Assignment: 1D and 2D data

Assignment: 1D and 2D data#

With one exception, you should be able to copy and paste code from the previous notebook and make minimal code modifications (e.g. changing the name of a variable, add a small computation…) to solve the following exercises.

Don’t think too “complicated”!

Importing the modules#

This one is easy. I’ll do it for you:

# Import the tools we are going to need today:
import matplotlib.pyplot as plt  # plotting library
import numpy as np  # numerical library
import xarray as xr  # netCDF library
import cartopy  # Map projections libary
import cartopy.crs as ccrs  # Projections list
# Some defaults:
plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size

Invariant data#

Download the ERA5_LowRes_Invariant.nc file, store it alongside the temperature one. Open it with xarray, and explore its content

# Your answer here

How many variables does the file have? What are their names and units? What do they represent?

# Your answer here

Topography according to ERA5#

Plot the model topography on a map in the Equal Earth projection. Interpret what you see.

# Your answer here

Compute the highest and lowest point on Earth according to these ERA5 data. Compare with the actual values. Discuss.

# Your answer here

Bonus question: we didn’t learn about how to find the location (in longitude and latitude) of the maximum value. Now use a web search or generative AI to find out how to do this. The task is to find out the longitude and latitude of the highest grid point in ERA5.

# Your answer here

Water covered areas on Earth according to ERA5#

Plot the land-sea mask on a map in the Equal Earth projection. Interpret what you see.

# Your answer here

Compute the percentage (%) of ocean cover accross latidudes (100% means there is only ocean at this specific latitude). Make a zonal plot showing your result. Interpret what you see.

# Your answer here

Now compute the % coverage of water over the planet. Hint: you will need to remember how to compute averages on a sphere! Compare your result with a google search.

# Your answer here

Temperature data#

Read the temperature data from the lesson:

# Your answer here

Timeseries#

Now select the temperature timeseries at two or three locations of your choice. Compute and compare their average temperature (in °C), and plot them on a single graph.

# Your answer here

2D timeslices#

Finally, select the decades 2015-2024 and 1979-1988 out of the 3D temperature data. Compute their time average. This should give you two 2D data arrays, yes? Each of them is a decadal average temperature. Now plot the difference between the last and the first decade of the ERA5 period. Interpret what you see!

# Your answer here