Ever since Roger Andrews sent me his spread sheet showing that 300 or so land based climate stations in the Southern hemisphere recorded little warming I have been trying to find out what exactly is going on. Does CO2 not force temperature down under? At this point, I should confess that at the moment I am looking for global warming where I least expect to find it and that means avoiding areas where there are a lot of people and where the Earth’s surface has been completely reworked by human beings.
I have also been examining the impact that GHCN V3 homogenisation has on the less processed V2 records. This is very time consuming and this is the last time that I will perform this exercise.
In summary, GHCN V2 records from 25 climate stations selected by the NASA GISS platform from southern Argentina, Chile and the Falkland Islands produce a completely flat time-temperature anomaly trend. I have succeeded again in not finding evidence for global warming in the southern hemisphere. The GHCN v3 homogenisation adjustments vary individual stations by up to ±2˚C and follow the same robotic style of exact decimal fraction adjustments seen elsewhere. In this case, these adjustments do add warming of the order 0.4˚C since 1888. GHCN V3 records and temperature reconstructions based upon them are to be avoided at all costs.
Figure 1 The beautiful Lago Argentine in Patagonia.
I clicked on southern Argentina, placed the beautiful Lago Argentine at the centre and the NASA GISS platform returned 25 stations within a 1200 km radius (Figures 2, 3, 4). Most of the stations are classified as rural although there are 7 larger towns. There is no evidence for urban warming in this remote and wild corner of the world and so all 25 stations are included in this summary.
Figure 2 Station list with Lago Argentin at the centre.
Figure 3 Station locations. Four stations are located on the Pacific coast, west of the Andes and two stations on the Falkland Islands.
Figure 4 At no point were all 25 stations simultaneously operational. In 1901, station number increased to 6, prior to then, results are based on a very low number of localities. In 1992 station number plunged to 5, presumably on the back of political woes in Argentina.
The V2 “Unadjusted” Data
Clicking through the records and charts it is easy to spot three main kinds of record. Flat, slowly warming and a couple show marked cooling in the 1960s. These are Valdivia and Puerto Mont, both on the Pacific Coast of Chile. While there may be a case for looking at the Pacific and Atlantic coasts separately, everything is lumped together in this summary.
The individual time temperature series (Figure 5) were converted to anomalies by deducting the mean temperature for a station from that station and then the average anomaly for the stack was calculated as shown in Figure 6*. Once again we have a totally flat line. No evidence for global warming in this remote part of South America.
[* I have had growing unease about my normalisation procedure. I believe it is the most correct option available and I did some checks before adopting it. I have now done a more thorough check on the central Australian data where I normalised to the means of the period 1965 to 1974 as detailed in this comment on Climate Etc. It makes no material difference to the outcome.]
Figure 5 Temperature spaghetti plot for 25 S American stations.
Figure 6 Trends don’t come much flatter than this. There is absolutely zero evidence for global warming in this remote corner of S America. The 1998 temperature top corresponds to the global top associated with the big El Niño that year. This pattern seems to repeat about every 50 years.
The V3 Adjusted Data
The exercise was repeated for the V3 homogenised records. In central Australia homogenisation did not significantly alter the average temperature. In Southern Africa it added a little warming. In South America it has added a significant amount of warming, about 0.5˚C since 1888 (Figure 7).
Figure 7 GHCN homogenisation has added about 0.4˚C warming since 1888 to this group of records.
Figure 8 In my workbook I lay the V3 spread sheet on top of the V2 spread sheet and create a new dT spread sheet by deducting the V3 matrix from the V2 matrix. The data then need to be cleaned since there are a large number of years where there is no data, there are a number of years where there is V2 data and no V3 and vice versa. The pattern of data cleaning and consistency is shown in Figure 9.
The resultant plot describes the pattern of data modification that V3 has done to V2. This is supposed to describe non-climatic trends identified in the data which is clearly nonsense. In this group of records there are in fact long strings of data that have not been modified (zero in cell in Figure 9).
Figure 9 This screen capture of my dT spread sheet shows the pattern of data modification and editing between V2 and V3. Empty cell = no data; zero in cell V2=V3; number in cell = dT adjustment; yellow cell V2 data exist V3 not; green in cell V3 data exist where V2 data do not. This latter category is a puzzle since V3 is supposed to be a derivative of V2. The scale of adjustment and edits in this data set is not as extensive as seen in Australia, Africa and Iceland. It is therefore somewhat ironic that the V3 adjustments have in this case biased the data towards a warming trend.
I’ve already completed analysis of Antarctica and E Siberia. N Scandinavia is almost done. What I’m seeing in the data is providing me with plenty of motivation to continue. I hope readers bear with me for another few weeks. I’ll keep trying to have at least one energy related post per week.
I’d like to conclude with this nice chart from commenter William that shows temperature anomalies for 9 stations from Chile that seem to corroborate and extend the conclusions drawn in this post.
Figure 10 dT trends for 9 Chilean stations from this comment by William.