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656 results for “albedo”
The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections
<p>These files are associated with the article "The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections". </p> <p>1. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/soil_hyper_albedo_RF_int.nc">soil_hyper_albedo_RF_int.nc</a> - hyperspectral soil albedo </p> <p>2. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/lai_hyper_albedo_RF_int.nc">lai_hyper_albedo_RF_int.nc</a> - hyperspectral surface albedo</p> <p>3. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_bl ...</a> - diagnostic results of the atmospheric model CAM between broadband and hyperspectral simulations. </p> <p>4. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_dif ...</a> - diagnostic results of the land model CLM between broadband and hyperspectral simulations. </p>
Carbon storage and carbon-equivalent albedo impact for US forests, by age and forest type
<p>These tables document estimates of carbon storage (Mg/ha +/- Standard Error) and carbon-equivalent albedo impacts (same units) of US forests by age and forest type (Healey et al., in review). Carbon estimates are derived from field measurements made by the USDA Forest Service on approximately 125,000 forested field plots (Domke et al., 2022). Soil organic carbon is omitted from these estimates, but all other above- and below-ground pools are included. Albedo impacts (time-dependent emissions equivalent, TDEE; Bright et al., 2016) were developed by applying atmospheric kernels (Bright and O'Halloran) to a new Landsat blue sky albedo product for the Landsat archive (Erb et al., 2022), as described by Healey et al. (in review). Standard error is supplied for each age/forest type bin for carbon storage, but upper and lower standard error bounds are specified for TDEE because log transformation creates an asymmetrical uncertainty envelope. </p> <p> </p> <p>Bright, Bogren, Bernier, Astrup, (2016). Carbon-equivalent metrics for albedo changes in land management contexts: Relevance of the time dimension. <em>Ecol. Appl.</em> 26, 1868–1880</p> <p>Bright, R. M., & O'Halloran, T. L. (2019). Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1. 0. <em>Geoscientific Model Development, </em>12(9), 3975-3990.</p> <p>Domke, Walters, Nowak, Greenfield, Smith, Nichols, Ogle, Coulston, Wirth (2022). Greenhouse Gas Emissions and Removals From Forest Land, Woodlands, Urban Trees, and Harvested Wood Products in the United States, 1990–2020. (US Dept. Ag. For. Service, Madison, WI; <a href="https://doi.org/10.2737/FS-RU-382">https://doi.org/10.2737/FS-RU-382</a>).</p> <p>Erb, Li, Sun, Paynter, Wang, & Schaaf, (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. <em>Remote Sensing</em>, <em>14</em>(21), 5320.</p> <p>Healey, Yang, Erb, Bright, Domke, Frescino, Schaaf, (in review) New satellite observations expose albedo dynamics offsetting half of carbon storage benefits in US forests.</p>
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: V - Change in summer albedo by climate scenario
These data contain albedo estimates from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. This file contains data for Figure 7.
McMurdo Dry Valleys LTER: Landscape Albedo in Taylor Valley, Antarctica from 2015 to 2019
This data package contains reflectance data and associated aerial images collected using a helicopter-suspended "albedo box," in which a shortwave radiometer and camera where mounted facing downward. The purpose of this study was to measure how surface reflectance varies within and across landscape types (glaciers, lakes, and soils) over the course of a single field season as well as across multiple field seasons. We made five flights in the 2015-2016 field season (20 November 2015, 7 December 2015, 24 December 2015, 5 January 2016, and 12 January 2016), five flights in the 2016-2017 field season (11 November 2016, 3 December 2016, 14 December 2016, 3 January 2017, 23 January 2017), four flights in the 2017-2018 field season (22 November 2017, 7 December 2017, 27 December 2017, 13 January 2018), and two flights in the 2018-2019 field season (23 November 2018, 9 January 2019). Flights originated from Lake Hoare field camp, flew down-valley over Canada Glacier, Lake Fryxell, and Commonwealth Glacier, then turned around and flew up-valley to Taylor Glacier Meteorological Station, after which they returned to Lake Hoare field camp. Flights took roughly one hour and were flown at approximately 25.72 m s-1 (50 knots) and 91.44 m (300 ft) above the ground surface. These data can be normalized to incoming solar radiation (measured in-situ at meteorological stations) to calculate landscape albedo. When collected several times throughout a season, these results can show how snow distribution and physical changes to glacier and lake ice impact the amount of incoming radiation that is absorbed, while also tracking the influence of deposited sediment on ice surfaces. Moreover, these data are important for quantifying the long-term changes in energy connectivity between the atmosphere and the landscape (i.e., addressing Hypothesis 1 of the MCM V funding cycle).
Spatial variability of the snowmelt-albedo feedback in Antarctica
<p>This dataset accompanies the manuscript "Spatial variability of the snowmelt-albedo feedback in Antarctica" by C.L. Jakobs et al., submitted to JGR: Earth Surface.</p> <p>File naming convention:</p> <p>Modelversion_domain_parameter_timeresolution_beginyear_endyear_experiment.nc</p> <p>where "experiment" is optional.</p> <p> </p> <p>Parameter names (note that precipitation and snowmelt are provided in units <em>per second</em>):</p> <ul> <li>LWin: downward longwave radiation [W/m<sup>2</sup>]</li> <li>LWout: upward longwave radiation [W/m<sup>2</sup>]</li> <li>precip: precipitation (liquid and solid) [kg/m<sup>2</sup>/s]</li> <li>QG: ground heat flux [W/m<sup>2</sup>]</li> <li>QL: latent heat flux [W/m<sup>2</sup>]</li> <li>QS: sensible heat flux [W/m<sup>2</sup>]</li> <li>snowmelt: surface melt rate [kg/m<sup>2</sup>/s]</li> <li>SWin: incoming shortwave radiation [W/m<sup>2</sup>]</li> <li>SWout: reflected shortwave radiation [W/m<sup>2</sup>]</li> <li>T2m: 2-meter temperature [K]</li> </ul> <p> </p> <p><strong>Note!</strong></p> <p>This dataset might not represent the latest version of these data. Please contact the Institute for Marine and Atmospheric Research Utrecht (imau@science.uu.nl) for information about the latest available version of these data.</p>
Dataset for "Comparison of climate response to ocean albedo modification and marine cloud brightening: A model study"
<p>Reproducible dataset for "Comparison of climate response to ocean albedo modification and marine cloud brightening: A model study"</p>
Shortwave surface albedo of glaciers in the central Chilean Andes
<p>Data used to analyse glacier surface albedo change in the central Chilean Andes for the manuscript:</p> <p>Glacier albedo reduction and drought effects in the extratropical Andes, 1986-2020</p> <p>Thomas E. Shaw1, Genesis Ulloa2, David Farías-Barahona3, Rodrigo Fernandez2, Jose Lattus2, James McPhee1,4</p> <p>1 Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile<br> 2 Department of Geology, Universidad de Chile, Santiago, Chile<br> 3 Institute für Geographie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany<br> 4 Department of Civil Engineering, Universidad de Chile, Santiago, Chile</p> <p>Corresponding author: Thomas E. Shaw (thomas.shaw@amtc.uchile.cl)<br> Keywords: Albedo, Andes, Glacier, Drought, Remote sensing, Climate</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Sub-folders:<br> <strong> [Albedo]:</strong><br> '<strong>Albedo_ChileanGlaciers_DATA.mat'</strong> = matlab file that contains a structure of all information for analyses. <br> 'DATA' structure contains: <br> NAME = Glacier name<br> ALBEDO = 3D albedo matrices for each named glacier<br> DEM = ASTER DEM of same resolution + size<br> DEMtif = as above, but within a georeferenced GRIDobj frame read by TopoToolbox<br> CLASS = classification as 0 (no data), 1 (ice) or 2 (snow)<br> OTSUindex = The histogram separation value per year (per glacier) based upon Otsu inter-class variance<br> SHADOW = Shadowed pixels based upon solar geometry<br> SOLAR_AZI = Solar Azimuth per year taken from Landsat metadata<br> SOLAR_ELE = Solar Elevation per year taken from Landsat metadata<br> NIR = The Near-Infrared band of Landsat images for the Osu classification<br> SLOPE = The calculated slope angle based upon the DEM (GRIDobj format)</p> <p><br> 'SHAPE' is an 18*1 structure of the imported shapefiles in matlab format. Can be plotted using 'mapshow'</p> <p> <strong>[Shapefiles]:</strong><br> '<strong>CentralChileGlaciers.shp</strong>' = Shapefile of glacier boundaries delineated based upon April 2020 3 m PlanetScope imagery.</p> <p><br> <strong>[Climate]:</strong><br> '<strong>Ta_Precip_HY.mat</strong>' = Mean Monthly Air temperature (°C) and monthly total precipitation (mm) at long term DGA weather stations for each Hydrological year (April-March)<br> 'TAmonth_HY' = A matrix of 35 x 12 mean month air temperatures (°C) where rows (x35) = the hydrological year starting 1985-1986 and columns (x12) = the months of the hydrological year so that the first column is April and the final column is March of the following year<br> 'PPmonth_HY' = As above but a 3D matrix of 35 x 12 x 3 for monthly sums of precipitation (mm). The rows and columns are defined above and the third dimension are the stations Riecillos (32.92°S, 70.35°W ,1290 m a.s.l.), Embalse Yeso (33.67°S, 70.08°W, 2475 m a.s.l.) and Rengo (34.19°S, 70.75°W, 515 m a.s.l.), respectively.<br> <br> </p> <p><br> <br> </p>
Data archive for the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method"
<p>Data archive accompanying the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method". In 2020 this article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em>. Data are uploaded in the form of ascii text files, Igor Pro experiment files (.pxp), and Jupyter notebook files. In addition, a Jupyter notebook file is included containing an implementation of the error model used in the paper.</p>
Boreal Winter Hadley Cell Contraction in Response to the Incorporation of a Comprehensive Ocean Surface Albedo in CESM2
<p>Simulated output for: Boreal Winter Hadley Cell Contraction in Response to the Incorporation of a Comprehensive Ocean Surface Albedo in CESM2</p>
Data of low snow surface albedo experiment with MIROC5.2
<p>Dataset of the low snow surface albedo experiment and the control experiment using the Atmospheric GCM of MIROC5.2.</p> <p>In the low snow surface albedo experiment, the surface albedo of snow was set considerably lower than the normal values used in the control experiment. Other parameters and forcing data in both the experiments are the same. Variables are limited to those used in the research paper.</p> <ul> <li>Latent heat flux at surface (evap)</li> <li>sensible heat flux at surface (sens)</li> <li>sea level pressure (slp)</li> <li>all-sky longwave radiation flux at surface (slr)</li> <li>clear-sky longwave radiation flux at surface(slrc)</li> <li>all-sky shortwave radiation flux at surface (ssr)</li> <li>clear-sky shortwave radiation flux at surface (ssrc)</li> <li>surface air temperature (T2)</li> </ul> <p>See the research paper for the detail.</p>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (500m)
<p>Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/.</p> <p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset follows the basic MODIS product format and employed the sinusoidal projection. It includes historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.</p>
MDAL v2: Experimental MSG daily albedo 01-11-2020 -- 31-10-2021
<p>This dataset was produced using an update to the MDAL retrieval algorithm (i.e. an update of the operational MDAL algorithm at time of publication). Depending on outcome of peer-review and EUMETSAT-internal review, the updated version will replace the currently operational one.</p>
Single scattering albedo dataset for Hammond et al. 2024
<p>Single scattering albedo (w) for surface types listed in Hammond et al. 2024. First column is wavelength in nm, second column is w.</p> <p>The script ssa_to_albedo.py shows how to convert this data to the spherical reflectance or geometric albedo. It can be converted to other quantities following Hapke 2012 (Theory of Reflectance and Emittance Spectroscopy), summarised in Hammond et al. 2024.</p> <p>This dataset utilises spectra acquired by Raymond E. Arvidson, Melinda D. Dyar, Bethany L. Ehlmann, William H. Farrand, George Mathew, Jack Mustard, Carle M. Pieters, Hiroshi Takeda, and the Planetary Geosciences Lab (PSI) with the NASA RELAB facility at Brown University. </p>
Measurement of the albedo of red soil in Ghana in 2020
<p>The albedo of red soil in Ghana was measured with an Albedometer in three different locations in 2020 following the <em>ASTM Standard E1918-06</em>. The dataset contains the calibration and the resolved measurements of the three other albedo measurement locations. Additionally, the uncertainty calculation is included in the dataset.</p>
Effects of Albedo on the MIR Emissivity Spectra of Silicates for Lunar Comparison
<p>We provide the laboratory VNIR and MIR spectra for the mineral samples included in the linked publication as well as the derived spectral feature values plotted in the figures therein. The Diviner, Kaguya, and Clementine OMAT datasets for the lunar regions and the examined regions of interest discussed are also included as MATLAB files. </p>
LULC input and CLM5 carbon output "Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benefits."
<p>Land use land cover (LULC) files for the CESM and UKESM simulations performed for the study "Chemistry-albedo feedbacks from reforestation reduce climate benefits and crop yields. The input data was processed to make it compatible for UKESM as described in Weber et al (2022) https://doi.org/10.5194/egusphere-2022-748.</p>
Data for: Connecting hemispheric asymmetries of planetary albedo and surface temperature
<p>Satellite measurements show that the Northern and Southern hemispheres reflect equal amounts of short-wave radiation ("albedo symmetry"), but no theory exists on if, how, and why the symmetry is established and maintained. Ambiguously, climate models are strongly biased in albedo symmetry but agree in the sign of the response to CO<sub>2</sub>. We find that mean-state biases in albedo symmetry and surface temperature asymmetry correlate negatively. Similarly, the response of albedo asymmetry to CO<sub>2</sub> forcing correlates negatively with the magnitude of the asymmetry in surface warming. This is true across many and within single climate model simulations: a too warm or stronger warming hemisphere is darker or darkens more than its counterpart. In the 21 years of observations we find the same tendency and hypothesize a) albedo symmetry is a function of the current climate state and b) we will observe an evolution towards albedo asymmetry in coming decades.</p>
Soil, litter and vegetation carbon for the 6 simulations performed in CLM5 of "Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benef"
<p>Soil, litter and vegetation carbon for the 6 simulations performed in CLM5 (Table S1) of "Chemistry-albedo feedbacks from reforestation reduce climate benefits and crop yields"</p> <p>i.clm5.global_MF_SSP1.h1.2015-2100_zip.nc - SSP126_MF_Land</p> <p>i.clm5.global_SSP1_nolulcc.clm2.h1.2015-2100_zip.nc - SSP126_2015_Land </p> <p>i.clm5.global_SSP1.h1.2015-2100_zip.nc - SSP126_Land</p> <p>i.clm5.global_MF_SSP3.02.h1.2015-2100_zip.nc - SSP370_MF_Land</p> <p>i.clm5.global_SSP3.02.h1.2015-2100_zip.nc - SSP370_Land</p> <p>i.clm5.global_SSP3_nolulcc.clm2.h1.2015-2100_zip.nc - SSP370_2015_Land</p> <p> </p> <p>These simulations were performed by Dr James King, University of Sheffield.</p> <p> </p>
Sensitivity of Arctic Surface Temperature to Including a Comprehensive Ocean Interior Reflectance to the Ocean Surface Albedo within the Fully Coupled CESM2
<p>CESM2 simulations were performed to study the light attenuation effects at the ocean surface layer on Arctic surface temperature. This dataset provides some simulated variables analyzed in our study.</p>
UKESM1 Data accompanying "Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benefit"
<p>UKESM1 Data accompanying "Chemistry-albedo feedbacks from reforestation reduce climate benefits and crop yields".</p> <p>The UKESM1_README provides an explanation of the diagnostics.</p> <p>These simulations were performed by Dr James Weber, University of Sheffield.</p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.