If AGW theorists were weathermen...?

Question

....what are the odds they would build a weather model predicting evening temperatures in excess of 200 degrees F, based upon readings collected between 9:00am and 10:00am?

Answer 1

if 9am was 25 years ago, and 10am is the present...  then I suppose you are correct.

But weathermen can't tell you accurately what your weather will be like in 30 days, 120 days, 1 year or 10 years.  If weathermen can't, then how can AGW or GW believers?  Based on Trends alone?  

AGW and GW is unprovable with the enormous lack of data, 120 years out of 4 billion?  All that faith, so little proof.   Kind of like the theory of creationism.

Answer 2

Freeman Dyson - Professor Emeritus of Physics - Princeton University said it best:



Short answer:

My first heresy says that all the fuss about global warming is grossly exaggerated. Here I am opposing the holy brotherhood of climate model experts and the crowd of deluded citizens who believe the numbers predicted by the computer models. [...] But I have studied the climate models and I know what they can do. The models solve the equations of fluid dynamics, and they do a very good job of describing the fluid motions of the atmosphere and the oceans. They do a very poor job of describing the clouds, the dust, the chemistry and the biology of fields and farms and forests. They do not begin to describe the real world that we live in. The real world is muddy and messy and full of things that we do not yet understand. It is much easier for a scientist to sit in an air-conditioned building and run computer models, than to put on winter clothes and measure what is really happening outside in the swamps and the clouds. That is why the climate model experts end up believing their own models.

And now the long answer: From a computer programmer.

What people do not understand is that there is no proof of "Man-Made" Global Warming without using irrelevant computer models. Yes computer models have a place in engineering but are utterly useless at fortune telling, I mean "climate prediction". With engineering you can build and test in the real world to confirm the computer model's accuracy. You can do not such thing with the planet Earth and it's climate. You cannot build a planet and it's atmosphere to "test" your computer climate model.

I am a computer analyst (the person who wrote this is) and can make a computer model do whatever I want by "tuning it" (adjusting variables by guessing until I get the answer I want or think is right).

GIGO: Garbage in = Garbage out

Computers need exact information and the exact procedures to process that information to get accurate answers, without that you get useless results, period. There is no way around this. Everything must be 100% understood and 100% accurate.

Computing incomplete, biased or flat out wrong data (guesses and assumptions) based on poorly understood climate physics into a "model" will give you junk results. Testing a model against past climate is an advanced exercise in curve fitting, nothing more and proves absolutely nothing. What this means is you are attempting to have your model's output match the existing historical output that has been recorded. For example matching the global mean temperature curve over 100 years. Even if you match this temperature curve with your model it is meaningless. Your model could be using some irrelevant calculation that simply matches the curve but does not relate to the real world. With a computer model there are an infinite number of ways to match the temperature curve but only one way that represents the real world. It is impossible for computer models to prove which combination of climate physics correctly matches the real world. Do not be fooled this logic is irrefutable by anyone who understands computer science and computer modeling.

To make matters worse it is not computer scientists creating these models but natural scientists coding them using Fortran. These natural scientists do not even begin to have the basic understanding of computer science or proper coding practices. Sloppy and buggy code is littered inside these climate model programs yet there is next to no accountability for any of this. How do you separate a programming error from a temperature anomaly? How can you replace observational data with a complex mathematical equation? You can't.

Processing more complex data in more complex ways via guessing gives you more complex junk results. But since the models have been "tuned" (guesstimated or deliberately altered to get the results they want) they get results that "seem" likely or even convincing to the average computer illiterate, except it is all based on a complex serious of guesses and assumptions and absolutely meaningless for prediction.

Nothing is emotional about computers they are pure logical machines, 1 + 1 must = 2. Imagine trying to use random numbers to get a right answer on a calculator but you do not know if you are to add or multiply those numbers and you have no way to confirm that "right answer" except to wait 50-100 years. Sound crazy? Welcome to Global Climate Modeling.

All the computer illiterates are convinced that because something is done on a "super computer" that costs "millions of dollars" it is infallible. The more complex the model, the more "mysterious" it seems to the average person. The public gives computer climate models this mystical aura because they are largely computer illiterate about how they actually work and when they hear the term "computer" they do not want to sound or feel stupid, so they nod their heads and go along with it.

What the modelers do is they keep playing with the numbers in a much more complex way until they think they guess right. A useless exercise. These same climate model computers are used to predict your weather and you know how accurate they are. But d**n! Al Gore and Gavin Schmidt can certainly tell your what the climate will be 50-100 years from now! Give me a break. Don't be fooled that modeling climate is different than the weather or one is more accurate than the other long term. The difference is simply a matter of resolution and scale.

Why are we not turning to models to predict the future for everything? Because they can't, not even remotely. Some of them work "sort of" for the weather in very, very short term results (1-3 days) until all the data they are processing that is wrong combined with all the data they are missing and the millions of variables they are not accounting for start to kick in and grow exponentially the farther out the model runs and wham - the model is wrong. No kidding, there are simply way too many variables that they cannot account for and the computer power necessary to even start to take these variables into account does not exist.

You are expected to believe that they can "model" the climate 50-100 years in the future when they cannot even give you accurate weather 3 days out? Don't be fools, I do this for a living, (well the person who wrote this does) Computer Models cannot predict the future with anything as complex as the Earth's climate.

Answer 3

Climate and weather are actually two different fields of study, and the level of predictability is comparably different. Think of this as the difference between trying to predict the height of the fifth wave from now that will come splashing up the beach versus predicting the height of tomorrow's high tide.

Climate is defined as weather averaged over a period of time, generally around 30 years. This averaging over time removes the random and unpredictable behavior of weather. This by no means says that it is necessarily easy to predict climate changes, but clearly seizing on the weatherman's weekly forecast failures to cast doubt on a climate model's 100 year projection is an argument of ignorance.

Answer 4

Weather and climate models harness computers for simulation.  Both have the problem that they are greatly flawed since the science is not well understood.  However, the models are quite different and not based on similar data sources.

Weather models mostly suffer from grid points (e.g. weather stations, airports) not close enough to each other and too many guessed constants (i.e. fudge factors) introduced just to approximate meaningful calculations/results.  Too much "local weather" occurs between grid points.  Climate models suffer from a lack of everything (e.g. accurate data, solid science backing, too many unknowns).

Technology has helped improve predictions a bit but we're far from truly accurate forecasts.  For instance, Doppler radar was a leap forward when it was introduced during the mid-80's.  Our scientific understanding has been steadily inching forward and there has been much progress in the last ~70 years.  However, our current knowledge base can still be considered as relatively infantile.  A true understanding of atmospheric physics or even climate science is quite distant.