MIROC5 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_MIROC5_abrupt4xCO2_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MIROC5 model output prepared for CMIP5 abrupt 4XCO2 |
| location | /shared/cmip5/data/abrupt4xCO2/atmos/mon/Amon/cli/MIROC.MIROC5/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cli_Amon_MIROC5_abrupt4xCO2_r1i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 128, lev: 40, lon: 256, time: 1812)
Coordinates:
* time (time) float64 15.5 45.0 74.5 ... 5.504e+04 5.507e+04 5.51e+04
* lev (lev) float64 0.9975 0.9915 0.983 ... 0.01861 0.0106 0.002905
* lat (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93
* lon (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6
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, 40, 2), meta=np.ndarray>
p0 (time) float32 100000.0 100000.0 100000.0 ... 100000.0 100000.0
a (time, lev) float64 dask.array<chunksize=(120, 40), meta=np.ndarray>
b (time, lev) float64 dask.array<chunksize=(120, 40), meta=np.ndarray>
ps (time, lat, lon) float32 dask.array<chunksize=(120, 128, 256), meta=np.ndarray>
a_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 40, 2), meta=np.ndarray>
b_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 40, 2), meta=np.ndarray>
lat_bnds (time, lat, bnds) float64 dask.array<chunksize=(120, 128, 2), meta=np.ndarray>
lon_bnds (time, lon, bnds) float64 dask.array<chunksize=(120, 256, 2), meta=np.ndarray>
cli (time, lev, lat, lon) float32 dask.array<chunksize=(120, 40, 128, 256), meta=np.ndarray>
Attributes:
institution: AORI (Atmosphere and Ocean Research Institute, Th...
institute_id: MIROC
experiment_id: abrupt4xCO2
source: MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); oce...
model_id: MIROC5
forcing: N/A
parent_experiment_id: piControl
parent_experiment_rip: r1i1p1
branch_time: 36500.0
contact: Masahiro Watanabe (hiro@aori.u-tokyo.ac.jp), Seit...
references: Watanabe et al., 2010: Improved climate simulatio...
initialization_method: 1
physics_version: 1
tracking_id: 67cee4f9-8a06-47c5-b859-0c3925961aab
product: output
experiment: abrupt 4XCO2
frequency: mon
creation_date: 2011-08-22T07:35:37Z
history: 2011-08-22T07:35:37Z CMOR rewrote data to comply ...
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Amon (27 April 2011) a5a1c518f52ae340313ba0...
title: MIROC5 model output prepared for CMIP5 abrupt 4XCO2
parent_experiment: pre-industrial control
modeling_realm: atmos
realization: 1
cmor_version: 2.7.1xarray.Dataset
- bnds: 2
- lat: 128
- lev: 40
- lon: 256
- time: 1812
- time(time)float6415.5 45.0 ... 5.507e+04 5.51e+04
- bounds :
- time_bnds
- units :
- days since 2100-1-1
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([1.55000e+01, 4.50000e+01, 7.45000e+01, ..., 5.50385e+04, 5.50690e+04, 5.50995e+04]) - lev(lev)float640.9975 0.9915 ... 0.0106 0.002905
- bounds :
- lev_bnds
- units :
- 1
- axis :
- Z
- positive :
- down
- long_name :
- hybrid sigma pressure coordinate
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- formula :
- p = a*p0 + b*ps
- formula_terms :
- p0: p0 a: a b: b ps: ps
array([0.997499, 0.991499, 0.982997, 0.971996, 0.958493, 0.94199 , 0.922486, 0.900483, 0.875977, 0.848972, 0.819967, 0.786951, 0.746923, 0.698885, 0.642833, 0.574472, 0.501478, 0.43645 , 0.379852, 0.330596, 0.287725, 0.250411, 0.21794 , 0.189681, 0.16508 , 0.143673, 0.125043, 0.108828, 0.094716, 0.082434, 0.071745, 0.062441, 0.054278, 0.046937, 0.040158, 0.033613, 0.026593, 0.018613, 0.010603, 0.002905]) - lat(lat)float64-88.93 -87.54 ... 87.54 88.93
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- latitude
- standard_name :
- latitude
array([-88.927735, -87.538705, -86.141472, -84.742386, -83.342596, -81.942466, -80.542146, -79.14171 , -77.741196, -76.340629, -74.940023, -73.539389, -72.138732, -70.738059, -69.337372, -67.936673, -66.535966, -65.135251, -63.73453 , -62.333803, -60.933072, -59.532337, -58.131598, -56.730857, -55.330112, -53.929366, -52.528617, -51.127867, -49.727115, -48.326361, -46.925606, -45.52485 , -44.124093, -42.723335, -41.322576, -39.921816, -38.521056, -37.120294, -35.719532, -34.31877 , -32.918007, -31.517244, -30.11648 , -28.715716, -27.314951, -25.914186, -24.513421, -23.112655, -21.71189 , -20.311124, -18.910357, -17.509591, -16.108824, -14.708057, -13.30729 , -11.906523, -10.505756, -9.104989, -7.704221, -6.303454, -4.902687, -3.501919, -2.101151, -0.700384, 0.700384, 2.101151, 3.501919, 4.902687, 6.303454, 7.704221, 9.104989, 10.505756, 11.906523, 13.30729 , 14.708057, 16.108824, 17.509591, 18.910357, 20.311124, 21.71189 , 23.112655, 24.513421, 25.914186, 27.314951, 28.715716, 30.11648 , 31.517244, 32.918007, 34.31877 , 35.719532, 37.120294, 38.521056, 39.921816, 41.322576, 42.723335, 44.124093, 45.52485 , 46.925606, 48.326361, 49.727115, 51.127867, 52.528617, 53.929366, 55.330112, 56.730857, 58.131598, 59.532337, 60.933072, 62.333803, 63.73453 , 65.135251, 66.535966, 67.936673, 69.337372, 70.738059, 72.138732, 73.539389, 74.940023, 76.340629, 77.741196, 79.14171 , 80.542146, 81.942466, 83.342596, 84.742386, 86.141472, 87.538705, 88.927735]) - lon(lon)float640.0 1.406 2.812 ... 357.2 358.6
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- longitude
- standard_name :
- longitude
array([ 0. , 1.40625, 2.8125 , ..., 355.78125, 357.1875 , 358.59375])
- time_bnds(time, bnds)float64dask.array<chunksize=(120, 2), meta=np.ndarray>
Array Chunk Bytes 28.99 kB 1.92 kB Shape (1812, 2) (120, 2) Count 48 Tasks 16 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 40, 2), meta=np.ndarray>
- formula :
- p = a*p0 + b*ps
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- units :
- 1
- formula_terms :
- p0: p0 a: a_bnds b: b_bnds ps: ps
Array Chunk Bytes 1.16 MB 76.80 kB Shape (1812, 40, 2) (120, 40, 2) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - p0(time)float32100000.0 100000.0 ... 100000.0
- long_name :
- vertical coordinate formula term: reference pressure
- units :
- Pa
array([100000., 100000., 100000., ..., 100000., 100000., 100000.], dtype=float32) - a(time, lev)float64dask.array<chunksize=(120, 40), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k)
Array Chunk Bytes 579.84 kB 38.40 kB Shape (1812, 40) (120, 40) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - b(time, lev)float64dask.array<chunksize=(120, 40), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k)
Array Chunk Bytes 579.84 kB 38.40 kB Shape (1812, 40) (120, 40) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - ps(time, lat, lon)float32dask.array<chunksize=(120, 128, 256), 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
- original_units :
- hPa
- history :
- 2011-08-22T07:35:37Z altered by CMOR: Converted units from 'hPa' to 'Pa'.
- cell_methods :
- time: mean
- cell_measures :
- area: areacella
Array Chunk Bytes 237.50 MB 15.73 MB Shape (1812, 128, 256) (120, 128, 256) Count 48 Tasks 16 Chunks Type float32 numpy.ndarray - a_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 40, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k+1/2)
Array Chunk Bytes 1.16 MB 76.80 kB Shape (1812, 40, 2) (120, 40, 2) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - b_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 40, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k+1/2)
Array Chunk Bytes 1.16 MB 76.80 kB Shape (1812, 40, 2) (120, 40, 2) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - lat_bnds(time, lat, bnds)float64dask.array<chunksize=(120, 128, 2), meta=np.ndarray>
Array Chunk Bytes 3.71 MB 245.76 kB Shape (1812, 128, 2) (120, 128, 2) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - lon_bnds(time, lon, bnds)float64dask.array<chunksize=(120, 256, 2), meta=np.ndarray>
Array Chunk Bytes 7.42 MB 491.52 kB Shape (1812, 256, 2) (120, 256, 2) Count 64 Tasks 16 Chunks Type float64 numpy.ndarray - cli(time, lev, lat, lon)float32dask.array<chunksize=(120, 40, 128, 256), 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
- original_name :
- QICEL+QICEC
- original_units :
- kg/kg
- history :
- 2011-08-22T07:35:37Z altered by CMOR: Converted units from 'kg/kg' to '1'. 2011-08-22T07:35:37Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20). 2011-08-22T07:35:37Z altered by CMOR: Inverted axis: lat.
- cell_methods :
- time: mean
- cell_measures :
- area: areacella
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_MIROC5_abrupt4xCO2_r0i0p0.nc areacella: areacella_fx_MIROC5_abrupt4xCO2_r0i0p0.nc
Array Chunk Bytes 9.50 GB 629.15 MB Shape (1812, 40, 128, 256) (120, 40, 128, 256) Count 48 Tasks 16 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), JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan)
- institute_id :
- MIROC
- experiment_id :
- abrupt4xCO2
- source :
- MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); ocean: COCO (COCO4.5, 256x224 L50); sea ice: COCO (COCO4.5); land: MATSIRO (MATSIRO, L6); aerosols: SPRINTARS (SPRINTARS 5.00, T85L40)
- model_id :
- MIROC5
- forcing :
- N/A
- parent_experiment_id :
- piControl
- parent_experiment_rip :
- r1i1p1
- branch_time :
- 36500.0
- contact :
- Masahiro Watanabe (hiro@aori.u-tokyo.ac.jp), Seita Emori (emori@nies.go.jp), Masayoshi Ishii (ism@jamstec.go.jp), Masahide Kimoto (kimoto@aori.u-tokyo.ac.jp)
- references :
- Watanabe et al., 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 6312-6335
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- 67cee4f9-8a06-47c5-b859-0c3925961aab
- product :
- output
- experiment :
- abrupt 4XCO2
- frequency :
- mon
- creation_date :
- 2011-08-22T07:35:37Z
- history :
- 2011-08-22T07:35:37Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Amon (27 April 2011) a5a1c518f52ae340313ba0aada03f862
- title :
- MIROC5 model output prepared for CMIP5 abrupt 4XCO2
- parent_experiment :
- pre-industrial control
- modeling_realm :
- atmos
- realization :
- 1
- cmor_version :
- 2.7.1