I have been spending some time (my wife would say “too much time”) examining how the Hansen Bias Method influences the temperature record. We have already observed that the Hansen method introduces an error in cases where the different versions are merely scribal variations. See http://www.climateaudit.org/?p=2019 and discussion.
The cause of the error has also been pinned down: in the case where a scribal version has only two of three monthly temperature values in a quarter available, Hansen calculates the anomalies of the available two months. It is important to note that the anomalies are the difference between the month’s recorded value and the month’s average value for the period of scribal record. Hansen takes these anomalies, averages them, and then sets the estimate of the “missing” month’s anomaly equal to this average. The “missing” monthly temperature value is then estimated by adding the estimated anomaly to the scribal record’s mean for the month. This occurs even when there is a temperature value available for the missing month in another scribal record. From the two available monthly values and the third, estimated monthly value a quarterly average is calculated, followed by a calculation of the annual average from the quarterly averages. Finally, for the two scribal records that are being combined, Hansen averages the annual averages for the overlap period, and, if there is a difference between the two averages, determines that to be a bias of one version relative to another and adjusts the earlier version downwards (or upwards) by the amount of the bias.
While the method clearly will corrupt the data set, there doesn’t seem to be any reason why it would introduce a material bias in northern hemisphere or global trends. We’ve observed cases in which the method caused early values to be falsely increased (Gassim) and cases where the method caused early values to be falsely reduced (Praha), and one’s first instinct is that Hansen’s method would not affect any overall numbers. (Of course that was one’s initial impression of the impact of the “Y2K” error on the US network.)
However, that proves not to be the case, because of a “perfect storm” so characteristic of climate errors.
Hansen’s network outside the US has 2 main components: GHCN records, which all too often end in 1990 for non-US stations (USHCN records continue up to date); and 1502 MCDW stations (mainly airports). The MCDW reports started in January 1987 and continue to the present day.
In Siberia, to take an important case under discussion, the overlap between the MCDW record and GHCN record is typically 4 years – from January 1987 to December 1990 or so. Here’s where the next twist in the perfect storm comes in. Instead of calculating annual averages over a calendar year, Hansen calculates them over a “meteorological year” of Dec-Nov. While there may be a good reason for this choice, it has an important interaction with his “Bias Method”.
Even if the two versions are temperature-for-temperature identical in the overlap period, the MCDW series is “missing” the December 1986 value and the 1987 DJF quarter must be “estimated”. Now suppose that Jan-Feb 1987 are “cold” (in anomaly terms) relative to December 1986 (also in anomaly terms). As it happens, this seems to be the case over large parts of Asia (other areas will be examined on another occasion). The variations in Asian anomalies are very large. Let’s say that over large regions of Asia, the Dec 1986 anomaly was 2.5 deg C higher than the Jan-Feb 1987 anomaly. And let’s say that all other values are scribally equal.
Under Hansen’s system of comparing annual anomalies, this difference of 2.5 deg C will enter into the average of 4 years ( in effect being divided by 48 months) and then rounded up to a “bias” of 0.1 deg C. Since the MCDW version is “cold” relative to the prior GHCN version, the GHCN version extending to earlier values will be lowered by 0.1 deg C.
It looks like there may be a domino effect if there is more than one series involved, with the third series extending to (say) the 1930s. Hansen combines the first two series (so that the deduction of 0.1 deg C is included in this interim step.) When the early scribal version is compared to the “merged” version, the early scribal version now appears to be running “warm” relative to the adjusted version by 0.1 deg C. So it “needs” to be lowered as well.
The net effect is to artificially increase the upward slope in the overall temperature trend for most of the stations we have studied. As noted earlier, this process can bias records in the other direction, but stations with the requisite conditions have been hard to come by – Gassim being one of the few.
To visually understand how the method works, I took the combined record for Erbogacen and corrupted it using the bias method. The data I use has already been processed through this method, but that is really unimportant. What is important is that I have a complete set of records that I can artificially split into two “scribal” versions. If the method for combining stations worked appropriately, the recombined record would be identical to the record I started with.
The first image shows the original record where I have defined two “scribal” versions that overlap in the period of 1987 through 1990:
The second image shows the result of estimating the DJF temperature for the latest scribal record, overlaying it with the older scribal record. As you can see, the estimated annual value falls below the actual value due entirely to the estimated DJF value.
The third image shows the impact of then using the bias method to recombine the two scribal records, using the estimated value for 1987 (derived from DJF) as input into the algorithm. This result is overlayed with the original record. As one can see, the bias method has “cooled” the early years of the original record.
The final image simply shows the trend lines for the original record and the recombined record. Clearly, the bias method has increased the apparent warming trend for this station.