I actually find some 3b1b content really useful. When I was learning linear algebra his course provided a visual representation for the fundamental operations and properties, which helped solidify and consolidate the formal stuff. The LLM videos are great too (ref https://xkcd.com/1838/).
To be fair, I think visual representations are particularly useful for reasoning about vectors and transformations, and I think I retain less from the videos that focus more on a specific advanced problem rather than the fundamentals of a topic. Those videos feel a bit more clickbaity too.
Why 2030? CO2 persists in the atmosphere for hundreds of years. This doesn't end in 2030.
The goal is not to generate hysteria; but to avoid it. Hitting one or more tipping points [1] is orders of magnitude more destructive than prioritising phasing out fossil fuels, and impossible to undo.
But I do understand that some people have reasons not to care about impacts that have an outsized effect on future generations, so to your request: a recent example of a list of impact predictions is the UK Government joint intelligence committee's national security assessment [2].
I think you are missing my point, and the point of the article: they are demonstrating that global temperature change that is not driven by volcanism, solar variation or El Niño is (in all likelihood, given the data) accelerating. They can do this because the effects of volcanism, solar variation and El Niño on global temperature can all be predicted from external measurements.
If you were trying to determine if the quantity of daylight increased over a week in spring, would you account for the differences caused by day and night? What about cloud cover? Or is that just massaging the data?
p.s. the cited methodology has >300 citations in peer reviewed publications, ref Web of Science
They use an established methodology (https://doi.org/10.1088/1748-95
9326/6/4/044022 - the methodology retains the average warming rate over the period since 1970 while smoothing fluctuations) to remove predictable temperature variations so they can isolate the effect they are trying to measure.
Just because they don't know exactly what past global temperatures would have been in the absence of El Niño doesn't mean it's statistically invalid to try and account for it.
Besides, temperature data to 2024 already shows accelerated warming with a confidence level that "exceeds 90% in two of the five data sets".
Add another year or two and it's likely we won't even need to smooth the curve to show accelerated warming at 95% confidence.
> Hyperbolic growth is what happens when the thing that's growing accelerates its own growth.
Quibble: when a growth rate of a metric is directly proportional to the metric's current value you will see exponential growth, not hyperbolic growth.
Hyperbolic growth is usually the result of a (more complex) second order feedback loop, as in, growth in A incites growth in B, which in turn incites growth in A.
The view I hold is that, as bad as the situation is, it's not hopeless and there is a lot that can be done that will make the situation better. "All we can save". I've heard it said in the context of the polycrisis that understanding leads to grief, which leads to action which leads to (solidly founded) hope.
People (and so societies) are hard-wired to be loss averse, which means the facts about what is at stake are more effective drivers of action than the promises of techno-optimism.
Not saying that there are not good optimistic views out there, just that I personally find a realistic view renders many of them quite flat. I think embracing false hope leaves us with a myopic lens through which to frame decisions and probably underprepared to deal with the future.
