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55 results for “CESM2”
CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia
<p><strong>General Info</strong></p> <p>This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively. </p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Dataset Contents </strong></p> <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> <p>The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET).</p> <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> <p>The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>)</p> <pre><code>model_scripts.tar</code></pre> <p>Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset. </p> <pre><code>glacier_final.tar</code></pre> <p>Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).</p>
CESM2 Idealized Experiment Output: Summer atmospheric response to zero May North American snow cover
<p>The National Center for Atmospheric Research’s Community Earth System Model version 2.2 (CESM2) (Danabasoglu et al., 2020) was run in the Atmospheric Model Intercomparison Project (AMIP) configuration. SSTs and sea-ice were prescribed as monthly varying seasonal cycles based on the observed climatology from 2005 to 2015 (i.e., component set: F2010climo) (Hurrell et al., 2008). We employed the Community Atmosphere Model version 6 (CAM6) (Bogenschutz et al., 2018)<span> </span>as the atmospheric component and the Community Land Model version 5 (CLM5) (Lawrence et al., 2019) as the land-surface component. Each model was run with a horizontal resolution of 0.9˚ latitude by 1.25˚ longitude.</p> <p>We ran a control simulation in this configuration for ten consecutive years. We then modified the land-surface restart files for May 1st of each year by reducing the snow cover over North America to zero. Using these modified files, we then completed a reduced snow simulation by rerunning three-month simulations from May through July for each of the ten years. </p>
CESM2 cloud locking suite single-level fields
<p>Selected single-level Community Atmosphere Model version 6 (CAM6) fields from four simulations of the Community Earth System Model version 2.0.1 (CESM2). Names of the simulations as they appear in the GRL manuscript (and corresponding native case name) are: CTL (B1850_c201_CTL), CLOCK (B1850_c201_CLOCK), FCTL (F1850JJB_c201_CTL), and FLOCK (F1850JJB_c201_CLOCK). Note that CLOCK only spans 24 yr while the others span 25 yr. All simulations are forced by prescribed pre-industrial atmospheric composition. CTL and CLOCK use prognostic atmosphere, ocean, land, and sea ice models; FCTL and FLOCK use prescribed monthly mean sea-surface temperatures and sea ice concentrations taken from CTL. In CLOCK and FLOCK, cloud properties seen by the radiation scheme are prescribed ("locked") and are sourced from randomly selected years of a 3-year data pool comprised of Years 20-22 of CTL.</p>
CESM2 cloud locking suite multi-level fields for FCTL simulation
<p>Selected multi-level Community Atmosphere Model version 6 (CAM6) fields from the FCTL simulation of the Community Earth System Model version 2.0.1 (CESM2). The simulation is labeled "FCTL" in the GRL manuscript but has a native case name of "F1850JJB_c201_CTL" in the file names. FCTL is forced by prescribed pre-industrial atmospheric composition, and monthly mean sea-surface temperatures and sea ice concentrations taken from an existing pre-industrial fully coupled simulation ("CTL").</p>
ARISE-SAI_1.5 : CESM2 Extreme Precipitation and Temperature Indices
<p>Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) is a set of simulations carried out with the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model, version 6 (CESM2(WACCM6)) that aims at simulating a plausible deployment of solar climate intervention of stratospheric aerosol injection to enable community assessment of responses of the Earth system.</p> <p>This dataset uses the first set of simulations, called ARISE-SAI-1.5, that utilized the middle-of-the-road SSP2-4.5 emission scenario, and targetted a global mean surface air temperature near 1.5°C above the pre-industrial value. ARISE-SAI-1.5 is described in Richter et al. (2022). Selected data are available at Richter & Visioni (2022a,b).</p> <p>The files contained here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022).</p> <p><strong>Precipitation Indices</strong></p> <p>PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT</p> <p><strong>Temperature Indices</strong></p> <p>TNN, TNX, FD, TR, TN90, TN10, TN90p, TN10p, TXX, TXN, ID, SU, TX90, TX10, TX10p, TX90p, WSDI</p> <p>Where T?10 is the number of days below an annual 10th percentile threshold and T?90 is the number of days above an annual 90th percentile threshold (i.e. around 30 days per year).</p> <p>T?10p as defined by ETCCDI is the frequency of days below the rolling 5-day average climatological day of year 10th percentile. This threshold is also used for the cold spell duration index (CSDI), or consecutive days that are cool for the season.</p> <p>T?90p as defined by ETCCDI is the frequency of days above the rolling 5-day average climatological day of year 90th percentile. This threshold is also used for the warm spell duration index (WSDI), or consecutive days that are warm for the season.</p>
Lateral_melting_TC_2022: Data for sea ice sensitivity to lateral melting, CESM2
<p>CESM2 model data for Smith, M. et al, Arctic sea ice sensitivity to lateral melting representation in a coupled climate model, In The Cryosphere, 2022</p>
Near-surface Temperature from CMIP6 NCAR CESM2 historical monthly dataset for CLIVAR CMIP6 Bootcamp
<p>This dataset has been created from CMIP6 data through CMIP6 online catalog. It is meant to be used for training purposes only.</p> <p> </p> <p>Data is from CESM2 (NCAR) and is a monthly dataset from 1850 to 2014 containing near-surface temperature (TAS).</p>
Downscaled 1 km RACMO2 data used in CESM2 Greenland SMB evaluation paper
<p>Post-processed RACMO2.3p2 data used to carry out part of the analysis in the paper titled “Present day Greenland ice sheet climate and surface mass balance in CESM2”, in review with JGR Earth Surface.</p> <p>This data stems from a RACMO2.3p2 regional climate simulation over Greenland at 11 km which was averaged over the period 1961-1990 and statistically downscaled to 1 km. The original resolution of this dataset is 11 km and further described in Noël et al., 2018, <a href="https://doi.org/10.5194/tc-12-811-2018">https://doi.org/10.5194/tc-12-811-2018</a></p> <p>This data is published for archiving purposes only. Data requests for the latest version of RACMO2 output can be made free of charge to Brice Noël (B.P.Y.Noel@uu.nl) and Michiel van den Broeke (M.R.vandenBroeke@uu.nl). In your request, please specify the variables of interest, time period and time frequency, and area of interest.</p> <p> </p>
CESM2 Atmospheric CO2 without agricultural management
This dataset was created to understand the impacts of agriculture on CO2 concentrations. Two 1-degree simulations were branched from the CMIP6 "CESM2-esm-hist" simulation in 1970. The first of these turned off the explicit representation of agriculture so that all crop areas are represented as "generic" C3 crops, where crop phenology is simulated as C3 grasses and do not include irrigation or fertilization (referred to as "generic crop"). The second uses the explicit representation of agriculture but removes industrial N fertilization (referred to as "no fertilization"). To ensure that changes in CO2 fluxes were minimally impacted by model drift, each simulation equilibrated carbon fluxes in 1970 by cycling over a single year of forcing for ten years. The CESM2 simulated these alternative representations of agriculture in a CO2 emissions-forced historical scenario following the "esm-hist" experimental protocol.
phenology data of lake mixing, and stratification based on the CESM2-LE output
<p>The phenology data of lake mixing, and stratification calculated from the daily output of CESM2 large ensemble. For more information about this dataset, please refer to the manuscript entitled 'Projected changes in the phenology of stratification and overturning in ice-covered lakes of the Northern Hemisphere (Lei Huang et al)'. For more information about CESM2 large ensemble, please refer to https://www.cesm.ucar.edu/community-projects/lens2. </p>
CESM2 land output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2015-2100. The files are labelled according to the experiments they were generated from (base, MF (Max Forest) and No LULCC). Output fields are as follows:</p> <p>discharge_plus_runoff: surface water availability (river discharge plus surface runoff), units m^-3 s^-1</p> <p>EFLX_LH_TOT: total latent heat flux from land to atmosphere, units W m^-2</p> <p>QFLX_EVAP_TOT: total evapotranspiration (canopy evaporation plus canopy transpiration plus soil evaporation), units kg m^-2 s^-1</p> <p>SOILLIQ: soil liquid water content, units kg m^-2</p> <p>SW_surface_albedo: surface albedo, units fraction</p> <p>TSA: 2m air temperature, units K</p> <p>VEGWP: vegetation water potential, units m</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the Max Forest scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the base scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 land use data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land use/ land cover input data for CESM2 which was used in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2000-2100. The files correspond to experiments described in the study as follows:</p> <p> </p> <p>Base: landuse.timeseries_0.9x1.25_SSP1-2.6_78pfts_CMIP6_simyr2000-2100_c220715.nc</p> <p>Max Forest: landuse.timeseries_0.9x1.25_hist_78pfts_SSPRFAFRS_SSP1_edit_xarray_4_simyr2000-2100_c221024.nc</p> <p>No LULCC: landuse.timeseries_0.9x1.25_hist_78pfts_SSPNOLULCC_3_simyr2000-2100_c221025.nc</p> <p> </p> <p>Files were created in collaboration by James A. King, James Weber, Peter Lawrence, and Stephanie Roe.</p> <p> </p>
CESM2 atmosphere output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the atmosphere output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation, for the No LULCC scenario. The datasets cover the period 2015-2100. Output fields are as follows:</p> <p>CCN3: concentration of cloud condensation nuclei at 0.1% supersaturation, units cm^-3</p> <p>CLDLOW: cloud fraction integrated between 1200-700 hPa, units fraction of grid cell</p> <p>CONCLD: convective cloud cover, units fraction of grid cell</p> <p>GCLDLWP: grid cell cloud water path, units kg m^-2</p> <p>LWCF_d1: clean longwave cloud forcing, units W m^-2</p> <p>OMEGA: vertical velocity, units Pa s^-1</p> <p>PRECT: total precipitation, units m s^-1</p> <p>SWCF_d1: clean shortwave cloud forcing, units W m^-2</p> <p>V: meridional wind, units m s^-1</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
Data from: Quantifying the impact of internal variability on the CESM2 control algorithm for stratospheric aerosol injection dataset
<p>Earth system models are a powerful tool to simulate the response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement tools from control theory, called "controllers", to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5C above pre-industrial temperatures. This work explores how internal (unforced) climate variability can impact controller-determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) simulations. Since the ARISE-SAI controller determines injection amounts by comparing global annual-mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE-SAI controller and data from CESM2 earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability.</p>
CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> <p>Decoding file names:</p> <p>Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend. </p> <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> <p>Variables:</p> <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul> <p> </p> <p> </p> <p> </p>
CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> <p>Details for files cesm2.[experiment name].SST.noise.nc</p> <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> <p>For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207</p> <p>For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374</p>
11 km RACMO2 data used in CESM2 Greenland SMB evaluation paper
<p>Post-processed RACMO2.3p2 data used to carry out part of the analysis in the paper titled “Present day Greenland ice sheet climate and surface mass balance in CESM2”, currently in review with JGR Earth Surface.</p> <p>This data stems from a RACMO2.3p2 regional climate simulation over Greenland at 11 km which was averaged over the period 1961-1990. This data is further described in Noël et al., 2018 : <a href="https://doi.org/10.5194/tc-12-811-2018">https://doi.org/10.5194/tc-12-811-2018</a></p> <p>This data is published for archiving purposes only. Data requests for the latest version of RACMO2 output can be made free of charge to Brice Noël (B.P.Y.Noel@uu.nl) and Michiel van den Broeke (M.R.vandenBroeke@uu.nl). In your request, please specify the variables of interest, time period and time frequency, and area of interest.</p> <p>NETCDF NAMING CONVENTION<br> <strong>_ymonmean</strong> : climatological mean over period<br> <strong>_ymonstd</strong> : climatological standard deviation over period</p>
Downscaled 1 km CESM2 data used in CESM2 Greenland SMB evaluation paper (HIST-EC)
<p>Monthly data from CESM2 simulation HIST-EC over the period 1960-1999, downscaled to the 1 km RACMO grid using elevation class output.</p> <p>Variable 'QICE' represents SMB as calculated internally by CLM.</p>
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