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16 results for “LGM”
The Human Niche Space of Post-LGM Late Upper Paleolithic Europe - Supplemental Material
<p>The data provided here are the supplemental information accompanying the journal article <strong>The Human Niche Space of Post-LGM Late Upper Paleolithic Europe: The Effects of Climate and Population Growth on Human Land Use</strong> by Yaworsky, Hussain, & Riede. All analyses were performed in R v4.5.0 and are documented in the HTML document, <strong>Supplemental 4</strong>.</p> <p>Version 1.2 of the Analysis Markdown Document incoporates changes made to functions within the package ENMeval.</p> <p>List of Supplemental Files:</p> <ol> <li><strong>Spatiotemporal Archaeological Observations - File name: <em>Archaeologicaldata_v1.csv</em></strong> <ol> <li>Archaeological observations derived from Kretschmer (2015) and supplemented with additional observations (see main paper for details).</li> </ol> </li> <li><strong>Summed Probability Estimate for Population Estimation - File name: <em>Population_SPD2.csv</em></strong><br> <ol> <li>Summed probability distribution estimating changes in relative population size across Europe from 22ka ago to 9.1ka ago using data from the P3K14C database (Bird et al, 2022; see <strong>Supplemental 4</strong> for details).</li> </ol> </li> <li><strong>Spatiotemporal Background Points - File name: </strong><em><strong>AbsencePointData.csv</strong></em><br> <ol> <li>Randomly generated background points. 100 random points were generated in each millennium.</li> </ol> </li> <li><strong>Analysis Markdown Document - File Name: </strong><em><strong>CLIOARCH_MD_v1.2.html</strong></em><br> <ol> <li>Markdown illustrating step-by-step the methods used to organize and analyze the data.</li> </ol> </li> <li><strong>High-Resolution Spatiotemporal Predictions - File name: </strong><em><strong>SDM_MainGIF.mp4</strong></em><br> <ol> <li>High-resolution mp4 file showing the predictions of the potential climate niche space for humans from 22ka to 9.1ka ago.</li> </ol> </li> <li><strong>Potential Niche Space 22ka to 9.1ka ago- File name: </strong><em><strong>Human_Niche_Size.csv</strong></em> <ol> <li>Quantification of the potential climate niche space for each century.</li> </ol> </li> </ol> <p>The Climate data are not provided due to their size but are sourced from Karger et al (2023) and are accessible <a href="https://chelsa-climate.org/">here (https://chelsa-climate.org/)</a>.</p> <p> </p>
Last Glacial Maximum (LGM) Run for UVic2.9.10 (MOBI2.2) Input Data
<p>Input data required for a simulation of the Last Glacial Maximum (LGM) simulation with the OSU version of the University of Victoria climate model (version 2.9.10) with the Model of Ocean Biogeochemistry and Isotopes (MOBI2.2).</p>
Atmosphere-cryosphere interactions during the last phase of the LGM (21 ka BP) in the European Alps
<p>This dataset refers to: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmosphere-cryosphere interactions at 21 ka BP in the European Alps, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-43, in review, 2022. </p> <p> </p> <p>We used the regional climate model RegCM4 to investigate the physical processes sustaining the glacier extent during the Last Glacial Maximum (LGM) and pre-industrial time (PI) over the European Alps. After a bias-correction of precipitation and temperature data, we reconstructed the environmental equilibrium line altitude (envELA) of the Alpine glaciers, which resulted consistent with geological records. </p> <p>#----------------------------------------------------------</p> <p> </p> <p>LGM in the file names referes to 21 ka BP</p> <p>PI refers to pre-industrial</p> <p>#----------------------------------------------------------</p> <p> </p> <p><strong>This dataset contains:</strong></p> <p><strong>NetCDF files ------------------------------------------------------------------------------------</strong></p> <p> </p> <ul> <li><strong>Monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>RegCM4 monthly mean near-surface air temperature (TAS)</li> <li>RegCM4 monthly mean precipitation (PR)</li> <li>model topography (topo)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = RCM forced with MPI-ESM-P</li> <li>remapped = no</li> <li>resolution = 12 km</li> <li>files = <ul> <li>LGM_PR_TAS_monmean.nc</li> <li>PI_PR_TAS_monmean.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Bias-corrected monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>model topography (topo)</li> <li>Bias-corrected RegCM4 monthly mean precipitation (PR)</li> <li>Bias-corrected RegCM4 monthly mean near-surface air temperature (TAS)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = bias-correction based on HISTALP (TAS) and LAPrec (PR) of RegCM4 data</li> <li>remapped = onto HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files= <ul> <li>LGM_PR_TAS_monmean_BC.nc</li> <li>PI_PR_TAS_monmean_BC.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>ELA</strong> <ul> <li>variables = <ul> <li>ELA </li> <li>average RegCM-HISTALP-LAPrec topography</li> </ul> </li> <li>units = m a.s.l.</li> <li>data = calculated from bias-corrected RegCM4 data</li> <li>method = Zebre et al. (2020)</li> <li>remapped = on HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files = <ul> <li>LGM_ELA.nc</li> <li>PI_ELA.nc</li> </ul> </li> </ul> </li> </ul> <p><br> <strong>csv files ------------------------------------------------------------------------------------</strong></p> <p><strong>* dates refer to model dates, not real ones!!!</strong><br> tj_700_hpa_pr_lgm : Tagliamento glacier daily wind and precipitation at the 21 ka BP<br> tj_700_hpa_pr_pi : Tagliamento glacier daily wind and precipitation at the PI<br> db_700_hpa_pr_lgm : Dora Baltea glacier daily wind and precipitation at 21 ka BP<br> db_700_hpa_pr_pi : Dora Baltea glacier daily wind and precipitation at the PI<br> r_700_hpa_pr_lgm : Rhine glacier daily wind and precipitation at 21 ka BP<br> r_700_hpa_pr_pi : Rhine glacier daily wind and precipitation at the PI<br> ist_700_hpa_pr_lgm : Inn-Salzach-Traun glacier daily wind and precipitation at 21 ka BP<br> ist_700_hpa_pr_pi : Inn-Salzach-Traun glacier daily wind and precipitation at the PI</p> <p> </p>
LGM-Lateglacial 3D ice surface reconstructions of the Dora Baltea glacier system (western Italian Alps)
<p>3D ice surface configurations of six LGM-Lateglacial ice stages of the Dora Baltea glacier system (western Italian Alps).</p> <p>Ice-configurations were obtained by combining existing and new chronological constraints from glacial and postglacial landforms/deposits from the Dora Baltea catchment into 2D and 3D ice surface reconstructions, similar to the approach of the GlaRe ArcGIS toolbox (Pellitero et al., 2016).</p> <p>Mean position of the study area: 45.7412/7.3978 (°N/°E, WGS84)</p>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Atmospheric methane since the LGM was driven by wetland sources
<p>Companion data set to Kleinen et al. (2023):<br> Thomas Kleinen, Sergey Gromov, Benedikt Steil, and Victor Brovkin<br> Atmospheric methane since the LGM was driven by wetland sources<br> Climate of the Past, 2023</p> <p>Model output from the MPIESM model, model experiments base and MWM.<br> See Kleinen et al. (2023) for details.</p> <p>Timeseries data plotted in all Figures:<br> Global mean temperature, total land carbon; CH4 concentrations and fluxes; NO and RC fluxes; atmospheric lifetimes.</p> <p>Time axis in netcdf files is negative years before present, i.e. year -20000 is 20000 years before present (present=1950 CE).<br> Time is represented as absolute time YYMMDD.f, with YY negative year BP, MM mmonth and DD day, f is fractional daytime.<br> </p>
Simulating the Laurentide Ice Sheet of the LGM (Datasets from Yelmo_v1.751 output simulations)
<p>Model output presented in Moreno-Parada, D., Alvarez-Solas, J., Blasco, J., Montoya, M., and Robinson, A.: Simulating the Laurentide ice sheet of the Last Glacial Maximum.</p>
ECHAM6-wiso annual mean data for LGM and PI
<p>This dataset contains ECHAM6-wiso annual mean values for 6 LGM and 2 PI simulations, described in <a href="https://doi.org/10.5194/cp-19-1275-2023">https://doi.org/10.5194/cp-19-1275-2023</a>:</p> <p>LGM_GLOMAP: uses SST and sea-ice from GLOMAP dataset.</p> <p>LGM_tierney2020: uses SST from Tierney et al. (2020) and sea-ice form GLOMAP.</p> <p>LGM_miroc4m_sst_glomap_sic: uses SST from MIROC 4m and sea-ice form GLOMAP.</p> <p>LGM_miroc4m_sst_and_sic: uses SST and sea ice from MIROC 4m.</p> <p>LGM_miroc4m_strong_AMOC_sst_glomap_sic: uses SST from MIROC 4m for strong AMOC phase and sea-ice from GLOMAP.</p> <p>LGM_miroc4m_strong_AMOC_sst_and_sic: uses SST and sea ice from MIROC 4m for strong AMOC phase.</p> <p>PI_amip_sst_and_sic: PI simulation with AMIP SST and sea ice. To be used with LGM_GLOMAP, LGM_tierney2020, LGM_miroc4m_sst_glomap_sic and LGM_miroc4m_strong_AMOC_sst_glomap_sic simulations.</p> <p>PI_amip_sst_miroc4m_sic: PI simulation with AMIP SST and MIROC 4m PI sea ice. To be used with LGM_miroc4m_sst_and_sic and LGM_miroc4m_strong_AMOC_sst_and_sic simulations.</p> <p>The data in the LGM simulations are 2m air temperature, precipitation amount, d18O of precipitation, vertically integrated water vapor content and u and v components of total water vapor transport. The data in the PI simulations are 2m air temperature, precipitation amount and d18O of precipitation.</p> <p>The 2 CSV files show values in d18O of snow on ice and temperature at polar ice core stations for both observations and ECHAM6-wiso simulations.</p>
OSU-UVic hosing run with LGM initial conditions (LGM Full)
<p>This directory contains model code, input, output, and scripts from a hosing (freshwater forcing in the North Atlantic) simulation with the OSU-UVic climate model (version 2.9.10) to investigate the effect of changes in the Atlantic Meridional Overturning Circulation (AMOC) on carbon and carbon-13 components in the ocean as described in Schmittner and Boling (2025) and Schmittner (2025).</p> <p>Model code is in the code/ subdirectory.<br>Model input data is in the data/ subdirectory and in the control.in and mk.in files.<br>Model output data is in the tavg*nc and tsi*nc files.<br>Ferret scripts used to produce the figures are in the ferret/ subdirectory.</p> <p>A more detailed description about the OSU-UVic climate model is available at https://github.com/OSU-CEOAS-Schmittner/UVic2.9 and https://doi.org/10.5281/zenodo.11224826.</p> <p>Andreas Schmittner (andreas.schmittner@oregonstate.edu)</p> <p>References:<br>Schmittner, A. and M. Boling (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon Components in the Ocean, Global Biogeochemical Cycles, submitted manuscript.<br>Schmittner, A. (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon-13 Components in the Ocean, Global Biogeochemical Cycles, submitted manuscript.</p>
Model output for PI, LGM, IS_albedo, IS_topo, LSC, ORB, GHG experiments
<p>Monthly mean outputs for LGM, PI, IS_topo, IS_albedo, GHG and ORB experiments.</p>
LGM simulations based on AWIESM
<p> </p> <p>Shi, X., Werner, M., Yang, H., D'Agostino, R., Liu, J., Yang, C., and Lohmann, G.: Unraveling the complexities of the Last Glacial Maximum climate: the role of individual boundary conditions and forcings, Clim. Past, 19, 2157–2175, https://doi.org/10.5194/cp-19-2157-2023, 2023.</p> <p> </p> <p> </p> <p><strong>The dataset in version 2 </strong>gives the raw model outputs from a pre-industrial (PI) and several Last Glacial Maximum (LGM) sensitivity simulations based on AWIESM. A more detailed description of the individual file is presented below:</p> <p>PI.tar.gz: model outputs for PI simulation.</p> <p>LGM-full-forced.tar.gz: model outputs for full forced LGM simulation.</p> <p>LGM-with-PI-greenhousegas.tar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the greenhouse gases are the same as for PI. </p> <p>LGM-with-PI-icesheets.tar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the ice sheets are the same as for PI. </p> <p>LGM-with-PI-orbital.tar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the orbital parameters are the same as for PI. </p> <p>LGM-with-PI-greenhousegas-and-icesheets.nctar.gz: model outputs for a sensitivity LGM simulation, in which all boundary conditions and forcings are set to the LGM values but the greenhouse gases and ice sheets are the same as for PI. </p> <p> </p> <p>Each file contains the following Netcdf:</p> <p>AWI_moisture_budget.nc: decomposition of the anomalous net precipitation in relative to LGM (not present for LGM-full-forced)</p> <p>pr.nc: precipitation</p> <p>psl.nc: sea level pressure</p> <p>ps.nc: surface pressure</p> <p>q.nc: specific humidity</p> <p>tas.nc: surface air temperature</p> <p>ts.nc: surface temperature</p> <p>u.nc: u-wind at all levels</p> <p>v.nc: v-wind at all levels</p> <p>zg.nc: geopotential height</p> <p> </p> <p><strong>The dataset in version 3</strong> gives the SST and vertical wind (omega) in all simulations.</p>
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c in Pliocene origins, Pleistocene refugia, and postglacial range expansions in southern devil scorpions (Vaejovidae: Vaejovis carolinianus)
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c. Localities used to test and train the model are indicated by
Pre-LGM Sea Levels within Antarctica
<p>A compilation of pre-LGM sea-level constraints from across Antarctica. Compiled for WALIS (<a href="https://alerovere.github.io/WALIS/">What is WALIS? | The World Atlas of Last Interglacial Shorelines (alerovere.github.io)</a>)</p>
Clinical Performance of the VivaDiag ™ COVID-19 lgM / IgG Rapid Test in Early Detecting the Infection of COVID-19
ClinicalTrials.gov study NCT04316728. IPD Sharing: YES. Countries: 1. Publications: 20.
The LGM and the Last Deglaciation lake expansion and its corresponding cold/wet climate change mode in the northern Qinghai-Tibet Plateau
<p><strong>SGH sediment dataset, Simulations of TRACE and PMIP3.</strong></p>
CESM2.1-CAM6-CLM4 2-degree amip-LGM
<p>Monthly mean RFMIP-like data from a 30-year simulation of CESM2 configured with CAM6 with finite-volume dynamical core on 2-degree grid and CLM4 as land model. Boundary conditions from Pedro DiNezio for last glacial maximum. </p> <p>The new gravity wave scheme in CAM6 is <strong>disabled</strong> in these simulations because the topography file used is incompatible.</p> <p>Compset: </p> <pre><code class="language-bash">1850_CAM60_CLM40%SP_CICE%PRES_DOCN%DOM_RTM_SGLC_SWAV</code></pre> <p>CAM namelist changes:</p> <pre><code class="language-bash">bnd_topo = "/glade/work/brianpm/model_data/topo_21ka_remap_19x25.mod.170428.sm9.nc" ch4vmr = 360e-9 co2vmr = 190e-6 f11vmr = 0.0 f12vmr = 0.0 n2ovmr = 240e-9 ! avoid new gravity wave scheme use_gw_oro = .true. use_gw_rdg_beta = .false.</code></pre> <p>CLM namelist changes:</p> <pre><code class="language-bash">fsurdat = '/glade/work/brianpm/model_data/surfdata_1.9x2.5_21ka.170505.modshelves.nc' urban_hac = 'OFF' finidat = 'b.e12.21ka.002.clm2.r.0301-01-01-00000.nc'</code></pre> <p>Coupler namelist changes:</p> <pre><code class="language-xml">&seq_infodata_inparm orb_iyear = -19050 orb_mode = 'fixed_year' /</code></pre> <p> </p>
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