Africa – Southern Nations

The Southern Nations of Africa

This posting completes the Africa series of Postings (and will be followed by an “aggregator posting” listing the continents). Please note that I’ve added a “dT/dt” category on the right hand side of the blog. I’m going to keep the dT/dt charts separate from the GIStemp and GHCN particular issues. This will make all of them easier to search.

OK, So the southern end of Africa. We would expect it to have much similarity over that tip of the continent. The land mass is relatively thin (as compared to the north) and most of it has coastal exposure. It is in some ways dominated by just 4 countries. Angola, Namibia, South Africa, Mozambique. The rest are smaller and in an inland cluster. So we will approach them in that same way. Starting in Angola, working down and around the Horn of Africa, and then up the other side, ending with the inland smaller countries.

Angola

Angola Monthly Anomalies and Running Total

Angola Monthly Anomalies and Running Total

Well. Astoundingly flat. Not even a dip in the baseline. No wonder it gets ‘cut off’ in the early ’80s.

Namibia

Namibia Monthly Anomalies and Running Total

Namibia Monthly Anomalies and Running Total

Well. Astoundingly flat. Wait… didn’t I just say that? No wonder that part of the globe shows up as missing on the GISS maps.

South Africa

South Africa Monthly Anomalies and Running Total

South Africa Monthly Anomalies and Running Total

Another incredibly flat “trend” though this time we do get about 1/2 C of rise out of the 1990 event (even with a tiny bulls eye) as a sort of step function, but no ongoing trend.

Lesotho

Entirely captured inside South Africa, so put here for comparison.

Lesotho Monthly Anomalies and Running Total

Lesotho Monthly Anomalies and Running Total

Too short to do much with, but flat for what it’s worth.

Mozambique

Mozambique Monthly Anomalies and Running Total

Mozambique Monthly Anomalies and Running Total

Well, I guess we know where all the ‘warming’ down near Madagascar can be found for “fill in” to those surrounding places which have a truncated or “holey” record. But man, what work it took to get it! Huge data drop outs, almost no cold going anomalies. All that just to stop a gentle decline and get all of what, 1/2 C out of it? Sheesh.

Needless to say, some clean recent data from Mozambique would be very interesting… If that weak “hockey blade” gets shown to be bogus, well, …

Zimbabwe (Rhodesia)

OK, a ‘flat roller’. It’s going nowhere, but bobbing up an down while it does it.

Zimbabwe Monthly Anomalies and Running Total

Zimbabwe Monthly Anomalies and Running Total

Dropping hard since 1998. Oh, look at that, cuts off in 2004. Have to ‘fill that in’ from somewhere else. Hey, Mozambique looks nice and warm ;-)

Botswana

Notice we jump from 1970 to 1981, then get 1987, 88, and 89 with almost no data. It’s a nice “partial baseline” dip, but nothing recent. So it will be filled in from “nearby”… and that would be, oh, Zambia…

Botswana Monthly Anomalies and Running Total

Botswana Monthly Anomalies and Running Total

How about by segments?

Botswana Monthly Anomalies and Running Total by Segments

Botswana Monthly Anomalies and Running Total by Segments

Where we have a nicely dropping trend turned into a “dip” at the thermometer count change up; then bookend it with a “lift” and later let GIStemp finish it out with “fill in”, “homogenizing”, and eventually even “The Reference Station Method” used on Grid / Box scale to make that Grid/Box anomaly from up to 1200 km away.

Zambia

Golly. Dropping like a stone up to the 1981 “lift” and then the 1991 “bulls eye” Pivot. Manages to turn it around nicely. Heck, we could even use some of this to “fill in” Zimbabwe… and maybe even Botswana…

Zambia Monthly Anomalies and Running Total

Zambia Monthly Anomalies and Running

A beauty of a dip in the 1951 – 1980 baseline too! Just because it’s no warmer than it was back in the 1920’s, no reason we can’t call this 1.5 C of “Global Warming” and “share the wealth” with all the adjoining countries that are just not going with the program. Heck, being nicely central in location, it can be used to warm just about every country in Southern Africa. What a nice convenient accident… please just ignore how much the thermometer count line has to jiggle around.

Malawi

Well, a rising early trend gives an upward tilt to the trend line for the early segment (though it is substantially flat for most of it’s history in the middle). Then…

Malawi Monthly Anomalies and Running Total

Malawi Monthly Anomalies and Running Total

What Happens! We’ve got a dropping trend line. Don’t even have a “dip” in the baseline and the 1990 Pivot gets us nothing (though in fairness it is encumbered by a load of dropped data in the late ’80s early ’90s) . I’m sure it’s just accidental that it cuts off in 2007 just after digging out of that -0.5 C hole it was in.

Conclusions

Having made over 150 various graphs in a couple of weeks, I never want to hear someone say I don’t do enough graphs ever again! ;-)

Southern Africa is not warming. Parts of it are cooling.

A few “well placed” buggered records can contribute a lot of “warming” to surrounding areas that are not “warming”, especially if the surrounding countries have their cooling or flat trends dropped.

Averaging a bunch of things together hides more than it reveals.

Given the “cooling” and “not warming” we found in other southern areas there is a very real potential for a global hemispheric oscillator to have been putting all the “cool” at the south end of the planet and all the “warm” at the north end for a few decades. Now that various ocean currents have ‘flipped’ it will be interesting to see if the 2010 ‘recompute’ of GHCN suddenly starts finding a lot of stations added in the south end of the globe. It would also be very interesting to find out if the satellite data produces maps of the hemispheres that could be used to “compare and contrast” and see if anything interesting is happening in the south vs the north. It’s certainly the case that an oscillator could easily have existed without showing up in the GHCN data set given how sparse the southern hemisphere coverage has been.

We really don’t know what our temperature history has been for most of the southern 1/3 of the globe. And we don’t really know what it is now. We have a few small spots with records, and those of dubious quality, that account for all the “global warming” in that 1/3 of the planet. That gets spread around to mask the fact that most of the nearby areas are flat of falling (or were before they were dropped on the floor…). Not exactly good evidence of any global warming pattern.

And then there is all that ice being added to Antarctica… but that’s another topic for another posting… For now, here is the “Antarctica” graph. But realize that it’s a splice nightmare with loads of divergent records being sucked into one graph.

Antarctica

Notice that the “tops” of the dT line (hot pink line) are fairly consistent at about the -1 C line right up to the 1990 Pivot. Notice too that the dT/yr (yearly anomaly average) just dies in volatility. That red line goes from a dramatic 3 C wiggler to nearly zero. That’s just wrong. At a minimum it says “nothing changes” in Antarctica. Hard to believe.

(It is counter cyclical with dT as dT is a running total of it, so it lags the moves in dT/yr)

Antarctica Monthly Anomalies and Running Total

Antarctica Monthly Anomalies and Running Total

It’s only the bottoms that rise. We’re not getting warmer in Antarctica, we’re just compressing the volatility of our measurements and converging on the long term stable top end. A look at the “monthly anomalies” shows that. Astounding compression of range. A lot of things that would have been considered “normal” in the past look like they are not making it to the record today. Some of that will be an artifact of more stations (the extreme of an average will not move as far as an individual station could move) and some may be the way our stations are now placed, compared to the past. But it’s a very strange trend in any case.

Either Antarctica is incredibly stable and our prior records are complete trash, or we have absolutely no idea what’s really going on as the record is cooked too much. Both ends of that chart can not be true and accurate.

About E.M.Smith

A technical managerial sort interested in things from Stonehenge to computer science. My present "hot buttons' are the mythology of Climate Change and ancient metrology; but things change...
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4 Responses to Africa – Southern Nations

  1. Ruhroh says:

    Rightio, more graphs per unit time than any other site I can think of at this moment.
    And I have greatly enjoyed your unique approach to flagging (the oddities) while you flog…
    But I hope it wasn’t all just for the carpers and carpettes;

    I have the vague sense that you were enjoying yourself, whilst demolishing so many of the treasured hiding places of the ‘cognoscenti’…

    They may notice that suddenly they are singing in a new register, falsetto…

    One new challenge; it would be great if you could express the dT/dt calculation in a formal, closed form kind of formula.
    Maybe LaTex or whatever cool guys now use…

    Anyway, simply smashing!
    RR

  2. E.M.Smith says:

    Running total of ( average for selected stationIDs of ( annual averages per thermometer of (monthly anomalies month over month) ) )

    I think that’s about as compact as I can get it…

    I make an “anomalies file” by doing the delta for each month from the last valid data item in that month for each thermometer/”Duplicate Number” (12 digit StationID). Past to present. (direction of time is somewhat optional here).

    Then I make a report that is the annual average of those monthly anomalies ( averaged for a collection of thermometers specified at run time). That is the dT/yr column that I sometimes plot but usually don’t.

    Then I make a running total over years of that dT/yr. (present to past) That is the dT line. The accumulated change of temperature. (with a count of thermometers by 11 digit StationID – not by “Duplicate Number”).

    Hope that helps…

    BTW, the Italians had an operatic form that might be appropriate to the rest of your comment: Envirato. Yes, a euphemism… but more, um, comfortable than some other terms.

    Yes, it was “fun” along with some of the “drudge”, but I’m ready for a rest from “canonical graphing”. Now it’s time to go back and find some of the less extreme oddities in the graphs, do some A/B compares, make some “Temperature rainbow hair” graphs. And, perhaps, finally follow up on all those things folks suggested that I investigate (when I couldn’t because I just HAD to get the set complete… I’m sure you know what that compulsion is like ;-)

    I’d also like to put a bit of time into trying to find out what that 1990 Pivot was caused by. It is, IMHO, the biggest Key Factor out there.

    FWIW, Africa shows footprints of being a testing grounds. The first place some techniques were applied or tried out. There are more divergent patterns and more variation in onset. I think it’s a very rich place to investigate the “how”s of things.

    But for now, the heavy lifting is done and it’s time for putty and spackle work for a few days. Some smaller and lighter bits of work and postings.

    At any rate, you now have the entire world in dT/dt graphical form to look at and to ponder. This is the data. The whole data and nothing but the data. Presented in summarized anomaly form.

    Free of various kinds of imaginings (like “fill in” and “homogenizing”) to the maximum extent possible while using the GHCN “UN-adjusted” data set. (Which does have some “fill-in” just not as much as the ‘adjusted’ or GIStemp products).

    And having looked long, hard, and close at each of these graphs (and the input reports to them) I have one major conclusion: CO2 can’t do that, but data handling processes can.

  3. vjones says:

    I was going to say take a well-earned rest, but on second thoughts – don’t. They say a change is as good as a rest. So please don’t stop, just turn your attention to another much-needed thorough examination.

    Wait till you see what I’m turing up in Canada (nearly ready to post).

    REPLY: [ Looking forward to the article! Post a link here to let me know when it’s ready! I AM going to rest, but in my own peculiar way… I will no long be ‘driven’ to complete the global set, so I’m going to do a few more “fluff” postings (that will probably interest more people than a gaggle of graphs did ;-) and I’m going to stroll through the canonical set of dT/dt graphs looking for “interesting bits” to explore (as we did in France and Marble Bar). Basically, a bit of a data and warming ‘walk about’ rather than goal directed behaviour. Yeah, strange way to ‘take a break’ but it’s what I do… -E.M. Smith ]

  4. eilert says:

    Can you supply me a list or were can I get a list, of the Namibia stations used. There were 5, then it drop to 1 in 2009. This single station is probably either Walvis bay at the coast or Windhoek inland at 1600m. The other stations I guess are the other mayor station. All of these report on a regular basis. In fact all have reported their temperatures on Weather Underground just 2 hours ago. Windhoek actually 9 minutes ago.

    REPLY: [ You can download all the data and station information from NCDC (directions are under the “GIStemp” tab up top, but you have to follow a few links to get to the data sources and download part). The stations in the “inventory” file for Namibia are:

    [chiefio@Hummer analysis]$ inin ^132
    13268014000 GROOTFONTEIN -19.60 18.12 1400 1450R -9HIDEno-9A-9SUCCULENT THORNSA 0
    13268110000 WINDHOEK -22.57 17.10 1700 1778U 61MVxxno-9A 2WARM GRASS/SHRUBC 50
    13268112000 J.G.STRIJDOM -22.48 17.47 1700 1685R -9HIDEno-9A-9WARM GRASS/SHRUBB 0
    13268300000 LUDERITZ(DIAZ -26.63 15.10 0 2R -9FLxxCO 1x-9SAND DESERT A 0
    13268312000 KEETMANSHOOP -26.53 18.12 1061 961S 10FLxxno-9x-9WARM GRASS/SHRUBA 0
    [chiefio@Hummer analysis]$

    “inin” is a little script I wrote that looks for things in the “station inventory” file and lines starting with the “country code” of 132 will be Namibia. The ‘survivor’ in 2009 is:

    [chiefio@Hummer analysis]$ more Temps/132.stns2009
    13268312000 KEETMANSHOOP -26.53 18.12 1061 961S 10FLxxno-9x-9WARM GRASS/SHRUBA 0
    [chiefio@Hummer analysis]$

    Hope that helps.. ;-)
    -E.M.Smith ]

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