Calculating ENSO with Xarray

Overview

In this tutorial, we perform and demonstrate the following tasks:

Load SST data from the CESM2 model
Mask data using .where()
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

For this tutorial, we import several Python packages. As plotting ENSO data requires a geographically accurate map, Cartopy is imported to handle geographic features and map projections. Xarray is used to manage raw data, and Matplotlib allows for feature-rich data plotting. Finally, a custom Pythia package is imported, in this case allowing access to the Pythia example data library.

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

Compute the anomalies

There are three main steps to obtain the anomalies from the Niño 3.4 dataset created in the previous set of examples. First, we use the groupby() method to convert to monthly data. Second, we subtract the mean sea-surface temperature (SST) from the monthly data. Finally, we obtain the anomalies by computing a weighted average. These steps are illustrated in the next example:

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'])

In this example, we smooth the data curve by applying a mean function with a 5-month moving window to the anomaly dataset. We then plot the smoothed data against the original data to demonstrate:

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'])
plt.title('SST anomaly over the Niño 3.4 region');

../../_images/a8603b699bcd3f7a8182b4052a99f3a735be344ecb17d5619e01bf9ea1048953.png

Since the ENSO index conveys deviations from a norm, the calculation of Niño data requires a standard deviation. In this example, we calculate the standard deviation of the SST in the Niño 3.4 region data, across the entire time period of the data array:

std_dev = tos_nino34.tos.std()
std_dev

<xarray.DataArray 'tos' ()>
array(1.8436452, dtype=float32)

The final step of the Niño 3.4 index calculation involves normalizing the data. In this example, we perform this normalization by dividing the smoothed anomaly data by the standard deviation calculated above:

normalized_index_nino34_rolling_mean = index_nino34_rolling_mean / std_dev

Visualize the computed Niño 3.4 index

In this example, we use Matplotlib to generate a plot of our final Niño 3.4 data. This plot is set up to highlight values above 0.5, corresponding to El Niño (warm) events, and values below -0.5, corresponding to 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

This tutorial covered the use of Xarray features, including selection, grouping, and statistical functions, to compute and visualize a data index important to climate science.

Resources and References

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

Calculating ENSO with Xarray

Overview

Prerequisites

Imports

The Niño 3.4 Index

Select the Niño 3.4 region

Compute the anomalies

Visualize the computed Niño 3.4 index

Summary

Resources and References

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