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There are different types of uncertainty. For the estimate of global mean the data is massively oversampled: for example for the NH monthly there are around 60 degrees of freedom in the data, meaning one could get a good estimate from around 60 stations, there are around 4,000, which reduces the impact of instrumental errors. Autocorrelation is high meaning an AR(1) model is appropriate in trend estimation.
Speaking of trends, a common denier trick is to look at a trend too short for the slope to have statistical significance, in the monthly data, anything less than 10 years is unlikely to achieve 95% significance.
My subconscious is annoying. It's got a mind of its own.
Is Global Warming statistically significant? Looks to me like it is not.
Based on what? The trends in the two datasets I showed the graph for since 1970 are 0.166 +/- 0.027 deg.C/decade, and 0.174 +/- 0.027 deg.C/decade, in line with model projections.
My subconscious is annoying. It's got a mind of its own.
Nonsense.
you don't need to look at trends to say its not since 1997 that Middlesborough were in an FA cup final. It's a fact not a statistical construct
and there has been no warming for nearly 20 years
(\__/)
(>'.'<)
("")("") Born to Drink. Forced to Work
There are different types of uncertainty. For the estimate of global mean the data is massively oversampled: for example for the NH monthly there are around 60 degrees of freedom in the data, meaning one could get a good estimate from around 60 stations, there are around 4,000, which reduces the impact of instrumental errors. Autocorrelation is high meaning an AR(1) model is appropriate in trend estimation.
Speaking of trends, a common denier trick is to look at a trend too short for the slope to have statistical significance, in the monthly data, anything less than 10 years is unlikely to achieve 95% significance.
AR(1) is only applicable to a timeseries that is (weakly) stationary. Is the mean and variance the same for all t? Is the autocorrelation for a particular lag the same for all the series?
At the moment there is an El Nino which will push up the global temps temporarily, but once that is through it will be followed by a pretty siginificant La Nina, which will shove the temps down.
It will be interesting to see where the temperatures are after these two events.
At the moment there is an El Nino which will push up the global temps temporarily, but once that is through it will be followed by a pretty siginificant La Nina, which will shove the temps down.
It will be interesting to see where the temperatures are after these two events.
It's not how fast the future might warm that should worry people, it's how fast the past is cooling.
It's surprising that any of us made it out of the ice-cave and past the wooly mammoths
(\__/)
(>'.'<)
("")("") Born to Drink. Forced to Work
AR(1) is only applicable to a timeseries that is (weakly) stationary. Is the mean and variance the same for all t? Is the autocorrelation for a particular lag the same for all the series?
I suspect not.
AR(1) is 'good enough' as a first approximation, certainly for policy/discussion purposes (When was the last time someone put a uncertainty interval on their 'no global warming for <insert number here> years?). If you want to remove autocorrelation altogether ARMA(1,1) gets you there ...
It's very significant in terms of taxes hitting your pockets...
It's a wonderful gravy train for those with their snouts in the trough.
I think we should have a tax on the devil. Everytime something bad happens or is predicted to happen, we pay a devil tax to help fight satan/lucifer and keep us safe.
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