MIROC4h 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/uo_Omon_MIROC4h_piControl_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MIROC4h model output prepared for CMIP5 pre-industrial control |
| location | /shared/cmip5/data/piControl/ocean/mon/Omon/uo/MIROC.MIROC4h/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/uo_Omon_MIROC4h_piControl_r1i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lev: 48, rlat: 912, rlon: 1280, time: 1200, vertices: 4)
Coordinates:
* time (time) float64 15.5 45.0 ... 3.645e+04 3.648e+04
* lev (lev) float64 1.25 3.75 ... 5.523e+03 5.688e+03
* rlat (rlat) float64 -85.5 -85.31 ... 85.12 85.31
* rlon (rlon) float64 0.0 0.2812 0.5625 ... 359.4 359.7
lat (rlat, rlon) float32 dask.array<chunksize=(912, 1280), meta=np.ndarray>
lon (rlat, rlon) float32 dask.array<chunksize=(912, 1280), meta=np.ndarray>
Dimensions without coordinates: bnds, vertices
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(6, 2), meta=np.ndarray>
lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(6, 48, 2), meta=np.ndarray>
sigma (time, lev) float64 dask.array<chunksize=(6, 48), meta=np.ndarray>
eta (time, rlat, rlon) float32 dask.array<chunksize=(6, 912, 1280), meta=np.ndarray>
depth (time, rlat, rlon) float32 dask.array<chunksize=(6, 912, 1280), meta=np.ndarray>
depth_c (time) float64 38.0 38.0 38.0 ... 38.0 38.0 38.0
nsigma (time) int32 8 8 8 8 8 8 8 8 ... 8 8 8 8 8 8 8 8
zlev (time, lev) float64 dask.array<chunksize=(6, 48), meta=np.ndarray>
sigma_bnds (time, lev, bnds) float64 dask.array<chunksize=(6, 48, 2), meta=np.ndarray>
zlev_bnds (time, lev, bnds) float64 dask.array<chunksize=(6, 48, 2), meta=np.ndarray>
rlat_bnds (time, rlat, bnds) float64 dask.array<chunksize=(6, 912, 2), meta=np.ndarray>
rlon_bnds (time, rlon, bnds) float64 dask.array<chunksize=(6, 1280, 2), meta=np.ndarray>
rotated_latitude_longitude (time) int32 -2147483647 ... -2147483647
lat_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(6, 912, 1280, 4), meta=np.ndarray>
lon_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(6, 912, 1280, 4), meta=np.ndarray>
uo (time, lev, rlat, rlon) float32 dask.array<chunksize=(6, 48, 912, 1280), meta=np.ndarray>
Attributes:
institution: AORI (Atmosphere and Ocean Research Institute, Th...
institute_id: MIROC
experiment_id: piControl
source: MIROC4h 2009 atmosphere: AGCM (AGCM5.8, T213L56);...
model_id: MIROC4h
forcing: N/A
parent_experiment_id: N/A
parent_experiment_rip: N/A
branch_time: 0.0
contact: Masahide Kimoto (kimoto@aori.u-tokyo.ac.jp), Masa...
references: Sakamoto et al., 2011: MIROC4h -- a new high-reso...
initialization_method: 1
physics_version: 1
tracking_id: 9db8b4de-58c0-4767-9052-6a65bfaa214f
product: output
experiment: pre-industrial control
frequency: mon
creation_date: 2011-07-18T08:14:32Z
history: 2011-07-18T08:14:32Z CMOR rewrote data to comply ...
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Omon (11 April 2011) eaf2e961aec11e7c91bc5e...
title: MIROC4h model output prepared for CMIP5 pre-indus...
parent_experiment: N/A
modeling_realm: ocean
realization: 1
cmor_version: 2.5.8xarray.Dataset
- bnds: 2
- lev: 48
- rlat: 912
- rlon: 1280
- time: 1200
- vertices: 4
- time(time)float6415.5 45.0 ... 3.645e+04 3.648e+04
- bounds :
- time_bnds
- units :
- days since 51-1-1
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 3.64235e+04, 3.64540e+04, 3.64845e+04]) - lev(lev)float641.25 3.75 ... 5.523e+03 5.688e+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, 6.500e+00, 1.000e+01, 1.450e+01, 2.000e+01, 2.650e+01, 3.400e+01, 4.300e+01, 5.400e+01, 6.700e+01, 8.200e+01, 9.900e+01, 1.180e+02, 1.405e+02, 1.680e+02, 2.005e+02, 2.380e+02, 2.830e+02, 3.380e+02, 4.080e+02, 4.980e+02, 6.080e+02, 7.380e+02, 8.880e+02, 1.058e+03, 1.248e+03, 1.448e+03, 1.648e+03, 1.848e+03, 2.048e+03, 2.248e+03, 2.448e+03, 2.648e+03, 2.848e+03, 3.048e+03, 3.248e+03, 3.448e+03, 3.648e+03, 3.848e+03, 4.048e+03, 4.273e+03, 4.523e+03, 4.773e+03, 5.023e+03, 5.273e+03, 5.523e+03, 5.688e+03]) - rlat(rlat)float64-85.5 -85.31 -85.12 ... 85.12 85.31
- bounds :
- rlat_bnds
- units :
- degrees
- axis :
- Y
- long_name :
- latitude in rotated pole grid
- standard_name :
- grid_latitude
array([-85.5 , -85.3125, -85.125 , ..., 84.9375, 85.125 , 85.3125])
- rlon(rlon)float640.0 0.2812 0.5625 ... 359.4 359.7
- bounds :
- rlon_bnds
- units :
- degrees
- axis :
- X
- long_name :
- longitude in rotated pole grid
- standard_name :
- grid_longitude
array([0.000000e+00, 2.812500e-01, 5.625000e-01, ..., 3.591562e+02, 3.594375e+02, 3.597188e+02]) - lat(rlat, rlon)float32dask.array<chunksize=(912, 1280), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- latitude coordinate
- units :
- degrees_north
- bounds :
- lat_vertices
Array Chunk Bytes 4.67 MB 4.67 MB Shape (912, 1280) (912, 1280) Count 995 Tasks 1 Chunks Type float32 numpy.ndarray - lon(rlat, rlon)float32dask.array<chunksize=(912, 1280), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- longitude coordinate
- units :
- degrees_east
- bounds :
- lon_vertices
Array Chunk Bytes 4.67 MB 4.67 MB Shape (912, 1280) (912, 1280) Count 995 Tasks 1 Chunks Type float32 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(6, 2), meta=np.ndarray>
Array Chunk Bytes 19.20 kB 96 B Shape (1200, 2) (6, 2) Count 600 Tasks 200 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(6, 48, 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 921.60 kB 4.61 kB Shape (1200, 48, 2) (6, 48, 2) Count 800 Tasks 200 Chunks Type float64 numpy.ndarray - sigma(time, lev)float64dask.array<chunksize=(6, 48), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: sigma(k)
Array Chunk Bytes 460.80 kB 2.30 kB Shape (1200, 48) (6, 48) Count 800 Tasks 200 Chunks Type float64 numpy.ndarray - eta(time, rlat, rlon)float32dask.array<chunksize=(6, 912, 1280), meta=np.ndarray>
- long_name :
- Sea Surface Height
- units :
- m
- original_units :
- cm
- history :
- 2011-07-18T08:14:22Z altered by CMOR: Converted units from 'cm' to 'm'.
- cell_methods :
- time: mean
Array Chunk Bytes 5.60 GB 28.02 MB Shape (1200, 912, 1280) (6, 912, 1280) Count 600 Tasks 200 Chunks Type float32 numpy.ndarray - depth(time, rlat, rlon)float32dask.array<chunksize=(6, 912, 1280), meta=np.ndarray>
- long_name :
- Sea Floor Depth
- comment :
- Ocean bathymetry.
- units :
- m
Array Chunk Bytes 5.60 GB 28.02 MB Shape (1200, 912, 1280) (6, 912, 1280) Count 800 Tasks 200 Chunks Type float32 numpy.ndarray - depth_c(time)float6438.0 38.0 38.0 ... 38.0 38.0 38.0
- long_name :
- vertical coordinate formula term: depth_c
array([38., 38., 38., ..., 38., 38., 38.])
- 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=(6, 48), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: zlev(k)
Array Chunk Bytes 460.80 kB 2.30 kB Shape (1200, 48) (6, 48) Count 800 Tasks 200 Chunks Type float64 numpy.ndarray - sigma_bnds(time, lev, bnds)float64dask.array<chunksize=(6, 48, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: sigma(k+1/2)
Array Chunk Bytes 921.60 kB 4.61 kB Shape (1200, 48, 2) (6, 48, 2) Count 800 Tasks 200 Chunks Type float64 numpy.ndarray - zlev_bnds(time, lev, bnds)float64dask.array<chunksize=(6, 48, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: zlev(k+1/2)
Array Chunk Bytes 921.60 kB 4.61 kB Shape (1200, 48, 2) (6, 48, 2) Count 800 Tasks 200 Chunks Type float64 numpy.ndarray - rlat_bnds(time, rlat, bnds)float64dask.array<chunksize=(6, 912, 2), meta=np.ndarray>
Array Chunk Bytes 17.51 MB 87.55 kB Shape (1200, 912, 2) (6, 912, 2) Count 800 Tasks 200 Chunks Type float64 numpy.ndarray - rlon_bnds(time, rlon, bnds)float64dask.array<chunksize=(6, 1280, 2), meta=np.ndarray>
Array Chunk Bytes 24.58 MB 122.88 kB Shape (1200, 1280, 2) (6, 1280, 2) Count 800 Tasks 200 Chunks Type float64 numpy.ndarray - rotated_latitude_longitude(time)int32-2147483647 ... -2147483647
- grid_mapping_name :
- rotated_latitude_longitude
- grid_north_pole_latitude :
- 77.0
- grid_north_pole_longitude :
- -40.0
- north_pole_grid_longitude :
- 90.0
array([-2147483647, -2147483647, -2147483647, ..., -2147483647, -2147483647, -2147483647], dtype=int32) - lat_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(6, 912, 1280, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 22.41 GB 112.07 MB Shape (1200, 912, 1280, 4) (6, 912, 1280, 4) Count 800 Tasks 200 Chunks Type float32 numpy.ndarray - lon_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(6, 912, 1280, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 22.41 GB 112.07 MB Shape (1200, 912, 1280, 4) (6, 912, 1280, 4) Count 800 Tasks 200 Chunks Type float32 numpy.ndarray - uo(time, lev, rlat, rlon)float32dask.array<chunksize=(6, 48, 912, 1280), meta=np.ndarray>
- standard_name :
- sea_water_x_velocity
- long_name :
- Sea Water X Velocity
- units :
- m s-1
- original_name :
- UO
- 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.
- original_units :
- cm/s
- history :
- 2011-07-18T08:14:22Z altered by CMOR: Converted units from 'cm/s' to 'm s-1'. 2011-07-18T08:14:22Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20).
- cell_methods :
- time: mean
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_MIROC4h_piControl_r0i0p0.nc
- grid_mapping :
- rotated_latitude_longitude
Array Chunk Bytes 268.96 GB 1.34 GB Shape (1200, 48, 912, 1280) (6, 48, 912, 1280) Count 600 Tasks 200 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), and JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan)
- institute_id :
- MIROC
- experiment_id :
- piControl
- source :
- MIROC4h 2009 atmosphere: AGCM (AGCM5.8, T213L56); ocean: COCO (COCO3.4, rotated pole 1280x912 L48); sea ice: COCO (COCO3.4); land: MATSIRO (MATSIRO, 2x3L5)
- model_id :
- MIROC4h
- forcing :
- N/A
- parent_experiment_id :
- N/A
- parent_experiment_rip :
- N/A
- branch_time :
- 0.0
- contact :
- Masahide Kimoto (kimoto@aori.u-tokyo.ac.jp), Masayoshi Ishii (ism@jamstec.go.jp)
- references :
- Sakamoto et al., 2011: MIROC4h -- a new high-resolution atmosphere-ocean coupled general circulation model. (in preparation); Tatebe et al., 2011: (in preparation)
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- 9db8b4de-58c0-4767-9052-6a65bfaa214f
- product :
- output
- experiment :
- pre-industrial control
- frequency :
- mon
- creation_date :
- 2011-07-18T08:14:32Z
- history :
- 2011-07-18T08:14:32Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Omon (11 April 2011) eaf2e961aec11e7c91bc5e7a112703ba
- title :
- MIROC4h model output prepared for CMIP5 pre-industrial control
- parent_experiment :
- N/A
- modeling_realm :
- ocean
- realization :
- 1
- cmor_version :
- 2.5.8