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Accessing Pangeo Climate Data

In this notebook, we select and pull CESM data from Pangeo using our pip-installable library, cesm.

# Import libraries
import os
import pandas as pd

## After pip install,
from cesm import cesm_pull as cpull

# Read in available datasets, filtered for NCAR's CESM
ncar_models = cpull.find_available_data()
ncar_models.drop(columns=['zstore']) # dropping link for cleaner ouput

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	dcpp_init_year	version
198051	CMIP	NCAR	CESM2	historical	r11i1p1f1	Amon	hurs	gn	NaN	20190514
198054	CMIP	NCAR	CESM2	historical	r11i1p1f1	Amon	huss	gn	NaN	20190514
198057	CMIP	NCAR	CESM2	historical	r11i1p1f1	Amon	pr	gn	NaN	20190514
198172	CMIP	NCAR	CESM2	historical	r11i1p1f1	Amon	tas	gn	NaN	20190514
437199	ScenarioMIP	NCAR	CESM2	ssp126	r11i1p1f1	Amon	huss	gn	NaN	20200528
437549	ScenarioMIP	NCAR	CESM2	ssp126	r11i1p1f1	Amon	tasmax	gn	NaN	20200528
437550	ScenarioMIP	NCAR	CESM2	ssp126	r11i1p1f1	Amon	tasmin	gn	NaN	20200528
437555	ScenarioMIP	NCAR	CESM2	ssp126	r11i1p1f1	Amon	tas	gn	NaN	20200528
437687	ScenarioMIP	NCAR	CESM2	ssp126	r11i1p1f1	Amon	hurs	gn	NaN	20200528
437792	ScenarioMIP	NCAR	CESM2	ssp126	r11i1p1f1	Amon	pr	gn	NaN	20200528
437906	ScenarioMIP	NCAR	CESM2	ssp585	r11i1p1f1	Amon	pr	gn	NaN	20200528
437907	ScenarioMIP	NCAR	CESM2	ssp585	r11i1p1f1	Amon	huss	gn	NaN	20200528
437918	ScenarioMIP	NCAR	CESM2	ssp585	r11i1p1f1	Amon	hurs	gn	NaN	20200528
437977	ScenarioMIP	NCAR	CESM2	ssp585	r11i1p1f1	Amon	tasmin	gn	NaN	20200528
437978	ScenarioMIP	NCAR	CESM2	ssp585	r11i1p1f1	Amon	tasmax	gn	NaN	20200528
437979	ScenarioMIP	NCAR	CESM2	ssp585	r11i1p1f1	Amon	tas	gn	NaN	20200528
438988	ScenarioMIP	NCAR	CESM2	ssp245	r11i1p1f1	Amon	tas	gn	NaN	20200528
439008	ScenarioMIP	NCAR	CESM2	ssp370	r11i1p1f1	Amon	tas	gn	NaN	20200528
439033	ScenarioMIP	NCAR	CESM2	ssp370	r11i1p1f1	Amon	tasmax	gn	NaN	20200528
439034	ScenarioMIP	NCAR	CESM2	ssp370	r11i1p1f1	Amon	tasmin	gn	NaN	20200528
439072	ScenarioMIP	NCAR	CESM2	ssp370	r11i1p1f1	Amon	pr	gn	NaN	20200528
439073	ScenarioMIP	NCAR	CESM2	ssp370	r11i1p1f1	Amon	huss	gn	NaN	20200528
439075	ScenarioMIP	NCAR	CESM2	ssp370	r11i1p1f1	Amon	hurs	gn	NaN	20200528
439080	ScenarioMIP	NCAR	CESM2	ssp245	r11i1p1f1	Amon	tasmax	gn	NaN	20200528
439227	ScenarioMIP	NCAR	CESM2	ssp245	r11i1p1f1	Amon	hurs	gn	NaN	20200528
439229	ScenarioMIP	NCAR	CESM2	ssp245	r11i1p1f1	Amon	huss	gn	NaN	20200528
439230	ScenarioMIP	NCAR	CESM2	ssp245	r11i1p1f1	Amon	pr	gn	NaN	20200528
439241	ScenarioMIP	NCAR	CESM2	ssp245	r11i1p1f1	Amon	tasmin	gn	NaN	20200528

# Assess unique values of ncar_models
meval = cpull.model_eval(ncar_models)

(28, 11)
activity_id :  ['ScenarioMIP', 'CMIP'] 

institution_id :  ['NCAR'] 

source_id :  ['CESM2'] 

experiment_id :  ['ssp370', 'historical', 'ssp245', 'ssp126', 'ssp585'] 

member_id :  ['r11i1p1f1'] 

table_id :  ['Amon'] 

variable_id :  ['tasmin', 'huss', 'tas', 'hurs', 'pr', 'tasmax'] 

grid_label :  ['gn'] 

version :  [20200528, 20190514] 

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
198051	CMIP	NCAR	CESM2	historical	r11i1p1f1	Amon	hurs	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...	NaN	20190514
437550	ScenarioMIP	NCAR	CESM2	ssp126	r11i1p1f1	Amon	tasmin	gn	gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2/ssp126...	NaN	20200528
437918	ScenarioMIP	NCAR	CESM2	ssp585	r11i1p1f1	Amon	hurs	gn	gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2/ssp585...	NaN	20200528
439033	ScenarioMIP	NCAR	CESM2	ssp370	r11i1p1f1	Amon	tasmax	gn	gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2/ssp370...	NaN	20200528
439227	ScenarioMIP	NCAR	CESM2	ssp245	r11i1p1f1	Amon	hurs	gn	gs://cmip6/CMIP6/ScenarioMIP/NCAR/CESM2/ssp245...	NaN	20200528

# Use the grab_data() function to check the output of one model variable
ds = cpull.grab_data(ncar_models, 439230)
ds

<xarray.Dataset>
Dimensions:    (lat: 192, nbnd: 2, lon: 288, time: 1032)
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
    lat_bnds   (lat, nbnd) float64 dask.array<chunksize=(192, 2), meta=np.ndarray>
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
    lon_bnds   (lon, nbnd) float64 dask.array<chunksize=(288, 2), meta=np.ndarray>
  * time       (time) object 2015-01-15 12:00:00 ... 2100-12-15 12:00:00
    time_bnds  (time, nbnd) object dask.array<chunksize=(1032, 2), meta=np.ndarray>
Dimensions without coordinates: nbnd
Data variables:
    pr         (time, lat, lon) float32 dask.array<chunksize=(288, 192, 288), meta=np.ndarray>
Attributes: (12/48)
    Conventions:            CF-1.7 CMIP-6.2
    activity_id:            ScenarioMIP
    branch_method:          standard
    branch_time_in_child:   735110.0
    branch_time_in_parent:  735110.0
    case_id:                1731
    ...                     ...
    tracking_id:            hdl:21.14100/077f7679-2555-431e-a864-29011759e8e7...
    variable_id:            pr
    variant_info:           CMIP6 SSP2-4.5 experiments (2015-2100) with CAM6,...
    variant_label:          r11i1p1f1
    netcdf_tracking_ids:    hdl:21.14100/077f7679-2555-431e-a864-29011759e8e7...
    version_id:             v20200528

xarray.Dataset

• Dimensions:
  • lat: 192
  • nbnd: 2
  • lon: 288
  • time: 1032
• Coordinates: (6)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    standard_name :

    latitude

    title :

    Latitude

    type :

    double

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
           -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
           -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
           -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
           -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
           -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
           -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
           -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
           -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
           -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
           -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
           -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
            23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
            28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
            34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
            40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
            45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
            51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
            57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
            62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
            68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
            73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
            79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
            85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])

  • lat_bnds
    (lat, nbnd)
    float64
    dask.array<chunksize=(192, 2), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 3.00 kiB 3.00 kiB Shape (192, 2) (192, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    axis :

    X

    bounds :

    lon_bnds

    standard_name :

    longitude

    title :

    Longitude

    type :

    double

    units :

    degrees_east

    valid_max :

    360.0

    valid_min :

    0.0

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • lon_bnds
    (lon, nbnd)
    float64
    dask.array<chunksize=(288, 2), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 4.50 kiB 4.50 kiB Shape (288, 2) (288, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • time
    (time)
    object
    2015-01-15 12:00:00 ... 2100-12-...

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    title :

    time

    type :

    double

    array([cftime.DatetimeNoLeap(2015, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 2, 14, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2100, 10, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 11, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2100, 12, 15, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(1032, 2), meta=np.ndarray>

    Array Chunk Bytes 16.12 kiB 16.12 kiB Shape (1032, 2) (1032, 2) Count 2 Tasks 1 Chunks Type object numpy.ndarray

• Data variables: (1)
  • pr
    (time, lat, lon)
    float32
    dask.array<chunksize=(288, 192, 288), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    chunits( ((1000 * PRECT)), units="kg m-2 s-1")

    description :

    at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)

    frequency :

    mon

    id :

    pr

    long_name :

    Precipitation

    mipTable :

    Amon

    out_name :

    pr

    prov :

    Amon ((isd.003))

    realm :

    atmos

    standard_name :

    precipitation_flux

    time :

    time

    time_label :

    time-mean

    time_title :

    Temporal mean

    title :

    Precipitation

    type :

    real

    units :

    kg m-2 s-1

    variable_id :

    pr

    Array Chunk Bytes 217.69 MiB 60.75 MiB Shape (1032, 192, 288) (288, 192, 288) Count 5 Tasks 4 Chunks Type float32 numpy.ndarray

• Attributes: (48)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  ScenarioMIP

  branch_method :

  standard

  branch_time_in_child :

  735110.0

  branch_time_in_parent :

  735110.0

  case_id :

  1731

  cesm_casename :

  b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.102

  contact :

  cesm_cmip6@ucar.edu

  creation_date :

  2020-05-26T20:12:57Z

  data_specs_version :

  01.00.31

  experiment :

  SSP-based RCP scenario with medium radiative forcing by the end of the century. Following approximately RCP4.5 global forcing pathway with SSP2 socioeconomic conditions. Radiative forcing reaches a level of 4.5 W/m2 in 2100. Concentration-driven.

  experiment_id :

  ssp245

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.ssp245.none.r11i1p1f1

  grid :

  native 0.9x1.25 finite volume grid (192x288 latxlon)

  grid_label :

  gn

  initialization_index :

  1

  institution :

  National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA

  institution_id :

  NCAR

  license :

  CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  mip_era :

  CMIP6

  model_doi_url :

  https://doi.org/10.5065/D67H1H0V

  nominal_resolution :

  100 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  historical

  parent_mip_era :

  CMIP6

  parent_source_id :

  CESM2

  parent_time_units :

  days since 0001-01-01 00:00:00

  parent_variant_label :

  r11i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  11

  realm :

  atmos

  source :

  CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)

  source_id :

  CESM2

  source_type :

  AOGCM BGC AER

  status :

  2020-09-05;created; by gcs.cmip6.ldeo@gmail.com

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  tracking_id :

  hdl:21.14100/077f7679-2555-431e-a864-29011759e8e7 hdl:21.14100/077f7679-2555-431e-a864-29011759e8e7

  variable_id :

  pr

  variant_info :

  CMIP6 SSP2-4.5 experiments (2015-2100) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1).

  variant_label :

  r11i1p1f1

  netcdf_tracking_ids :

  hdl:21.14100/077f7679-2555-431e-a864-29011759e8e7 hdl:21.14100/077f7679-2555-431e-a864-29011759e8e7

  version_id :

  v20200528

# Plot the model variable (precipitation) at one timestamp
ds.sel(time='2015-01-15')['pr'].plot(figsize=(14, 10));

# Assess the selected model variables to check for consistency in dims/coords
## All CMIP data have same coords, and all ScenarioMIP data have same coords
cpull.data_eval(ncar_models)

	activity_id	experiment_id	variable_id	lat_size	lat_min	lat_max	lon_size	lon_min	lon_max	time_size	time_min	time_max
198051	CMIP	historical	hurs	192	-90.0	90.0	288	0.0	358.75	1980	1850-01-15 12:00:00	2014-12-15 12:00:00
198054	CMIP	historical	huss	192	-90.0	90.0	288	0.0	358.75	1980	1850-01-15 12:00:00	2014-12-15 12:00:00
198057	CMIP	historical	pr	192	-90.0	90.0	288	0.0	358.75	1980	1850-01-15 12:00:00	2014-12-15 12:00:00
198172	CMIP	historical	tas	192	-90.0	90.0	288	0.0	358.75	1980	1850-01-15 12:00:00	2014-12-15 12:00:00
437687	ScenarioMIP	ssp126	hurs	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437199	ScenarioMIP	ssp126	huss	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437792	ScenarioMIP	ssp126	pr	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437555	ScenarioMIP	ssp126	tas	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437549	ScenarioMIP	ssp126	tasmax	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437550	ScenarioMIP	ssp126	tasmin	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439227	ScenarioMIP	ssp245	hurs	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439229	ScenarioMIP	ssp245	huss	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439230	ScenarioMIP	ssp245	pr	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
438988	ScenarioMIP	ssp245	tas	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439080	ScenarioMIP	ssp245	tasmax	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439241	ScenarioMIP	ssp245	tasmin	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439075	ScenarioMIP	ssp370	hurs	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439073	ScenarioMIP	ssp370	huss	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439072	ScenarioMIP	ssp370	pr	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439008	ScenarioMIP	ssp370	tas	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439033	ScenarioMIP	ssp370	tasmax	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
439034	ScenarioMIP	ssp370	tasmin	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437918	ScenarioMIP	ssp585	hurs	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437907	ScenarioMIP	ssp585	huss	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437906	ScenarioMIP	ssp585	pr	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437979	ScenarioMIP	ssp585	tas	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437978	ScenarioMIP	ssp585	tasmax	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00
437977	ScenarioMIP	ssp585	tasmin	192	-90.0	90.0	288	0.0	358.75	1032	2015-01-15 12:00:00	2100-12-15 12:00:00

# Check how many variables each model has
## The 2 extra in ScenarioMIP are for 'tasmin' and 'tasmax'
model_groups = pd.DataFrame({
    '# variables': ncar_models.groupby(['activity_id', 'experiment_id']).size()
})
model_groups

		# variables
activity_id	experiment_id
CMIP	historical	4
ScenarioMIP	ssp126	6
	ssp245	6
	ssp370	6
	ssp585	6

# Read in the selected model data!
if os.path.exists('../data/historical.zarr'):
    print('Data already downloaded!')
else:
    cpull.model_pull()

Completed downloading ('CMIP', 'historical')!
Completed downloading ('ScenarioMIP', 'ssp126')!
Completed downloading ('ScenarioMIP', 'ssp245')!
Completed downloading ('ScenarioMIP', 'ssp370')!
Completed downloading ('ScenarioMIP', 'ssp585')!

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