inmcm4 model output prepared for CMIP5 abrupt 4XCO2
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_inmcm4_abrupt4xCO2_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | inmcm4 model output prepared for CMIP5 abrupt 4XCO2 |
| location | /shared/cmip5/data/abrupt4xCO2/atmos/mon/Amon/cli/INM.inmcm4/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cli_Amon_inmcm4_abrupt4xCO2_r1i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 120, lev: 21, lon: 180, time: 1800)
Coordinates:
* time (time) float64 15.5 45.0 74.5 ... 5.467e+04 5.47e+04 5.473e+04
* lev (lev) float64 0.993 0.975 0.95 0.91 ... 0.041 0.0256 0.016 0.01
* lat (lat) float64 -89.25 -87.75 -86.25 -84.75 ... 86.25 87.75 89.25
* lon (lon) float64 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
Dimensions without coordinates: bnds
Data variables:
time_bnds (time, bnds) float64 dask.array<chunksize=(120, 2), meta=np.ndarray>
lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
p0 (time) float32 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
a (time, lev) float64 dask.array<chunksize=(120, 21), meta=np.ndarray>
b (time, lev) float64 dask.array<chunksize=(120, 21), meta=np.ndarray>
ps (time, lat, lon) float32 dask.array<chunksize=(120, 120, 180), meta=np.ndarray>
a_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
b_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
lat_bnds (time, lat, bnds) float64 dask.array<chunksize=(120, 120, 2), meta=np.ndarray>
lon_bnds (time, lon, bnds) float64 dask.array<chunksize=(120, 180, 2), meta=np.ndarray>
cli (time, lev, lat, lon) float32 dask.array<chunksize=(120, 21, 120, 180), meta=np.ndarray>
Attributes:
institution: INM (Institute for Numerical Mathematics, Moscow...
institute_id: INM
experiment_id: abrupt4xCO2
source: inmcm4 (2009)
model_id: inmcm4
forcing: N/A
parent_experiment_id: piControl
branch_time: 87600.0
contact: Evgeny Volodin, volodin@inm.ras.ru,INM RAS, Gubki...
history: Output from /data5/volodin/4CO2B 2010-06-23T13:05...
comment: no comments
references: Volodin, Diansky, Gusev 2010. Climate model INMCM...
initialization_method: 1
physics_version: 1
tracking_id: aec75a5e-1706-4faf-b8ab-7856a5af5e32
product: output
experiment: abrupt 4XCO2
frequency: mon
creation_date: 2010-06-23T13:05:32Z
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Amon (12 May 2010) 12e195f2fdc87c907b38b38e...
title: inmcm4 model output prepared for CMIP5 abrupt 4XCO2
parent_experiment: pre-industrial control
modeling_realm: atmos
realization: 1
cmor_version: 2.0.0xarray.Dataset
- bnds: 2
- lat: 120
- lev: 21
- lon: 180
- time: 1800
- time(time)float6415.5 45.0 ... 5.47e+04 5.473e+04
- bounds :
- time_bnds
- units :
- days since 2090-1-1
- calendar :
- 365_day
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 5.46735e+04, 5.47040e+04, 5.47345e+04]) - lev(lev)float640.993 0.975 0.95 ... 0.016 0.01
- bounds :
- lev_bnds
- units :
- 1
- axis :
- Z
- positive :
- down
- long_name :
- hybrid sigma pressure coordinate
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- formula :
- p(n,k,j,i) = a(k)*p0 + b(k)*ps(n,j,i)
- formula_terms :
- p0: p0 a: a b: b ps: ps
array([0.993 , 0.975 , 0.95 , 0.91 , 0.85 , 0.78 , 0.7 , 0.62 , 0.54 , 0.46 , 0.38 , 0.3 , 0.23 , 0.17 , 0.12 , 0.09 , 0.065 , 0.041 , 0.0256, 0.016 , 0.01 ]) - lat(lat)float64-89.25 -87.75 ... 87.75 89.25
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- latitude
- standard_name :
- latitude
array([-89.25, -87.75, -86.25, -84.75, -83.25, -81.75, -80.25, -78.75, -77.25, -75.75, -74.25, -72.75, -71.25, -69.75, -68.25, -66.75, -65.25, -63.75, -62.25, -60.75, -59.25, -57.75, -56.25, -54.75, -53.25, -51.75, -50.25, -48.75, -47.25, -45.75, -44.25, -42.75, -41.25, -39.75, -38.25, -36.75, -35.25, -33.75, -32.25, -30.75, -29.25, -27.75, -26.25, -24.75, -23.25, -21.75, -20.25, -18.75, -17.25, -15.75, -14.25, -12.75, -11.25, -9.75, -8.25, -6.75, -5.25, -3.75, -2.25, -0.75, 0.75, 2.25, 3.75, 5.25, 6.75, 8.25, 9.75, 11.25, 12.75, 14.25, 15.75, 17.25, 18.75, 20.25, 21.75, 23.25, 24.75, 26.25, 27.75, 29.25, 30.75, 32.25, 33.75, 35.25, 36.75, 38.25, 39.75, 41.25, 42.75, 44.25, 45.75, 47.25, 48.75, 50.25, 51.75, 53.25, 54.75, 56.25, 57.75, 59.25, 60.75, 62.25, 63.75, 65.25, 66.75, 68.25, 69.75, 71.25, 72.75, 74.25, 75.75, 77.25, 78.75, 80.25, 81.75, 83.25, 84.75, 86.25, 87.75, 89.25]) - lon(lon)float640.0 2.0 4.0 ... 354.0 356.0 358.0
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- longitude
- standard_name :
- longitude
array([ 0., 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40., 42., 44., 46., 48., 50., 52., 54., 56., 58., 60., 62., 64., 66., 68., 70., 72., 74., 76., 78., 80., 82., 84., 86., 88., 90., 92., 94., 96., 98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118., 120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142., 144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166., 168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190., 192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214., 216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238., 240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262., 264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286., 288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310., 312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334., 336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.])
- time_bnds(time, bnds)float64dask.array<chunksize=(120, 2), meta=np.ndarray>
Array Chunk Bytes 28.80 kB 1.92 kB Shape (1800, 2) (120, 2) Count 45 Tasks 15 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
- formula :
- p(n,k,j,i) = a(k)*p0 + b(k)*ps(n,j,i)
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- units :
- 1
- formula_terms :
- p0: p0 a: a_bnds b: b_bnds ps: ps
Array Chunk Bytes 604.80 kB 40.32 kB Shape (1800, 21, 2) (120, 21, 2) Count 60 Tasks 15 Chunks Type float64 numpy.ndarray - p0(time)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- long_name :
- vertical coordinate formula term: reference pressure
- units :
- Pa
array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)
- a(time, lev)float64dask.array<chunksize=(120, 21), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k)
Array Chunk Bytes 302.40 kB 20.16 kB Shape (1800, 21) (120, 21) Count 60 Tasks 15 Chunks Type float64 numpy.ndarray - b(time, lev)float64dask.array<chunksize=(120, 21), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k)
Array Chunk Bytes 302.40 kB 20.16 kB Shape (1800, 21) (120, 21) Count 60 Tasks 15 Chunks Type float64 numpy.ndarray - ps(time, lat, lon)float32dask.array<chunksize=(120, 120, 180), 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
Array Chunk Bytes 155.52 MB 10.37 MB Shape (1800, 120, 180) (120, 120, 180) Count 45 Tasks 15 Chunks Type float32 numpy.ndarray - a_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k+1/2)
Array Chunk Bytes 604.80 kB 40.32 kB Shape (1800, 21, 2) (120, 21, 2) Count 60 Tasks 15 Chunks Type float64 numpy.ndarray - b_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 21, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k+1/2)
Array Chunk Bytes 604.80 kB 40.32 kB Shape (1800, 21, 2) (120, 21, 2) Count 60 Tasks 15 Chunks Type float64 numpy.ndarray - lat_bnds(time, lat, bnds)float64dask.array<chunksize=(120, 120, 2), meta=np.ndarray>
Array Chunk Bytes 3.46 MB 230.40 kB Shape (1800, 120, 2) (120, 120, 2) Count 60 Tasks 15 Chunks Type float64 numpy.ndarray - lon_bnds(time, lon, bnds)float64dask.array<chunksize=(120, 180, 2), meta=np.ndarray>
Array Chunk Bytes 5.18 MB 345.60 kB Shape (1800, 180, 2) (120, 180, 2) Count 60 Tasks 15 Chunks Type float64 numpy.ndarray - cli(time, lev, lat, lon)float32dask.array<chunksize=(120, 21, 120, 180), meta=np.ndarray>
- standard_name :
- mass_fraction_of_cloud_ice_in_air
- long_name :
- Mass Fraction of Cloud Ice
- comment :
- Report on model layers (not standard pressures). Include both large-scale and convective cloud. Calculate 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. Include precipitating hydrometeors ONLY if the precipitating hydrometeor affects the calculation of radiative transfer in model.
- units :
- 1
- original_name :
- cli
- cell_methods :
- time: mean (interval: 1 month)
- cell_measures :
- area: areacella
- history :
- 2010-06-23T13:05:01Z altered by CMOR: Reordered dimensions, original order: time lat lon lev.
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_fx_inmcm4_abrupt4xCO2_r0i0p0.nc areacella: areacella_fx_inmcm4_abrupt4xCO2_r0i0p0.nc
Array Chunk Bytes 3.27 GB 217.73 MB Shape (1800, 21, 120, 180) (120, 21, 120, 180) Count 45 Tasks 15 Chunks Type float32 numpy.ndarray
- institution :
- INM (Institute for Numerical Mathematics, Moscow, Russia)
- institute_id :
- INM
- experiment_id :
- abrupt4xCO2
- source :
- inmcm4 (2009)
- model_id :
- inmcm4
- forcing :
- N/A
- parent_experiment_id :
- piControl
- branch_time :
- 87600.0
- contact :
- Evgeny Volodin, volodin@inm.ras.ru,INM RAS, Gubkina 8, Moscow, 119333 Russia,+7-495-9383904
- history :
- Output from /data5/volodin/4CO2B 2010-06-23T13:05:01Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- comment :
- no comments
- references :
- Volodin, Diansky, Gusev 2010. Climate model INMCM4.0. Izvestia RAS. Atmospheric and oceanic physics, V.46, N4, in print.
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- aec75a5e-1706-4faf-b8ab-7856a5af5e32
- product :
- output
- experiment :
- abrupt 4XCO2
- frequency :
- mon
- creation_date :
- 2010-06-23T13:05:32Z
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Amon (12 May 2010) 12e195f2fdc87c907b38b38e15851337
- title :
- inmcm4 model output prepared for CMIP5 abrupt 4XCO2
- parent_experiment :
- pre-industrial control
- modeling_realm :
- atmos
- realization :
- 1
- cmor_version :
- 2.0.0