The system is equally complex for both, but while a random, unpredictable event can completely change the weather tomorrow, over a longer time those events won’t matter as much anymore and the system is much easier to predict at this larger scale.
That’s just based on probability: If you roll 1 die, I can’t tell you what face it will show. But if you rolled 100 dice 600 times, I can tell you pretty accurately how many of each face you’ll see: about 10’000 each. This looks like a way more complex system, but it isn’t: it’s the original repeated 60’000 times. Could my prediction be wrong? Yes. Is it likely to be far off? No.
(Going beyond ELI5 here: Based on a Chernoff upper bound, the probability than any face shows up more often than 10500 times is less than 0.025%. At just 11000 of one face, you’re at a chance of less than 10^(-13)%.)
Of course the prediction isn’t accurate down to the single die anymore, but we’re talking about 60’000 dice rolls, nobody cares. Just like in climate models nobody cares whether it’s going to rain in Hinterschlumpfhausen, Germany on April 1, 2033 at 6 pm. Only the large scale predictions are actually important (are there 10’000 or 11’000 6s? / will it be on average 2.5° or 2° warmer?)
Here’s an analogy. Suppose you have one six-sided die and one twenty-sided die. You roll both of them and keep track of the average values of each die.
You can’t predict in advance what numbers you’ll get on one roll in particular. But over many rolls, you can be confident that the average value of the twenty-sided die will be higher than the average value of the six-sided die.
Predicting the weather is like predicting the value of one particular roll. Predicting the climate is like predicting the average of many rolls.
Imagine tracking a molecule of air as it moves around inside a balloon: its speed, trajectory, spin, etc. Now try to predict how that molecule will behave as it moves through that balloon as it bounces off of the rubber and into other air molecules.
I hope you can understand that this would be incredibly, INCREDIBLY hard to do with accuracy given how many variables you’d need to track, and how small variations can throw off your estimates very rapidly.
BUT… what if instead of tracking the behavior of individual molecules, we track the behavior of all those trillions of air molecules as a whole: temperature, pressure, volume, etc. This is much easier, because the individual behaviors of all those particles will average out. This is how we get the ideal gas law: PV = nRT.
Another way to think of it is to consider predicting the results of a coin flip: how many times will it land on heads? You’ll only be able to get it right 50% of the time. But what if we consider 10 coins, and consider the results as a whole: how many times will the heads come up between 40% and 60% of the time? That’s a lot easier, and can be mathematically tracked along what’s called a [normal distribution](https://www.fourmilab.ch/rpkp/experiments/statistics.html). Furthermore, when you increase that number to 100 coins, or 1000 coins, that normal distribution gets narrower and narrower, because as you add more coins the system “averages out” more and more.
The larger a system is, the more accurately you can gauge its “average state.” Tomorrow’s weather is a much smaller system than the overall climate (which can be roughly seen as an average of a region’s weather patterns over a long period of time). The idiosyncrasies of daily changes to the weather effectively average out over a long period and is easier to predict, and trends are easier to observe.
(1) Predictions about future climate tend to be very general (things like “average worldwide temperature increase of X degrees”) while weather predictions tend to be a lot more precise (“You’ll see 3-5 inches of rain in this city between 9am and 1pm tomorrow.”) The more precise the prediction, the easier it is to miss.
(2) We don’t really yet know if predictions about the climate are accurate because they’re largely predictions of events that are still to come.
Because complex systems regress towards a median, in accordance with a model’s predictions, especially over long intervals, whereas short-term predictions are subject to greater volatility. In other words, it’s much easier to say that 1,000 cars will go down the road, than it is to say that car X will take the next off-ramp.
I think the predictions people are looking for regarding weather are far more precise.
For example the prediction about climate that comes to my mind is “the average temperature of earth will rise”.
(Stuff like a rising water level are direct conclusions of this one very vague prediction.
But if you are talking about weather you want to know what will the temperature be next Tuesday at exactly 4 pm in this very specific city.
Another thing to keep in my mind with weather you get a new chance to check the predictions everyday.
While with climate it needs a few years to get an answer.
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