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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Intensity-Modulated Continuous-Wave Lidar at 1.57 Micrometer for Atmospheric CO2 Measurements

Understanding the earth's carbon cycle is essential for diagnosing current and predicting future climates, which requires precise global measurements of atmospheric CO2 through space missions. The Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) space mission will provide accurate global atmospheric CO2 measurements to meet carbon science requirements. The joint team of NASA Langley Research Center and ITT Exelis, Inc proposes to use the intensity-modulated, continuous-wave (IM-CW) lidar approach for the ASCENDS mission. Prototype instruments have been developed and used to demonstrate the power, signal-to-noise ratio, precision and accuracy, spectral purity, and stability of the measurement and the instrument needed for atmospheric CO2 observations from space. The ranging capability from laser platform to ground surfaces or intermediate backscatter layers is achieved by transmitted range-encoded IM laser signals. Based on the prototype instruments and current lidar technologies, space lidar systems and their CO2 column measurements are analyzed. These studies exhibit a great potential of using IM-CW lidar system for the active space CO2 mission ASCENDS.

Lin, Bing↗
NASA NTRS1999

Modeling Mesoscale Eddies in the North Atlantic Ocean

Ocean modeling plays an important role in understanding the current climatic conditions and predicting the future climate change. Modeling the ocean at eddy-permitting and/or eddy resolving resolutions (1/3 degree or higher) has a two-fold objective. One part is to represent the ocean as realistically as possible, because mesoscale eddies have an impact on the large-scale circulation. The second objective is to learn how to represent effects of mesoscale eddies without explicitly resolving them. This is particularly important for climate models which cannot be run at eddy-resolving resolutions because of the computational constraints. At JPL, a 1/6 degree latitude by 1/6 degree longitude with 37 vertical levels Atlantic Ocean model has been developed. The model is based on the Parallel Ocean Program (POP) developed at Los Alamos National Laboratory (LANL). Using the 256-processor Cray T3D, we have conducted a 40-year integration of this Atlantic eddy-resolving ocean model. A regional analysis demonstrate that many observed features associated with the Caribbean Sea eddies can be realistically simulated by this model. Analysis of this Atlantic eddy-resolving ocean model further suggests that these Caribbean Sea eddies are connected with eddies formed outside the Caribbean Sea at the confluence of the North Brazil Current (NBC) and the North Equatorial Countercurrent. The diagram of the model simulated surface current shows that the Caribbean eddies ultimately originate in the NBC retroflection region, traveling more than a year from the North Brazil coast through the Lesser Antilles into the Caribbean Sea and eventually into the Gulf of Mexico. Additional information is contained in the original.

Chao, Yi↗
NASA NTRS1997

Optimization of a Parallel Ocean General Circulation Model

Global climate modeling is one of the grand chalenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting the future climate change.

Optimization Ocean Modeling Parallel Computations↗
NASA NTRS2012

Likelihood-Based Climate Model Evaluation

Climate models are deterministic, mathematical descriptions of the physics of climate. Confidence in predictions of future climate is increased if the physics are verifiably correct. A necessary, (but not sufficient) condition is that past and present climate be simulated well. Quantify the likelihood that a (summary statistic computed from a) set of observations arises from a physical system with the characteristics captured by a model generated time series. Given a prior on models, we can go further: posterior distribution of model given observations.

climate models↗
NASA NTRS2000

Global Warming in the 21st Century: An Alternate Scenario

Evidence on a broad range of time scales, from Proterozoic to the most recent periods, shows that the Earth's climate responds sensitively to global forcings. In the past few decades the Earth's surface has warmed rapidly, apparently in response to increasing anthropogenic greenhouse gases in the atmosphere. The conventional view is that the current global warming rate will continue or accelerate in the 21st century. I will describe an alternate scenario that would slow the rate of global warming and reduce the danger of dramatic climate change. But reliable prediction of future climate change requires improved knowledge of the carbon cycle and global observations that allow interpretation of ongoing climate change.

Hansen, James E.↗
NASA NTRS2007

Contrasts Between Precipitation over Mediterranean Sea and Adjacent Continental Areas Based on Decadal Scale Satellite Estimates

Most knowledge concerning the last century's climatology and climate dynamics of precipitation over the Mediterranean Sea basin is based on observations taken from rain gauges surrounding the sea itself. In turn, most of the observations come from Southern Europe, with many fewer measurements taken from widely scattered sites situated over North Africa, the Middle East, and the Balkans. This aspect of research on the Mediterranean Sea basin is apparent in a recent compilation of studies presented in book form concerning climate variability of the Mediterranean region [Lionello, P., P. Malanotte-Rizzoli, and R. Boscolo (eds.), 2006: Mediterranean Climate Variability. Elsevier, Amsterdam, 9 chapters.] In light of this missing link to over-water observations, this study (in conjunction with four companion studies by Z. Haddad, A. Mugnai, T. Nakazawa, and G. Stephens) will contrast the nature of precipitation variability directly over the Mediterranean Sea to precipitation variability over the surrounding land areas based on three decades of satellite-based precipitation estimates which have stood up well to validation scrutiny. The satellite observations are drawn from the Global Precipitation Climatology Project (GPCP) dataset extending back to 1979 and the TRMM Merged Algorithm 3b42 dataset extending back to 1998. Both datasets are mostly produced from microwave measurements, excepting the period from 1979 to mid-1987 when only infrared satellite measurements were available for the GPCP estimates. The purpose of this study is to emphasize how the salient properties of precipitation variability over land and sea across a hierarchy of space and time scales, and the salient differences in these properties, might be used in guiding short-term climate models to better predictions of future climate states under different regional temperature-change scenarios.

Smith, Eric A.↗
NASA NTRS2000

Remote Sensing of Global Wetland Dynamics

Although natural wetlands only cover about 4% of the earth's ice-free land surface, they are the world's largest methane (CH4) source and the only one dominated by climate. In addition, wetlands affect climate by modulating temperatures and heat fluxes, storing water, increasing evaporation, and altering the seasonality of runoff and river discharge to the oceans. Current CH4 emissions from wetlands are relatively well understood but the sensitivity of wetlands and their emissions to climate variations remains the largest uncertainty in the global CH4 cycle and could strongly influence predictions of future climate. Therefore, characterizing climate-sensitive processes prevailing in the world's wetlands is crucial to understanding and predicting physical and biogeochemical responses of wetlands to interannual and longer-term climate variations. Recent research has resulted in the first generation of models to predict methane emissions from wetlands but the models must still be applied to static data on wetland distributions. Moreover, no models currently exist to realistically predict the distribution and dynamics of wetlands themselves for the current, or any other, climate. The dominant obstacle to modeling wetland dynamics has been lack of remote sensing techniques and data useful for characterizing quantitatively the seasonal and interannual variations of wetlands. We report on initial remote sensing studies undertaken to validate a global hydrological model linking rivers, takes and wetlands. Using a combination of SSM/I microwave and TOPEX Poseidon altimetry data sets, we developed and applied techniques to quantify inundation extent and duration for several large wetlands in tropical Africa and South America. Our initial results indicate that seasonally-inundated wetlands can be well characterized over large spatial scales and at monthly time scales using these remote sensing data. The results also confirm that currently available remote sensing products can be applied to provide crucial data to the research community modeling global hydrological and biogeochemical cycles.

Matthews, Elaine↗
NASA NTRS2015

Lidar Measurements of Atmospheric CO2 From Regional to Global Scales

Atmospheric CO2 is a critical forcing for the Earth's climate and the knowledge on its distributions and variations influences predictions of the Earth's future climate. Large uncertainties in the predictions persist due to limited observations. This study uses the airborne Intensity-Modulated Continuous-Wave (IMCW) lidar developed at NASA Langley Research Center to measure regional atmospheric CO2 spatio-temporal variations. Further lidar development and demonstration will provide the capability of global atmospheric CO2 estimations from space, which will significantly advances our knowledge on atmospheric CO2 and reduce the uncertainties in the predictions of future climate. In this presentation, atmospheric CO2 column measurements from airborne flight campaigns and lidar system simulations for space missions will be discussed. A measurement precision of approx.0.3 ppmv for a 10-s average over desert and vegetated surfaces has been achieved. Data analysis also shows that airborne lidar CO2 column measurements over these surfaces agree well with in-situ measurements. Even when thin cirrus clouds present, consistent CO2 column measurements between clear and thin cirrus cloudy skies are obtained. Airborne flight campaigns have demonstrated that precise atmospheric column CO2 values can be measured from current IM-CW lidar systems, which will lead to use this airborne technique in monitoring CO2 sinks and sources in regional and continental scales as proposed by the NASA Atmospheric Carbon and Transport â€" America project. Furthermore, analyses of space CO2 measurements shows that applying the current IM-CW lidar technology and approach to space, the CO2 science goals of space missions will be achieved, and uncertainties in CO2 distributions and variations will be reduced.

Lin, Bing↗
NASA NTRS2020

Using Climate Models to Learn: Investigating the Phenomenon of Increasing Surface Air Temperatures Using a Global Climate Modeling Approach

Since 1880, Earth’s average global temperature has increased by approximately 1.4 °F (0.8 °C). Approximately two-thirds of the warming has occurred since 1975 (Wuebbles, Easterling, Hayhoe, et al. 2017). Despite consensus about global climate change (GCC) within the scientific community, students struggle to comprehend - why temperatures are increasing globally - at what rate this change is occurring, and - how climate scientists predict Earth’s future climate. These challenges are due, in part, to the abstraction inherent to understanding GCC, a phenomenon that occurs over long periods of time (past and future), and over large regions (regional and global). With the adoption of the Next Generation Science Standards (NGSS Lead States 2013), GCC is emphasized at the secondary school level, with a focus on analyzing and understanding the evidence for GCC (HS-ESS3-1), climate modeling (HS-ESS2-6, HS-ESS3-5), and the impact of humans on the environment (HS-LS2-7, HS-ESS3-6).

climate models↗
NASA NTRS2006

Role of Climate Change in Global Predictions of Future Tropospheric Ozone and Aerosols

A unified tropospheric chemistry-aerosol model within the Goddard Institute for Space Studies general circulation model II is applied to simulate an equilibrium CO2-forced climate in the year 2100 to examine the effects of climate change on global distributions of tropospheric ozone and sulfate, nitrate, ammonium, black carbon, primary organic carbon, secondary organic carbon, sea salt, and mineral dust aerosols. The year 2100 CO2 concentration as well as the anthropogenic emissions of ozone precursors and aerosols/aerosol precursors are based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) A2. Year 2100 global O3 and aerosol burdens predicted with changes in both climate and emissions are generally 5-20% lower than those simulated with changes in emissions alone; as exceptions, the nitrate burden is 38% lower, and the secondary organic aerosol burden is 17% higher. Although the CO2-driven climate change alone is predicted to reduce the global O3 concentrations over or near populated and biomass burning areas because of slower transport, enhanced biogenic hydrocarbon emissions, decomposition of peroxyacetyl nitrate at higher temperatures, and the increase of O3 production by increased water vapor at high NOx levels. The warmer climate influences aerosol burdens by increasing aerosol wet deposition, altering climate-sensitive emissions, and shifting aerosol thermodynamic equilibrium. Climate change affects the estimates of the year 2100 direct radiative forcing as a result of the climate-induced changes in burdens and different climatological conditions; with full gas-aerosol coupling and accounting for ozone and direct radiative forcings by the O2, sulfate, nitrate, black carbon, and organic carbon are predicted to be +0.93, -0.72, -1.0, +1.26, and -0.56 W m(exp -2), respectively, using present-day climate and year 2100 emissions, while they are predicted to be +0.76, -0.72, 0.74, +0.97, and -0.58 W m(exp -2), respectively, with year 2100 climate and emissions.

Liao, Hong↗
NASA NTRS2006

Observational Evidence of Changes in Water Vapor, Clouds, and Radiation at the ARM SGP Site

Characterizing water vapor and cloud effects on the surface radiation budget is critical for understanding the current climate because water vapor is the most important greenhouse gas in the atmosphere and clouds are one of the largest sources of uncertainty in predicting potential future climate change. Several studies have shown that insolation over land declined until 1990 then increased until the present. Using 8 years of surface data, we observed the increasing trend of insolation from 1997 to 2000, but detected a significant decrease from 2001 to 2004. The variation of cloud fraction mirrors that of insolation with an overall increase of 1 percent per year. Under clear-sky conditions, water vapor changes have a greater impact on longwave flux than on insolation.

Dong, Xiquan↗
NASA NTRS2006

Observational Evidence of Changes in Water Vapor, Clouds, and Radiation at the ARM SGP Site

Characterizing water vapor and cloud effects on the surface radiation budget is critical for understanding the current climate because water vapor is the most important greenhouse gas in the atmosphere and clouds are one of the largest sources of uncertainty in predicting potential future climate change. Several studies have shown that insolation over land declined until 1990 then increased until the present. Using 8 years of data collected at the ARM Southern Great Plains (SGP) surface site, we found that the insolation increased from 1997 to 2000, but significantly decreased from 2001 to 2004, changes that exactly mirror the variation in the second-order fit of cloud fraction. Under clear-sky conditions, the rates of water vapor, insolation and downwelling longwave (LW) flux are -0.166 cm/yr, 0.48 Wm(exp -2)/yr, and -1.16 Wm(exp -2)/yr, respectively, indicating that water vapor changes are more important for LW flux than for insolation.

Dong, Xiquan↗
NASA NTRS2016

Oceanic Transport of Surface Meltwater from the Southern Greenland Ice Sheet

The Greenland ice sheet has undergone accelerating mass losses during recent decades. Freshwater runoff from ice melt can influence fjord circulation and dynamic1 and the delivery of bioavailable micronutrients to the ocean. It can also have climate implications, because stratification in the adjacent Labrador Sea may influence deep convection and the strength of the Atlantic meridional overturning circulation. Yet, the fate of the meltwater in the ocean remains unclear. Here, we use a high-resolution ocean model to show that only 1-15% of the surface meltwater runoff originating from southwest Greenland is transported westwards. In contrast, up to 50-60% of the meltwater runoff originating from southeast Greenland is transported westwards into the northern Labrador Sea, leading to significant salinity and stratification anomalies far from the coast. Doubling meltwater runoff, as predicted in future climate scenarios, results in a more-than-double increase in anomalies offshore that persists further into the winter. Interannual variability in offshore export of meltwater is tightly related to variability in wind forcing. The new insight that meltwaters originating from the west and east coasts have different fates indicates that future changes in mass loss rates and surface runoff will probably impact the ocean differently, depending on their Greenland origins.

Physical oceanography↗
NASA NTRS2013

Challenges in Quantifying Pliocene Terrestrial Warming Revealed by Data-Model Discord

Comparing simulations of key warm periods in Earth history with contemporaneous geological proxy data is a useful approach for evaluating the ability of climate models to simulate warm, high-CO2 climates that are unprecedented in the more recent past. Here we use a global data set of confidence-assessed, proxy-based temperature estimates and biome reconstructions to assess the ability of eight models to simulate warm terrestrial climates of the Pliocene epoch. The Late Pliocene, 3.6-2.6 million years ago, is an accessible geological interval to understand climate processes of a warmer world4. We show that model-predicted surface air temperatures reveal a substantial cold bias in the Northern Hemisphere. Particularly strong data-model mismatches in mean annual temperatures (up to 18 C) exist in northern Russia. Our model sensitivity tests identify insufficient temporal constraints hampering the accurate configuration of model boundary conditions as an important factor impacting on data- model discrepancies. We conclude that to allow a more robust evaluation of the ability of present climate models to predict warm climates, future Pliocene data-model comparison studies should focus on orbitally defined time slices.

Salzmann, Ulrich↗
NASA NTRS2019

SWIRP: Compact Submm-Wave and LWIR Polarimeters for Cirrus Ice Properties

Providing critical information on the make-up and distribution of aerosols and clouds, which in turn improve predictions of future climate conditions and help us assess the impacts of aerosols on human health;Addressing key questions about how changing cloud cover and precipitation will affect climate, weather, and Earth's energy balance in the future, advancing understanding of the movement of air and energy in the atmosphere and its impact on weather, precipitation, and severe storms;

Wu, Dong L.↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events, and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, enable detection of atmospheric features during daytime operations with a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
NASA NTRS1998

On the Tropical Rainfall Measuring Mission (TRMM): Bringing NASA's Earth System Science Program to the Classroom

The Tropical Rainfall Measuring Mission is the first mission dedicated to measuring tropical and subtropical rainfall using a variety of remote sensing instrumentation, including the first spaceborne rain-measuring radar. Since the energy released when tropical rainfall occurs is a primary "fuel" supply for the weather and climate "engine"; improvements in computer models which predict future weather and climate states may depend on better measurements of global tropical rainfall and its energy. In support of the STANYS conference theme of Education and Space, this presentation focuses on one aspect of NASA's Earth Systems Science Program. We seek to present an overview of the TRMM mission. This overview will discuss the scientific motivation for TRMM, the TRMM instrument package, and recent images from tropical rainfall systems and hurricanes. The presentation also targets educational components of the TRMM mission in the areas of weather, mathematics, technology, and geography that can be used by secondary school/high school educators in the classroom.

Shepherd, J. Marshall↗