The drum and Antarctica share two features:

Individual membrane elements tend to drag their neighbours up and down with them as may temperature elements.

The drum is constrained at the rim. Antarctica is constrained by the surrounding ocean. Temperature variability is likely to be least constrained away from the ocean.

Alex

However, there can be a problem in those cases where the size of the value is related to the fact that it is missing – certainly a possibility in this case because the presence of clouds and the surface temperature can be related.

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While without further information we wouldn’t know if the presence of clouds and surface temperature are strongly related (or, if they are, exactly how they are correlated), we can say with certainty that the cloud masking will preferentially affect certain geographical locations: namely West Antarctica and the coastlines. Cloud cover is least over the plateau. It may be entirely coincidental, but the areas in both the PCA and AVHRR recons that show the strongest warming from 1957-2006 are the areas with the most cloud cover . . . and, hence, the largest number of data points removed via masking.
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I have not had any luck so far getting into GISMO at NSIDC (can’t get the Java applet to work), but while we await Steig’s processed data, I was going to start analyzing cloud cover in the same grid cell size as what Steig used over the same period and see if there is any actual correlation between cloud cover (and, by extension, missing data) and warming trend.

With all of the concurrent threads, it is hard to figure out where to post a comment.

The situation is pretty clear now. The satellite data was reduced (presumably from a total of 300 x 5509 = 1,652,700 points) to just three PC series totalling 900 points to describe all of the variation in the monthly temperatures for the entire Antarctic continent for a 25 year period. These 900 values were first stretched using the partially-complete surface data to 1800 using RegEM and then to 3,305,400 values to draw the picture on the cover of Nature.

Was this successfully done? I agree with you. Until we can get to see at least the initial satellite values from which the three PCs were calculated, I don’t think that there is much more that we can do in directly evaluating the quality of the reconstruction.

How do you calculate PC’s with missing data from the cloud masking?? This is why I think we need the code and raw data for removing the cloud data.

There are many possible ways (not all necessarily appropriate for a given situation) to infill the values. Simple local geographic interpolation, “climate” using monthly means, and EM (without the Reg) which uses statistical relationships with all observed values to estimate the missing measurements are some possible methods. If the number of infilled values is small compared to the entire data set, then the impact of the infilling of the PCs extracted could be relatively low. However, there can be a problem in those cases where the size of the value is related to the fact that it is missing – certainly a possibility in this case because the presence of clouds and the surface temperature can be related.

I didn’t see the reply, these threads are long and complicated. When you first found the 3 pcs I started thinking about how the covariance matrices are calculated and decided the same thing you did, it doesn’t matter whether you use 3 or 5509 because it’s the same information.

Since the weighting of the 3 pc’s after 1982 doesn’t include surfacestation info I used the same values to reconstruct the continent with JeffC’s surface station info.

Anyway it would be very very slow to run 5509 series through the masher so 3 is fine and I also think this is what they did.

I can’t hazard a guess as to how they got these PCs. They could have been calculated by Dr. Steig and crew in an unmentioned step or they may have actually received the values already in that form. Who knows?

How do you calculate PC’s with missing data from the cloud masking?? This is why I think we need the code and raw data for removing the cloud data.

first principal component … significantly correlated with the SAM index(?)

The thoroughness and elegance of the statistical deconstruction is impressive — what seems somewhat short changed in a discussion of the actual physical processes that might account for what Steig et al contend. In short, does their interpretation of the physical meaning of their first 3 PCs make sense?