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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leveraging the signature of heterotrophic respiration on atmospheric CO 2 for model benchmarking

Spatial and temporal variations in atmospheric carbon dioxide (CO 2 ) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO 2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO 2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO 2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO 2 , in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations

Abstract. One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG.

Bhattacharya, Bimal K.↗
DOE OSTI2023

Soil organic matter diagenetic state informs boreal forest ecosystem feedbacks to climate change

The fate of soil organic carbon (SOC) in boreal forests is dependent on the integrative ecosystem response to climate change. For example, boreal forest productivity is often nitrogen (N) limited, and climate warming can enhance N cycling and primary productivity. However, the net effect of this feedback on the SOC reservoir and its longevity with climate change remain unclear due to difficulty in detecting small differences between large and variable carbon (C) fluxes needed to determine net changes in soil reservoirs. The diagenetic state of SOC – resulting from the physicochemical and biological transformations that alter the original biomolecular composition of detrital inputs to soil over time – is useful for tracing the net response of SOC at the timescales relevant to climate change not usually discernible from fluxes and stocks alone. Here, we test two hypotheses using a mesic boreal forest climate transect: (1) the SOC diagenetic state is maintained across this climosequence, and (2) the maintenance of the SOC diagenetic state is a consequence of coupled soil C and N cycling, signifying the role of enhanced N cycling supporting SOC inputs that maintain SOC stocks within the warmer-climate forests. Shifts in nonvascular to vascular plant inputs with climate observed in these and other boreal forests highlighted the need to carefully separate biogeochemical indicators of SOC source from those signifying diagenetic alteration. We thus evaluated and applied lignin biomarkers to assess the diagenetic alteration of SOC in these boreal forest organic soils and directly compared the lignin diagenetic state with that of soil organic nitrogen (SON) assessed through amino acid composition. The lignin diagenetic state remained constant across the climate transect, indicating a balance between the input and removal of lignin in these mesic boreal forests. When combined with previous knowledge of these forest ecosystems, including the diagenetic state of SON and direct measures of C fluxes and stocks, the results indicate a coupled increase in C and N cycling with climate warming that supports forest productivity and maintains SOC stocks. This balance could markedly shift as other factors begin to limit forest productivity (e.g., trace nutrients, water) with further climate change or affect forest nutrient allocation (e.g., forest age or compositional change). Further application of the approach presented here could be used to detect the limits of this and other ecosystem–climate feedbacks, by providing a tractable and parameterizable index of the lignin state across large spatial scales, necessary for ecosystem-scale parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CERF: IM3 Projected Western US Power Plant Locations

Overview The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial python package that provides new power plant locations at a 1km resolution. The model ingests U.S. state or regional-scale electricity system capacity expansion plans, such as those produced by the Global Change Analysis Model (GCAM-USA), and identifies feasible, site-specific locations for individual new power plants (renewable and non-renewable). CERF combines high-resolution geospatial suitability analyses with an economic algorithm that selects individual plant siting locations based on grid interconnection costs and the locational marginal value of new generation. The model incorporates a wide range of dynamic constraints and opportunities, such as protected lands, population density, existing infrastructure, and water availability. This dataset provides CERF power plant siting results for IM3 Phase 2 simulations across eight different scenarios for the Western US through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 CERF siting results in this dataset correspond to capacity expansion plans in the GCAM-USA IM3 Phase 2 simulation data and are available for each of the above scenarios. Data Details Temporal Range: 2015-2055 in 5-year timesteps. Note that 2015 is the experiment base year and 2020 and beyond represent model simulation years. Spatial Range: Plant locations are provided for the eleven states in the Western US including Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Spatial Resolution: 1 km-squared, provided in x and y coordinates Geospatial Projection: Albers Equal Area Conic (ESRI:102003) File Type: csv The dataset contains subdirectories for each of the eight scenarios described in the overview. Each scenario folder contains two subfolders with the following information: 1. Power Plant Data This directory contains a single .csv file of power plant locations for both pre-existing (non-CERF sited plants in operation in 2015) and new (CERF-sited) power plants across the temporal range along with additional CERF model output parameters for CERF-sited plants. Plant with a siting year earlier than 2020 correspond to facilities that are operational leading into the first timestep CERF simulation. For a more detailed description of CERF model output parameters, see the CERF model documentation. Note that the cerf_plant_id parameter is unique within each scenario file but not across scenario files. Parameter Descriptions scenario - Name of scenario cerf_plant_id - Unique siting identifier cerf_sited - If True, indicates that plant was sited by CERF model. If False, indicates pre-existing facility region_name - Name of region (state) tech_id - Technology ID tech_name - Full generation technology name inclusive of cooling type (if applicable) and additional characteristics tech_simple - Simplified generation technology type unit_size_mw - Power plant unit size (MW) xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) index - Index position in the flattend 2D array buffer_in_km - Exclusion buffer around site (km) sited_year - Year of siting retirement_year - Year of retirement lmp_zone - Locational marginal price (LMP) zone ID locational_marginal_price_usd_per_mwh - Locational marginal price ($/MWh) generation_mwh_per_year - Generation output (MWh/yr) operating_cost_usd_per_year - Cost of plant operations ($/yr) net_operational_value - Net operational value based on LMP and and operating costs ($/yr) interconnection_cost - Cost of interconnection for transmission & gas pipeline (if applicable) net_locational_cost -- Difference of interconnection cost and operating value ($/yr) capacity_factor_fraction - Capacity factor (fraction) carbon_capture_rate_fraction - Carbon capture rate (fraction) fuel_co2_content_tons_per_btu - Fuel CO2 content (tons/Btu) fuel_price_usd_per_mmbtu - Fuel price ($/MMBtu) fuel_price_esc_rate_fraction - Fuel price escalation rate (fraction) heat_rate_btu_per_kWh - Heat rate (Btu/kWh) lifetime_yrs - Technology lifetime for annuity (years) operational_life_yrs - Operational lifetime for retirement (years) variable_om_usd_per_mwh - Variable operation and maintenance costs of yearly capacity use ($/MWh) variable_om_esc_rate_fraction - Variable operation and maintenance costs escalation rate (fraction) carbon_tax_usd_per_ton - Carbon tax ($/ton) carbon_tax_esc_rate_fraction - Carbon tax escalation rate (fraction) 2. Storage Data This directory contains information on new and pre-existing energy storage facilities operational in each timestep along with various storage operational parameters. The 2015 timestep provides pre-existing energy storage data and corresponds with facilities that are operational leading into the first model simulation timestep. Note that coordinates in the storage files correspond to the interconnection point on the grid (substation location), not individual energy storage locations. Energy storage is added in a cumulative process at each given interconnection point. That is, each individual file provides the total operational storage capacity interconnected to the specified substation for the given timestep, inclusive of previously installed storage at that location and new storage installed in that timestep at that location. Parameters scenario - Name of scenario timestep - Simulation timestep name - Unique storage identifier s_typ - Type of energy storage technology (battery or pumped storage hydro) s_node - Node ID of interconnecting substation xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) charge_rate - Maximum charge rate (power capacity) of storage system (MW) discharge_rate - Maximum discharge rate (power capacity) of storage system (MW) duration - Duration of storage system (hours) max_SoC - Allowed maximum state of charge (energy capacity) of storage system (MWh) min_SoC -Allowed minimum state of charge (energy capacity) of storage system (MWh) charge_eff - Efficiency of charge (fraction between 0 and 1) discharge_eff - Efficiency of discharge (fraction between 0 and 1) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

CERF↗
DOE OSTI2025

TRAILS Output Files

Overview This data repository contains ZIP files that store compressed versions of the output of running the WaterPaths utility planning and management tool in the DU Re-Evaluation mode (to download the tool, please see this GitHub repository). The tool was used to simulate the six-utility North Carolina Research Triangle problem. Details on the contents of each ZIP file can be seen below. Data details Temporal range: Weekly data for 2,344 weeks from 2015 to 2060 (45 years). Spatial range: Six water utilities in the North Carolina Research Triangle region (0: Chapel Hil/OWASA, 1: Durham, 2: Cary, 3: Raleigh, 4: Pittsboro, and 5: Chatham) File types: CSV and OUT Different solutions available The solution numbers correspond to the different pathway strategies (henceforth referred to as "solutions") discussed in paper's main and supporting text (abstract and link to the paper here). They are as follows: Sol92: The Durham-focused pathway strategy Sol132: The Raleigh-focused pathway strategy Sol140: The regionally-robust pathway strategy Objectives files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Objectives_RDMXX_solsXX_to_XX.csv files. Each CSV file will consist of a row representing all the objective values for that specific solution, while every six columns represents the reliability, restriction frequency, infrastructure net present value ($ mil), peak financial cost, worst-case cost, and unit cost ($ per MG; in that order) for each of the six utilities. There will be 1,000 such files, denoting the performance of the six utilities across the 1,000 deeply uncertain states of the world (DU SOWs). Pathway files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Pathways_sXX_RDMXX.out file. Each OUT corresponds to the set of infrastructure being triggered in a specific DU SOW, and each file will have the name file will consist of four tab-delimited columns that are described as follows: Realization: The realization in which an infrastructure options being triggered utility: The utility currently triggering infrastructure week: The week in which a specific infrastructure option is being triggered infra.: The infrastructure option being triggered If the OUT file contains only the header line, no infrastructure was triggered for that specific DU SOW. Policies files These files can be obtained by unzipping Policies.zip. Each of the 1,000 CSV files within the unzipped folder will contain weekly water use restriction policies for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: 0rest_m: restriction multiplier for utility 0 (values between 0 and 1) 1rest_m: restriction multiplier for utility 1 (values between 0 and 1) 2rest_m: restriction multiplier for utility 2 (values between 0 and 1) 3rest_m: restriction multiplier for utility 3 (values between 0 and 1) 4rest_m: restriction multiplier for utility 4 (values between 0 and 1) 5rest_m: restriction multiplier for utility 5 (values between 0 and 1) 0transf: transfer volume for utility 0 (in MGD) 1transf: transfer volume for utility 1 (in MGD) 2transf: transfer volume for utility 2 (in MGD) 3transf: transfer volume for utility 3 (in MGD) 4transf: transfer volume for utility 4 (in MGD) 5transf: transfer volume for utility 5 (in MGD) Water Sources files These files can be obtained by unzipping WaterSources_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each water source for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xvolume: available water volume from source X (in MGD) Xs_area: surface area of source X (in ACF) Xdemand: demand drawn from a water source from source X (in MGD) Xup_spill: upstream spillage from source X (in MGD) Xww_inflow: wastewater inflow from source X (in MGD) Xcatch_inflow: upstream catchment inflow to source X (in MGD) Xevap: evaporation multiplier for source X (values between 0 and 1) Xds_spill: downstream spillage from source X (in MGD) X_Y_alloc_cap: the allocated capacity from source X to utility Y (values between 0 and 1) X_Y_alloc_dem: the allocated demand from source X to utility Y (values between 0 and 1) Xtrmt_alloc_Y: the allocated treatment capacity from source X to utility Y (values between 0 and 1) Utilities files These files can be obtained by unzipping Utilities_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each utility for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xst_vol: total available storage volume of utility X (in MG) Xcapacity: total storage capacity of utility X (in MG) Xnet_inf: : net inflow for all storage infrastructure for utility X (in MGD) Xst_rof: short term ROF for utility X (values between 0 and 1) Xst_stor_rof: short-term storage ROF for utility X (values between 0 and 1) Xst_trmt_rof: short-term treatment ROF for utility X (values between 0 and 1) Xlt_rof: long-term ROF for utility X (values between 0 and 1) Xlt_stor_rof: long-term storage ROF for utility X (values between 0 and 1) Xlt_trmt_rof: long-term treatment ROF for utility X (values between 0 and 1) Xrest_demand: restricted demand for utility X (in MGD) Xunrest_demand: unrestricted demand for utility X (in MGD) Xunfulf_demand: unfulfilled demand for utility X (in MGD) Xwastewater: wastewater return for utility X (in MGD) Xtreat_capacity: total treatment capacity for utility X (in MG) Xcont_fund: reserve (contingency) fund balance for utility X Xins_pout: insurance payout for utility X (% annual volumetric revenue) Xins_price: insurance price for utility X (% annual volumetric revenue) Xinfra_npv: infrastructure net present value for utility ($mil) Xst_vol: total available storage volume of utility X (in MG) Xdebt_serv: debt service for utility X (usually once per year if the infrastructure is triggered; % annual volumetric revenue) Xstor_vol: total stored volume (in MGD) Xobs_ann_dem: observed annual demand for utility X (in MGD) Xproj_dem: projected annual demand for utility X (in MGD) Xpv_debt_serv: present value of debt service payments for utility X (% annual volumetric revenue) Xgross_rev: gross revenue for utility X ($mil) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

Artificial Intelligence↗
DOE OSTI2016

Root bacterial endophytes alter plant phenotype, but not physiology

Plant traits, such as root and leaf area, influence how plants interact with their environment and the diverse microbiota living within plants can influence plant morphology and physiology. Here, we explored how three bacterial strains isolated from the Populus root microbiome, influenced plant phenotype. Here, we chose three bacterial strains that differed in predicted metabolic capabilities, plant hormone production and metabolism, and secondary metabolite synthesis. We inoculated each bacterial strain on a single genotype of Populus trichocarpa and measured the response of plant growth related traits (root:shoot, biomass production, root and leaf growth rates) and physiological traits (chlorophyll content, net photosynthesis, net photosynthesis at saturating light–A sat , and saturating CO 2 –A max ). Overall, we found that bacterial root endophyte infection increased root growth rate up to 184% and leaf growth rate up to 137% relative to non-inoculated control plants, evidence that plants respond to bacteria by modifying morphology. However, endophyte inoculation had no influence on total plant biomass and photosynthetic traits (net photosynthesis, chlorophyll content). In sum, bacterial inoculation did not significantly increase plant carbon fixation and biomass, but their presence altered where and how carbon was being allocated in the plant host.

58 GEOSCIENCES↗
DOE OSTI2021

Grid Service Values of Generic Marginal Building Flexibility in Modeled 2030 U.S. Power Systems

The datasets include the capacity, energy, and ancillary service values of a marginal kilowatt-hour (kWh) of generic, daily, shiftable building flexibility as a presumed market entrant in the 2030 U.S. power systems. The results should be interpreted along with the caveats listed in "Valuation of Building Flexibility: Grid Service Value for Initial Market Entrants in Projected 2030 United States Power Systems", which also documents the methods used for the study. The factors examined in the study include: grid scenario, region, original usage hour in the day (local time), and building flexibility parameters - efficiency, dissipation, and shifting window include max pre-shift and max post-shift. Filenames in the datasets: The filenames contain information on the grid scenario, and the building flexibility efficiency and dissipation. For example: MidCase_2030_efficiency1.25_dissipation0.05_value.csv contains all results under Mid RE 2020, efficiency = 1.25, and dissipation = 0.05. * MidCase = Mid RE Each .csv file contains: region: The location of the building flexibility, aligned with U.S. Energy Information Administration (EIA) National Energy Modeling System (NEMS) Electricity Market Module regions. max_pre_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift earlier. max_post_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift later. local_datetime: Original datetime of 1kWh of building energy consumption. local_orig_h: Original usage hour (1-24) of building energy consumption, ignores daylight saving. local_shift_to: The datetime to when building energy consumption shifts, if shifting happens. If no shifting occurs, this cell is left blank. energy: Net energy value of energy shifting. capacity: Net capacity value of energy shifting. shifting_value: Net energy plus capacity value of energy shifting. spin: Spin reserve value at the original datetime of consumption. flex: Flexible reserve value at the original datetime of consumption. reg: Regulation reserve value at the original datetime of consumption. total_profit: Assuming the building flexibility is capable of providing energy, capacity, and ancillary services, total profit is the maximum value of energy shifting value, spin reserve value, flexible reserve value, and regulation reserve value - one of the four choices at any given hour - because we do not allow its value to be double-counted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1973

Martian surface coordinates

The resolution of the pictures from the Mariner 9 mission determines the distribution of the control points, as well as the gaps in the planet-wide control net. After discussing the photogrammetric equations and the computational method used to establish the planet-wide control net and the secondary net, the camera stations and the assumed physical properties of Mars are considered. Subsequent paragraphs contain a description of the control points, their measurements, and a discussion of how distortions are removed. Finally, results of the computations are given, with tables of coordinates for the control points.

Davies, M. E.↗
NASA NTRS1974

Cruise performance of an isolated 1.15 pressure ratio turbofan propulsion system simulator at Mach numbers from 0.6 to 0.85

An isolated 1.15 pressure ratio turbofan engine simulator was tested at Mach numbers from 0.6 to 0.85. At Mach 0.75 the net propulsive force of the fan and nacelle (excluding core thrust) was 73 percent of the ideal fan net thrust. Internal losses amounted to 7 percent, and external drag amounted to 20 percent of the ideal fan net thrust. External pressure and friction drag were about equal. The propulsive efficiency with a 90 percent efficient fan would have been 63 percent. For the aerodynamic characteristics of the nacelle that was tested, increasing the fan pressure ratio to approximately 1.35 would have resulted in a maximum propulsive efficiency of 67 percent.

Steffen, F. W.↗
NASA NTRS1975

Theoretical monochromatic-wave-induced currents in intermediate water with viscosity and nonzero mass transport

Wave-induced mass-transport current theories with both zero and nonzero net mass (or volume) transport of the water column are reviewed. A relationship based on the Longuet-Higgens theory is derived for wave-induced, nonzero mass-transport currents in intermediate water depths for a viscous fluid. The relationship is in a form useful for experimental applications; therefore, some design criteria for experimental wave-tank tests are also presented. Sample parametric cases for typical wave-tank conditions and a typical ocean swell were assessed by using the relation in conjunction with an equation developed by Unluata and Mei for the maximum wave-induced volume transport. Calculations indicate that substantial changes in the wave-induced mass-transport current profiles may exist dependent upon the assumed net volume transport. A maximum volume transport, corresponding to an infinite channel or idealized ocean condition, produces the largest wave-induced mass-transport currents. These calculations suggest that wave-induced mass-transport currents may have considerable effects on pollution and suspended-sediments transport as well as buoy drift, the surface and midlayer water-column currents caused by waves increasing with increasing net volume transports. Some of these effects are discussed.

Talay, T. A.↗
NASA NTRS1977

Observations in energy balance in man during spaceflight

An investigation was undertaken of the changes in metabolic energy balance which occur in weightlessness. Daily energy intake was determined each day throughout the 28-, 59-, and 84-day flights for each of the nine Skylab astronauts. The energy content of the urine and feces was also measured. Changes in body composition were inferred from measurements of weight, volume, water, and total exchangeable potassium before and after flight. During flight, changes were followed by a daily measurement of body mass and by metabolic balance. Examination of the data reveal losses in body weight during the 1st and 2nd months of flight, a loss in body water and protein during the 1st month and a loss of fat during the 1st, 2nd, and 3rd months of flight. The energy input was about 41.7 kcal/kg per day on the ground, and 43.7 kcal/kg per day after 3 months in space. The increase in net energy input of about 1.6% per month is significant (P less than 0.05). When the net energy input is expressed on the basis of total body potassium, the increase in the resulting normalized net energy input of about 3.7% per month is also significant (P less than 0.05).

Rambaut, P. C.↗
NASA NTRS1978

The annual variation in the global heat balance of the earth

An annual variation with a range of 31 W/sq m is found in the global net radiation balance of the earth. The net radiation flux values measured from satellites and the changes in total heat content computed from independent sets of atmospheric and oceanic data show annual variations which are consistent with each other in both phase and magnitude. The net energy gain and loss by the planet within a year is stored and released within the system primarily by the oceans.

Ellis, J. S.↗
NASA NTRS1975

Relative ages of flow units in Mare Imbrium and Sinus Iridum

A method for determining relative ages from low-resolution Lunar Orbiter photographs of lunar plains has been developed and applied to Mare Imbrium and Sinus Iridum. The method is an extension of previous ones and uses the shapes of craters and a small-impact erosion model to estimate the net accumulated impact flux. The net impact flux that has accumulated on a surface of uniform age is a measure of relative age of that surface and can be estimated by the largest diameters of craters that have been degraded by the net impact flux. Frequency distributions or relative ages indicate that there are four major units present in Mare Imbrium and Sinus Iridum. The older mare units are generally found along the edge of the basin and the younger units in the center. A similar distribution of ages of units in Mare Serenitatis shows the two basins have had similar filling histories. Filling histories for other circular basins may be similar to these two.

Boyce, J. M.↗
NASA NTRS1979

Control networks for the Galilean satellites, November 1979

Pictures of the four Galilean satellites taken as the two Voyager spacecraft approached Jupiter during March and July 1979 are presented. Control nets of the Galilean satellites, computed photogrammetrically, and measurements of the mean radii are presented. The pictures in the control nets are identified, the coordinates of the control points are given, and identifications of some of the control points are shown on figures. The use of star field pictures to compute the focal lengths of the camera is discussed and the geometric relationship between the narrow and wide and angle cameras is reported. A description of the coordinate systems of the Galilean satellites is presented and the status of the control net computations is reported.

Davies, M. E.↗
NASA NTRS1981

Radiation budget measurement/model interface research

The NIMBUS 6 data were analyzed to form an up to date climatology of the Earth radiation budget as a basis for numerical model definition studies. Global maps depicting infrared emitted flux, net flux and albedo from processed NIMBUS 6 data for July, 1977, are presented. Zonal averages of net radiation flux for April, May, and June and zonal mean emitted flux and net flux for the December to January period are also presented. The development of two models is reported. The first is a statistical dynamical model with vertical and horizontal resolution. The second model is a two level global linear balance model. The results of time integration of the model up to 120 days, to simulate the January circulation, are discussed. Average zonal wind, meridonal wind component, vertical velocity, and moisture budget are among the parameters addressed.

Vonderhaar, T. H.↗
NASA NTRS1981

Analysis of the surface heat balance over the world ocean

The net surface heat fluxes over the global ocean for all calendar months were evaluated. To obtain a formula in the form Qs = Q2(T*A - Ts), where Qs is the net surface heat flux, Ts is the sea surface temperature, T*A is the apparent atmospheric equilibrium temperature, and Q2 is the proportionality constant. Here T*A and Q2, derived from the original heat flux formulas, are functions of the surface meteorological parameters (e.g., surface wind speed, air temperature, dew point, etc.) and the surface radiation parameters. This formulation of the net surface heat flux together with climatological atmospheric parameters provides a realistic and computationally efficient upper boundary condition for oceanic climate modeling.

Esbenson, S. K.↗
NASA NTRS1981

Earth radiation budgets

The annual and seasonal averaged earth atmosphere radiation budgets, derived from the most complete set of satellite observations available in late 1979, are presented. The budgets are derived using a composite of 48 monthly mean radiation budget maps. The annual, global average emitted infrared flux is 234 W/sq m, the planetary albedo is 0.30, and the net flux is zero within measurement uncertainty. In addition, the annual cycle of net flux is studied in detail, and the observed globally averaged net flux is found to display an annual cycle that is of similar magnitude and phase to the annual cycle imposed by the influence of sun-earth distance variations on solar radiation input into the atmosphere.

Stephens, G. L.↗
NASA NTRS1981

Energy balance in solar and stellar chromospheres

Net radiative cooling rates for quiet and active regions of the solar chromosphere and for two stellar chromospheres are calculated from corresponding atmospheric models. Models of chromospheric temperature and microvelocity distributions are derived from observed spectra of a dark point within a cell, the average sun and a very bright network element on the quiet sun, a solar plage and flare, and the stars Alpha Boo and Lambda And. Net radiative cooling rates due to the transitions of various atoms and ions are then calculated from the models as a function of depth. Large values of the net radiative cooling rate are found at the base of the chromosphere-corona transition region which are due primarily to Lyman alpha emission, and a temperature plateau is obtained in the transition region itself. In the chromospheric regions, the calculated cooling rate is equal to the mechanical energy input as a function of height and thus provides a direct constraint on theories of chromospheric heating.

Avrett, E. H.↗

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