Using “home made” statistics

Since I am still at the Fields Institute in Toronto, enjoying a workshop on Impacts of Climate Change on Economics, Finance, and Insurance, I wanted to share some experience, from this summer. After three years of lockdown because of the covid situation, the family has been able to travel, and we went to France, so that our kids could see their grand-parents (the last visit was a long time ago). And it was hot, very hot. While I was chating with my dad, about the weather, and told me that he had a lot of connected devices in the house, including measures of the temperature. One of the device was in a place where nothing did not really change over time. So I thought it could be sufficient to get robust data. My goal was to see how the popular IPCC graph was on real data

When I got the data, I did plot them, and did compare the distribution back in 2012, and in 2022 (or to be honest, half 2021-half 2022). As for the IPCC graph, I assume a Gaussian distribution.

As expected, there is a clear shift to the right (that is “climate change”). But the most scary part, was actually the linear trend,

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -637.30455   80.44650  -7.922 3.01e-15 ***
x              0.32273    0.03988   8.092 7.72e-16 ***
---
Signif. codes:  0***0.001**0.01*0.05 ‘.’ 0.1 ‘ ’ 1

with a slope of 0.322, meaning that the average temperature is increasing by 0.322 degrees per year ! That is more than 3°C over the past ten years ! Let me write it again : in a house, +3°C on average over the past ten years.

I thought there were some issues with the data. So I tried to collect some official data, and since there were no official records in their village, I did use the data from Lyon (which is 80 kilometers from their house).

The shift on the right is confirmed here, but unfortuntely, I could not get data after 2020.  Now

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -567.27953   96.26577  -5.893 4.17e-09 ***
x              0.28803    0.04776   6.031 1.81e-09 ***
---
Signif. codes:  0***0.001**0.01*0.05 ‘.’ 0.1 ‘ ’ 1

And here again, I have a slope close to 0.3. So again, mainland, about +3°C over the past 10 years was observed. You might not find that scary, but I do think that it is scary !


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