Surely you should not estimate missing data in order to evaluate a trend? The missing data increases the uncertainty of your evaluation and cannot be replaced by estimates without loss of both data integrity and scientific integrity.

Correct, the magnitude of the fluctuations is not significant. It is significant enough, however, to cause changes in the ever-so-important temperature rankings we have all come to know and love. I found that surprising – it was not something I expected to see.

Something else I did not expect to see was how much a specific year’s average can change over time. Take the “hottest year on record” – 1998. On Oct. 13, 1999 the anomaly was 65. On Jan. 26, 2001 it had risen to 66. On Feb. 11, 2002 it was 67. By Apr. 19, 2003 it was 70, which is where it stands today after a brief interlude back at 69 in mid-2006. That’s an almost 10% drift upward, due solely to the fact (as it appears) to more recent temperatures being added to the record. One would think an agency as august as NASA would be able to develop a more stable methodology. It just looks sloppy.

So if there is a “gotcha” it is that we have found their process to add additional uncertainty beyond that already present in the data.

Most of the monthly data change were on the order of + / – 1 or 0.01C. I also did the same with the annual data and again most of the annual changes were + / – 1 with a couple being +/- 2. So I agree that there are a significant number of changes, but I do not think the changes are large enough to be relevant. I seem to recall that the accuracy of the GISS is +/- 0.1C and more the further back you go – I am sure some of the people here will be able to supply a more accurate figure. I also did a comparison between the monthly anomalies and the annual ones to see if the annual changes were being caused by the monthly ones and – to within round off error – they seemed to be.

A question further up caused me to look at the overall trends for the annual record (D-N). From the two cases above I plotted the annual and using the trend function on Excel (yeah, I know its for wimps, but I was in a hurry) I found the trend for one to be +0.5695 and for the other to be +0.5742. The R2 value was almost identical (0.6323 and 0.6341).

As a final check I plotted to annual values against each other. I figure this would give me a good idea of how close they were. A perfect match would have a slope of 1. The slope in this case was 1.006.

So I don’t think there is a “gotch-ya” hiding there. However I do tend to agree with the people who say it is a strange way to calculate a missing value. If the data series was fairly flat (no trend) then it might make sense. But I don’t see any rationale for doing it this way.

John

Having said that:

Earle:
I hear what you are saying regarding pre-satellite data. It is rather suspicious, to put it kindly. There isn’t anything to compare against.

Would you agree that in the satellite period the data tell the same story? If, in the future, the satellite history diverges then I would agree that the books are being cooked.

There is, after all, no controlling authority overseeing the ‘Science’.

It’s not the end that is of concern, it is the beginning. GISS matches the satellite record to some degree, true. But it reqrites history by applying adjustments to past temperatures. Look at the adjustments applied in the 100 years prior to the satellite record and you will see a remarkable similarity to the alleged increase in golbal temperature since the start of the industrial revolution.

Thanks jeez!

Look at March numbers for GISS. No it most certainly does NOT work and does NOT match other records. Time for them to fiddle with the numbers again.

Check it out, John. You made top 100 WordPress (#60).

John G, that was essentially my point.

The $64K question is, does the estimated data effect the global temperature average and trend?

I await the average and trend calculated with and without the estimated data with considerable interest.

To expand, I posted the following comment on CA:

Obviously no method is perfect. Initially I thought a method like Hu’s would suffice, but it assumes climate behaves normally. We know it does not.

Example: Where I live, November of 2006 was normal, December 2006 was way above average, and January 2007 was slightly below normal. If I were missing the December 2006 data point, just about any scheme I come up with will grossly under-estimate that temperature.

Step forward one year later. November was slightly above normal, December was slightly below normal, and January was even colder than the previous year. If I were missing December of 2007 but had both adjacent months as well as all three corresponding months from the previous year, I would over-estimate the December 2007 temperature.

Note however that the method GISS uses does not use adjacent months – it uses seasonal months. The autumn season is Sep-Oct-Nov. If N is missing it is derived from Sep-Oct. You and I might think it more reasonable to derive it from Oct-Dec. Likewise for missing Sep. Only Oct would be derived from adjacent months. In fact, only four of the missing seasons would be derived from missing months. If an entire season is missing, the relationship gets really strange.

Estimation of values during the intermediate steps of determining an average global temperature should be avoided. This is particularly true of the earliest steps, because the uncertainty of that estimation only grows as more and more results are derived from it.

In this case, estimation begins very early in the process. One of the first steps is to take each scribal record and estimate seasonal averages when months are missing. Then missing annual averages are estimated if any seasonal averages are estimated or missing. These scribal records are then combined with other scribal records to form a station record, and the consequence of the estimation ripples throughout the resulting record. GISS then applies a homogeneity adjustment to this data containing significant numbers of estimates. After that GISS goes through gridcell calculations and then the final calculation of the annual global temperature.

The only purpose of the estimation seems to be to calculate an annual average for each scribal record prior to combining, adjusting, griding and averaging. I propose that annual averages are not important as intermediate steps. Instead of calculating an annual global average temperature, calculate a monthly average temperature. If a data point is missing, then it is not adjusted, gridded or averaged. It simply is not included. There should be enough other data points from around the world to calculate an average temperature with some degree of certainty (of course, it is desirable that this degree of certainty be published).