I’d made a couple of trial runs at a “monthly anomaly trend” graph based roughly on a running total of the monthly dT/dt values. There were two issues.
First, the graphs were very “busy” and it was hard to hear what they had to say in the clutter.
Second, they were ‘quirky’ in that the same attribute that gave the dT/dt graphs the nice “bullseye” marker where things changed oddly, also put some odd processing artifacts in the graphs. This was basically a side effect of how “Duplicate Numbers” were handled.
The “Duplicate Number” (as NCDC calls them) or the “Modification History Flag” as the code named it, tells you that some change of processing history applies to a thermometer data run. Most of the thermometer records have multiple “Dup Flags”. For the dT/dt series I wanted to emphasize this ‘splice’ so I deliberately let the “difference” show up as a line of zeros in a data set when the “Dup Number” changed. (As in First Differences where a new record starts with a line of zeros as the first time you see a value it has no difference from itself). This involved deliberately suppressing the “overlap” period that most records have. (Since the records are almost always identical, doing an average of X with Y when both are the same yields the same answer…) and the “bullseye” was a great diagnostic artifact.
On the monthly running total graphs the discontinuity was enhanced too much. For Mauritius, for example, we got a jump up in December that other months did not flaunt so much, due entirely to that ‘reset on dup number change’.
In this version I’ve gone ahead and “feathered the splice”. There is typically a couple of years of overlap between each Dup Number. Rather than suppress that and enhance the detection of a transition, in this version I gently ‘feather the edge’ by blending the disjoint Duplicate Number records into one record (and thus also avoid the ‘line of zeros’ on a reset from a record change).
This is done by the expedient of simply averaging the temperatures together for a given thermometer during the overlap period. I may someday change this to averaging anomalies, but it doesn’t really matter. Inspection of the records shows that they are almost universally identical, and where there is a difference it is usually in the 1/10 C space or is a missing datum in one series but present in the other.
So for an overlapping record, I’ll average the temperatures for EACH single thermometer and splice the different Duplicate Number series together into a single data series. This can have a small impact on the “warming” found. In most cases I looked at, it flattens the overall graph by a fractional amount. This is likely an artifact of the “lost data” in the gap of zeros being from, typically, the same year for large groups of thermometers. That is, it is a ‘splice artifact’ from the concentration of the change in a single year. This blending via averaging method of splicing will give a smoother (and more accurate) rendition of actual temperature changes in the thermometer record since we will only take a line of zeros on the very first record for a given thermometer.
Basically, the forensic value of flagging the splice drops, but the “fidelity to actual history of thermometer reading” goes up. Especially in the detail of individual months where a single dropped datum might put a 2 C ‘step’ into a line of total anomaly to date at that ‘enhanced splice effect join’ point.
Also, in these graphs, I’ve gone to emphasizing the trend line for each month. You can still see individual months ‘cumulative anomaly’ volatility and relative position, but I think that seeing the overall trend line is usually the most informative.
OK, enough on process. If folks really care, I’ll provide code and tutoring on how to reproduce any particular variation on these graphs and methods. For now, on to some more graphs and analysis…
As before, you can click on a graph to get a very large version to look at in more detail.
Not really much different overall. We still see some months falling while others are rising. Neat trick, eh? With the emphasis on trend lines you can see, for example, that the orange November line is strongly dropping, yet when The Great Dying of Thermometers begins, it makes a hard “U Turn” and heads up. It is a month with a strong down trend into the baseline, but it also is clearly behaving differently from some of the other months.
IMHO, this is a strong “Smoking Gun” that the ‘warming’ in the record is not a CO2 effect, but that it is rather a “thermometer selection and splicing” artifact. We will see very similar effects in each graph.
In this case we have about 1/2 the months dropping and about 1/2 rising. Not quite a “global warming” effect… It’s a race condition between these two groups of changes that results in a net warming or cooling. The “dT” line is the net. That’s the “hot pink” trend line. You will notice that for Australia it has a very small (trivial, really) positive slope. The bulk of the “warming” for GISS or CRU would come from that “dip” in the baseline during that 1951 to 1991 span; and with most of that coming from just a couple of individual monthly trends.
What’s more, there are even times when adjacent countries or thermometers have the same months going in different directions (or different months going in the same direction). The sheer chaos of which way months are trending argues strongly against the CO2 ‘broadly warming’ thesis and argues strongly for an “instrumentation and process” artifacts explanation.
Some Specific Australian Stations
We saw this Marble Bar set earlier as part of a ‘splice set’ example in the “Mysterious Marble Bar” posting. In this graph we can see the “boom town” warming, but with monthly detail on trends.
Interesting that even in a “boom town” with growing UHI there were some months rising while others fall. August dropping, May flat, October on a rocket ride. Looks more like local effects than CO2 to me.
Here we see Darwin, dropping:
So it’s getting hot in Marble Bar, but Darwin is cooling off. What’s “Global” about that?
And Hobart down in Tasmania:
January and December falling, June July August and September rising. So it’s cooling off in summer but winter is warming? Sounds like a UHI issue to me. That the ‘shoulder months’ are basically flat also argues for fuel driven effect.
What a pretty “bow tie” effect!
For comparison, we have some more southern hemisphere graphs:
We note that unlike Australia, November is rising nicely. And April is flat where it was rising nicely in Australia. One is left to wonder how such trends can be reconciled with a generally diffuse physical effect as CO2 is supposed to be.
Nearby is Mauritius:
And for comparison at the top of Africa:
Over time thermometers move away from the coastal water and into the hotter interior. I suspect that contributes to things like that very hot December.
Another “bow tie” with a different set of risers and fallers:
June, July, and August are just flying, the rest of the year not so much… Wonder if there is more jet traffic at the airports in those months?
Just south in Argentina:
What a mess. More dynamic range than Brazil, but still has months going in all directions.
I like Chile:
Though I’m wondering what the deal is with September and especially June. Sudden outburst of heaters in June or something?
Elsewhere in the Pacific
Dropping like a rock in most months.
Though a lot of the drop looks like it comes out of that high start, prior to the added thermometers splice. Then again, the 1930’s – 40’s were reported warm in many places.
How about New Zealand?
What a mix! August on fire, September calming, but back to warming again in December. March relatively flat (slight down trend) with April and July dropping. But what is happening in May and June! Who lit them on fire? I’m sure someone can figure out what’s going on here, but it won’t be easy. And I’d just love to hear how CO2 is supposed to cause that selective warming over that many years.
Still with a strong wedgee going on. Amazing really, how divergent the months are.
Blending or “feathering together” the duplicate numbers smoothes some of the individual months but does not strongly impact the overall character of the graphs nor the conclusions I would draw from them. It does make the “trend line” a good method for seeing the divergent nature of individual months; by station, by country, and even between regions.
The warming seen in some places looks, to me, like it varies more by individual location issues than by any global effect.