CMIP6 Precip Analysis Example¶
In [1]:
import os
os.environ['NUMPY_EXPERIMENTAL_ARRAY_FUNCTION'] = '0'
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import gcsfs
from tqdm.autonotebook import tqdm
from xhistogram.xarray import histogram
%matplotlib inline
plt.rcParams['figure.figsize'] = 12, 6
%config InlineBackend.figure_format = 'retina'
In [2]:
# Cluster was created via the dask labextension
# Delete this cell and replace with a new one
from dask.distributed import Client
from dask_kubernetes import KubeCluster
cluster = KubeCluster()
cluster.adapt(minimum=1, maximum=20, interval='2s')
client = Client(cluster)
client
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In [3]:
df = pd.read_csv('https://storage.googleapis.com/pangeo-cmip6/pangeo-cmip6-zarr-consolidated-stores.csv')
df.head()
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In [4]:
df_3hr_pr = df[(df.table_id == '3hr') & (df.variable_id == 'pr')]
len(df_3hr_pr)
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In [5]:
df.head()
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In [6]:
run_counts = df_3hr_pr.groupby(['source_id', 'experiment_id'])['zstore'].count()
run_counts
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In [7]:
source_ids = []
experiment_ids = ['historical', 'ssp585']
for name, group in df_3hr_pr.groupby('source_id'):
if all([expt in group.experiment_id.values
for expt in experiment_ids]):
source_ids.append(name)
source_ids
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In [8]:
source_id = source_ids[0]
def load_pr_data(source_id, expt_id):
"""
Load 3hr precip data for given source and expt ids
"""
uri = df_3hr_pr[(df_3hr_pr.source_id == source_id) &
(df_3hr_pr.experiment_id == expt_id)].zstore.values[0]
gcs = gcsfs.GCSFileSystem(token='anon')
ds = xr.open_zarr(gcs.get_mapper(uri), consolidated=True)
return ds
In [9]:
def precip_hist(ds, nbins=100, pr_log_min=-3, pr_log_max=2):
"""
Calculate precipitation histogram for a single model.
Lazy.
"""
assert ds.pr.units == 'kg m-2 s-1'
# mm/day
bins_mm_day = np.hstack([[0], np.logspace(pr_log_min, pr_log_max, nbins)])
bins_kg_m2s = bins_mm_day / (24*60*60)
pr_hist = histogram(ds.pr, bins=[bins_kg_m2s], dim=['lon']).mean(dim='time')
log_bin_spacing = np.diff(np.log(bins_kg_m2s[1:3])).item()
pr_hist_norm = 100 * pr_hist / ds.dims['lon'] / log_bin_spacing
pr_hist_norm.attrs.update({'long_name': 'zonal mean rain frequency',
'units': '%/Δln(r)'})
return pr_hist_norm
def precip_hist_for_expts(dsets, experiment_ids):
"""
Calculate histogram for a suite of experiments.
Eager.
"""
# actual data loading and computations happen in this next line
pr_hists = [precip_hist(ds).load()
for ds in [ds_hist, ds_ssp]]
pr_hist = xr.concat(pr_hists, dim=xr.Variable('experiment_id', experiment_ids))
return pr_hist
In [10]:
results = {}
for source_id in tqdm(source_ids):
# get a 20 year period
ds_hist = load_pr_data(source_id, 'historical').sel(time=slice('1980', '2000'))
ds_ssp = load_pr_data(source_id, 'ssp585').sel(time=slice('2080', '2100'))
pr_hist = precip_hist_for_expts([ds_hist, ds_ssp], experiment_ids)
results[source_id] = pr_hist
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def plot_precip_changes(pr_hist, vmax=5):
"""
Visualize the output
"""
pr_hist_diff = (pr_hist.sel(experiment_id='ssp585') -
pr_hist.sel(experiment_id='historical'))
pr_hist.sel(experiment_id='historical')[:, 1:].plot.contour(xscale='log', colors='0.5', levels=21)
pr_hist_diff[:, 1:].plot.contourf(xscale='log', vmax=vmax, levels=21)
In [12]:
title = 'Change in Zonal Mean Rain Frequency'
for source_id, pr_hist in results.items():
plt.figure()
plot_precip_changes(pr_hist)
plt.title(f'{title}: {source_id}')
