1. Double up the number of pennisular stations from 15 to 30.
2. The new 15 stations have identical temperature records to the existing 15. The “new” stations could be visualized as being 10 feet away from the existing stations.
3. Re-run the RegEm analysis and calculate the continent wide trend. If RegEm is correct, then there should be no change in trend. If the method is poor, the trend will change.

There is an expressive word in Hawaiian pidgen for what I think about this: shibai.

To watch you guys tease out the story is amusing and stimulating. Even if The Team were all bullet-proof statisticians, I would still think that recreating a temperature history by making up temperatures where there are no observations is shibai, at least when the making up is on the scale we see in Steig.

But it’s all about process in the audit, ain’t it?

Gee, hope I didn’t cross the snip line.

I’ve had that happen there before, but thanks for the warning. The reason I even went over there was that several people had mentioned Jeff & Jeff’s work, but hadn’t described what the result was (and I’m not sure I did it justice). The link to the article had been dismissed as not worthy of reading even on the slowest of days. I really wanted to see if Gavin knew that RegEM doesn’t factor in distances when doing its magic.

I think he knows that, but then I’m not sure he understands the issue that this can lead to. And that is that the easiest way to warm the antarctic is to take more measurements on the peninsula. RegEM will handle the rest…

(note, this is my take on what J&J have shown… if that’s not accurate, then please let me know!)

While I take Jason’s point, on the other hand, the value of missing data will always be unrelated to the fact that it is missing.

The data are assumed to be mssing at random relative to the data field, not the x, y geo-coordinates. The reality is that data sensors are most likely to fail under extreme weather, which in Antarctica means extreme cold. I note further that the data field in fact covaries with x, y, z, with south pole and higher elevations being far colder than more northerly locations at lower elevation. Therefore it is a bit disingenuous to dismiss someone’s concern about data missing larger from the colder interior continental region. ie. The data are probably not missing at random with regard to the temperature data field. (Of course you have no way of knowing this because the missing data are not known to you. That is why it is an assumption that is so easy to pretend is true.)

Re: Dean P (#44),
You must be careful in talking with the dismissive ones at RC. They would rather so show you to be an idiot – especially if you smell like a skeptic – than take the time necessary to fully answer (and clarify, if necessary) your question. As my comment above would indicate.

While I take Jason’s point, on the other hand, the value of missing data will always be unrelated to the fact that it is missing.

The value is/was the value, it does not change by the fact of observation, at least not till you get down to subatomic observations.

The speed at which I drove to work this morning is unrelated to whether a cop with a laser gun was hiding behind a billboard (purely hypothetical, we don’t have billboards in Hawaii).

What happens when you do that? How does the reconstruction compare to other methods (e.g.,GISS & HadCRUT).

The data in the interior is different because it is in the interior.

The data in the interior is missing because it was in the interior.

It is plainly NOT the case that the value of the data is unrelated to the fact that it is missing. In fact, both are primarily the result of a single factor: the geographic location.

It is remarkable that Gavin can understand the basic principle and then so spectacularly fail to apply it.

http://www.rochester.edu/College/PSC/clarke/204/Freedman91.pdf

Absolutely delightful reading if you are interested in statistical analysis of data (it focuses social science but is still useful for thinking about the stuff here).