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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The Shared Socio-economic Pathways: Trajectories for human development and global environmental change

This Special Issue presents the Shared Socio-economic Pathways (SSPs), a set of 5 storylines on possible trajectories for human development and global environmental change during the 21st century. The information presented in the Special Issue has been developed over the last few years as a joint community effort and includes both qualitative narratives and results of quantitative model analysis. The SSPs form part of a larger set of community scenarios for analysis of climate change. Such community scenarios form a key tool for analysis of analysis of global environmental change and sustainable development problems. They allow exploration of different futures with and without response strategies. They also form a key tool to link across different disciplines and scales. They allow the same scenarios to be used in research on driving forces of climate change, the physical climate system, climate impacts and adaptation and mitigation strategies. Such scenarios can also link research across different research areas (e.g. climate change and biodiversity analysis) or be used across different scales (global, regional and local scales). Finally, they also have a key function as a consistent data source (population projections, climate projections and insights into land and energy development, for instance).

Van Vuuren, Detlef↗
NASA NTRS2016

GC31G-1182: Opennex, a Private-Public Partnership in Support of the National Climate Assessment

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX is funded as an enabling tool for sustaining the national climate assessment. Over the past five years, researchers have used the NEX platform and produced a number of data sets highly relevant to the National Climate Assessment. These include high-resolution climate projections using different downscaling techniques and trends in historical climate from satellite data. To enable a broader community in exploiting the above datasets, the NEX team partnered with public cloud providers to create the OpenNEX platform. OpenNEX provides ready access to NEX data holdings on a number of public cloud platforms along with pertinent analysis tools and workflows in the form of Machine Images and Docker Containers, lectures and tutorials by experts. We will showcase some of the applications of OpenNEX data and tools by the community on Amazon Web Services, Google Cloud and the NEX Sandbox.

datasets↗
NASA NTRS2019

Small Satellites for NASA Earth Science

NASA’s Earth Science Division (ESD) seeks to develop a scientific understanding of Earth and its response to natural and human-induced changes. Earth is a system comprised of diverse components interacting in complex ways. Understanding Earth’s atmosphere, surface and interior, oceans and surface water, ice and snow, and life as a single connected system is necessary in order to improve our predictions of climate, weather, and natural hazards. The ESD’s Flight Program consists of a coordinated series of satellite and airborne systems providing long and short-term, global and regional observations. In addition, the Flight Program provides infrastructure for operating these missions, processing their scientific data, and distributing them on a free and open basis to researchers, operational users, and the public. The Flight Program currently has 24 operating Earth observing space missions and instruments. There are 18 more missions and instruments planned for launch over the next five years. These comprise missions recommended by the National Academies 2017 Earth Science Decadal Survey, missions and selected instruments to ensure availability of key climate data sets, operational missions to sustain the land imaging provided by the Landsat system, and small-sized competitively selected orbital and instrument missions of opportunity belonging to the Earth Venture (EV) program. The Earth Science Decadal Survey, released in early 2018, recommended four new Flight Program elements in addition to the above activities that comprise the Program of Record (POR). Small satellites (~500 kg or less) are essential components of these activities. Presently, there is an increasing use of micro and nanosatellites (or CubeSats) in constellations to support NASA ESD’s scientific objectives. These include the Cyclone Global Navigation Satellite System (CYGNSS) for observing tropical cyclone intensification and genesis factors, the Timed-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, and the Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) CubeSat mission. ESD small satellite initiatives like the Small Satellite Constellation Data Buy and Venture Class Launch Services (VCLS) are also underway. The Earth Science Technology Office’s (ESTO) In-Space Validation of Earth Science Technologies (InVEST) and the Venture Technology program elements have launched seven 3U and 6U CubeSat missions to validate advanced instruments and related technologies. An equivalent number of InVEST and other technology demonstration CubeSats are being prepared for launch in the next year. An overview of plans and current status including topics related to small satellite enabling activities will be presented.

Neeck, Steven P.↗
NASA NTRS2014

Remote Sensing of Above Cloud Aerosols

The direct and indirect radiative effects of aerosols suspended in the atmosphere above clouds (ACA) are a highly uncertain component of both regional and global climate. Much of this uncertainty is observational in nature most orbital remote sensing algorithms were not designed to simultaneously retrieve aerosol and cloud optical properties in the same vertical column. Thus the climate modeling community has limited data to inform model development efforts, which encapsulate the current understanding of climate. Furthermore, field measurements have identified regions with consistent ACA, and regional simulations show that the radiative forcing may be significant. For this reason, there has been a recent push to develop the ability to determine ACA distribution, optical properties and cloud interactions, while also providing a means to validate models. Several algorithms have been created that utilize existing instruments for these purposes. However, the observational data sources, algorithm characteristics, geophysical assumptions and retrieved products from these methods are highly variable. This chapter is a review of these techniques, their uncertainties, and the associated validation efforts. We will also discuss the future of ACA remote sensing, both with regards to new instruments and the potential for new algorithms.

Aerosols↗
DOE OSTI2020

A Multiyear Gridded Data Ensemble of Surface Biogenic Carbon Fluxes for North America: Evaluation and Analysis of Results

Accurate and fine-scale estimates of biogenic carbon fluxes are critical for measuring and monitoring the biosphere's responses and feedback to the climate system. Currently available data products from flux towers and model-intercomparison projects struggle to adequately represent spatiotemporal dynamics of surface biogenic carbon fluxes, and to quantify their uncertainties, which also are crucial to atmospheric inversion systems. To address these gaps, we introduce a new perturbed-parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3-hourly scales for North America at ~500-m and 5-km resolutions. We first use the Extended Fourier Amplitude Sensitivity Testing to choose the three most sensitive parameters to be perturbed, maximum light use efficiency (E max ), optimal temperature of photosynthesis (T opt ), and temperature response of respiration (Q 10 ). The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned E max with site-level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site-level results at both monthly and 3-hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales. Finally, the ensemble's seasonality agrees well with other models for most biome types and in high and middle latitudes, but inconsistencies are found in subtropical and tropical ecoregions and for annual totals over North America.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Photosynthesis phenology, as defined by solar-induced chlorophyll fluorescence, is overestimated by vegetation indices in the extratropical Northern Hemisphere

Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. In this work, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analyzing the Inflation Reduction Act and the Bipartisan Infrastructure Law for Their Effects on Nuclear Cost Data

Decarbonizing to meet aggressive climate change mitigation targets requires energy transition within all sectors. In the industrial sector, emissions will need to decrease by 65–90% by 2050 to avert global warming greater than 1.5°C (IPCC 2022). The Inflation Reduction Act (IRA), Bipartisan Infrastructure Law (BIL), and Defense Production Act (DPA) have clean energy requirements and provide financial incentives to accelerate the use of clean energy technologies in the industrial sector. It is important to note that IRA is the most extensive action ever taken by Congress and the U.S. government to combat climate change (US CBO 2021, 2022). The energy system provisions comprise most of the estimated climate and energy support. A better understanding of those provisions in the above mentioned acts and laws is crucial to assessing their impact on the equivalent energy costs to the power plant owners (impact on net revenue in $/MWh) across different energy technologies, market deployment potential offered to different energy technologies applications, and energy system research modeling. The purpose of the report is to shed light on IRA and BIL provisions with particular attention to impacts on the nuclear industry. The report also seeks to understand potential equivalent energy cost savings for nuclear energy technologies from other laws and programs in conjunction with IRA, BIL, loan program guarantees, and DPA. The report reviews recent legislation on energy policy and translates that policy to impacts on equivalent nuclear costs for the purpose of modeling policy in energy scenarios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1990

A satellite retrieval of the shortwave heating of the atmosphere and the surface - Relationship to the general circulation, interannual climate variability, and the cryosphere

Data from several Nimbus-7 instruments and monthly averaged, multilayer delta-Eddington radiative transfer calculations are used to estimate the full vertical profile of the SW heating in the surface and the atmosphere. The noontime physical profile of clouds, water vapor, and surface albedo is used to compute a daily, monthly averaged top-of-atmosphere albedo (TOA), which was produced with noontime Nimbus-7 data, and a profile of SW heating for the atmosphere and surface. The daily, monthly averaged TOA albedo for July 1983 is compared with the ERBE daily, monthly averaged TOA albedo for July 1985. The sensitivity of the vertical SW heating profiles to the retrieval input assumptions is shown.

Charlock, Thomas P.↗
NASA NTRS2023

Interconnected Hydrologic Extreme Drivers and Impacts Depicted By Remote Sensing Data Assimilation

In a changing climate, the likelihood of hydrologic extremes has been increasing as climate change can impact both means and extremes4 of hydrologic cycle processes, potentially resulting in an increased frequency of floods in some regions and decreases in others. In a warming world, the physical processes that affect hydrologic response, such as rain-on snow runoff events, are also changing, such that the seasonality of streamflow has been shifting. The geography of rain-on-snow runoff events is predicted to move from low to high elevations. In addition to floods, there is also potential for an increase in dry extremes in a warming world with increased drought frequency and occurrences in many parts of the world. The increased frequency of drought and heatwave events is expected to have consequences such as escalating crop failures in future projection scenarios1. Thus, the consensus of literature shows that climate change is increasing the magnitude and frequency of extreme hydrologic events, and the human influence in many of these events is substantial.

Timothy M. Lahmers↗
DOE OSTI2014

ARM Data File Standards Version: 1.0

The Atmospheric Radiation Measurement (ARM) Climate Research Facility performs routine in situ and remote-sensing observations to provide a detailed and accurate description of the Earth atmosphere in diverse climate regimes. The result is a diverse data sets containing observational and derived data, currently accumulating at a rate of 30 TB of data and 150,000 different files per month (http://www.archive.arm.gov/stats/storage2.html). Continuing the current processing while scaling this to even larger sizes is extremely important to the ARM Facility and requires consistent metadata and data standards. The standards described in this document will enable development of automated analysis and discovery tools for the ever-growing volumes of data. It also will enable consistent analysis of the multiyear data, allow for development of automated monitoring and data health status tools, and facilitate development of future capabilities for delivering data on demand that can be tailored explicitly to user needs. This analysis ability will only be possible if the data follows a minimum set of standards. This document proposes a hierarchy that includes required and recommended standards.

Kehoe, Kenneth↗
NASA NTRS2011

Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum

The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

Jin, Zhonghai↗
DOE OSTI2018

Deep learning tackles cloud detection

To better understand Earth systems, atmospheric scientists use cloud data collected by a remote sensing tool called lidar. These data are an important part of global forecasting, but analyzing lidar images by hand is time-consuming. Researchers at Pacific Northwest National Laboratory teamed up to see if a deep learning model could process lidar data better than current automatic detection techniques.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Open-Source Science-Driven Development of the Science Data System (SDS) for Earth System Observatory (ESO) Atmospheric Missions

The NASA Earth System Observatory (ESO) atmospheric missions will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The Science Data System (SDS) will deploy the adaptive processing system (APS) developed within the Cloud to manage the research and operational processing of ESO atmospheric mission orbital and suborbital sensors and curate these data for near real-time and collection reprocessing and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage and distribution. Further, the SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The SDS follows NASA’s commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the SDS system components will be developed with open-source concepts including components of APS itself as well as ESO atmospheric mission algorithms. This presentation describes the framework of the SDS and its integral part in facilitating OSS within the ESO atmospheric missions.

David M. Giles↗
NASA NTRS2007

Antarctic Circumpolar Current Transport Variability during 2003-05 from GRACE

Gravity Recovery and Climate Experiment (GRACE) gravity data spanning January 2003-November 2005 are used as proxies for ocean bottom pressure (BP) averaged over 1 month, spherical Gaussian caps 500 km in radius, and along paths bracketing the Antarctic Circumpolar Current's various fronts. The GRACE BP signals are compared with those derived from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean modeling-assimilation system, and to a non-Boussinesq version of the Regional Ocean Model System (ROMS). The discrepancy found between GRACE and the models is 1.7 cm H2O (1 cm H2O approx. 1 hPa), slightly lower than the 1.9 cmH2O estimated by the authors independently from propagation of GRACE errors. The northern signals are weak and uncorrelated among basins. The southern signals are strong, with a common seasonality. The seasonal cycle GRACE data observed in the Pacific and Indian Ocean sectors of the ACC are consistent, with annual and semiannual amplitudes of 3.6 and 0.6 cmH2O (1.1 and 0.6 cm H2O with ECCO), the average over the full southern path peaks (stronger ACC) in the southern winter, on days of year 197 and 97 for the annual and semiannual components, respectively; the Atlantic Ocean annual peak is 20 days earlier. An approximate conversion factor of 3.1 Sv (Sv equiv 10(exp 6)cu m/s) of barotropic transport variability per cm H2O of BP change is estimated. Wind stress data time series from the Quick Scatterometer (QuikSCAT), averaged monthly, zonally, and over the latitude band 40(deg)- 65(deg)S, are also constructed and subsampled at the same months as with the GRACE data. The annual and semiannual harmonics of the wind stress peak on days 198 and 82, respectively. A decreasing trend over the 3 yr is observed in the three data types.

Gravity Recovery and Climate Experiment (GRACE)↗
DOE OSTI2018

Climate reconstruction for the Entre‐Douro‐e‐Minho region ( NW Portugal) between AD 1626 and AD 1820: synthesis of viticulture data and foraminiferal evidence

This work presents two novel climate‐related time series for the northwest of Portugal. The first is an AD 1626–1820 triennial‐resolved wine production series, based on the Benedictine accounts from six monasteries of the Entre‐Douro‐e‐Minho ( EDM ) region. The second, an AD 1654–2010 benthic foraminiferal record from the Caminha salt marsh, located in the lower estuary of the Minho River. The series were analysed together for the common period to outline how both palaeoclimatic proxies respond to the most likely natural environmental drivers of temporal variability, solar forcing included. Singular spectral analysis revealed a common significant multidecadal periodicity agreeing with recognized long‐term changes in solar activity, i.e. the Lower Gleissberg cycle (50–80 years). The application of wavelet analysis allowed the detection of high coherence at this time scale (centred at c . 64 years) between marsh foraminifera and both total solar irradiance and the North Atlantic Oscillation index. This relationship persists throughout the c . AD 1730–1875 period. The continuous wavelet transform results for wine production were inconclusive. As the time‐span analysed is recognized as one of high socio‐economic and political distress, the main human‐driven impacts on wine production, particularly in the two periods of greatly reduced solar activity – the Maunder and Dalton Minima – are reviewed in the light of the available historical records. In addition to a documented climate‐related agricultural crisis in Portugal, damage and losses to wine production may have been triggered by several local and international conflicts in which the country was involved. But to what extent the two influences contributed to the wine production variations observed in the EDM region during both periods remains an open question.

Moreno, João↗
DOE OSTI2020

The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models

Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global high-resolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December–January–February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Seasonal variability of mesospheric water vapor

Ground-based spectral line measurements of the 22.2 GHz atmospheric water vapor line in emission were made at the JPL in order to obtain data in a dry climate, and to confirm similar measurements made at the Haystack Observatory. The results obtained from March 1984 to July 1984 and from December 1984 to May 1985, were based on data recorded by a HP9816 microcomputer. The instrument spectrometer was a 64 channel, 62.5 kHz resolution filter bank. Data indicates the existence of a seasonal variation in the abundance of water vapor in the upper mesosphere, with mixing ratios higher in summer than in spring. This is consistent with recent theoretical and observational results. In the area of semiannual oscillation, Haystack data are more consistent than those of JPL, indicating an annual cycle with abundances at maximum in summer and minimum in winter.

Schwartz, P. R.↗
NASA NTRS1988

Estimating surface soil moisture from satellite microwave measurements and a satellite derived vegetation index

Normalized 18-GHz microwave brightness temperatures, T(B), and a vegetation index determined from satellite radiometer data are combined with climatically modeled surface moisture estimates to constrain a simple physically based soil moisture model. It is found that the normalized T(B) values correlated well with soil moisture when the data were segregated by vegetation index range, but less so when all the data were combined. By using the vegetation index parameter, the model is shown to account for about 70 percent of the variability in modeled surface soil moisture.

Owe, Manfred↗