Quote:
Originally Posted by
El Kabong
Quote:
Originally Posted by
bcollins
Quote:
Originally Posted by
El Kabong
Quote:
Originally Posted by
bcollins
The thing is NASA and NOAA got caught fudging the numbers as have other "scientists" ....so what do they have to do to get you to question them? They got caught LYING, FACT.
Fact? Gotta be careful with those. That's the whole point. Even more important is to check your sources.
Fox's Doocy: NASA fudged data to make the case for global warming | PunditFact
Again, the experts are experts for a reason. Rather than jumping on every story that shares your belief, the better idea is to keep an open mind and not try to arrive at erroneous conclusions. All scientists do make mistakes, but the community as a whole is just waiting for such high profile mistakes - that's the stuff careers are made of.
They've used
COMPUTER MODELS which have....has one been right yet? Anyway they used those computer models rather than the actual data. Sorry, they've been lying and skewing the numbers, and shit if THAT doesn't sway you look at the laws the "green" politicians try to pass to curb CO2 emissions....anything WE (the United States) doesn't put in the atmosphere China, Russia, and India have no problem picking up that slack
AND THEN SOME!!!
It's a hoax, a façade, a flimflam, bogus, untrue, etc
Ok - now we are into my realm of expertise. I think you have some fundamental misconceptions about the science here. I'll try to explain it a little, without getting into the math too much.
With "pure" mathematics, results are proven using nothing but logic, beginning with a fundamental collection of axioms. This is pretty much the only type of hard "proof" in any physical science.
With applied mathematics - which the science of climatology relies upon - the results are more along the lines of "evidence" that supports a conclusion. This is the type of result you can expect to see in the other physical sciences - physics, chemistry, biology, geology, etc. There are some exceptions, but pure mathematics is pretty much the sole bastion of "proof".
Now, the (classical) modeling process essentially consists of using systems of differential equations to simulate a physical process. There are a wealth of DEs out there that apply to a given physical process and many of these have been around for centuries. These descriptions of physical phenomenon in such a precise characterization are among some of the crowning achievements of humankind and rational, logical thinking. What's nice about these equations is that anyone on the planet with an understanding of how they work can use them to predict a given phenomenon - it has been independently verified so many times that they are beyond reproach. There are entire buildings filled with literature on these partial differential equations. If you don't believe this, I'll be happy to provide more details.
When creating a mathematical model of this type, the adaptation to a particular scenario is usually done through modification of parameter values. This is where the data comes in. When creating a model of the type used by climate scientists, the data will be used to give a range of parameter values. The scientist will then run a large number of simulations on the computer, typically varying the parameters in a systematic manner and observing the prediction of the model. In other words, the computer models depend on the data - there can be no separation of the two!
Any time a new and more accurate way of generating data comes into play, the models are typically adjusted to take into account any new information. Thus, the science here isn't the type of pure mathematics, where it is proven once and that's it - here, the results are constantly updated as new information comes to light.
What's cool is that this is only the "classical" approach to modeling. The guys on the cutting edge are working on newer and more accurate modeling techniques. One newer method is called stochastic modeling (newer means the last century or so) which incorporates uncertainty quantification into the differential equations themselves. This allows for the concept of "randomness" to be built into the model itself - which is obviously useful in a field such as climatology! The downside is that the mathematical analysis becomes much, much more difficult, as well as the computational methods of simulation. One of my advisers runs a group at the Oak Ridge lab in this area. It is very cutting edge and very new and EXTREMELY difficult. With the advent of supercomputers, this approach will hopefully yield more and more accurate models in the future. There are even more modeling techniques coming up the pipe - data driven modeling techniques are another bright light - so this is a continually evolving science.
Now, you can see how complex this can all be, so when you say that NASA/NOAA have been "fudging" the data, it is an incorrect statement, or at least a very inaccurate one. As the report above indicates, they have updated techniques used to derive that data, but now it matches data found INDEPENDENTLY by multiple other sources.
It is interesting to note that the source of the claim that NASA/NOAA "fudged" data has been called out by other climate change skeptics as using shoddy analysis.
You are correct in your statement that we have no control over the emission regulations in place for other countries. However, this has no effect on the data itself and is furthermore irrelevant to the conversation.