Understanding GISS Temperature Adjustments

A couple of weeks ago something struck me as odd. Paul Homewood had been going on about all sorts of systematic temperature adjustments, showing clearly that the past has been cooled between the UHCN “raw data” and the GISS Homogenised data used in the data sets. When I looked at eight stations in Paraguay, at Reykjavik and at two stations on Spitzbergen I was able to corroborate this result. Yet Euan Mearns has looked at groups of stations in central Australia and Iceland, in both finding no warming trend between the raw and adjusted temperature data. I thought that Mearns must be wrong, so when he published on 26 stations in Southern Africa1, I set out to evaluate those results, to find the flaw. I have been unable to fully reconcile the differences, but the notes I have made on the Southern African stations may enable a greater understanding of temperature adjustments. What I do find is that clear trends in the data across a wide area have been largely removed, bringing the data into line with Southern Hemisphere trends. The most important point to remember is that looking at data in different ways can lead to different conclusions.

Net difference and temperature adjustments

I downloaded three lots of data – raw, GCHNv3 and GISS Homogenised (GISS H), then replicated Mearns’ method of calculating temperature anomalies. Using 5 year moving averages, in Chart 1 I have mapped the trends in the three data sets.

There is a large divergence prior to 1900, but for the twentieth century the warming trend is not excessively increased. Further, the warming trend from around 1900 is about half of that in the GISTEMP Southern Hemisphere or global anomalies. Looked in this way Mearns would appear to have a point. But there has been considerable downward adjustment of the early twentieth century warming, so Homewood’s claim of cooling the past is also substantiated. This might be the more important aspect, as the adjusted data makes the warming since the mid-1970s appear unusual.

Another feature is that the GCHNv3 data is very close to the GISS Homogenised data. So in looking the GISS H data used in the creation of the temperature data sets is very much the same as looking at GCHNv3 that forms the source data for GISS.

But why not mention the pre-1900 data where the divergence is huge?

The number of stations gives a clue in Chart 2.

It was only in the late 1890s that there are greater than five stations of raw data. The first year there are more data points left in than removed is 1909 (5 against 4).

Removed data would appear to have a role in the homogenisation process. But is it material? Chart 3 graphs five year moving averages of raw data anomalies, split between the raw data removed and retained in GISS H, along with the average for the 26 stations.

Where there are a large number of data points, it does not materially affect the larger picture, but does remove some of the extreme “anomalies” from the data set. But where there is very little data available the impact is much larger. That is particularly the case prior to 1910. But after 1910, any data deletions pale into insignificance next to the adjustments.

The Adjustments

I plotted the average difference between the Raw Data and the adjustment, along with the max and min values in Chart 4.

The max and min of net adjustments are consistent with Euan Mearns’ graph “safrica_deltaT” when flipped upside down and made back to front. It shows a difficulty of comparing adjusted, where all the data is shifted. For instance the maximum figures are dominated by Windhoek, which I looked at a couple of weeks ago. Between the raw data and the GISS Homogenised there was a 3.6oC uniform increase. There were a number of other lesser differences that I have listed in note 3. Chart 5 shows the impact of adjusting the adjustments is on both the range of the adjustments and the pattern of the average adjustments.

Comparing this with this average variance between the raw data and the GISS Homogenised shows the closer fit if the adjustments to the variance. Please note the difference in scale on Chart 6 from the above!

In the earlier period has by far the most deletions of data, hence the lack of closeness of fit between the average adjustment and average variance. After 1945, the consistent pattern of the average adjustment being slightly higher than the average variance is probably due to a light touch approach on adjustment corrections than due to other data deletions. The might be other reasons as well for the lack of fit, such as the impact of different length of data sets on the anomaly calculations.

Update 15/03/15

Of note is that the adjustments in the early 1890s and around 1930 is about three times the size of the change in trend. This might be partly due to zero net adjustments in 1903 and partly due to the small downward adjustments in post 2000.

The consequences of the adjustments

It should be remembered that GISS use this data to create the GISTEMP surface temperature anomalies. In Chart 7 I have amended Chart 1 to include Southern Hemisphere annual mean data on the same basis as the raw data and GISS H.

It seems fairly clear that the homogenisation process has achieved bringing the Southern Africa data sets into line with the wider data sets. Whether the early twentieth century warming and mid-century cooling are outliers that have been correctly cleansed is a subject for further study.

What has struck me in doing this analysis is that looking at individual surface temperature stations becomes nonsensical, as they are grid reference points. Thus comparing the station moves for Reykjavik with the adjustments will not achieve anything. The implications of this insight will have to wait upon another day.

Kevin Marshall

Notes

1. 26 Data sets

The temperature stations, with the periods for the raw data are below.

Location

Lat

Lon

ID

Pop.

Years

Harare

17.9 S

31.1 E

156677750005

601,000

1897 – 2011

Kimberley

28.8 S

24.8 E

141684380004

105,000

1897 – 2011

Gwelo

19.4 S

29.8 E

156678670010

68,000

1898 – 1970

Bulawayo

20.1 S

28.6 E

156679640005

359,000

1897 – 2011

Beira

19.8 S

34.9 E

131672970000

46,000

1913 – 1991

Kabwe

14.4 S

28.5 E

155676630004

144,000

1925 – 2011

Livingstone

17.8 S

25.8 E

155677430003

72,000

1918 – 2010

Mongu

15.2 S

23.1 E

155676330003

< 10,000

1923 – 2010

Mwinilunga

11.8 S

24.4 E

155674410000

< 10,000

1923 – 1970

Ndola

13.0 S

28.6 E

155675610000

282,000

1923 – 1981

Capetown Safr

33.9 S

18.5 E

141688160000

834,000

1880 – 2011

Calvinia

31.5 S

19.8 E

141686180000

< 10,000

1941 – 2011

East London

33.0 S

27.8 E

141688580005

127,000

1940 – 2011

Windhoek

22.6 S

17.1 E

132681100000

61,000

1921 – 1991

Keetmanshoop

26.5 S

18.1 E

132683120000

10,000

1931 – 2010

Bloemfontein

29.1 S

26.3 E

141684420002

182,000

1943 – 2011

De Aar

30.6 S

24.0 E

141685380000

18,000

1940 – 2011

Queenstown

31.9 S

26.9 E

141686480000

39,000

1940 – 1991

Bethal

26.4 S

29.5 E

141683700000

30,000

1940 – 1991

Antananarivo

18.8 S

47.5 E

125670830002

452,000

1889 – 2011

Tamatave

18.1 S

49.4 E

125670950003

77,000

1951 – 2011

Porto Amelia

13.0 S

40.5 E

131672150000

< 10,000

1947 – 1991

Potchefstroom

26.7 S

27.1 E

141683500000

57,000

1940 – 1991

Zanzibar

6.2 S

39.2 E

149638700000

111,000

1880 – 1960

Tabora

5.1 S

32.8 E

149638320000

67,000

1893 – 2011

Dar Es Salaam

6.9 S

39.2 E

149638940003

757,000

1895 – 2011

2. Temperature trends

To calculate the trends I used the OLS method, both from the formula and using the EXCEL “LINEST” function, getting the same answer each time. If you are able please check my calculations. The GISTEMP Southern Hemisphere and global data can be accessed direct from the NASA GISS website. The GISTEMP trends are from the skepticalscience trends tool. My figures are:-

3. Adjustments to the Adjustments

Location

Recent adjustment

Other adjustment

Other Period
Antananarivo

0.50

 

 
Beira

 

0.10

Mid-70s + inter-war
Bloemfontein

0.70

 

 
Dar Es Salaam

0.10

 

 
Harare

 

1.10

About 1999-2002
Keetmanshoop

1.57

 

 
Potchefstroom

-0.10

 

 
Tamatave

0.39

 

 
Windhoek

3.60

 

 
Zanzibar

-0.80

 

 

Windhoek Temperature adjustments

At Euan Mearn’s blog I made reference to my findings, posted in full last night, that in the Isfjord Radio weather station had adjustments that varied between +4.0oC in 1917 to -1.7oC in the 1950s. I challenged anyone to find bigger adjustments than that. Euan came back with the example of Windhoek in South Africa, claiming 5oC of adjustments between the “raw” and GISS homogenised data.

I cry foul, as the adjustments are throughout the data set. J

That is the whole of the data set has been adjusted up by about 4 oC!

However, comparing the “raw” with the GISS homogenised data, with 5 year moving averages, (alongside the net adjustments) there are some interesting features.

The overall temperatures have been adjusted up by around 4oC, but

  • From the start of the record in 1920 to 1939 the cooling has been retained, if not slightly amplified.
  • The warming from 1938 to 1947 of 1.5oC has been erased by a combination of deleting the 1940 to 1944 data and reducing the 1945-1948 adjustments by 1.4oC.
  • The 1945-1948 adjustments, along with random adjustments and deletion of data mostly remove the near 1.5oC of cooling from the late 1940s to mid-1950s and the slight rebound through to the early 1960s.
  • The early 1970s cooling and the warming to the end of the series in the mid-1980s is largely untouched.

The overall adjustments leave a peculiar picture that cannot be explained by a homogenisation algorithm. The cooling in the 1920s offsets the global trend. Deletion of data and the adjustments in the data counter the peak of warming in the early 1940s in the global data. Natural variations in the raw data between the late 1940s and 1970 appear to have been removed, then the slight early 1970s cooling and the subsequent warming in the raw data left alone. However, the raw data shows average temperatures in the 1980s to be around 0.8oC higher than in the early 1920s. The adjustments seem to have removed this.

This removal of the warming trend tends to disprove something else. There appears to be no clever conspiracy, with a secret set of true figures. Rather, there are a lot of people dipping in to adjusting adjusted data to their view of the world, but nobody really questioning the results. They have totally lost sight of what the real data actually is. If they have compared the final adjusted data with the raw data, then they realised that the adjustments had managed to have eliminated a warming trend of over 1 oC per century.

Kevin Marshall