SEARCH · Engineering Papers

Results for “climate data record”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 847 records · Page 47

All sources NASA NTRS DOE OSTI

NASA NTRS2026

Petermann Glacier on the Brink: Progress, Challenges and Insights

Petermann Glacier, the largest marine-terminating glacier in northern Greenland based on catchment area and ice discharge, plays a key role in regulating ice discharge from the Greenland Ice Sheet into the Arctic Ocean. With an upstream catchment connected to the ice sheet interior via a deep subglacial canyon, its future stability has major implications for sea level rise. In this review, we synthesize recent advances in understanding Petermann’s dynamics across three critical interfaces: the ice–ocean, ice–atmosphere, and ice–bed boundaries. At the surface, observations show that reanalysis products underestimate air temperatures and melt, while regional climate models diverge significantly in their estimates of surface mass balance, underscoring the need for improved in situ data and models. At the ocean boundary, enhanced basal melting driven by both subglacial runoff and Atlantic water intrusions is identified as the dominant driver of recent mass loss of Petermann Glacier, with continued warming posing a serious threat to the stability of the floating tongue. At the bed, new geophysical synthesis reveals complex geology, likely spatial variability in geothermal heat flux, and the influence of the megacanyon on seasonal hydrology and velocity fluctuations. Petermann’s mass balance has been negative in recent decades without corresponding flow acceleration. However, the glacier has undergone significant calving events, and the current rifting that began in September 2025 highlights its vulnerability. This upcoming calving event underscores the timeliness of this review, as it will put Petermann Glacier’s terminus at its most retreated position since records began in 1923. The anticipated retreat of the ice tongue also reduces buttressing and brings the terminus closer to the grounding zone, and modeling studies suggest that calving within 12 km of the grounding zone could potentially trigger dynamic retreat, accelerating ice discharge, and a doubling of flow speeds. We conclude that improved observations, sustained monitoring of oceanographic and atmospheric properties, and high-resolution modeling are critical to constraining projections of Petermann Glacier’s future and its role in the stability of the Greenland Ice Sheet.

Dominik Fahrner↗

DOE OSTI2024

Vegetation Warming Experiment: Environmental Conditions, Utqiagvik (Barrow), Alaska, 2018

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 16 June - 24 September, 2018. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Vegetation Warming Experiment: Environmental conditions, Utqiagvik (Barrow), Alaska, 2019

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 19 June – 25 September, 2019. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017–2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Southern Ocean Climate and Sea Ice Anomalies Associated with the Southern Oscillation

The anomalies in the climate and sea ice cover of the Southern Ocean and their relationships with the Southern Oscillation (SO) are investigated using a 17-year of data set from 1982 through 1998. We correlate the polar climate anomalies with the Southern Oscillation index (SOI) and examine the composites of these anomalies under the positive (SOI > 0), neutral (0 > SOI > -1), and negative (SOI < -1) phases of SOL The climate data set consists of sea-level pressure, wind, surface air temperature, and sea surface temperature fields, while the sea ice data set describes its extent, concentration, motion, and surface temperature. The analysis depicts, for the first time, the spatial variability in the relationship of the above variables and the SOL The strongest correlation between the SOI and the polar climate anomalies are found in the Bellingshausen, Amundsen and Ross sea sectors. The composite fields reveal anomalies that are organized in distinct large-scale spatial patterns with opposing polarities at the two extremes of SOI, and suggest oscillating climate anomalies that are closely linked to the SO. Within these sectors, positive (negative) phases of the SOI are generally associated with lower (higher) sea-level pressure, cooler (warmer) surface air temperature, and cooler (warmer) sea surface temperature in these sectors. Associations between these climate anomalies and the behavior of the Antarctic sea ice cover are clearly evident. Recent anomalies in the sea ice cover that are apparently associated with the SOI include: the record decrease in the sea ice extent in the Bellingshausen Sea from mid- 1988 through early 199 1; the relationship between Ross Sea SST and ENSO signal, and reduced sea ice concentration in the Ross Sea; and, the shortening of the ice season in the eastern Ross Sea, Amundsen Sea, far western Weddell Sea, and the lengthening of the ice season in the western Ross Sea, Bellingshausen Sea and central Weddell Sea gyre over the period 1988-1994. Four ENSO episodes over the last 17 years contributed to a negative mean in the SOI (-0.5). In each of these episodes, significant retreats in the Bellingshausen/Amundsen Sea were observed providing direct confirmation of the impact of SO on the Antarctic sea ice cover.

Kwok, R.↗

NASA NTRSDate not supplied

Updated PSC Climatology Based on Caliop Measurements From 2006-2023

After more than three decades of research, the roles of polar stratospheric clouds (PSCs) in stratospheric ozone depletion are well established. Heterogeneous reactions on PSCs convert the stable chlorine reservoirs HCl and ClONO2 to chlorine radicals that destroy ozone catalytically. PSCs also prolong ozone depletion by delaying chlorine deactivation through the removal of gas-phase HNO3 and H2O by sedimentation of large nitric acid trihydrate (NAT) and ice particles. A substantial recovery of the ozone layer is expected by the middle of this century with reduced global production of ozone depleting substances in accordance with the Montreal Protocol and subsequent amendments. But as climate changes, leading to a colder and perhaps wetter stratosphere and upper troposphere, reliable model predictions of recovery of the Antarctic ozone hole and of potentially more severe ozone depletion in the Arctic are challenging. This is due both to a lack of detailed understanding of the underlying physics and the fact that many global models use simple parameterizations that do not accurately represent PSC processes. A more complete picture of PSC processes on vortex-wide scales has emerged from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument on the CALIPSO satellite that has been observing PSCs at latitudes up to 82 degrees in both hemispheres since June 2006. The CALIOP Version 2.0 (v2) PSC algorithm was recently developed to address known deficiencies in previous algorithms and includes additional refinements to increase the robustness of the inferred PSC composition. In this paper, we present an updated PSC reference data record and comprehensive climatology constructed by applying the v2 algorithm to the more than 17-year CALIOP spaceborne lidar dataset. In addition, we will examine the potential impact of aerosol and water vapor injections into the stratosphere from the January 2022 Hunga Tonga eruption on PSC occurrence in both the Arctic and Antarctic regions.

polar stratospheric clouds↗

DOE OSTI2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

The Evolution of Tropical Precipitation Patterns During ENSO Events Using 21+ Years of GPCP Merged Data

The ENSO phenomenon is characterized by fluctuations in the climate system of the tropical Pacific. Quantifying changes in the precipitation component of this system is important in understanding the distribution of heating in the atmosphere which drives the large-scale circulation and affects the weather patterns in the mid-latitudes. Monitoring precipitation anomalies in the Pacific is also an important component for tracking the evolution of ENSO. The most timely and complete observations of the earth come from satellite instruments. In this study, the state of the art satellite-gauge merged monthly precipitation data set from the Global Precipitation Climatology Project (GPCP) is used to depict tropical rainfall patterns during ENSO events over the past two decades and quantify these patterns using indices. This analysis will be complemented by daily precipitation data which can resolve the Madden-Julian Oscillation and westerly wind burst events. The 1997-98 El Nino and 1998-2000 La Nina were the best observed ENSO cycle in the historic record. Prior to the El Nino (in terms of anomalous warming of the east Pacific) dry anomalies over the Maritime Continent were observed in February 1997 as a westerly wind burst advected convection to the east. The largest SST anomalies occurred around November-December 1997, which were followed by the largest precipitation anomalies in the beginning of 1998. The largest precipitation departures from normal were not colocated with the SST anomalies, but were further west, In the spring of 1998 negative precipitation anomalies to the north of the equator intensified, signaling the mature phase of the El Nino. A rapid increase in the precipitation-based La Nina index from December-January 1998 to March-April 1998 signaled the coming La Nina. The 1982-1983 El Nino was comparable in strength (according to several indices) and the precipitation patterns evolved in a similar fashion. For the 1998-2000 La Nina, the coldest anomalies, were confined to the central equatorial Pacific, while the driest anomalies were found in the west Pacific,

Curtis, Scott↗

NASA NTRS2014

MODIS NDVI Change Detection Techniques and Products Used in the Near Real Time ForWarn System for Detecting, Monitoring, and Analyzing Regional Forest Disturbances

This presentation discusses MODIS NDVI change detection methods and products used in the ForWarn Early Warning System (EWS) for near real time (NRT) recognition and tracking of regionally evident forest disturbances throughout the conterminous US (CONUS). The latter has provided NRT forest change products to the forest health protection community since 2010, using temporally processed MODIS Aqua and Terra NDVI time series data to currently compute and post 6 different forest change products for CONUS every 8 days. Multiple change products are required to improve detectability and to more fully assess the nature of apparent disturbances. Each type of forest change product reports per pixel percent change in NDVI for a given 24 day interval, comparing current versus a given historical baseline NDVI. EMODIS 7 day expedited MODIS MOD13 data are used to obtain current and historical NDVIs, respectively. Historical NDVI data is processed with Time Series Product Tool (TSPT); and 2) the Phenological Parameters Estimation Tool (PPET) software. While each change products employ maximum value compositing (MVC) of NDVI, the design of specific products primarily differs in terms of the historical baseline. The three main change products use either 1, 3, or all previous years of MVC NDVI as a baseline. Another product uses an Adaptive Length Compositing (ALC) version of MVC to derive an alternative current NDVI that is the freshest quality NDVI as opposed to merely the MVC NDVI across a 24 day time frame. The ALC approach can improve detection speed by 8 to 16 days. ForWarn also includes 2 change products that improve detectability of forest disturbances in lieu of climatic fluctuations, especially in the spring and fall. One compares current MVC NDVI to the zonal maximum under the curve NDVI per pheno-region cluster class, considering all previous years in the MODIS record. The other compares current maximum NDVI to the mean of maximum NDVI for all previous MODIS years.

Spruce, Joseph P.↗

NASA NTRS2020

Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission Integration and Testing

This paper describes the plans, flows, key facilities, components and equipment necessary to fully integrate, functionally test and qualify the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Observatory. PACE is currently in the design phase of mission implementation. It is scheduled to launch in 2022, extending and improving NASA's twenty-year record of satellite observations of global ocean biology, aerosols and clouds. PACE will advance the assessment of ocean health by measuring the distribution of phytoplankton, which are small plants and algae that sustain the marine food web. It will also continue systematic records of key atmospheric variables associated with air quality and the Earth's climate. The PACE observatory is comprised of the spacecraft and three instruments, an Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone). The spacecraft and the OCI, which is the primary instrument, are developed and integrated at the NASA Goddard Space Flight Center (GSFC). The OCI is a hyper-spectral scanning (HSS) radiometer designed to measure spectral radiances from the ultraviolet to shortwave infrared (SWIR) to enable advanced ocean color and heritage cloud and aerosol particle science. The HARP2 and SPEXone are secondary instruments on the PACE observatory, acquired outside of GSFC. The Hyper-Angular Rainbow Polarimeter instrument (HARP2) is a wide swath imaging polarimeter that is capable of characterizing atmospheric aerosols for purposes of sensor atmospheric correction as well as atmospheric science. The SPEXone provides atmospheric aerosol and cloud data at high temporal and spatial resolution. This paper will focus on the Integration and Test (I&T) activities for the PACE mission at NASA GSFC. This I&T phase consists of mechanical, electrical and thermal integration and test of all the spacecraft subsystems and the integration of the instruments with the spacecraft. The PACE observatory environmental tests include electromagnetic interference (EMI)/electromagnetic compatibility (EMC), vibration, acoustics, shock, thermal balance, thermal vacuum, mass properties and center of gravity. This paper will also discuss the observatory shipment to the launch site as well as the launch site processing.

Petro, Susanna↗

DOE OSTI2021

A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale

Wildfire is an ecological disturbance that disrupts the hydrological cycle. In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States. Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to “Water-in-the-West” through negative impacts to water supply and its quality, and is a direct threat to downstream communities, groundwater, and drinking water supply infrastructure. While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context: 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.

58 GEOSCIENCES↗

NASA NTRS2013

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Vegetation↗

DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series: Supporting Data

This dataset consists of soil temperature time series that were used to estimate soil thermal diffusivity and its uncertainty trough the probabilistic modelling approach developed and presented in the article named "Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series" and published in Earth Surface Dynamics. There are two compressed (.zip) files that contains synthetic (Synthetic_soiltemp_Teller.zip) and field (Field_soiltemp_Teller.zip) data. There is one MATLAB file that requires MATLAB to execute but any text editor can open it. The Synthetic_soiltemp_Teller.zip file includes 5 comma-delimited data files (.csv) each of which contains soil temperature time series generated through forward modeling (i.e., heat-conduction process in a heterogeneous medium using an explicit finite difference method) to mimic various types of temperature gradients, trend and fluctuations. These synthetic soil temperatures were used to investigate the impact of different environmental conditions on the uncertainty of thermal diffusivity estimates. The Field_soiltemp_Teller.zip file contains 28 comma-delimited data files (.csv) out of which (a) 27 files includes soil temperature time series recorded from 27 temperature probes located in a site along Teller Road about 40 km northwest of Nome, Alaska (64.72°N, 165.94°W), (b) one includes the name and coordinates of the 27 probes. These field soil temperatures were used to infer soil thermal diffusivity at numerous locations and depths in a discontinuous permafrost environment, and to evaluate the links between the estimated soil thermal diffusivity values and soil physical properties. The comma-delimited data files (.csv) of the synthetic and field soil temperature time series includes date and time (UTC) in the first column and soil temperature from 5 cm below the ground surface to 1.05 m depth (with 5 or 10 cm spacing between sensors) in the other columns. The measurements were acquired every 15 minutes. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Contributions to Climate Research Using the AIRS Science Team Version-5 Products

This paper compares recent spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLRCLR (Clear Sky OLR) as determined using CERES and AIRS observations over the time period September 2002 through June 2010. We find excellent agreement in OLR anomaly time series of both data sets in almost every detail, down to the 1 x 1 spatial grid point level. This extremely close agreement of OLR anomaly time series derived from observations by two different instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper then examines anomaly time series of AIRS derived products over the extended time period September 2002 through April 2011. We show that OLR anomalies during this period are closely in phase with those of an El Nino index, and that recent global and tropical mean decreases in OLR and OLR(sub CLR) are a result of a transition from an El Nino condition at the beginning of the data record to La Nina conditions toward the end of the data period. This relationship can be explained by temporal changes of the distribution of mid-tropospheric water vapor and cloud cover in two spatial regions that are in direct response to El Nino/La Nina activity which occurs outside these spatial regions

Susskind, Joel↗

DOE OSTI2022

Spatially calibrating polycyclic aromatic hydrocarbons (PAHs) as proxies of area burned by vegetation fires: Insights from comparisons of historical data and sedimentary PAH fluxes

Many regions worldwide have experienced increasing wildfire activity in recent years and climate changes are predicted to result in more frequent and severe fires. Reconstruction of past fire activity offers paleoenvironmental context for modern and future burning. Pyrogenic polycyclic aromatic hydrocarbons (PAHs) have been increasingly used as a molecular biomarker for fire occurrence in the paleorecord and offer opportunity for nuanced reconstructions of fire characteristics. A suite of PAHs are produced during combustion, and the emission amount and assemblage is influenced by many variables including fuel type, fire temperature, and oxygen availability. Despite recent advances in understanding the controls and taphonomy of these biomass burning markers, the spatial scale of this proxy is unknown. In this paper, measurements of PAH fluxes preserved in a lake sediment archive from the Sierra Nevada, California were compared with a historical geographic information system dataset of area burned up to 150 km distance from the lake to determine the spatial scales for which these biomarkers are reliable proxies of burning. Comparisons of PAH fluxes with charcoal accumulation rates in the same sediments suggest that pyrogenic particulate transport modulates low to mid-molecular weight PAHs via adsorption. Overall, the results indicate that PAH records integrate a combination of spatial signals of area burned and measurement of individual PAHs may enable cross-scale paleofire reconstructions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Infrasound and Gravity Waves Over the Andes Observed by a Pressure Sensor on Board a Stratospheric Balloon

The study of infrasound (acoustic) and gravity waves sources and propagation in the atmosphere of a planet gives us precious insight on atmosphere dynamics, climate, and even internal structure. The implementation of modern pressure sensors with high rate sampling on stratospheric balloons is improving their study. In our work we analyzed the data from the National Aeronautics and Space Administration Ultra Long Duration Balloon mission (16 May to 30 June 2016). Here, we focus on the balloon's transit of the Andes Mountains. We detected gravity waves that are associated to troposphere convective activity and mountain waves. An increase of the horizontal wavelengths from 50 to 70 km with increasing distance to the mountains is favoring the presence of mountain waves. We also report on the detection of infrasounds generated by the mountains in the 0.01–0.1 Hz range with a pressure amplitude increase by a factor 2 relative background signal. Besides, we characterized the decrease of microbaroms power when the balloon was flying away from the ocean coast. These observations suggest, in a way similar to microseisms for seismometers, that microbaroms are the main background noise sources recorded in the stratosphere even far from the ocean sources. Finally, we observed a broadband signal above the Andes, between 0.45 and 2 Hz, probably associated with a thunderstorm. The diversity of geophysical phenomena captured in less than a day of observation stresses the interest of high rate pressure sensors on board long-duration balloon missions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1986

The CDF software package

The concepts that are fundamental to the new Common Data Format are outlined. With Pilot Climate Data System (PCDS) Version 4.0, the Common Data Format (CDF) will supersede the Climate Data File (also CDF) or earlier versions. This new format incorporates generalizations in both design and terminology that make it applicable to multidisciplinary data sets. Furthermore, the new CDF will be made available to programmers as a software package that shields them from the low-level details of file formats. The CDF interface routines create an abstract conceptual environment for the scientific programmer. The principle concept for visualizing a CDF is known as the basic grid. A basic grid is an n-dimensional block by means of which a CDF is constructed. The size of the block may vary from one CDF to another, but is consistent within any individual CDF. Thus, the basic grid serves as a fundamental uniform building block for a CDF. The number of grid dimensions and the size of each dimension are chosen by the scientist/programmer to represent the patterns by which data are structured. The uniform grid structure appears to the programmer to be propagated into each record for each variable. The CDF stores a variable value for each lattice point of the grid for each record. These data values are inserted and retrieved simply by specifying the variable's identifier, a record number, and the indices that specify the lattice point of interest.

Wilson, C. E.↗

NASA NTRS2010

Secondary Forests from Agricultural Abandonment in Amazonia 2000-2009

Ongoing negotiations to include reducing emissions from tropical deforestation and forest degradation (REDD+) in a post-Kyoto climate agreement highlight the critical role of satellite data for accurate and transparent accounting of forest cover changes. In addition to deforestation and degradation, knowledge of secondary forest dynamics is essential for full carbon accounting under REDD+. Land abandonment to secondary forests also frames one of the key tradeoffs for agricultural production in tropical forest countries-whether to incentivize secondary forest growth (for carbon sequestration and biodiversity conservation) or low-carbon expansion of agriculture or biofuels production in areas of secondary forests. We examined patterns of land abandonment to secondary forest across the arc of deforestation in Brazil and Bolivia using time series of annual Landsat and MODIS data from 2000-2009. Rates of land abandonment to secondary forest during 2002-2006 were less than 5% of deforestation rates in these years. Small areas of new secondary forest were scattered across the entire arc of deforestation, rather than concentrated in any specific region of the basin. Taken together, our analysis of the satellite data record emphasizes the difficulties of addressing the pool of new secondary forests in the context of REDD+ in Amazonia. Due to the small total area of secondary forests, land sparing through agricultural intensification will be an important element of efforts to reduce deforestation rates under REDD+ while improving agricultural productivity in Amazonia.

Morton, Douglas↗