Time – temperature series for 26 selected climate stations in southern Africa are presented. The stations are spread from Capetown to Zanzibar. The mean result for GHCN V2 (unadjusted) is a flat temperature record from 1880 to 2011. The data seem to record the mid-1970s cooling recognised in Central Australian records and overall the results are closely aligned with Central Australia where station selection criteria were objective.
The GHCN v3.1* homogenised records have been subject to wholesale data deletion, data addition and data change. And yet, as was the case in Central Australia, the net result on the homogenised time temperature series is minimal, has tended to reduce variance and in part erase structure that may be due to natural climatic cycles. In southern Africa, homogenisation does not appear to be the source of warming present in climate reconstructions but evidently absent in the raw records I selected.
(* note that Roger advises that there is uncertainty about what generation of records are archived at this web link. They are GHCN homogenised records of uncertain provenance. They are evidently the station records used by GISS temp.)
Temperature anomalies for 26 southern African stations. The trend line, rising perhaps 0.1˚C in 130 years is effectively flat. The data display cyclicality with an approximate 85 year cycle. A cold period, 1890-93 appears to repeat in 1974-76. The latter cold period is also seen in Australian records. These mark the low points. The high points are in the 1920s and recent decades. 2005 is a high outlier caused in part by a low number of operational stations (9). The amplitude of variation is roughly ±0.7˚C. The data suggest that a regional temperature change of 1.4˚C in 43 years is not unusual.
This is my third detailed post hunting for global warming. The first two looked at central Australia and Iceland where no evidence for warming in the last 100 years was found. Southern Africa makes it 3 out of 3. In central Australia I used all 30 records returned by the NASA GISS web platform and in Iceland I used all 8 Icelandic records that exist. Objectivity was assured. In this post necessity has required a less objective selection of climate stations which I am fully aware is open to criticism. If I have subjectively de-seleceted urban stations that show warming I’m more than happy to discuss this in comments. But beware, a station that shows warming needs to be surrounded by stations that also show warming for the data to be valid (see Appendix 1).
Station Selection Criteria
In Australia I used the NASA GISS web platform, clicked on Alice Springs, and this produced a list of 30 climate stations within a 1000 km radius, covering a huge part of that continent, and I used them all. In Iceland I used the data from all 8 climate stations. In southern Africa, objective station selection has been more challenging.
In southern Africa clicking on the NASA GISS map returns very large numbers of rural records that span 1969-1991. This makes it difficult to objectively select stations with long records that are not affected by urban warming and so I clicked around looking for long records. Urban records that showed warming were rejected while urban records that did not show warming were selected. My eye was also drawn to the large number of records that showed mid-1970s cooling and I regarded that as a hallmark of station quality.
Within congruous climatic regions, temperature trends are also expected to be congruous. In other words they should all be pretty much the same (Appendix 2). Inspection of Figure 1 shows that the selected records are all trending flat, and so if a record shows warming my rationale is that there is likely something wrong with it. See for example Pretoria (Appendix 1).
I am fully aware that this subjective station selection is wide open to criticism. But commenters are invited to click around and see if they can find geographic clusters of warming records. Lourenco Marques/Countinho is a good place to start. It has been warming since 1910, but the nearest rural record, Skukuza is completely flat as are the majority of rural records in a 600 km radius. On this list, check out Inhambane and Chipinge, two long records that I missed on my selection survey. Both completely flat and record 1970s cooling.
GHCN V2 Unadjusted Data
Figure 1 Raw temperature records for the 26 selected stations. The range in temperature between localities is enormous from 15˚C in Bethal to 26 ˚C in Zanzibar. Bethal is a small farming township in S Africa, 26˚S while Zanzibar is an island off the east coast 6˚S of the equator. Bethal is at an altitude of 5,518 ft (higher than Ben Nevis) hence the cool climate. There is a paucity of long, old records and also a paucity of recent records owing to station closures. Sharp eyes will see that there is no overall warming trend, but a tendency for saucer shaped records with a warm past and a warm present.
Figure 2 How to make sense of the spaghetti in Figure 1? Each station record was converted to an anomaly by deducting the mean temperature for that station from the temperature – time series. Taking the arithmetic mean of the stack provides this dT anomaly trend. There are a number of key observations:
- A linear regression through the data is effectively a flat line. There is no warming in these southern African stations.
- There are two short cold periods, 1890-93 appears to repeat in 1974-76. The latter cold period is also seen in Australian records where the cold period spans 1974 to 1977 (one year longer).
- The cold periods mark the low points of a circa 83 year cycle with gradual warming and cooling in between.
- There is a high temperature outlier in 2005 brought about in part by a low number of operational stations (9).
Mid 1970s Cooling?
Figure 3 In scanning a large number of records a mid-1970s feature caught my eye that I had also seen in Australia. The period 1974-1976 appears to have experienced regional cooling.
GHCN V3.1 Adjusted Data
Figure 4 The GHCN V3.1 data for the same stations.
Figure 5 The difference between V2 and V 3.1 data. These are the changes made to “raw records” under the banner of correcting for non-climatic artefacts. It needs to be noted that the changes are concentrated in a ±1˚C band and records are both warmed and cooled. But then some huge corrections are applied to certain records, almost 5˚C in the case of Windhoek and over 3˚C in the case of Keetmanshoop.
Figure 6 Screen shot of my spread sheet used to make Figure 5. Making Figure 5 was a lot of work since the V3.1 has so many edits compared to V2. The coding above is as follows:
Empty cell = no data V2 and V3.1; number in cell V2-V3.1; zero in cell V2=V3.1; yellow cell V2 data deleted in V3.1; green in cell V3.1 data exists where V2 data does not.
A huge amount of data has been deleted in V3.1 with a strong tendency to chop the old part of long records, arguably some of the most valuable data. And there is a tendency to generate new data at the young end of records where no V2 data exists. This combined with Figure 5 looks like shocking, mass manipulation of temperature records.
Figure 7 The V2 and V3.1 record count shows the true scale of data deletion and how around 1990 data deletion gives way to data creation. The chart also shows how, in keeping with other parts of the world, station closures has resulted in the recent past being represented by meagre numbers of stations.
Figure 8 But then making a dT plot for the remaining V3.1 records, we find that, like Australia, the impact of all the editing is minimal. Note that the pre-1894 data were erratic and they are not shown. A slight warming gradient is added to what was a flat record. But the warming is spread across the whole record and not post-1970. The 1970s cool period is even preserved. What has happened is that the variance is reduced and the clear cycles seen in the raw records have been straightened. Removing variance is I guess what homogenisation is supposed to do but here it is likely removing natural climate cycles from the data. And that gives me cause for concern.
I really don’t understand what GHCN are up to. The wholesale manipulation of the raw data displayed in Figures 5 and 6 is appalling. And yet, the net impact is minimal. Why do it? I do not believe that it is homogenisation of data that has resulted in flat temperature records being turned into warming records, but as yet I do not know what the cause of “spurious warming” is.
Figure 9 To conclude this section, the chart shows dT_V2 minus dT_V3.1 which, by definition, is an aggregate view from GHCN of how non-climatic artefacts have affected temperature records in the 26 stations selected.
I began this line of enquiry into temperature records with the aim of testing the veracity of GHCN homogenisation methodology. What I have found in Australia and southern Africa is that homogenisation results in the mass deletion of data, the addition of data and wholesale overwriting of original records but the technique does not greatly distort the overall temperature history. The extensive data editing shown in Figures 5 and 6 has essentially a zero outcome. So why do it?
The big surprise has been that central Australia and southern Africa show little evidence of warming. This is more than a little perplexing since GISS, NCDC, Hadcrut4 and BEST all show significant southern hemisphere warming. BEST shows warming in South Africa that I would assert is absent in raw, unpolluted data.
Figure 10 Time temperature trend for South Africa from BEST. Includes data from 193 stations. BEST’s focus has been on quantity where the focus should be on quality. How do flat raw records get turned into this?
Roger Andrews has pointed out in Homogenizing the World that these 4 reconstructions do not agree with each other in the southern hemisphere and that the land temperature record in the southern hemisphere, when area weighted, accounts for only 10% of the global picture.
My approach is biased towards avoiding heavily populated prosperous areas. I suspect that moving into SW Australia and using warming urban records from Africa would produce some warming that would appear attributable to human activity. I can’t help but feel that there is either some flaw in my methodology and logic or in the methodology employed by everyone else (apart from Roger).
Appendix 1: Bad Records
Figure 11 Hunting for long continuous records, these ones caught my eye. Durban, Port Elizabeth and Diego Suarez each have a step down of between 1 to 2˚C. Had these features been aligned they may have meant something. But they are not. Step changes like this are perhaps linked to a station move, but it’s a lot of work to find out. Rather than make assumptions and try to correct records such as these I believe it is simply better to leave them out. There are more than enough “good records” to build the story.
Pretoria is an example of an urban record that shows significant warming. Pretoria University has a flat to cooling record that records the 1970s cooling and probably provides the real picture. Urban records that show warming like Pretoria should always be rejected in my opinion unless supported by the majority of surrounding records. I have used neither of the Pretoria records.
Appendix 2: Congruous Temperature Trends
The UK provides a good example of congruous temperature trends. ALL records display the same structure over a large area. Lerwick on the Shetland Islands is 1200 kms north of Southampton on the south coast of England. If a UK climate record did not conform to this regional trend there would most likely be something wrong with it.
Figure 12 Tmax, 5y running averages for 23 UK stations.