Climate Data Tasks

Climate Data

Climate change research is supported by both the physical theories underlying the climate and a mountain of data -- all tied together using a large number of simulations and models. The data (CO2, temp, etc...) comes in two forms: direct measurements made by scientists over the last couple of hundred years; and indirect measurements inferred from ice-cores, tree rings, isotope ratios, etc...

There are some really amazing climate data visualisations available, e.g.,

• What's really warming the world (Published in Bloomberg 2015)
• Visualizing Climate Data (Climate.gov)
• Visualization global warming (Mashable 2016)
  • The Climate Spiral Demonstrates the Power of Sharing Creative Ideas (Published 2019) [twitter]

Asides:

• If you're interested in understanding some more about how different factors affect the climate, then the Monash Simple Climate Model is a good place to start.
• Lots more info at climate.nasa.gov and data at climate.gov/maps-data and worldclim.org/data

For the tasks below you have been provided with primary and secondary data in Comma Separated Value (CSV) files -- at the bottom of the page. You have also been given the spreadsheet and Plotly visualisations; however, please do not rely on these too much, as you need to be able to perform and design the visualisations for yourselves.

Task 2 -- Temperature and CO2 over the last 2000 years

In this exercise we'll use the Scripps Merged CO2 data, along with the combined temperature data from the Jones & Mann 2004 paper. Import/copy the two CSV files from the folder below into a spreadsheet and make separate scatter plots. Calculate their moving averages and add to the scatter plots.

You can make a two-axis charts in Excel and Google Sheets, but the two sets of dependent variable (y-axes) data needs to occur at the same dependent variable (x-axis) values. This is not the case with our data, as the CO2 data gives a point only every couple of decades, while the temperature data is every year. You can fix it by using a VLOOKUP or some interpolation.... or you can use something like Plotly which is happy to have to separate scatter plots on the same chart.

Use the skills from Task 1 to produce a visualisation like that below. Think about your design choices: Colour & Opacity, Axes, Grids, Titles, Legends, Data Range, Moving Average / Smoothings, Annotations.

Explore how the graph changes when you adjust the y-axes ranges -- it moves the separate scatter plots up and down with respect to each other and can make it look like CO2 increase precedes or follows the temperature increase.