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There's no record and/or proof that man can predict global weahter 100 years in advance accurately yet you claim it is much easier than predicting short term weather fluctuations accurately. Interesting.
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Climate models can reconstruct the past 130 years with a pretty high accuracy. Given that the models are based off of physics and not statistics (i.e. they don't use training data) this says mountains about their usefulness.
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No it doesn't, that's just plain wrong. As others have pointed out the models better fit past data. That doesn't say a thing about their predictive value.
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Well in the past they knew the exact date and time that certain volcanoes exploded. And as I explained before deviations in the predictions can be attributed to short term noise like Pinatubo, a strong El Nino, etc. This noise was accounted for but obviously there are time differences. Knowing exactly when and where the short term noise hits will ALWAYS make the past reconstructions more accurate than the future predictions. This would be true even if God himself came down and endorsed the models as being 100% correct.
Your statement of: "doesn't say a thing about their predictive value" is rather strong. I would understand your statement if the model was statistical but it's not. I would understand if you said it was "incomplete" but you didn't.
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I thought I had jumped up and down, screamed at the top of my lungs, etc. with caveats stating that I see a great of value in climate science and the development of climate models. Apparently I haven't done this enough so I'm now officially jumping up and down, screaming at the top of my lungs and proclaiming to the world that I see a great deal of value in climate science and the development of climate models.
I think this is another caveat I've made but not strongly enough. I'm officially jumping up and down and screaming the following at the top of my lungs. The most dire predictions made by climate models may be correct and thus erring on the side of caution is a prudent to follow IMO. We just don't know if the most dire of predictions are correct.
With those caveats in mind. I understand fully that climate models aren't intended to make accurate short term predictions about weather and such. My statements about the predictive value of climate models is not criticism, they're facts. The predictive value of climate models are unproven because they're relatively new. Statistics vs. physics have nothing to do with the idea that they're unproven. At the end of this post I'm going to post an article that encapsulates the ideas that I've been stating about the models. Here's a simple way to understand what I'm talking about, there are climate models that have vastly different predictions and I'm quite sure that they fit well with past data. Models fit well with past data by definition or they're not models.
I'd say that the chances that current climate models weigh all the factors that go into climate correctly is small. Note that this entertains the possibility that greenhouse gas emissions may be more destructive than what the models tell us. I also believe that the chances that the models get better as we learn more over time (a long time albeit) is very,very high.
Here's the article which encapsulates a lot of thoughts on the climate models. I'm not necessarily agreeing with the portion about the motivation to get research money part but more with the ideas of the complicated nature of the models and the physical influences on climate. Saying climate models are unproven isn't a negative criticism it's a fact. And it's a fact that should be more than obvious to anyone who actually understands the nature of climate science to at least some degree; understands something about models and what they're used for; understands that climatic periods are relatively long; and that climate science is relatively new. Very new when we compare climate history of the world and the onset of climate models.
Numerical Models, Integrated Circuits and Global Warming Theory
To me here's the crux of the problem with taking what the models predict as fact:
Finally, the computer calculation can commence: A unit of time (a second, minute, day) is assumed to pass and the computer calculates the next "state" of the model based on the initial conditions, the boundary conditions and the other equations of the model. This process is repeated again and again, with the new state being the initial condition for calculating the subsequent state, until e.g. 100 years has passed.
Errors can accumulate rapidly. Let's list some of the factors that must be included (by no means an exhaustive list):
Solar flux
Gravity, Pressure
Temperature
Density
Humidity
Earth's rotation
Surface temperature
Currents in the Ocean (e.g., Gulf Stream)
Greenhouse gases
CO2 dissolved in the oceans
Polar ice caps
Infrared radiation
Cosmic rays (ionizing radiation)
Earth's magnetic field
Evaporation
Precipitation
Cloud formation
Reflection from clouds
Reflection from snow
Volcanoes
Soot formation
Trace compounds
And many, many others
Even if mathematics could be developed to accurately model each of these factors, the combined model would be infinitely complex requiring some simplifications. Simplifications in turn amount to judgment calls by the modeler. Can we ignore the effects of trace compounds? Well, we were told that trace amounts of chlorofluoro compounds had profound effects on the ozone layer, necessitating the banning of their use in refrigerators and as aerosol spray propellants. Can we ignore cosmic rays? Well, they cause ions (electrically charged molecules) which affect the ozone layer and also catalyze formation of rain-drops and soot particles.
As with all models, it is perilous to ignore factors in the absence of complete experimental data which might have otherwise have significant effect.
Unproven != wrong.
Unproven != right.
Unproven != worthless.