MIROC4h model output prepared for CMIP5 historical
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/sic_OImon_MIROC4h_historical_r3i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MIROC4h model output prepared for CMIP5 historical |
| location | /shared/cmip5/data/historical/seaIce/mon/OImon/sic/MIROC.MIROC4h/r3i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/sic_OImon_MIROC4h_historical_r3i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, rlat: 912, rlon: 1280, time: 672, vertices: 4)
Coordinates:
* time (time) float64 15.5 45.0 ... 2.041e+04 2.044e+04
* rlat (rlat) float64 -85.41 -85.22 ... 85.22 85.41
* rlon (rlon) float64 0.1406 0.4219 ... 359.6 359.9
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=(12, 2), meta=np.ndarray>
rlat_bnds (time, rlat, bnds) float64 dask.array<chunksize=(12, 912, 2), meta=np.ndarray>
rlon_bnds (time, rlon, bnds) float64 dask.array<chunksize=(12, 1280, 2), meta=np.ndarray>
rotated_latitude_longitude (time) int32 -2147483647 ... -2147483647
lat_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(12, 912, 1280, 4), meta=np.ndarray>
lon_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(12, 912, 1280, 4), meta=np.ndarray>
sic (time, rlat, rlon) float32 dask.array<chunksize=(12, 912, 1280), meta=np.ndarray>
Attributes:
institution: AORI (Atmosphere and Ocean Research Institute, Th...
institute_id: MIROC
experiment_id: historical
source: MIROC4h 2009 atmosphere: AGCM (AGCM5.8, T213L56);...
model_id: MIROC4h
forcing: GHG, SA, Oz, LU, Sl, Vl, SS, Ds, BC, MD, OC (GHG ...
parent_experiment_id: piControl
parent_experiment_rip: r1i1p1
branch_time: 21900.0
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: 9472c019-6b14-4948-ada2-4d1b659e9237
product: output
experiment: historical
frequency: mon
creation_date: 2011-06-01T14:38:13Z
history: Mon Aug 8 14:34:03 2011: ncatted -a branch_time,...
Conventions: CF-1.4
project_id: CMIP5
table_id: Table OImon (11 April 2011) c685058342b6615c12d34...
title: MIROC4h model output prepared for CMIP5 historical
parent_experiment: pre-industrial control
modeling_realm: seaIce ocean
realization: 3
cmor_version: 2.5.8xarray.Dataset
- bnds: 2
- rlat: 912
- rlon: 1280
- time: 672
- vertices: 4
- time(time)float6415.5 45.0 ... 2.041e+04 2.044e+04
- bounds :
- time_bnds
- units :
- days since 1950-1-1
- calendar :
- gregorian
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 2.03775e+04, 2.04080e+04, 2.04385e+04]) - rlat(rlat)float64-85.41 -85.22 ... 85.22 85.41
- bounds :
- rlat_bnds
- units :
- degrees
- axis :
- Y
- long_name :
- latitude in rotated pole grid
- standard_name :
- grid_latitude
array([-85.40625, -85.21875, -85.03125, ..., 85.03125, 85.21875, 85.40625])
- rlon(rlon)float640.1406 0.4219 ... 359.6 359.9
- bounds :
- rlon_bnds
- units :
- degrees
- axis :
- X
- long_name :
- longitude in rotated pole grid
- standard_name :
- grid_longitude
array([1.406250e-01, 4.218750e-01, 7.031250e-01, ..., 3.592969e+02, 3.595781e+02, 3.598594e+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 30 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 30 Tasks 1 Chunks Type float32 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(12, 2), meta=np.ndarray>
Array Chunk Bytes 10.75 kB 1.92 kB Shape (672, 2) (120, 2) Count 21 Tasks 7 Chunks Type float64 numpy.ndarray - rlat_bnds(time, rlat, bnds)float64dask.array<chunksize=(12, 912, 2), meta=np.ndarray>
Array Chunk Bytes 9.81 MB 1.75 MB Shape (672, 912, 2) (120, 912, 2) Count 28 Tasks 7 Chunks Type float64 numpy.ndarray - rlon_bnds(time, rlon, bnds)float64dask.array<chunksize=(12, 1280, 2), meta=np.ndarray>
Array Chunk Bytes 13.76 MB 2.46 MB Shape (672, 1280, 2) (120, 1280, 2) Count 28 Tasks 7 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, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, ... -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647, -2147483647], dtype=int32) - lat_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(12, 912, 1280, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 12.55 GB 2.24 GB Shape (672, 912, 1280, 4) (120, 912, 1280, 4) Count 28 Tasks 7 Chunks Type float32 numpy.ndarray - lon_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(12, 912, 1280, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 12.55 GB 2.24 GB Shape (672, 912, 1280, 4) (120, 912, 1280, 4) Count 28 Tasks 7 Chunks Type float32 numpy.ndarray - sic(time, rlat, rlon)float32dask.array<chunksize=(12, 912, 1280), meta=np.ndarray>
- standard_name :
- sea_ice_area_fraction
- long_name :
- Sea Ice Area Fraction
- comment :
- fraction of grid cell covered by sea ice.
- units :
- %
- original_name :
- AI
- history :
- 2011-06-01T14:38:12Z altered by CMOR: Converted units from '' to '%'. 2011-06-01T14:38:12Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20).
- cell_methods :
- time: mean
- cell_measures :
- area: areacello
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_seaIce_fx_MIROC4h_historical_r0i0p0.nc areacello: areacello_fx_MIROC4h_historical_r0i0p0.nc
- grid_mapping :
- rotated_latitude_longitude
Array Chunk Bytes 3.14 GB 560.33 MB Shape (672, 912, 1280) (120, 912, 1280) Count 21 Tasks 7 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 :
- historical
- 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 :
- GHG, SA, Oz, LU, Sl, Vl, SS, Ds, BC, MD, OC (GHG includes CO2, N2O, methane, and fluorocarbons; Oz includes OH and H2O2)
- parent_experiment_id :
- piControl
- parent_experiment_rip :
- r1i1p1
- branch_time :
- 21900.0
- 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 :
- 9472c019-6b14-4948-ada2-4d1b659e9237
- product :
- output
- experiment :
- historical
- frequency :
- mon
- creation_date :
- 2011-06-01T14:38:13Z
- history :
- Mon Aug 8 14:34:03 2011: ncatted -a branch_time,global,m,d,21900.0 sic_OImon_MIROC4h_historical_r3i1p1_195001-195012.nc 2011-06-01T14:38:13Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table OImon (11 April 2011) c685058342b6615c12d34dc34a53e6fa
- title :
- MIROC4h model output prepared for CMIP5 historical
- parent_experiment :
- pre-industrial control
- modeling_realm :
- seaIce ocean
- realization :
- 3
- cmor_version :
- 2.5.8