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238 results for “Atmospheric Model”
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (No Long-term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
Datasets for "A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models"
<p>This repository contains the post-processed model outputs from HAMSTER v1.2.0 as used in the following paper: </p> <p>Keune, J., Schumacher, D. L., and Miralles, D. G.: A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models, Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, 2022.<br> <br> The data set contains (1) global validation statistics for the three fluxes (evaporation, precipitation, sensible heat), and (2) the climatological source regions of precipitation and heat for Denver, Beijing and Windhoek. The former are found in the directory 'validation/global', and the latter are found in the directories '1001' (Denver), '3001' (Beijing) and '5002' (Windhoek). Multiple experiments were performed to assess the uncertainty of the source regions. Thus, multiple files exist, that show the same variables but for multiple experiments (indicated by the names "ALLPBL", "RH-10-20", "SOD08-SCH19", "SCH20", "FAS19" in the file name). For the moisture source regions, the uncertainty of the attribution methodology was assessed; these are indicated by the different folders, i.e. 'linear_upscaled' and 'random2_upscaled'. For each city and each experiment, the climatologically averaged source regions ('_mean.nc') and the climatologically averaged individual backward day contributions ('_bwmean.nc') are provided. Data sets are in the netCDF format and contain metadata following the CF convention.</p>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
Global atmospheric simulation using the Super-Parameterized Community Atmosphere Model
<p>A 1-month subset from a global atmospheric simulation using the Super-Parameterized Community Atmosphere Model (SP-CAM), which implements the Multi-scale Modeling Framework (MMF) described in Khairoutdinov and Randall (2001) and Khairoutdinov et al. (2005). The version of the SP-CAM used here is described in Marchand et al. (2009) and Ovtchinnikov et al (2006), and was run at Pacific Northwest National Laboratory with DOE support. It is based on CAM 3.0 for the global atmospheric component, and uses the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003). This simulation is configured with CAM running the finite volume dynamical core on a 2x2.5 degree latitude-longitude grid with 26 vertical levels. The embedded CRM (SAM) is configured with 64 horizontal columns at 4 km grid spacing with 24 vertical levels (sharing the bottom 24 levels with the CAM grid), and single-moment microphysics. The simulation was initialized on 1 September 1997 and runs through June 2002, forced with observed monthly-mean sea surface temperatures. Only the month of July 2000 is uploaded here, which is what is required to reproduce the results in Hillman et al. (2018).</p>
Atmospheric climate model output of the COSMO-CLM2 regional climate model hindcast run over Antarctica (1987-2016)
<p>The dataset contains monthly output of a COSMO-CLM² (COSMO-CLM coupled to the Community Land Model) atmospheric hindcast simulation over Antarctica which is described and evaluated in the following paper: </p> <p>Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J.T.M., Medley, B., Gorodetskaya, I.V., Vanden Broucke, S., van Lipzig, N.P.M., 2019. A new Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-year Hindcast with COSMO-CLM² over Antarctica. Journal of Geophysical Research: Atmospheres, 124, 1405-1427. (doi:10.1029/2018JD028862)</p> <p>Details of the model simulation:<br> - COSMO-CLM version 5.0_clm6<br> - Community Land Model version 4.5<br> - Horizontal resolution: 0.25°x0.25°<br> - Vertical resolution: 40 levels<br> - Time period: 1987-2016 (excluding 4 years of spin-up)<br> - Driving model: ERA-Interim<br> </p> <p>The data provided here has a monthly time resolution and contains the monthly average of all variables except denoted otherwise below. As such, each file consists of 360 time steps.<br> - AEVAP_S: Surface evaporation [kg m-2] (summed value for each month)<br> - ALB: Surface albedo [-] (only for austral summer months)<br> - ALHFL_S: Surface latent heat flux [W m-2]<br> - ALWD_S: Downward longwave radiation at the surface [W m-2]<br> - ALWU_S: Upward longwave radiation at the surface [W m-2]<br> - ASHFL_S: Surface sensible heat flux [W m-2]<br> - ASOB_S: Surface net downward shortwave radiation [W m-2]<br> - ASWDIFD_S: Diffuse downward shortwave radiation at the surface [W m-2]<br> - ASWDIFU_S: Diffuse upward shortwave radiation at the surface [W m-2]<br> - ASWDIR_S: Direct downward shortwave radiation at the surface [W m-2]<br> - ATHB_S: Surface net downward longwave radiation at the surface [W m-2]<br> - P: Pressure at 40 vertical levels [Pa]<br> - QV: Specific humidity at 40 vertical levels [kg kg-1]<br> - RH2M: Relative humidity at 2 meter [%]<br> - SNOW_GSP: Surface snowfall amount [kg m-2] (summed value for each month)<br> - T2M: Temperature at 2 meter [K]<br> - T: Temperature at 40 vertical levels [K]<br> - WS10M: Wind speed at 10 meter [m s-1]<br> - WS: Wind speed at 40 vertical levels [m s-1]</p>
UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems.</p> <p>Key:</p> <p>"summaryday.nc" contain eleven months of data in each year, excluding either February or March.</p> <p>"summarydat2.nc" contain one month of data in each year, either February or March.</p> <p>"last5" indicates that for this simulation only the last five years of data are available.</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"llcsemu" are simulations with the Lambert-Lewis emulator.</p> <p>"gremu" are simulations with the Gregory-Rowntree emulator.</p> <p>"llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>"30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.</p> <p> </p>
Modelling of Stably Stratified Atmospheric Boundary Layers with Varying Stratifications
<p>This repository contains data that was used for publishing article called <a href="https://link.springer.com/article/10.1007%2Fs10546-020-00527-8"><em>Modelling of Stably Stratified Atmospheric Boundary Layers with Varying Stratifications</em></a>. The repository compliments the publication in the sense that it provides qualitative insight for comparison and exploration.</p> <p><strong>Keywords</strong>: GABLS1, Open data, Stably-stratified turbulence, Turbulence parametrization</p> <p>The data is stored inside sixteen files. The file names are split into a part that describes variables and part that describes simulation. Here's an example of a file name:</p> <p>budgets.cr0375.csv</p> <p>The first part <em>budgets</em> refers to variables inside the file and the second part <em>cr0375</em> refers to forcing conditions (in this example cooling rate of 0.375 Kelvin per hour) used in the simulation.</p> <p><strong>Variables</strong>:</p> <ul> <li>mean wind speed and mean potential temperature (<em>first_order_stat</em>)</li> <li>variance and covariance variables that describe turbulence properties (<em>second_order_stat)</em></li> <li>variables in the turbulent kinetic energy and half the temperature variance equations <em>(budgets</em>)</li> <li>contain values for model coefficients that can be used for calculating second order statistics <em>(lambda_beta_coeffs)</em></li> </ul> <p><strong>Simulations</strong>:</p> <ul> <li>cooling rate at the surface 0.25 Kelvin per hour <em>(cr025)</em></li> <li>cooling rate at the surface 0.375 Kelvin per hour <em>(cr0375)</em></li> <li>cooling rate at the surface 0.5 Kelvin per hour (<em>cr05)</em></li> <li>cooling rate at the surface 1.0 Kelvin per hour <em>(cr1)</em></li> </ul> <p><strong>Note</strong>: The results presented in the repository are taken after the ninth hour of the simulation while the results in the published paper is averaged between the eight and ninth hour. This difference should be negligible.</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>
Model Outputs for Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry
<p>Includes the model output files used in calculations regarding the research article "Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry". Output files contains: 1) surface Mn concentrations, annual; 2) Mn deposition, monthly; 3) soil Mn map; 4) soil Mn "pseudo" turnover time.</p>
On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity
<p><strong>Overview</strong></p> <p>The HDF5 file contains primary measurement data and secondary processing data that was used to assess clear sky effective emissivity and transmissivity estimates and generate the results in the associated manuscript (accepted and forthcoming).</p> <p>Data is indexed by solar time and provided per site for years 2010 through 2015. Sample Python code is provided to reconstruct training and validation sets by concatenating all 'tra' or 'val' samples across sites. Results can be explored by modifying choice of filters and constructing new training and validation sets.</p> <p><strong>Data usage</strong></p> <p>The usage of the data presented here is intended for research and development purposes only and implies explicit reference to the paper:<br><em>Matsunobu, L. M., & Coimbra, C. F. M. (2024). On effective spectral wideband models for clear sky atmospheric emissivity and transmissivity. Journal of Geophysical Research: Atmospheres, 129, e2023JD039798. https://doi.org/10.1029/2023JD039798</em></p> <p><strong>Data description</strong></p> <p>Column names and descriptions are as follows:<br>- dlw_m: measured downwelling longwave [W/m^2]<br>- ghi_m: measured global horizontal irradiance [W/m^2]<br>- dni_m: measured direct normal irradiance [W/m^2]<br>- dhi_m: measured diffuse horizontal irradiance [W/m^2]<br>- rh_m: measured relative humidity [%]<br>- pa_m: measured atmospheric pressure [hPa]<br>- t_m: measured temperature [K]<br>- sza: solar zenith angle [deg]<br>- ghi_c: clear sky global horizontal irradiance [W/m^2]<br>- dni_c: clear sky direct normal irradiance [W/m^2]<br>- dhi_c: clear sky diffuse horizontal irradiance [W/m^2]<br>- cs1: clear sky filter 1<br>- cs2: clear sky filter 2<br>- site_elev: station elevation [m]<br>- clr_pct: fraction of samples identified as clear for the given site and day<br>- clr_num: number of samples identified as clear for the given site and day<br>- pw_hpa: water vapor partial pressure [hPa]<br>- alt_correction: altitude correction<br>- tra: indicate if sample is included in training set<br>- val: indicate if sample is included in validation set<br>- sqrt_pw: square root of non-dimensional water vapor partial pressure<br>- e_sky: effective clear sky emissivity</p> <p>The last two columns, 'sqrt_pw' and 'e_sky' represent the input and target for linear regression, i.e. e_sky = c_1 + (c_2 * sqrt_pw).<br>Altitude corrected sky emissivity, or expected emissivity for a station at sea-level, is found by e_sky - alt_correction.</p> <p><strong>Sample code (Python v3.8)</strong></p> <pre>import pandas as pd site = "GWC" # or other station code df = pd.read_hdf("data.h5", key=site) # import single site</pre> <p>Training and validation sets can be reconstructed as below. Linear regression on 'sqrt_pw' to predict 'e_sky' - 'alt_correction' in the resultant training set will reproduce results in the associated manuscript.</p> <pre>training = [] validation = [] surfrad_sites = ['BON', 'DRA', 'FPK', 'GWC', 'PSU', 'SXF', 'TBL'] for site in surfrad_sites: # loop through sites df = pd.read_hdf("data.h5", key=site) df["site"] = site # add site name training.append(df.loc[df.tra]) # append samples marked as training validation.append(df.loc[df.val]) # append samples marked as validation # join respective set samples across sites training = pd.concat(training, ignore_index=False) validation = pd.concat(validation, ignore_index=False)</pre> <p>Reproduce regression results</p> <pre>from sklearn.linear_model import LinearRegression c1 = 0.6 # set intercept (c1 constant) x = training.sqrt_pw.to_numpy().reshape(-1, 1) y = training.e_sky - training.alt_correction - c1 # adjust for altitude and c1 y = y.to_numpy().reshape(-1, 1) model = LinearRegression(fit_intercept=False) model.fit(x, y) c2 = model.coef_[0][0] print(f"c1={c1:.3f}, c2={c2:.3f}") # output: c1=0.600, c2=1.652</pre>
Supporting data for "Global Model of Atmospheric Chlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, observation tables, and Python scripts for reproducing the analysis results/ figures presented in "Global Model of Atmospheric Chlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information.</p>
Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the outputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in the urban sensors section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p> </p>
Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort.</p> <p>The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p><strong>Note this data should be used only for non-commercial purposes.</strong></p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data
<p>This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite<br> Observations and 3-D Model Simulations" submitted to ACP.</p> <p>The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.</p>
Monthly methane emissions estimated with the atmospheric inversion model CarbonTracker Europe - CH4
<p>Monthly estimates of global methane emissions from CarbonTracker Europe - CH4 (CTE-CH4). CTE-CH4 is a Bayesian inversion framework based on an ensemble Kalman filter algorithm using the Eulerian global atmospheric transport model TM5. The gridded fluxes are available with a resolution of 1.0x1.0 degrees and in units of kgCH4/m2/month. The gridded flux file contains variables for posterior fluxes from soils (bio_flux_opt) and anthropogenic sources (anth_flux_opt) and the total posterior flux (total_flux_opt). Priors used: Anthropogenic: EDGAR v6, biosphere/wetlands (soils): LPX-Bern DYPTOP v1.4, Ocean: Weber et al. (2019), Biomass burning: GFED v4.1, Termites: VISIT. A more detailed setup of the inversion is documented in Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. Note: Fluxes are optimised at 1.0x1.0 degrees in northern high latitudes (USA, Canada, Europe and Russia), but are also provided here at the same resolution for other regions.</p>
Models and Datasets for "Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium"
<p><strong>Associated publication:</strong> <em>Karamperidou, C., Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium, Nature Communications Earth & Environment, (2024)</em></p> <p> </p> <p><strong>This repository contains:</strong></p> <ul> <li>the architecture and weights of PaleoBlockNet v1.0</li> <li>the following ensemble DL reconstructions of JJA frequency of blocked days inferred by PaleoBlockNet: <ol> <li>the 10-member NTREND-based DL reconstruction; uses as input the NTREND DA N.Hemisphere MJJA surface temperature anomaly by King et al. (2021)</li> <li>the 100-member PHYDA-based DL reconstruction; uses as input the PHYDA JJA surface temperature anomaly by Steiger et al. (2018)</li> <li>the 12-member LME-based DL reconstruction; uses as input the CESM-LME surface temperature anomaly; this is a sensitivity experiment (see publication for details).</li> </ol> </li> <li>Integrated Gradients that assign importance to the input features for PaleoblockNet's blocking inferences </li> <li>train-validate-test samples to use with sample scripts from the Gituhub repo github/ckaramp-research/paleoblocknet</li> </ul> <p> </p> <p><strong>If you use this dataset, please cite the associated publication and the present repository.</strong></p> <p>To <strong>interactively explore</strong> the datasets, a web interface has been developed and can be accessed at <a href="https://www2.hawaii.edu/~ckaramp/paleoblocknet">https://www2.hawaii.edu/~ckaramp/paleoblocknet</a></p> <p>Contact the author Christina Karamperidou (<a title="Karamperidou Research Group" href="https://www2.hawaii.edu/~ckaramp" target="_blank" rel="noopener">https://www2.hawaii.edu/~ckaramp</a>) for more information about the details of these datasets.</p>
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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.