CCSM4 model output prepared for CMIP5 historical
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/thetao_Omon_CCSM4_historical_r6i1p1.yaml")
ds=cat.netcdf.read()
Metadata
title | CCSM4 model output prepared for CMIP5 historical |
location | /shared/cmip5/data/historical/ocean/mon/Omon/thetao/NCAR.CCSM4/r6i1p1 |
tags | gridded,global,model,monthly |
catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/thetao_Omon_CCSM4_historical_r6i1p1.yaml |
last updated | 2019-07-15 |
Dataset Contents
<xarray.Dataset> Dimensions: (bnds: 2, i: 320, j: 384, lev: 60, time: 1872, vertices: 4) Coordinates: * time (time) float64 6.753e+05 6.753e+05 ... 7.321e+05 7.322e+05 * lev (lev) float64 5.0 15.0 25.0 ... 4.875e+03 5.125e+03 5.375e+03 * j (j) int32 1 2 3 4 5 6 7 8 ... 377 378 379 380 381 382 383 384 * i (i) int32 1 2 3 4 5 6 7 8 ... 313 314 315 316 317 318 319 320 lat (j, i) float32 dask.array<chunksize=(384, 320), meta=np.ndarray> lon (j, i) float32 dask.array<chunksize=(384, 320), meta=np.ndarray> Dimensions without coordinates: bnds, vertices Data variables: time_bnds (time, bnds) float64 dask.array<chunksize=(120, 2), meta=np.ndarray> lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 60, 2), meta=np.ndarray> lat_vertices (time, j, i, vertices) float32 dask.array<chunksize=(120, 384, 320, 4), meta=np.ndarray> lon_vertices (time, j, i, vertices) float32 dask.array<chunksize=(120, 384, 320, 4), meta=np.ndarray> thetao (time, lev, j, i) float32 dask.array<chunksize=(120, 60, 384, 320), meta=np.ndarray> Attributes: institution: NCAR (National Center for Atmospheric Resea... institute_id: NCAR experiment_id: historical source: CCSM4 model_id: CCSM4 forcing: Sl GHG Vl SS Ds SD BC MD OC Oz AA LU parent_experiment_id: piControl parent_experiment_rip: r1i1p1 branch_time: 9.0 contact: cesm_data@ucar.edu comment: CESM home page: http://www.cesm.ucar.edu references: Gent P. R., et.al. 2011: The Community Clim... initialization_method: 1 physics_version: 1 tracking_id: def83323-1ce4-453a-939c-fd845bbc5a77 acknowledgements: The CESM project is supported by the Nation... cesm_casename: b40.20th.track1.1deg.012 cesm_repotag: ccsm4_0_beta53 cesm_compset: B20TRCN resolution: f09_g16 (0.9x1.25_gx1v6) forcing_note: Additional information on the external forc... processed_by: dfeddema on mirage0 at 20120605 -041642.317 processing_code_information: Last Changed Rev: 839 Last Changed Date: 20... product: output experiment: historical frequency: mon creation_date: 2012-06-05T10:16:46Z history: 2012-06-05T10:16:46Z CMOR rewrote data to c... Conventions: CF-1.4 project_id: CMIP5 table_id: Table Omon (12 January 2012) 980e0aeb02de42... title: CCSM4 model output prepared for CMIP5 histo... parent_experiment: pre-industrial control modeling_realm: ocean realization: 6 cmor_version: 2.8.1
xarray.Dataset
- bnds: 2
- i: 320
- j: 384
- lev: 60
- time: 1872
- vertices: 4
- time(time)float646.753e+05 6.753e+05 ... 7.322e+05
- bounds :
- time_bnds
- units :
- days since 0000-01-01 00:00:00
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([675265.5, 675295. , 675324.5, ..., 732113.5, 732144. , 732174.5])
- lev(lev)float645.0 15.0 ... 5.125e+03 5.375e+03
- bounds :
- lev_bnds
- units :
- m
- axis :
- Z
- positive :
- down
- long_name :
- ocean depth coordinate
- standard_name :
- depth
array([5.000000e+00, 1.500000e+01, 2.500000e+01, 3.500000e+01, 4.500000e+01, 5.500000e+01, 6.500000e+01, 7.500000e+01, 8.500000e+01, 9.500000e+01, 1.050000e+02, 1.150000e+02, 1.250000e+02, 1.350000e+02, 1.450000e+02, 1.550000e+02, 1.650984e+02, 1.754790e+02, 1.862913e+02, 1.976603e+02, 2.097114e+02, 2.225783e+02, 2.364088e+02, 2.513702e+02, 2.676542e+02, 2.854837e+02, 3.051192e+02, 3.268680e+02, 3.510935e+02, 3.782276e+02, 4.087846e+02, 4.433777e+02, 4.827367e+02, 5.277280e+02, 5.793729e+02, 6.388626e+02, 7.075633e+02, 7.870025e+02, 8.788252e+02, 9.847059e+02, 1.106204e+03, 1.244567e+03, 1.400497e+03, 1.573946e+03, 1.764003e+03, 1.968944e+03, 2.186457e+03, 2.413972e+03, 2.649001e+03, 2.889385e+03, 3.133405e+03, 3.379793e+03, 3.627670e+03, 3.876452e+03, 4.125768e+03, 4.375392e+03, 4.625190e+03, 4.875083e+03, 5.125028e+03, 5.375000e+03])
- j(j)int321 2 3 4 5 6 ... 380 381 382 383 384
- units :
- 1
- long_name :
- cell index along second dimension
array([ 1, 2, 3, ..., 382, 383, 384], dtype=int32)
- i(i)int321 2 3 4 5 6 ... 316 317 318 319 320
- units :
- 1
- long_name :
- cell index along first dimension
array([ 1, 2, 3, ..., 318, 319, 320], dtype=int32)
- lat(j, i)float32dask.array<chunksize=(384, 320), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- latitude coordinate
- units :
- degrees_north
- bounds :
- lat_vertices
Array Chunk Bytes 491.52 kB 491.52 kB Shape (384, 320) (384, 320) Count 75 Tasks 1 Chunks Type float32 numpy.ndarray - lon(j, i)float32dask.array<chunksize=(384, 320), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- longitude coordinate
- units :
- degrees_east
- bounds :
- lon_vertices
Array Chunk Bytes 491.52 kB 491.52 kB Shape (384, 320) (384, 320) Count 75 Tasks 1 Chunks Type float32 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(120, 2), meta=np.ndarray>
Array Chunk Bytes 29.95 kB 1.92 kB Shape (1872, 2) (120, 2) Count 48 Tasks 16 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 60, 2), meta=np.ndarray>
Array Chunk Bytes 1.80 MB 115.20 kB Shape (1872, 60, 2) (120, 60, 2) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - lat_vertices(time, j, i, vertices)float32dask.array<chunksize=(120, 384, 320, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 3.68 GB 235.93 MB Shape (1872, 384, 320, 4) (120, 384, 320, 4) Count 64 Tasks 16 Chunks Type float32 numpy.ndarray - lon_vertices(time, j, i, vertices)float32dask.array<chunksize=(120, 384, 320, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 3.68 GB 235.93 MB Shape (1872, 384, 320, 4) (120, 384, 320, 4) Count 64 Tasks 16 Chunks Type float32 numpy.ndarray - thetao(time, lev, j, i)float32dask.array<chunksize=(120, 60, 384, 320), meta=np.ndarray>
- standard_name :
- sea_water_potential_temperature
- long_name :
- Sea Water Potential Temperature
- units :
- K
- original_name :
- TEMP
- comment :
- TEMP no change, units from C to K
- original_units :
- degC
- history :
- 2012-06-05T10:16:42Z altered by CMOR: Converted units from 'degC' to 'K'. 2012-06-05T10:16:42Z altered by CMOR: replaced missing value flag (9.96921e+36) with standard missing value (1e+20).
- cell_methods :
- time: mean (interval: 30 days)
- cell_measures :
- area: areacello volume: volcello
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_CCSM4_historical_r0i0p0.nc areacello: areacello_fx_CCSM4_historical_r0i0p0.nc volcello: volcello_fx_CCSM4_historical_r0i0p0.nc
Array Chunk Bytes 55.21 GB 3.54 GB Shape (1872, 60, 384, 320) (120, 60, 384, 320) Count 48 Tasks 16 Chunks Type float32 numpy.ndarray
- institution :
- NCAR (National Center for Atmospheric Research) Boulder, CO, USA
- institute_id :
- NCAR
- experiment_id :
- historical
- source :
- CCSM4
- model_id :
- CCSM4
- forcing :
- Sl GHG Vl SS Ds SD BC MD OC Oz AA LU
- parent_experiment_id :
- piControl
- parent_experiment_rip :
- r1i1p1
- branch_time :
- 9.0
- contact :
- cesm_data@ucar.edu
- comment :
- CESM home page: http://www.cesm.ucar.edu
- references :
- Gent P. R., et.al. 2011: The Community Climate System Model version 4. J. Climate, doi: 10.1175/2011JCLI4083.1
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- def83323-1ce4-453a-939c-fd845bbc5a77
- acknowledgements :
- The CESM project is supported by the National Science Foundation and the Office of Science (BER) of the U.S. Department of Energy. NCAR is sponsored by the National Science Foundation. Computing resources were provided by the Climate Simulation Laboratory at the NCAR Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation and other agencies.
- cesm_casename :
- b40.20th.track1.1deg.012
- cesm_repotag :
- ccsm4_0_beta53
- cesm_compset :
- B20TRCN
- resolution :
- f09_g16 (0.9x1.25_gx1v6)
- forcing_note :
- Additional information on the external forcings used in this experiment can be found at http://www.cesm.ucar.edu/CMIP5/forcing_information
- processed_by :
- dfeddema on mirage0 at 20120605 -041642.317
- processing_code_information :
- Last Changed Rev: 839 Last Changed Date: 2012-06-03 22:21:24 -0600 (Sun, 03 Jun 2012) Repository UUID: d2181dbe-5796-6825-dc7f-cbd98591f93d
- product :
- output
- experiment :
- historical
- frequency :
- mon
- creation_date :
- 2012-06-05T10:16:46Z
- history :
- 2012-06-05T10:16:46Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Omon (12 January 2012) 980e0aeb02de4233251f100571014e41
- title :
- CCSM4 model output prepared for CMIP5 historical
- parent_experiment :
- pre-industrial control
- modeling_realm :
- ocean
- realization :
- 6
- cmor_version :
- 2.8.1