bcc-csm1-1-m 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/hus_Amon_bcc-csm1-1-m_historical_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | bcc-csm1-1-m model output prepared for CMIP5 historical |
| location | /shared/cmip5/data/historical/atmos/mon/Amon/hus/BCC.bcc-csm1-1-m/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/hus_Amon_bcc-csm1-1-m_historical_r1i1p1.yaml |
| last updated | 2017-07-11 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 160, lon: 320, plev: 17, time: 1956)
Coordinates:
* time (time) float64 15.5 45.0 74.5 105.0 135.5 ... 0.0 0.0 0.0 0.0 0.0
* plev (plev) float64 1e+05 9.25e+04 8.5e+04 7e+04 ... 3e+03 2e+03 1e+03
* lat (lat) float64 -89.14 -88.03 -86.91 -85.79 ... 86.91 88.03 89.14
* lon (lon) float64 0.0 1.125 2.25 3.375 ... 355.5 356.6 357.8 358.9
Dimensions without coordinates: bnds
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>
lat_bnds (time, lat, bnds) float64 dask.array<chunksize=(360, 160, 2), meta=np.ndarray>
lon_bnds (time, lon, bnds) float64 dask.array<chunksize=(360, 320, 2), meta=np.ndarray>
hus (time, plev, lat, lon) float32 dask.array<chunksize=(360, 17, 160, 320), meta=np.ndarray>
Attributes:
institution: Beijing Climate Center(BCC),China Meteorological ...
institute_id: BCC
experiment_id: historical
source: bcc-csm1-1-m:atmosphere: BCC_AGCM2.2 (T106L26); ...
model_id: bcc-csm1-1-m
forcing: Nat Ant GHG SD Oz Sl Vl SS Ds BC OC
parent_experiment_id: piControl
parent_experiment_rip: r1i1p1
branch_time: 344.0
contact: Dr. Tongwen Wu (twwu@cma.gov.cn)
history: Output from monthly mean data 2011-12-21T08:47:00...
comment: The main difference between BCC-CSM1-1-M and BCC-...
initialization_method: 1
physics_version: 1
tracking_id: d943f8ad-c55e-4f75-8a66-eb107f6f9964
product: output
experiment: historical
frequency: mon
creation_date: 2011-12-21T08:47:05Z
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Amon (11 April 2011) 1cfdc7322cf2f4a3261482...
title: bcc-csm1-1-m model output prepared for CMIP5 hist...
parent_experiment: pre-industrial control
modeling_realm: atmos
realization: 1
cmor_version: 2.5.6xarray.Dataset
- bnds: 2
- lat: 160
- lon: 320
- plev: 17
- time: 1956
- time(time)float6415.5 45.0 74.5 ... 0.0 0.0 0.0
- bounds :
- time_bnds
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([15.5, 45. , 74.5, ..., 0. , 0. , 0. ])
- plev(plev)float641e+05 9.25e+04 ... 2e+03 1e+03
- units :
- Pa
- axis :
- Z
- positive :
- down
- long_name :
- pressure
- standard_name :
- air_pressure
array([100000., 92500., 85000., 70000., 60000., 50000., 40000., 30000., 25000., 20000., 15000., 10000., 7000., 5000., 3000., 2000., 1000.]) - lat(lat)float64-89.14 -88.03 ... 88.03 89.14
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- latitude
- standard_name :
- latitude
array([-89.141519, -88.029429, -86.910771, -85.790629, -84.669924, -83.548947, -82.427818, -81.306595, -80.18531 , -79.063982, -77.942624, -76.821243, -75.699844, -74.578432, -73.457008, -72.335576, -71.214136, -70.09269 , -68.97124 , -67.849784, -66.728326, -65.606864, -64.485399, -63.363932, -62.242462, -61.120991, -59.999518, -58.878044, -57.756569, -56.635092, -55.513614, -54.392135, -53.270655, -52.149175, -51.027694, -49.906212, -48.784729, -47.663246, -46.541763, -45.420279, -44.298794, -43.177309, -42.055824, -40.934338, -39.812852, -38.691366, -37.56988 , -36.448393, -35.326906, -34.205418, -33.083931, -31.962443, -30.840955, -29.719467, -28.597979, -27.476491, -26.355002, -25.233514, -24.112025, -22.990536, -21.869047, -20.747558, -19.626069, -18.50458 , -17.383091, -16.261601, -15.140112, -14.018622, -12.897133, -11.775643, -10.654153, -9.532664, -8.411174, -7.289684, -6.168194, -5.046704, -3.925215, -2.803725, -1.682235, -0.560745, 0.560745, 1.682235, 2.803725, 3.925215, 5.046704, 6.168194, 7.289684, 8.411174, 9.532664, 10.654153, 11.775643, 12.897133, 14.018622, 15.140112, 16.261601, 17.383091, 18.50458 , 19.626069, 20.747558, 21.869047, 22.990536, 24.112025, 25.233514, 26.355002, 27.476491, 28.597979, 29.719467, 30.840955, 31.962443, 33.083931, 34.205418, 35.326906, 36.448393, 37.56988 , 38.691366, 39.812852, 40.934338, 42.055824, 43.177309, 44.298794, 45.420279, 46.541763, 47.663246, 48.784729, 49.906212, 51.027694, 52.149175, 53.270655, 54.392135, 55.513614, 56.635092, 57.756569, 58.878044, 59.999518, 61.120991, 62.242462, 63.363932, 64.485399, 65.606864, 66.728326, 67.849784, 68.97124 , 70.09269 , 71.214136, 72.335576, 73.457008, 74.578432, 75.699844, 76.821243, 77.942624, 79.063982, 80.18531 , 81.306595, 82.427818, 83.548947, 84.669924, 85.790629, 86.910771, 88.029429, 89.141519]) - lon(lon)float640.0 1.125 2.25 ... 357.8 358.9
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- longitude
- standard_name :
- longitude
array([ 0. , 1.125, 2.25 , ..., 356.625, 357.75 , 358.875])
- time_bnds(time, bnds)float64dask.array<chunksize=(360, 2), meta=np.ndarray>
Array Chunk Bytes 31.30 kB 5.76 kB Shape (1956, 2) (360, 2) Count 18 Tasks 6 Chunks Type float64 numpy.ndarray - lat_bnds(time, lat, bnds)float64dask.array<chunksize=(360, 160, 2), meta=np.ndarray>
Array Chunk Bytes 5.01 MB 921.60 kB Shape (1956, 160, 2) (360, 160, 2) Count 24 Tasks 6 Chunks Type float64 numpy.ndarray - lon_bnds(time, lon, bnds)float64dask.array<chunksize=(360, 320, 2), meta=np.ndarray>
Array Chunk Bytes 10.01 MB 1.84 MB Shape (1956, 320, 2) (360, 320, 2) Count 24 Tasks 6 Chunks Type float64 numpy.ndarray - hus(time, plev, lat, lon)float32dask.array<chunksize=(360, 17, 160, 320), meta=np.ndarray>
- standard_name :
- specific_humidity
- long_name :
- Specific Humidity
- units :
- 1
- original_name :
- Q
- original_units :
- kg/kg
- history :
- 2011-12-21T08:46:59Z altered by CMOR: Converted units from 'kg/kg' to '1'.
- cell_methods :
- time: mean (interval: 20 mintues)
- cell_measures :
- area: areacella
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_bcc-csm1-1-m_historical_r0i0p0.nc areacella: areacella_fx_bcc-csm1-1-m_historical_r0i0p0.nc
Array Chunk Bytes 6.81 GB 1.25 GB Shape (1956, 17, 160, 320) (360, 17, 160, 320) Count 18 Tasks 6 Chunks Type float32 numpy.ndarray
- institution :
- Beijing Climate Center(BCC),China Meteorological Administration,China
- institute_id :
- BCC
- experiment_id :
- historical
- source :
- bcc-csm1-1-m:atmosphere: BCC_AGCM2.2 (T106L26); land: BCC_AVIM1.1;ocean: MOM4_L40v2 (tripolar, 1 lon x (1-1/3) lat, L40);sea ice: SIS (tripolar,1 lon x (1-1/3) lat)
- model_id :
- bcc-csm1-1-m
- forcing :
- Nat Ant GHG SD Oz Sl Vl SS Ds BC OC
- parent_experiment_id :
- piControl
- parent_experiment_rip :
- r1i1p1
- branch_time :
- 344.0
- contact :
- Dr. Tongwen Wu (twwu@cma.gov.cn)
- history :
- Output from monthly mean data 2011-12-21T08:47:00Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- comment :
- The main difference between BCC-CSM1-1-M and BCC-CSM1-1 is their horizontal resolutions of the atmospheric component and the corresponding mask of sea-land. The experiment starts from piControl run at year 344. RCP8.5 scenario forcing data are used beyond year 2005.
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- d943f8ad-c55e-4f75-8a66-eb107f6f9964
- product :
- output
- experiment :
- historical
- frequency :
- mon
- creation_date :
- 2011-12-21T08:47:05Z
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Amon (11 April 2011) 1cfdc7322cf2f4a32614826fab42c1ab
- title :
- bcc-csm1-1-m model output prepared for CMIP5 historical
- parent_experiment :
- pre-industrial control
- modeling_realm :
- atmos
- realization :
- 1
- cmor_version :
- 2.5.6