The data release consists of a single NetCDF file with results from a suite of ice sheet model simulations. We ran with Community Ice Sheet Model (CISM2) with input from models used in the Palaeoclimate Modelling Intercomparison 3 (PMIP3). The NetCDF file contains output from model year 50,000 for a limited number of variables to keep the file size reasonably small. This subset of variables are the ones we focus our analysis and paper on.

This data set consists of monthly long-term mean temperature data (degrees C) for the last glacial maximum (21 ka) downscaled to a 10-km grid of North America. The 10-km data were derived using simulated temperature data from 10 general circulation models (GCMs; CCSM4, CNRM-CM5, COSMOS-ASO, FGOALS-g2, GISS-E2-R, IPSL-CM5A-LR, MIROC-ESM, MPI-ESM-P-OA, MPI-ESM-P-OAC, and MRI-CGCM3) run under the PMIP3/CMIP5 (Paleoclimate Modelling Intercomparison Project phase 3 / Coupled Model Intercomparison Project phase 5) “lgm” and “piControl” experiments. The lgm and piControl data are available from the Earth System Grid - Center for Enabling Technologies (ESG-CET; https://esgf-node.llnl.gov/projects/esgf-llnl/). Additional information about the data is available from the CMIP5 (https://pcmdi.llnl.gov/mips/cmip5/) and PMIP3 (https://pmip3.lsce.ipsl.fr/) web sites. The names of the lgm and piControl files we used are listed in the “source_file” global attribute of each GCM temperature netCDF file in this data release. For each GCM, the PMIP3/CMIP5 lgm temperature data were bias corrected using long-term mean differences calculated as the lgm long-term mean minus the piControl long-term mean. These long-term mean differences were regridded to a North America 10-km Lambert azimuthal equal-area grid using the CDO (Climate Data Operators, https://code.mpimet.mpg.de/projects/cdo) bilinear interpolation function “remapbil”. We used ICE-5G (VM2) data (Peltier, 2004, https://doi.org/10.1146/annurev.earth.32.082503.144359) to identify grid cells with ice cover at 21 ka. The interpolated long-term mean differences were applied to CRU CL 2.0 (1961-1990 30-year mean) climate data (New et al., 2002, https://doi.org/10.3354/cr021001). The CRU CL 2.0 data were also regridded to the 10-km grid using local lapse-rate adjusted interpolation (Praskievicz and Bartlein, 2014, https://doi.org/10.1016/j.jhydrol.2014.06.017). The ensemble mean data were calculated using the bias corrected temperature data from each of the 10 GCM simulations.

This data set consists of monthly long-term mean temperature data (degrees C) for the last glacial maximum (21 ka) downscaled to a 10-km grid of North America. The 10-km data were derived using simulated temperature data from 10 general circulation models (GCMs; CCSM4, CNRM-CM5, COSMOS-ASO, FGOALS-g2, GISS-E2-R, IPSL-CM5A-LR, MIROC-ESM, MPI-ESM-P-OA, MPI-ESM-P-OAC, and MRI-CGCM3) run under the PMIP3/CMIP5 (Paleoclimate Modelling Intercomparison Project phase 3 / Coupled Model Intercomparison Project phase 5) “lgm” and “piControl” experiments. The lgm and piControl data are available from the Earth System Grid - Center for Enabling Technologies (ESG-CET; https://esgf-node.llnl.gov/projects/esgf-llnl/). Additional information about the data is available from the CMIP5 (https://pcmdi.llnl.gov/mips/cmip5/) and PMIP3 (https://pmip3.lsce.ipsl.fr/) web sites. The names of the lgm and piControl files we used are listed in the “source_file” global attribute of each GCM temperature netCDF file in this data release. For each GCM, the PMIP3/CMIP5 lgm temperature data were bias corrected using long-term mean differences calculated as the lgm long-term mean minus the piControl long-term mean. These long-term mean differences were regridded to a North America 10-km Lambert azimuthal equal-area grid using the CDO (Climate Data Operators, https://code.mpimet.mpg.de/projects/cdo) bilinear interpolation function “remapbil”. We used ICE-5G (VM2) data (Peltier, 2004, https://doi.org/10.1146/annurev.earth.32.082503.144359) to identify grid cells with ice cover at 21 ka. The interpolated long-term mean differences were applied to CRU CL 2.0 (1961-1990 30-year mean) climate data (New et al., 2002, https://doi.org/10.3354/cr021001). The CRU CL 2.0 data were also regridded to the 10-km grid using local lapse-rate adjusted interpolation (Praskievicz and Bartlein, 2014, https://doi.org/10.1016/j.jhydrol.2014.06.017). The ensemble mean data were calculated using the bias corrected temperature data from each of the 10 GCM simulations.

This data set comprises results from a hybrid glacier evolution model that uses the mass balance module of the Python Glacier Evolution Model (PyGEM) and the glacier dynamics module of the Open Global Glacier Model (OGGM). Output parameters include projections of glacier mass change, fixed runoff, and various mass balance components at regionally aggregated and glacier scales.

This data set provides daily estimates of snow depth and snow density for snow-on-sea-ice in the Arctic Ocean over a 41-year period using a Lagrangian snow-evolution model forced with NASA’s Modern Era Retrospective-Analysis for Research Applications Version 2 (MERRA-2) and the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis, generation 5 (ERA5).

The Local Analysis and Prediction System (LAPS), run by the NOAA's Forecast Systems Laboratory (FSL), combines numerous observed meteorological data sets into a collection of atmospheric analyses.

This dataset includes model projections of seasonal temperature (T), precipitation (P), and runoff (R) from 214 climate simulations from coupled model intercomparison project (CMIP) 3 and CMIP5 scenarios for 19-year periods centered on 2030, 2060, and 2090. The summaries of the climate model projections are presented as percentiles (5th, 25th, 50th, 75th, and 95th) of seasonal (October through March, January through March, April through June, and July through September) changes in T, P, and R for the 214 climate models. The metrics are calculated from variables previously summarized across the conterminous United States for hydrologic response units of the Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014). T, P, and R were previously derived using a monthly water balance model (Bock and others, 2016; 2017). Names, sources, and references of the climate inputs are described in Bock and others (2017).

This dataset includes model projections of seasonal temperature (T), precipitation (P), and runoff (R) from 214 climate simulations from coupled model intercomparison project (CMIP) 3 and CMIP5 scenarios for 19-year periods centered on 2030, 2060, and 2090. The summaries of the climate model projections are presented as percentiles (5th, 25th, 50th, 75th, and 95th) of seasonal (October through March, January through March, April through June, and July through September) changes in T, P, and R for the 214 climate models. The metrics are calculated from variables previously summarized across the conterminous United States for hydrologic response units of the Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014). T, P, and R were previously derived using a monthly water balance model (Bock and others, 2016; 2017). Names, sources, and references of the climate inputs are described in Bock and others (2017).

With the upgraded Land Surface Models (LSMs) and updated forcing data sets, the GLDAS version 2.1 (GLDAS-2.1) production stream serves as a replacement for GLDAS-001. The entire GLDAS-001 collection from January 1979 through March 2020 was decommissioned on June 30, 2020 and removed from the GES DISC system. However, the replacement for GLDAS-001 monthly and 3-hourly 1.0 x 1.0 degree products from CLM Land Surface Model currently are not available yet. Once their replacement data products become available, the DOIs of GLDAS-001 CLM data products will direct to the GLDAS-2.1 CLM data products. This data set contains a series of land surface parameters simulated from the Common Land Model (CLM) V2.0 model in the Global Land Data Assimilation System (GLDAS). The data are in 1.0 degree resolution and range from January 1979 to present. The temporal resolution is 3-hourly. This simulation was forced by a combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET). The simulation was initialized on 1 January 1979 using soil moisture and other state fields from a GLDAS/CLM model climatology for that day of the year. WGRIB or another GRIB reader is required to read the files. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. For more information, please see the README document.

This dataset provides monthly-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.