In recent months I’ve had a series of posts looking at the temperature histories of a number of land areas in the Southern Hemisphere [1, 2, 3, 4, 5]. This was in response to a post by Roger Andrews where he presented an analysis of about 300 climate stations from the Southern Hemisphere that, when combined, showed substantially less warming than the reconstructions presented by various groups (BEST, GISS, HadCRUT) . I found this to be both intriguing and important and wanted to see if I could replicate Roger’s result.
Figure 1 Note that this chart has an expanded Y-axis scale and the grid lines are at 0.1˚C intervals. A regression through all the data using station average as the base indicates warming of +0.18˚C per century, i.e. close to zero. The black trend lines are parallel to the regression and show there are rising tops and bottoms in an overall slowly rising trend. The alternative view is a flat trend from 1880 to the mid-1970s with a step change to warmer temperatures across the mid-1970s cold snap.
- The average time-temperature anomaly series for 174 climate stations from New Zealand, Central Australia , Southern Africa [2,3], Patagonia , Paraguay and Antarctica  are presented in Figure 1. A simple regression through the data with no weighting indicates warming of +0.23˚C since 1880 equivalent to +0.18˚C per century. This is substantially less than S hemisphere land temperature reconstructions reported by BEST, GISS and HadCRUT.
- Comparing with Roger Andrews’ reconstruction the difference is less than 0.1˚C. I have managed to replicate Roger’s result.
- My default method is to use each station’s average temperature as a base for calculating anomalies. All anomalies have also been calculated using a fixed base period of 1963 to 1992. Doing so makes no material difference. Warming is reduced to +0.21˚C since 1880 using the fixed base.
- Area weighting the results produces a trend of +0.19˚C warming since 1880. Area weighting lends substantial weight to Antarctica (only 14 records) where the main data series begin in 1954 and possible methodological problems are identified with lending 55% weight to only 14 stations with records that begin in the mid point of the time series.
- The mid-1970s stand out as an anomalous cool period seen in records throughout Central Australia and Southern Africa. It was also cool, although not anomalously so, in S America, Antarctica and New Zealand at this time.
- The structure of the data is one of cyclical warming to circa 1914 followed by cyclical cooling to the mid-1970s followed by cyclical warming to the present day. There is no evidence for warming linked to the 1998 el nino in these data. Nor is there evidence for a pause in warming in the Southern Hemisphere since 1998.
- The data presented here are not intended to provide full cover of the Southern Hemisphere. Coverage will be extended at some point. But there is probably sufficient cover to be representative of the southern hemisphere land and surrounding marine conditions. There is scant evidence for significant warming in these data suggesting global warming is confined mainly to the Northern Hemisphere.
GHCN V2 data as downloaded from the NASA GISS web interface are used throughout. These V2 data have been subjected to minor adjustments by GHCN, which is undesirable, but the level of adjustment is substantially less than the V3 homogenised data.
The areas of study have been selected to as far as possible avoid large numbers of people and reworking of the land surface by Man although this has not always been possible, for example in Southern Africa. Urban records present a dilemma. The most valuable records are long and continuous and these are often from urban areas. Urban records that show clear signs of urban warming were not used. It was only in Southern Africa that 7 urban records were rejected on this basis.
In many cases I allowed the NASA GISS station selector map to choose the stations for me. For example placing Alice Springs at the centre of a NASA GISS search returns 30 records in a 1000 km radius around Alice Springs. This was done in New Zealand, Patagonia and Paraguay. However, in Southern Africa the GISS interface returns very large numbers of short rural records and I therefore clicked around on the map searching for the longer continuous records. Similarly in Antarctica I clicked around on the map looking for the longer and continuous records. In addition to the continental areas, the islands of South Orkney (Base Orcadas), South Georgia (Grytviken) and Kerguelen are included.
Most of the individual areas have been presented before as individual posts [1, 2, 3, 4, 5]. New Zealand and Paraguay have not and these data are presented as appendices to this post.
Figure 2 S Hemisphere map showing the distribution of areas sampled. These have in general been chosen to avoid large centres of human population and prosperity. I will return to sample more areas and it will be interesting to see to what extent population density and / or latitude impacts the results.
The time series distribution of stations is shown in Figure 3. While 174 stations have been examined, the maximum operational at any one time was 146 in the mid 1970s. The minimum number was 7 in the early 1880s and this may impart some small sample bias to the earliest part of the composite record. From 1889 there were at least 12 operational stations and small sample bias diminishes thereafter as increasing numbers of stations come on line.
It is assumed that the commissioning of stations was a random process and that station number growth to the mid 1970s should not impart any bias to the composite data with one exception. Antarctica, that represents 55% of the total land area (of the sampled land area), only came on line with 14 stations around 1956. If those data are area weighted this results in a major data structure discontinuity.
A source of possible concern is the mass program of station closures that took place in 1990/91. We go from 118 to 72 stations in 2 years. If this process was non-random then it has potential to bias results from that time onwards.
Figure 3 The distribution of operational stations from the group of 174 selected stations.
With discontinuous time-temperature records it is essential to convert station temperatures to anomalies to minimise the effect of the discontinuous series. The conventional way to do this is to use the mean temperature for a base period, for example 1951 to 1980 favoured by GISS and others, and to deduct the mean temperature from that base period from the whole time-temperature series to produce a time-anomaly (dT) series. This is undoubtedly one approach. But what if a station record has no data for the base period?
Approaching this as a novice some months ago I decided the best and simplest way to do this was to use the average temperature for a station as the base. This uses all the data in the station normalisation procedure which I instinctively feel is correct. One consequence of this is to have a non-uniform base time and it was anticipated that this procedure may suppress differences while using a fixed base period may amplify them. There is no absolutely correct way around this. When my Central Australian post appeared on Climate Etc some weeks ago there was a disproportionate amount of interest in “flawed normalisation” as a scapegoat for what the data really showed.
And so for the S Hemisphere I have re-calculated all records using a fixed base period of 1963 to 1992 (at the recommendation of Roger Andrews) which is a time period that catches most but not all records. Where a record is not represented in 1963-1992 I have used the station average instead. As we shall see none of this has any significant relevance to the substance of the debate.
Figure 4 Using a 1963 to 1992 base period for normalisation has no material effect on the results. Somewhat surprisingly the slope is slightly reduced compared with the station average base (Figure 1).
Figure 4 shows the stack of 174 stations replotted against the 1963 to 1992 base period. You will be hard pressed to see any difference between this and Figure 1 that uses the station average base. A regression through the 1963-92 base data produces a +0.21˚C since 1880, slightly but not significantly less than the station base method.
The main results have by now already been presented. These large areas of the southern hemisphere land mass show little significant warming since 1880. To place this in context the results are re-plotted at a more conventional scale in Figure 5. This shows that from 1880 to 1973 (almost 100 years) the trend was effectively flat. In the mid 1970s, centred on 1976, something strange happened to Southern Hemisphere climate. A marked cool period, accompanied by higher rainfall, gave way to an era of marginally higher temperatures, perhaps 0.2˚C warmer than the previous era. This is what climatologists should be seeking to explain. I do not believe it has anything to do with mankind’s activities.
The cool feature centred on 1976 is present in most Central Australian and Southern African records and is very real. It is probably over – represented in this data set since I have 79 records from these two areas (45% of the total records for 31% of the land area). But it was also cool, although not anomalously so, in S America, Antarctica and New Zealand at this time. Roger Andrews, Crutem4 and BEST all pick out this anomaly (see below) but GISS Temp does not. Evidently GHCN V3 homogenisation has homogenised this feature out of existence.
Figure 5 This chart plotted at different Y-axis scale provides an alternative perspective.
I have marked a few other events on Figure 5. Krakatoa erupted in August 1883. 1884 was a little cool in the S hemisphere, but not uncommonly so. 1891 was as cool. The mass station closures of 1990/91 do not really appear to have affected the structure of the time-temperature profile. The 1998 El Niño temperature spike that all climate watchers are familiar with also appears to be absent in these S hemisphere records although present in GISS Temp and Crutem4. As discussed below, the presence of the 1998 El Niño spike in Crutem4 looks particularly suspicious since it is quite clearly absent in the 174 records analysed here.
Many workers and some commentators like to see a smoothed moving average. There is certainly a place for this with certain kinds of data. The S hemisphere land data is not noisy and does not really require smoothing but a centred 5Y moving average is shown in Figure 6 nonetheless.
Figure 6 A 5Y centred moving average reveals the general structure of the data. A regression from 1880 to 1980 actually shows a gently cooling trend. If CO2 radiatively forces temperatures then it failed to do so in these parts of the S hemisphere during that 100 year period. This chart does show a little warming since 1980 but don’t be deceived by the scale. Recent peaks are +0.3˚C compared with +0.1˚C back in 1914. Recent warming is real but trivial.
The professional reconstructions build a global grid and seek to allocate a temperature to most or all cells within that grid in some cases regardless of whether or not there is a temperature record for it. The temperature history for each grid cell is weighted according to the grid cell area. This appears to be a sound methodology until one actually attempts to do this, which then makes one aware of the limitations.
With the current set of records, Antarctica exemplifies this problem. For the main continental area the main record series begins in the mid-1950s. We quite simply do not have a temperature record for the main continent before then. There are longer records from the Antarctic peninsula and the islands to the NE (South Orkney and South Georgia) but this area is quite clearly in a totally different climatic regime  and should absolutely not be used to model or project temperatures southwards to The Continent.
Figure 7 Areas and weights used to calculate the area weighted temperature-time series (Figure 8). Some of the areas are approximations. Prior to the mid 1950s the reconstruction shown in Figure 8 does not include Antarctica. Area weighting lends a large amount of significance to the 14 Antarctic records from the mid 1950s on.
The philosophy I have followed in selecting “good” records from a particular area is to identify records that exhibit similar trends or features. For example, records from Central Australia and Southern Africa that show the mid 1970s cooling bear a hall mark of good data quality. In working across a geographic area one develops a sense that all records are recording more or less the same temperature history, absolute temperatures being controlled by latitude and altitude. Each of the areas shown in Figure 2 can be regarded as having congruous records with the exception of South America where there is a higher degree of interwoven variability.
The area of each group shown in Figure 2 has been estimated and the area used to weight the mean time-temperature series for that group (Figure 7). The weight distribution varies as the time series come and go. For example the weights of Central Australia and Southern Africa are halved when data from Antarctica arrive in the mid 1950s (Figure 7). The outcome of this exercise is shown in Figure 8. The gradient is little changed but the distribution is more variable because of the different way the calculation is done and the weight given to the more variable Antarctic records.
Figure 8 The area weighted chart is not analogous to Figure 1. Figure 1 is the average of the stack of 174 records. This chart is the sum of weighted averages for 7 areas + the three islands (that have tiny weight). Warming of +0.15˚C per century is little changed from Figure 1. Note that prior to 1956 there was only single station patchy records for Antarctica and since these records are lent 55% weight the Antarctic series begins in 1956 when there were 5 operational stations.
Comparison With Other Reconstructions
One of the main objectives of this project is to try and get to the bottom of the warming seen in time-temperature series for the Southern Hemisphere produced by large well funded groups like NASA GISS, UK HadCru and BEST. I have tortured the data for the 174 stations I selected every which way I know and cannot squeeze more than +0.18˚C per century out of them. I have been told repeatedly that I am wasting my time. So many EXPERTS working this data all come up with the same conclusions. Well not quite. There is about as much difference between EM and CRUTEM4 as there is between CRUTEM4 and BEST/GISS Temp.
Figure 9 Roger Andrews’ reconstruction had about 300 records and more complete cover of the Southern Hemisphere. He is using a different base period that accounts for the gross offset between the two series (this applies to all the comparisons). There is a high degree of congruity, i.e. the series are to large extent going up and down in unison.
Figure 10 Deducting EM from RA produces a fairly flat trend with a gradient much less than 0.1˚C per century. The main difference between myself and Roger probably lies in the greater geographic cover in his data series.
Figure 11 There appears to be an even higher degree of congruity between EM and CRUTEM4 (Crutem4 data kindly supplied by Roger Andrews) suggesting that similar data lies beneath these to reconstructions. CRUTEM4 for example picks out the mid-1970s cooling. One exception is the 1998 El Niño. CRUTEM4 has a large temperature spike in 1998 that is absent from the 174 records I examined. CRUTEM4 begins below and ends above the EM series giving rise to a warming trend that is evidently absent in the “raw” records.
Figure 12 Deducting EM from CRUTEM4 produces a warming differential of about +0.5˚C between the two. This is the closest match I have to any of the “official” reconstructions. Trying to track down the origin of this warming that appears to be absent in the “raw” GHCN V2 records is one of the primary objectives of this project.
Figure 13 There is also a high degree of congruity between BEST and EM but the BEST series begins way lower and ends way higher (once again thanks to Roger Andrews for the annualised BEST land S hemisphere series). The high degree of congruity suggests that both they and I must be doing something right. But the origin of the gradient in the BEST data that seems absent in the “raw” records Roger and I have analysed remains a mystery. I am hopeful that Judy Curry may run this post and that Stephen Mosher who blogs there may begin to provide some form of explanation.
Figure 14 Deducting EM from BEST produces a warming differential of about +0.9˚C between the two.
Figure 15 GISS Temp probably gives the poorest match to the EM data series. This time the GISS Temp data comes from the NASA website from a link I believe Gavin pointed me to. As with the other “official” series GISS begins below EM and ends higher producing a temperature gradient that seems absent in the raw records that both Roger and I selected.
Figure 16 Deducting EM from GISS Temp produces a warming differential of about +0.9˚C between the two.
I believe that myself and Roger Andrews have sufficient data to provide a representative picture of Southern Hemisphere land temperature history and our data shows a warming trend of between +0.2 to +0.3˚C since 1880, significantly below the “official” reconstructions. The most perplexing is BEST who claim to be a group of sceptics that set out to test the veracity of GISS and HadCrut and yet have managed to generate warming from what are effectively flat records. The objective of this project is to try and discover where any errors lie. While non-representative cover may be a small part of that story I am not prepared to accept this as the whole explanation. I believe the answer may lie in a combination of the following:
- Homogenisation of data (adjustments of raw records) that is known to add +0.3 to +0.5˚C to the GHCN V3 series even although the main man-made artefact is UHI that should result in the application of a net cooling correction. Is the GHCN v3 homogenisation residual evenly spread across the globe? Or is it over-represented in the S Hemisphere to mask the inconvenience of little warming across half of Earth?
- The inclusion of UHI warmed urban records
- The adjustment of records to a regional expectation (BEST). Does this for example adjust S hemisphere records to a N hemisphere expectation? And are UHI warmed urban records included in that expectation? Has the CRUTEM4 1998 spike been imported from the N as an expectation?
- The projection of temperatures into grid cells where there are no records.
I believe the most reliable way to get an accurate picture of global time-temperature is a simple analysis of what are viewed as the most reliable raw records. A degree of processing to create anomalies is required and area weighting may help ensure representative cover. Any processing beyond that opens the door to the introduction of biases and since further processing is not necessary it should be avoided.
 Temperature Adjustments in Australia
 The Hunt For Global Warming: Southern Africa
 The Hunt For Global Warming: Southern Africa Part 2
 The Hunt for Global Warming: South America
 The Hunt For Global Warming: Antarctica
 Homogenizing the World
[Note added 14th April: One of the comparisons I forgot to do was with the satellite data. The chart shows UAH, lower troposphere, land only, hopefully annual mean. I think this is pretty good and tends to conform the veracity of the satellite record. NOAA NCDC]
Appendix A New Zealand
Figure A1 15 climate stations in New Zealand selected by the NASA GISS interface.
Figure A2 T spaghetti for New Zealand. There are 5 long records and visual inspection shows no clear gradient.
Figure A3 dT spaghetti for New Zealand. There is a high degree of congruity between the records, especially post-1905.
Figure A4 T anomalies calculated relative to station base mean and 1963 to 1992 base period. There is clearly little difference between the two although the 63 to 92 base has a slightly steeper gradient. While a regression shows a slight gradient the overall trend is flat since it lacks rising tops and bottoms.
Appendix B Paraguay and surrounding areas
Figure B1 34 climate stations in Paraguay and surrounding countries selected by the NASA GISS interface placing Puerto Casado at the centre. Many of these have a somewhat chaotic record and it was difficult to select good from bad and so the simple solution was to use them all.
Figure B2 T spaghetti for Paraguay and surroundings. There is a degree of congruity since many of the spikes go up and down together. But overall there is a higher degree of variability in these records compared with other areas. See for example Figure A2.
Figure B3 dT spaghetti for Paraguay and surroundings. A spike down can be seen in 1955 but otherwise the dT trends appear quite chaotic lacking any clear gradient.
Figure B4 T anomalies calculated relative to station mean base. Despite the more variable nature of the individual records this all seems to come out in the wash and the average of the stack is fairly typical for the Southern Hemisphere with a small positive gradient and gently rising tops and bottoms.
Roger Andrews provided initial inspiration and back up data. My son Neil Mearns compiled the New Zealand and Paraguay records and charts.
Note added 23rd April
Since making this post I have conducted a number of methodology checks that can be found at the link below: