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/cli_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/cli/MRI.MRI-CGCM3/r1i1p1 |
tags | gridded,global,model,monthly |
catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/cli_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> cli (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: 3b94bccd-58df-4c4c-b229-73df20d3144d product: output experiment: RCP8.5 frequency: mon creation_date: 2011-06-05T20:55:06Z 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: 2.6.0
xarray.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 - cli(time, lev, lat, lon)float32dask.array<chunksize=(120, 35, 160, 320), 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 :
- CWCICE
- cell_methods :
- time: mean (interval: 30 minutes)
- cell_measures :
- area: areacella
- history :
- 2011-06-05T20:55:06Z 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 2011-06-05T20:55:06Z 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 :
- 3b94bccd-58df-4c4c-b229-73df20d3144d
- product :
- output
- experiment :
- RCP8.5
- frequency :
- mon
- creation_date :
- 2011-06-05T20:55:06Z
- 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