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]]>him as no one else know such detailed about my difficulty.

You’re wonderful! Thanks! ]]>

In looking at 8.5 million records we found this:

http://joannenova.com.au/2012/03/australian-temperature-records-shoddy-inaccurate-unreliable-surprise/

Yes, the USA has always worked in deg F, but here we found some complications with conversion to deg C. The second troublesome period is at the end of mercury thermometers/start of thermistors. It’s far from clear that the methodology changeover was neutral. That’s the next WIP.

Also, on a quick read of the maps you give, Australia looks wrong. There are many stations that should be acceptable dots on your global maps, at least 100.

]]>The more I look at the GIStemp process and results, the stronger becomes my suspicion that “Airport Heat Islands” are a major issue.

]]>The Duke.edu line is a useful primer and reference. In fact, I provided that same link to CoRev a few weeks ago.

The claim isn’t that AR(1) models are always wrong. Far from it. It’s oftentimes a simple way to make a series stationary. The problem is that it’s very crude and in this day and age it’s hard to see why you wouldn’t want to run normal ARIMA tests. Instead of taking first lags, take the first difference and then see if you need additional lags. Or maybe you need an MA term if the series is mildly overdifferenced. If you’re going to restrict yourself to univariate models, then you at least should try and squeeze as much information as you can out of that time series.

BTW…quite wrong about the 2SB handle. Not even close.

]]>It’s bated not baited breath. The humor in using baited because of 2SB’s handle is rather weak, IMO. For even worse, try googling ‘slug bait lyrics’, possibly NSFW if you actually go to the link.

]]>http://www.arl.noaa.gov/documents/JournalPDFs/SanterEtal.JGR2000.pdf

If values of e(t) are not statistically independent, as is often the case with temperature data (see Table 3), the NAIVE approach must be modified. There are various ways of accounting for temporal autocorrelation in e(t) [see, e.g., Wigley and Jones, 1981; Bloomfield and Nychka, 1992; Wilks, 1995; Ebisuzaki, 1997; Bretherton et al., 1999]. The simplest way [Bartlett, 1935; Mitchell et al., 1966] uses an effective sample size ne based on r1, the lag-1 autocorrelation coefficient of e(t):

Since I do not use subscripts, here is my representation of the equation given in the Santer 2000 paper:

neff = nt (1-r1)/(1+r1)

where neff is the effective sample size, nt is the actual sample size and r1 is the lag-1 autocorrelation coefficient of the regression residuals, (e(t).

Jeff ID @ Post#110, why not have 2SBs do an analysis on the temperature series used in Santer et al (2008)? If there is any condenscention to be had let it be aimed at our antagonists.

Remember Santer was intent on showing these CIs for the trends calculated in his paper were very large and thus we could not show significant differences between the observed and climate modeled results. Santer, in passing, finally admitted that using a difference series between the surface and troposphere temperature series greatly reduced the AR(1) values and made it easier to detect significant differences. As a layperson I suggested using differences in all the Santer analysis. Also brought into play is the use of monthly versus yearly data. While it is true that the number of degrees of freedom are reduced using yearly data, AR(1) is reduced for yearly data and the resultant adjustment used for AR(1).

It is correct, I think, to use the data that show the least amount of AR(1) and avoid having to refit the model. I think UC at CA has always commented about avoiding using data that have large AR(1) values by finding the right data or model.

Finally, it is a good sign that 2SBs has not been offended by our comments to this point and ran off after telling us how poorly behaved we are. Antt, (slug) baited breath indeed.

I found the following link to be a good one for better understanding of time series modeling by laypersons like me:

http://www.duke.edu/~rnau/411arim.htm

and others using rand in place of arim and etc.

]]>“…Remember, adding two logs is the same as multiplying two numbers…”

Hmm. Maybe people would be more tolerant of your errors and misunderstandings as a newcomer to this field if you didn’t make condescending remarks to a distinguished professor of statistics…

I also wait for your analysis with baited breath. But be warned that many of the commentators on this site will have at least as much expertise as you in time series analysis (and some considerably more).

]]>Ok, rather than argue details with you about induced relationships from fitting simple linear trends to more complicated situations,I will go along with Jeff and wait for your analysis.

]]>My concern was with how the slope of the trend was arrived at. Confidence intervals are useful and important for forecasting, but have limited value when the question concerns the data generating model itself.

Regarding the level of sophistication, you make a fair point. Not sure what’s appropriate.

]]>