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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

The Coupling of Solution Chemistry to Plant Nutrient Demand in an on Demand Nutrient Delivery System

The goal of the proposal will be to determine the suitability of the DASI instrument in providing a signal that can be recognized and be utilized as an indicator of plant stress. The method to be utilized for evaluating stress is the presentation of an every increasing level of nutrient deficiency and salinity stress (addition of salt (NACl) or increasing concentration of balanced nutrient) while simultaneously recording spectral reflectance using the DASI instrument and monitoring the traditional processes of gas exchange and nutrient uptake parameters. In this manner, we will be able to directly compare the DASI measurements with known stresses as determined by the traditional gas exchange and nutrient uptake measures of stress. We anticipate that the DASI will provide a sensitive identifier of plant stress; recording signals of the resulting changes in plant metabolism in real time, far before any visible effects of stress could be observed. Thus, there is a potential for very early management intervention to correct a stress condition before damage could develop. The present response time for the observation of visual symptoms of plant stress is considerable and only provides an indication that a stress is present after it has been present for an extended period of time. Thus, the impact of a plant-based life support function will have already been significant. An additional benefit of this research to regenerative life support will be the characterization of a potential recovery scenario from various degrees of stress. The experimental approach to be employed includes the removal of the stress at various points in the stress gradient and the characterization of plant performance and reflectance spectra during recovery from various degrees of stress. Spectral reflectance imaging techniques have been developed and used to measure the biochemical composition of plants and relate these characteristics to the fluxes of biochemical elements within the ecosystem.

Savage, Wayne↗
NASA NTRSDate not supplied

An In-Time Aviation Safety Management System (IASMS) Concept of Operations for Part 139 Airports

Today’s airports are complex multi-faceted ecosystems. Currently, of the 517certificatedairports, 270arerequired to use safety management systems (SMSs) to identify and mitigate known hazards and emergent risks and to voluntarily share safety data with commercial operators and tenants. Airports manage a wide variety of hazards. These traffic hubs have direct responsibilities, such as removing foreign object debris from runways and taxiways and configuring runways to help prevent against incursions and tail strikes during takeoff. To ensure safety in the future NAS, the National Academies recommended an In-time Aviation Safety Management System (IASMS). An IASMS will employ services, functions, and capabilities (SFCs) to identify and mitigate hazards that are proactively and predictively managed based on data analytics of detected anomalies, precursors, and trends. SFCs would scale with airport complexity and environmental conditions using increasingly automated systems to respond proactively to hazards and, by using integrated data sources and predictive safety analytical methods, discover new, never before seen risks.

Ellis, K↗
NASA NTRS2019

Simulating response versus scan angle characterization on OCI for the upcoming PACE mission

The Plankton, Aerosol, Cloud ocean Ecosystem (PACE) mission Ocean Color Instrument (OCI) is the next generation ocean color sensor following on from the SeaWiFS and VIIRS designs, to be launched into low Earth orbit in 2022. Like those heritage sensors, OCI has cross track scanning fore-optics, the reflectivity of which (referred to here as the response versus scan angle or RVS) must be characterized prior to launch. The expected test setup, component reflectivity measurements, instrument model, and heritage sensor testing were used to generate simulated RVS data. This simulated data set was then used to verify the functionality of the analysis software in preparation for the upcoming ground test campaign. The analysis software is designed to characterize the RVS for all OCI bands over the full range of scan angles to be measured on orbit as well as verify compliance with the sensor design requirements in near real-time.

PACE↗
NASA NTRS2002

Optical properties of intact leaves for estimating chlorophyll concentration

Changes in leaf chlorophyll content can serve as relative indicators of plant vigor and environmental quality. This study identified reflectance, transmittance, and absorptance wavebands and band ratios within the 400- to 850-nm range for intact leaves that could be used to estimate extracted leaf chlorophyll per unit leaf area (areal concentration) with minimal error. Leaf optical properties along with chlorophyll a, b, and a + b concentrations were measured for the planar-leaved sweetgum (Liquidambar styraciflua L.), red maple (Acer rubrum L.), wild grape (Vitis rotundifolia Michx.), and switchcane [Arundinaria gigantea (Walter) Muhl.], and for needles of longleaf pine (Pinus palustris Miller). Generally, reflectance, transmittance, and absorptance corresponded most precisely with chlorophyll concentrations at wavelengths near 700 nm, although regressions were also strong in the 550- to 625-nm range. A power function was superior to a simple linear function in yielding low standard deviations of the estimate (s). When data were combined among the planar-leaved species, s values were low at approximately 50 mumol/m2 out of a 940 mumol/m2 range in chlorophyll a + b at best-fit wavelengths of 707 to 709 nm. Minimal s values for chlorophyll a + b ranged from 32 to 62 mumol/m2 across species when band ratios having numerator wavelengths of 693 to 720 nm were used with the application of a power function. Optimal denominator wavelengths for the band ratios were 850 nm for reflectance and transmittance and 400 nm for absorptance. This information can be applied in designing field portable chlorophyll meters and in the landscape-scale remote sensing of plant responses to the environment.

Chlorophyll/analysis↗
NASA NTRS2020

NASA-ISRO Synthetic Aperture Radar (NISAR) Mission

NISAR is a multi-disciplinary Earth-observing radar mission that makes global measurements of land surface changes that will greatly improve Earth system models. NISAR data will clarify spatially and temporally complex phenomena, including ecosystem disturbances, ice sheet collapse, and natural hazards including earthquakes, tsunamis, volcanoes, and landslides. It provides societally relevant data that will enable better protection of life and property. The mission, a NASA-ISRO partnership, uses two fully polarimetric SARs, one at L-band (L-SAR) and one at S-band (S-SAR), in exact repeating orbits every 12 days that allows interferometric combination of data on repeated passes. NASA provides the L SAR; a shared deployable reflector; an engineering payload that supports mission-specific data handling, navigation and communication functions; science observation planning and L SAR data processing. ISRO provides the S-SAR, spacecraft, launch vehicle, satellite operations, and S-SAR data processing. The mission will be launched from the Satish Dhawan Space Centre, Sriharikota, India. Mission development has addressed many unique challenges and incorporates many “firsts” for a jointly-developed free-flyer radar science mission.

Sarma, C V H S↗
NASA NTRS2021

Cutting out the Middleman: Calibrating and Validating a Dynamic Vegetation Model (ED2-PROSPECT5) Using Remotely Sensed Surface Reflectance

Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure. However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations. Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance. In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”). In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products. We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.

Alexey N Shiklomanov↗
NASA NTRS1993

Functional patterns in an annual grassland during an AVIRIS overflight

This study relates Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery to ground measurements of vegetation distribution, physiology, and productivity at Stanford University's Jasper Ridge Biological Preserve. Primary efforts focused on a 9-ha region of annual grassland where we completed a detailed ground-based study in conjunction with a 15 May 1991 AVIRIS overflight. Spectral mixture analysis and the normalized difference vegetation index (NDVI) calculated from AVIRIS data were used to evaluate spatial patterns of vegetation type, productivity, and potential physiological activity. Concurrent ground sampling revealed a high degree of correlation between NDVI and estimates of canopy chemistry, structure, productivity, and CO2 flux, supporting the use of imaging spectrometry to estimate spatial and temporal trends in vegetation physiology and productivity in this relatively simple grassland ecosystem. Geostatistical analyses of both ground and AVIRIS data supported the conclusion that the AVIRIS pixel size was suitable for describing the influence of major landscape features in this grassland and that spatial detail would be lost at slightly larger pixel sizes typical of other imaging spectrometers.

Gamon, John A.↗
NASA NTRS2021

TLSLEAF: Automatic Leaf Angle Estimates From Single-Scan Terrestrial Laser Scanning

Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf–wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.

3D↗
NASA NTRS2024

In-Time Safety Management for Part 139 Airports

Today’s airports are complex multi-faceted ecosystems. Currently, of the 517 certificated airports, 270 are required to use safety management systems (SMSs) to identify and mitigate known hazards and emergent risks and to voluntarily share safety data with commercial operators and tenants. Airports manage a wide variety of hazards. These traffic hubs have direct responsibilities, such as removing foreign object debris from runways and taxiways and configuring runways to help prevent against incursions and tail strikes during takeoff. To ensure safety in the future NAS, the National Academies recommended an In-time Aviation Safety Management System (IASMS). An IASMS will employ services, functions, and capabilities (SFCs) to identify and mitigate hazards that are proactively and predictively managed based on data analytics of detected anomalies, precursors, and trends. SFCs would scale with airport complexity and environmental conditions using increasingly automated systems to respond proactively to hazards and, by using integrated data sources and predictive safety analytical methods, discover new, never before seen risks.

IASMS↗
NASA NTRS1992

A model of the CO2 exchanges between biosphere and atmosphere in the tundra

A physical model of the soil thermal regime in a permafrost terrain has been developed and validated with soil temperature measurements at Barrow, Alaska. The model calculates daily soil temperatures as a function of depth and average moisture contents of the organic and mineral layers using a set of five climatic variables, i.e., air temperature, precipitation, cloudiness, wind speed, and relative humidity. The model is not only designed to study the impact of climate change on the soil temperature and moisture regime, but also to provide the input to a decomposition and net primary production model. In this context, it is well known that CO2 exchanges between the terrestrial biosphere and the atmosphere are driven by soil temperature through decomposition of soil organic matter and root respiration. However, in tundra ecosystems, net CO2 exchange is extremely sensitive to soil moisture content; therefore it is necessary to predict variations in soil moisture in order to assess the impact of climate change on carbon fluxes. To this end, the present model includes the representation of the soil moisture response to changes in climatic conditions. The results presented in the foregoing demonstrate that large errors in soil temperature and permafrost depth estimates arise from neglecting the dependence of the soil thermal regime on soil moisture contents. Permafrost terrain is an example of a situation where soil moisture and temperature are particularly interrelated: drainage conditions improve when the depth of the permafrost increases; a decrease in soil moisture content leads to a decrease in the latent heat required for the phase transition so that the heat penetrates faster and deeper, and the maximum depth of thaw increases; and as excepted, soil thermal coefficients increase with moisture.

Labgaa, Rachid R.↗
NASA NTRS2000

BOREAS HYD-1 Soil Hydraulic Properties

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-1 team coordinated a program of data collection to measure and monitor soil properties in collaboration with other science team measurement needs. This data set contains soil hydraulic properties determined at the Northern Study Area (NSA) and Southern Study Area (SSA) flux tower sites based on analysis of in situ tension infiltrometer tests and laboratory-determined water retention from soil cores collected during the 1994-95 field campaigns. Results from this analysis are saturated hydraulic conductivity, and fitting parameters for the van Genuchten-Mualem soil hydraulic conductivity and water retention function at flux tower sites. The data are contained in tabular ASCII files. The HYD-01 soil hydraulic properties data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Hall, Forrest G.↗
NASA NTRS1990

Toward applied behavior analysis of life aloft

This article deals with systems at multiple levels, at least from cell to organization. It also deals with learning, decision making, and other behavior at multiple levels. Technological development of a human behavioral ecosystem appropriate to space environments requires an analytic and synthetic orientation, explicitly experimental in nature, dictated by scientific and pragmatic considerations, and closely approximating procedures of established effectiveness in other areas of natural science. The conceptual basis of such an approach has its roots in environmentalism which has two main features: (1) knowledge comes from experience rather than from innate ideas, divine revelation, or other obscure sources; and (2) action is governed by consequences rather than by instinct, reason, will, beliefs, attitudes or even the currently fashionable cognitions. Without an experimentally derived data base founded upon such a functional analysis of human behavior, the overgenerality of "ecological systems" approaches render them incapable of ensuring the successful establishment of enduring space habitats. Without an experimentally derived function account of individual behavioral variability, a natural science of behavior cannot exist. And without a natural science of behavior, the social sciences will necessarily remain in their current status as disciplines of less than optimal precision or utility. Such a functional analysis of human performance should provide an operational account of behavior change in a manner similar to the way in which Darwin's approach to natural selection accounted for the evolution of phylogenetic lines (i.e., in descriptive, nonteleological terms). Similarly, as Darwin's account has subsequently been shown to be consonant with information obtained at the cellular level, so too should behavior principles ultimately prove to be in accord with an account of ontogenetic adaptation at a biochemical level. It would thus seem obvious that the most productive conceptual and methodological approaches to long-term research investments focused upon human behavior in space environments will require multidisciplinary inputs from such wide-ranging fields as molecular biology, environmental physiology, behavioral biology, architecture, sociology, and political science, among others.

Review↗
NASA NTRS2020

Marine wild-capture fisheries after nuclear war

Nuclear war, beyond its devastating direct impacts, is expected to cause global climatic perturbations through injections of soot into the upper atmosphere. Reduced temperature and sunlight could drive unprecedented reductions in agricultural production, endangering global food security. However, the effects of nuclear war on marine wild-capture fisheries, which significantly contribute to the global animal protein and micronutrient supply, remain unexplored. We simulate the climatic effects of six war scenarios on fish biomass and catch globally, using a state-of-the-art Earth system model and global process-based fisheries model. We also simulate how either rapidly increased fish demand (driven by food shortages) or decreased ability to fish (due to infrastructure disruptions), would affect global catches, and test the benefits of strong pre-war fisheries management. We find a decade-long negative climatic impact that intensifies with soot emissions, with global biomass and catch falling by up to 18 ± 3% and 29 ± 7% after a US-Russia war under business-as-usual fishing – similar in magnitude to the end-of-century declines under unmitigated global warming. When war occurs in an overfished state, increasing demand increases short-term (1-2 year) catch by at most ~30% followed by precipitous declines of up to ~70%, thus offsetting only a minor fraction of agricultural losses. However, effective pre-war management that rebuilds fish biomass could ensure a short-term catch buffer large enough to replace ~43 ± 35% of today’s global animal protein production. This buffering function in the event of a global food emergency adds to the many previously-known economic and ecological benefits of effective and precautionary fisheries management.

food from the ocean↗
NASA NTRS2021

Application of Radon Transform to Multi-Angle Measurements Made by the Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I: Theory and Tests on Simulated Data.

The Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances in 9 spectral channels. The RSP was a prototype for the Aerosol Polarimetry Sensor (APS) launched on-board the NASA Glory satellite. Currently the retrieval algorithms developed for the RSP are being adopted for the measurements of the space-borne polarimeters on the upcoming NASA’s Plankton, Aerosol, Cloud Ocean Ecosystem (PACE)satellite mission. The RSP’s uniquely high angular resolution coupled with the high frequency of measurements allows for characterization of liquid water cloud droplet sizes using the polarized rainbow structure. It also provides geometric constraints on the cumulus cloud’s 2D cross section yielding the cloud’s geometric shape estimates. In this study we further build on the latter technique to develop a new tomographic approach to retrieval of cloud internal structure from remote sensing measurements. While tomography in the strict definition is a technique based on active measurements yielding a tomogram (directional optical thickness as a function of angle and offset of the view ray), we developed a “semi-tomographic” approach in which tomogram of the cloud is estimated from passive observations instead of being measured directly. This tomogram is then converted into 2D spatial distribution of the extinction coefficient using inverse Radon transform (filtered back projection) which is the standard tomographic procedure used e.g., in medical CT scans. This algorithm is computationally inexpensive compared to techniques relying on highly-multi-dimensional least-square fitting; it does not require iterative 3D RT simulations. The resulting extinction distribution is defined up to an unknown constant factor, so we discuss the ways to calibrate it using additional independent measurements. In the next step we use the profile of the droplet size distribution parameters from the cloud’s side (derived by fitting the polarized rainbows) to convert the 2D extinction distribution into that of the droplet number concentration. We illustrate and validate the proposed technique using 3D-RT-simulatedRSP observations of a LES-generated Cu cloud. Quantitative comparisons between the retrieved and the original optical and microphysical parameters are presented.

clouds↗
NASA NTRSDate not supplied

Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model

The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.

Jeremy Alan Kravitz↗
NASA NTRS2016

Mid and Thermal Infrared Remote Sensing at the Jet Propulsion Laboratory

The mid and thermal infrared (MTIR) for the Earth surface is defined between 3 and 14µm. In the outer solar system, objects are colder and their Planck response shifts towards longer wavelengths. Hence for these objects (e.g. icy moons, polar caps, comets, Europa), the thermal IR definition usually stretches out to 50µm and beyond. Spectroscopy has been a key part of this scientific exploration because of its ability to remotely determine elemental and mineralogical composition. Many key gas species such as methane, ammonia, sulfur, etc. also have vibrational bands which show up in the thermal infrared spectrum above the background response. Over the past few decades, the Jet Propulsion Laboratory has been building up a portfolio of technology to capture the MTIR for various scientific applications. Three recent sensors are briefly reviewed: The airborne Hyperspectral thermal emission spectrometer (HyTES), the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Mars Climate Sounder (MCS)/DIVINER. Each of these sensors utilize a different technology to provide a remote sensing product based on MTIR science. For example, HyTES is a push-brooming hyperspectral imager which utilizes a large format quantum well infrared photodetector (QWIP). The goal is to transition this to a new complementary barrier infrared photodetector (CBIRD) with a similar long wave cut-off and increased sensitivity. ECOSTRESS is a push-whisk Mercury Cadmium Telluride (MCT) based high speed, multi-band, imager which will eventually observe and characterize plant/vegetation functionality and stress index from the International Space Station (ISS) across the contiguous United States (CONUS). MCS/DIVINER utilizes thermopile technology to capture the thermal emission from the polar caps and shadow regions of the moon. Each sensor utilizes specific JPL technology to capture unique science. similar long wave cut-off and increased sensitivity. ECOSTRESS is a push-whisk Mercury Cadmium Telluride (MCT) based high speed, multi-band, imager which will eventually observe and characterize plant/vegetation functionality and stress index from the International Space Station (ISS) across the contiguous United States (CONUS). MCS/DIVINER utilizes thermopile technology to capture the thermal emission from the polar caps and shadow regions of the moon. Each sensor utilizes specific JPL technology to capture unique science.

Johnson, William R.↗
NASA NTRS2003

Lipid Biomarkers for a Hypersaline Microbial Mat Community

The use of lipid biomarkers and their carbon isotopic compositions are valuable tools for establishing links to ancient microbial ecosystems. As witnessed by the stromatolite record, benthic microbial mats grew in shallow water lagoonal environments where microorganisms had virtually no competition apart from the harsh conditions of hypersalinity, desiccation and intense light. Today, the modern counterparts of these microbial ecosystems find appropriate niches in only a few places where extremes eliminate eukaryotic grazers. Answers to many outstanding questions about the evolution of microorganisms and their environments on early Earth are best answered through study of these extant analogs. Lipids associated with various groups of bacteria can be valuable biomarkers for identification of specific groups of microorganisms both in ancient organic-rich sedimentary rocks (geolipids) and contemporary microbial communities (membrane lipids). Use of compound specific isotope analysis adds additional refinement to the identification of biomarker source, so that it is possible to take advantage of the 3C-depletions associated with various functional groups of organisms (i.e. autotrophs, heterotrophs, methanotrophs, methanogens) responsible for the cycling of carbon within a microbial community. Our recent work has focused on a set of hypersaline evaporation ponds at Guerrero Negro, Baja California Sur, Mexico which support the abundant growth of Microcoleus-dominated microbial mats. Specific biomarkers for diatoms, cyanobacteria, archaea, green nonsulfur (GNS), sulfate reducing, and methanotrophic bacteria have been identified. Analyses of the ester-bound fatty acids indicate a highly diverse microbial community, dominated by photosynthetic organisms at the surface.

Jahnke, Linda L.↗
NASA NTRS2017

Large-Scale Circulation and Climate Variability

The causes of regional climate trends cannot be understood without considering the impact of variations in large-scale atmospheric circulation and an assessment of the role of internally generated climate variability. There are contributions to regional climate trends from changes in large-scale latitudinal circulation, which is generally organized into three cells in each hemisphere-Hadley cell, Ferrell cell and Polar cell-and which determines the location of subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift poleward during warmer periods, which could result in poleward shifts in precipitation patterns, affecting natural ecosystems, agriculture, and water resources. In addition, regional climate can be strongly affected by non-local responses to recurring patterns (or modes) of variability of the atmospheric circulation or the coupled atmosphere-ocean system. These modes of variability represent preferred spatial patterns and their temporal variation. They account for gross features in variance and for teleconnections which describe climate links between geographically separated regions. Modes of variability are often described as a product of a spatial climate pattern and an associated climate index time series that are identified based on statistical methods like Principal Component Analysis (PC analysis), which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster analysis.

Perlwitz, J.↗