Climate Syndrome: China Meltdown?

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.

Figure 1. China – cumulative change of temperature (dT) and change per year (dT/yr)

E.M. Smith kindly sent me his dT analysis of data from China (this is a version of First Differences). Figure 1 shows my plot of his data, and it is interesting to note that it is fairly flat.  In fact of you plot a linear regression of the dT data between 1880 and 1990 the trend is just 0.09 deg.C per century.  However, global warming really takes off in China in the 1990s. What a hockey stick!

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. 

 Figure 2. Counts of stations plotted again the year when the station ceased
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.

Figure 3. Counts of stations plotted again the year when the station was added to the GHCN v2.mean file. 

In my file the data starts in 1880 and in Figure 3 you can see Beijing and Shanghai at the extreme right of the plot as the original two stations. A few additional stations are added in the late 19th century, but many stations are added after 1908 including, increasingly from 1933, ones with an overall cooling trend. From 1950, of the ones that are added, proportionately more of them have a warming trend.  Could this show up in the raw data? 
  • 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
Now, before looking at this, we have to remember that an overall warming or cooling trend is just that; the world experienced cooling in the period 1940-1970 (as in Fig. 9 here), so we might expect some cooling in almost all stations in that period. 

Figure 4. Trend additions to dT data from Figure 1 overlaid with the overall station count.
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

So the effect seen in Turkey in the previous post is even more in evidence here in China – major inflections in the shape of the raw data as a result of changes in station numbers and, as the analysis tentatively suggests, possibly station trend.  As I said at the beginning – interesting.  The question is – how does this bear though into the adjusted data (Figure 1 and 4 use unadjusted or ‘raw’ data)?  And is this effect lost in the way stations are combined and anomalised in the final product? Now that we know “where the holes in the road are deep” I think it is clear where to look further. It is simple, the thermometer record in China is not stable; we need to be sure that this is not affecting the overall output and resulting in overstatement of any warming experienced by China.
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4 Responses to Climate Syndrome: China Meltdown?

  1. E.M.Smith says:

    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.)

  2. VJones says:

    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.

  3. Anonymous says:

    Another Fine job, mate!

    Otherwise absorbed,

  4. VJones says:

    @RR – Thank you!

    @pass – thank you for the positive comment, however I have deleted your comment due to the hidden hyperlinks after the comment.

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