An article published today in the Bulletin of the American Meteorological Society may be the last interview with the father of chaos theory, MIT professor Dr. Edward Lorenz, and has essential implications for climate modelling. In the 2007 interview, Dr. Lorenz confirms that chaos theory proves that weather and climate cannot be predicted beyond the very short term [about 3 weeks], and that even with today's state-of-the-art observing systems and models, weather [or climate] still cannot be predicted even 2 weeks in advance.
Dr. Lorenz notes that although other fields that deal with complex, non-linear systems have accepted the implications of chaos theory, some meteorologists and climatologists remain reluctant to accept the implications of chaos theory, namely that long-term climate forecasting is impossible.
This guy started with a fundamental misunderstanding of the function of climate models and then ran with it.
Not that you'd question what he said even if I explained it to you.
So here you have a mathematical determination that it isn't possible to use computer models to predict climate for years into the future. It's a theory that is widely accepted by other fields yet some climate scientists refuse to acknowledge its implications.
The reason being, of course, in my opinion, that those guys are getting millions in funding for claiming that they can predict future climate, it's a hotly politicized field that gets a lot of money for toeing that political line, and they know we'll have to wait 30 to 50 years or so to find out they were wrong.
You're confusing climate modeling with weather forecasting, talking about trends and observations and what it may mean in general sense decades or more from now is not the same as predicting that a storm will occur in 3 weeks.
The same principles apply to both weather and climate when it comes to computer models. The fellow was clearly referring to climate models.
Not really, no. There are pretty important differences.
Ok, I'll bite. Like what?
The same principles apply to both weather and climate when it comes to computer models. The fellow was clearly referring to climate models.
A weather forecast basically says "This is what we think the weather will be at this point at this time." It's a prediction. We know the inputs (current weather conditions) and therefore say this will be the result.
A climate model does not do this.
Rather, a climate model says "we think the average (weather factor) will be somewhere in this range during this period." Furthermore it's not even really a prediction in the sense that it says "this is what will happen." Rather, climate models say "If X inputs occur, we expect the result to, on average, be within this range X% of the time."
So, when you look at a climate model giving you expected temperature and its uncertainty range, they're saying, "we expect the global average temperature to be within this range 95% of the time." (assuming that model's graph uses two standard deviations anyway)
The bold item above? This is a significant portion of the error you guys keep pointing at in climate models over the recent period. The models were based on a different solar output than actually occurred. X Input didn't occur: the sun dipped to a lower output than anticipated. Therefore it's literally to be expected that climate models would somewhat overshoot actual temperatures. There's more to it, obviously. Climate is really complicated. There will always be more to research and improve models with.
A weather forecast basically says "This is what we think the weather will be at this point at this time." It's a prediction. We know the inputs (current weather conditions) and therefore say this will be the result.
A climate model does not do this.
Rather, a climate model says "we think the average (weather factor) will be somewhere in this range during this period." Furthermore it's not even really a prediction in the sense that it says "this is what will happen." Rather, climate models say "If X inputs occur, we expect the result to, on average, be within this range X% of the time."
So, when you look at a climate model giving you expected temperature and its uncertainty range, they're saying, "we expect the global average temperature to be within this range 95% of the time." (assuming that model's graph uses two standard deviations anyway)
The bold item above? This is a significant portion of the error you guys keep pointing at in climate models over the recent period. The models were based on a different solar output than actually occurred. X Input didn't occur: the sun dipped to a lower output than anticipated. Therefore it's literally to be expected that climate models would somewhat overshoot actual temperatures. There's more to it, obviously. Climate is really complicated. There will always be more to research and improve models with.
That's a lot of hand waving on your part. How do the models for climate and weather actually differ? Climate models attempt to predict precipitation and other parameters over time and space as well, you know, and you might have noticed that weather forecasts are issued in terms of ranges and probabilities.
Climate models take current conditions and then attempt to calculate what the conditions will be in the next time frame. Then they take those predictions and use them to estimate conditions in the time frame after that, and so on and so forth. Weather models use exactly the same method. Any errors in estimating conditions from one time frame to the next tend to get magnified, which is why weather models are known to be inaccurate after so many iterations -- no more than 10 days or so. (Their predictions have, unlike climate models for the most part, actually been compared to what happens.)
Except that's not exactly what they're doing.
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