NorESM2-MM output prepared for CMIP6
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_NorESM2-MM_piControl_r1i1p1f1_gn.yaml")
ds=cat.netcdf.read()
Metadata
| title | NorESM2-MM output prepared for CMIP6 |
| location | /shared/cmip6/data/piControl/ocean/mon/Omon/thetao/NCC.NorESM2-MM/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/thetao_Omon_NorESM2-MM_piControl_r1i1p1f1_gn.yaml |
| last updated | 2020-10-23 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, i: 360, j: 385, rho: 53, time: 6000, vertices: 4)
Coordinates:
* time (time) float64 4.377e+05 4.377e+05 ... 6.201e+05
* rho (rho) float64 1.027e+03 1.028e+03 ... 1.038e+03
* j (j) int32 1 2 3 4 5 6 7 ... 379 380 381 382 383 384 385
* i (i) int32 1 2 3 4 5 6 7 ... 354 355 356 357 358 359 360
latitude (j, i) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>
longitude (j, i) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>
Dimensions without coordinates: bnds, vertices
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(120, 2), meta=np.ndarray>
rho_bnds (time, rho, bnds) float64 dask.array<chunksize=(120, 53, 2), meta=np.ndarray>
vertices_latitude (time, j, i, vertices) float64 dask.array<chunksize=(120, 385, 360, 4), meta=np.ndarray>
vertices_longitude (time, j, i, vertices) float64 dask.array<chunksize=(120, 385, 360, 4), meta=np.ndarray>
thetao (time, rho, j, i) float32 dask.array<chunksize=(120, 53, 385, 360), meta=np.ndarray>
Attributes:
Conventions: CF-1.7 CMIP-6.2
activity_id: CMIP
branch_method: Hybrid-restart from year 1200-01-01 of piContr...
branch_time: 0.0
branch_time_in_child: 0.0
branch_time_in_parent: 438000.0
contact: Please send any requests or bug reports to nor...
creation_date: 2019-11-22T12:18:02Z
data_specs_version: 01.00.31
experiment: pre-industrial control
experiment_id: piControl
external_variables: areacello volcello
forcing_index: 1
frequency: mon
further_info_url: https://furtherinfo.es-doc.org/CMIP6.NCC.NorES...
grid: tripolar grid with 1deg nominal resolution, ve...
grid_label: gn
history: 2019-11-22T12:18:02Z ; CMOR rewrote data to be...
initialization_index: 1
institution: NorESM Climate modeling Consortium consisting ...
institution_id: NCC
mip_era: CMIP6
model_id: NorESM2-MM
nominal_resolution: 100 km
parent_activity_id: CMIP
parent_experiment_id: piControl-spinup
parent_mip_era: CMIP6
parent_source_id: NorESM2-MM
parent_sub_experiment_id: none
parent_time_units: days since 0001-01-01
parent_variant_label: r1i1p1f1
physics_index: 1
product: model-output
realization_index: 1
realm: ocean
run_variant: N/A
source: NorESM2-MM (2017):
aerosol: OsloAero
atmos:...
source_id: NorESM2-MM
source_type: AOGCM AER BGC
sub_experiment: none
sub_experiment_id: none
table_id: Omon
table_info: Creation Date:(24 July 2019) MD5:0bb394a356ef9...
title: NorESM2-MM output prepared for CMIP6
variable_id: thetao
variant_label: r1i1p1f1
license: CMIP6 model data produced by NCC is licensed u...
cmor_version: 3.5.0
tracking_id: hdl:21.14100/a60ce48a-42e0-4a22-8265-70c0c1108de7xarray.Dataset
- bnds: 2
- i: 360
- j: 385
- rho: 53
- time: 6000
- vertices: 4
- time(time)float644.377e+05 4.377e+05 ... 6.201e+05
- bounds :
- time_bnds
- units :
- days since 0001-01-01 00:00
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([437650.5, 437680. , 437709.5, ..., 620058.5, 620089. , 620119.5])
- rho(rho)float641.027e+03 1.028e+03 ... 1.038e+03
- bounds :
- rho_bnds
- units :
- kg m-3
- axis :
- Z
- positive :
- down
- long_name :
- potential density referenced to 2000 dbar
- standard_name :
- sea_water_potential_density
array([1027.22 , 1027.72 , 1028.202, 1028.681, 1029.158, 1029.632, 1030.102, 1030.567, 1031.026, 1031.477, 1031.92 , 1032.352, 1032.772, 1033.176, 1033.564, 1033.932, 1034.279, 1034.602, 1034.9 , 1035.172, 1035.417, 1035.637, 1035.832, 1036.003, 1036.153, 1036.284, 1036.398, 1036.497, 1036.584, 1036.66 , 1036.728, 1036.789, 1036.843, 1036.893, 1036.939, 1036.982, 1037.022, 1037.06 , 1037.096, 1037.131, 1037.166, 1037.199, 1037.231, 1037.264, 1037.295, 1037.327, 1037.358, 1037.388, 1037.419, 1037.45 , 1037.48 , 1037.58 , 1037.8 ]) - j(j)int321 2 3 4 5 6 ... 381 382 383 384 385
- units :
- 1
- long_name :
- cell index along second dimension
array([ 1, 2, 3, ..., 383, 384, 385], dtype=int32)
- i(i)int321 2 3 4 5 6 ... 356 357 358 359 360
- units :
- 1
- long_name :
- cell index along first dimension
array([ 1, 2, 3, ..., 358, 359, 360], dtype=int32)
- latitude(j, i)float64dask.array<chunksize=(385, 360), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- latitude
- units :
- degrees_north
- bounds :
- vertices_latitude
Array Chunk Bytes 1.11 MB 1.11 MB Shape (385, 360) (385, 360) Count 245 Tasks 1 Chunks Type float64 numpy.ndarray - longitude(j, i)float64dask.array<chunksize=(385, 360), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- longitude
- units :
- degrees_east
- bounds :
- vertices_longitude
Array Chunk Bytes 1.11 MB 1.11 MB Shape (385, 360) (385, 360) Count 245 Tasks 1 Chunks Type float64 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(120, 2), meta=np.ndarray>
Array Chunk Bytes 96.00 kB 1.92 kB Shape (6000, 2) (120, 2) Count 150 Tasks 50 Chunks Type float64 numpy.ndarray - rho_bnds(time, rho, bnds)float64dask.array<chunksize=(120, 53, 2), meta=np.ndarray>
Array Chunk Bytes 5.09 MB 101.76 kB Shape (6000, 53, 2) (120, 53, 2) Count 200 Tasks 50 Chunks Type float64 numpy.ndarray - vertices_latitude(time, j, i, vertices)float64dask.array<chunksize=(120, 385, 360, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 26.61 GB 532.22 MB Shape (6000, 385, 360, 4) (120, 385, 360, 4) Count 200 Tasks 50 Chunks Type float64 numpy.ndarray - vertices_longitude(time, j, i, vertices)float64dask.array<chunksize=(120, 385, 360, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 26.61 GB 532.22 MB Shape (6000, 385, 360, 4) (120, 385, 360, 4) Count 200 Tasks 50 Chunks Type float64 numpy.ndarray - thetao(time, rho, j, i)float32dask.array<chunksize=(120, 53, 385, 360), meta=np.ndarray>
- standard_name :
- sea_water_potential_temperature
- long_name :
- Sea Water Potential Temperature
- comment :
- Please note that the layer depth information is stored separately in "zfull" and "zhalf" while approximate layer density values are stored together with "msftmrhoz"., CMIP_table_comment: Diagnostic should be contributed even for models using conservative temperature as prognostic field.
- units :
- degC
- original_name :
- temp
- cell_methods :
- area: mean where sea time: mean
- cell_measures :
- area: areacello volume: volcello
- history :
- 2019-11-22T12:18:02Z altered by CMOR: Converted type from 'd' to 'f'.
Array Chunk Bytes 176.30 GB 3.53 GB Shape (6000, 53, 385, 360) (120, 53, 385, 360) Count 150 Tasks 50 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.7 CMIP-6.2
- activity_id :
- CMIP
- branch_method :
- Hybrid-restart from year 1200-01-01 of piControl-spinup
- branch_time :
- 0.0
- branch_time_in_child :
- 0.0
- branch_time_in_parent :
- 438000.0
- contact :
- Please send any requests or bug reports to noresm-ncc@met.no.
- creation_date :
- 2019-11-22T12:18:02Z
- data_specs_version :
- 01.00.31
- experiment :
- pre-industrial control
- experiment_id :
- piControl
- external_variables :
- areacello volcello
- forcing_index :
- 1
- frequency :
- mon
- further_info_url :
- https://furtherinfo.es-doc.org/CMIP6.NCC.NorESM2-MM.piControl.none.r1i1p1f1
- grid :
- tripolar grid with 1deg nominal resolution, vertical density coordinate
- grid_label :
- gn
- history :
- 2019-11-22T12:18:02Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.
- initialization_index :
- 1
- institution :
- NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway
- institution_id :
- NCC
- mip_era :
- CMIP6
- model_id :
- NorESM2-MM
- nominal_resolution :
- 100 km
- parent_activity_id :
- CMIP
- parent_experiment_id :
- piControl-spinup
- parent_mip_era :
- CMIP6
- parent_source_id :
- NorESM2-MM
- parent_sub_experiment_id :
- none
- parent_time_units :
- days since 0001-01-01
- parent_variant_label :
- r1i1p1f1
- physics_index :
- 1
- product :
- model-output
- realization_index :
- 1
- realm :
- ocean
- run_variant :
- N/A
- source :
- NorESM2-MM (2017): aerosol: OsloAero atmos: CAM-OSLO (1 degree resolution; 288 x 192; 32 levels; top level 3 mb) atmosChem: OsloChemSimp land: CLM landIce: CISM ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]) ocnBgchem: HAMOCC seaIce: CICE
- source_id :
- NorESM2-MM
- source_type :
- AOGCM AER BGC
- sub_experiment :
- none
- sub_experiment_id :
- none
- table_id :
- Omon
- table_info :
- Creation Date:(24 July 2019) MD5:0bb394a356ef9d214d027f1aca45853e
- title :
- NorESM2-MM output prepared for CMIP6
- variable_id :
- thetao
- variant_label :
- r1i1p1f1
- license :
- CMIP6 model data produced by NCC is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https:///pcmdi.llnl.gov/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
- cmor_version :
- 3.5.0
- tracking_id :
- hdl:21.14100/a60ce48a-42e0-4a22-8265-70c0c1108de7
