CCSM4 model output prepared for CMIP5 RCP4.5
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_rcp45_r6i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | CCSM4 model output prepared for CMIP5 RCP4.5 |
| location | /shared/cmip5/data/rcp45/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_rcp45_r6i1p1.yaml |
| last updated | 2019-10-16 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, i: 320, j: 384, lev: 60, time: 1140, vertices: 4)
Coordinates:
* i (i) int32 1 2 3 4 5 6 7 8 ... 313 314 315 316 317 318 319 320
* j (j) int32 1 2 3 4 5 6 7 8 ... 377 378 379 380 381 382 383 384
lat (j, i) float32 dask.array<chunksize=(384, 320), meta=np.ndarray>
* lev (lev) float64 5.0 15.0 25.0 ... 4.875e+03 5.125e+03 5.375e+03
lon (j, i) float32 dask.array<chunksize=(384, 320), meta=np.ndarray>
* time (time) float64 7.322e+05 7.322e+05 ... 7.668e+05 7.668e+05
Dimensions without coordinates: bnds, vertices
Data variables:
lat_vertices (time, j, i, vertices) float32 dask.array<chunksize=(48, 384, 320, 4), meta=np.ndarray>
lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(48, 60, 2), meta=np.ndarray>
lon_vertices (time, j, i, vertices) float32 dask.array<chunksize=(48, 384, 320, 4), meta=np.ndarray>
thetao (time, lev, j, i) float32 dask.array<chunksize=(48, 60, 384, 320), meta=np.ndarray>
time_bnds (time, bnds) float64 dask.array<chunksize=(48, 2), meta=np.ndarray>
Attributes:
institution: NCAR (National Center for Atmospheric Resea...
institute_id: NCAR
experiment_id: rcp45
source: CCSM4
model_id: CCSM4
forcing: Sl GHG SS Ds SA BC MD OC Oz AA
parent_experiment_id: historical
parent_experiment_rip: r6i1p1
branch_time: 20.0
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: a24da314-231e-4f79-b03f-627f6d0b5d5a
acknowledgements: The CESM project is supported by the Nation...
cesm_casename: b40.rcp4_5.1deg.006
cesm_repotag: ccsm4_0_beta53
cesm_compset: BRCP45CN
resolution: f09_g16 (0.9x1.25_gx1v6)
forcing_note: Additional information on the external forc...
processed_by: mai on mirage2 at 20120605 -163848.906
processing_code_information: Last Changed Rev: 839 Last Changed Date: 20...
product: output
experiment: RCP4.5
frequency: mon
creation_date: 2012-06-05T22:38:53Z
history: Wed Jun 6 06:04:41 2012: ncks -F -d time,1...
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Omon (12 January 2012) 980e0aeb02de42...
title: CCSM4 model output prepared for CMIP5 RCP4.5
parent_experiment: historical
modeling_realm: ocean
realization: 6
cmor_version: 2.8.1
NCO: 4.1.0xarray.Dataset
- bnds: 2
- i: 320
- j: 384
- lev: 60
- time: 1140
- vertices: 4
- 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)
- 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)
- 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 50 Tasks 1 Chunks Type float32 numpy.ndarray - 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]) - 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 50 Tasks 1 Chunks Type float32 numpy.ndarray - time(time)float647.322e+05 7.322e+05 ... 7.668e+05
- bounds :
- time_bnds
- units :
- days since 0000-01-01 00:00:00
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([732205.5, 732235. , 732264.5, ..., 766788.5, 766819. , 766849.5])
- lat_vertices(time, j, i, vertices)float32dask.array<chunksize=(48, 384, 320, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 2.24 GB 235.93 MB Shape (1140, 384, 320, 4) (120, 384, 320, 4) Count 44 Tasks 11 Chunks Type float32 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(48, 60, 2), meta=np.ndarray>
Array Chunk Bytes 1.09 MB 115.20 kB Shape (1140, 60, 2) (120, 60, 2) Count 44 Tasks 11 Chunks Type float64 numpy.ndarray - lon_vertices(time, j, i, vertices)float32dask.array<chunksize=(48, 384, 320, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 2.24 GB 235.93 MB Shape (1140, 384, 320, 4) (120, 384, 320, 4) Count 44 Tasks 11 Chunks Type float32 numpy.ndarray - thetao(time, lev, j, i)float32dask.array<chunksize=(48, 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 :
- 2012-06-05T22:38:49Z altered by CMOR: Converted units from 'degC' to 'K'. 2012-06-05T22:38:49Z 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_rcp45_r0i0p0.nc areacello: areacello_fx_CCSM4_rcp45_r0i0p0.nc volcello: volcello_fx_CCSM4_rcp45_r0i0p0.nc
Array Chunk Bytes 33.62 GB 3.54 GB Shape (1140, 60, 384, 320) (120, 60, 384, 320) Count 33 Tasks 11 Chunks Type float32 numpy.ndarray - time_bnds(time, bnds)float64dask.array<chunksize=(48, 2), meta=np.ndarray>
Array Chunk Bytes 18.24 kB 1.92 kB Shape (1140, 2) (120, 2) Count 33 Tasks 11 Chunks Type float64 numpy.ndarray
- institution :
- NCAR (National Center for Atmospheric Research) Boulder, CO, USA
- institute_id :
- NCAR
- experiment_id :
- rcp45
- source :
- CCSM4
- model_id :
- CCSM4
- forcing :
- Sl GHG SS Ds SA BC MD OC Oz AA
- parent_experiment_id :
- historical
- parent_experiment_rip :
- r6i1p1
- branch_time :
- 20.0
- 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 :
- a24da314-231e-4f79-b03f-627f6d0b5d5a
- 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.rcp4_5.1deg.006
- cesm_repotag :
- ccsm4_0_beta53
- cesm_compset :
- BRCP45CN
- 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 :
- mai on mirage2 at 20120605 -163848.906
- 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 :
- RCP4.5
- frequency :
- mon
- creation_date :
- 2012-06-05T22:38:53Z
- history :
- Wed Jun 6 06:04:41 2012: ncks -F -d time,13,60 thetao_Omon_CCSM4_rcp45_r6i1p1_200501-200912.nc thetao_Omon_CCSM4_rcp45_r6i1p1_200601-200912.nc 2012-06-05T22:38:53Z 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 RCP4.5
- parent_experiment :
- historical
- modeling_realm :
- ocean
- realization :
- 6
- cmor_version :
- 2.8.1
- NCO :
- 4.1.0