MIROC5 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/clw_Amon_MIROC5_rcp85_r2i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MIROC5 model output prepared for CMIP5 RCP8.5 |
| location | /shared/cmip5/data/rcp85/atmos/mon/Amon/clw/MIROC.MIROC5/r2i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/clw_Amon_MIROC5_rcp85_r2i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 128, lev: 40, lon: 256, time: 1140)
Coordinates:
* time (time) float64 5.696e+04 5.698e+04 ... 9.157e+04 9.16e+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=(48, 2), meta=np.ndarray>
lev_bnds (time, lev, bnds) float64 dask.array<chunksize=(48, 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=(48, 40), meta=np.ndarray>
b (time, lev) float64 dask.array<chunksize=(48, 40), meta=np.ndarray>
ps (time, lat, lon) float32 dask.array<chunksize=(48, 128, 256), meta=np.ndarray>
a_bnds (time, lev, bnds) float64 dask.array<chunksize=(48, 40, 2), meta=np.ndarray>
b_bnds (time, lev, bnds) float64 dask.array<chunksize=(48, 40, 2), meta=np.ndarray>
lat_bnds (time, lat, bnds) float64 dask.array<chunksize=(48, 128, 2), meta=np.ndarray>
lon_bnds (time, lon, bnds) float64 dask.array<chunksize=(48, 256, 2), meta=np.ndarray>
clw (time, lev, lat, lon) float32 dask.array<chunksize=(48, 40, 128, 256), meta=np.ndarray>
Attributes:
institution: AORI (Atmosphere and Ocean Research Institute, Th...
institute_id: MIROC
experiment_id: rcp85
source: MIROC5 2010 atmosphere: MIROC-AGCM6 (T85L40); oce...
model_id: MIROC5
forcing: GHG, SA, Oz, LU, Sl, Vl, SS, Ds, BC, MD, OC (GHG ...
parent_experiment_id: historical
parent_experiment_rip: r2i1p1
branch_time: 56940.0
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: be7835a4-9ce6-418c-8e09-b0144b03d9e2
product: output
experiment: RCP8.5
frequency: mon
creation_date: 2011-12-01T09:37:43Z
history: 2011-12-01T09:37:43Z CMOR rewrote data to comply ...
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Amon (26 July 2011) 976b7fd1d9e1be31dddd28f...
title: MIROC5 model output prepared for CMIP5 RCP8.5
parent_experiment: historical
modeling_realm: atmos
realization: 2
cmor_version: 2.7.1xarray.Dataset
- bnds: 2
- lat: 128
- lev: 40
- lon: 256
- time: 1140
- time(time)float645.696e+04 5.698e+04 ... 9.16e+04
- bounds :
- time_bnds
- units :
- days since 1850-1-1
- calendar :
- noleap
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([56955.5, 56985. , 57014.5, ..., 91538.5, 91569. , 91599.5])
- 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=(48, 2), meta=np.ndarray>
Array Chunk Bytes 18.24 kB 1.92 kB Shape (1140, 2) (120, 2) Count 33 Tasks 11 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(48, 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 729.60 kB 76.80 kB Shape (1140, 40, 2) (120, 40, 2) Count 44 Tasks 11 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=(48, 40), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k)
Array Chunk Bytes 364.80 kB 38.40 kB Shape (1140, 40) (120, 40) Count 44 Tasks 11 Chunks Type float64 numpy.ndarray - b(time, lev)float64dask.array<chunksize=(48, 40), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k)
Array Chunk Bytes 364.80 kB 38.40 kB Shape (1140, 40) (120, 40) Count 44 Tasks 11 Chunks Type float64 numpy.ndarray - ps(time, lat, lon)float32dask.array<chunksize=(48, 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 :
- 2011-12-01T09:37:42Z altered by CMOR: Converted units from 'hPa' to 'Pa'.
- cell_methods :
- time: mean
- cell_measures :
- area: areacella
Array Chunk Bytes 149.42 MB 15.73 MB Shape (1140, 128, 256) (120, 128, 256) Count 33 Tasks 11 Chunks Type float32 numpy.ndarray - a_bnds(time, lev, bnds)float64dask.array<chunksize=(48, 40, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k+1/2)
Array Chunk Bytes 729.60 kB 76.80 kB Shape (1140, 40, 2) (120, 40, 2) Count 44 Tasks 11 Chunks Type float64 numpy.ndarray - b_bnds(time, lev, bnds)float64dask.array<chunksize=(48, 40, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k+1/2)
Array Chunk Bytes 729.60 kB 76.80 kB Shape (1140, 40, 2) (120, 40, 2) Count 44 Tasks 11 Chunks Type float64 numpy.ndarray - lat_bnds(time, lat, bnds)float64dask.array<chunksize=(48, 128, 2), meta=np.ndarray>
Array Chunk Bytes 2.33 MB 245.76 kB Shape (1140, 128, 2) (120, 128, 2) Count 44 Tasks 11 Chunks Type float64 numpy.ndarray - lon_bnds(time, lon, bnds)float64dask.array<chunksize=(48, 256, 2), meta=np.ndarray>
Array Chunk Bytes 4.67 MB 491.52 kB Shape (1140, 256, 2) (120, 256, 2) Count 44 Tasks 11 Chunks Type float64 numpy.ndarray - clw(time, lev, lat, lon)float32dask.array<chunksize=(48, 40, 128, 256), meta=np.ndarray>
- standard_name :
- mass_fraction_of_cloud_liquid_water_in_air
- long_name :
- Mass Fraction of Cloud Liquid Water
- comment :
- Includes both large-scale and convective cloud. Calculate as the mass of cloud liquid water in the grid cell divided by the mass of air (including the water in all phases) in the grid cells. Precipitating hydrometeors are included ONLY if the precipitating hydrometeors affect the calculation of radiative transfer in model.
- units :
- 1
- original_name :
- QLIQL+QLIQC
- original_units :
- kg/kg
- history :
- 2011-12-01T09:37:42Z altered by CMOR: Converted units from 'kg/kg' to '1'. 2011-12-01T09:37:42Z altered by CMOR: replaced missing value flag (-999) with standard missing value (1e+20). 2011-12-01T09:37:43Z 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_rcp85_r0i0p0.nc areacella: areacella_fx_MIROC5_rcp85_r0i0p0.nc
Array Chunk Bytes 5.98 GB 629.15 MB Shape (1140, 40, 128, 256) (120, 40, 128, 256) Count 33 Tasks 11 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 :
- rcp85
- 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 :
- GHG, SA, Oz, LU, Sl, Vl, SS, Ds, BC, MD, OC (GHG includes CO2, N2O, methane, and fluorocarbons; Oz includes OH and H2O2; LU excludes change in lake fraction)
- parent_experiment_id :
- historical
- parent_experiment_rip :
- r2i1p1
- branch_time :
- 56940.0
- 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 :
- be7835a4-9ce6-418c-8e09-b0144b03d9e2
- product :
- output
- experiment :
- RCP8.5
- frequency :
- mon
- creation_date :
- 2011-12-01T09:37:43Z
- history :
- 2011-12-01T09:37:43Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Amon (26 July 2011) 976b7fd1d9e1be31dddd28f5dc79b7a1
- title :
- MIROC5 model output prepared for CMIP5 RCP8.5
- parent_experiment :
- historical
- modeling_realm :
- atmos
- realization :
- 2
- cmor_version :
- 2.7.1