Since the claim about what these purported 97% support is vague, the first step is to clearly define what the Hell we are trying to determine scientist might possibly agree on.
WHAT THE HELL is it, EXACTLY, that 97% of scientists are supposed to agree with?
Answer that question first.
NEXT: Define what you mean by a climate scientist. Your local news weather(wo)man who may or may not have a bachelor’s degree in meteorology? Your high school earth science teacher? Dr. Lindzen, who virtually invented the field?
Once you’ve answered those two questions, then you can take a nose count of whom from the set of what we have agreed to call climate scientists agree with what specific propositions.
I am a scientist. A computer scientist, in fact, with a strong background in the natural sciences. I know the models are BS, never mind the data. The most notorious, if not the most egregious, perhaps, is Mann’s model which is the source of the infamous Hockey Stick Graph. Pretty much any dataset put into it will generate the same results. None of those results, however, match reality. That is just on the model side.
It turns out that the data selected for his (Mann’s) adventure in creative modeling is equally cooked, a sort of double-whammy cheat so that if you tested the data or the model independently with an honest model or honest data you’d still end up with his results or something close, and if you didn’t use either you could easily be discredited for allegedly doing what Mann et. al. actually DID do.
Globull warming is nothing but an attempt to shame and blackmail the West into transferring most of their wealth to turd world dictators in the insane hope that it will somehow raise the people under those dictators out of abject poverty.
The 97% claim is a myth. It is the result of an extremist alarmist group divining the beliefs of the authors of a number of papers on climate change by reading those papers rather than by actually asking the authors. Any rational individual can see the problem with that.
When the actual authors were interviewed later the real result was something like 52%. Keep in mind that these are people that make their living doing climate research on your dime, not representative of the larger scientific community. What would you expect them to say?
Princeton Professor Denies Global Warming Theory Jan. 12, 2009
Princeton Physics Professor Discredits Anthropogenic Climate Change Theory Dec. 21, 2016
German scientists reject man-made global warming
Real Scientist Uncover Serious Flaw In Global Warming Data
Physicist Howard Hayden's one-letter disproof of global warming claims [pre-Climategate]
'Consensus' On Man-Made Global Warming Collapses in 2008
Perth electrical engineer's discovery will change climate change debate October 04, 2015
Top Physicist Freeman Dyson: Obama 'Took the Wrong Side' on Climate Change October 14, 2015
Global Warming Petition Project Scientists who reject AGW
31,487 American scientists have signed this petition, including 9,029 with PhDs
Prominent Scientists Declare Climate Claims Ahead of UN Summit 'Irrational' * 'Based On Nonsense' * 'Leading us down a false path' November 19, 2015
This is really simple. Start the model in 1960 and see if it fits today. If so, count me a believer. If not . . . . .
Consensus is not Science, Consensus is Politics or Business or the kiss at the door at the end of a Date.
The best take down of climate science “models” was an older scientist saying that if they were valid, we wouldn’t have competing models with wide ranging projections but a single MODEL that was correct.
How reliable are climate models?
How reliable were election models built by the same data scientists?
Models depend on several things.
First is a large amount of data to establish the norm, the current state.
Then analyze the data. Find the patterns. Patterns occur in small and large granularity. They occur over many dimensions.
Then determine why the patterns exist. Where is the cause and effect. Correlation is not causation is the common warning.
Eventually you use this data to predict the future.
So let’s look where data scientists have massive amounts of data, the best tools available. Most large IT shops have data scientists who build models to predict the response time and cpu consumption of critical systems.
These data scientists collect massive amounts of data on current production systems. They collect massive amounts of test data on new systems not yet in production. They build models that predict the behavior of the new system when it goes into production.
The track record of these highly educated, highly skilled data scientists who (for the most part) lack bias in the desired outcome are only marginally better than the flip of a coin. They have a high rate of false positives and a high rate of false negatives.
Over their objections systems are put in production that perform very well despite the dire predictions. And with their endorsement systems are put in production that bring down the entire business.
These data scientists have far more data than climate scientists. So how reliable are the climate scientists’ models? In computer systems we find out shortly after put in production... if not in test. With climate it will take many decades to validate a model.
The real climate science challenge is for the weatherman in all 50 states to produce an accurate down to the degree weather forecast for the next day.
I trust their intellects more, much more, than I do Bill Nye's.