cause major shifts to the permafrost with resulting impacts to hydro-ecosystems across the high latitudes of the globe. However, it is challenging to examine streamflow shifts in these regions owing to a paucity of data, discontinuity of records, and other issues related to data consistency and accuracy. Methods Recent trends for long-term periods (1990–2021, 1976–2021) in observed minimum, mean, and maximum seasonal and annual streamflow were analyzed for a range of watersheds across North America affected by varying degrees of permafrost coverage. Results Streamflow trend analysis revealed that areas affected by permafrost are changing variably over the periods in terms of maximum, mean, and minimum seasonal and annual streamflow. These changes indicate a significant shift occurring in the most recent 46 years towards increasing mean streamflow for the dominant (> 50%) permafrost systems. Meanwhile, minimum streamflow increases for all permafrost-dominant systems and many of the other permafrost-affected systems across the seasons and annual periods considered, with the greatest number of significant changes in streamflow over other metrics. Maximum streamflow is shifting variably with significant increases in the permafrost-dominant systems in winter and fall over longer time periods of analysis. Our analysis suggests that streamflow trends are driven by climate (precipitation, followed by temperature), while variables such as permafrost coverage only appear important in the most recent 32-year period. Discussion The increases in streamflow trends observed in this study are reflective of deepening active layers and thawing permafrost, indicating that the entire hydrograph is undergoing change within permafrost-dominant streamflow systems as the Arctic moves towards a warmer future under climate change. Despite the many challenges to understanding changing streamflow in cold regions, there are new products and datasets in development that are increasingly allowing researchers to better understand the patterns of change in Arctic and subarctic systems affected by permafrost, offering a range of new tools, which, along with continued observational records, may help in improved understanding of changing Arctic streamflow patterns.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

MUlti-SpEctral, MUlti-SpEcies, MUlti-SatEllite (MUSES) Retrieval Algorithm: Towards Extending Multi-Decadal NASA EOS Atmospheric Composition Data Records

Multi-Spectra, Multi-Species, Multi-Sensors (MUSES): Builds off of heritage from the Tropospheric Emission Spectrometer (TES) optimal estimation (OE) algorithm to combine a priori and satellite data, including rigorous error analysis diagnostics and observation operators needed for trend analysis, climate model evaluation, and data assimilation; has generic design to incorporate forward model radiances from hyperspectral measurements from multiple sensors into the joint retrieval algorithm.

Fu, Dejian↗
NASA NTRS2011

Estimation and Validation of Oceanic Mass Circulation from the GRACE Mission

Since the launch of the Gravity Recovery And Climate Experiment (GRACE) in March 2002, the Earth's surface mass variations have been monitored with unprecedented accuracy and resolution. Compared to the classical spherical harmonic solutions, global high-resolution mascon solutions allows the retrieval of mass variations with higher spatial and temporal sampling (2 degrees and 10 days). We present here the validation of the GRACE global mascon solutions by comparing mass estimates to a set of about 100 ocean bottom pressure (OSP) records, and show that the forward modelling of continental hydrology prior to the inversion of the K-band range rate data allows better estimates of ocean mass variations. We also validate our GRACE results to OSP variations modelled by different state-of-the-art ocean general circulation models, including ECCO (Estimating the Circulation and Climate of the Ocean) and operational and reanalysis from the MERCATOR project.

Boy, J.-P.↗
DOE OSTI2020

A method to generate heating and cooling schedules based on data from connected thermostats

Internet-connected thermostats are a promising new source of temperature and operational data in homes because they record inside temperatures, setpoints, and HVAC runtimes every five minutes. Over 20 million Internet-connected thermostats have already been installed in American homes. Data from about 20,000 connected thermostats were collected and organized by climate zone, number of occupants, floor area, and day type. A method was developed to create up to 40 representative temperature schedules which, together, can more accurately capture the diversity of heating and cooling behaviors. These results are suitable for input into schedules for building energy simulation models. This information enables more realistic simulations of American heating and cooling behavior, leading to more accurate estimates of energy consumption and savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1981

Climate model postprocessing

The development of new postprocessing software of the climate modeling group is summarized. Code, test, and perform simulations with global general circulation models are described. The models improve understanding and ability to predict the vagaries of weather and climate. To learn from and utilize the model results, it is necessary to create elaborate postprocessing software to allow analysis of the large volume of data produced. The models produce sigma history tapes. The sigma history records are interpolated to pressure history records, which are written on a pressure history tape. The model results are analyzed on pressure surfaces, with snap shots or time averages.

Abeles, J.↗
NASA NTRS1988

Do volcanic eruptions affect climate? Sulfur gases may cause cooling

The relationship between volcanic eruptions on earth and the observed climatic changes is investigated. The results of the comparison and analyses of volcanologic and climatologic data sets for the years between 1880 and 1980 indicate that changes in temperature caused by even of the largest eruptions recorded during this time were about the same as normal variations in temperature. However, when temperature records for several months or years preceding and following a given eruption were analyzed, a statistically significant temperature decrease of 0.2-0.5 C was found for the periods of one to two years immediately following some of the 19th and 20th century explosive events that prodiced large aerosol clouds (e.g., Krakatau and Agung eruptions). It is suggested that the content of sulfur in the erupted magma determines the size of aerosol cloud producing the cooling effect.

Self, Stephen↗
NASA NTRS2000

Science Writers' Guide to TERRA

The launch of NASA's Terra spacecraft marks a new era of comprehensive monitoring of the Earth's atmosphere, oceans, and continents from a single space-based platform. Data from the five Terra instruments will create continuous, long-term records of the state of the land, oceans, and atmosphere. Together with data from other satellite systems launched by NASA and other countries, Terra will inaugurate a new self-consistent data record that will be gathered over the next 15 years. The science objectives of NASAs Earth Observing System (EOS) program are to provide global observations and scientific understanding of land cover change and global productivity, climate variability and change, natural hazards, and atmospheric ozone. Observations by the Terra instruments will: provide the first global and seasonal measurements of the Earth system, including such critical functions as biological productivity of the land and oceans, snow and ice, surface temperature, clouds, water vapor, and land cover; improve our ability to detect human impacts on the Earth system and climate, identify the "fingerprint" of human activity on climate, and predict climate change by using the new global observations in climate models; help develop technologies for disaster prediction, characterization, and risk reduction from wildfires, volcanoes, floods, and droughts, and start long-term monitoring of global climate change and environmental change.

Source record↗
NASA NTRS2021

Depletion & Recovery of the O3 Layer Observed from Space Over the Past 50 Years of the Anthropocene

This year we are celebrating the 50th anniversary of the launch of the backscatter UV (BUV) instrument on NASA’s Nimbus-4 satellite. Since then 10 more advanced instruments, but based on the same basic measurement principle, have been launched on polar orbiting NASA and NOAA satellites. The datasets produced by these instruments are unique for they have captured the period before the onset of the decline of the ozone layer due to manmade chemicals, the declining period, and the present slow recovery period. This period includes the rapid development of the iconic anthropocene era feature called the “Ozone Hole” . However, stitching together data from 11 separate satellite instruments to create a homogeneous record has presented many challenges. The first two NASA instruments suffered considerable degradation after launch that required the development of novel techniques to characterize their calibration. The next 7 instruments were launched on NOAA’s polar orbiting weather satellites that were not designed for long-term monitoring of climate variables. In particular, they didn’t have the station keeping capabilities needed to maintain a stable equator crossing time. Drifting orbits not only stress the retrieval algorithm and the calibration system of UV instruments but also make the interpretation of data difficult because the upper stratospheric ozone has systematic and complex diurnal variabilities that are comparable to the anthropogenic signal one wants to extract. We will highlight our recent efforts in untangling this knot. Finally, we will discuss the results from the OMPS limb scattering instrument, a recent addition to this constellation, that has been operating on the Suomi NPP satellite since October 2011. It is helping us capture how the ozone layer in the lower stratosphere and troposphere is evolving in response to the climate change and rising chemical pollutants in some parts of the world, while the upper stratosphere slowly recovers due to phaseout of the chemicals that initiated the decline of the ozone layer in the late 70s.

P Bhartia↗
DOE OSTI2025

Hot droughts in the Amazon provide a window to a future hypertropical climate

Tropical forests represent the warmest and wettest of Earth’s biomes, but with continued anthropogenic warming, they will be pushed to climate states with no current analogue. Droughts in the tropics are already becoming more intense as they occur at successively higher temperatures. Here, in this study, we synthesize multiple datasets to assess the effects of hot droughts on a central Amazon forest. First, a more than 30-year record of annually resolved forest demographic data from a selective logging experiment showed higher tree mortality during intense droughts, particularly among fast-growing pioneer species with low wood density. Second, analysis of ecophysiological field measurements from the 2015 and 2023 El Niño droughts identified a soil moisture threshold beyond which transpiration rates rapidly declined. As rainless days beyond this threshold continued, drought conditions intensified, increasing the potential for tree mortality from hydraulic failure and carbon starvation. Third, analyses from the Coupled Model Intercomparison Project Phase 6 demonstrated that under high-emission scenarios, a large area of tropical forest will shift to a hotter ‘hypertropical’ climate by 2100. Last, under a hypertropical climate, temperature and moisture conditions during typical dry season months will more frequently exceed identified drought mortality thresholds, elevating the risk of forest dieback. Present-day hot droughts are harbingers of this emerging climate, offering a window for studying tropical forests under expected extreme future conditions.

drought↗
NASA NTRS2020

Satellite Monitoring of Global Surface Soil Organic Carbon Dynamics Using the SMAP Level 4 Carbon Product

Soil organic carbon (SOC) is an important metric of soil health and the terrestrial carbon balance. Short‐term climate variations affect SOC through changes in temperature and moisture, which control vegetation growth and soil decomposition. We evaluated a satellite data‐driven carbon model, operating under the NASA Soil Moisture Active‐Passive (SMAP) mission, as a means of monitoring global surface SOC dynamics. The SMAP Level 4 Carbon (L4C) product estimates a daily global carbon budget including surface (0‐ to 5‐cm depth) SOC. We found that the L4C mean latitudinal SOC distribution is generally consistent with alternative assessments from static soil inventory records and dynamic global vegetation models (r ≥ 0.89). Within forest systems, based on inventory data, L4C SOC is most similar in magnitude to litterfall but is correlated with coarse woody debris ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0001) and total SOC ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0002). L4C SOC is sensitive to seasonal and annual climate variability, with mean residence times that range from 1.5 years in the wet tropics to 17 years in the cold tundra. Incorporating soil moisture retrievals from the SMAP L‐band (1.4 GHz) microwave radiometer within the L4C algorithm provides enhanced soil moisture sensitivity under low‐to‐moderate vegetation cover (<5 kg/sq.m vegetation water content). The L‐band soil moisture had the greatest impact on the L4C carbon budget in semiarid regions, which span almost 60% of the globe and account for substantial variability in the terrestrial carbon sink. The L4C operational product enables prognostic investigations into effects of recent climate trends and anomalies (e.g., droughts and pluvials) on shallow soil carbon dynamics.

K. Arthur Endsley↗
DOE OSTI2020

Comparing traditional and Bayesian approaches to ecological meta‐analysis

Abstract Despite the wide application of meta‐analysis in ecology, some of the traditional methods used for meta‐analysis may not perform well given the type of data characteristic of ecological meta‐analyses. We reviewed published meta‐analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies ( n i ) and the number of studies or records ( k ) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta‐analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that n i and k were highly variable, distributions were right‐skewed and were generally small (median n i = 5, median k = 44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval (CI) based on the t distribution that uses an adjusted standard error (the Hartung–Knapp–Sidik–Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z distribution CIs had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta‐analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random‐effects meta‐analysis, the performance of the best frequentist and Bayesian methods was similar for the same combinations of factors ( k and mean replication), though the Bayesian approach had higher than nominal (>95%) coverage for the mean effect when k was very low ( k < 15). Our literature review suggests that many meta‐analyses that used z distribution or bootstrapping CIs may have overestimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta‐analyses.

Pappalardo, Paula↗
DOE OSTI2020

Global Climate

The 2019 global lower tropospheric temperature (LTT), which encompasses the atmosphere from the surface to ~10 km, ranked second warmest in seven datasets and first or third in the remaining two (Fig. 2.7). These records extend back to 1958 using radiosonde (balloon-borne instrumentation) data and one reanalysis dataset (JRA55), which demonstrate reasonable agreement with the 40+ year satellite record (since late 1978) and two other reanalysis datasets (since 1979 and 1980, ERA5 and MERRA2, respectively). Aweak El Niño contributed to increased global temperatures as 2019 values were +0.44° to +0.68°C higher than the 1981–2010 average (depending on the dataset), being just slightly cooler (~0.07°C on average) than the record warm year of 2016. At least fourof the five globally complete datasets (ERA5, MERRA2, JRA55, RSS, UAH) recorded each of the four months—June, September, November, and December—as experiencing their warmest monthly global LTT.

58 GEOSCIENCES↗
DOE OSTI2024

Assessing Historical Extreme Weather Event Impacts

Understanding how past extreme weather events have affected a site is an integral part of site-level resilience planning. Energy and water resilience planning has been a key priority for the federal government for many years and agencies look to develop processes for identifying and addressing critical resilience gaps at their facilities and across their sites. The purpose of this information paper is to help inform how organizations could begin structuring a comprehensive process for recording the impacts of extreme weather events in order to facilitate climate vulnerability assessments, and thus, resilience planning. The paper highlights current limitations for developing event history assessments and suggests a framework for more consistently capturing key data points.

54 ENVIRONMENTAL SCIENCES↗