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Genuine question here, if predicting the weather for a localized area such as a small region Cali is "tea leaves and Tarot cards", why are we so confident in th

Genuine question here, if predicting the weather for a localized area such as a small region Cali is "tea leaves and Tarot cards", why are we so confident in the prediction of the climate for the entire planet?

js2 10y ago
Q: If forecasts can’t get next week’s weather right, how can we trust predictions for decades or centuries from now? A: Weather and climate are not the same. Weather is individual, day-to-day atmospheric events; climate is the statistical average of those events. Weather is short-term and chaotic and is thus inherently unpredictable beyond a few days. Climate is long-term average weather and is controlled by larger forces, such as the composition of the atmosphere, and is thus more predictable on longer timescales. For the same reasons, a cold winter in one region does not disprove global warming. As an analogy, while it is impossible to predict the age at which any particular man will die, we can say with high confidence that the average age of death for men in industrialized countries is about 75. The individual is analogous to weather, whereas the statistical average is analogous to climate. https://www.climatecommunication.org/questions/predictions/ https://www.climatecommunication.org/questions/predictions/
- 234dd57d2c8dba 10y ago
  Thank you, that helped quite a bit. It makes sense, as predicting when a single individual will die (a single area will have <x> weather), is different than, the observed trend is males live to avg. 75 years. Makes sense to the layman!
- refurb 10y ago
  OK, I'm going to play devil's advocate. That answer doesn't really seem all that good since it doesn't address the predictability at all. To use the human lifespan argument, yes we can say the average lifespan for men is 75, but that's looking backwards. How good would we be at predicting future life expectancy? I'd say we're probably pretty bad at it. Also, yes, we're trying to predict the average temperature of the earth with climate models. However, that average is determined by the climate in a number of different areas. Someone please explain this to me.
  - Cogito 10y ago
    I'll take a stab :) You ask "how good are we at predicting future life expectancy?" Unfortunately I can't do an analysis right now, so hopefully this qualitative discussion will help answer it for you. If you look at historical life expectancy, grouped by cohort, you'll see a relatively smooth function with easily identifiable trends. So, group everybody into the year they were born, and graph their average lifespan. Past trends do not guarantee future performance, however a smooth function implies an underlying order to these observations. This is the core thesis everything else stems from then; we assume there is a relationship between the year someone was born, and their average lifespan. Now a function relating just the year they were born to their average lifespan is almost certainly too simplistic. Where they were born, their socioeconomic status, and so much more will affect that number. The problem we have is that many of these parameters are hidden to us. Even worse, we can't even say for sure what all the parameters are! What we can do, however, is estimate the effect of these parameters, and even estimate the effect of parameters we don't know exist. This process is inherently fuzzy, as statistics and modelling from less than perfect knowledge must be, but the models that have been created have proved to have great predictive power. The existence and general profitability of the life insurance industry is evidence to that. We use models all the time, and for the most part this goes unquestioned. Regardless of your model of the sun and planets, it better predict the sun rising tomorrow, or else it's got some pretty big gaps. If someone told you they predicted the sun would not rise tomorrow, you'd be rightfully discredulous. But you'd be as equally discredulous if someone told you it was impossible to predict the future, so we really don't know if the sun will come up or not. We test our models on their predictive power. Even if our models were bad, we don't simply throw them away because they are not perfect. We work to refine them and make them more accurate. Asimov's essay on the relativeness of 'wrong' is well worth the read. So, life expectancy is a decent model, and is constantly being improved upon as we learn more about the world. Something may come along and cause us all to die, or live forever, but that remote possibility is no reason to throw our hands in the air and say the whole exercise is pointless. Similarly, our climate models may be inaccurate or not account for some unknown future event, but that is not a reason to stop refining them, nor a reason to say "we can't know the future so this is pointless."
    - 10y ago
- caf 10y ago
  Another analogy: we can't look at a single atomic of Uranium and tell when it's going to decay - but that doesn't stop us knowing enough about the bulk nucleonics of Uranium to be able to build sophisticated reactors.
ajb 10y ago
Imagine a cannonball, on its flight through the air. Attached to it is a feather. We can't predict the fluctuations of the feather, as it is whipsawed by the airstream of the cannonball, but this has almost no effect on the trajectory of the composite.
taneq 10y ago
For the same reason that, even though it's 'tea leaves and tarot cards' trying to predict the exact geometry of surface waves in a square meter of ocean, we can predict the time and height of a high tide with high accuracy.
thisrod 10y ago
Do a search for "Lorentz attractor". It's the result of an early numerical weather model. The weather is a particular point on the twisted surface; it is impossible to calculate what point you'll reach at a given time, for reasons that you'll find in a search for "Lyapurnov exponent". The climate is the butterfly shape. If you run the simulation with different parameters, you can look and see how this changes. The Lorentz equations are much simpler than a realistic weather model, but the same principles apply to the complicated models, and to the laws of physics that the atmosphere actually follows.

js2 10y ago

Q: If forecasts can’t get next week’s weather right, how can we trust predictions for decades or centuries from now? A: Weather and climate are not the same. Weather is individual, day-to-day atmospheric events; climate is the statistical average of those events. Weather is short-term and chaotic and is thus inherently unpredictable beyond a few days. Climate is long-term average weather and is controlled by larger forces, such as the composition of the atmosphere, and is thus more predictable on longer timescales. For the same reasons, a cold winter in one region does not disprove global warming. As an analogy, while it is impossible to predict the age at which any particular man will die, we can say with high confidence that the average age of death for men in industrialized countries is about 75. The individual is analogous to weather, whereas the statistical average is analogous to climate. https://www.climatecommunication.org/questions/predictions/ https://www.climatecommunication.org/questions/predictions/

234dd57d2c8dba 10y ago
Thank you, that helped quite a bit. It makes sense, as predicting when a single individual will die (a single area will have <x> weather), is different than, the observed trend is males live to avg. 75 years. Makes sense to the layman!
refurb 10y ago
OK, I'm going to play devil's advocate. That answer doesn't really seem all that good since it doesn't address the predictability at all. To use the human lifespan argument, yes we can say the average lifespan for men is 75, but that's looking backwards. How good would we be at predicting future life expectancy? I'd say we're probably pretty bad at it. Also, yes, we're trying to predict the average temperature of the earth with climate models. However, that average is determined by the climate in a number of different areas. Someone please explain this to me.
- Cogito 10y ago
  I'll take a stab :) You ask "how good are we at predicting future life expectancy?" Unfortunately I can't do an analysis right now, so hopefully this qualitative discussion will help answer it for you. If you look at historical life expectancy, grouped by cohort, you'll see a relatively smooth function with easily identifiable trends. So, group everybody into the year they were born, and graph their average lifespan. Past trends do not guarantee future performance, however a smooth function implies an underlying order to these observations. This is the core thesis everything else stems from then; we assume there is a relationship between the year someone was born, and their average lifespan. Now a function relating just the year they were born to their average lifespan is almost certainly too simplistic. Where they were born, their socioeconomic status, and so much more will affect that number. The problem we have is that many of these parameters are hidden to us. Even worse, we can't even say for sure what all the parameters are! What we can do, however, is estimate the effect of these parameters, and even estimate the effect of parameters we don't know exist. This process is inherently fuzzy, as statistics and modelling from less than perfect knowledge must be, but the models that have been created have proved to have great predictive power. The existence and general profitability of the life insurance industry is evidence to that. We use models all the time, and for the most part this goes unquestioned. Regardless of your model of the sun and planets, it better predict the sun rising tomorrow, or else it's got some pretty big gaps. If someone told you they predicted the sun would not rise tomorrow, you'd be rightfully discredulous. But you'd be as equally discredulous if someone told you it was impossible to predict the future, so we really don't know if the sun will come up or not. We test our models on their predictive power. Even if our models were bad, we don't simply throw them away because they are not perfect. We work to refine them and make them more accurate. Asimov's essay on the relativeness of 'wrong' is well worth the read. So, life expectancy is a decent model, and is constantly being improved upon as we learn more about the world. Something may come along and cause us all to die, or live forever, but that remote possibility is no reason to throw our hands in the air and say the whole exercise is pointless. Similarly, our climate models may be inaccurate or not account for some unknown future event, but that is not a reason to stop refining them, nor a reason to say "we can't know the future so this is pointless."
  - 10y ago
caf 10y ago
Another analogy: we can't look at a single atomic of Uranium and tell when it's going to decay - but that doesn't stop us knowing enough about the bulk nucleonics of Uranium to be able to build sophisticated reactors.

ajb 10y ago

Imagine a cannonball, on its flight through the air. Attached to it is a feather. We can't predict the fluctuations of the feather, as it is whipsawed by the airstream of the cannonball, but this has almost no effect on the trajectory of the composite.

taneq 10y ago

For the same reason that, even though it's 'tea leaves and tarot cards' trying to predict the exact geometry of surface waves in a square meter of ocean, we can predict the time and height of a high tide with high accuracy.

thisrod 10y ago

Do a search for "Lorentz attractor". It's the result of an early numerical weather model. The weather is a particular point on the twisted surface; it is impossible to calculate what point you'll reach at a given time, for reasons that you'll find in a search for "Lyapurnov exponent". The climate is the butterfly shape. If you run the simulation with different parameters, you can look and see how this changes. The Lorentz equations are much simpler than a realistic weather model, but the same principles apply to the complicated models, and to the laws of physics that the atmosphere actually follows.