CESM2 LENS2 on AWS - Climate Variability Cookbook

# Display output of plots directly in Notebook
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

import intake
import numpy as np
import pandas as pd
import xarray as xr

catalog = intake.open_esm_datastore(
    'https://raw.githubusercontent.com/NCAR/cesm2-le-aws/main/intake-catalogs/aws-cesm2-le.json'
)
catalog

catalog.df.to_csv('namelist.csv')

catalog = intake.open_esm_datastore(
    'https://ncar-cesm-lens.s3-us-west-2.amazonaws.com/catalogs/aws-cesm1-le.json'
)
catalog

catalog = intake.open_esm_datastore(
    'https://ncar-cesm-lens.s3-us-west-2.amazonaws.com/catalogs/aws-cesm1-le.json'
)
catalog

catalog.df.to_csv('namelistCESM1.csv')

import xarray as xr
import numpy as np
import climdex.utils as cutils

import climdex.precipitation as pdex

indices = pdex.indices(time_dim='time')
   # compute total monthly precipitation;
   # your data should be daily or sub-daily time scale
#ptot = indices.PRECC
indices

<climdex.precipitation.PrecipitationIndices at 0x7f76caeeb0e0>

#indices = pdex.indices(time_dim='time')
   # compute total monthly precipitation;
   # your data should be daily or sub-daily time scale
#ptot = indices.prcptot(data, period='1M')

catalog_subset = catalog.search(variable='TREFHT', frequency='daily')
catalog_subset

catalog_subset.df

dsets = catalog_subset.to_dataset_dict(storage_options={'anon':True})

#indices = pdex.indices(time_dim='time')
   # compute total monthly precipitation;
   # your data should be daily or sub-daily time scale
#ptot = indices.TREFHT(data, period='1M')

dsets

{'atm.HIST.daily': <xarray.Dataset> Size: 6GB
 Dimensions:    (time: 25550, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
     member_id  int64 8B ...
   * time       (time) object 204kB 1850-01-01 12:00:00 ... 1919-12-31 12:00:00
     time_bnds  (time, nbnd) object 409kB dask.array<chunksize=(12775, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     TREFHT     (time, lat, lon) float32 6GB dask.array<chunksize=(576, 192, 288), meta=np.ndarray>
 Attributes: (12/25)
     Conventions:                       CF-1.0
     NCO:                               4.4.2
     Version:                           $Name$
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
     logname:                           mudryk
     ...                                ...
     intake_esm_attrs:units:            K
     intake_esm_attrs:start_time:       1850-01-01 12:00:00
     intake_esm_attrs:end_time:         1919-12-31 12:00:00
     intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-H...
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.HIST.daily,
 'atm.20C.daily': <xarray.Dataset> Size: 278GB
 Dimensions:    (member_id: 40, time: 31390, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 320B 1 2 3 4 5 6 7 ... 35 101 102 103 104 105
   * time       (time) object 251kB 1920-01-01 12:00:00 ... 2005-12-31 12:00:00
     time_bnds  (time, nbnd) object 502kB dask.array<chunksize=(15695, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     TREFHT     (member_id, time, lat, lon) float32 278GB dask.array<chunksize=(1, 576, 192, 288), meta=np.ndarray>
 Attributes: (12/25)
     Conventions:                       CF-1.0
     NCO:                               4.4.2
     Version:                           $Name$
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.001.cam.i....
     logname:                           mudryk
     ...                                ...
     intake_esm_attrs:units:            K
     intake_esm_attrs:start_time:       1920-01-01 12:00:00
     intake_esm_attrs:end_time:         2005-12-31 12:00:00
     intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-2...
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.20C.daily,
 'atm.RCP85.daily': <xarray.Dataset> Size: 307GB
 Dimensions:    (member_id: 40, time: 34675, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 320B 1 2 3 4 5 6 7 ... 35 101 102 103 104 105
   * time       (time) object 277kB 2006-01-01 12:00:00 ... 2100-12-31 12:00:00
     time_bnds  (time, nbnd) object 555kB dask.array<chunksize=(17338, 2), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     TREFHT     (member_id, time, lat, lon) float32 307GB dask.array<chunksize=(1, 576, 192, 288), meta=np.ndarray>
 Attributes: (12/26)
     Conventions:                       CF-1.0
     NCO:                               4.4.2
     Version:                           $Name$
     host:                              tcs-f02n07
     important_note:                    This data is part of the project 'Blin...
     initial_file:                      b.e11.B20TRC5CNBDRD.f09_g16.105.cam.i....
     ...                                ...
     intake_esm_attrs:units:            K
     intake_esm_attrs:start_time:       2006-01-01 12:00:00
     intake_esm_attrs:end_time:         2100-12-31 12:00:00
     intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-R...
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.RCP85.daily,
 'atm.CTRL.daily': <xarray.Dataset> Size: 145GB
 Dimensions:    (member_id: 1, time: 656634, lat: 192, lon: 288, nbnd: 2)
 Coordinates:
   * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
   * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
   * member_id  (member_id) int64 8B 1
   * time       (time) object 5MB 0402-01-01 12:00:00 ... 2200-12-31 12:00:00
     time_bnds  (time, nbnd) object 11MB dask.array<chunksize=(82080, 1), meta=np.ndarray>
 Dimensions without coordinates: nbnd
 Data variables:
     TREFHT     (member_id, time, lat, lon) float32 145GB dask.array<chunksize=(1, 576, 192, 288), meta=np.ndarray>
 Attributes: (12/25)
     Conventions:                       CF-1.0
     NCO:                               4.3.4
     Version:                           $Name$
     case:                              b.e11.B1850C5CN.f09_g16.005
     initial_file:                      /glade/p/cesm/cseg//inputdata/atm/cam/...
     logname:                           mai
     ...                                ...
     intake_esm_attrs:units:            K
     intake_esm_attrs:start_time:       0402-01-01 12:00:00
     intake_esm_attrs:end_time:         2200-12-31 12:00:00
     intake_esm_attrs:path:             s3://ncar-cesm-lens/atm/daily/cesmLE-C...
     intake_esm_attrs:_data_format_:    zarr
     intake_esm_dataset_key:            atm.CTRL.daily}

dsets.keys()

dict_keys(['atm.HIST.daily', 'atm.20C.daily', 'atm.RCP85.daily', 'atm.CTRL.daily'])

historical = dsets['atm.HIST.daily']

historical

import matplotlib.pyplot as plt
plt.imshow(historical['TREFHT'].data[0], origin='lower')

#plt.colorbar() # for the colorbar

import matplotlib.pyplot as plt
import numpy as np

# Create sample data
data = np.random.rand(10, 10)

# Create a plot with imshow
plt.imshow(data, cmap='viridis')
plt.colorbar() # Add a colorbar to the plot
plt.title("Sample Data with Colorbar")
plt.show()

Climate Variability Cookbook