MIROC4h model output prepared for CMIP5 10- or 30-year run initialized in year 1980
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/snd_OImon_MIROC4h_decadal1980_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MIROC4h model output prepared for CMIP5 10- or 30-year run initialized in year 1980 |
| location | /shared/cmip5/data/decadal1980/seaIce/mon/OImon/snd/MIROC.MIROC4h/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/snd_OImon_MIROC4h_decadal1980_r1i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, rlat: 912, rlon: 1280, time: 360, vertices: 4)
Coordinates:
* time (time) float64 15.5 45.0 ... 1.091e+04 1.094e+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=(120, 2), meta=np.ndarray>
rlat_bnds (time, rlat, bnds) float64 dask.array<chunksize=(120, 912, 2), meta=np.ndarray>
rlon_bnds (time, rlon, bnds) float64 dask.array<chunksize=(120, 1280, 2), meta=np.ndarray>
rotated_latitude_longitude (time) int32 -2147483647 ... -2147483647
lat_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(120, 912, 1280, 4), meta=np.ndarray>
lon_vertices (time, rlat, rlon, vertices) float32 dask.array<chunksize=(120, 912, 1280, 4), meta=np.ndarray>
snd (time, rlat, rlon) float32 dask.array<chunksize=(120, 912, 1280), meta=np.ndarray>
Attributes:
institution: AORI (Atmosphere and Ocean Research Institute, Th...
institute_id: MIROC
experiment_id: decadal1980
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: 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: 2dfe5a78-36be-4520-9fc9-a2f85e7c34d1
product: output
experiment: 10- or 30-year run initialized in year 1980
frequency: mon
creation_date: 2011-06-13T17:22:49Z
history: 2011-06-13T17:22:49Z CMOR rewrote data to comply ...
Conventions: CF-1.4
project_id: CMIP5
table_id: Table OImon (11 April 2011) c685058342b6615c12d34...
title: MIROC4h model output prepared for CMIP5 10- or 30...
parent_experiment: N/A
modeling_realm: seaIce
realization: 1
cmor_version: 2.5.8xarray.Dataset
- bnds: 2
- rlat: 912
- rlon: 1280
- time: 360
- vertices: 4
- time(time)float6415.5 45.0 ... 1.091e+04 1.094e+04
- bounds :
- time_bnds
- units :
- days since 1981-1-1
- calendar :
- gregorian
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([ 15.5, 45. , 74.5, ..., 10880.5, 10911. , 10941.5])
- 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 10 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 10 Tasks 1 Chunks Type float32 numpy.ndarray
- time_bnds(time, bnds)float64dask.array<chunksize=(120, 2), meta=np.ndarray>
Array Chunk Bytes 5.76 kB 1.92 kB Shape (360, 2) (120, 2) Count 9 Tasks 3 Chunks Type float64 numpy.ndarray - rlat_bnds(time, rlat, bnds)float64dask.array<chunksize=(120, 912, 2), meta=np.ndarray>
Array Chunk Bytes 5.25 MB 1.75 MB Shape (360, 912, 2) (120, 912, 2) Count 12 Tasks 3 Chunks Type float64 numpy.ndarray - rlon_bnds(time, rlon, bnds)float64dask.array<chunksize=(120, 1280, 2), meta=np.ndarray>
Array Chunk Bytes 7.37 MB 2.46 MB Shape (360, 1280, 2) (120, 1280, 2) Count 12 Tasks 3 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], dtype=int32) - lat_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(120, 912, 1280, 4), meta=np.ndarray>
- units :
- degrees_north
Array Chunk Bytes 6.72 GB 2.24 GB Shape (360, 912, 1280, 4) (120, 912, 1280, 4) Count 12 Tasks 3 Chunks Type float32 numpy.ndarray - lon_vertices(time, rlat, rlon, vertices)float32dask.array<chunksize=(120, 912, 1280, 4), meta=np.ndarray>
- units :
- degrees_east
Array Chunk Bytes 6.72 GB 2.24 GB Shape (360, 912, 1280, 4) (120, 912, 1280, 4) Count 12 Tasks 3 Chunks Type float32 numpy.ndarray - snd(time, rlat, rlon)float32dask.array<chunksize=(120, 912, 1280), meta=np.ndarray>
- standard_name :
- surface_snow_thickness
- long_name :
- Snow Depth
- comment :
- the mean thickness of snow in the ocean portion of the grid cell (averaging over the entire ocean portion, including the snow-free ocean fraction). Reported as 0.0 in regions free of snow-covered sea ice.
- units :
- m
- original_name :
- HSG
- original_units :
- cm
- history :
- 2011-06-13T17:22:48Z altered by CMOR: Converted units from 'cm' to 'm'. 2011-06-13T17:22:48Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20).
- cell_methods :
- time: mean area: mean where sea
- cell_measures :
- area: areacello
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_seaIce_fx_MIROC4h_decadal1980_r0i0p0.nc areacello: areacello_fx_MIROC4h_decadal1980_r0i0p0.nc
- grid_mapping :
- rotated_latitude_longitude
Array Chunk Bytes 1.68 GB 560.33 MB Shape (360, 912, 1280) (120, 912, 1280) Count 9 Tasks 3 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 :
- decadal1980
- 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 :
- 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 :
- 2dfe5a78-36be-4520-9fc9-a2f85e7c34d1
- product :
- output
- experiment :
- 10- or 30-year run initialized in year 1980
- frequency :
- mon
- creation_date :
- 2011-06-13T17:22:49Z
- history :
- 2011-06-13T17:22:49Z 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 10- or 30-year run initialized in year 1980
- parent_experiment :
- N/A
- modeling_realm :
- seaIce
- realization :
- 1
- cmor_version :
- 2.5.8