MIROC5 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/so_Omon_MIROC5_piControl_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MIROC5 model output prepared for CMIP5 pre-industrial control |
| location | /shared/cmip5/data/piControl/ocean/mon/Omon/so/MIROC.MIROC5/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/so_Omon_MIROC5_piControl_r1i1p1.yaml |
| last updated | 2021-02-25 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lev: 51, rlat: 225, rlon: 259, time: 7980, vertices: 4)
Coordinates:
* lev (lev) float64 1.25 3.75 7.5 ... 5.375e+03 5.55e+03 1.676e+155
* rlat (rlat) float64 -88.08 -85.91 -85.41 ... 85.91 88.08 1.071e+156
* rlon (rlon) float64 -0.7031 0.7031 2.109 ... 9.333e+155 4.553e+156
* time (time) float64 15.5 45.0 74.5 ... 2.445e+05 2.445e+05
lat (time, rlat, rlon) float32 dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
lon (time, rlat, rlon) float32 dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
Dimensions without coordinates: bnds, vertices
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(12, 2), meta=np.ndarray>
lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(12, 51, 2), meta=np.ndarray>
sigma (time, lev) float64 dask.array<chunksize=(12, 51), meta=np.ndarray>
eta (time, rlat, rlon) float32 dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
depth (time, rlat, rlon) float32 dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
depth_c (time) float64 50.0 50.0 50.0 50.0 ... 50.0 50.0 50.0 50.0
nsigma (time) int32 8 8 8 8 8 8 8 8 8 8 8 8 ... 8 8 8 8 8 8 8 8 8 8 8
zlev (time, lev) float64 dask.array<chunksize=(12, 51), meta=np.ndarray>
sigma_bnds (time, lev, bnds) float64 dask.array<chunksize=(12, 51, 2), meta=np.ndarray>
zlev_bnds (time, lev, bnds) float64 dask.array<chunksize=(12, 51, 2), meta=np.ndarray>
rlat_bnds (time, rlat, bnds) float64 dask.array<chunksize=(12, 225, 2), meta=np.ndarray>
rlon_bnds (time, rlon, bnds) float64 dask.array<chunksize=(12, 259, 2), meta=np.ndarray>
lat_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(12, 225, 259, 4), meta=np.ndarray>
lon_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(12, 225, 259, 4), meta=np.ndarray>
so (time, lev, rlat, rlon) float32 dask.array<chunksize=(12, 51, 225, 259), meta=np.ndarray>
Attributes:
institution: AORI (Atmosphere and Ocean Research Institute, Th...
institute_id: MIROC
experiment_id: piControl
source: MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); oce...
model_id: MIROC5
forcing: N/A
parent_experiment_id: N/A
parent_experiment_rip: N/A
branch_time: 0.0
contact: Masahiro Watanabe (hiro@aori.u-tokyo.ac.jp), Seit...
references: Watanabe et al., 2010: Improved climate simulatio...
initialization_method: 1
physics_version: 1
tracking_id: 4592ad53-0d73-49f0-9e2c-3b7a745a240f
product: output
experiment: pre-industrial control
frequency: mon
creation_date: 2011-09-16T10:07:57Z
history: 2011-09-16T10:07:57Z CMOR rewrote data to comply ...
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Omon (26 July 2011) 25bb94a0408beca44c0f5b6...
title: MIROC5 model output prepared for CMIP5 pre-indust...
parent_experiment: N/A
modeling_realm: ocean
realization: 1
cmor_version: 2.7.1xarray.Dataset
- bnds: 2
- lev: 51
- rlat: 225
- rlon: 259
- time: 7980
- vertices: 4
- lev(lev)float641.25 3.75 ... 5.55e+03 1.676e+155
- bounds :
- lev_bnds
- axis :
- Z
- long_name :
- ocean sigma over z coordinate
- standard_name :
- ocean_sigma_z
- formula :
- for k <= nsigma: z = eta + sigma*(min(depth_c,depth)+eta) ; for k > nsigma: z = zlev
- formula_terms :
- sigma: sigma eta: eta depth: depth depth_c: depth_c nsigma: nsigma zlev: zlev
array([1.250000e+000, 3.750000e+000, 7.500000e+000, 1.250000e+001, 1.875000e+001, 2.625000e+001, 3.500000e+001, 4.500000e+001, 5.500000e+001, 7.000000e+001, 9.000000e+001, 1.100000e+002, 1.300000e+002, 1.550000e+002, 1.850000e+002, 2.150000e+002, 2.450000e+002, 2.800000e+002, 3.200000e+002, 3.600000e+002, 4.000000e+002, 4.450000e+002, 4.950000e+002, 5.500000e+002, 6.100000e+002, 6.800000e+002, 7.600000e+002, 8.500000e+002, 9.500000e+002, 1.060000e+003, 1.190000e+003, 1.340000e+003, 1.510000e+003, 1.700000e+003, 1.900000e+003, 2.125000e+003, 2.375000e+003, 2.625000e+003, 2.875000e+003, 3.125000e+003, 3.375000e+003, 3.625000e+003, 3.875000e+003, 4.125000e+003, 4.375000e+003, 4.625000e+003, 4.875000e+003, 5.125000e+003, 5.375000e+003, 5.550000e+003, 1.675976e+155]) - rlat(rlat)float64-88.08 -85.91 ... 88.08 1.071e+156
- bounds :
- rlat_bnds
- units :
- degrees
- axis :
- Y
- long_name :
- latitude in rotated pole grid
- standard_name :
- grid_latitude
array([-8.808128e+001, -8.591256e+001, -8.541256e+001, ..., 8.591256e+001, 8.808128e+001, 1.071452e+156]) - rlon(rlon)float64-0.7031 0.7031 ... 4.553e+156
- bounds :
- rlon_bnds
- units :
- degrees
- axis :
- X
- long_name :
- longitude in rotated pole grid
- standard_name :
- grid_longitude
array([-7.031250e-001, 7.031250e-001, 2.109375e+000, ..., 3.578906e+002, 9.333091e+155, 4.553417e+156]) - time(time)float6415.5 45.0 ... 2.445e+05 2.445e+05
- bounds :
- time_bnds
- units :
- days since 2000-1-1
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([1.550000e+01, 4.500000e+01, 7.450000e+01, ..., 2.444735e+05, 2.445040e+05, 2.445345e+05]) - lat(time, rlat, rlon)float32dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- latitude coordinate
- units :
- degrees_north
- bounds :
- lat_vertices
Array Chunk Bytes 1.86 GB 2.80 MB Shape (7980, 225, 259) (12, 225, 259) Count 4649 Tasks 665 Chunks Type float32 numpy.ndarray - lon(time, rlat, rlon)float32dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- longitude coordinate
- units :
- degrees_east
- bounds :
- lon_vertices
Array Chunk Bytes 1.86 GB 2.80 MB Shape (7980, 225, 259) (12, 225, 259) Count 4649 Tasks 665 Chunks Type float32 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(12, 2), meta=np.ndarray>
Array Chunk Bytes 127.68 kB 192 B Shape (7980, 2) (12, 2) Count 1995 Tasks 665 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(12, 51, 2), meta=np.ndarray>
- formula :
- for k <= nsigma: z = eta + sigma*(min(depth_c,depth)+eta) ; for k > nsigma: z = zlev
- standard_name :
- ocean_sigma_z
- units :
- formula_terms :
- sigma: sigma_bnds eta: eta depth: depth depth_c: depth_c nsigma: nsigma zlev: zlev_bnds
Array Chunk Bytes 6.51 MB 9.79 kB Shape (7980, 51, 2) (12, 51, 2) Count 4000 Tasks 665 Chunks Type float64 numpy.ndarray - sigma(time, lev)float64dask.array<chunksize=(12, 51), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: sigma(k)
Array Chunk Bytes 3.26 MB 4.90 kB Shape (7980, 51) (12, 51) Count 3996 Tasks 665 Chunks Type float64 numpy.ndarray - eta(time, rlat, rlon)float32dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
- long_name :
- Sea Surface Height
- units :
- m
- original_units :
- cm
- history :
- 2011-09-16T10:07:57Z altered by CMOR: Converted units from 'cm' to 'm'.
- cell_methods :
- time: mean
Array Chunk Bytes 1.86 GB 2.80 MB Shape (7980, 225, 259) (12, 225, 259) Count 4016 Tasks 665 Chunks Type float32 numpy.ndarray - depth(time, rlat, rlon)float32dask.array<chunksize=(12, 225, 259), meta=np.ndarray>
- long_name :
- Sea Floor Depth
- comment :
- Ocean bathymetry.
- units :
- m
Array Chunk Bytes 1.86 GB 2.80 MB Shape (7980, 225, 259) (12, 225, 259) Count 4677 Tasks 665 Chunks Type float32 numpy.ndarray - depth_c(time)float6450.0 50.0 50.0 ... 50.0 50.0 50.0
- long_name :
- vertical coordinate formula term: depth_c
array([50., 50., 50., ..., 50., 50., 50.])
- nsigma(time)int328 8 8 8 8 8 8 8 ... 8 8 8 8 8 8 8 8
- long_name :
- vertical coordinate formula term: nsigma
array([8, 8, 8, ..., 8, 8, 8], dtype=int32)
- zlev(time, lev)float64dask.array<chunksize=(12, 51), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: zlev(k)
Array Chunk Bytes 3.26 MB 4.90 kB Shape (7980, 51) (12, 51) Count 3996 Tasks 665 Chunks Type float64 numpy.ndarray - sigma_bnds(time, lev, bnds)float64dask.array<chunksize=(12, 51, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: sigma(k+1/2)
Array Chunk Bytes 6.51 MB 9.79 kB Shape (7980, 51, 2) (12, 51, 2) Count 4000 Tasks 665 Chunks Type float64 numpy.ndarray - zlev_bnds(time, lev, bnds)float64dask.array<chunksize=(12, 51, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: zlev(k+1/2)
Array Chunk Bytes 6.51 MB 9.79 kB Shape (7980, 51, 2) (12, 51, 2) Count 4000 Tasks 665 Chunks Type float64 numpy.ndarray - rlat_bnds(time, rlat, bnds)float64dask.array<chunksize=(12, 225, 2), meta=np.ndarray>
Array Chunk Bytes 28.73 MB 43.20 kB Shape (7980, 225, 2) (12, 225, 2) Count 4000 Tasks 665 Chunks Type float64 numpy.ndarray - rlon_bnds(time, rlon, bnds)float64dask.array<chunksize=(12, 259, 2), meta=np.ndarray>
Array Chunk Bytes 33.07 MB 49.73 kB Shape (7980, 259, 2) (12, 259, 2) Count 4008 Tasks 665 Chunks Type float64 numpy.ndarray - lat_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(12, 225, 259, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 7.44 GB 11.19 MB Shape (7980, 225, 259, 4) (12, 225, 259, 4) Count 4684 Tasks 665 Chunks Type float32 numpy.ndarray - lon_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(12, 225, 259, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 7.44 GB 11.19 MB Shape (7980, 225, 259, 4) (12, 225, 259, 4) Count 4684 Tasks 665 Chunks Type float32 numpy.ndarray - so(time, lev, rlat, rlon)float32dask.array<chunksize=(12, 51, 225, 259), meta=np.ndarray>
- standard_name :
- sea_water_salinity
- long_name :
- Sea Water Salinity
- units :
- psu
- original_name :
- SO
- comment :
- The most bottom level is a bottom boundary layer (BBL). The BBL in MIROC5 is 80 meters in thickness on 49N-90N and 54S-90S.
- cell_methods :
- time: mean
- cell_measures :
- area: areacello volume: volcello
- history :
- 2011-09-16T10:07:57Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20).
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_MIROC5_piControl_r0i0p0.nc areacello: areacello_fx_MIROC5_piControl_r0i0p0.nc volcello: volcello_fx_MIROC5_piControl_r0i0p0.nc
Array Chunk Bytes 94.87 GB 142.66 MB Shape (7980, 51, 225, 259) (12, 51, 225, 259) Count 4690 Tasks 665 Chunks Type float32 numpy.ndarray
- institution :
- AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan), NIES (National Institute for Environmental Studies, Ibaraki, Japan), JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan)
- institute_id :
- MIROC
- experiment_id :
- piControl
- source :
- MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); ocean: COCO (COCO4.5, 256x224 L50); sea ice: COCO (COCO4.5); land: MATSIRO (MATSIRO, L6); aerosols: SPRINTARS (SPRINTARS 5.00, T85L40)
- model_id :
- MIROC5
- forcing :
- N/A
- parent_experiment_id :
- N/A
- parent_experiment_rip :
- N/A
- branch_time :
- 0.0
- contact :
- Masahiro Watanabe (hiro@aori.u-tokyo.ac.jp), Seita Emori (emori@nies.go.jp), Masayoshi Ishii (ism@jamstec.go.jp), Masahide Kimoto (kimoto@aori.u-tokyo.ac.jp)
- references :
- Watanabe et al., 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 6312-6335
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- 4592ad53-0d73-49f0-9e2c-3b7a745a240f
- product :
- output
- experiment :
- pre-industrial control
- frequency :
- mon
- creation_date :
- 2011-09-16T10:07:57Z
- history :
- 2011-09-16T10:07:57Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Omon (26 July 2011) 25bb94a0408beca44c0f5b601258a94e
- title :
- MIROC5 model output prepared for CMIP5 pre-industrial control
- parent_experiment :
- N/A
- modeling_realm :
- ocean
- realization :
- 1
- cmor_version :
- 2.7.1