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It's because the model results project more snow and less snow. More rain and less rain. More severe weather and less severe weather. The example cited by the G

It's because the model results project more snow and less snow. More rain and less rain. More severe weather and less severe weather. The example cited by the GP illustrates this - 2 years ago during the drought, climate models predicted continuing severe droughts for California. Now, they predict continuing increased precipitation. The results haven't actually changed, just different subsets are being reported. The global results are better than the regional results, but tend to run hotter than observations, suggesting they don't yet account for the full range of natural variability. Statistics don't help you predict a coin flip (actually I think tails is ever-so-slightly more likely) Edit: the point is not that a particular model can't make predictions. Some models will actually be correct. We just don't know which regional models are correct, and because of the range of results, they cannot all be correct. Therefore, as a whole, the regional models have no predictive power. The stock market is a good example. Lots of people make lots of money betting on their models, but lots of people lose lots of money when their model stops working. There is also a general rising trend underlying the whole system.

KirinDave 9y ago
The model that predicts your lifespan predicts people with more and less life, with varying probability. It actually does have predictive power for your lifespan, but it does so in a probabilistic way. The idea that a distribution doesn't have power because it is a distribution is a bizarre opinion to encounter in a community that prides itself on education, computer science, business acumen, machine learning, and informed decision making.
- jackmott 9y ago
  it is pretty similar to the latest meme on climate change which is that imperfect models are not suitable for making policy decisions. so I guess we have perfect models for economics and social issues and geopolitical issues now.....so that we can make any policy decision.
jackmott 9y ago
the model predicts more heat, not less. the model predicts less ice, not more. period. so, you've got that.
cjbprime 9y ago
> (actually I think tails is ever-so-slightly more likely) Why do you say this? For which coin?
- kbutler 9y ago
  Heads tends to be heavier (not sure if this is a general rule). Discussion of US penny: http://www.smithsonianmag.com/science-nature/gamblers-take-note-the-odds-in-a-coin-flip-arent-quite-5050-145465423/ http://www.smithsonianmag.com/science-nature/gamblers-take-n... But with a coin flip, the initial side up is a bigger bias.
  - KirinDave 9y ago
    … Its precisely statistics that can help us identify these trends in coin flips and if there actually is observable effects to these weights.
    - kbutler 9y ago
      Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. None of those apply to climate models, especially regional models. Statistics make it easy to mislead yourself that the precision of your numbers implies correctness - like the joke, How do you know economists have a sense of humor? They use decimal points.
      - KirinDave 9y ago
        > Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? No. Never did. > Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. ... This is actually the opposite of the truth. When we understand the system fully and can fully enumerate it's interal states and every relevant input, that's when there is no point in statistical modeling. I really think you fundamentally misunderstood your statatistics education.
digitalzombie 9y ago
> Statistics don't help you predict a coin flip (actually I think tails is ever-so-slightly more likely) Uh... yes it does. We have laws of large number and expected value. You just have to do a binomial distribution and use the data to fit your model. Statistical learning is a thing. Element of statistical learning is a classic made by several famous statisticians who are responsible for LASSO, RIDGE, Elasticnet regressions. Bayesian network is also a thing. Likewise with Bayesian Hierarchical Modeling (HM). A coin flip is actually the easiest thing to predict and for advance statistical learning model such as HM they start out with Binomial Distribution and usually the example is a coin flip.

KirinDave 9y ago

The model that predicts your lifespan predicts people with more and less life, with varying probability. It actually does have predictive power for your lifespan, but it does so in a probabilistic way. The idea that a distribution doesn't have power because it is a distribution is a bizarre opinion to encounter in a community that prides itself on education, computer science, business acumen, machine learning, and informed decision making.

jackmott 9y ago
it is pretty similar to the latest meme on climate change which is that imperfect models are not suitable for making policy decisions. so I guess we have perfect models for economics and social issues and geopolitical issues now.....so that we can make any policy decision.

jackmott 9y ago

the model predicts more heat, not less. the model predicts less ice, not more. period. so, you've got that.

cjbprime 9y ago

> (actually I think tails is ever-so-slightly more likely) Why do you say this? For which coin?

kbutler 9y ago
Heads tends to be heavier (not sure if this is a general rule). Discussion of US penny: http://www.smithsonianmag.com/science-nature/gamblers-take-note-the-odds-in-a-coin-flip-arent-quite-5050-145465423/ http://www.smithsonianmag.com/science-nature/gamblers-take-n... But with a coin flip, the initial side up is a bigger bias.
- KirinDave 9y ago
  … Its precisely statistics that can help us identify these trends in coin flips and if there actually is observable effects to these weights.
  - kbutler 9y ago
    Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. None of those apply to climate models, especially regional models. Statistics make it easy to mislead yourself that the precision of your numbers implies correctness - like the joke, How do you know economists have a sense of humor? They use decimal points.
    - KirinDave 9y ago
      > Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? No. Never did. > Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. ... This is actually the opposite of the truth. When we understand the system fully and can fully enumerate it's interal states and every relevant input, that's when there is no point in statistical modeling. I really think you fundamentally misunderstood your statatistics education.

digitalzombie 9y ago

> Statistics don't help you predict a coin flip (actually I think tails is ever-so-slightly more likely) Uh... yes it does. We have laws of large number and expected value. You just have to do a binomial distribution and use the data to fit your model. Statistical learning is a thing. Element of statistical learning is a classic made by several famous statisticians who are responsible for LASSO, RIDGE, Elasticnet regressions. Bayesian network is also a thing. Likewise with Bayesian Hierarchical Modeling (HM). A coin flip is actually the easiest thing to predict and for advance statistical learning model such as HM they start out with Binomial Distribution and usually the example is a coin flip.