G'day Count,
I actually wrote a rather long post about exactly what I was aiming to do with my research. This included obtaining the raw data where only monthly averaged data is currently available and the avareging technique does not accord with a "standard" I wish to impose or is unknown. It also includes having several data sets. Starting with the raw data. There would be a set weighted for concentration just like the Monte Carlo bit here suggests and there would be adjusted data for urban effect and other adjustments used in a standardised form as well the method currently adopted by various groups. The whole idea would be to show the effects of adjustments and whether they are necessary to get to a more accurate figure or just manipulate the data. I would also like to include a data set where all known effects on the weather station are recorded and their effects analysed. This, however, would have to be a very small subset because of the immense amount of work required. I have gone to the trouble of obtaining histories of weather stations and it takes a massive effort. Often the detail is just not known and you have to snoop around with town historians, very old members of a community, retired postal managers, etc. This is not something you can do for 7,000 weather stations or even 700. I was hoping that by setting up a standard averaging data set that representative locales for each region could be selected and those could be examined in great detail but am not sure whether this is feasible and whether I will receive any assistance or not.
My biggest problem is I have no expertise in the analysis of data. I freely admit I had never heard of Monte Carlo (except as a gambling location) before this thread. My expertise lies in looking for flaws in other's work with data. It is not the same thing at all.
At no point have I thought to use similations. You may think it the "orthodox" approach but I consider similulations another name for guessing and cloaking it so it appears to be science.
I do not even understand your reasoning in similuting "typical" data for weather stations that shows no trend up. I wouldn't know "typical" data for a weather station if it jumped up and bit me.
I sort of understand your point in using some made up set of data and applying the adjustments made by NASA, and various other institutes to see what happens but the trouble is I'm sure that if I created five different data sets, I'd get five extremely diverse results. I could create a data set that showed the adjustments caused huge increases in the trend and I'm sure someone else could create data that showed the reverse. It would depend on what the data was.
I do know for instance that the majority of weather stations show a cooling trend. It seems that the trend upwards is because those that show a warming trend do so at a higher rate than those showing a cooling trend. The other problem is that very cold climates have much wider variations than say the tropics, where the variations are very small. So having a great many tropical weather stations showing a very slight cooling trend can be completely obliterated by adding in only a small number of arctic stations that show a pronounced warming trend.
So the data set that showed no trend could be devised that had an even distribution of upward trends and downward trends. Applying the adjustments would then give you a certain result. Another set showing no trend could be closer to the real world, with a higher number showing a cooling trend of a smaller amplitude than the smaller number showing a warming trend with the overall result of no trend. It would not take much actually to change the current data to show a cooling trend. You only have to change a few intense warming trend stations down to negatives. But doing the same adjustments would yield quite a different result.
I believe that all this would show is that the bias inherent in the production of the false data in the first place would determine the result that was obtained, and thus any analysis could be rightly discredited.
I do not hold much hope for raw data, consistently averaged and using standard adjustments applied in a rational fashion, even if it shows no cooling trend, in the unadjusted raw data, in the averaged data, in the adjusted data, will be widely accepted. But at least a better alternative data set will be available for the few that do realise that there are serious flaws in the current data set. With time it might actually start to be used, especially if the climate catches up on those that are so sure of global warming and a cooling trend turns up.
That is a big fear of mine actually. You'd think I'd welcome a sudden cooling trend because it would support my arguments about the flaws in the research and data. But today there was a major article in our local paper about fish stock depletion. This is a serious problem. Overfishing could have horrendous concenquences for other reasons than simply fish will disappear from the tables of people. Actually fish will not disappear as a great deal of it is now farmed but overfishing is still a very serious problem. Whole species are disappearing and may completely disappear or take centuries to regenerate. But this whole article had to mention global warming as a big issue in that the loss of fish would mean that the oceans would be more susceptibla to global warming. That slant will doom this research and similar research to being ignored if global warming is shown to be a fantasy.
Indeed, a great deal of science will be looked upon by politicians and the general public with great suspicion. It took years for people to realise that viruses and spyware were not being made up to sell software but rather a problem that really needed tackling simply because most people had been taken in by the Y2K "disaster" predictions and had become much more cynical. Imagine the impact of global warming to be shown as a complete con. Global warming leaves Y2K for dead in the prediction stakes, and in the amount of issues that are now completely tied to it. Environmental groups could find themselves bankrupt very quickly, even those such as WWF or small groups interested in protecting a local species really in severe danger. Who's going to care about pollution in general when the biggest pollutant for years has been said to be CO2, a benefcial gas, that does a great deal of good. DDT at least actually did do some harm, even if it was enormously exagerated. CO2 on the other hand is not a pollutant. It might be contributing to a climate change but otherwise the gas has no detrimental effects in the quantities currently in the air or predicted before carbon based energy starts to peter out.
So a sudden realisation that global warming isn't really based on good science especially considering the huge amounts of money given to the research of the "problem" by various governments, by donations etc, imho, would be a disaster, almost as bad as global warming itself. Unlike an argument over the origins of the universe and the big bang, global warming, is thought by the average person to be a problem that will effect them. So if tomorrow it was shown that the big bang is not likely to be correct, little harm would occur to science. Show global warming to be less than certain, or worse, completely wrong, and the harm will echo for probably decades.
Regards
Richard
