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/snomelt_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/snomelt/MIROC.MIROC4h/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/snomelt_OImon_MIROC4h_decadal1980_r1i1p1.yaml |
| last updated | 2013-05-26 |
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>
snomelt (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: f9bfa08e-6fbf-4ca5-b873-6ad6e1537d4c
product: output
experiment: 10- or 30-year run initialized in year 1980
frequency: mon
creation_date: 2011-06-13T19:31:58Z
history: 2011-06-13T19:31:58Z 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 - snomelt(time, rlat, rlon)float32dask.array<chunksize=(120, 912, 1280), meta=np.ndarray>
- standard_name :
- surface_snow_melt_flux
- long_name :
- Snow Melt Rate
- comment :
- the rate of change of snow mass due to melting, divided by the area of the ocean portion of the grid cell. Reported as 0.0 in regions free of sea ice. Includes falling snow that melts on impact with the surface.
- units :
- kg m-2 s-1
- original_name :
- IMRSNO
- original_units :
- g/cm^2/s
- history :
- 2011-06-13T19:31:57Z altered by CMOR: Converted units from 'g/cm^2/s' to 'kg m-2 s-1'. 2011-06-13T19:31:57Z 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 :
- f9bfa08e-6fbf-4ca5-b873-6ad6e1537d4c
- product :
- output
- experiment :
- 10- or 30-year run initialized in year 1980
- frequency :
- mon
- creation_date :
- 2011-06-13T19:31:58Z
- history :
- 2011-06-13T19:31:58Z 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