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19 results for “Atmospheric forcing”
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
FESOM2 simulations with increasing sea-ice model complexity under different atmospheric forcings
<p><strong>Introduction</strong></p> <p>This dataset has been compiled in support of the paper "Impact of sea-ice model complexity on the performance of an unstructured sea-ice/ocean model under different atmospheric forcings" by Zampieri et al., submitted to the Journal of Advances in Modeling Earth Systems (JAMES) published by the American Geophysical Union (AGU).</p> <p><strong>Scientific description of the dataset</strong></p> <p>The dataset contains the results of sea-ice simulations performed with the Finite-volumE Sea ice-Ocean Model version 2 (FESOM2), based on six model configurations: C1-E, C1-N, C2-E, C2-N, C3-E, and C3-N. As described in the paper, the complexity of the sea-ice model increases from the setup C1 to C3. The suffix -E and -N indicate respectively the ERA5 and NCEP atmospheric forcings used as boundary conditions for the FESOM2 model. As two iterations of the Green's function approach for the optimization of the parameter space have been performed, each configuration features three separate simulations: a control run (cnt), a first-round of optimization (opt_1), and a second and final round of optimization (opt_2). The parameter optimization is based on various sea-ice observations retrieved over the period 2002–2015. In total, 18 simulations compose the dataset (6 configurations x 3 realizations). The following 2D monthly-averaged variables are provided: the sea-ice concentration, the sea-ice thickness, the meridional and zonal components of the sea-ice velocity, and the snow thickness on top of the sea ice. The fields are defined on a global unstructured mesh denominated "CORE2", which is also included in the database.</p> <p><strong>Technical description of the dataset</strong></p> <p>As an unstructured model output is not widely diffused in the sea-ice community, we include here some suggestions for handling and analyzing the simulation results.</p> <p>The files can be interpolated to a regular grid using the following <strong><a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></strong> commands:</p> <ol> <li>Add grid description to model file: <strong><em>cdo setgrid,CORE2_mesh.nc var.fesom.yyyy.nc temp.nc</em></strong></li> <li>Interpolate to regular grid: <strong>cdo remapycon,r360x180 temp.nc var.fesom.interpolated.yyyy.nc</strong></li> </ol> <p>Furthermore, the python package<strong> <a href="https://code.mpimet.mpg.de/projects/cdo">pyfesom2</a></strong> can be used for plotting the unstructured model data and for interpolating it to a regular grid. The R package <strong><a href="https://github.com/FESOM/spheRlab">spheRlab</a></strong> can be used for plotting the model data directly on its unstructured grid and for performing further analysis. More information can be found on the <strong><a href="https://fesom.de/cmip6/work-with-awi-cm-unstructured-data/">FESOM website</a></strong>.</p> <p>The following naming convention is adopted for the model variables:</p> <ul> <li><strong><em>a_ice</em></strong> → sea-ice concentration</li> <li><strong><em>m_ice</em></strong> → sea-ice volume per unit area of ice</li> <li><strong><em>m_snow </em></strong>→ snow-volume per unit area of ice</li> <li><strong><em>vice</em></strong> → meridional component of the sea-ice velocity</li> <li><strong><em>uice</em></strong> → zonal component of the sea-ice velocity</li> </ul> <p>Three types of simulation are included:</p> <ul> <li><strong>cnt </strong>→ control run before the parameters optimization (2000–2019)</li> <li><strong>opt_1 </strong>→ after the first iteration of the parameter optimization method (2000–2015)</li> <li><strong>opt_2</strong> → after the second iteration of the parameter optimization method (2000–2019)</li> </ul> <p>Do not hesitate to contact the corresponding author (lorenzo.zampieri@awi.de) for additional information about the data processing and for any other issue with this dataset.</p> <p> </p> <p> </p>
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.</p>
Comparison of radiative transfer schemes for the calculation of aerosol radiative forcing in Mars' atmosphere
<p>Output datasets for Figures (fig. 1 to 10) for the intercomparison of radiative transfer algorithms for the calculation of aerosol radiative forcing in the Martian atmosphere.</p>
Model data used for the paper: "Heat extremes driven by amplification of phase-locked circumglobal waves forced by topography in an idealized atmospheric model"
<p>Atmospheric model output to reproduce the results of the study: "<strong>Heat Extremes Driven by Amplification of Phase-Locked Circumglobal Waves Forced by Topography in an Idealized Atmospheric Model" </strong>published in Geophysical Research letters.<br> <br> Authors: B. Jiménez-Esteve. K. Kornhuber and D. I.V. Domeisen <br> <br> DOI: <a href="https://doi.org/10.1029/2021GL096337">https://doi.org/10.1029/2021GL096337</a><br> <br> For more information about the model setup and the design of the experiments please refer to the above publication.</p>
Top-of-the-atmosphere radiative forcing by aerosol due to continuous OCS injection near the tropical tropopause simulated by EMAC
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figure 4.</p> <p>The dataset contains additional stratospheric aerosol forcing for injections of 6 Tg S a<sup>-1</sup> OCS for several years over 5 tropical cities at the tropopause (97 hPa) calculated with the EMAC (ECHAM5/MESSy Atmospheric Chemistry) CCM (e.g. Brühl et al., 2018; Schallock et al., 2023). Data are given for a four year time series starting in January 2017.</p> <p>- - - - - - - - - - - -</p> <p>File format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> time, 10 hourly, as 'day since 1997-01-01' (note that the two variables named time4 and time6 are identical)</p> <p>Parameters:</p> <p> SOLFORCCSO: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> TOTFORCCSO: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> SOLFORCCSO_FREE: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p> TOTFORCCSO_FREE: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p>- - - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Brühl, C., Schallock, J., Klingmüller, K., Robert, C., Bingen, C., Clarisse, L., Heckel, A., North, P., and Rieger, L.: Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using Aerosol CCI satellite data, Atmos. Chem. Phys., 18, 12845-12857, 10.5194/acp-18-12845-2018, 2018</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl<br> sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Schallock, J., Brühl, C., Bingen, C., Höpfner, M., Rieger, L., and Lelieveld, J.: Reconstructing volcanic radiative forcing since 1990, using a comprehensive emission inventory and spatially resolved sulfur injections from satellite data in a chemistry climate model, Atmos. Chem. Phys., 23, 1169-1207, 10.5194/acp-23-1169-2023, 2023.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.</p>
Coupled Ocean-atmospheric forcing on Indian Summer Monsoon variability during the middle Holocene: Insights from the Core Monsoon Zone speleothem record.
<p>Stable oxygen and carbon isotope data of stalagmite sample during middle Holocene time from Mahadev cave of Jagdalpur region in Central India.</p>
Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US
<p>The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.</p> <p>The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels.</p> <p>The data files include:</p> <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]: half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol> <p> </p> <p><strong>References</strong></p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p> </p>
Atmospheric forcing and OGCM conditions for the year 2019 over the South China Sea
<p>The atmospheric forcing (<a href="13253327" target="_blank" rel="noopener noreferrer">atmospheric_ECMWF_forcing.tar</a>) comes from the 3-hourly output of the ECMWF 1/8<sup>o</sup> atmospheric analysis.</p> <p>Initial ocean conditions and lateral ocean boundary conditions are prescribed from the daily outputs of the global ocean 1/12<sup>o</sup> analysis PSY4QV3R1 from the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="13253327" target="_blank" rel="noopener noreferrer">copernicus_OGCM_forcing.tar</a>).</p> <p>The data cover the entire year of 2019.</p> <p> </p>
Compensating atmospheric adjustments reduce the volcanic forcing from Hunga stratospheric water vapor enhancement
<p>The uploaded scripts and data generate the figures of the manuscript "Compensating atmospheric adjustments reduce the volcanic forcing from Hunga stratospheric water enhancement."</p>
Datasets for "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the datasets for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations".</p>
High quality figures of "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the figures for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations" in their original resolution, ensuring clarity the high-quality visual representations for readers.</p>
Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier
<p>This Zenodo repository contains all datasets (IPSL CMIP6 data, glaciological data, SAFRAN data), ElmerIce codes and Python Jupyter Notebook used in the study reported in the article <strong><em>"Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier"</em></strong></p>
Contrasting aerosol effects on longwave cloud forcing in South East Asia and Amazon simulated with Community Atmosphere Model version 5.3
<p>Aerosols modify cloud microphysical and radiative properties and thus impact the shortwave (SW) and longwave cloud forcing (LWCF). This study first reports the finding of contrasting aerosol effects on LWCF in South East Asia and Amazon in the Community Atmosphere Model version 5.3 (CAM5.3), which corresponds to the sum of LW indirect and semi-direct effects investigated in Ghan et al., (2012). A series of numerical experiments is conducted to decompose the complex aerosol effects on LWCF. Our analysis indicates that the cooling (negative aerosol effects on LWCF) in Amazon is due mainly to the aerosol effects on warm clouds and the inhibition of vertical motion by the aerosol-induced radiative cooling. In contrast, the warming (positive aerosol effects on LWCF) in South East Asia is due mainly to the aerosol effect on homogeneous freezing, thus reducing the ice particle size and prolonging the existence of ice cloud. Our results emphasize that a comprehensive analysis of integrated aerosol effects on both warm and ice clouds is necessary for better understanding the aerosol effects on LWCF.</p>
Atmospherically-forced and chaotic interannual variability of the sea level and its components over 1993-2015 from the OCCIPUT ensemble simulations
<p>This data set contains the interannual variability fields for the sea level (ssh_var_inter_1993_2015_annuel.tar.gz) and its steric (hsterica_var_inter_1993_2015_annuel.tar.gz) and manometric (obp_var_inter_1993_2015_annuel.tar.gz) components over 1993-2015 from the OCCIPUT ensemble simulations. It is used in the paper « Atmospherically forced and chaotic interannual variability of regional sea level and its components over 1993-2015 » published in Journal of Geophysical Research - Oceans.</p> <p>This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (Bessières et al., 2017, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (Madec, 2008). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in Brankart, 2013) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis.</p> <p> </p> <p>For each member, the simulated sea surface height (SSH) over 1993-2015 is considered. As NEMO is a Boussinesq model, it conserves volume instead of mass. Therefore, the steric effect is missing into the global mean sea level change (Greatbatch 1994). To overcome this issue, we remove the global mean estimate for the sea level time series at each grid point. Then the sea level anomalies obtained are averaged per year and a linear trend is removed from each member. The same processes are applied to the steric and manometric sea level time series.</p> <p> </p> <p>Here is an example of the file header</p> <p><em><strong>dimensions:</strong></em></p> <p><em><strong>member = UNLIMITED ; // (50 currently)</strong></em></p> <p><em><strong>time = 23 ;</strong></em></p> <p><em><strong>y = 1021 ;</strong></em></p> <p><em><strong>x = 1442 ;</strong></em></p> <p><em><strong>variables:</strong></em></p> <p><em><strong> float ssh(member, time, y, x) ;</strong></em></p> <p><em><strong> ssh:long_name = "sea level interannual variability" ;</strong></em></p> <p><em><strong> ssh:standard_name = "sea_level interannual variability" ;</strong></em></p> <p><em><strong> ssh:units = "m" ;</strong></em></p> <p><em><strong> ssh:FillValue = "nan" ;</strong></em></p> <p><em><strong> float nav_lat(member, y, x) ;</strong></em></p> <p><em><strong> nav_lat:axis = "Y" ;</strong></em></p> <p><em><strong> nav_lat:long_name = "Latitude" ;</strong></em></p> <p><em><strong> nav_lat:standard_name = "latitude" ;</strong></em></p> <p><em><strong> nav_lat:units = "degrees_north" ;</strong></em></p> <p><em><strong> float nav_lon(member, y, x) ;</strong></em></p> <p><em><strong> nav_lon:axis = "Y" ;</strong></em></p> <p><em><strong> nav_lon:long_name = "Longitude" ;</strong></em></p> <p><em><strong> nav_lon:standard_name = "longitude" ;</strong></em></p> <p><em><strong> nav_lon:units = "degrees_east" ;</strong></em></p> <p><em><strong> float time(time) ;</strong></em></p> <p><em><strong> time:long_name = "time" ;</strong></em></p> <p><em><strong> time:standard_name = "time" ;</strong></em></p> <p><em><strong> time:units = "years since 1992" ;</strong></em></p> <p> </p> <p>nav_lat and nav_lon represent the latitude and longitude of the NEMO model whereas var represents the interannual variability time series. </p>
LBA-ECO CD-36 South American Land Data Assimilation System Atmospheric Forcing Data
This data set provides South American Land Data Assimilation System (SALDAS) forcing data including atmospheric fields necessary for land surface modeling for South America which are derived by combining modeled and observation based sources. The forcing data cover the entire continent of South America at 0.125 degree resolution and are built around the model-calculated values of air temperature, wind speed and specific humidity at two meters, surface pressure, downward shortwave and longwave surface radiation, and precipitation from the South American Regional Reanalysis (SARR). These SARR data (Aravequia et al. 2007), which were released in 2006 by INPE/CPTEC, are a medium-term, dynamically consistent, high-resolution (0.125 degree), high-frequency, atmospheric dataset covering South America. The forcing data are available at a 3-hourly time step for a 5-year period from 2000 to 2004. There are 60 monthly *.zip files with each zipped file containing ~240 3-hourly time step data files for that particular month in NetCDF format. Each zipped file is approximately one GB in size.
Lidar Atmospheric Sensing Experiment (LASE) Data Obtained During the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX)
The Lidar Atmospheric Sensing Experiment (LASE) Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX) data set was collected over the Western Atlantic Ocean in July 1996. The overall goal of TARFOX was to reduce uncertainties in the effects of aerosols on climate by determining the direct radiative impacts, as well as the chemical, physical, and optical properties, of the aerosols carried over the western Atlantic Ocean from the United States. LASE is an airborne autonomous DIAL system which produces measurements of aerosols and water vapor vertical profiles from the aircraft altitude down to the surface. Such profiles show the vertical context in which the TARFOX in situ and radiometric measurements are made, thus supporting the vertical extension of the in situ measurements and detecting any unsampled layers or inhomogeneities, which would impact the airborne and satellite radiative flux measurements. Note that the LASE_TARFOX data set is also available under the TARFOX project as the TARFOX_LASE data set. The data files included in these two data sets are identical.
Atmospheric forcing dataset for Numerical study of the seasonal thermal and gas regimes of the large artificial lake in Western Europe using LAKE2.0
<p>Input dataset for the LAKE2.0 model (http://tesla.parallel.ru/Viktor/LAKE/wikis/LAKE-model) that was used for the experiments in the "Numerical study of the seasonal thermal and gas regimes of the large artificial lake in Western Europe using LAKE2.0" article. Archive contains the atmospheric forcing file itself, file with the header description, and the setup and driver files for the LAKE2.0 model.</p> <p>Source code of the current version of the LAKE2.0 model used in the work mentioned above, pre-compiled exec of the FLake model as well as its source code (freely available under the terms of the MIT license) are also provided as separate archive files.</p> <p> </p>
Comparison of Radiative Transfer schemes for the calculation of aerosol radiative forcing in Mars' atmosphere 2.0
<p>Data-sets of figures (1 to 10) for the Comparison of radiative transfer schemes for the calculation of aerosol radiative forcing in the atmosphere of Mars. (Revision: 2)</p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.