bcc-csm1-1 model output prepared for CMIP5 RCP4.5
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/mrsos_Lmon_bcc-csm1-1_rcp45_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | bcc-csm1-1 model output prepared for CMIP5 RCP4.5 |
| location | /shared/cmip5/data/rcp45/land/mon/Lmon/mrsos/BCC.bcc-csm1-1/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/mrsos_Lmon_bcc-csm1-1_rcp45_r1i1p1.yaml |
| last updated | 2013-05-28 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 64, lon: 128, time: 3540)
Coordinates:
* time (time) float64 15.5 45.0 74.5 ... 7.329e+04 7.332e+04 7.335e+04
* lat (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86
* lon (lon) float64 0.0 2.812 5.625 8.438 ... 348.8 351.6 354.4 357.2
depth float64 0.05
Dimensions without coordinates: bnds
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(1128, 2), meta=np.ndarray>
lat_bnds (time, lat, bnds) float64 dask.array<chunksize=(1128, 64, 2), meta=np.ndarray>
lon_bnds (time, lon, bnds) float64 dask.array<chunksize=(1128, 128, 2), meta=np.ndarray>
depth_bnds (time, bnds) float64 dask.array<chunksize=(1128, 2), meta=np.ndarray>
mrsos (time, lat, lon) float32 dask.array<chunksize=(1128, 64, 128), meta=np.ndarray>
Attributes:
institution: Beijing Climate Center (BCC), China Meteorologica...
institute_id: BCC
experiment_id: rcp45
source: bcc-csm1-1:atmosphere: BCC_AGCM2.1 (T42L26); lan...
model_id: bcc-csm1-1
forcing: Nat Ant GHG SD Oz Sl SS Ds BC OC
parent_experiment_id: historical
parent_experiment_rip: r1i1p1
branch_time: 2006.0
contact: Dr. Tongwen Wu (twwu@cma.gov.cn)
history: Output from monthly mean data 2012-07-19T10:36:41...
comment: Future projection (2006-2099) forced with prescri...
initialization_method: 1
physics_version: 1
tracking_id: a42715e4-afaf-47c5-989a-b27b5ee97f4a
product: output
experiment: RCP4.5
frequency: mon
creation_date: 2012-07-19T10:36:41Z
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Lmon (11 April 2011) 738e0f61cb381d2b73d949...
title: bcc-csm1-1 model output prepared for CMIP5 RCP4.5
parent_experiment: historical
modeling_realm: land
realization: 1
cmor_version: 2.5.6xarray.Dataset
- bnds: 2
- lat: 64
- lon: 128
- time: 3540
- time(time)float6415.5 45.0 ... 7.332e+04 7.335e+04
- bounds :
- time_bnds
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 7.32885e+04, 7.33190e+04, 7.33495e+04]) - lat(lat)float64-87.86 -85.1 -82.31 ... 85.1 87.86
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- latitude
- standard_name :
- latitude
array([-87.8638 , -85.096527, -82.312912, -79.525604, -76.7369 , -73.947517, -71.157753, -68.36776 , -65.577606, -62.787354, -59.997021, -57.206631, -54.416199, -51.625732, -48.835239, -46.044727, -43.254196, -40.46365 , -37.673088, -34.882523, -32.091946, -29.301359, -26.510769, -23.720175, -20.929575, -18.138971, -15.348365, -12.557756, -9.767145, -6.976533, -4.185921, -1.395307, 1.395307, 4.185921, 6.976533, 9.767145, 12.557756, 15.348365, 18.138971, 20.929575, 23.720175, 26.510769, 29.301359, 32.091946, 34.882523, 37.673088, 40.46365 , 43.254196, 46.044727, 48.835239, 51.625732, 54.416199, 57.206631, 59.997021, 62.787354, 65.577606, 68.36776 , 71.157753, 73.947517, 76.7369 , 79.525604, 82.312912, 85.096527, 87.8638 ]) - lon(lon)float640.0 2.812 5.625 ... 354.4 357.2
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- longitude
- standard_name :
- longitude
array([ 0. , 2.8125, 5.625 , 8.4375, 11.25 , 14.0625, 16.875 , 19.6875, 22.5 , 25.3125, 28.125 , 30.9375, 33.75 , 36.5625, 39.375 , 42.1875, 45. , 47.8125, 50.625 , 53.4375, 56.25 , 59.0625, 61.875 , 64.6875, 67.5 , 70.3125, 73.125 , 75.9375, 78.75 , 81.5625, 84.375 , 87.1875, 90. , 92.8125, 95.625 , 98.4375, 101.25 , 104.0625, 106.875 , 109.6875, 112.5 , 115.3125, 118.125 , 120.9375, 123.75 , 126.5625, 129.375 , 132.1875, 135. , 137.8125, 140.625 , 143.4375, 146.25 , 149.0625, 151.875 , 154.6875, 157.5 , 160.3125, 163.125 , 165.9375, 168.75 , 171.5625, 174.375 , 177.1875, 180. , 182.8125, 185.625 , 188.4375, 191.25 , 194.0625, 196.875 , 199.6875, 202.5 , 205.3125, 208.125 , 210.9375, 213.75 , 216.5625, 219.375 , 222.1875, 225. , 227.8125, 230.625 , 233.4375, 236.25 , 239.0625, 241.875 , 244.6875, 247.5 , 250.3125, 253.125 , 255.9375, 258.75 , 261.5625, 264.375 , 267.1875, 270. , 272.8125, 275.625 , 278.4375, 281.25 , 284.0625, 286.875 , 289.6875, 292.5 , 295.3125, 298.125 , 300.9375, 303.75 , 306.5625, 309.375 , 312.1875, 315. , 317.8125, 320.625 , 323.4375, 326.25 , 329.0625, 331.875 , 334.6875, 337.5 , 340.3125, 343.125 , 345.9375, 348.75 , 351.5625, 354.375 , 357.1875]) - depth()float640.05
- units :
- m
- axis :
- Z
- positive :
- down
- long_name :
- depth
- standard_name :
- depth
- bounds :
- depth_bnds
array(0.05)
- time_bnds(time, bnds)float64dask.array<chunksize=(1128, 2), meta=np.ndarray>
Array Chunk Bytes 56.64 kB 38.59 kB Shape (3540, 2) (2412, 2) Count 6 Tasks 2 Chunks Type float64 numpy.ndarray - lat_bnds(time, lat, bnds)float64dask.array<chunksize=(1128, 64, 2), meta=np.ndarray>
Array Chunk Bytes 3.62 MB 2.47 MB Shape (3540, 64, 2) (2412, 64, 2) Count 8 Tasks 2 Chunks Type float64 numpy.ndarray - lon_bnds(time, lon, bnds)float64dask.array<chunksize=(1128, 128, 2), meta=np.ndarray>
Array Chunk Bytes 7.25 MB 4.94 MB Shape (3540, 128, 2) (2412, 128, 2) Count 8 Tasks 2 Chunks Type float64 numpy.ndarray - depth_bnds(time, bnds)float64dask.array<chunksize=(1128, 2), meta=np.ndarray>
Array Chunk Bytes 56.64 kB 38.59 kB Shape (3540, 2) (2412, 2) Count 8 Tasks 2 Chunks Type float64 numpy.ndarray - mrsos(time, lat, lon)float32dask.array<chunksize=(1128, 64, 128), meta=np.ndarray>
- standard_name :
- moisture_content_of_soil_layer
- long_name :
- Moisture in Upper Portion of Soil Column
- comment :
- the mass of water in all phases in a thin surface soil layer.
- units :
- kg m-2
- original_name :
- MRSOS
- cell_methods :
- time: mean (interval: 20 mintues) area: mean where land
- cell_measures :
- area: areacella
- history :
- 2012-07-19T10:36:41Z altered by CMOR: Treated scalar dimension: 'depth'.
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_land_fx_bcc-csm1-1_rcp45_r0i0p0.nc areacella: areacella_fx_bcc-csm1-1_rcp45_r0i0p0.nc
Array Chunk Bytes 116.00 MB 79.04 MB Shape (3540, 64, 128) (2412, 64, 128) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray
- institution :
- Beijing Climate Center (BCC), China Meteorological Administration,China
- institute_id :
- BCC
- experiment_id :
- rcp45
- source :
- bcc-csm1-1:atmosphere: BCC_AGCM2.1 (T42L26); land: BCC_AVIM1.0;ocean: MOM4_L40 (tripolar, 1lon x (1-1/3) lat, L40);sea ice: SIS (tripolar, 1 lon x (1-1/3) lat)
- model_id :
- bcc-csm1-1
- forcing :
- Nat Ant GHG SD Oz Sl SS Ds BC OC
- parent_experiment_id :
- historical
- parent_experiment_rip :
- r1i1p1
- branch_time :
- 2006.0
- contact :
- Dr. Tongwen Wu (twwu@cma.gov.cn)
- history :
- Output from monthly mean data 2012-07-19T10:36:41Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- comment :
- Future projection (2006-2099) forced with prescribed concentration scenario of RCP4.5. The carbon cycle in land and ocean component model is considered,but the time-evolution of CO2 concentration in atmospheric component is prescribed.
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- a42715e4-afaf-47c5-989a-b27b5ee97f4a
- product :
- output
- experiment :
- RCP4.5
- frequency :
- mon
- creation_date :
- 2012-07-19T10:36:41Z
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Lmon (11 April 2011) 738e0f61cb381d2b73d949ee76b893de
- title :
- bcc-csm1-1 model output prepared for CMIP5 RCP4.5
- parent_experiment :
- historical
- modeling_realm :
- land
- realization :
- 1
- cmor_version :
- 2.5.6