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/thetao_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/thetao/MIROC.MIROC5/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/thetao_Omon_MIROC5_piControl_r1i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lev: 50, rlat: 224, rlon: 256, time: 8040, vertices: 4)
Coordinates:
* time (time) float64 15.5 45.0 74.5 ... 2.445e+05 2.445e+05
* lev (lev) float64 1.25 3.75 7.5 ... 5.125e+03 5.375e+03 5.55e+03
* rlat (rlat) float64 -88.08 -85.91 -85.41 ... 85.41 85.91 88.08
* rlon (rlon) float64 -0.7031 0.7031 2.109 ... 355.1 356.5 357.9
lat (rlat, rlon) float32 dask.array<chunksize=(224, 256), meta=np.ndarray>
lon (rlat, rlon) float32 dask.array<chunksize=(224, 256), 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, 50, 2), meta=np.ndarray>
sigma (time, lev) float64 dask.array<chunksize=(12, 50), meta=np.ndarray>
eta (time, rlat, rlon) float32 dask.array<chunksize=(12, 224, 256), meta=np.ndarray>
depth (time, rlat, rlon) float32 dask.array<chunksize=(12, 224, 256), 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, 50), meta=np.ndarray>
sigma_bnds (time, lev, bnds) float64 dask.array<chunksize=(12, 50, 2), meta=np.ndarray>
zlev_bnds (time, lev, bnds) float64 dask.array<chunksize=(12, 50, 2), meta=np.ndarray>
rlat_bnds (time, rlat, bnds) float64 dask.array<chunksize=(12, 224, 2), meta=np.ndarray>
rlon_bnds (time, rlon, bnds) float64 dask.array<chunksize=(12, 256, 2), meta=np.ndarray>
lat_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(12, 224, 256, 4), meta=np.ndarray>
lon_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(12, 224, 256, 4), meta=np.ndarray>
thetao (time, lev, rlat, rlon) float32 dask.array<chunksize=(12, 50, 224, 256), 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: c32b2772-dc17-4cc9-8877-4cfa7a2ac78d
product: output
experiment: pre-industrial control
frequency: mon
creation_date: 2011-09-16T10:07:46Z
history: 2011-09-16T10:07:46Z 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: 50
- rlat: 224
- rlon: 256
- time: 8040
- vertices: 4
- 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]) - lev(lev)float641.25 3.75 ... 5.375e+03 5.55e+03
- 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([1.250e+00, 3.750e+00, 7.500e+00, 1.250e+01, 1.875e+01, 2.625e+01, 3.500e+01, 4.500e+01, 5.500e+01, 7.000e+01, 9.000e+01, 1.100e+02, 1.300e+02, 1.550e+02, 1.850e+02, 2.150e+02, 2.450e+02, 2.800e+02, 3.200e+02, 3.600e+02, 4.000e+02, 4.450e+02, 4.950e+02, 5.500e+02, 6.100e+02, 6.800e+02, 7.600e+02, 8.500e+02, 9.500e+02, 1.060e+03, 1.190e+03, 1.340e+03, 1.510e+03, 1.700e+03, 1.900e+03, 2.125e+03, 2.375e+03, 2.625e+03, 2.875e+03, 3.125e+03, 3.375e+03, 3.625e+03, 3.875e+03, 4.125e+03, 4.375e+03, 4.625e+03, 4.875e+03, 5.125e+03, 5.375e+03, 5.550e+03]) - rlat(rlat)float64-88.08 -85.91 ... 85.91 88.08
- bounds :
- rlat_bnds
- units :
- degrees
- axis :
- Y
- long_name :
- latitude in rotated pole grid
- standard_name :
- grid_latitude
array([-88.081279, -85.912558, -85.412558, ..., 85.412558, 85.912558, 88.081279]) - rlon(rlon)float64-0.7031 0.7031 ... 356.5 357.9
- bounds :
- rlon_bnds
- units :
- degrees
- axis :
- X
- long_name :
- longitude in rotated pole grid
- standard_name :
- grid_longitude
array([ -0.703125, 0.703125, 2.109375, ..., 355.078125, 356.484375, 357.890625]) - lat(rlat, rlon)float32dask.array<chunksize=(224, 256), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- latitude coordinate
- units :
- degrees_north
- bounds :
- lat_vertices
Array Chunk Bytes 229.38 kB 229.38 kB Shape (224, 256) (224, 256) Count 3345 Tasks 1 Chunks Type float32 numpy.ndarray - lon(rlat, rlon)float32dask.array<chunksize=(224, 256), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- longitude coordinate
- units :
- degrees_east
- bounds :
- lon_vertices
Array Chunk Bytes 229.38 kB 229.38 kB Shape (224, 256) (224, 256) Count 3345 Tasks 1 Chunks Type float32 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(12, 2), meta=np.ndarray>
Array Chunk Bytes 128.64 kB 192 B Shape (8040, 2) (12, 2) Count 2010 Tasks 670 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(12, 50, 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 6.43 MB 9.60 kB Shape (8040, 50, 2) (12, 50, 2) Count 2680 Tasks 670 Chunks Type float64 numpy.ndarray - sigma(time, lev)float64dask.array<chunksize=(12, 50), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: sigma(k)
Array Chunk Bytes 3.22 MB 4.80 kB Shape (8040, 50) (12, 50) Count 2680 Tasks 670 Chunks Type float64 numpy.ndarray - eta(time, rlat, rlon)float32dask.array<chunksize=(12, 224, 256), meta=np.ndarray>
- long_name :
- Sea Surface Height
- units :
- m
- original_units :
- cm
- history :
- 2011-09-16T10:07:46Z altered by CMOR: Converted units from 'cm' to 'm'.
- cell_methods :
- time: mean
Array Chunk Bytes 1.84 GB 2.75 MB Shape (8040, 224, 256) (12, 224, 256) Count 2010 Tasks 670 Chunks Type float32 numpy.ndarray - depth(time, rlat, rlon)float32dask.array<chunksize=(12, 224, 256), meta=np.ndarray>
- long_name :
- Sea Floor Depth
- comment :
- Ocean bathymetry.
- units :
- m
Array Chunk Bytes 1.84 GB 2.75 MB Shape (8040, 224, 256) (12, 224, 256) Count 2680 Tasks 670 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, 50), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: zlev(k)
Array Chunk Bytes 3.22 MB 4.80 kB Shape (8040, 50) (12, 50) Count 2680 Tasks 670 Chunks Type float64 numpy.ndarray - sigma_bnds(time, lev, bnds)float64dask.array<chunksize=(12, 50, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: sigma(k+1/2)
Array Chunk Bytes 6.43 MB 9.60 kB Shape (8040, 50, 2) (12, 50, 2) Count 2680 Tasks 670 Chunks Type float64 numpy.ndarray - zlev_bnds(time, lev, bnds)float64dask.array<chunksize=(12, 50, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: zlev(k+1/2)
Array Chunk Bytes 6.43 MB 9.60 kB Shape (8040, 50, 2) (12, 50, 2) Count 2680 Tasks 670 Chunks Type float64 numpy.ndarray - rlat_bnds(time, rlat, bnds)float64dask.array<chunksize=(12, 224, 2), meta=np.ndarray>
Array Chunk Bytes 28.82 MB 43.01 kB Shape (8040, 224, 2) (12, 224, 2) Count 2680 Tasks 670 Chunks Type float64 numpy.ndarray - rlon_bnds(time, rlon, bnds)float64dask.array<chunksize=(12, 256, 2), meta=np.ndarray>
Array Chunk Bytes 32.93 MB 49.15 kB Shape (8040, 256, 2) (12, 256, 2) Count 2680 Tasks 670 Chunks Type float64 numpy.ndarray - lat_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(12, 224, 256, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 7.38 GB 11.01 MB Shape (8040, 224, 256, 4) (12, 224, 256, 4) Count 2680 Tasks 670 Chunks Type float32 numpy.ndarray - lon_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(12, 224, 256, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 7.38 GB 11.01 MB Shape (8040, 224, 256, 4) (12, 224, 256, 4) Count 2680 Tasks 670 Chunks Type float32 numpy.ndarray - thetao(time, lev, rlat, rlon)float32dask.array<chunksize=(12, 50, 224, 256), meta=np.ndarray>
- standard_name :
- sea_water_potential_temperature
- long_name :
- Sea Water Potential Temperature
- units :
- K
- original_name :
- TO
- 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 :
- 2011-09-16T10:07:46Z 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 92.21 GB 137.63 MB Shape (8040, 50, 224, 256) (12, 50, 224, 256) Count 2010 Tasks 670 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 :
- c32b2772-dc17-4cc9-8877-4cfa7a2ac78d
- product :
- output
- experiment :
- pre-industrial control
- frequency :
- mon
- creation_date :
- 2011-09-16T10:07:46Z
- history :
- 2011-09-16T10:07:46Z 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