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08 October 2021

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CESM2-Large Ensemble Reproduction of Kay et al. 2015#

Introduction#

This Jupyter Notebook demonstrates how one might use the NCAR Community Earth System Model v2 (CESM2) Large Ensemble (CESM2-LE) data hosted on AWS S3. The notebook shows how to reproduce figure 2 from the Kay et al. (2015) paper describing the CESM LENS dataset (doi:10.1175/BAMS-D-13-00255.1), with the LENS2 dataset.

There was a previous notebook which explored this use case, put together by Joe Hamman and Anderson Banihirwe, accessible on the Pangeo Gallery using this link. The specific figure we are replicating is shown below.

cesm1-lens

With the CESM2-LE dataset, we have 100 members which span from 1850 to 2100; whereas the original LENS dataset’s 40 member ensemble include data from 1920 to 2100. In this notebook, we include the ensemble spread from 1850 to 2100, comparing observations from the HADCRUT4 dataset when available. We also explore a more interactive version of this figure!

Imports#

%matplotlib inline
import warnings

warnings.filterwarnings("ignore")
# Silence dask.distributed logs
import logging

logger = logging.getLogger("distributed.utils_perf")
logger.setLevel(logging.ERROR)

import holoviews as hv
import hvplot.pandas
import hvplot.xarray
import intake
import numpy as np
import pandas as pd
import xarray as xr
from distributed import Client
from ncar_jobqueue import NCARCluster

hv.extension('bokeh')

Spin up a Cluster#

cluster = NCARCluster(
    memory="50GB",
    walltime='2:00:00',
    cores=4,
    processes=1,
    resource_spec='select=1:ncpus=1:mem=50GB',
)
cluster.scale(20)
client = Client(cluster)
client

Client

Client-5de8302f-2d27-11ec-9b41-3cecef1b1236

Connection method: Cluster object Cluster type: dask_jobqueue.PBSCluster
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/37823/status

Cluster Info

Read in Data#

Use the Intake-ESM Catalog to Access the Data#

We are currently working on transferring these datasets to the cloud (AWS). The total size of the CESM2-LE dataset is ~4 Petabytes (4,000 Terabytes), but we are only transferring 500 TB of that output to AWS. This process can take a while, since we need to convert from netCDF to Zarr (a more cloud friendly data format), then transfer the data to AWS. The datasets required for this example are now available from AWS!

Update 14 October 2021: We updated the catalog here to point to data on AWS

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

aws-cesm2-le catalog with 28 dataset(s) from 293 asset(s):

unique
component 4
spatial_domain 3
frequency 3
path 284
experiment 2
forcing_variant 2
variable 57
long_name 57
start_time 4
end_time 6

Subset for Daily Temperature Data (TREFHT)#

We use Intake-ESM here to query for our dataset, focusing the smoothed biomass burning (smbb) experiment!

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

aws-cesm2-le catalog with 4 dataset(s) from 4 asset(s):

unique
component 1
spatial_domain 1
frequency 1
path 4
experiment 2
forcing_variant 2
variable 1
long_name 1
start_time 2
end_time 2 
catalog_subset.df
component spatial_domain frequency path experiment forcing_variant variable long_name start_time end_time
0 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-histori... historical cmip6 TREFHT Reference height temperature 1850-01-01 12:00:00 2014-12-31 12:00:00
1 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-histori... historical smbb TREFHT Reference height temperature 1850-01-01 12:00:00 2014-12-31 12:00:00
2 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-ssp370-... ssp370 cmip6 TREFHT Reference height temperature 2015-01-01 12:00:00 2100-12-31 12:00:00
3 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-ssp370-... ssp370 smbb TREFHT Reference height temperature 2015-01-01 12:00:00 2100-12-31 12:00:00

Taking a look at the dataframe, we see there are two files - one for the historical run, and the other a future scenario (ssp370)

catalog_subset.df
component spatial_domain frequency path experiment forcing_variant variable long_name start_time end_time
0 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-histori... historical cmip6 TREFHT Reference height temperature 1850-01-01 12:00:00 2014-12-31 12:00:00
1 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-histori... historical smbb TREFHT Reference height temperature 1850-01-01 12:00:00 2014-12-31 12:00:00
2 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-ssp370-... ssp370 cmip6 TREFHT Reference height temperature 2015-01-01 12:00:00 2100-12-31 12:00:00
3 atm global daily s3://ncar-cesm2-lens/atm/daily/cesm2LE-ssp370-... ssp370 smbb TREFHT Reference height temperature 2015-01-01 12:00:00 2100-12-31 12:00:00

Load in our datasets using .to_dataset_dict()#

We use .to_dataset_dict() to return a dictionary of datasets, which we can now use for the analysis

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

--> The keys in the returned dictionary of datasets are constructed as follows:
	'component.experiment.frequency.forcing_variant'
100.00% [4/4 00:07<00:00]

Here are the keys for each dataset - you will notice they follow the groupby attributes, with component.experiment.frequency.forcing_variant, one for the future and the other historical here

dsets.keys()

dict_keys(['atm.ssp370.daily.smbb', 'atm.historical.daily.smbb', 'atm.ssp370.daily.cmip6', 'atm.historical.daily.cmip6'])

Let’s load these into separate datasets, then merge these together!

historical_smbb = dsets['atm.historical.daily.smbb']
future_smbb = dsets['atm.ssp370.daily.smbb']

historical_cmip6 = dsets['atm.historical.daily.cmip6']
future_cmip6 = dsets['atm.ssp370.daily.cmip6']

Now, we can merged the historical and future scenarios. We will drop any members that do not include the full 1850-2100 output!

merge_ds_smbb = xr.concat([historical_smbb, future_smbb], dim='time')
merge_ds_smbb = merge_ds_smbb.dropna(dim='member_id')

merge_ds_cmip6 = xr.concat([historical_cmip6, future_cmip6], dim='time')
merge_ds_cmip6 = merge_ds_cmip6.dropna(dim='member_id')

For ease of plotting, we convert the cftime.datetime times to regular datetime

merge_ds_smbb['time'] = merge_ds_smbb.indexes['time'].to_datetimeindex()
merge_ds_cmip6['time'] = merge_ds_cmip6.indexes['time'].to_datetimeindex()

Finally, we can load in just the variable we are interested in, Reference height temperature (TREFHT). We will separate this into two arrays:

  • t_smbb - the entire period of data for the smoothed biomass burning experiment (1850 to 2100)

  • t_cmip6 - the entire period of data for the cmip6 biomass burning experiment (1850 to 2100)

  • t_ref - the reference period used in the Kay et al. 2015 paper (1961 to 1990)

t_smbb = merge_ds_smbb.TREFHT
t_cmip6 = merge_ds_cmip6.TREFHT
t_ref = t_cmip6.sel(time=slice('1961', '1990'))

Let’s take a look at our datasets - note how many members we have available for each biomass burning variant

t_smbb

<xarray.DataArray 'TREFHT' (member_id: 35, time: 91615, lat: 192, lon: 288)>
dask.array<getitem, shape=(35, 91615, 192, 288), dtype=float32, chunksize=(1, 730, 192, 288), chunktype=numpy.ndarray>
Coordinates:
  * member_id  (member_id) <U12 'r11i1231p1f2' 'r11i1281p1f2' ... 'r20i1301p1f2'
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
  * time       (time) datetime64[ns] 1850-01-01T12:00:00 ... 2100-12-31T12:00:00
Attributes:
    cell_methods:  time: mean
    long_name:     Reference height temperature
    units:         K
t_cmip6

<xarray.DataArray 'TREFHT' (member_id: 49, time: 91615, lat: 192, lon: 288)>
dask.array<getitem, shape=(49, 91615, 192, 288), dtype=float32, chunksize=(1, 730, 192, 288), chunktype=numpy.ndarray>
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
  * member_id  (member_id) <U12 'r10i1181p1f1' 'r10i1231p1f1' ... 'r9i1301p1f1'
  * time       (time) datetime64[ns] 1850-01-01T12:00:00 ... 2100-12-31T12:00:00
Attributes:
    cell_methods:  time: mean
    long_name:     Reference height temperature
    units:         K

Read in the Grid Data#

We also have Zarr stores of grid data, such as the area of each grid cell. We will need to follow a similar process here, querying for our experiment and extracting the dataset

grid_subset = catalog.search(
    component='atm', frequency='static', experiment='historical', forcing_variant='smbb'
)

We can load in the dataset, calling pop_item, which grabs the dataset, assigning _ to the meaningless key

_, grid = grid_subset.to_dataset_dict(
    aggregate=False, zarr_kwargs={"consolidated": True}, storage_options={'anon': True}
).popitem()
grid

--> The keys in the returned dictionary of datasets are constructed as follows:
	'component.spatial_domain.frequency.path.experiment.forcing_variant.variable.long_name.start_time.end_time'
100.00% [1/1 00:00<00:00] 
<xarray.Dataset>
Dimensions:   (lat: 192, lon: 288, ilev: 31, lev: 30, bnds: 2, slat: 191, slon: 288)
Coordinates: (12/21)
    P0        float64 ...
    area      (lat, lon) float32 dask.array<chunksize=(192, 288), meta=np.ndarray>
    gw        (lat) float64 dask.array<chunksize=(192,), meta=np.ndarray>
    hyai      (ilev) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    hyam      (lev) float64 dask.array<chunksize=(30,), meta=np.ndarray>
    hybi      (ilev) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    ...        ...
    ntrn      int32 ...
  * slat      (slat) float64 -89.53 -88.59 -87.64 -86.7 ... 87.64 88.59 89.53
  * slon      (slon) float64 -0.625 0.625 1.875 3.125 ... 355.6 356.9 358.1
    time      object ...
    w_stag    (slat) float64 dask.array<chunksize=(191,), meta=np.ndarray>
    wnummax   (lat) int32 dask.array<chunksize=(192,), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
    *empty*
Attributes:
    intake_esm_varname:      None
    intake_esm_dataset_key:  atm.global.static.s3://ncar-cesm2-lens/atm/stati...

Extract the Grid Area and Compute the Total Area#

Here, we extract the grid area and compute the total area across all grid cells - this will help when computing the weights…

cell_area = grid.area.load()
total_area = cell_area.sum()
cell_area

<xarray.DataArray 'area' (lat: 192, lon: 288)>
array([[2.9948368e+07, 2.9948368e+07, 2.9948368e+07, ..., 2.9948368e+07,
        2.9948368e+07, 2.9948368e+07],
       [2.3957478e+08, 2.3957478e+08, 2.3957478e+08, ..., 2.3957478e+08,
        2.3957478e+08, 2.3957478e+08],
       [4.7908477e+08, 4.7908477e+08, 4.7908477e+08, ..., 4.7908477e+08,
        4.7908477e+08, 4.7908477e+08],
       ...,
       [4.7908477e+08, 4.7908477e+08, 4.7908477e+08, ..., 4.7908477e+08,
        4.7908477e+08, 4.7908477e+08],
       [2.3957478e+08, 2.3957478e+08, 2.3957478e+08, ..., 2.3957478e+08,
        2.3957478e+08, 2.3957478e+08],
       [2.9948368e+07, 2.9948368e+07, 2.9948368e+07, ..., 2.9948368e+07,
        2.9948368e+07, 2.9948368e+07]], dtype=float32)
Coordinates:
    P0       float64 nan
    area     (lat, lon) float32 2.995e+07 2.995e+07 ... 2.995e+07 2.995e+07
    gw       (lat) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
    ntrk     int32 0
    ntrm     int32 0
    ntrn     int32 0
    time     object 1850-02-01 00:00:00
    wnummax  (lat) int32 1543505128 10970 1543505128 ... 2146959360 0 2146959360
Attributes:
    cell_methods:  time: mean
    long_name:     area of grid box
    units:         m2

Compute the Weighted Annual Averages#

Here, we utilize the resample(time="AS") method which does an annual resampling based on start of calendar year.

Note we also weight by the cell area, multiplying each value by the corresponding cell area, summing over the grid (lat, lon), then dividing by the total area

Setup the Computation - Below, we lazily prepare the calculation!#

You’ll notice how quickly this cell runs - which is suspicious đŸ‘€ - we didn’t actually do any computation, but rather prepared the calculation

%%time
t_ref_ts = ((t_ref.resample(time="AS").mean("time")).weighted(cell_area).mean(('lat', 'lon'))).mean(
    dim=("time", "member_id")
)

t_smbb_ts = (t_smbb.resample(time="AS").mean("time")).weighted(cell_area).mean(('lat', 'lon'))

t_cmip6_ts = (t_cmip6.resample(time="AS").mean("time")).weighted(cell_area).mean(('lat', 'lon'))

CPU times: user 2.15 s, sys: 23.6 ms, total: 2.17 s
Wall time: 2.29 s

Compute the Averages#

At this point, we actually compute the averages - using %%time to help us measure how long it takes to compute…

%%time
t_ref_mean = t_ref_ts.compute()

CPU times: user 19.1 s, sys: 663 ms, total: 19.7 s
Wall time: 59.3 s

%%time
t_smbb_mean = t_smbb_ts.compute()

CPU times: user 2min 11s, sys: 4.35 s, total: 2min 15s
Wall time: 5min 46s

%%time
t_cmip6_mean = t_cmip6_ts.compute()

CPU times: user 2min 27s, sys: 4.82 s, total: 2min 32s
Wall time: 5min 51s

Convert to Dataframes#

Our output data format is still an xarray.DataArray which isn’t neccessarily ideal… we could convert this to a dataframe which would be easier to export.

For example, you could call anomaly_smbb.to_csv('some_output_file.csv') to export to csv, which you could then share with others!

We can convert our xarray.DataArray to a pandas dataframe using the following:

t_smbb_ts_df = t_smbb_mean.to_series().unstack().T
t_cmip6_ts_df = t_cmip6_mean.to_series().unstack().T

Now that we have our dataframes, we can compute the anomaly by subtracting the mean value t_ref_mean

anomaly_smbb = t_smbb_ts_df - t_ref_mean.data
anomaly_cmip6 = t_cmip6_ts_df - t_ref_mean.data

Grab some Observational Data#

In this case, we use the HADCRUT4 dataset. We include the url here, which can be used to remotely access the data.

# Observational time series data for comparison with ensemble average
obsDataURL = (
    "https://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/cru/hadcrut4/air.mon.anom.median.nc"
)

ds = xr.open_dataset(obsDataURL).load()
ds

<xarray.Dataset>
Dimensions:    (lat: 36, lon: 72, time: 2060, nbnds: 2)
Coordinates:
  * lat        (lat) float32 87.5 82.5 77.5 72.5 ... -72.5 -77.5 -82.5 -87.5
  * lon        (lon) float32 -177.5 -172.5 -167.5 -162.5 ... 167.5 172.5 177.5
  * time       (time) datetime64[ns] 1850-01-01 1850-02-01 ... 2021-08-01
Dimensions without coordinates: nbnds
Data variables:
    time_bnds  (time, nbnds) datetime64[ns] 1850-01-01 1850-01-31 ... 2021-08-31
    air        (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan
Attributes:
    platform:                        Surface
    title:                           HADCRUT4 Combined Air Temperature/SST An...
    history:                         Originally created at NOAA/ESRL PSD by C...
    Conventions:                     CF-1.0
    Comment:                         This dataset supersedes V3
    Source:                          Obtained from http://hadobs.metoffice.co...
    version:                         4.2.0
    dataset_title:                   HadCRUT4
    References:                      https://www.psl.noaa.gov/data/gridded/da...
    DODS_EXTRA.Unlimited_Dimension:  time

Compute the Weighted Temporal Mean from Seasons#

This observational dataset comes in as seasonal values, but we want yearly averages.. this requires weighting by the length of each season, so we include this helper function, weighted_temporal_mean!

def weighted_temporal_mean(ds):
    """
    weight by days in each month
    """
    time_bound_diff = ds.time_bnds.diff(dim="nbnds")[:, 0]
    wgts = time_bound_diff.groupby("time.year") / time_bound_diff.groupby("time.year").sum(
        xr.ALL_DIMS
    )
    np.testing.assert_allclose(wgts.groupby("time.year").sum(xr.ALL_DIMS), 1.0)
    obs = ds["air"]
    cond = obs.isnull()
    ones = xr.where(cond, 0.0, 1.0)
    obs_sum = (obs * wgts).resample(time="AS").sum(dim="time")
    ones_out = (ones * wgts).resample(time="AS").sum(dim="time")
    obs_s = (obs_sum / ones_out).mean(("lat", "lon")).to_series()
    return obs_s

Let’s apply this to our dataset now!

obs_s = weighted_temporal_mean(ds)
obs_s

time
1850-01-01   -0.338822
1851-01-01   -0.245482
1852-01-01   -0.291014
1853-01-01   -0.342457
1854-01-01   -0.276820
                ...   
2017-01-01    0.777498
2018-01-01    0.641953
2019-01-01    0.809306
2020-01-01    0.865248
2021-01-01    0.683412
Freq: AS-JAN, Length: 172, dtype: float64

Convert the dataset into a dataframe#

obs_df = pd.DataFrame(obs_s).rename(columns={0: 'value'})
obs_df
value
time
1850-01-01 -0.338822
1851-01-01 -0.245482
1852-01-01 -0.291014
1853-01-01 -0.342457
1854-01-01 -0.276820
... ...
2017-01-01 0.777498
2018-01-01 0.641953
2019-01-01 0.809306
2020-01-01 0.865248
2021-01-01 0.683412

172 rows Ă— 1 columns

Plot the Output#

In this this case, we use hvPlot to plot the output! We setup the plots in the following cell

smbb_plot = (
    anomaly_smbb.hvplot.scatter(
        'time',
        height=300,
        width=500,
        color='lightgrey',
        legend=False,
        xlabel='Year',
        ylabel='Global Mean \n Temperature Anomaly (K)',
        hover=False,
    )
    * anomaly_smbb.mean(1).hvplot.line('time', color='k', line_width=3, label='ensemble mean')
    * obs_df.hvplot.line('time', color='red', label='observations')
).opts(title='Smoothed Biomass Burning')


cmip6_plot = (
    anomaly_cmip6.hvplot.scatter(
        'time',
        height=300,
        width=500,
        color='lightgrey',
        legend=False,
        xlabel='Year',
        ylabel='Global Mean \n Temperature Anomaly (K)',
        hover=False,
    )
    * anomaly_cmip6.mean(1).hvplot.line('time', color='k', line_width=3, label='ensemble mean')
    * obs_df.hvplot.line('time', color='red', label='observations').opts(
        title='CMIP6 Biomass Burning'
    )
)

# Include both plots in the same column
(smbb_plot + cmip6_plot).cols(1)

Spin Down the Cluster#

After we are done, we can spin down our cluster

cluster.close()
client.close()
ESDS Progress Over the Past Few Months   Reading WRF data into Xarray and Visualizing the Output using hvPlot
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