40 Comments
author

I use thermometers, not thermostats :-)

I use them because they directly measure temperature, pure and simple.

Proxies like tree-rings, while allowing for longer duration (since some trees have existed longer than the oldest thermometers) have confounding factors: the amount of rain, the availability of nutrients, other local ecological stresses, etc.

Expand full comment

Nothing to add - I just can't get over how refreshing it is to come across people who think clearly! Thanks SO much.

Expand full comment

What do I see from your data? I'm no expert but I see very little going on beyond "normal" oscillations.

Expand full comment

What I see is a varied rising or falling overall, depending on the location. If anything, it is fascinating to see some areas’ cooling trends amidst others trending warmer. I live in Redlands, not far from PS, and I think it’s getting warmer, but I know for a fact two things: it was always hot from time to time (childhood here in the 60s), a solid 5 month season of cough-inducing smog (not mostly gone), and I’m willing to recognize I am influenced by the constant barrage of news telling me the earth is burning up.

Personally, I am suspicious of human hubris that would somehow poopoo solar influence and think we are the ne plus ultra of influencers on planetary climate.

Expand full comment

I apologize but if you don’t mind can you can Into further depth on why you used thermostats over proxies(tree rings, etc) to determine long term climate temperature?

Expand full comment

Weather is complex. Temperature is just one indicator. What about rainfall (same rain in longer period is better for vegetation)? Wind (dries vegetation)? Humidity?

Expand full comment

Thanks for this very interesting analysis.

The least squares fit to the medians is heavily influenced by very few data points. My suggestion is that you use a robust regression, such as a Theil-Sen estimator. You can also do a bootstrap on this estimator to obtain a confidence interval on the (robust) slope. Finally, you can do a histogram of the (robust) slopes across the different locations.

By the way, it would be better to subtract the local average temperatures before you compute these slopes, rather than the global average, as you have done. This way, the slopes are easier to compare.

And finally, a non-statistical remark: there is clearly a bias in only using airport data for your analysis. This is because the micro climate at airports is non-representative both because of the construction materials used, as well as because the locations were carefully selected to reduce the influence of certain environmental factors. This does not discredit the analysis but it is a caveat that should at least be stated.

Expand full comment

You say “The global median temperature (of the entire dataset) is uniformly subtracted from every data point, to keep things relative to an arbitrary x-axis of 0.” and then seem to ask why most of the cities don’t vary much from 0. What does an equivalent plot of that global median temperature show? If it is rising over this 80 year period and you are subtracting that out, then why would you expect to see big changes in the city data?

Expand full comment

> How do you even define "global temperature"?

This is an interesting question. Doing some Googling, it looks like the answer is, very roughly:

1. Collect all the time series from around the world that you can.

2. Define a grid that covers the globe.

3. Average the temperature time series located within each grid cell, to get a single time series for that cell.

4. Average all the time series across all the cells to get a single "global average temperature."

Here's an example of this process, including all the data cleaning steps I didn't include above: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2005JD006548

Expand full comment

I was curious if there is a known effect specific to airport. One commenter already speculated it could be changing runway materials. Or perhaps air conditioner upgrades that increased efficiency. Or who knows. I’ll search some more but in the meantime, found this. Want to run your process on some airports in Greece? https://link.springer.com/article/10.1007/s10584-019-02634-z

Expand full comment
Jun 22, 2022·edited Jun 22, 2022

You maybe missing the trees for the forest. Global or local mean or median temperature is a nebulous (no pun intended) indicator. It's the increases in daily weather variance, incomprehensibly-fast melting of major glacial masses, rising sea levels, and frequency and intensity of storms, fires, famines, and species' extinctions that are the "forest". These are consequences of anthropogenic GHGs that reduced Arctic sea ice (nearing an inevitable blue ocean event) and increased global sea thermal energy absorption which destabilized the jet stream from more predictable cycles. GHGs lead to a -11 F snowpocalypse in Austin, megadrought in US West and SW, "Hurricane Alley" shifting eastward, 100 F in the high Arctic town of Verkhoyansk, water trains of India, and slumping and melting of Siberian and other permafrost areas releasing absurd amounts of methane.

Expand full comment

Why does the display and trendlines of the data use the median of temperature data instead of the mean? The mean would be the natural choice as a higher average temperature necessitates a higher overall energy density, while the median can disguise the temperature changes in extremes. (For example, there seems to be a trend in newfoundland of the coldest temperatures growing warmer over time, but the median doesn't change much at all.)

Expand full comment

Interesting approach! You might find my own "from scratch" analysis worth reading, it used NASA's dataset and compared to a random walk of yearly deltas:

https://statisticsblog.com/2012/12/03/the-surprisingly-weak-case-for-global-warming/

Expand full comment

I had a similar question a few years ago and grabbed the weather data for the Sydney observatory itself (not Sydney airport). The observatory had a longer time series than the airport FWIW. Data sources here: http://www.bom.gov.au/climate/data/stations/

My analysis is here:

http://blog.ifost.org.au/2017/01/farewell-to-cold-winters-and-hello.html

There are github links in there for a Jupyter notebook to reproduce it.

I was doing a different analysis (basically, what was happening to minimum and maximum daily temperatures), and it sure looks like there's a clear effect there.

I'm struggling to come up with why there's a difference in our analyses, though.

Idea 1: Could there be an airport-specific effect happening there? Change of runway materials or airport design? (The Australian bureau of meteorology data is just a zip of a CSV file, so I suspect it wouldn't require any changes in your R code. That would you let confirm whether the phenomenon is airport-specific.

Idea 2: Maybe if I re-do my analysis on Sydney airport and I'm able to confirm a growing loss-of-winter effect there too (it's not that far away from Sydney observatory, so hard to see how there couldn't be)... maybe your results and the loss-of-winter effect can both co-exist? I'm not sure what that means though.

Expand full comment

Very cool. Your charting gave me a few ideas for a project I'm working on that's similar but only for the US (https://wxrecords.com). The data source is the RCC-ACIS API (https://www.rcc-acis.org/docs_webservices.html).

Another fun tool is the query tool attached to the API - https://xmacis.rcc-acis.org/

Expand full comment

Air has a much lower heat density than water or stone, so temperature readings are going to vary faster and by larger amounts compared to more dense mediums. Combined with the small rise of global temperatures that the scientists have measured, I think any global change is simply being lost in the noise of the daily air temperature readings you're looking at.

Expand full comment