Tutorial 7: Other Computational Tools in Xarray

Tutorial 7: Other Computational Tools in Xarray#

Week 1, Day 1, Climate System Overview

Content creators: Sloane Garelick, Julia Kent

Content reviewers: Katrina Dobson, Younkap Nina Duplex, Danika Gupta, Maria Gonzalez, Will Gregory, Nahid Hasan, Paul Heubel, Sherry Mi, Beatriz Cosenza Muralles, Jenna Pearson, Agustina Pesce, Chi Zhang, Ohad Zivan

Content editors: Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan

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Our 2024 Sponsors: CMIP, NFDI4Earth

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Pythia credit: Rose, B. E. J., Kent, J., Tyle, K., Clyne, J., Banihirwe, A., Camron, D., May, R., Grover, M., Ford, R. R., Paul, K., Morley, J., Eroglu, O., Kailyn, L., & Zacharias, A. (2023). Pythia Foundations (Version v2023.05.01) https://zenodo.org/record/8065851

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Tutorial Objectives#

Estimated timing of tutorial: 15 minutes

Thus far, we’ve learned about various climate processes in the videos, and we’ve explored tools in Xarray that are useful for analyzing and interpreting climate data in the tutorials.

In this tutorial, you’ll continue using the SST data from CESM2 and practice using some additional computational tools in Xarray to resample your data, which can help with data comparison and analysis. The functions you will use are:

.resample(): Groupby-like functionality specifically for time dimensions. Can be used for temporal upsampling and downsampling. Additional information about resampling in Xarray can be found here.
.rolling(): Useful for computing aggregations on moving windows of your dataset e.g. computing moving averages. Additional information about resampling in Xarray can be found here.
.coarsen(): Generic functionality for downsampling data. Additional information about resampling in Xarray can be found here.

Setup#

# installations ( uncomment and run this cell ONLY when using google colab or kaggle )
#!pip install pythia_datasets

# imports
import matplotlib.pyplot as plt
import xarray as xr
from pythia_datasets import DATASETS

Figure Settings#

Video 1: Carbon Cycle and the Greenhouse Effect#

Section 1: High-level Computation Functionality#

In this tutorial, you will learn about several methods for dealing with the resolution of data. Here are some links for quick reference, and we will go into detail in each of them in the sections below.

.resample(): Groupby-like functionality especially for time dimensions. Can be used for temporal upsampling and downsampling
.rolling(): Useful for computing aggregations on moving windows of your dataset e.g. computing moving averages
.coarsen(): Generic functionality for downsampling data

First, let’s load the same data that we used in the previous tutorials (monthly SST data from CESM2):

Section 1.1: Resampling Data#

For upsampling or downsampling temporal resolutions, we can use the .resample() method in Xarray. For example, you can use this function to downsample a dataset from hourly to 6-hourly resolution.

Our original SST data is monthly resolution. Let’s use .resample() to downsample to annual frequency:

# resample from a monthly to an annual frequency
tos_yearly = ds.tos.resample(time="AS")
tos_yearly

<string>:6: FutureWarning: 'AS' is deprecated and will be removed in a future version. Please use 'YS' instead of 'AS'.

DataArrayResample, grouped over '__resample_dim__'
15 groups with labels 2000-01-01, 00:00:00, ..., 201....

# calculate the global mean of the resampled data
annual_mean = tos_yearly.mean()
annual_mean_global = annual_mean.mean(dim=["lat", "lon"])
annual_mean_global.plot()

[<matplotlib.lines.Line2D at 0x7f779837a160>]

../../../_images/01bdf55684f0d2890833989dd8dea6dab0ace3fc4bb93be66ee7ef55447c99d8.png

Section 1.4: Compare the Resampling Methods#

Now that we’ve tried multiple resampling methods on different temporal resolutions, we can compare the resampled datasets to the original.

original_global = ds.mean(dim=["lat", "lon"])

original_global.tos.plot(size=6)
coarse_data.tos.plot()
tos_m_avg_global.plot()
annual_mean_global.plot()


plt.legend(
    [
        "original data (monthly)",
        "coarsened (4 months)",
        "moving average (6 months)",
        "annually resampled (12 months)",
    ]
)

<matplotlib.legend.Legend at 0x7f77997af040>

../../../_images/8f9b59cf47788793a63f825d2940b0646db37d6bf1cf1b1c3d7e748b29596ce0.png

Questions 1.4: Climate Connection#

What type of information can you obtain from each time series?
In what scenarios would you use different temporal resolutions?

Click for solution

Summary#

In this tutorial, we’ve explored Xarray tools to simplify and understand climate data better. Given the complexity and variability of climate data, tools like .resample(), .rolling(), and .coarsen() come in handy to make the data easier to compare and find long-term trends. You’ve also looked at valuable techniques like calculating moving averages.

Resources#

Code and data for this tutorial is based on existing content from Project Pythia.