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60 results for “cesm”
Datasets and analysis scripts for air-sea flux study using CESM-MOM6
<p>This repository provides the fully-coupled CESM-MOM6 simulation datasets for the ocean surface and the analysis scripts for studying air-sea flux variability. This study aimed to quantify the effects of the stochastic ocean density corrections on the ocean-intrinsic component of air-sea fluctuations, which is the strongest at mesoscales, i.e., 10-1000 Km. </p>
PICO simulation of the CESM-BEC
<p>This is ocean biogeochemistry tracer and tracer flux output from the PICO simulation of the Community Earth System Model, Biogeochemical Ecosystem Cycling (CESM-BEC) model as detailed in Letscher et al. (2023) in <em>Global Biogeochemical Cycles</em>. Output is the final twenty year annually averaged tracer concentrations and fluxes from a 300 year simulation using CORE II external forcing for the years 1948-2009. The resolution is nominally 3 deg latitude by 3 deg longitude with 62 vertical layers. </p>
Multi-scale sea ice kinematics modeling dataset with a tripolar grid hierarchy (TS grids) in CESM
<p>1. We design a new tripolar grid generation method for global ocean-sea ice modeling. The generated grid is orthogonal and compatible with ocean and sea ice models. A hierarchy of ocean-sea ice model grid is constructed and incorporated in CESM by using the grid generation method. The generated tripolar grids are regarded as TS045, TS015 and TS005, with the nominal resolution of 0.45<sup>o </sup>(800x560), 0.15<sup>o</sup> (2400x1680) and 0.05<sup>o </sup>(7200x5040), respectively. The resolution range for the grid hierarchy covers climate modeling to sub-mesoscale capable for ocean modeling.</p> <p>2. Atmosphere forced simulations based on CESM D-type experiments are carried out for TS grid. Both TS045 and TS015 experiments start with no sea ice, integrate for 42 years. TS005 starts from the equilibrium state of TS015 result (36 year), runs for another 7 years.</p> <p>3. TS0*.grid files include necessary grid information, such as grid latitude, longitude, etc. TS0*.kmt files denote the deepest level at each grid location. Bilinear interpolators from TS grid to atmosphere T62 grid are provided with map*.nc files. The model outputs at year 42 are provided with T*.nc files. The last two kinds of netcdf files are compressed with Linux command "gzip".</p>
Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations
<p>Leaf level broadband reflectance and transmittance computed from leaf traits.</p> <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> <p>Global scale simulation results</p> <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> <p>Code changes</p> <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>
CESM model results - Effect of nitrogen limitation and soil processes on Holocene greening of the Sahara
<p>The model results are made in EAPL, Yonsei University, South Korea (https://eapl.yonsei.ac.kr). The purpose of the simulations were to find out the effect of nitrogen limitation and soil processes on Holocene greening of the Sahara. CESM model v1.2 was used for this simulation.</p>
Idealized single-forcing GCM simulations with NCAR CESM
<p>This repository contains a set of single-forcing general circulation model (GCM) simulations run with the National Center for Atmospheric Research (NCAR) Community Earth System Model version 1.2 (CESM1.2). In each of these equilibrium simulations, one climate forcing was altered while all others were held constant at preindustrial levels, modeling the climate response to individual climate forcings. Simulations were run for obliquity (low and high obliquity), precession (four phases of the precession cycle with high eccentricity, and one simulation with eccentricity set to zero), half CO<sub>2</sub>, and LGM-sized ice sheets. The values chosen for the orbital simulations represent the extreme values of the past 900 thousand years.</p> <p>Simulations were run for at least 500 years, and forcings do not change from year to year. The uploaded files are 100 year (or longer) monthly climatologies. For most simulations, variables are presented for the atmosphere (atm), sea ice (cice), land (clm2), and river runoff (rtm). For the ice sheets simulation and its corresponding preindustrial simulation (0ka), only atmospheric output is presented; to inquire about other fields for those simulations, please contact Pedro DiNezio at pdn@ig.utexas.edu. Ocean files are not currently available; please contact Michael Erb at michael.p.erb@gmail.com if you are interested in those results.</p> <p> </p> <p>=== FORCINGS ===</p> <p>Preindustrial climate forcings for the 0ka_0urb simulation were set to the following values:</p> <p> - Obliquity: 23.44107°</p> <p> - Longitude of perihelion: 102.7242°</p> <p> - Eccentricity: 0.01670772</p> <p> - CO<sub>2</sub>: 284.7 ppm</p> <p> - Ice sheets: 0 ka BP</p> <p>The remaining simulations explore the climate response to a change in one of these forcings, with all other forcings set to preindustrial levels. Forcings are specified as follows:</p> <p> - lo_obliq and hi_obliq: Obliquity is set to 22.079° or 24.480°, respectively.</p> <p> - 0_AEQ, 90_WSOL, 180_VEQ, and 270_SSOL: Perihelion occurs at the NH autumnal equinox, winter solstice, vernal equinox, or summer solstice, respectively, with eccentricity set to 0.0493. This corresponds to a longitude of perihelion of 0°, 90°, 180°, or 270°, respectively.</p> <p> - ECC_0: Eccentricity is set to 0.</p> <p> - half_CO2: CO<sub>2</sub> is set to 142.35 ppm.</p> <p> - 21kaGlac: Ice sheets and sea level are set to Last Glacial Maximum (LGM) levels. Ice sheets come from the Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) reconstruction, with ice shelves added in the western Labrador Sea.</p> <p>A note about preindustrial simulations: Some details of the model setup differ between the ice sheet simulation and the other simulations. Because of this, if a preindustrial control simulation is wanted for analysis, it is recommended that you use certain preindustrial simulations for certain comparisons, as follows:</p> <p>- 0ka_0urb (BG1850C5CN): Control simulation for all simulations except ice sheets.</p> <p>- 0ka (B1850C5): Control simulation for ice sheets (21kaGlac) simulation.</p> <p> </p> <p>=== NOTES ===</p> <p>More detailed description of these simulations, as well as results, can be found in the following papers:</p> <p>Erb, M. P., C. S. Jackson, A. J. Broccoli, D. W. Lea, P. J. Valdes, M. Crucifix, and P. N. DiNezio, in press: Model evidence for a seasonal bias in Antarctic ice cores. <em>Nature Communications.</em></p> <p>Bosmans, J. H. C., M. P. Erb, A. M. Dolan, S. S. Drijfhout, E. Tuenter, F. J. Hilgen, D. Edge, J. O. Pope, and L. J. Lourens, in press: Response of the Asian summer monsoons to idealized precession and obliquity forcing in a set of GCMs. <em>Quat. Sci. Rev.</em></p> <p>Bhattacharya, T., J. E. Tierney, and P. DiNezio, 2017: Glacial reduction of the North American Monsoon via surface cooling and atmospheric ventilation. <em>Geophys. Res. Lett.</em>, <strong>44</strong>, 5113-5122, doi:10.1002/2017GL073632.</p> <p>DiNezio, P. N., J. E. Tierney, B. L. Otto-Bliesner, A. Timmermann, T. Bhattacharya, N. Rosenbloom, and E. Brady, in review: Glacial changes in tropical climate amplified by the Indian Ocean.</p> <p>Note that the monthly data analyzed in these papers is sometimes converted to a common fixed-angular calendar in which every "month" corresponds to a 30° arc of Earth's orbit. This was done because changes in precession affect the speed at which Earth travels through different parts of its orbit according to Kepler's second law, complicating the comparison of months in different precession experiments. However, the results provided in this repository use the model's original fixed-day calendar.</p> <p>Computing resources (ark:/85065/d7wd3xhc) were provided by the Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies.</p> <p>If you use these simulations for research, please let the authors know.</p> <p>For a similar set of experiments using another model (GFDL CM2.1), see doi:10.5281/zenodo.1194480.</p> <p>Contact:<br> Michael Erb<br> Postdoctoral Scholar at Northern Arizona University<br> michael.p.erb@gmail.com</p>
Land use and volcanic forcing files for past2k CESM simulation
<p>Data associated with the manuscript entitled "Asymmetric Cooling of the Atlantic and Pacific Arctic during the Past Two Millennia: A Dual Observation-Modeling Study".</p> <p> Transient simulations for the past 2,000 years (past2k) have been proposed for the Paleoclimate Model Intercomparison Phase 4 (PMIP4) contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Jungclaus et al., 2017). Forcing data for the past2k experiments was compiled by PMIP4. However, there was no land-cover change available for prior to 850 CE when this past2k CESM simulation was started. We therefore constructed a land cover forcing (Dataset1.tar) for this past2k simulation from 1 CE to 849 CE as a superposition of HYDE3.1 cropland changes (Goldewijk et al. 2010, 2011) onto the land cover forcing for the CESM Last Millennium Ensemble runs (LME, Otto-Blisner et al. 2016) for 850 CE. Land cover forcing for this past2k CESM simulation from 850 CE to 2005 CE is taken from LME, which implements grassland changes in addition to cropland changes as compiled by PMIP3.</p> <p>PMIP4 Easy Volcanic Aerosol data (EVA, Toohey et al. 2016) was used to generate volcanic aerosol forcing file for the past2k simulation with CESM. EVA provides space-time distribution of volcanic aerosol mass, effective radius, and optical depth. CESM reads in space-time distribution of volcanic aerosol mass, but assumes constant effective radius while computing optical depth. In order for CESM to match EVA optical depth, EVA aerosol mass needed to be scaled up by a factor of 1.67 based on a set of sensitivity experiments for the Tambora eruption in 1815. The forcing file (Dataset2.nc) is included for others to use.</p> <p>Data citation: Zhong, Y., Jahn, A., Miller, G. H., Geirsdottir, A. (2018). Asymmetric Cooling of the Atlantic and Pacific Arctic during the Past Two Millennia: A Dual Observation-Modeling Study. <em>Submitted to GRL</em>.</p>
CESM FKESSLER input data for running CESM in docker container
This dataset contains input data for running CESM 2.1.1 with FKESSLER compset and resolution T31_g37. <pre>create_newcase --case /home/cesm/cases/fkessler --compset FKESSLER \ --res T31_g37 --compset FKESSLER --machine espresso --run-unsupported </pre>
CESM F1850 input data for running CESM in docker container
<p>This dataset contains input data for running CESM 2.1.1 with F1850 compset and resolution f09_g17.</p> <p> </p> <pre>create_newcase --case /home/cesm/cases/B1850 --compset B1850 \ --res f09_g17 --machine espresso --run-unsupported && \ cd /home/cesm/cases/B1850 </pre> <p>The case uses <a href="https://bioconda.github.io/recipes/cesm/README.html">cesm from bioconda</a>.</p>
CESM input data for running CESM historic with CAM6 and CLM5 (no ocean) in docker container
<p>CESM docker container for HIST_CAM60_CLM50%BGC_CICE%PRES_DOCN%DOM_MOSART_CISM2%NOEVOLVE_SWAV compset and resolution f19_g17 using <a href="https://bioconda.github.io/recipes/cesm/README.html">bioconda cesm docker</a> as a base image.</p>
CESM and FOCI model data as supplementary data for Climate Index Collection based on model data (CICMoD)
<p>The Community Earth System Model (CESM) and the Flexible Ocean and Climate Infrastructure (FOCI) are both fully-coupled, global climate models that provide state-of-the-art computer simulations of the Earth's past, present, and future climate states.</p> <p>This dataset contains results from control runs with conditions of year 1850 without additional external forcing for 1000 years and 999 years for FOCI and CESM, respectively.</p> <p>Included features are:</p> <ul> <li>sea surface temperature</li> <li>surface air temperature</li> <li>sea level pressure</li> <li>sea surface salinity</li> <li>geopotential height (500mb)</li> <li>precipitation</li> </ul>
Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"
<p><strong>General Info</strong></p> <p>This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “<em>Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” </em>that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>.</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>Raw Data</strong></p> <p>Raw monthly and daily unstructured HMA VR model output are available on request. </p> <p><strong>Dataset Contents</strong></p> <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> <p>These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA. </p> <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> <p>These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.</p>
LPS's over Bay of Bengal and TC's over West North Pacific (WNP) simulated in the CESM 1.2 WNP SST warming experiment
<p>This repository contains the dataset as explained below:</p> <p>1) This dataset contains low-pressure systems (LPS) tracks over the Bay of Bengal (BoB) and West North Pacific (WNP) tropical cyclones (TCs) simulated in CESM 1.2 WNP SST warming experiment conducted by Srujan and Sandeep (2023) submitted to Nature Geosciences. The LPS and TCs are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015).</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p>
LPS's over Bay of Bengal and TC's over West North Pacific (WNP) simulated in the CESM 1.2 WNP SST warming experiment
<p>This repository contains the dataset as explained below:</p> <p>1) This dataset contains low-pressure systems (LPS) tracks over the Bay of Bengal (BoB) and West North Pacific (WNP) tropical cyclones (TCs) simulated in CESM 1.2 WNP SST warming experiment conducted by Srujan and Sandeep (2023) submitted to Nature Geosciences. The LPS and TCs are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015). The file names start with "<strong>cyc_</strong>". This will be useful for reproducing Fig. 1 and Fig. A2</p> <p>2) The data helps reproduce the Heating rate due to the moist process (Fig. 2) starts with "<strong>DT_</strong>"</p> <p>3) The data helps reproduce the propagation of rossbywave (Fig. 3) starting with "<strong>regcof_</strong>"</p> <p>4) The data helps reproduce the input SST pattern (Fig. A1) starting with "<strong>sst_</strong>"</p> <p>5) The data helps reproduce the Hovmuller plot (Fig. A3) starting with "<strong>comp_</strong>"</p> <p>6) The data helps reproduce the lag1 mslp anomaly (Fig. A4) starting with "<strong>slpcomp_</strong>"</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p>
Exceptional multi-year prediction skill of the Kuroshio Extension in the high-resolution CESM decadal prediction system
The Kuroshio Extension (KE) has far-reaching influences on climate as well as on local marine ecosystems. Thus, skillful multi-year to decadal prediction of the KE state and understanding sources of skill are valuable. Retrospective forecasts using the high-resolution CESM show exceptional skill in predicting KE variability up to lead year 4, substantially higher than the skill found in a similarly configured low-resolution CESM. The higher skill is attained because the high-resolution system can more realistically simulate the westward Rossby wave propagation of initialized ocean anomalies in the central North Pacific and their expression within the sharp KE front, and does not suffer from spurious variability near Japan present in the low-resolution CESM that interferes with the incoming wave propagation. These results argue for the use of high-resolution models for future studies that aim to predict changes in western boundary current systems and associated biological fields.
Data for manuscript "Modes of Variability in E3SM and CESM Large Ensembles"
An adequate characterization of internal modes of climate variability (MoV) is prerequisite for both accurate seasonal predictions and the attribution and detection of forced climate change in nature. Assessing the fidelity of climate models in simulating MoV is therefore essential; however, doing so is complicated by the large intrinsic variations in MoV and the limited span of the observational record. Large ensembles (LEs) provide a unique opportunity to assess model fidelity in simulating MoV and quantify inter-model contrasts. In this work, these goals are pursued in four recently produced LEs: the Energy Exascale Earth System Model (E3SM) versions 1 and 2 LEs, and the Community Earth System Model (CESM) versions 1 and 2 LEs. In general, the representation of MoV is found to improve across successive E3SM and CESM versions concurrent with improved simulation of the base state climate. The patterns of global coupled modes and many extratropical modes are well simulated by both E3SM2 and CESM2, though various persistent shortcomings are identified. The results both demonstrate the successes of these recent model versions and suggest the potential for continued improvement in the representation of MoV with advances in model physics.
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</p>
Postprocessed output from VR-CESM with refinement over the greater Greenland area and a CAM-SE control experiment
<p>This dataset contains post-processed monthly output from two VR-CESM simulations that were performed with CAM5.4 and CLM5, with refinement patches over the greater Greenland area. Data from a standard, quasi-uniform CAM-SE simulations is included as well. These are the data that are analysed and discussed by our paper in The Cryosphere, <a href="https://www.the-cryosphere-discuss.net/tc-2018-257/">https://www.the-cryosphere-discuss.net/tc-2018-257/</a>.</p> <p>The postprocessing involved (A) regridding from the respective unstructured CAM grids to regular latitude-longitude grids to allow for easy plotting and comparison, (B) vertical interpolation of some atmospheric fields to constant pressure levels, and (C) averaging in time to compute climatological means and variances.</p> <p><strong>Contact</strong><br> Leo van Kampenhout (L.vankampenhout@uu.nl)</p> <p><strong>Raw data</strong><br> The raw, ungridded monthly timeseries data are currently available on NCAR's HPSS tape system, under file path /home/lvank/archive_pp, and can be requested through the contact person. There exists also daily data for selected variables.</p> <p><strong>Dataset contents</strong></p> <pre><code>Global_Uniform.tar Global_VR28.tar Global_VR55.tar</code></pre> <p>Atmospheric CAM output regridded to a global regular 1 degree latitude-longitude grid. Variables are PHIS, T200, T500, T700, Z200, Z500, Z700.</p> <pre><code>Greenland_0.25_Uniform.tar Greenland_0.25_VR28.tar Greenland_0.25_VR55.tar</code></pre> <p>Atmospheric CAM output and CLM land model output regridded to a 0.25x0.25 degree grid stretching from 40N - 90N and 100W - 0W. Variables are FLDS, FLNS, FSDS, FSNS, H2OSNO, LHFLX, SHFLX, PHIS, PRECC, PRECL, PRECSC, PRECSL, TGCLDCWP, TREFHT, TSOI_10CM, TS, U10.</p> <pre><code>acab_c2b8_UNI_fdm.004_timmean.nc acab_c2b8_VRGRN_28.005_timmean.nc acab_c2b8_VRGRN_55.005_timmean.nc</code></pre> <p>Downscaled SMB on the 4km CISM grid, in meters of ice equivalent. These files represent time means over the period 1980-1999.</p> <pre><code> cism_thickness.nc </code></pre> <p>CISM ice thickness and ice elevation at 4 km. Note that the ice sheet was non-evolving in our simulations, so both these fields are constant in time.</p>
Results of transient experiment using CESM
<p>precipitation, geopotential height field datasets from the results of transient experiment using CESM, and ozone forcing used in this model.</p>
Inputdata for VR_CESM 2.2.beta01.ver3 (ne0np4uoslo30x8)
<p>Input data needed for use with a docker container with the variable resolution CESM</p> <p>From ne30np4 (global mesh) to x8 refinment</p> <p>Resolution ne0uoslone30x8_ne0uoslone30x8_mt12</p> <p>Compset longname is HIST_CAM60_CLM50%BGC_CICE%PRES_DOCN%DOM_MOSART_CISM2%NOEVOLVE_SWAV</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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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.