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23 results for “historical simulations”
Simulations of Historical Impacts of Climate Change and Atmospheric Chemistry at Harvard Forest 1850-2019
This study is a model application aimed at simulating historical carbon (C), nitrogen (N), and water dynamics at a hardwood forest stand at Harvard Forest from 1850 to 2019. We applied the PnET-CN-daily model with a reconstructed historical climate and air quality scenario derived from field observations and regional model simulations. The model outputs were calibrated with field measurements conducted at Harvard Forest. We used field measurements of aboveground biomass (AGB) and foliar mass near the EMS tower to calibrate ecosystem C pools. Gross primary production (GPP), net ecosystem exchange (NEE), and respiration from the EMS eddy flux tower were used to calibrate C fluxes. Net N mineralization data from the chronic N amendment experiment, along with other N dynamics data collected at Harvard Forest, were used to calibrate N pools and fluxes. Additionally, evapotranspiration (ET) and soil water content from the EMS tower were used to calibrate water fluxes. To isolate the effects of individual environmental factors on C, N, and water dynamics, we ran the PnET-CN-daily model with a series of theoretical scenarios. These scenarios were developed based on the reconstructed historical climate and air quality data while keeping non-target input factors at pre-industrial levels. The considered environmental factors include climate, carbon dioxide (CO2) concentration, atmospheric N deposition, and ozone (O3) concentration. This approach allowed us to decompose the influence of each factor on ecosystem dynamics by comparing model outputs across different scenarios.
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
HadCM3 historical simulation with transient tracers
<p>Excess temperature, CFCs, SF6 and bomb C14 from the HadCM3 historical simulation (1860-2008) (_hist files). These tracers are also integrated using the control ocean transport (_ctrl files). The experiment is designed to test the Green's function method for estimating excess heat in the ocean.</p>
Dataset to "Applied Research of the Hygrothermal Behaviour of an Internally Insulated Historic Wall without Vapour Barrier: In Situ Measurements and Dynamic Simulations"
<p>This record contains raw data of a 3-month monitoring period of the HeLLo project.</p> <p>The datafiles titled MeasLog_YYYY-MM-DD.dat correspond to the raw data used for the data analysis presented in “Hygrothermal analysis at critical points of an internally insulated historic wall without vapour barrier: in situ measurements and dynamic simulation”, submitted for publication in journal <em>energies</em>.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, corresponds to daily registered data monitored every minute.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuratio, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643).</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.</p>
North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations
<p>This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x.</p> <p><br>The data are provided as netcdf files.</p> <p>The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245).</p> <p>Each file contains data for the following one dimensional variables:</p> <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul> <p> </p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </p> <p> </p>
Post-processed output from WACCM: historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN) and sensitivity studies (compset FW)
<p>This dataset contains post-processed monthly output from WACCM (version 4) simulations:</p> <ol> <li>historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN, B55TRWCN.zip)</li> <li>sensitivity study: 50-years experiments designed with different geomagnetic and solar conditions (compset FW, FW.zip)</li> </ol> <p>The postprocessing involved (1) zonal averaging, (2) Transformed Eulerian mean (TEM) budget calculation in log-pressure coordindates from model output (h0 files).</p> <p><strong>Contact</strong><br> Monika Szelag (monika.szelag@fmi.fi)</p> <p><strong>Raw data</strong><br> The raw, ungridded monthly data (2TB, h0 files) are currently available on FMI storage system, under path /ibrix/arch/waccm, and can be requested through the contact person. </p> <p><strong>Dataset contents</strong></p> <pre><code>B55TRWCN.zip files: vdk.zonal.002.1957-2005.nc vdk.zonal.003.1957-2005.nc vdk.zonal.004.1957-2005.nc tem.vdk.zonal.002.1957-2005.nc tem.vdk.zonal.003.1957-2005.nc tem.vdk.zonal.004.1957-2005.nc</code></pre> <p>WACCM output: historical simulations, zonal averages, 3 ensemble members of 49 years each (002,003,004).</p> <p>Variables are <strong>NOY, O3, OH, QJOULE, QRL_TOT, QRLNLTE, QRS_TOT, T, U</strong> (vdk.zonal.00[234].1957-2005.nc) and <strong>delf, fphi, fz, vres, wres, z3</strong> (tem.vdk.zonal.00[234].1957-2005.nc</p> <pre><code>FW.zip files: zonal_F2000_max.nc zonal_F2000_avg.nc zonal_F2000_min.nc tem_zonal_F2000_max.nc tem_zonal_F2000_avg.nc tem_zonal_F2000_min.nc</code></pre> <p>WACCM output: perpetual year 2000, zonal averages, 50 years experiments with 3 different solar and geomagnetic conditions (min, max, avg).</p> <p>Variables are <strong>NOY, NOX, O3, OH, QRL_TOT, QRS_TOT, T, U, V, CO, CO2, PS, TS</strong> (zonal_F2000_*.nc) and <strong>delf, fphi, fz, vres, wres, z3</strong> (tem_zonal_F2000_*.nc)</p> <p> </p>
Global Water Quality Data: Historical Nitrogen and Phosphorus Loads and Concentrations Simulated by CoSWAT-WQ v1.0
<p>Output data on global TN and TP water quality and scripts for the historical period (1995 - 2016) generated by CoSWAT-WQ v1.0. Please check the readme file for data description. The data includes river flow, , nitrogen and Phosphorus loads and concetrations at monthly timestep.</p>
The simulated monthly runoff data in the historical period and under future climate scenarios of the Yarlung Zangbo River Basin
<p>This data provides the simulated monthly runoff data under the historical period (1979-2014) and future (2049-2084) climate scenarios for four sub-basins of the Yarlung Zangbo River Basin, including Nugexia, Nuxia, Lasha, and Rikaze.<br> This runoff data is simulated based on the GR4J model coupled with a simple degree-day snow module. The GR4J_SNOW performs parameterization and calculates runoff on each grid cell, and the gridded simulated runoff then converges to the outlet of the sub-basin.<br> Time series of the daily records for meteorological forcing data (precipitation, air temperature, vapor pressure, wind speed, downward long-wave radiation, and downward short-wave radiation) from 1979-2014 was provided by China Meteorological Forcing Dataset (CMFD). <br> Future climate scenarios were generated using the combined climate forcing data together with scaling factors obtained from empirical downscaling of 30 available CMIP5 models (28 GCMs for RCP4.5 and 29 GCMs for RCP8.5). The simulated runoff under RCP4.5 and RCP8.4 are the ensemble averages of 28 and 29 simulated runoff results, respectively.</p>
Daily streamflow at 0.5 deg resolution generated using 0.22 deg runoff from the historical (1986-2005) and two future scenarios' (RCP 4.5 and 8.5, 2081-2100) simulations of CanRCM4 for its North American domain
<p>These data are provided in support of the following manuscript which is currently (July 2024) under revision for the <strong>Hydrology and Earth System Science</strong> journal. The details about how these data were generated can be found in this manuscript (or its final accepted version, hoping our manuscript will be accepted). <br><br><a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/</a><br><br>The effect of climate change on the simulated streamflow of six Canadian rivers based on the CanRCM4 regional climate model<br>Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha <br><br>The netcdf files provide here contain two variables.<br><br>1) streamflow, variable name is fout_land, units are m3/s<br>2) flow velocity, variale name is velocity, units are m/s<br><br><br></p>
Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations.</p> <p>FILE DESCRIPTION:<br> NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end. </p> <p>FILE DESCRIPTION:<br> NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
The role of fire in the carbon dynamics of the boreal forest II. - Eurasia model simulations of historical fire disturbance and carbon dynamics (1000-2002).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
The role of fire in the carbon dynamics of the boreal forest III. - North America model simulations of historical fire disturbance and carbon dynamics (1900-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
NZESM model data of simulation u-bl274 (historical run)
<p>Subset data of NZESM historical simulation u-bl274.</p>
UKESM model data of simulation u-bm456 (historical run)
<p>Subset data of UKESM historical simulation u-bm456</p>
Time series of annual TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models on a 2 x 2 deg grid. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the CMIP5 future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in a separate file. </p> <p>Each file contains the time series of the 40-year linear trend for tas at each grid point from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</p>
Time series of Area mean TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the Arctic (ARC) and the Eastern Arctic (eARC) mean annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in separate files. The Arctic is defined as the area north of 70 N, and the eastern Arctic is defined as 0 - 180 E and north of 70 N. Each file contains the time series of the 40-year linear trend for the respective area mean tas from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</p>
Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
<p><span>Tree spatial patterns in dry coniferous forests of the western US, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre-existing forest structure shapes tree spatial patterns is limited. In this study, we (1) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed-conifer forest, (2) estimated tree mortality, and (3) examined tree spatial patterns of live trees before and after fire, and of fire-killed trees. Tree mortality in the historical period was clustered and density-dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, post-fire tree patterns were more uniform and devoid of moderately sized tree groups. Post-fire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western US. This divergence suggests that decades of forest dynamics without significant disturbances has altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.</span></p>
Maximum Web-bulb-temperature in monsoon Asia with EC-Earth3 historical simulation
<p>The WBTmax-Asia-EC-Earth3 dataset is comprised of the NEX-GDDP-CMIP6 dataset (EC-Earth3 outputs of tasmax, huss) and ERA5 dataset (sp) using the algorithm described in Davies-Jones (2008), implemented by Buzan (2015), and ported to Matlab by Dr Robert Kopp (Rutgers, 2016). The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) (EC-Earth3) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four “Tier 1” greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). This dataThe purpose of this dataset is to provide a set of high-resolution, bias-corrected climate change projections that can be used to evaluate extreme heat over East Asia.</p>
Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
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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.