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At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Impact of Assimilating GOES-Derived Land Surface Variables into the PSU/NCAR MM5

Land surface processes are known to have a profound impact on the overlying atmosphere over a wide range of spatial and temporal scales. Many atmospheric numerical models include special parameterizations to improve the specification and partitioning of surface fluxes which are critical to the accurate prediction of warm season boundary layer behavior, organized mesoscale circulations, and convective precipitation. However, the added degrees of freedom resulting from the inclusion of vegetation and soil schemes require the specification of additional surface parameters such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, and the vertical distribution of soil moisture. As satellite data have become more readily available in recent years, many investigations have attempted to use these new measurements to infer missing components of the surface energy budget. Sensitivity studies have shown land-skin temperature (LST) tendencies during the mid-morning hours are strongly sensitive to the surface moisture availability (a function of soil wetness and vegetation) and less sensitive to other parameters such as surface roughness. Based upon results from these and other studies, developed a simple technique that dynamically assimilates Geostationary Operational Environmental Satellite (GOES) derived land-surface products into the surface energy budget of a mesoscale model. The purpose of this paper is to demonstrate that assimilating the GOES satellite data has the potential to improve the representation of land surface characteristics within the model without prior knowledge of the land surface characteristics. The assimilation technique is presented in Section 2 and the numerical experiments are detailed in Section 3. Preliminary results and conclusions are presented in Sections 4 and 5, respectively.

Lapenta, William M.↗
DOE OSTI2022

Variability in observed stable water isotopes in snowpack across a mountainous watershed in Colorado

In this study, isotopic information from 81 snowpits was collected over a 5-year period in a large, Colorado watershed. Data spans gradients in elevation, aspect, vegetation, and seasonal climate. They are combined with overlapping campaigns for water isotopes in precipitation and snowmelt, and a land-surface model for detailed estimates of snowfall and climate at sample locations. Snowfall isotopic inputs, describe the majority of δ18O snowpack variability. Aspect is a secondary control, with slightly more enriched conditions on east and north facing slopes. This is attributed to preservation of seasonally enriched snowfall and vapour loss in the early winter. Sublimation, expressed by decreases in snowpack d-excess in comparison to snowfall contributions, increases at low elevation and when seasonal temperature and solar radiation are high. At peak snow accumulation, post-depositional fractionation appears to occur in the top 25 ± 14% of the snowpack due to melt-freeze redistribution of lighter isotopes deeper into the snowpack and vapour loss to the atmosphere during intermittent periods of low relative humidity and high windspeed. Relative depth of fractionation increases when winter daytime temperatures are high and winter precipitation is low. Once isothermal, snowpack isotopic homogenization and enrichment was observed with initial snowmelt isotopically depleted in comparison to snowpack and enriching over time. The rate of δ18O increase (d-excess decrease) in snowmelt was 0.02‰ per day per 100-m elevation loss. Isotopic data suggests elevation dictates snowpack and snowmelt evolution by controlling early snow persistence (or absence), isotopic lapse rates in precipitation and the ratio of energy to snow availability. Hydrologic tracer studies using stable water isotopes in basins of large topographic relief will require adjustment for these elevational controls to properly constrain stream water sourcing from snowmelt.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. A systematic evaluation of the interaction between coastal catchment properties and carbon dioxide (CO2) emission by soil respiration is significant for assessing carbon dynamics and predicting the future trajectory of atmospheric CO2 concentrations in coastal TAIs. The soil CO2 efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation index (kNDVI), climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, there is no one-size-fits-all approach to modeling carbon fluxes in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Microwave remote sensing: Active and passive. Volume 2 - Radar remote sensing and surface scattering and emission theory

The fundamental principles of radar backscattering measurements are presented, including measurement statistics, Doppler and pulse discrimination techniques, and associated ambiguity functions. The operation of real and synthetic aperture sidelooking airborne radar systems is described, along with the internal and external calibration techniques employed in scattering measurements. Attention is given to the physical mechanisms responsible for the scattering emission behavior of homogeneous and inhomogeneous media, through a discussion of surface roughness, dielectric properties and inhomogeneity, and penetration depth. Simple semiempirical models are presented. Theoretical models involving greater mathematical sophistication are also given for extended ocean and bare soil surfaces, and the more general case of a vegetation canopy over a rough surface.

Ulaby, F. T.↗
NASA NTRS1984

Red and near-infrared sensor response to off-nadir viewing

It has first been intended to employ the Advanced Very High Resolution Radiometer (AVHRR) as a meteorological instrument to aid in detecting clouds and atmospheric temperature stratifications over large areas. However, more recently the satellite data have been used for monitoring large areas of vegetation with emphasis on the visible (VIS) and near-infrared (NIR) bands. The present investigation is concerned with the objective to model ranges in AVHRR VIS and NIR radiances and normalized differences expected under typical AVHRR viewing and illumination conditions. Atmospheric data sets reported by Dave (1978) and Dave and Braslau (1975) were used. It is shown that sensor response to atmospheric path length can be substantial for the AVHRR VIS and NIR channels and normalized difference (ND) values. However, errors can be minimized by high sun and clear atmosphere viewing.

Holben, B.↗
NASA NTRS1984

Relating the microwave backscattering coefficient to leaf area index

This paper examines the relationship between the microwave backscattering coefficient of a vegetation canopy, sigma (can, 0) and the canopy's leaf area index (LAI). The relationship is established through the development of one model for corn and sorghum and another for wheat. Both models are extensions of the cloud model of Attema and Ulaby (1978). Analysis of experimental data measured at 8.6, 13.0, 17.0, and 35.6 GHz indicates that most of the temporal variations of sigma (can, 0) can be accounted for through variations in green LAI alone, if the latter is greater than 0.5.

Ulaby, F. T.↗
NASA NTRS1992

Factors controlling sulfur gas exchange in Sphagnum-dominated wetlands

Atmosphere-peatland exchange of reduced sulfur gases was determined seasonally in fen in NH, and in an artificially-acidified fen at the Experimental Lakes Area (ELA) in Canada. Dimethyl sulfide (DMS) dominated gas fluxes at rates as high as 400 nmol/m(sup -2)hr(sup -1). DMS fluxes measured using enclosures were much higher than those calculated using a stagnant-film model, suggesting that Sphagnum regulated efflux. Temperature controlled diel and seasonal variability in DMS emissions. Use of differing enclosure techniques indicated that vegetated peatlands consume atmospheric carbonyl sulfide. Sulfate amendments caused DMS and methane thiol concentrations in near-surface pore waters to increase rapidly, but fluxes of these gases to the atmosphere were not affected. However, emission data from sites experiencing large differences in rates of sulfate deposition from the atmosphere suggested that chronic elevated sulfate inputs enhance DMS emissions from northern wetlands.

Demello, William Zamboni↗
NASA NTRS1994

Remote sensing of forest ecosystem dynamics: Measurements and modeling

The development of an integrated approach to the modeling of forest dynamics encompassing submodels of forest growth and succession, soil processes and radiation interactions, is reported. Remote sensing technology is a key element of this study in that it provides data for developing, initializing, updating, and validating the models. The objectives are reviewed, the data collected and models in use are discussed, and a framework for studying interactions between the forest growth, soil process and energy interaction components, is described. Remote sensing technology used in the study includes optical and microwave field, aircraft and satellite borne instruments. The types of data collected during intensive field and aircraft campaigns included bidirectional reflectance, thermal emittance and multifrequency, multipolarization synthetic aperture radar backscatter. Synthetic imagery of derived products such as forest biomass and NDVI (Normalized Difference Vegetative Index), and collections of ground data are being assembled in a georeferenced data base. These data are used to drive or test multidiscipline simulations of forested ecosystems. Enhancements to the modeling environment permit considerable flexibility in configuring simulations and selecting results for reporting and graphical display.

Williams, Darrel L.↗
NASA NTRS1973

Land use mapping and modelling for the Phoenix Quadrangle

The author has identified the following significant results. Comparison of 9 x 9 MSS band images and color composites made from bands 4, 5, and 6 showing vegetated areas near Phoenix during the summer and fall seasons aided in definitely establishing that certain land areas were being used as agricultural land and not as rangeland. Agricultural land, which appeared to be fallow, idle, or not irrigated, often became more readily identifiable as agricultural land when comparing different images of identical land areas which have been affected by seasonal vegetation changes. Experimentation with color density slicing portions of 9 x 9 MSS band 7 transparency showing the central urban core of phoenix enabled dense commercial and industrial areas to be separated from less dense urbanized land uses; however, loss of resolution produced results of limited usefulness. The best results in agricultural areas near Sun City were obtained using MSS band 5 imagery. Discrimination of different land uses in both urban and agricultural areas which were color density sliced was not possible to the degree of accuracy necessary to make mapping feasible. Examination of MSS transparencies and color composites allowed updating of a map of land use change in the Phoenix Quadrangle.

Place, J. L.↗
NASA NTRS1973

Land use mapping and modelling for the Phoenix Quadrangle

The author has identified the following significant results. Comparison of 9 x 9 MSS band images and color composites made from bands 4, 5, and 6 showing vegetated areas near Phoenix during the summer, fall, and winter seasons aided in definitely establishing that certain land areas were being used as agricultural land and not as rangeland. Agricultural land, which appeared to be fallow, idle, or not irrigated, often became more readily identifiable as agricultural land when comparing different images of identical land areas which have been affected by seasonal vegetation changes. Experimentation with the Bausch and Lomb Zoom Transferscope using MSS images of identical areas in the same spectral band from different time periods, with a quick flip method of alternately viewing the frame areas, enabled rapid detection of a major land use change from agricultural to urban use on the northwest fringe of the metropolitan Phoenix area. The best results in this case were obtained when comparing MSS band 5 images. Examination of MSS transparencies and color composites allowed further updating of a map of land use change in the Phoenix Quadrangle.

Place, J. L.↗
DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Maps of Arctic vegetation leaf nitrogen concentration, albedo and plant functional type (PFT) derived from imaging spectroscopy data, Council watershed, Seward Peninsula, Alaska, 2019

Remote sensing maps of surface albedo, leaf nitrogen content, and plant functional types (PFTs) derived from NASA's Airborne Visible / Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) by the Terrestrial Ecosystem Science & Technology (TEST) group at Brookhaven National Laboratory. The AVIRIS-NG imaging spectroscopy data (380 ~ 2510 nm) was collected as a part of the collaboration between NASA's Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE's Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps for the NGEE-Arctic Council watershed created using AVIRIS-NG imagery collected on July 9th, 2019. The map data and metadata are provided as image (ENVI, *.png) and text (*.txt, *hdr) formats. Additional supporting map quicklooks are provided as *.png files and GIS *.kml files. Detailed description of the methods for each map are provided in this document. These datasets are provided in support of Figure 6 in Nelson et al., (2022), "Remote Sensing of Tundra Ecosystems using High Spectral Resolution Reflectance: Opportunities and Challenges". The full citation can be found within the references section. Note that the AVIRIS-NG leaf nitrogen product included in this dataset is a preliminary product and is provided for demonstration purposes only. It is not recommended that the map be used for scientific applications. For future updates on these products, please contact the authors.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Use of MODIS Satellite Data to Evaluate Juniperus spp. Pollen Phenology to Support a Pollen Dispersal Model, PREAM, to Support Public Health Allergy Alerts

Juniperus spp. pollen is a significant aeroallergen that can be transported 200-600 km from the source. Local observations of Juniperus spp. phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. Methods: The Dust REgional Atmospheric Model (DREAM)is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust. We successfully modified the DREAM model to incorporate pollen transport (PREAM) and used MODIS satellite images to develop Juniperus ashei pollen input source masks. The Pollen Release Potential Source Map, also referred to as a source mask in model applications, may use different satellite platforms and sensors and a variety of data sets other than the USGS GAP data we used to map J. ashei cover type. MODIS derived percent tree cover is obtained from MODIS Vegetation Continuous Fields (VCF) product (collection 3 and 4, MOD44B, 500 and 250 m grid resolution). We use updated 2010 values to calculate pollen concentration at source (J. ashei ). The original MODIS derived values are converted from native approx. 250 m to 990m (approx. 1 km) for the calculation of a mask to fit the model (PREAM) resolution. Results: The simulation period is chosen following the information that in the last 2 weeks of December 2010. The PREAM modeled near-surface concentrations (Nm-3) shows the transport patterns of J. ashei pollen over a 5 day period (Fig. 2). Typical scales of the simulated transport process are regional.

Luvall, J. C.↗
DOE OSTI2022

Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska: Supporting Data

Remote sensing maps of plant functional type (PFT) fractional cover (FCover), dominant PFT, and FCover uncertainty derived from NASA's Airborne Visible / Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380-2510 nm) was collected as a part of the collaboration between NASA's Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE's Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps of the NGEE-Arctic Council watershed on the Seward Peninsula, Alaska, created using AVIRIS-NG imagery collected on July 9th, 2019. The map data and metadata are provided as GeoTIFF (*.tif), ENVI image (*.dat), and text (*.txt, *hdr) formats. Additional map quicklooks are provided as *.pdf files and GIS *.kml files. These datasets are provided in support of Yang et al., (2023), "Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska".The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Changes in the Carbon Cycle of Amazon Ecosystems During the 2010 Drought

Satellite remote sensing was combined with the NASA-CASA carbon cycle simulation model to evaluate the impact of the 2010 drought (July through September) throughout tropical South America. Results indicated that net primary production (NPP) in Amazon forest areas declined by an average of 7% in 2010 compared to 2008. This represented a loss of vegetation CO2 uptake and potential Amazon rainforest growth of nearly 0.5 Pg C in 2010. The largest overall decline in ecosystem carbon gains by land cover type was predicted for closed broadleaf forest areas of the Amazon River basin, including a large fraction of regularly flooded forest areas. Model results support the hypothesis that soil and dead wood carbon decomposition fluxes of CO2 to the atmosphere were elevated during the drought period of 2010 in periodically flooded forest areas, compared to forests outside the main river floodplains.

Potter, Christophera↗
NASA NTRS2020

Global Carbon Cycle and Climate Feedbacks in the NASA GISS ModelE2.1

We present results from the NASA GISS ModelE2.1‐G‐CC Earth System Model with coupled climate‐carbon cycle simulations that were submitted to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Coupled Climate‐Carbon Cycle MIP (C4MIP). Atmospheric CO2 concentration and carbon budgets for the land and ocean in the historical simulations were generally consistent with observations. Low simulated atmospheric CO2 concentrations during 1850‐1950 were due to excess uptake from prescribed land cover change, which erroneously replaced arid shrublands with higher biomass crops, and assumed high 2004 LAI values in vegetated lands throughout the historical simulation. At the end of the historical period, slightly higher simulated CO2 than observed resulted from the land being an insufficient net carbon sink, despite the net effect of CO2 fertilization and warming‐induced increases to leaf photosynthetic capacity. The global ocean carbon uptake agreed well with the observations with the largest discrepancies in the low latitudes. Future climate projection at 2091‐2100 agreed with CMIP5 models in the northward shift, of temperate deciduous forest climate and expansion across Eurasia along 60 °N latitude, and dramatic regional biome shifts from drying and warming in continental Europe. Carbon feedback parameters were largely similar to the CMIP5 model ensemble. For our model, the variation of land feedback parameters within the uncertainty arises from the fertilization feedback being less sensitive due to lack of increased vegetation growth, and the comparably more negative ocean carbon‐climate feedback is due to the large slowdown of the Atlantic overturning circulation.

Carbon cycle↗