FLASH!! NOAA drives stake through heart of alarmists!!!

[Crick]

Dave, if you can't follow the conversation, why not just sit quietly in the corner with your six-shooters and your comic books and wait till the grown-ups are done talking?

 

[SSDD]

Dave, if you can't follow the conversation, why not just sit quietly in the corner with your six-shooters and your comic books and wait till the grown-ups are done talking?

You claimed that data from 50 years ago was inaccurate and needed to be changed to make it accurate...he asked how you know it to be inaccurate and you think that is an irrelevant question?

You simply make claims with no expectation that you should have to substantiate them. Lets hear a rational, scientifically valid reason for systematically lowering the temperature record of 50 years ago.

For that matter, lets hear a rational, scientifically valid justification for the arbitrarily selected temperature upon which temperature anomalies are based. Are you sure that that temperature was not simply the temperature at the end of a cooling period? What valid scientific reasoning establishes that temperature as the "normal" temperature for planet earth?

 

[itfitzme]

You couldn't carry a pencil for a real mathematician.. The purpose of a trend line is to estimate the 1st derivative of a data set. That is always a USEFUL estimate in that sense.. Even in the presence of data with high deviation or noise. No different than a selective filtering operation to accentuate different derivatives of the process.. Problem with poor math education is that they tell you WHAT TO DO -- but rarely explain what the tools are actually for.. Where your "education" failed you is that nobody is ATTEMPTING to find the underlying equation for the Earth temperature versus Time curve BECAUSE THERE ISN'T ONE.... So the "errors" in fit are MEANINGLESS. It is simply to establish the dtemp/dtime.... Go be a victim...

Or better yet -- go bug the market analysts on Wall Street for applying trend lines to high variance data.. Tell THEM they can't do that unless their p-values are appropriate..

@Flac

And yet, for allll your ranting, you've said nothing that changes the fact that the OP and the article that it highlights are irrelevant because the graph is meaningless. It is mesningless because the regression has no meaning.

Every statistic that is calculated using sum of squares has an associated p-value that defnes the significance of the statistic. And every coefficient has a conficdence imterval. Lacking those, the regression line is meaningless. And everything hinges on that. The line is insignificant. Watt's conclusions are insignificant. The OP is insignificant. And your opinions are insignificant.

If you cannot address the fundamentals of the science correctly, then nothing you have to present has any significance.

And it is obvious to everyone here that all you can do is whine.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem.. WHEN to apply certain tests and how to INTERPRET those tests is more important than mindlessly following the functions offered in Excel..

So --- If I run a 3 or 4 year low pass filter over the temp chart data to "smooth" the high frequency data --- Have I affected the 1st derivative (slope) estimate? (remember that error term of the regression is ASSUMED to have specific properties for the P-test)

After I filter and the variance is REDUCED -- fit that to the best trend line.. Is the P-Value BETTER or WORSE?

Actually -- the number of samples is far more important to the significance of the slope estimation in this case than goodness of the raw linear fit of the data.

You just add more bs on top of bs.

The trend line is a best estimate. A best estimate has a confidence interval. It also has a p-value, the probability of getting the same result from random chance. No published confidence interval and no p-value, no meaning.

The statistics are affected by a number of factors, sample size and variance are two. The slope is as well.

You csn prove that the trend line is precise, just by coming up with the p-value and confidence interval. It's that simple. No need for bullshit about low pass filters. Just do the stats correctly.

 

[daveman]

Dave, if you can't follow the conversation, why not just sit quietly in the corner with your six-shooters and your comic books and wait till the grown-ups are done talking?

Oh, eat shit, you pompous stuffed shirt. Your cult has been caught cooking the books more than once. Get it? AGW is based on lies. We prove it to you, and what's your response? Post more of their lies.

It's a tropism. No independent thought required. Which is good, because you're not capable of it.

 

[itfitzme]

You couldn't carry a pencil for a real mathematician.. The purpose of a trend line is to estimate the 1st derivative of a data set. That is always a USEFUL estimate in that sense.. Even in the presence of data with high deviation or noise. No different than a selective filtering operation to accentuate different derivatives of the process.. Problem with poor math education is that they tell you WHAT TO DO -- but rarely explain what the tools are actually for.. Where your "education" failed you is that nobody is ATTEMPTING to find the underlying equation for the Earth temperature versus Time curve BECAUSE THERE ISN'T ONE.... So the "errors" in fit are MEANINGLESS. It is simply to establish the dtemp/dtime.... Go be a victim...

Or better yet -- go bug the market analysts on Wall Street for applying trend lines to high variance data.. Tell THEM they can't do that unless their p-values are appropriate..

@Flac

And yet, for allll your ranting, you've said nothing that changes the fact that the OP and the article that it highlights are irrelevant because the graph is meaningless. It is mesningless because the regression has no meaning.

Every statistic that is calculated using sum of squares has an associated p-value that defnes the significance of the statistic. And every coefficient has a conficdence imterval. Lacking those, the regression line is meaningless. And everything hinges on that. The line is insignificant. Watt's conclusions are insignificant. The OP is insignificant. And your opinions are insignificant.

If you cannot address the fundamentals of the science correctly, then nothing you have to present has any significance.

And it is obvious to everyone here that all you can do is whine.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem.. WHEN to apply certain tests and how to INTERPRET those tests is more important than mindlessly following the functions offered in Excel..

So --- If I run a 3 or 4 year low pass filter over the temp chart data to "smooth" the high frequency data --- Have I affected the 1st derivative (slope) estimate? (remember that error term of the regression is ASSUMED to have specific properties for the P-test)

After I filter and the variance is REDUCED -- fit that to the best trend line.. Is the P-Value BETTER or WORSE?

Actually -- the number of samples is far more important to the significance of the slope estimation in this case than goodness of the raw linear fit of the data.

The confidence interval of the slope is dependent onn the variance of the data

"Confidence intervals are preferred to*point estimates*and to interval estimates, because only confidence intervals indicate (a) the precision of the estimate and (b) the uncertainty of the estimate."

The slope coefficient is a point estimate. It is the average value for the slope estimate.

Inference in Linear Regression

Yale University

The MINITAB output provides a great deal of information. Under the equation for the regression line, the output provides the least-squares estimate for the constant*b0*and the slopeb1. Since*b1*is the coefficient of the explanatory variable "Sugars," it is listed under that name. The calculated standard deviations for the intercept and slope are provided in the second column.

Predictor.....Coef........StDev......T...........P
Constant.....59.284....1.948......30.43....0.000
Sugars......-2.4008....0.2373....-10.12...0.000

S = 9.196.....R-Sq = 57.7%......R-Sq(adj) = 57.1%

There it is again from Yale, this time with Minitab. The results of a linear regression include the standard deviation and p-value for the coefficients.

No stdev and p-value, no meaning.

One standard deviation is about 68% confidence level Three standard deviations is about 95% confidence level. To get 99.7% level of confidence, requires three standard deviations

"In*statistics, the*68–95–99.7 rule, also known as the*three-sigma rule*or empirical rule, states that nearly all values lie within three*standard deviationsof the*mean*in a*normal distribution.

68.27% of the values lie within one standard deviation of the mean. Similarly, 95.45% of the values lie within two standard deviations of the mean. Nearly all (99.73%) of the values lie within three standard deviations of the mean."

The slope coefficient is a point estimate. The 95% confidence interval for the slope estimate is

b2 +/- 2*stdev

Clearly, the linear regression line is only a point estimate, the mean, of all of the slopes that fit the date. Oh, here is the thing. The probability that the slope is exactly equal the point estimate is zero.

I've gotta go to work. The next lesson will be on calculating the stdev of the slope coefficient.

Yammering on and on as you do means nothing. You can 'this, that, or the other thing" to the end of time. It isn't a substitute for the facts and doing the work.

Without the variance and probability for the point estimate, including the slope coefficient, it means nothing.

 

[IanC]

itfitzme's biggest problem with trying to claim CO2 causation of temperature increase is the 'hidden factor fallacy (or fraud)'.

it's like a wife saying that the family is going into $250 debt per month because her husband spends $250 on booze every month. perfect correlation. but then the husband says it's her fault because she spends 250 bucks a month on cigarettes. again perfect correlation. it depends on who defines the factors to be analyzed, and even whether all the factors are known. CO2 doesnt work so well in explaining the MWP or the LIA. but for the 80's and 90's it was perfect. now, not so much.

 

[IanC]

A safe assumption is that the newer dataset is more accurate. That would be the paranoia-free position.

The difference between the two, as explained by the people who did the adjusting, is that such adjustments correct for errors and differences such as time-of day, location changes, analog to digital and general magic ;-),

Why go back and alter data from over 50 years ago? What is the reason for altering it?

Wow...

If you don't care whether its accurate, why do you care if it even exists?

adjustments circa 2000

adjustments circa 2013

what is interesting to me is what doesnt seem to be on the graphs. no marked spike in corrections when a wholesale conversion to digital thermometers and 'morning readings'. at least in the 2000 graph the adjustments levelled off a bit after 1990. but that plateau disappeared in the 2013 edition.

the early work into TOBS used coarse decadal estimate figures for time of temperature readings. those coarse estimates didnt show up then, and they dont show up now on individual stations that should only have one TOBS adjustment per change in observation time.

 

[itfitzme]

itfitzme's biggest problem with trying to claim CO2 causation of temperature increase is the 'hidden factor fallacy (or fraud)'.

it's like a wife saying that the family is going into $250 debt per month because her husband spends $250 on booze every month. perfect correlation. but then the husband says it's her fault because she spends 250 bucks a month on cigarettes. again perfect correlation. it depends on who defines the factors to be analyzed, and even whether all the factors are known. CO2 doesnt work so well in explaining the MWP or the LIA. but for the 80's and 90's it was perfect. now, not so much.

I'm not claiming anything except,watts,graph is ,wrong and west westwall doesn't know what he's talking about.

Oh, and you as well.

 

[itfitzme]

So, back to walleyes question if a lowpass filter has an associated probability and a confidence interval.

The answer can be had simply by asking of a ramping sinusoid can be sampled such that the result is a decreasing line.

Of walleyes is half as smart as he think he is, he can answer that easily. He will surely squirm away as usual.

The answer is absolutely. Every descrete time sampling filtere has a probability of yielding an output that isn't representative of the original signal. Digital communications requires statistics and probability as part of signal analysis and filter design. Every communications engineer studies statistics.

A sample of yeary temp is a descrete time sample.

That just proves that, yes, Watts graph is meaningless because no confidence interval and p- value is given.

The problem, which I'll get to later, is even worse because it is obvious by inspection thatr the confidence interval is so large thay the linear regression is meaningless.

Being an asshole, waleyed, doesn't make you any less wrong. I get that all that adrenaline makes you feel like you are. What is does do is make you both an asshole and stupid. There's a common phrase for that ..... what is it? You are a what?

Do you really need me to show you a sampled ramped sinusoid that gives a decreasing line, or can you manage that on your own?

I'd like to see you stop being intentionally wrong. I doubt that willl happen.

Well, back to work.

 

[westwall]

So, back to walleyes question if a lowpass filter has an associated probability and a confidence interval.

The answer can be had simply by asking of a ramping sinusoid can be sampled such that the result is a decreasing line.

Of walleyes is half as smart as he think he is, he can answer that easily. He will surely squirm away as usual.

The answer is absolutely. Every descrete time sampling filtere has a probability of yielding an output that isn't representative of the original signal. Digital communications requires statistics and probability as part of signal analysis and filter design. Every communications engineer studies statistics.

A sample of yeary temp is a descrete time sample.

That just proves that, yes, Watts graph is meaningless because no confidence interval and p- value is given.

The problem, which I'll get to later, is even worse because it is obvious by inspection thatr the confidence interval is so large thay the linear regression is meaningless.

Being an asshole, waleyed, doesn't make you any less wrong. I get that all that adrenaline makes you feel like you are. What is does do is make you both an asshole and stupid. There's a common phrase for that ..... what is it? You are a what?

Do you really need me to show you a sampled ramped sinusoid that gives a decreasing line, or can you manage that on your own?

I'd like to see you stop being intentionally wrong. I doubt that willl happen.

Well, back to work.

Are you on drugs? This is one of the most incoherent posts I've seen in a while. You OK?

 

[CrusaderFrank]

A safe assumption is that the newer dataset is more accurate. That would be the paranoia-free position.

The difference between the two, as explained by the people who did the adjusting, is that such adjustments correct for errors and differences such as time-of day, location changes, analog to digital and general magic ;-),

Why go back and alter data from over 50 years ago? What is the reason for altering it?

Wow...

If you don't care whether its accurate, why do you care if it even exists?

...and the best gauge of "Accuracy" is making sure it validates the AGWCult model.

When the data refuses to validate your theory, you alter the data.

 

[CrusaderFrank]

Why go back and alter data from over 50 years ago? What is the reason for altering it?

Wow...

If you don't care whether its accurate, why do you care if it even exists?

adjustments circa 2000

adjustments circa 2013

what is interesting to me is what doesnt seem to be on the graphs. no marked spike in corrections when a wholesale conversion to digital thermometers and 'morning readings'. at least in the 2000 graph the adjustments levelled off a bit after 1990. but that plateau disappeared in the 2013 edition.

the early work into TOBS used coarse decadal estimate figures for time of temperature readings. those coarse estimates didnt show up then, and they dont show up now on individual stations that should only have one TOBS adjustment per change in observation time.

The existing Data had DENIER!!!! Tendencies and had to be corrected

 

[flacaltenn]

@Flac

And yet, for allll your ranting, you've said nothing that changes the fact that the OP and the article that it highlights are irrelevant because the graph is meaningless. It is mesningless because the regression has no meaning.

Every statistic that is calculated using sum of squares has an associated p-value that defnes the significance of the statistic. And every coefficient has a conficdence imterval. Lacking those, the regression line is meaningless. And everything hinges on that. The line is insignificant. Watt's conclusions are insignificant. The OP is insignificant. And your opinions are insignificant.

If you cannot address the fundamentals of the science correctly, then nothing you have to present has any significance.

And it is obvious to everyone here that all you can do is whine.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem.. WHEN to apply certain tests and how to INTERPRET those tests is more important than mindlessly following the functions offered in Excel..

So --- If I run a 3 or 4 year low pass filter over the temp chart data to "smooth" the high frequency data --- Have I affected the 1st derivative (slope) estimate? (remember that error term of the regression is ASSUMED to have specific properties for the P-test)

After I filter and the variance is REDUCED -- fit that to the best trend line.. Is the P-Value BETTER or WORSE?

Actually -- the number of samples is far more important to the significance of the slope estimation in this case than goodness of the raw linear fit of the data.

You just add more bs on top of bs.

The trend line is a best estimate. A best estimate has a confidence interval. It also has a p-value, the probability of getting the same result from random chance. No published confidence interval and no p-value, no meaning.

The statistics are affected by a number of factors, sample size and variance are two. The slope is as well.

You csn prove that the trend line is precise, just by coming up with the p-value and confidence interval. It's that simple. No need for bullshit about low pass filters. Just do the stats correctly.

You didn't answer the questions. Do I damage the trend line estimate estimate if I roll off and smooth the data vector? If not -- does the p-value get BETTER or WORSE ? You are still telling me the answer but working the wrong problem..

After you answer those 2 questions.. Tell me what would happen to your precious p-value if the data set was NOT ALREADY Annually FILTERED.. - If you substituted daily, or MONTHLY resolution to the data.. If the data set was MONTHLIES and not YEARLIES might the p-values change?

The probability test is constructed by assuming a random nature of the error term.. The mistake you are making is that In this case, you do not have multiple draws on the data. It is what is. And you can't draw it again. Surface temp is not stationary in any sense and it is not driven by closed form simple linear estimate or polynomial.. So the probability value may be interesting, but does not invalidate the best estimate of slope. The error term in this case is a specific and isolated observation and NOT a normal random process.

So --- genius ---- if you have a process that has a NON-STATIONARY MEAN and a NON-STATIONARY VARIANCE -- how do you set up a probability test on it's 1st derivative?

What is the likelihood of any particular slope in a NON-Stationary process? A STANDARD p-test will not reveal that. It is NOT just a integral of a Normal distribution.. NOR is it simply simply dependent on the variance of the data vector.

Give me the probability distribution of slopes for arbitrary snippets of temperature records.... Doesn't matter HOW MANY ill-informed climatologists have ATTEMPTED to do this and failed to solve the CORRECT problem.. They still insist on MISAPPLYING p-values to this problem...

 

[Procrustes Stretched]

"While we can’t say there has been a statistically significant cooling trend, even though the slope of the trend is downward, we also can’t say there’s been a statistically significant warming trend either." -- NOAA shows ?the pause? in the U.S. surface temperature record over nearly a decade | Watts Up With That?


Global Surface Temperature Anomalies | Monitoring References | National Climatic Data Center (NCDC)

 

[CrusaderFrank]

It's only accurate when it agrees with the models, er, just like Michelson Morley's ether experiments and Einsteins Cosmological Constant, yeah, that's how real scientists roll

 

[itfitzme]

So, back to walleyes question if a lowpass filter has an associated probability and a confidence interval.

The answer can be had simply by asking of a ramping sinusoid can be sampled such that the result is a decreasing line.

Of walleyes is half as smart as he think he is, he can answer that easily. He will surely squirm away as usual.

The answer is absolutely. Every descrete time sampling filtere has a probability of yielding an output that isn't representative of the original signal. Digital communications requires statistics and probability as part of signal analysis and filter design. Every communications engineer studies statistics.

A sample of yeary temp is a descrete time sample.

That just proves that, yes, Watts graph is meaningless because no confidence interval and p- value is given.

The problem, which I'll get to later, is even worse because it is obvious by inspection thatr the confidence interval is so large thay the linear regression is meaningless.

Being an asshole, waleyed, doesn't make you any less wrong. I get that all that adrenaline makes you feel like you are. What is does do is make you both an asshole and stupid. There's a common phrase for that ..... what is it? You are a what?

Do you really need me to show you a sampled ramped sinusoid that gives a decreasing line, or can you manage that on your own?

I'd like to see you stop being intentionally wrong. I doubt that willl happen.

Well, back to work.

Are you on drugs? This is one of the most incoherent posts I've seen in a while. You OK?

Like I said, you'de squirm away.

 

[itfitzme]

You couldn't carry a pencil for a real mathematician.. The purpose of a trend line is to estimate the 1st derivative of a data set. That is always a USEFUL estimate in that sense.. Even in the presence of data with high deviation or noise. No different than a selective filtering operation to accentuate different derivatives of the process.. Problem with poor math education is that they tell you WHAT TO DO -- but rarely explain what the tools are actually for.. Where your "education" failed you is that nobody is ATTEMPTING to find the underlying equation for the Earth temperature versus Time curve BECAUSE THERE ISN'T ONE.... So the "errors" in fit are MEANINGLESS. It is simply to establish the dtemp/dtime.... Go be a victim...

Or better yet -- go bug the market analysts on Wall Street for applying trend lines to high variance data.. Tell THEM they can't do that unless their p-values are appropriate..

@Flac

And yet, for allll your ranting, you've said nothing that changes the fact that the OP and the article that it highlights are irrelevant because the graph is meaningless. It is mesningless because the regression has no meaning.

Every statistic that is calculated using sum of squares has an associated p-value that defnes the significance of the statistic. And every coefficient has a conficdence imterval. Lacking those, the regression line is meaningless. And everything hinges on that. The line is insignificant. Watt's conclusions are insignificant. The OP is insignificant. And your opinions are insignificant.

If you cannot address the fundamentals of the science correctly, then nothing you have to present has any significance.

And it is obvious to everyone here that all you can do is whine.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem.. WHEN to apply certain tests and how to INTERPRET those tests is more important than mindlessly following the functions offered in Excel..

So --- If I run a 3 or 4 year low pass filter over the temp chart data to "smooth" the high frequency data --- Have I affected the 1st derivative (slope) estimate? (remember that error term of the regression is ASSUMED to have specific properties for the P-test)

After I filter and the variance is REDUCED -- fit that to the best trend line.. Is the P-Value BETTER or WORSE?

Actually -- the number of samples is far more important to the significance of the slope estimation in this case than goodness of the raw linear fit of the data.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem..


So everyone at work think you're an idiot too.

 

[westwall]

@Flac

And yet, for allll your ranting, you've said nothing that changes the fact that the OP and the article that it highlights are irrelevant because the graph is meaningless. It is mesningless because the regression has no meaning.

Every statistic that is calculated using sum of squares has an associated p-value that defnes the significance of the statistic. And every coefficient has a conficdence imterval. Lacking those, the regression line is meaningless. And everything hinges on that. The line is insignificant. Watt's conclusions are insignificant. The OP is insignificant. And your opinions are insignificant.

If you cannot address the fundamentals of the science correctly, then nothing you have to present has any significance.

And it is obvious to everyone here that all you can do is whine.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem.. WHEN to apply certain tests and how to INTERPRET those tests is more important than mindlessly following the functions offered in Excel..

So --- If I run a 3 or 4 year low pass filter over the temp chart data to "smooth" the high frequency data --- Have I affected the 1st derivative (slope) estimate? (remember that error term of the regression is ASSUMED to have specific properties for the P-test)

After I filter and the variance is REDUCED -- fit that to the best trend line.. Is the P-Value BETTER or WORSE?

Actually -- the number of samples is far more important to the significance of the slope estimation in this case than goodness of the raw linear fit of the data.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem..


So everyone at work think you're an idiot too.

And you're still so incoherent that you gave me pos rep instead of neg rep because you're blind as well as stoned

 

[flacaltenn]

@Flac

And yet, for allll your ranting, you've said nothing that changes the fact that the OP and the article that it highlights are irrelevant because the graph is meaningless. It is mesningless because the regression has no meaning.

Every statistic that is calculated using sum of squares has an associated p-value that defnes the significance of the statistic. And every coefficient has a conficdence imterval. Lacking those, the regression line is meaningless. And everything hinges on that. The line is insignificant. Watt's conclusions are insignificant. The OP is insignificant. And your opinions are insignificant.

If you cannot address the fundamentals of the science correctly, then nothing you have to present has any significance.

And it is obvious to everyone here that all you can do is whine.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem.. WHEN to apply certain tests and how to INTERPRET those tests is more important than mindlessly following the functions offered in Excel..

So --- If I run a 3 or 4 year low pass filter over the temp chart data to "smooth" the high frequency data --- Have I affected the 1st derivative (slope) estimate? (remember that error term of the regression is ASSUMED to have specific properties for the P-test)

After I filter and the variance is REDUCED -- fit that to the best trend line.. Is the P-Value BETTER or WORSE?

Actually -- the number of samples is far more important to the significance of the slope estimation in this case than goodness of the raw linear fit of the data.

Half my day is spent working with people that have a correct answer.. But they are working the wrong problem..


So everyone at work think you're an idiot too.

They love me for my unlimited patience and humor..

How about we approach this from neutral ground.. I'll concede that that there IS a probability test for significance of that data, but you'd have to search the deep statistics lit for the PROPER way to handle it. AND I'll concede that the data shown in the graph NEEDS to be tested for significance. But the assumptions of a p-test for linear regression that you get out STANDARD Stat pkgs is not appropriate.

Here's the reason.. And I think I caught several people saying the same thing over the years --- It's bloody STUPID to be testing significance of the fit of ANY process that is HIGHLY SUSPECTED to have higher order components OR does not meet the limiting requirements of a p-test on slope. You can see this from folks that put R(sq) values on the same linear regressions of temperatures. You KNOW it's not linear -- why are you CORRELATING IT IN THE FIRST PLACE !!!

In the end the real problem that everyone wants to know here is when to declare that a temperature record has trendline that reaches significance. You are NOT testing an H(0) hypothesis on possible other slopes in a Normal distribution assuming that REAL temperatures are well behaved noise. So that problem is better solved by adding more degrees of freedom (more points) and using careful filtering to reduce the inherent variance BEFORE running a linear regression.. The problem is to estimate the 1st derivative --- NOT find the polynomial that describes the process. And that particular temp snippet is not long enough to run an adequate filter.

BUT -- that doesn't mean the GRAPH is garbage. Or the Trendline is garbage. It is what it is.... It would be much harder than you or Anthony Watts or even many of the climatologists think to test the REAL significance of these snippets..

That ^^^^^^^^ is why the folks I work with end up loving my crotchedy ass..

 

[skookerasbil]

Gentlemen.......go check out the thread I just posted up on how many Americans think the science is settled!!!!


You will laugh your balls off!!!!!!!!


We can almost bow out of this stoopid forum at this point........."climate change" is now seen as a mega-hoax!!