As I foreshadowed in a recent post, for plotting recent monthly data I wanted to shift from anomalies based on a past period (1961-1990) to one based on the present. For each station and each month, I would use the present value of a weighted linear regression as the expectation, and the anomaly would be the deviation from that.
The reason was mainly that I suspected that irregular happenings in the history of the stations was distorting the anomaly base, and creating noise in the anomaly plot which isn't needed. In my most recent post I traced the prominent deviations due to Nitchequon and Shahr-e-kord to gaps in the record and noted big (and probably correct) adjustments made by GHCN.
I've done it, and the monthly maps now use this basis. I think it has been very successful in removing this source of error. Of course, it also means that the anomalies do not give any measure of AGW. For that the right source is the trend map.
Below the jump, I'll illustrate the improvement.
I haven't updated some earlier maps (June 2012, Nov 2011), so you can use these for comparison. Here is a snapshot of North America for June:
 |  | |
| June 2012 using anomaly base 1961-1990 | June 2012 using anomaly from present estimate |
Not only is the Nitchequon dip in Quebec gone, but the US is very much smoother. I have often commented previously how these plots seem less smooth in the US; that may well be due to a greater frequency of station changes. Anyway, it's much less true now.
It also shows more emphatically how spatially correlated are the changes in individual station monthly averages.
Posts
Great idea!
ReplyDeleteThe other problem which bothers me is how if coverage is not constant you can get a different global mean depending on your choice of baseline period. (I've been encountering that mostly at the level of gridded data rather than station data though - I haven't thought about the station data case.)
Kevin C