In my last post, I talked about how I was building a climate model. It was pretty much just your average light hearted blog post. This one is not. The results that I was excited to see were not exactly exciting as such. The results of my model show that Sydney’s temperature is rising at a rate of 0.01°C every year. And over the past 200 years, there has been a 2°C increase in temperature.
This may also seem like not a lot. But with the the climate changing globally, a global average temperature rise of 2°C would mean irreversible changes in our environment according to the UNCC and the threshold used in the Paris Agreement.
Building The Model
My target area was Sydney. Sydney is home to one of the oldest observatories in the country and has records dating back into the 1860s, making this the perfect site to get data from for my model. Usually to see a climate pattern you need at least 30 years of data. But I wanted to see the change since pre industrial times.
The Model
To make my model I used the numpy, pandas, matplotlib, seaborn and scipy libraries to build it. These libraries are super useful for data analysis and generating graphs.
Pandas was used for cleaning the data, extracting what I needed from it, and formatting it in a way that I could make a graph from it. All the other libraries I used were for the formatting of the graphs. Using a matplotlib base, and seaborn to get a better looking graph. numpy and scipy were used to get specific information out of the graph like trendline, slope of trendline and R^2 values.
I loaded in the data on maximum temperature, minimum temperature and precipitation (e.g. rain, snow, etc) Weather and climate data is available on the Bureau of Meteorology website under Climate Data Online. It’s a great resource for this kind of thing as it is easily downloaded in csv file. This kind of data is also available for most countries meteorological organisations.
Results…
My initial plots, weren’t great. They were hard to read due to how much data was on there and the trends weren’t super clear.
Figure 1 (plot shows maximum temperature in Sydney from 1860 to 2020)
Figure 2 (plot shows minimum temperature in Sydney from 1860 to 2020)
Figure 3 (plot of precipitation in Sydney from 1860 to 2020)
The temperature plots show the cyclic changes of the seasons and known climatic events like ENSO, but a general trend is hard to see and I wanted a number to show as a result. So my next plots a trend line added. The trend line shows more distinctly a change over time than these line plots.
These looked like this:
Figure 3 (plot of maximum temperature in Sydney from 1860 – 2024. Intercept: 22.15, R²: 0.0183, Slope per year: 0.013554 °C/year)
Figure 4 (Minimum temperature in Sydney from 1860 to 2024 Intercept: 14.15, R²: 0.0151, Slope per year: 0.010298 °C/year)
Figure 5 (precipitation in Sydney from 1860 – 2024. Intercept: 100.73, R²: 0.0000, Slope per year: -0.006365 °C/year)
Sydney’s Temperature Is Rising…
You might notice that there is a slope of about 0.01 °C/year in the temperature graphs. Though this does not seem like a lot, since pre industrial times, Sydney’s temperature is rising by nearly 2°C, reaching a 1.94°C change.
This doesn’t sound like a lot but there is a noticeable difference between the past average maximum temperature of 22°C and the current average maximum temperature of 24°C.
The precipitation graph has a much less noticeable change. Though admittedly a line graph like this is probably not the most ideal way to show this, but there is a very small amount of change, showing Sydney is receiving about 1mm less rain than pre industrial times on average. This might not seem like a lot, but once again this loss will be felt in the environment.
Future Projects
Though I enjoyed learning how to make models like this, the results it showed are rather grim. I think the next model I will make will measure changes in the Arctic vs Antarctic. This is because I have read a number of papers specifying that the Southern Hemisphere is more susceptible to an increase in temperature due to it being covered in more ocean (oceans absorb a lot of heat) than its’ northern counterpart.
Leave a Reply