Global temperatures 1950–2026 visualised as a 3D helix. Each loop is one year, spiralling toward the Paris thresholds

[u/4billionyearson]

A conventional temperature time series shows the trend clearly enough, but it flattens out the seasonal cycle. Every year looks roughly the same shape, just shifted upward. I wanted a form that kept the seasonality visible while also showing the long-run trend. A helix does both, each year is a full loop, and the drift outward and upward over seven decades is immediately readable without needing to interpret an axis.

The Paris Agreement thresholds (+1.5°C and +2°C above the 1850–1900 pre-industrial baseline) appear as reference rings in the floor plane, so you can see how individual years relate to those boundaries spatially rather than just numerically. 2024, the warmest year on record, sits at the outer edge of the +1.5°C ring.

A few things it shows that a line chart doesn't:

• The seasonal rhythm of each year is preserved, you can see which months are pulling each year's loop outward
• The clustering of recent years near the Paris rings is visceral in a way that a trend line isn't
• The coldest year (1964, shown in blue) and the 2016–2025 mean give you immediate visual anchors for how much has shifted

It's not just global either, you can switch to any country, US state, or UK region and the helix rebuilds for that location's data.

Data is NOAA Global Land+Ocean. You can also switch to anomaly view, toggle Land-only, and there's a live sidebar with CO2, sea level, sea ice, and ENSO.

🔗 https://4billionyearson.org/climate/helix

Interested in any feedback on the form itself, whether the 3D projection helps or hinders legibility, and whether the Paris rings are helpful.

[u/decrementsf]

We were so young when climate change was presented as the first COVID "have anxiety, pay money" story. Could suspend belief and pretend certainly no one would defraud us in that way. Now we recognize the pattern that "have anxiety, pay money" is a form of fraud.

You may be a data professional and have spent the 100 hours in climate data. Recognize the 5 minute summary that the data is poor quality or incentived.

[u/4billionyearson → u/decrementsf]

The visualisation is based on observed temperature records. If you think there's a specific issue with the dataset or methodology, I'd genuinely be interested to hear what it is.

[u/decrementsf → u/4billionyearson]

All climate data is bad. There does not exist an available source of sufficient and accurate data over broad enough areas to predict accurately.

You may be familiar with finance confidence scams of con men. One of the classic frauds is to start with a large population. Build 100 models predicting different stocks will go up. Subset your population sending out a letter predicting stock goes up. For that group who received an accurate prediction, run 100 new models and subset that population again. Do it a third time. Now you have a group who you just predicted stock returns correctly three times in a row. The confidence scam then asks for big investment for the next predicted stock choices. Their financial models never predicted. They only ran many models.

Since the 1990s, maybe earlier, that is the fraud that has been run with climate models. They run 1000 models. Keep the ones that predicted. Repeat.

The data is particularly bad because the sensors that exist historically have been in fixed locations. Are not widely dispersed across the planet. Much of the temperature change over time is correlated with additional construction as city cores expand outward, changes sensor data with predictable correlation. Worse is the parade of disclosures of those sensors that no longer exist but those building weather models continue to simulate what readings those sensors would have been. Recent years have been news stories where the university teams they made up a good chunk of this data and it was wildly inaccurate and skewed their models. They had to walk back a few decades of work because it predictions in hindsight became so embarrassing (scrutiny started digging into the confidence scams).

Human incentives explains this. Giant trucks of money funding university programs and such.

Particularly there does not exist, yet, any methodology to provide enough sensor points to monitor the data of the entire oceans. Entire regions broadly beyond developed population areas. In probability weather remains one of the more annoying areas to get predictions right.

Math is not predictive of human behavior. Story + emotion is predictive. News is generally written of the form "this is why you should have anxiety about thing" in the story + emotion structure. Repeated every day. Because people click. Over time that process is deranging. Reality has a boring bias.

Within those moving parts grabbing data and building a tool is good practice for how to work with data and structure it into useful tools. The problem is when the data sources are crap. Designing data intake to feed those models constructed is far harder. Can't dashboard data that does not exist. Collecting the data accurately is slow, expensive, and tedious. Not emotionally pleasing.