MPI-ESM-LR model output prepared for CMIP5 RCP8.5
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cli_Amon_MPI-ESM-LR_rcp85_r3i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MPI-ESM-LR model output prepared for CMIP5 RCP8.5 |
| location | /shared/cmip5/data/rcp85/atmos/mon/Amon/cli/MPI-M.MPI-ESM-LR/r3i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cli_Amon_MPI-ESM-LR_rcp85_r3i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 96, lev: 47, lon: 192, time: 1140)
Coordinates:
* time (time) float64 5.699e+04 5.702e+04 ... 9.163e+04 9.166e+04
* lev (lev) float64 0.9961 0.9826 0.959 ... 4.225e-05 9.816e-06
* lat (lat) float64 -88.57 -86.72 -84.86 -83.0 ... 84.86 86.72 88.57
* lon (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1
Dimensions without coordinates: bnds
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(48, 2), meta=np.ndarray>
lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(48, 47, 2), meta=np.ndarray>
ap (time, lev) float64 dask.array<chunksize=(48, 47), meta=np.ndarray>
b (time, lev) float64 dask.array<chunksize=(48, 47), meta=np.ndarray>
ps (time, lat, lon) float32 dask.array<chunksize=(48, 96, 192), meta=np.ndarray>
ap_bnds (time, lev, bnds) float64 dask.array<chunksize=(48, 47, 2), meta=np.ndarray>
b_bnds (time, lev, bnds) float64 dask.array<chunksize=(48, 47, 2), meta=np.ndarray>
lat_bnds (time, lat, bnds) float64 dask.array<chunksize=(48, 96, 2), meta=np.ndarray>
lon_bnds (time, lon, bnds) float64 dask.array<chunksize=(48, 192, 2), meta=np.ndarray>
cli (time, lev, lat, lon) float32 dask.array<chunksize=(48, 47, 96, 192), meta=np.ndarray>
Attributes:
institution: Max Planck Institute for Meteorology
institute_id: MPI-M
experiment_id: rcp85
source: MPI-ESM-LR 2011; URL: http://svn.zmaw.de/svn/cosm...
model_id: MPI-ESM-LR
forcing: GHG Oz SD Sl Vl LU
parent_experiment_id: historical
parent_experiment_rip: r3i1p1
branch_time: 56978.0
contact: cmip5-mpi-esm@dkrz.de
history: Model raw output postprocessing with modelling en...
references: ECHAM6: n/a; JSBACH: Raddatz et al., 2007. Will t...
initialization_method: 1
physics_version: 1
tracking_id: 27eb162d-c861-4b70-a339-7633299e1801
product: output
experiment: RCP8.5
frequency: mon
creation_date: 2011-07-12T18:21:31Z
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Amon (27 April 2011) a5a1c518f52ae340313ba0...
title: MPI-ESM-LR model output prepared for CMIP5 RCP8.5
parent_experiment: historical
modeling_realm: atmos
realization: 3
cmor_version: 2.5.9xarray.Dataset
- bnds: 2
- lat: 96
- lev: 47
- lon: 192
- time: 1140
- time(time)float645.699e+04 5.702e+04 ... 9.166e+04
- bounds :
- time_bnds
- units :
- days since 1850-1-1 00:00:00
- calendar :
- proleptic_gregorian
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([56993.5, 57023. , 57052.5, ..., 91599.5, 91630. , 91660.5])
- lev(lev)float640.9961 0.9826 ... 9.816e-06
- bounds :
- lev_bnds
- units :
- 1
- axis :
- Z
- positive :
- down
- long_name :
- hybrid sigma pressure coordinate
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- formula :
- p = ap + b*ps
- formula_terms :
- ap: ap b: b ps: ps
array([9.961500e-01, 9.826500e-01, 9.589556e-01, 9.276460e-01, 8.908394e-01, 8.501503e-01, 8.068475e-01, 7.620137e-01, 7.164562e-01, 6.707188e-01, 6.252446e-01, 5.803910e-01, 5.363946e-01, 4.934380e-01, 4.517182e-01, 4.114154e-01, 3.726136e-01, 3.354221e-01, 3.000480e-01, 2.666209e-01, 2.352179e-01, 2.059397e-01, 1.787885e-01, 1.537470e-01, 1.306586e-01, 1.092082e-01, 8.982902e-02, 7.309098e-02, 5.886336e-02, 4.689628e-02, 3.694117e-02, 2.875524e-02, 2.206205e-02, 1.662554e-02, 1.228782e-02, 8.893068e-03, 6.291450e-03, 4.342560e-03, 2.918469e-03, 1.903934e-03, 1.199425e-03, 7.259045e-04, 4.202813e-04, 2.284638e-04, 1.097754e-04, 4.224661e-05, 9.815866e-06]) - lat(lat)float64-88.57 -86.72 ... 86.72 88.57
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- latitude
- standard_name :
- latitude
array([-88.572166, -86.722534, -84.861969, -82.99894 , -81.134979, -79.270561, -77.405891, -75.541061, -73.676132, -71.811134, -69.946083, -68.080994, -66.215874, -64.350731, -62.485569, -60.620396, -58.755211, -56.890015, -55.024807, -53.159595, -51.294376, -49.429153, -47.563927, -45.698692, -43.833458, -41.96822 , -40.102978, -38.237736, -36.37249 , -34.507244, -32.641994, -30.776745, -28.911493, -27.04624 , -25.180986, -23.315731, -21.450476, -19.585218, -17.719961, -15.854704, -13.989446, -12.124187, -10.258928, -8.393669, -6.528409, -4.66315 , -2.79789 , -0.93263 , 0.93263 , 2.79789 , 4.66315 , 6.528409, 8.393669, 10.258928, 12.124187, 13.989446, 15.854704, 17.719961, 19.585218, 21.450476, 23.315731, 25.180986, 27.04624 , 28.911493, 30.776745, 32.641994, 34.507244, 36.37249 , 38.237736, 40.102978, 41.96822 , 43.833458, 45.698692, 47.563927, 49.429153, 51.294376, 53.159595, 55.024807, 56.890011, 58.755211, 60.620396, 62.485569, 64.350731, 66.215874, 68.080994, 69.946083, 71.811134, 73.676132, 75.541061, 77.405891, 79.270561, 81.134979, 82.99894 , 84.861969, 86.722534, 88.572166]) - lon(lon)float640.0 1.875 3.75 ... 356.2 358.1
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- longitude
- standard_name :
- longitude
array([ 0. , 1.875, 3.75 , 5.625, 7.5 , 9.375, 11.25 , 13.125, 15. , 16.875, 18.75 , 20.625, 22.5 , 24.375, 26.25 , 28.125, 30. , 31.875, 33.75 , 35.625, 37.5 , 39.375, 41.25 , 43.125, 45. , 46.875, 48.75 , 50.625, 52.5 , 54.375, 56.25 , 58.125, 60. , 61.875, 63.75 , 65.625, 67.5 , 69.375, 71.25 , 73.125, 75. , 76.875, 78.75 , 80.625, 82.5 , 84.375, 86.25 , 88.125, 90. , 91.875, 93.75 , 95.625, 97.5 , 99.375, 101.25 , 103.125, 105. , 106.875, 108.75 , 110.625, 112.5 , 114.375, 116.25 , 118.125, 120. , 121.875, 123.75 , 125.625, 127.5 , 129.375, 131.25 , 133.125, 135. , 136.875, 138.75 , 140.625, 142.5 , 144.375, 146.25 , 148.125, 150. , 151.875, 153.75 , 155.625, 157.5 , 159.375, 161.25 , 163.125, 165. , 166.875, 168.75 , 170.625, 172.5 , 174.375, 176.25 , 178.125, 180. , 181.875, 183.75 , 185.625, 187.5 , 189.375, 191.25 , 193.125, 195. , 196.875, 198.75 , 200.625, 202.5 , 204.375, 206.25 , 208.125, 210. , 211.875, 213.75 , 215.625, 217.5 , 219.375, 221.25 , 223.125, 225. , 226.875, 228.75 , 230.625, 232.5 , 234.375, 236.25 , 238.125, 240. , 241.875, 243.75 , 245.625, 247.5 , 249.375, 251.25 , 253.125, 255. , 256.875, 258.75 , 260.625, 262.5 , 264.375, 266.25 , 268.125, 270. , 271.875, 273.75 , 275.625, 277.5 , 279.375, 281.25 , 283.125, 285. , 286.875, 288.75 , 290.625, 292.5 , 294.375, 296.25 , 298.125, 300. , 301.875, 303.75 , 305.625, 307.5 , 309.375, 311.25 , 313.125, 315. , 316.875, 318.75 , 320.625, 322.5 , 324.375, 326.25 , 328.125, 330. , 331.875, 333.75 , 335.625, 337.5 , 339.375, 341.25 , 343.125, 345. , 346.875, 348.75 , 350.625, 352.5 , 354.375, 356.25 , 358.125])
- time_bnds(time, bnds)float64dask.array<chunksize=(48, 2), meta=np.ndarray>
Array Chunk Bytes 18.24 kB 2.11 kB Shape (1140, 2) (132, 2) Count 30 Tasks 10 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(48, 47, 2), meta=np.ndarray>
- formula :
- p = ap + b*ps
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- units :
- 1
- formula_terms :
- ap: ap_bnds b: b_bnds ps: ps
Array Chunk Bytes 857.28 kB 99.26 kB Shape (1140, 47, 2) (132, 47, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - ap(time, lev)float64dask.array<chunksize=(48, 47), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: ap(k)
- units :
- Pa
Array Chunk Bytes 428.64 kB 49.63 kB Shape (1140, 47) (132, 47) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - b(time, lev)float64dask.array<chunksize=(48, 47), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k)
Array Chunk Bytes 428.64 kB 49.63 kB Shape (1140, 47) (132, 47) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - ps(time, lat, lon)float32dask.array<chunksize=(48, 96, 192), meta=np.ndarray>
- standard_name :
- surface_air_pressure
- long_name :
- Surface Air Pressure
- comment :
- not, in general, the same as mean sea-level pressure
- units :
- Pa
- cell_methods :
- time: mean
- cell_measures :
- area: areacella
Array Chunk Bytes 84.05 MB 9.73 MB Shape (1140, 96, 192) (132, 96, 192) Count 30 Tasks 10 Chunks Type float32 numpy.ndarray - ap_bnds(time, lev, bnds)float64dask.array<chunksize=(48, 47, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: ap(k+1/2)
- units :
- Pa
Array Chunk Bytes 857.28 kB 99.26 kB Shape (1140, 47, 2) (132, 47, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - b_bnds(time, lev, bnds)float64dask.array<chunksize=(48, 47, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k+1/2)
Array Chunk Bytes 857.28 kB 99.26 kB Shape (1140, 47, 2) (132, 47, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - lat_bnds(time, lat, bnds)float64dask.array<chunksize=(48, 96, 2), meta=np.ndarray>
Array Chunk Bytes 1.75 MB 202.75 kB Shape (1140, 96, 2) (132, 96, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - lon_bnds(time, lon, bnds)float64dask.array<chunksize=(48, 192, 2), meta=np.ndarray>
Array Chunk Bytes 3.50 MB 405.50 kB Shape (1140, 192, 2) (132, 192, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - cli(time, lev, lat, lon)float32dask.array<chunksize=(48, 47, 96, 192), meta=np.ndarray>
- standard_name :
- mass_fraction_of_cloud_ice_in_air
- long_name :
- Mass Fraction of Cloud Ice
- comment :
- Includes both large-scale and convective cloud. This is calculated as the mass of cloud ice in the grid cell divided by the mass of air (including the water in all phases) in the grid cell. It includes precipitating hydrometeors ONLY if the precipitating hydrometeors affect the calculation of radiative transfer in model.
- units :
- 1
- cell_methods :
- time: mean
- cell_measures :
- area: areacella
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_MPI-ESM-LR_rcp85_r0i0p0.nc areacella: areacella_fx_MPI-ESM-LR_rcp85_r0i0p0.nc
- history :
- 2011-07-12T18:21:31Z altered by CMOR: Inverted axis: lev.
Array Chunk Bytes 3.95 GB 457.41 MB Shape (1140, 47, 96, 192) (132, 47, 96, 192) Count 30 Tasks 10 Chunks Type float32 numpy.ndarray
- institution :
- Max Planck Institute for Meteorology
- institute_id :
- MPI-M
- experiment_id :
- rcp85
- source :
- MPI-ESM-LR 2011; URL: http://svn.zmaw.de/svn/cosmos/branches/releases/mpi-esm-cmip5/src/mod; atmosphere: ECHAM6 (REV: 4619), T63L47; land: JSBACH (REV: 4619); ocean: MPIOM (REV: 4619), GR15L40; sea ice: 4619; marine bgc: HAMOCC (REV: 4619);
- model_id :
- MPI-ESM-LR
- forcing :
- GHG Oz SD Sl Vl LU
- parent_experiment_id :
- historical
- parent_experiment_rip :
- r3i1p1
- branch_time :
- 56978.0
- contact :
- cmip5-mpi-esm@dkrz.de
- history :
- Model raw output postprocessing with modelling environment (IMDI) at DKRZ: URL: http://svn-mad.zmaw.de/svn/mad/Model/IMDI/trunk, REV: 3412 2011-07-12T18:21:14Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- references :
- ECHAM6: n/a; JSBACH: Raddatz et al., 2007. Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty first century? Climate Dynamics, 29, 565-574, doi 10.1007/s00382-007-0247-8; MPIOM: Marsland et al., 2003. The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Modelling, 5, 91-127; HAMOCC: http://www.mpimet.mpg.de/fileadmin/models/MPIOM/HAMOCC5.1_TECHNICAL_REPORT.pdf;
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- 27eb162d-c861-4b70-a339-7633299e1801
- product :
- output
- experiment :
- RCP8.5
- frequency :
- mon
- creation_date :
- 2011-07-12T18:21:31Z
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Amon (27 April 2011) a5a1c518f52ae340313ba0aada03f862
- title :
- MPI-ESM-LR model output prepared for CMIP5 RCP8.5
- parent_experiment :
- historical
- modeling_realm :
- atmos
- realization :
- 3
- cmor_version :
- 2.5.9