Xarray 7: Calculating ENSO with Xarray

Contents

Xarray 7: Calculating ENSO with Xarray¶

Objectives¶

Load SST data from CESM2
Mask data using .where() method
Compute climatologies and anomalies using .groupby()
Use .rolling() to compute moving average
Compute, normalize, and plot the Niño 3.4 Index

Prerequisites¶

Concepts	Importance	Notes
Introduction to Xarray	Necessary
Computation and Masking	Necessary

Time to learn: 20 minutes

Imports¶

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import xarray as xr
from pythia_datasets import DATASETS

Compute monthly climatology¶

Compute the monthly climo for area averaged total SST from Niño 3.4 region, and subtract climatology from area averaged total SST time series to obtain anomalies

gb = tos_nino34.tos.groupby('time.month')
tos_nino34_anom = gb - gb.mean(dim='time')
index_nino34 = tos_nino34_anom.weighted(tos_nino34.areacello).mean(dim=['lat', 'lon'])

Smooth the anomalies with a 5-month running mean¶

index_nino34_rolling_mean = index_nino34.rolling(time=5,center=True).mean()

index_nino34.plot(size=8)
index_nino34_rolling_mean.plot()
plt.legend(['anomaly', '5-month running mean anomaly']);

../../_images/4506d345c68670c8acf702186a87abc4fe9bf474de33eaa122c5935aa9f3d96a.png

Normalize the smoothed values¶

We’ll normalize the values by dividing the rolling mean by the standard deviation.

std_dev = tos_nino34.tos.std()
std_dev

<xarray.DataArray 'tos' ()>
array(1.84363365)

normalized_index_nino34_rolling_mean = index_nino34_rolling_mean / std_dev

Visualize the computed Niño 3.4 index.¶

We’ll highlight values in excess of 0.5, roughly corresponding to El Niño (warm) / La Niña (cold) events.

fig = plt.figure(figsize=(12, 6))

plt.fill_between(
    normalized_index_nino34_rolling_mean.time.data,
    normalized_index_nino34_rolling_mean.where(
        normalized_index_nino34_rolling_mean >= 0.4
    ).data,
    0.4,
    color='red',
    alpha=0.9,
)
plt.fill_between(
    normalized_index_nino34_rolling_mean.time.data,
    normalized_index_nino34_rolling_mean.where(
        normalized_index_nino34_rolling_mean <= -0.4
    ).data,
    -0.4,
    color='blue',
    alpha=0.9,
)

normalized_index_nino34_rolling_mean.plot(color='black')
plt.axhline(0, color='black', lw=0.5)
plt.axhline(0.4, color='black', linewidth=0.5, linestyle='dotted')
plt.axhline(-0.4, color='black', linewidth=0.5, linestyle='dotted')
plt.title('Niño 3.4 Index');

../../_images/9a4df8028a3744361353741e1850da4e4470340089a6325c47720aeb497ebd1e.png

Summary¶

We have applied a variety of Xarray’s selection, grouping, and statistical functions to compute and visualize an important climate index.

What’s next?¶

In the next notebook, we will use Xarray to analyze and visualize NWP model output in another common format, GRIB.

Resources and References¶

Niño 3.4 Index
Matplotlib’s fill_between method
Matplotlib’s axhline method (see also its analogous axvline method)

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Xarray 6: Computations and Masks

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Xarray 8: GRIB Data