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Update:
As noted within the comments under, GISS updated the GLB.Ts+dSST anomalies which indicate a substantial 0.67 degC value for March. This addition of March 2008 temperature information towards the record induced a corresponding drop in annual common temperature for the a long time 1946 and 1903. In line with GISS, 1946 is now colder than 1960 and 1972, and 1903 dropped into a tie with 1885, 1910 and 1912.
That’s really neat.
End update.
In February I wrote a post asking How much Estimation is too much Estimation? I pointed out that a big number of station records contained estimates for that annual common. Furthermore, the number of stations used to calculate the yearly common had been dropping precipitously for your past 20 years. One was left to wonder just how accurate the reported global average really was and how meaningful rankings of the warmest many years had become.
One question that popped into my mind back then was whether or not – with all of the estimation going on – the historical record was static. One could reasonably expect that the report is static. After all,
Windows 7 Home Basic Product Key, once an estimate for a given year is calculated there is no reason to change it, correct? That would be true if your estimate did not rely on new knowledge added to the record, in particular temperatures collected at a future date. But inside the case of GISStemp, this is exactly what is done.
Last September I mentioned that an estimate of a seasonal or quarterly temperature when one month is missing from the report depends heavily on averages for all three months in that quarter. This can be expressed by the following equation, where are the months from the quarter (in no particular order) and one of the three months is missing:
In the above, T is temperature, q is the given quarter, n is the given year,
Microsoft Office 2007 Standard Key, and N is all decades of the file.
One can readily see that as new temperatures are added to your record, the common monthly temperatures will change. Because those common monthly temperatures change, the estimated quarterly temperatures will change, as will the estimated yearly averages.
Interestingly, application of the “bias method” used to combine a station’s scribal records can have a ripple effect all the way back for the beginning of a station’s history. This is because the first yearly regular in every scribal file is estimated, and the bias method relies on the overlap between all decades of document, estimated or not. Recall that annual averages are calculated from December of the prior year through November of the current year. However, all scribal records begin in January (well, I have not found one that does not begin in January), so that first winter common is estimated due towards the missing December appeal. Thus, with the bias method, at least one of the two records contains estimated yearly values.
Of course, it is fair to ask whether or not this ultimately has any effect on the global annual averages reported by GISS. One does not have to look very hard to find out that the answer is “yes”.
On March 29 I downloaded the GLB.Ts.txt file from GISS and compared it to a copy I had from late August 2007. I was surprised to find several hundred differences in monthly temperature. Intrigued, I decided to take a trip back in time via the “Way Back Machine”.
Here I found 32 versions of GLB.Ts.txt going back to September 24, 2005. I was a bit disappointed the report did not go back further, but was later surprised at how many historical changes can occur in a brief 2 1/2 years.The first thing I did was eliminate versions where no changes to your data were made. I then compared the number of monthly differences between the remaining sequential records and built the following table. Here I show the “Prior” report compared to the next sequential record (referred to as “Current”). The number of changes made towards the monthly document between Prior and Current is shown inside the “Updates” column (this column does not count additions for the record – only changes to existing data are counted). The number of valid months contained in the Prior file is from the “Months” column. “Change” is simply the percent Updates made to Months.
On regular 20% of the historical report was modified 16 times from the last 2 1/2 a long time. The largest single jump was 0.27 C. This occurred between the Oct 13,
Office 2007 Standard Product Key, 2006 and Jan 15, 2007 records when Aug 2006 changed from an anomoly of +0.43C to +0.70C, a change of nearly 68%.
Wow.
The next question I had was “how often are the months within specific many years modified?” As can be seen inside the next chart, a surprising number of the earliest monthly averages are modified time and again.
I was surprised at how much of the pre-Y2K temperature report changed! My personal favorite change was between the August 16, 2007 file and the March 29, 2008 file. Suddenly, in the later file, the J-D yearly temperature for 1880 could now be calculated. In all previous versions the temperature could not be determined.
But some will want to know only how this process affects the rankings for that top 10 warmest years. Because the history goes back for the middle of 2005,
Windows 7 Ultimate Product Key, I explored this question only for that years before 2005. While the overall ranking from top to bottom does change from one report for the other, the top 10 prior to 2005 does not change much. However, the top two do exchange position frequently, as can be seen from the following table:
I will note that the overall trend in changes between now and Sep. 24, 2005 is very close to zero. If one compares the latest file with the one from Sep 24, 2005, it can be seen that the earliest and latest many years are adjusted lower today than in 2005, while the middle many years are adjusted higher. However, this is purely coincidence. If one compares the file from Aug. 2007 with the latest file, it appears the earliest temperatures have been adjusted downward, leading to an overall upward trend. Surely other comparisons will yield a downward tend. It is by pure chance that we have selected two endpoint datasets that appear to have no effect on the tend.
It is at this point I would like to ask, does anyone have a copy of the GISS monthly and yearly temperatures – the equivalent to GLB.Ts.txt – from a date earlier than Sep. 24, 2005?
In the meantime, will the real historical document please stand up?
with all of the estimation going on –
Linear Mode
