CMCC-CM model output prepared for CMIP5 pre-industrial control
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/tauuo_Omon_CMCC-CM_piControl_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | CMCC-CM model output prepared for CMIP5 pre-industrial control |
| location | /shared/cmip5/data/piControl/ocean/mon/Omon/tauuo/CMCC.CMCC-CM/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/tauuo_Omon_CMCC-CM_piControl_r1i1p1.yaml |
| last updated | 2015-01-20 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, i: 182, j: 149, time: 3960, vertices: 4)
Coordinates:
* time (time) float64 15.5 45.0 74.5 ... 3.606e+03 3.636e+03
* j (j) int32 1 2 3 4 5 6 7 8 ... 142 143 144 145 146 147 148 149
* i (i) int32 1 2 3 4 5 6 7 8 ... 175 176 177 178 179 180 181 182
lat (j, i) float32 dask.array<chunksize=(149, 182), meta=np.ndarray>
lon (j, i) float32 dask.array<chunksize=(149, 182), meta=np.ndarray>
Dimensions without coordinates: bnds, vertices
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(120, 2), meta=np.ndarray>
lat_vertices (time, j, i, vertices) float32 dask.array<chunksize=(120, 149, 182, 4), meta=np.ndarray>
lon_vertices (time, j, i, vertices) float32 dask.array<chunksize=(120, 149, 182, 4), meta=np.ndarray>
tauuo (time, j, i) float32 dask.array<chunksize=(120, 149, 182), meta=np.ndarray>
Attributes:
institution: CMCC - Centro Euro-Mediterraneo per i Cambiamenti...
institute_id: CMCC
experiment_id: piControl
source: CMCC-CM
model_id: CMCC-CM
forcing: Nat,GHG,SA,TO,Sl
parent_experiment_id: N/A
parent_experiment_rip: N/A
branch_time: 0.0
contact: Silvio Gualdi (gualdi@bo.ingv.it)
history: Model output postprocessed with CDO (https://cod...
comment: Equilibrium reached after more than 600-year spin...
references: model described in the documentation at http://ww...
initialization_method: 1
physics_version: 1
tracking_id: 2a6c6831-e28a-4ae5-bff9-8258aa81379c
product: output
experiment: pre-industrial control
frequency: mon
creation_date: 2012-03-06T14:37:05Z
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Omon (27 April 2011) 340eddd4fd838d90fa9ffe...
title: CMCC-CM model output prepared for CMIP5 pre-indus...
parent_experiment: N/A
modeling_realm: ocean
realization: 1
cmor_version: 2.7.1xarray.Dataset
- bnds: 2
- i: 182
- j: 149
- time: 3960
- vertices: 4
- time(time)float6415.5 45.0 ... 3.606e+03 3.636e+03
- bounds :
- time_bnds
- calendar :
- standard
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([ 15.5, 45. , 74.5, ..., 3575.5, 3606. , 3636.5])
- j(j)int321 2 3 4 5 6 ... 145 146 147 148 149
- units :
- 1
- long_name :
- cell index along second dimension
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149], dtype=int32) - i(i)int321 2 3 4 5 6 ... 178 179 180 181 182
- units :
- 1
- long_name :
- cell index along first dimension
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182], dtype=int32) - lat(j, i)float32dask.array<chunksize=(149, 182), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- latitude coordinate
- units :
- degrees_north
- bounds :
- lat_vertices
Array Chunk Bytes 108.47 kB 108.47 kB Shape (149, 182) (149, 182) Count 160 Tasks 1 Chunks Type float32 numpy.ndarray - lon(j, i)float32dask.array<chunksize=(149, 182), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- longitude coordinate
- units :
- degrees_east
- bounds :
- lon_vertices
Array Chunk Bytes 108.47 kB 108.47 kB Shape (149, 182) (149, 182) Count 160 Tasks 1 Chunks Type float32 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(120, 2), meta=np.ndarray>
Array Chunk Bytes 63.36 kB 1.92 kB Shape (3960, 2) (120, 2) Count 99 Tasks 33 Chunks Type float64 numpy.ndarray - lat_vertices(time, j, i, vertices)float32dask.array<chunksize=(120, 149, 182, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 1.72 GB 52.07 MB Shape (3960, 149, 182, 4) (120, 149, 182, 4) Count 132 Tasks 33 Chunks Type float32 numpy.ndarray - lon_vertices(time, j, i, vertices)float32dask.array<chunksize=(120, 149, 182, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 1.72 GB 52.07 MB Shape (3960, 149, 182, 4) (120, 149, 182, 4) Count 132 Tasks 33 Chunks Type float32 numpy.ndarray - tauuo(time, j, i)float32dask.array<chunksize=(120, 149, 182), meta=np.ndarray>
- standard_name :
- surface_downward_x_stress
- long_name :
- Surface Downward X Stress
- comment :
- This is the stress on the liquid ocean from overlying atmosphere, sea ice, ice shelf, etc.
- units :
- N m-2
- original_name :
- sozotaux
- cell_methods :
- time: mean (interval: 1 month) area: mean where sea
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_CMCC-CM_piControl_r0i0p0.nc
Array Chunk Bytes 429.55 MB 13.02 MB Shape (3960, 149, 182) (120, 149, 182) Count 99 Tasks 33 Chunks Type float32 numpy.ndarray
- institution :
- CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna, Italy
- institute_id :
- CMCC
- experiment_id :
- piControl
- source :
- CMCC-CM
- model_id :
- CMCC-CM
- forcing :
- Nat,GHG,SA,TO,Sl
- parent_experiment_id :
- N/A
- parent_experiment_rip :
- N/A
- branch_time :
- 0.0
- contact :
- Silvio Gualdi (gualdi@bo.ingv.it)
- history :
- Model output postprocessed with CDO (https://code.zmaw.de/projects) 2012-03-06T14:37:05Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- comment :
- Equilibrium reached after more than 600-year spin-up at pre-industrial GHG concentrations after which data were output with nominal date of January 1550
- references :
- model described in the documentation at http://www.cmcc.it/data-models/models
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- 2a6c6831-e28a-4ae5-bff9-8258aa81379c
- product :
- output
- experiment :
- pre-industrial control
- frequency :
- mon
- creation_date :
- 2012-03-06T14:37:05Z
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Omon (27 April 2011) 340eddd4fd838d90fa9ffe1345ecbd73
- title :
- CMCC-CM model output prepared for CMIP5 pre-industrial control
- parent_experiment :
- N/A
- modeling_realm :
- ocean
- realization :
- 1
- cmor_version :
- 2.7.1