Always always bet on reality.
The blunt truth is that every, every model is wrong.
The important and difficult thing is determining how wrong the results are and then what conclusions can reasonably be made given the accuracy of the results.
The assumptions have to be factored in. Which effects were simplified, which ones were ignored totally. What is the range of values where certain assumptions are more valid? How good is the data being input into the model?
So, let’s review the bidding. The IPCC and the models on which it premises its version of reality are wrong on rainfall. They are wrong on GHG concentrations and behavior. Models are wrong on Antarctica, on Andean snowpack, on Bangladesh, on ocean temperatures, and wrong on the Northwest Passage. Roy Spencer’s research appears to have affirmed that models are demonstrably and fatally wrong on the threshold question of climate sensitivity.
Huh... that's not confidence inspiring.
Well what are they going to do with data of that reliability?
Other than that, why, those models are perfectly wonderful tools on which to premise trillion-dollar economic decisions!
That's not good. Not good at all.
Computer models make very nice graphs and can create reams of data. Their parameters can be varied to make lots of studies and comparisons...
but if the model itself is deficient, then all you have is very pretty garbage.
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