MRI-CGCM3 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_MRI-CGCM3_rcp85_r1i1p1.yaml")
ds=cat.netcdf.read()Metadata
| title | MRI-CGCM3 model output prepared for CMIP5 RCP8.5 |
| location | /shared/cmip5/data/rcp85/atmos/mon/Amon/clw/MRI.MRI-CGCM3/r1i1p1 |
| tags | gridded,global,model,monthly |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/clw_Amon_MRI-CGCM3_rcp85_r1i1p1.yaml |
| last updated | 2013-06-14 |
Dataset Contents
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 160, lev: 35, lon: 320, time: 1140)
Coordinates:
* time (time) float64 5.699e+04 5.702e+04 ... 9.163e+04 9.166e+04
* lev (lev) float64 0.995 0.9825 0.965 ... 0.02447 0.01782 0.01269
* lat (lat) float64 -89.14 -88.03 -86.91 -85.79 ... 86.91 88.03 89.14
* lon (lon) float64 0.0 1.125 2.25 3.375 ... 355.5 356.6 357.8 358.9
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, 35, 2), meta=np.ndarray>
p0 (time) float32 101325.0 101325.0 101325.0 ... 101325.0 101325.0
a (time, lev) float64 dask.array<chunksize=(120, 35), meta=np.ndarray>
b (time, lev) float64 dask.array<chunksize=(120, 35), meta=np.ndarray>
ps (time, lat, lon) float32 dask.array<chunksize=(120, 160, 320), meta=np.ndarray>
a_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 35, 2), meta=np.ndarray>
b_bnds (time, lev, bnds) float64 dask.array<chunksize=(120, 35, 2), meta=np.ndarray>
lat_bnds (time, lat, bnds) float64 dask.array<chunksize=(120, 160, 2), meta=np.ndarray>
lon_bnds (time, lon, bnds) float64 dask.array<chunksize=(120, 320, 2), meta=np.ndarray>
clw (time, lev, lat, lon) float32 dask.array<chunksize=(120, 35, 160, 320), meta=np.ndarray>
Attributes:
institution: MRI (Meteorological Research Institute, Tsukuba, ...
institute_id: MRI
experiment_id: rcp85
source: MRI-CGCM3 2011 atmosphere: GSMUV (gsmuv-110112, T...
model_id: MRI-CGCM3
forcing: GHG, SA, Oz, LU, Sl, Vl, BC, OC (GHG includes CO2...
parent_experiment_id: historical
parent_experiment_rip: r1i1p1
branch_time: 56978.0
contact: Seiji Yukimoto (yukimoto@mri-jma.go.jp)
history: Output from /sharex2/cmip5/rcp85/run-C3_rcp8501/g...
references: Model described by Yukimoto et al. (Technical Rep...
initialization_method: 1
physics_version: 1
tracking_id: 7cf83f33-140b-482a-bb92-2d729badf70c
product: output
experiment: RCP8.5
frequency: mon
creation_date: 2011-06-05T20:32:37Z
Conventions: CF-1.4
project_id: CMIP5
table_id: Table Amon (27 April 2011) a5a1c518f52ae340313ba0...
title: MRI-CGCM3 model output prepared for CMIP5 RCP8.5
parent_experiment: historical
modeling_realm: atmos
realization: 1
cmor_version: 2.6.0xarray.Dataset
- bnds: 2
- lat: 160
- lev: 35
- lon: 320
- time: 1140
- time(time)float645.699e+04 5.702e+04 ... 9.166e+04
- bounds :
- time_bnds
- units :
- days since 1850-01-01
- calendar :
- standard
- axis :
- T
- long_name :
- time
- standard_name :
- time
array([56993.5, 57023. , 57052.5, ..., 91599.5, 91630. , 91660.5])
- lev(lev)float640.995 0.9825 ... 0.01782 0.01269
- 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([0.994996, 0.98249 , 0.964983, 0.942465, 0.914922, 0.882353, 0.845271, 0.804677, 0.761574, 0.716962, 0.671845, 0.626721, 0.582097, 0.538472, 0.495847, 0.454224, 0.414116, 0.37602 , 0.339429, 0.303853, 0.269806, 0.237784, 0.20779 , 0.179825, 0.15389 , 0.129985, 0.108109, 0.088385, 0.070994, 0.056 , 0.043351, 0.032904, 0.024469, 0.017815, 0.01269 ]) - lat(lat)float64-89.14 -88.03 ... 88.03 89.14
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- latitude
- standard_name :
- latitude
array([-89.14152, -88.02943, -86.91077, -85.79063, -84.66992, -83.54895, -82.42782, -81.30659, -80.18531, -79.06398, -77.94262, -76.82124, -75.69984, -74.57843, -73.45701, -72.33558, -71.21414, -70.09269, -68.97124, -67.84978, -66.72833, -65.60686, -64.4854 , -63.36393, -62.24246, -61.12099, -59.99952, -58.87804, -57.75657, -56.63509, -55.51361, -54.39214, -53.27066, -52.14917, -51.02769, -49.90621, -48.78473, -47.66325, -46.54176, -45.42028, -44.29879, -43.17731, -42.05582, -40.93434, -39.81285, -38.69137, -37.56988, -36.44839, -35.32691, -34.20542, -33.08393, -31.96244, -30.84096, -29.71947, -28.59798, -27.47649, -26.355 , -25.23351, -24.11203, -22.99054, -21.86905, -20.74756, -19.62607, -18.50458, -17.38309, -16.2616 , -15.14011, -14.01862, -12.89713, -11.77564, -10.65415, -9.53266, -8.41117, -7.28968, -6.16819, -5.0467 , -3.92521, -2.80372, -1.68223, -0.56074, 0.56074, 1.68223, 2.80372, 3.92521, 5.0467 , 6.16819, 7.28968, 8.41117, 9.53266, 10.65415, 11.77564, 12.89713, 14.01862, 15.14011, 16.2616 , 17.38309, 18.50458, 19.62607, 20.74756, 21.86905, 22.99054, 24.11203, 25.23351, 26.355 , 27.47649, 28.59798, 29.71947, 30.84096, 31.96244, 33.08393, 34.20542, 35.32691, 36.44839, 37.56988, 38.69137, 39.81285, 40.93434, 42.05582, 43.17731, 44.29879, 45.42028, 46.54176, 47.66325, 48.78473, 49.90621, 51.02769, 52.14917, 53.27066, 54.39214, 55.51361, 56.63509, 57.75657, 58.87804, 59.99952, 61.12099, 62.24246, 63.36393, 64.4854 , 65.60686, 66.72833, 67.84978, 68.97124, 70.09269, 71.21414, 72.33558, 73.45701, 74.57843, 75.69984, 76.82124, 77.94262, 79.06398, 80.18531, 81.30659, 82.42782, 83.54895, 84.66992, 85.79063, 86.91077, 88.02943, 89.14152]) - lon(lon)float640.0 1.125 2.25 ... 357.8 358.9
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- longitude
- standard_name :
- longitude
array([ 0. , 1.125, 2.25 , ..., 356.625, 357.75 , 358.875])
- time_bnds(time, bnds)float64dask.array<chunksize=(120, 2), meta=np.ndarray>
Array Chunk Bytes 18.24 kB 1.92 kB Shape (1140, 2) (120, 2) Count 30 Tasks 10 Chunks Type float64 numpy.ndarray - lev_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 35, 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 638.40 kB 67.20 kB Shape (1140, 35, 2) (120, 35, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - p0(time)float32101325.0 101325.0 ... 101325.0
- long_name :
- vertical coordinate formula term: reference pressure
- units :
- Pa
array([101325., 101325., 101325., ..., 101325., 101325., 101325.], dtype=float32) - a(time, lev)float64dask.array<chunksize=(120, 35), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k)
Array Chunk Bytes 319.20 kB 33.60 kB Shape (1140, 35) (120, 35) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - b(time, lev)float64dask.array<chunksize=(120, 35), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k)
Array Chunk Bytes 319.20 kB 33.60 kB Shape (1140, 35) (120, 35) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - ps(time, lat, lon)float32dask.array<chunksize=(120, 160, 320), 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 233.47 MB 24.58 MB Shape (1140, 160, 320) (120, 160, 320) Count 30 Tasks 10 Chunks Type float32 numpy.ndarray - a_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 35, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k+1/2)
Array Chunk Bytes 638.40 kB 67.20 kB Shape (1140, 35, 2) (120, 35, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - b_bnds(time, lev, bnds)float64dask.array<chunksize=(120, 35, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k+1/2)
Array Chunk Bytes 638.40 kB 67.20 kB Shape (1140, 35, 2) (120, 35, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - lat_bnds(time, lat, bnds)float64dask.array<chunksize=(120, 160, 2), meta=np.ndarray>
Array Chunk Bytes 2.92 MB 307.20 kB Shape (1140, 160, 2) (120, 160, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - lon_bnds(time, lon, bnds)float64dask.array<chunksize=(120, 320, 2), meta=np.ndarray>
Array Chunk Bytes 5.84 MB 614.40 kB Shape (1140, 320, 2) (120, 320, 2) Count 40 Tasks 10 Chunks Type float64 numpy.ndarray - clw(time, lev, lat, lon)float32dask.array<chunksize=(120, 35, 160, 320), 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 :
- CWCWAT
- cell_methods :
- time: mean (interval: 30 minutes)
- cell_measures :
- area: areacella
- history :
- 2011-06-05T20:32:37Z altered by CMOR: replaced missing value flag (-9.99e+33) with standard missing value (1e+20).
- associated_files :
- baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_MRI-CGCM3_rcp85_r0i0p0.nc areacella: areacella_fx_MRI-CGCM3_rcp85_r0i0p0.nc
Array Chunk Bytes 8.17 GB 860.16 MB Shape (1140, 35, 160, 320) (120, 35, 160, 320) Count 30 Tasks 10 Chunks Type float32 numpy.ndarray
- institution :
- MRI (Meteorological Research Institute, Tsukuba, Japan)
- institute_id :
- MRI
- experiment_id :
- rcp85
- source :
- MRI-CGCM3 2011 atmosphere: GSMUV (gsmuv-110112, TL159L48); ocean: MRI.COM3 (MRICOM-3_0-20101116, 1x0.5L51); sea ice: MRI.COM3; land: HAL (HAL_cmip5_v0.31_04); aerosol: MASINGAR-mk2 (masingar_mk2-20110111_0203, TL95L48)
- model_id :
- MRI-CGCM3
- forcing :
- GHG, SA, Oz, LU, Sl, Vl, BC, OC (GHG includes CO2, CH4, N2O, CFC-11, CFC-12, and HCFC-22)
- parent_experiment_id :
- historical
- parent_experiment_rip :
- r1i1p1
- branch_time :
- 56978.0
- contact :
- Seiji Yukimoto (yukimoto@mri-jma.go.jp)
- history :
- Output from /sharex2/cmip5/rcp85/run-C3_rcp8501/grads/atm_eta_avr_mon.ctl 2011-06-05T20:32:37Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.
- references :
- Model described by Yukimoto et al. (Technical Report of the Meteorological Research Institute, 2011, 64, 83pp.)
- initialization_method :
- 1
- physics_version :
- 1
- tracking_id :
- 7cf83f33-140b-482a-bb92-2d729badf70c
- product :
- output
- experiment :
- RCP8.5
- frequency :
- mon
- creation_date :
- 2011-06-05T20:32:37Z
- Conventions :
- CF-1.4
- project_id :
- CMIP5
- table_id :
- Table Amon (27 April 2011) a5a1c518f52ae340313ba0aada03f862
- title :
- MRI-CGCM3 model output prepared for CMIP5 RCP8.5
- parent_experiment :
- historical
- modeling_realm :
- atmos
- realization :
- 1
- cmor_version :
- 2.6.0