What I always find so hard to believe is that, knowing how well we can predict weather and other complicated systems, climate change science somehow is able to make 1/10 centigrade precision predictions on time horizons of decades and centuries.
"Average annual global temperature in a given year" and "temperature in my city tomorrow" are fundamentally very different types of predictions.
Often it is easier to accurately forecast gross dynamics on a long time frame than it is to forecast precise dynamics the exact same process over a short time frame.
You don't even need to understand the math or physics to see why this is intuitively true.
Consider e.g. predicting minutiae about the behavior of a fetus over the next week ("how many fist clenches", "how many kicks") vs. predicting which week the baby will be born -- the latter is substantially easier than the former despite the longer time frame.
Or, more to the point, consider forecasting the position of a particular cloud of molecules in a pot of water being bought to boil vs forecasting the temperature of the water in the pot in 5 minutes. The latter is hilariously trivial -- a small child can be taught how to do this with excellent accuracy. The former is some horrendously difficult phd level fluid mechanics and even then hard/impossible.
In some sense, an educated intuition is exactly the opposite of yours -- it'd be surprising if we were this good at extremely fine-grained weather prediction but couldn't guess the annualized average temperature of the entire system in 50 years. The latter is a much simpler statistic because the timescales and physical scales take a lot of the difficult stochasticity out of the forecasting problem.
Also when are people held accountable for their models being wrong and the output that comes from that.
Climate models has a terrible track record and have failed to materialize over and over again.
We have environmental crises all over the place which we should be focused on. The two are not the same thing and climate activism seems to not care about that at all, ie the issue of EVs and their super non green batteries or the near slave labor in terrible conditions resource extraction.
There is a good reason to be skeptical of the regulations derived from these models when they are wrong all the time.
But in general, the changes we’ve seen so far are in line with predictions and there’s no reason to be more skeptical than normal of this science.
(As a side note, the resource extraction required to run a petrol engine is also not very green and often connected to human rights abuses. Not to downplay the issues in lithium sourcing, there are horrible conditions that need to be fixed, but the solution is not “oil”.)
The "no oil" people may be right; that would make me a horrible person. But, make the prediction -- I'll "convert" if it comes true. Science for the win.
https://climate.nasa.gov/news/2943/study-confirms-climate-mo...
None of the things you mention after this sentence have anything to do with climate models.
> cenovus up, pembina up, taiwan semi up
At best, this is equivalent to saying health nuts have been disproven because you made money buying Coca Cola stock.
The general relationship between increased CO2 and warming has been known since the 19th century, though they were off by a factor of two on the slope back then. Modern climate models have a lot of moving and it's not clear they're actually better than the "Assume a spherical^H^H^H^H homogeneous atmosphere" models but the basic physics and general trend line are hard to ignore.
Casting blame is a common denier tactic [1] used despite the models being useful and accurate [2].
The "whataboutisms" you mention are another common tactic. [3] Blaming EV batteries is a red herring; they have much lower lifecycle emissions than gas-based engines. [4]
[1] https://skepticalscience.com/climate-models.htm [2] https://climate.nasa.gov/news/2943/study-confirms-climate-mo... [3] https://www.cambridge.org/core/journals/global-sustainabilit... [4] https://arstechnica.com/cars/2021/07/electric-cars-have-much...
Can you predict the average height of all the residents of your city? A demographer can given an answer that is a lot more accurate.
And predictions about life expectancy become easier in aggregate, too.
These are known stable parameters, we have a long recorded history of resource use and the insulative properties of gases in a mixture are tabled.
It's also well known in numerical modelling and physics why a number of systems have easy to predict long term coarse behaviour while also having short term impossible to predict fine grained behaviour, this exactly addresses your question about how can climate (coarse long term) be predicted when weather (short term, fine grained) is difficult.
See the Dzhanibekov Effect and the work of both Smale and Lorentz for insight.
A rotating (about intermediate axis) wing nut has a determined long arc trajectory of its CoG (centre of gravity) .. but an unpredictable short term tumble about its CoG.
Source: am data scientist.