6 ms·

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

by toxik 4y ago

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.

NationalPark 4y ago
Can we predict weather that well? The 10 day forecasts my phone gives are mostly ok, not great, and often change at the last minute.
- toxik 4y ago
  That was my point.
js8 4y ago
Do you also find it hard to believe that casino can say your chance of winning roulette is 1/37 (and you're gonna lose money), despite the fact every roll is random? Both are just law of large numbers.
- prottog 4y ago
  Strange to compare the random results of a spinning wheel three feet wide in an air-conditioned room to a planetwide system, isn't it?
  - johnnymorgan 4y ago
    Not really, they both rely on chance to win
  - orbifold 4y ago
    Well in both cases you can ignore a lot of unknown physical details and still reach a valid conclusion. In the case of the climate this is done in terms of energy balance for example. The details are super complicated but you also have historical data that you can use to calibrate and validate your models on.
thwayunion 4y ago
If climate models were predicting the temperature of your city on March 21, 2120 then your intuition here would be wise. But that is not at all what these models are predicting. They are predicting global average annual temperatures and other gross, planet-scale statistics. "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.
- johnnymorgan 4y ago
  The point he is making is the disconnect between models and reality. 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.
  - 4y ago
avaldez_ 4y ago
for starters, weather and climate and different concepts. it's easier to predict the long term probability distribution of a coin toss than a single event result.
bertil 4y ago
Can you predict the height of the next person that will turn a corner? You likely won’t any less than three inches off. 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.
p0pcult 4y ago
Aggregates (global temp average over a year) are generally more predictable than the underlying datapoints (daily local weather). Source: am data scientist.
myrmidon 4y ago
Compare with how hard it is to exactly predict if a patient is gonna survive the next day-- while assuming that *anyone* (for now) is gonna be dead in 120 years is a pretty safe bet. And predictions about life expectancy become easier in aggregate, too.
guerrilla 4y ago
Why would you be confused about this? Temperature of a function of energy. We're talking about a global average temperature. Energy is being captured and retained. That raises the global average temperature. The specifics don't matter. Predicting the weather on the other hand is very complicated because you're asking what specifically will happen in a specific place. The variables going into that are many and even if measured extremely accurately, it's not good enough because weather is chaotic.
- toxik 4y ago
  This is reductionist, the specifics of what goes on at the surface do matter, that is the whole point of the climate change debate. How much chemical energy can we dig up and how do the byproducts affect the radiative ability of the planet (ie green house gases).
  - defrost 4y ago
    > How much chemical energy can we dig up and how do the byproducts affect the radiative ability of the planet (ie green house gases). 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.

by toxik 4y ago

NationalPark 4y ago

Can we predict weather that well? The 10 day forecasts my phone gives are mostly ok, not great, and often change at the last minute.

toxik 4y ago
That was my point.

js8 4y ago

Do you also find it hard to believe that casino can say your chance of winning roulette is 1/37 (and you're gonna lose money), despite the fact every roll is random? Both are just law of large numbers.

prottog 4y ago
Strange to compare the random results of a spinning wheel three feet wide in an air-conditioned room to a planetwide system, isn't it?
- johnnymorgan 4y ago
  Not really, they both rely on chance to win
- orbifold 4y ago
  Well in both cases you can ignore a lot of unknown physical details and still reach a valid conclusion. In the case of the climate this is done in terms of energy balance for example. The details are super complicated but you also have historical data that you can use to calibrate and validate your models on.

thwayunion 4y ago

If climate models were predicting the temperature of your city on March 21, 2120 then your intuition here would be wise. But that is not at all what these models are predicting. They are predicting global average annual temperatures and other gross, planet-scale statistics. "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.

johnnymorgan 4y ago
The point he is making is the disconnect between models and reality. 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.
- 4y ago

avaldez_ 4y ago

for starters, weather and climate and different concepts. it's easier to predict the long term probability distribution of a coin toss than a single event result.

bertil 4y ago

Can you predict the height of the next person that will turn a corner? You likely won’t any less than three inches off. 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.

p0pcult 4y ago

Aggregates (global temp average over a year) are generally more predictable than the underlying datapoints (daily local weather). Source: am data scientist.

myrmidon 4y ago

Compare with how hard it is to exactly predict if a patient is gonna survive the next day-- while assuming that *anyone* (for now) is gonna be dead in 120 years is a pretty safe bet. And predictions about life expectancy become easier in aggregate, too.

guerrilla 4y ago

Why would you be confused about this? Temperature of a function of energy. We're talking about a global average temperature. Energy is being captured and retained. That raises the global average temperature. The specifics don't matter. Predicting the weather on the other hand is very complicated because you're asking what specifically will happen in a specific place. The variables going into that are many and even if measured extremely accurately, it's not good enough because weather is chaotic.

toxik 4y ago
This is reductionist, the specifics of what goes on at the surface do matter, that is the whole point of the climate change debate. How much chemical energy can we dig up and how do the byproducts affect the radiative ability of the planet (ie green house gases).
- defrost 4y ago
  > How much chemical energy can we dig up and how do the byproducts affect the radiative ability of the planet (ie green house gases). 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.