Tutorial 6: Compute and Plot Temperature Anomalies

Tutorial 6: Compute and Plot Temperature Anomalies#

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

Production editors: Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan

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: 20 minutes

In the previous tutorials, we have explored global climate patterns and processes, focusing on the terrestrial, atmospheric, and oceanic climate systems. We have understood that Earth’s energy budget, primarily controlled by incoming solar radiation, plays a crucial role in shaping Earth’s climate. In addition to these factors, other significant long-term climate forcings can influence global temperatures. To gain insight into these forcings, we need to look into historical temperature data, as it offers a valuable point of comparison for assessing changes in temperature and understanding climatic influences.

Recent and future temperature change is often presented as an anomaly relative to a past climate state or historical period. For example, past and future temperature changes relative to pre-industrial average temperature are a common comparison.

In this tutorial, our objective is to deepen our understanding of these temperature anomalies. We will compute and plot the global temperature anomaly from 2000-01-15 to 2014-12-1, providing us with a clearer perspective on recent climatic changes.

Setup#

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

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

Figure Settings#

Video 1: Orbital Cycles#

Section 1: Compute Anomaly#

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

We’ll compute the climatology using xarray’s .groupby() operation to split the SST data by month. Then, we’ll remove this climatology from our original data to find the anomaly:

Let’s try plotting the anomaly of a specific location:

tos_anom.sel(lon=310, lat=50, method="nearest").plot()
plt.ylabel("tos anomaly")

Text(0, 0.5, 'tos anomaly')

../../../_images/a4a330ce014a8037ac12acb393465143043701db336f56bd005a917703c65e07.png

Next, let’s compute and visualize the mean global anomaly over time. We need to specify both lat and lon dimensions in the dim argument to .mean():

unweighted_mean_global_anom = tos_anom.mean(dim=["lat", "lon"])
unweighted_mean_global_anom.plot()
# aesthetics
plt.ylabel("Global mean tos anomaly")
plt.xlabel("Time (months)")

Text(0.5, 0, 'Time (months)')

../../../_images/36cdecd1ba85931a8a34e5051005b07f7ca199c94b22da7c5ffd1565f246566f.png

Notice that we called our variable unweighted_mean_global_anom. Next, we are going to compute the weighted_mean_global_anom. Why do we need to weight our data? Grid cells with the same range of degrees latitude and longitude are not necessarily same size. Specifically, grid cells closer to the equator are much larger than those near the poles, as seen in the figure below (Djexplo, 2011, CC-BY).

Therefore, an operation which combines grid cells of different size is not scientifically valid unless each cell is weighted by the size of the grid cell. Xarray has a convenient .weighted() method to accomplish this.

Let’s first load the grid cell area data from another CESM2 dataset that contains the weights for the grid cells:

Let’s calculate area-weighted mean global anomaly:

weighted_mean_global_anom = tos_anom.weighted(
    areacello).mean(dim=["lat", "lon"])

Let’s plot both unweighted and weighted means:

unweighted_mean_global_anom.plot(size=7)
weighted_mean_global_anom.plot()
plt.legend(["unweighted", "weighted"])
plt.ylabel("Global mean tos anomaly")
plt.xlabel("Time (months)")

Text(0.5, 0, 'Time (months)')

../../../_images/f2a82dfec0e98dd371dd4316cc4d26919c45c4d5959d38a552451e15f215988c.png

Questions 1: Climate Connection#

What is the significance of calculating area-weighted mean global temperature anomalies when assessing climate change? How are the weighted and unweighted SST means similar and different?
What overall trends do you observe in the global SST mean over this time? How does this magnitude and rate of temperature change compare to past temperature variations on longer timescales (refer back to the figures in the video/slides)?

Click for solution

Summary#

In this tutorial, we focused on historical temperature changes. We computed and plotted the global temperature anomaly from 2000 to 2014 and compared a weighted and unweighted time series of the global mean SST anomaly. This helped us enhance our understanding of recent climatic changes and their potential implications for the future.

Resources#

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