Tutorial 5: Xarray Data Analysis and Climatology#

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, Sherry Mi, Beatriz Cosenza Muralles, Jenna Pearson, Agustina Pesce, Chi Zhang, Ohad Zivan

Content editors: Jenna Pearson, Chi Zhang, Ohad Zivan

Production editors: Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan

Our 2023 Sponsors: NASA TOPS and Google DeepMind

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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#

Global climate can vary on long timescales, but it’s also important to understand seasonal variations. For example, seasonal variations in precipitation associated with the migration of the Intertropical Convergence Zone (ITCZ) and monsoon systems occur in response to seasonal changes in temperature. In this tutorial, we will use data analysis tools in Xarray to explore the seasonal climatology of global temperature. Specifically, in this tutorial, we’ll use the groupby operation in Xarray, which involves the following steps:

Split: group data by value (e.g., month).
Apply: compute some function (e.g., aggregate) within the individual groups.
Combine: merge the results of these operations into an output dataset.

Setup#

# imports
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from pythia_datasets import DATASETS
import pandas as pd
import matplotlib.pyplot as plt

Figure Settings#

# @title Figure Settings
import ipywidgets as widgets  # interactive display

%config InlineBackend.figure_format = 'retina'
plt.style.use(
    "https://raw.githubusercontent.com/ClimateMatchAcademy/course-content/main/cma.mplstyle"
)

Video 1: Terrestrial Temperature and Rainfall#

Tutorial slides#

These are the slides for the videos in all tutorials today

Section 1: GroupBy: Split, Apply, Combine#

Simple aggregations (as we learned in the previous tutorial) can give useful summary of our dataset, but often we would prefer to aggregate conditionally on some coordinate labels or groups. Xarray provides the so-called groupby operation which enables the split-apply-combine workflow on Xarray DataArrays and Datasets. The split-apply-combine operation is illustrated in this figure from Project Pythia:

The split step involves breaking up and grouping an xarray Dataset or DataArray depending on the value of the specified group key.
The apply step involves computing some function, usually an aggregate, transformation, or filtering, within the individual groups.
The combine step merges the results of these operations into an output xarray Dataset or DataArray.

We are going to use groupby to remove the seasonal cycle (“climatology”) from our dataset, which will allow us to better observe long-term trends in the data. See the xarray groupby user guide for more examples of what groupby can take as an input.

Let’s start by loading the same data that we used in the previous tutorial (monthly SST data from CESM2):

Then, let’s select a gridpoint closest to a specified lat-lon (in this case let’s select 50ºN, 310ºE), and plot a time series of SST at that point (recall that we learned this is Tutorial 2). The annual cycle will be quite pronounced. Note that we are using the nearest method (see Tutorial 2 for a refresher) to find the points in our datasets closest to the lat-lon values we specify. What this returns may not match these inputs exactly.

ds.tos.sel(
    lon=310, lat=50, method="nearest"
).plot()  # time range is 2000-01-15 to 2014-12-15

[<matplotlib.lines.Line2D at 0x7fa686c7a830>]

../../../_images/W1D1_Tutorial5_17_1.png

This plot is showing changes in monthly SST between 2000-01-15 to 2014-12-15. The annual cycle of SST change is apparent in this figure, but to understand the climatatology of this region, we need to calculate the average SST for each month over this time period. The first step is to split the data into groups based on month.

Section 1.1: Split#

Let’s group data by month, i.e. all Januaries in one group, all Februaries in one group, etc.

ds.tos.groupby(ds.time.dt.month)

DataArrayGroupBy, grouped over 'month'
12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12.

In the above code, we are using the .dt DatetimeAccessor to extract specific components of dates/times in our time coordinate dimension. For example, we can extract the year with ds.time.dt.year. See also the equivalent Pandas documentation.

Xarray also offers a more concise syntax when the variable you’re grouping on is already present in the dataset. This is identical to ds.tos.groupby(ds.time.dt.month):

ds.tos.groupby("time.month")

DataArrayGroupBy, grouped over 'month'
12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12.

Summary#

In this tutorial, we focused on exploring seasonal climatology in global temperature data using the split-apply-combine approach in Xarray. By utilizing the split-apply-combine approach, we gained insights into the seasonal climatology of temperature and precipitation data, enabling us to analyze and understand the seasonal variations associated with global climate patterns.

Resources#

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

Climatematch Academy: Computational Tools for Climate Science

Tutorial 5: Xarray Data Analysis and Climatology

Contents

Tutorial 5: Xarray Data Analysis and Climatology#

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Tutorial Objectives#

Setup#

Figure Settings#

Video 1: Terrestrial Temperature and Rainfall#

Tutorial slides#

Section 1: GroupBy: Split, Apply, Combine#

Section 1.1: Split#

Section 1.2: Apply & Combine#

Section 1.2.1: Compute the Climatology#

Questions 1.2.1: Climate Connection#

Section 1.2.2: Spatial Variations#

Questions 1.2.1: Climate Connection#

Summary#

Resources#