“Only he that has travelled the road knows where the holes are deep” (Chinese Proverb)
And so it is with climate data for China, for which there is only word – ‘interesting’…but I leave that to you to judge.
China is of similar size to the US – 9,600,000 sq. kilometers (3,700,000 sq. miles) and has 417 stations reporting climate data, of which (as of January 2009) 414 made it through into the GIStemp set of stations contributing to their climate analysis. The longest serving stations are Bejing and Shanghai. As we know from the “station drop-out problem“, China is one of those countries that for which there was a large addition of thermometers in the 1950s and a massive loss of them from the GHCN record in 1990. Most of the discussions of this problem, for example at Lucia’s site The Blackboard here and here, insist there is no effect on a global scale of this loss of thermometers. Nor should there be any effect from dropping thermometers from specifically cold regions (here). Nonetheless I am interested to see how this change affects the raw data.
I’ve been mainly concerned with trend data – i.e. using tables of the trend produced by a linear regression of individual station data. I’ve analysed data from the GISS combined/adjusted data set – i.e. that which has already gone though the GIStemp adjustment process that corrects for UHI effects etc. and therefore will actually contribute to the GIStemp output data; here I’ve only divided it by whether individual stations have an overall warming or cooling trend across the station data history.
providing data in the GHCN v2.mean file.
I’ve used this data to plot (Figure 2) counts of years when stations were dropped from the database and clearly the data shows a small drop-off in thermometer numbers in 1988 and a huge loss in 1990. This is where, if there is one, I would expect to see an immediate effect on temperature differences. In 2009 we are left with twenty eight stations with an overall warming trend, of which eight are rural stations, and just three with a cooling trend (only one rural – 1939-present).
I’ve also examined in the same way the year of addition of stations. In this case an immediate effect would perhaps not be apparent as I would expect it might take several years for the trends of the new stations to assert themselves over the ‘noise’ of yearly weather fluctuations.
- A change from the addition of stations from 1908 (and perhaps from 1933)
- Change from the addition of stations in the 1950s
- Change from the loss of stations from 1988 and/or 1990
Linear regressions for periods are as follows:
1908-1950: y = 0.0118x – 24.2451, R^2 = 0.03141
1951-1988: y = 0.0013x – 4.3841, R^2 = 0.0032
1988-2010: y = 0.0668x – 134.24, R^2 = 0.7828
I see that you, too, are comfortable with having “time run both ways” in graphs… But I’d caution that I “got a bit of flack” (AKA feedback 😉 that it was confusing to some folks… so you might want to have the graphs have “time run the same way” or at least placard them with a notice when “time runs backwards” with 2009 on the left…
I like the trend analysis approach. I’m not set up for it, but it is a very intriguing thing… Which was was a station headed when it was added or dropped. Might I suggest Fiji as a candidate? It has a very complex set of adds/drops that look to be “trying” to make a “little dipper” in the baseline. But without the trend data I’m just speculating.
Oh, and Singapore has a great Hockey Stick. Would be interesting to see the “trend data” for the station change there too… Might help sort out the “thermometer change” vs “Duplicate Number / Mod Flag change” as causal agent for the hockey stick blade…
(Trying again with name/url rather than “wordpress” auth.)
Do send Fiji and Singapore data and I’ll give them a go. I have sent you a data file which might be useful to scan when setting about a new country.
Trend can be very misleading depending on start and end date, and because we are dealing with temperature/climate cycles in very many places, however it can be a useful guide to what is happening. The key thing is looking at the individual station data as well when possible. Then you really get a feel for it.
Another Fine job, mate!
Otherwise absorbed,
RR
@RR – Thank you!
@pass – thank you for the positive comment, however I have deleted your comment due to the hidden hyperlinks after the comment.