Guest post by Ed Thurstan
Abstract
It is apparent from the data that CRUTEM4 temperatures adjustments have, in part, been made with reference only to the earlier CRUTEM3 data, rather than raw temperature data. Further, the adjustments depend on the month for the data, and these adjustments are made for 20 or 30 consecutive years.
In the case of Adelaide (946720), for 30 years from 1857, CRUTEM4
- Lowers all January temps by 1.4oC
- Lowers all Feb temps by 0.9oC
- Lowers all March temps by 1.7oC
- With April to December all lowered by 0.5 to 1.1oC.
Thereafter, there are no adjustments until 2000, when a smattering of adjustments appear, mostly raising the temperature.
There are many examples of this practice. The total effect of all the differences between CRUTEM4 and CRUTEM3 where there is corresponding data is to accentuate warming trend by lowering pre 1995 temps by 0.1 to 0.2oC, and raising post 1995 temps by a similar amount.
This does not mean that the overall effect of all CRUTEM4 updates will induce the same magnitude change in HADCRUT4 anomaly, as no account has been taken of deleted and added stations, or relative numbers of stations in the database, vs the number which display differences as analysed here.
I believe that CRUTEM4 is seriously flawed, due to the apparent selective mechanical adjustments to blocks of station temperatures where the criteria for adjustment are a function of the month name. Until this is adequately explained, CRUTEM4 should be withdrawn.
Background
In March 2012 the Hadley Climate Research Unit released the land temperature dataset CRUTEM4, along with the station data from which it was constructed. A cursory inspection of the new dataset revealed some irregularities in Australian data. In particular, there were puzzling differences between CRUTEM3 and CRUTEM4 data.
A program was written to compare the two complete sets CRUTEM data, and highlight the differences. It compares the two sets to report:
· Stations in CRUTEM3 which do not appear in CRUTEM4. That is, they have been dropped in the construction of CRUTEM4.
· Stations in CRUTEM4 which do not appear in CRUTEM3. That is, new stations.
· Stations which appear in both sets and which have an arithmetical difference in any month of any year. The whole 12 months are reported for these. All missing data (reported as -99 in CRUTEM data) was converted to zero. This means that some data is lost if a valid temperature appears in one set with a matching -99 in the other dataset.
To exclude minor differences due to issues (like roundoff, precision) which could arise in comparing two datasets derived in different ways, a threshold of 0.22 was applied. That is, years are selected only if they contain differences whose absolute value exceeds 0.22oC.
Some simple statistics about the two databases include:
1. Station/Years of data: In CRUTEM3 set – 399,303; In CRUTEM4 set – 466,246.
2. Number of -99 months: In CRUTEM3 set – 674,993; In CRUTEM4 set – 568,606.
3. Number of CRUTEM3 stations dropped from CRUTEM4 286
4. Number of stations added to CRUTEM4 738
5. Matching station years where there is at least 1 month difference 104,296
6. Total stations in CRUTEM3 5,113
7. Total stations in CRUTEM4 5,565
Results
Old temperature data has been adjusted
In the following examples, only a snapshot of small parts of the data is presented to illustrate the point. Positive differences imply that CRUTEM4 is higher than CRUTEM3.
30050 is LERWICK UK; 42160 is ILULISSAT-JAKOBSHAVN Greenland;66450 is BASEL-BINNINGEN Switzerland; 67000 is GENEVE-COINTRIN Switzerland.
Adjustments of this magnitude can be seen through most of the CRUTEM4 database, but especially in Europe and adjacent areas. With data from such early years being so sparse, it is difficult to see both why such adjustments have been made, and the basis on which they were made. They were clearly made with reference solely to CRUTEM3 data, and not from original data. There seems little value in adjusting such early data, unless the purpose is to lower early temperatures.
There are strange repeating adjustments
Strange adjustments have been systematically applied to CRUTEM3 data to create CRUTEM4 data. For example, for a period of 22 years, from 1951 to 1972 Station 915540 (Vanuatu) has the following set of adjustments applied:
With the original CRUTEM3 data showing
1951 26.5 26.3 26.5 26.0 25.2 24.4 24.2 24.1 24.2 25.1 25.6 26.1
1952 26.8 26.6 26.3 25.6 24.8 23.9 23.0 22.6 22.8 23.8 24.5 24.8
1953 25.3 25.4 25.1 24.3 23.7 22.6 22.5 23.1 23.1 24.2 25.0 24.4
1954 25.2 25.1 24.8 24.2 23.6 23.3 23.3 22.2 23.5 23.7 23.9 24.8
1955 24.6 25.2 24.1 24.1 23.9 22.6 23.1 22.3 23.7 24.0 24.5 24.7
1956 24.9 25.6 24.8 24.3 23.9 23.7 22.4 22.5 24.1 24.4 24.4 25.7
1957 25.7 25.7 25.1 25.0 23.1 21.6 21.7 23.2 23.1 23.5 24.3 25.0
1958 25.6 26.3 25.7 24.9 23.9 23.6 21.5 22.0 23.9 23.8 24.1 25.4
1959 25.3 25.7 25.4 24.6 23.7 23.1 23.4 22.8 23.3 24.2 25.3 25.5
1960 24.9 24.8 24.9 24.4 24.1 23.6 22.6 23.3 24.0 23.8 24.7 24.4
1961 26.0 26.6 26.0 25.0 24.9 24.3 23.9 23.7 24.3 24.5 25.1 25.7
1962 26.1 25.8 25.2 24.9 24.7 23.8 23.3 24.1 23.9 24.4 24.7 25.3
1963 25.6 25.9 25.6 25.0 23.7 24.4 23.0 23.8 23.7 23.6 24.4 25.3
1964 25.8 26.5 26.2 25.3 24.5 24.4 23.2 24.0 23.9 24.5 25.5 25.4
1965 25.5 25.8 25.5 24.6 23.5 23.1 22.5 21.9 22.8 23.1 24.1 25.4
1966 25.8 25.9 25.8 25.1 23.5 23.4 22.4 23.0 23.3 23.8 24.3 24.5
1967 25.4 26.0 25.6 24.6 24.7 24.0 22.9 23.5 23.8 24.1 24.2 25.5
1968 25.5 26.0 25.4 24.5 24.0 23.5 22.9 22.6 23.2 24.1 24.5 25.3
1969 25.6 25.9 26.1 25.6 25.0 24.4 23.3 23.6 23.2 24.1 24.7 25.3
1970 25.9 26.0 26.5 25.1 24.4 23.8 23.6 24.2 24.0 24.5 24.5 25.5
1971 25.3 25.7 25.2 24.8 24.1 24.1 23.2 23.9 23.9 24.1 24.5 24.8
1972 25.3 25.6 25.2 24.8 25.1 23.7 22.4 21.7 23.1 24.0 25.2 25.5
And the original CRUTEM4 data showing
1951 26.9 26.7 26.9 27.0 26.0 25.0 25.0 25.3 25.2 25.9 26.4 26.7
1952 27.2 27.0 26.7 26.6 25.6 24.5 23.8 23.8 23.8 24.6 25.3 25.4
1953 25.7 25.8 25.5 25.3 24.5 23.2 23.3 24.3 24.1 25.0 25.8 25.0
1954 25.6 25.5 25.2 25.2 24.4 23.9 24.1 23.4 24.5 24.5 24.7 25.4
1955 25.0 25.6 24.5 25.1 24.7 23.2 23.9 23.5 24.7 24.8 25.3 25.3
1956 25.3 26.0 25.2 25.3 24.7 24.3 23.2 23.7 25.1 25.2 25.2 26.3
1957 26.1 26.1 25.5 26.0 23.9 22.2 22.5 24.4 24.1 24.3 25.1 25.6
1958 26.0 26.7 26.1 25.9 24.7 24.2 22.3 23.2 24.9 24.6 24.9 26.0
1959 25.7 26.1 25.8 25.6 24.5 23.7 24.2 24.0 24.3 25.0 26.1 26.1
1960 25.3 25.2 25.3 25.4 24.9 24.2 23.4 24.5 25.0 24.6 25.5 25.0
1961 26.4 27.0 26.4 26.0 25.7 24.9 24.7 24.9 25.3 25.3 25.9 26.3
1962 26.5 26.2 25.6 25.9 25.5 24.4 24.1 25.3 24.9 25.2 25.5 25.9
1963 26.0 26.3 26.0 26.0 24.5 25.0 23.8 25.0 24.7 24.4 25.2 25.9
1964 26.2 26.9 26.6 26.3 25.3 25.0 24.0 25.2 24.9 25.3 26.3 26.0
1965 25.9 26.2 25.9 25.6 24.3 23.7 23.3 23.1 23.8 23.9 24.9 26.0
1966 26.2 26.3 26.2 26.1 24.3 24.0 23.2 24.2 24.3 24.6 25.1 25.1
1967 25.8 26.4 26.0 25.6 25.5 24.6 23.7 24.7 24.8 24.9 25.0 26.1
1968 25.9 26.4 25.8 25.5 24.8 24.1 23.7 23.8 24.2 24.9 25.3 25.9
1969 26.0 26.3 26.5 26.6 25.8 25.0 24.1 24.8 24.2 24.9 25.5 25.9
1970 26.3 26.4 26.9 26.1 25.2 24.4 24.4 25.4 25.0 25.3 25.3 26.1
1971 25.7 26.1 25.6 25.8 24.9 24.7 24.0 25.1 24.9 24.9 25.3 25.4
1972 25.7 26.0 25.6 25.8 25.9 24.3 23.2 22.9 24.1 24.8 26.0 26.1
Similar adjustments can be seen in many other stations. The above Vanuatu data comes from a tropical area where temperature varies only 2 to 4oC over the year. In this situation, an adjustment of 0.4 to 1.2oC degree seems extreme. Furthermore, there are no adjustments after 1972. But the same type of adjustment appears in more temperate 946720 (Adelaide Australia). For the 30 year period 1857 to 1886 the following adjustment has been applied to CRUTEM3 to create CRUTEM4.
when the two Datasets show
CRUTEM3
1857 22.6 28.3 19.7 17.5 13.0 11.6 11.8 12.2 14.2 15.4 18.4 23.4
1858 26.4 24.0 22.3 18.3 13.5 12.0 10.3 11.7 12.6 15.8 21.1 22.5
1859 23.8 22.4 20.3 17.1 13.2 10.9 10.7 12.4 13.3 17.7 19.7 22.9
1860 26.1 23.8 22.4 17.0 14.4 12.5 11.7 13.4 15.5 17.0 19.9 22.5
1861 23.2 22.3 23.2 19.1 14.2 13.7 10.6 11.4 14.3 17.7 20.0 19.8
1862 25.4 22.4 23.0 17.0 14.9 11.9 13.2 13.1 15.3 18.6 21.4 23.6
1863 23.5 24.0 22.3 20.7 15.8 13.3 11.7 12.2 13.5 16.3 19.0 21.9
1864 23.0 21.9 21.1 18.1 15.3 11.7 11.5 11.7 15.4 16.1 20.6 21.0
1865 21.6 21.7 21.3 19.5 13.6 11.8 10.6 12.7 14.6 17.1 21.7 20.8
1866 23.9 25.2 21.2 19.0 15.8 12.6 11.6 12.6 13.8 16.6 17.8 22.1
1867 24.1 24.3 20.7 18.2 15.6 14.0 11.5 12.6 13.2 16.2 18.9 20.1
1868 20.6 22.9 23.4 17.8 15.8 12.1 10.4 12.1 14.6 17.9 20.2 22.0
1869 22.1 22.6 21.3 17.4 13.4 12.4 11.3 12.8 11.8 16.4 20.4 21.7
1870 23.4 25.3 21.7 18.7 13.9 12.7 10.8 11.5 12.9 17.2 18.0 21.9
1871 23.0 23.8 20.3 18.6 15.4 13.4 11.6 13.4 14.8 16.6 18.6 24.0
1872 25.9 23.3 22.4 17.1 13.5 12.6 11.1 9.9 14.1 16.2 21.0 20.0
1873 23.9 22.6 19.9 16.7 14.9 11.8 10.7 12.6 14.2 18.3 17.3 23.6
1874 24.0 21.7 19.8 19.4 14.0 11.7 9.8 11.5 12.1 17.6 17.9 21.9
1875 23.6 22.9 20.9 18.1 13.2 11.9 10.6 12.2 13.8 16.6 18.2 19.3
1876 22.8 22.1 23.8 16.5 13.3 10.8 10.1 11.5 13.5 16.1 18.6 23.4
1877 23.1 24.7 20.3 17.7 14.2 11.7 11.2 13.6 12.4 16.1 17.2 20.6
1878 25.6 23.0 21.6 18.3 14.1 9.9 11.7 12.9 14.3 17.5 20.1 21.4
1879 24.4 23.9 20.9 18.6 12.4 11.6 10.1 12.1 13.2 16.2 18.6 21.1
1880 25.6 26.4 21.5 17.5 13.7 12.1 10.4 12.9 13.7 15.4 17.8 21.9
1881 23.2 21.7 21.4 17.4 14.9 10.6 10.6 11.8 13.9 15.3 18.6 21.3
1882 22.9 24.0 22.6 17.4 15.1 10.6 9.6 11.2 14.0 16.9 20.8 21.6
1883 23.6 21.9 20.9 18.4 13.2 13.1 10.8 11.3 12.4 15.3 19.6 20.9
1884 21.2 23.6 22.1 17.1 13.9 12.2 9.8 13.3 14.1 15.5 18.9 19.5
1885 21.6 21.6 18.9 17.2 15.9 10.8 10.7 12.8 14.0 18.0 19.2 23.2
1886 24.4 20.6 20.4 17.3 14.0 11.6 11.6 12.4 16.1 14.7 19.9 21.9
CRUTEM4
1857 21.2 27.4 18.0 16.4 12.1 11.0 11.4 11.4 13.7 14.7 17.5 22.6
1858 25.0 23.1 20.6 17.2 12.6 11.4 9.9 10.9 12.1 15.1 20.2 21.7
1859 22.4 21.5 18.6 16.0 12.3 10.3 10.3 11.6 12.8 17.0 18.8 22.1
1860 24.7 22.9 20.7 15.9 13.5 11.9 11.3 12.6 15.0 16.3 19.0 21.7
1861 21.8 21.4 21.5 18.0 13.3 13.1 10.2 10.6 13.8 17.0 19.1 19.0
1862 24.0 21.5 21.3 15.9 14.0 11.3 12.8 12.3 14.8 17.9 20.5 22.8
1863 22.1 23.1 20.6 19.6 14.9 12.7 11.3 11.4 13.0 15.6 18.1 21.1
1864 21.6 21.0 19.4 17.0 14.4 11.1 11.1 10.9 14.9 15.4 19.7 20.2
1865 20.2 20.8 19.6 18.4 12.7 11.2 10.2 11.9 14.1 16.4 20.8 20.0
1866 22.5 24.3 19.5 17.9 14.9 12.0 11.2 11.8 13.3 15.9 16.9 21.3
1867 22.7 23.4 19.0 17.1 14.7 13.4 11.1 11.8 12.7 15.5 18.0 19.3
1868 19.2 22.0 21.7 16.7 14.9 11.5 10.0 11.3 14.1 17.2 19.3 21.2
1869 20.7 21.7 19.6 16.3 12.5 11.8 10.9 12.0 11.3 15.7 19.5 20.9
1870 22.0 24.4 20.0 17.6 13.0 12.1 10.4 10.7 12.4 16.5 17.1 21.1
1871 21.6 22.9 18.6 17.5 14.5 12.8 11.2 12.6 14.3 15.9 17.7 23.2
1872 24.5 22.4 20.7 16.0 12.6 12.0 10.7 9.1 13.6 15.5 20.1 19.2
1873 22.5 21.7 18.2 15.6 14.0 11.2 10.3 11.8 13.7 17.6 16.4 22.8
1874 22.6 20.8 18.1 18.3 13.1 11.1 9.4 10.7 11.6 16.9 17.0 21.1
1875 22.2 22.0 19.2 17.0 12.3 11.3 10.2 11.4 13.3 15.9 17.3 18.5
1876 21.4 21.2 22.1 15.4 12.4 10.2 9.7 10.7 13.0 15.4 17.7 22.6
1877 21.7 23.8 18.6 16.6 13.3 11.1 10.8 12.8 11.9 15.4 16.3 19.8
1878 24.2 22.1 19.9 17.2 13.2 9.3 11.3 12.1 13.8 16.8 19.2 20.6
1879 23.0 23.0 19.2 17.5 11.5 11.0 9.7 11.3 12.7 15.5 17.7 20.3
1880 24.2 25.5 19.8 16.4 12.8 11.5 10.0 12.1 13.2 14.7 16.9 21.1
1881 21.8 20.8 19.7 16.3 14.0 10.0 10.2 11.0 13.4 14.6 17.7 20.5
1882 21.5 23.1 20.9 16.3 14.2 10.0 9.2 10.4 13.5 16.2 19.9 20.8
1883 22.2 21.0 19.2 17.3 12.3 12.5 10.4 10.5 11.9 14.6 18.7 20.1
1884 19.8 22.7 20.4 16.0 13.0 11.6 9.4 12.5 13.6 14.8 18.0 18.7
1885 20.2 20.7 17.2 16.1 15.0 10.2 10.3 12.0 13.5 17.3 18.3 22.4
1886 23.0 19.7 18.7 16.2 13.1 11.0 11.2 11.6 15.6 14.0 19.0 21.1
The updates accentuate the global warming argument
There is pronounced tendency to flex the graph of global temperature vs time in a way which accentuates warming in recent years. However, the dominant effect is to lower temperatures prior to 1995.
The differences between CRUTEM3 and CRUTEM4, with zero tolerance, were consolidated to annual temperature differences, and plotted against time along with the count of the stations which contributed to the graph. This gives
The adjustments indicate that in CRUTEM4:
· A comparatively small number of stations have been cooled between about 1820 and 1900 anywhere up to 0.4oC.
· A substantial number of stations have been cooled about 0.1oC between about 1910 and 1995.
· Between about 100 and 400 stations have been warmed by up to about 0.2oC since about 1995.
This does not translate into the same change in the anomaly vs time graph, because it is not the whole dataset, but simply the changed data between CRUTEM3 and CRUTEM4. Nor does it take into account the effect of dropped and added stations. But it does indicate the direction of the change.
Nitpicking Differences
The nature of the CRUTEM database makes it obviously difficult to manage. Data comes from many sources. It is unreasonable to expect Hadley or UEA personnel to understand the geography of the data they receive. So errors will appear in the database. Some positional errors can be dismissed on the grounds that CRUTEM is directed at anomalies. This means that if a station’s position data is wrongly represented, then provided the error is contained within the same 5o v 5o gridcell, the anomaly is unaffected.
However, it is difficult to see how Station number 237070, name listed as “Unknown, Russia”, with a Lat/Long of -99.9/-999.9 (ie unknown), could escape being found by Quality Control, while still feeding temperatures to the gridding/anomaly calculation. Station 288020 is similarly identified, and also supplies data.
“Normals” are, by convention, calculated over 30 years from 1961 to 1990. While this appears to be observed, it is quite common to calculate Standard Deviations over a different period, commonly 1941 to 1990.
Extreme differences occur when the same station Number is used for two different locations. For example, In CRUTEM3, Station 840270 is the high altitude Tulcan El Rosal in Ecuador. But in CRUTEM4 station 840270 is Esmeraldas Tachina, also in Ecuador, but several hundred km distant from its CRUTEM3 namesake, and at almost sea level. This appears to be a CRUTEM3 error, probably difficult to find once the error is made.
Sydney Airport (947670) is still badly identified. The data runs from 1859 to the present, but only data after 1990 comes from Sydney Airport. The earlier data comes from Sydney Observatory, 10km away.
Effect of added and deleted stations
No attempt is made in this report to assess the effect of the 286 station deletions and the 738 additional stations, except to observe that most of the additions are high latitude NH stations.
Discussion
When updating a database such as CRUTEM, I would expect the steps to be roughly
· Tidy up the precursor database, which should be mostly stable data that has had years of scrutiny, and which would require limited correction or additions.
· Delete data which is considered poor quality.
· Add new data, edited and homogenised.
· Run a check as has been done in this report, looking at differences which might suggest irregularities.
But Hadley/CRU do not have appears to have done this. They have added and deleted stations, but it seems strange that very early data – 18th and 19th Century data, should be added, especially when much of it is sparse, of perhaps questionable quality and not germane to the current temperature/time/CO2 discussion.
But the biggest problem with the new HADCRUT4 database is the frequent practice of systematic, repetitive temperature modifications on blocks of station data. What reason could there be for making the following set of adjustments on Adelaide data for every year from 1857 to 1886 ?
The perpetrators of this change have clearly not gone back to original raw data and re-appraised their original homogenisation processes. They have simply taken every year of CRUTEM3 data (with it’s possible homogenisation adjustments), from 1857 to 1886, and
· Deducted 1.4oC if the month name was January,
· Deducted 0.9oC if February,
· Deducted 1.7oC if March
· And so on.
It is difficult to conceive what the justification could be for these changes. There are many instances of such changes in HADCRUT4, covering periods of 5 to 30 years, in both ancient and recent data. The adjustment vector differs in each, but the magnitudes of the individual elements are equally large. Their signs may vary.
The temperature differences, where there are corresponding data in each of the datasets works to promote the impression of a temperature surge in the 1990s.
It has done this by lowering temperatures before about 1995, and raising them thereafter.
The effect of this on the final gridded anomaly data has yet to be calculated. But it will be less than depicted above. The above graph shows the difference in the data induced by changes in data which appear in both CRUTEM3 and CRUTEM4. It does not include the effect of
· Dropped stations
· Added stations
· Stations which record a valid temperature in one set, but a Null (-99) in the other.
There are other relatively minor issues regarding data quality. I am surprised that neither CRU quality control nor users of the data have picked up these errors.
The CRUTEM4 database appears even more like one which has been assembled by amateurs with little concept of accuracy or integrity. If you were of a suspicious nature, you might get the impression that the changes are targeted at accentuating the Warmist message.
Until the matters raised in this report have been adequately explained, CRUTEM4, and therefore HADCRUT4 are seriously flawed.
Acknowledgements
Thank you Warwick Hughes (http://www.warwickhughes.com/blog/) for encouraging me to write this report, and for helpful suggestions on the construction of the report.
Data available
The original data is available at: http://www.cru.uea.ac.uk/cru/data/temperature/.
The data and processing programs used in preparation of this report are available from the author at thurstan@bigpond.net.au., or from Warwick Hughes blog http://www.warwickhughes.com/blog/ They are in two zipped packages.
Data
This comprises
· The CRUTEM3 and CRUTEM4 station data for all examples mentioned in this report.
· The output of a full CRUTEM run, with some analysis of the output. It will not run under XL97, which is limited to 64K rows, when this workbook holds over 400,000 rows.
This is about 26mb compressed.
Programs
· An Excel 2010 Workbook. The one that produces the output used in this report. It is a Visual Basic driven Excel workbook. It extracts stations missing from each dataset, and the differences. An untested XL97 version is included. It will not run the full CRUTEM database, as XL97 is restricted to 64K rows, and CRUTEM generates over 100,000. But it is possible to split the two sets of input into smaller parcels. Australia/New Zealand generated only about 650 rows of output.
· Instructions for use are on Worksheet “MAIN”.
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Reading the article prompted me to download the data and do some digging. Rather than trust the data, I generated my own header files by grepping the data files. My V3 header file matched Hadley’s. My V4 header file did not match Hadley’s. A quick check showed that the Hadley V4 header file has several extra stations in Poland that do not show up in the Hadley V4 data file, let alone the Hadley V3 data file. Wattsupwiththat???
The ID numbers are 121050 121500 121950 122500 122400 125200 125300 125500 125850 125950 126250 126950
Re: Contamination & evidence.
Thank you, Ed & Warwick, for this large computational ecercise and code. My contribution is smaller, but demands similar answers. Here is a slight variation of a post of some data I put on Jo Nova’s site earlier today, ref Australia, Darwin.
You guys who are arguing need to look at evidence. Back to basics. You are talking about a temperature rise.
The BoM is talking about a temperature rise. They have released a new data set of temperatures for about 100 stations, called Acorn. It’s online. Then, you can go online to a different part of the BoM home page and get another version.
I’ll show you both. Then I’ll ask you which version I should use in this example (Darwin).
http://www.geoffstuff.com/Darwin3preExcel2010.xls
There are a couple of pages of data here, so you can DIY. However, if you look at the graphs on sheet 1, one of the products shows a warming Darwin, the other shows a cooling Darwin. If this exercise was studied and repeated all over the world, we might be able to conclude that most of the warming is manufactured by fiddling with numbers. I’ve done it for more Australian stations, similar conclusions.
If you are expert enough to comment on comparisons, then you should be expert enough to tell us which of the graphs, both being handed out today by our BoM, should be used in constructing comparisons. If you can’t do that, then you have failed to use due diligence in your science.
As someone who has been a committed sceptic for the last 10 years, a supporter of Anthony and all the other heroes (and I consider myself a modest one, putting my arse on the line, along with Andrew Bolt down under) Given the climate of persecution on this matter, I am curious about this abrupt response to a post of mine yesterday:
“[snip OT . . read Tips & Notes for what you are looking for . . kbmod]”
I was simply asking what had happened to Goddard’s blog. Maybe there is some politically correct line that I have crossed..
[REPLY: It was Off-topic, but there is nothing wrong with Steve Goddard or his blog. There was a disturbance not long ago that you can read about here. -REP]
I was simply wondering what this was about http://www.real-science.com/
[it seems the blog has been the subject of a conflict and the current postings seem to be part of that, it is apparent that Mr. Goddard is not in control at the moment. . . I apologize for my rather curt response to you earlier and I never intended any discourtesy . . kbmod]
Steven Mosher says:
May 3, 2012 at 3:47 pm
Why is the extra data from the SH not been available before? Many decades of SH data don’t just appear out of nowhere. For long records they would have been available for ages. I don’t see any difference to cherry picking the data and moving the chairs around the room, to show more warming to counter the amount that didn’t occur. With that many stations available (thousands) it is very easy to cherry pick 500+ of them every decade or so for confirmation bias.
Matt G says:
May 4, 2012 at 9:55 am (Edit)
Steven Mosher says:
May 3, 2012 at 3:47 pm
Why is the extra data from the SH not been available before? Many decades of SH data don’t just appear out of nowhere. For long records they would have been available for ages. I don’t see any difference to cherry picking the data and moving the chairs around the room, to show more warming to counter the amount that didn’t occur. With that many stations available (thousands) it is very easy to cherry pick 500+ of them every decade or so for confirmation bias.
#####################
Why is the extra data not been available before.
Some of it has been available but nobody saw the need to actually use it.
Lets see if I can explain. GHCN Monthly was a project started long ago. The set out to look at
all the records they could and select a subset. After the project was complete there was no real effort put into collecting more data FOR THAT COLLECTION. over time people stopped giving reports to the folks who run that data. Efforts concentrated on ‘homogenization.
CRU takes about 98% of its data from GHCN. 2% it got from other sources. There was no effort to get more data because no body saw the need. Plus, its really boring work and you cannot
write a good science paper based on adding a few hundred stations here and there.
To some extent cwith climategate people saw the opportunity to finally do this work
and assemble the data that has always been there.
Meanwhile other data collections continue to be made: Ghcn daily, GSOD, the colonial records. They have been there but nobody wanted to undertake the effort to slog through it all.
My experience. I finished slogging thru GHCN daily. It starts with over 80000 individual files
I worked on it for months. In the end, guess what? the answer is the same. Try to publish that as a paper. here is another experience. env canada. 7-8K sites. a couple months of work.
A brutal problem because they dont have their data on an FTP site. ever scrape 7000 web pages?
Result? The answer doesnt change.
GSOD data. Thousands of sites. Answer? the same.
When I say the answer doesnt change I mean in any scientifically interesting way. A bump here
a valley there, but its still warming. little changes in the bumps and valleys are not scientifically interesting to Nature or Science. No scientist would waste his time on it.
As we sit here there are millions of boxes of data that have not been digitized. The science and the statistics tell us that the answer in the unopened boxes will NOT be significantly different from the answer calculated from the sample we have. here is what I know. Give me ANY latitude, longitude and altitude and I can tell you the temperature within x.y C ( wait for the publication) The information in the un opened boxes will tighten that range, but long term means will not change in any scientifically interesting way. That means doing a bunch of work and its hard to publish a paper that confirms what we already know.
Skeptics ( me included) thought more data might change the answer. It didnt. Sorry.
This is part of the reason people like McIntyre and me suggested that the temperature series be taken over by an independent agency. An agency that doesnt have to Publish science papers. An agency that compiles all the data into one official series. Some steps have been taken in that direction
There are two efforts aimed at this. One is Berkeley earth and the other is being headed up by Peter Thorne. Both of these groups dont have the luxury of not publishing.
As for cherry picking series. that is SUPER EASY to test. Lets be objective.
Hypothesis: CRU cherry picked series
How do I test that? Well easy peasy.
A. I duplicate CRU’s method and pick different stations.
B. I create a better method and USE ALL THE DATA
fair test it seems to me.
What is the answer? The answer you get from using CRUs method ( which I emulated and verified) with different data, gives the same answer. And the answer you get from a better method ( Least squares or Kridging) and ALL THE DATA… is the same.
conclusion: either CRU did not cherry pick or if they did it doesnt change the answer.
of course at the micro level if you compare one needle in the haystack to another needle in the haystack you will find differences. But in the end the answer doesnt change. Finding out that the 1930s were .04C cooler than thought, ISNT INTERESTING. Adding arctic stations and finding that the current estimate goes up by .05C ISNT INTERESTING, especially when you consider what the error bounds were TO BEGIN WITH. the changes are not interesting, not scientifically relevant. C02 still causes warming. the interesting question is HOW MUCH?
studying the temperature record cannot answer that question, in fact we know it cant. By itself the temperature record can tell us precious little about sensitivity. Sensitivity is underdetermined by the short temperature record. At best you can get an estimate of the TRANSIENT climate response out of the series, but evern there you have big error bars.
The record is scientifically boring.minor changes in it are a little less boring.
The adjustment trend also shows in a comparison to HadSST3:
http://img850.imageshack.us/img850/2079/hadcrut4vshadsst3.png
The sea surface temperatures have slightly lower variance, but does it make sense that the averages diverge 0.1K? Before ~1985, the difference is pretty random and close to zero, but towards the end it’s consistently positive.
Steven Mosher says:
May 4, 2012 at 1:24 pm
Thank you for your responce, can’t disagree with the majority of what you have responded with. Only little concern is that athough the long term trend may not change hardly what ever general set is used, including all the data. The short term periods or individul years do chop and change and can make different years cooler or warmer than others. Hence, the last several few years were made warmer with HADTEMP4 compared with HADTEMP3.
Air temperature alone can’t tell us about sensitivity whether it is short or medium term. Other factors how they behave during these time frames are considered to beable to find a reasonable sensitivity. The longer the period goes on the sensitivity is increasingly exposed, so either contributes tiny or large impact it wil become known. Anyway I agree with Richard lindzen that sensitivity is low. The change in global cloud albedo is having a large affect on temperatures relative to the increase over the last century.
Matt.
Understand what the definition of sensitivity is:
What is the change in temperature (C), given a change in Forcing (Watts)
so if the sun goes up by 1 watt, how much does the temperature go up?
Here is a simple analogy. You are sitting in your car. You slam the pedal down and apply
100 horsepower. What is the velocity you will see from this forcing?
Well, it is time dependent. and it depends on your tires, on the headwind, on the track, on your power train losses. Lots of things. Now, hold all those variables constant and apply 100 horsepower and let the car get to a final top speed, where it is no longer increasing.
Lets say its 105 mph. Thats the response we talk about when we talk about equillbrium
climate response. When the sun goes up by one watt, how does temperature respond.
Well, we dont look at the very next day. The very next day could be cloudy. We have to look
over a long time scale. We cant control all the other variables. and there are feedbacks. Some act on short scale, some act on long scale. back to the car. Suppose I look at the car after
10 seconds. There im looking at the transient response. First I have to overcome inertia and that takes time. maybe I spin the tires, crap. Anyways the transient response will be related to
the equillibrium response but its not exact.
With the climate you have a system with huge inertia. The transient response is much smaller than the response after decades or centuries. looking at the 160 years of land surface data you can get a sense of the transient response. Sometimes the tires spin. Sometimes you hit a head wind. It takes centuries to reach the equillbrium response.
Lindzen? if you look at the data he looked at you can only get the transient. Same thing with volcanoes. Transient.
I find it odd that people have such certainty over sensitivity when it comes to lindzens results.
I wouldnt discount him out of hand, but his results are far from certain.
Stephen – taking your analogy for climate sensitivity to a 1 watt increase in solar energy further. We know that 3.5 billion years ago the Earth had liquid oceans while the solar radiation was about 80 watts less than today. The sun gradually brightened by ~0.02 watts/million years. The Earth’s temperature has changed very little since then, in fact it seems to have cooled – otherwise we would not be here able to discuss it. How can it then even be possible for water feedbacks to be positive. Recent geological evidence rules out CO2 or Methane GHG as the cause. Water must therefore act to stabilise temperatures on Earth in the long term. Feedbacks must be negative around -2 watts/m2K^-1 to explain this. So we can expect AGW < 1 degC – not really a big deal.
Steven Mosher says:
May 4, 2012 at 2:47 pm
This link below also supports why only air temperature is no good for deducing sensitivity and also a good description of the situation. Have to distinguish between regular ocean cycles like ENSO that result from regularly changing cloud levels in the tropics. The feedback with regarding CO2 effect on clouds has to be established and this can’t be achieved by air temperature alone.
http://www.sciencebits.com/OnClimateSensitivity
Whether it would take centuries to responce is very unlikely, the extra CO2 molecules only absorb a tiny amount of energy and this happens almost immediately. There has to be an almost immediate responce taking other factors into account or it is not there to be accounted for. Finally, the step up in global temperatures after each significant El NIno show the majority of the warming can’t be caused by CO2 unless CO2 caused the El Nino’s.
Matt, you fundamentally misunderstand the warming mechanism of C02. it has NOTHING to do with the absorbing of heat or the heat capacity of C02.
If the atmosphere had no absorbing GHG gases the earth would radiate from the surface.
But we have gases which absorb, reflect, and retransmit Long wave radiation. As a result the earth radiates from about 5-6km. This is called the effective radiating altitude.
Because the earth has a lapse rate ( higher is colder ) that means the earth radiates to space from a colder place than the surface. Physically, since colder bodies lose energy via radiation slower than hotter bodies do, the earth radiates more slowly than it would if it radiated from the surface. That means the surface is warmer than it would be otherwise. It cools less rapidly than it would with no GHG atmosphere. The silvered liner of a thermos doesnt “trap” heat by its heat capacity, it DELAYS the loss of energy via radiation. Your coffee is not WARMED by the reflection of radiation, it loses heat less rapidly.
When we add C02 to the atmosphere we raise the level at which the earth radiates to space.
So, over time, that altitude increases and earth radiates to space more slowly. That means over time the surface cools less rapidly. This effect is small in day to day terms, small in monthly, yearly and even decadal terms. Yes, the earth continues big swings with el nino and other internally driven variations, but over time, over long periods of time the increased opacity of the atmosphere results in a raising of the ERL. A higher ERL means we radiate from a colder place in the atmosphere. That means slower loses of energey to space. That means a surface that cools less rapidly. its got NOTHING to do with the heat capacity of C02
I don’t know where else to ask this. I watched a documentary a couple of days ago on the reason the Maya civilisation collapsed – Dick Gill spent 20 years exploring it and shows it was drought. The Mayan area has no natural lakes, rivers or underground water, relies completely on water collected during the summer rainy season, around 800 AD this failed. During the telling of it he said that normally the rains come because of a particular high pressure system which more or less stays put, somewhere in the Atlantic I think, but that this moved considerably further south than it normally does which altered the climate by making it colder in the north, which in turn didn’t bring the rains up into the area. All this to ask, is this the mechanism which triggers the El Nins? If so, what moves the high pressure system?
http://topdocumentaryfilms.com/ancient-apocalypse-maya-collapse/
The graphic and that explantion are towards the end of the docu, sorry can’t say exactly but around forty minutes in.
Steven Mosher says:
May 4, 2012 at 6:13 pm
I didn’t explain it well enough.
The CO2 molecule absorbs energy then releases energy, absorbs energy then releases energy. (continuous cycle) If it can’t be absorbed it is refected or retransmitted. While it is doing this it delays the energy loss to to upper atmosphere. One CO2 molecule can’t delay energy more than it’s heat capacity at the frequency any one moment this occurs.
Matt, you still dont get it. The effect is cause by increasing the opacity of the atmosphere.
At a given concentration of C02 the earth will radiate to space. That altitude is called the ERL.
Above this altitude the concentration of C02 and other gases is such that the radiation escapes
to space. Call this concentration X.
When you add C02 to the atmosphere the altitude at which Concentration X occurs increases.
That means the earth radiates from a HIGHER altitude. for example: at 280 ppm the
altitude the earth radiates is say 5.5km. Above 5.5km the concentration is such that the radiation escapes to space. call that concentration X. When you add more C02, the altitude
at which X occurs is Higher. The earth radiates from a higher colder place.In short, the amount of C02 above the ERL is constant. Its the amount that allows radiation to escape freely to space.
raise the total C02 in the atmosphere, and the ALTITUDE at which the ERL occurs goes up.
That means the rate of energy loss decreases. The surface cools less rapidly as a result.
You are confusing yourself
start here
http://www.aos.wisc.edu/~aos121br/radn/radn/sld012.htm
graduate to here
http://geosci.uchicago.edu/~rtp1/papers/PhysTodayRT2011.pdf
While Crutem4 is now on woodfortrees, there is a slight problem. It only goes to the end of 2010. Mathematicians may wish to improve on my crude analysis, but for what it is worth, here is what I did. I took the slope of Crutem3 from September 2001 to December 2010. Then I found the slope from September 2001 to March 2012. The drop for the additional 15 months was 0.0083. The slope of Crutem4 from September 2001 to December 2010 was 0.0083. So if I am allowed to make the assumption that when Crutem4 is completely updated, that there would be a similar drop, there will be NO temperature change in land for the past 10 years and 7 months. Granted, it is not over 15 years like Hadcrut3, Hadsst2 and RSS. But it is long enough to adopt a bit of a wait and see attitude before spendings billions that may not be needed.
http://www.woodfortrees.org/plot/crutem4vgl/from:2001.66/plot/crutem4vgl/from:2001.66/trend/plot/crutem3vgl/from:2001.66/plot/crutem3vgl/from:2001.66/to:2011/trend/plot/crutem3vgl/from:2001.66/trend
Mosher, I thought you said you meant the _trend_ in the 30’s go down in Hadcrut4. Now you go back to repating that the decade was cooler than thought. But the average anomaly is slightly (about 0.015C) higher in the 30’s in hadcrut4 compared to hadcrut3. It’s not a big deal, I’m just puzzled you keep ignoring that.
“Adjustments of this magnitude can be seen through most of the CRUTEM4 database, but especially in Europe and adjacent areas. With data from such early years being so sparse, it is difficult to see both why such adjustments have been made, and the basis on which they were made”
“There are strange repeating adjustments”
Ed you are certainly pointing at big flaws in CRU’s methodology to prepare the data for analysis. To my understanding CRU further blows their own credibility with the new version.
Great report!
P. Solar says:
May 3, 2012 at 10:43 am
“What is notable here is not just cooling of earlier temps and bumping up or recent ones. There is a general reduction in the long term variations, ie removal (reduction) of the natural cycles. ”
Exactly – to better fit models – not the other way around …
Steven Mosher says:
May 3, 2012 at 1:51 pm
“1. Berkeley Proved no such thing.”
—————————————————————————–
Steven other people understands things differently. Here from Tamino’s blog at the time:
berkeley-team-says-global-warming-not-due-to-urban-heating:
” the results from the Berkeley team have confirmed that the other main global temperature estimates (NASA GISS, NOAA/NCDC, and HadCRU) got it right, and that station siting/urban heat island effects are not responsible for any of the observed temperature increase. The real reason all these analyses (including Berkeley’s) show temperature rise is: the globe is warming.”
Berkeley themselves do not go so far, but leave room for this interpretation:
“We observe the opposite of an urban heating effect over the period 1950 to 2010, with a
slope of -0.19 ± 0.19 °C/100yr. This is not statistically consistent with prior estimates, but it
does verify that the effect is very small,”
——————————————————————————
Steven Mosher says:
2. The last study Zeke, nick stokes and I did, suggested a UHI trend from 1979-2010
That trend, about .04C per decade, is consistent with the handful of regional
studies of UHI which all show trend bias of .03C to .125C per decade over the same
period. It is REGIONALLY variable. One UHI does not fit all. In the SH, UHI is much
smaller. In china and japan and Korean building practice drives it higher.
The argument that the whole database is contaminated is lacking one thing: evidence.”
——————————————————————————
As I posted above the UHI trend is different per the region but also period of time – very depending of the time of urbanisation – the time when the city grows – what you seem to ignore.
I have not seen the study you mention, but am not surprised by the relative small amount of UHI effect in the period 1979-2010.
You miss to address the influence of the demographic development which is in my view very relevant.
You do not specify where is “region variable”, but let me make a guess: it is more relevant at high latitudes. And as we do not have many cities in the south at high latitudes it is more relevant for the northern hemisphere high latitudes. If this is true, it is again an argument to my hypothesis that UHI effect on grows plays a relevant role in the measurement of data in North America, Europe, and Russian cities 19th and early 20th century.
You say that the evidence is lacking – but first the simple existence of the UHI is a first evidence to it. To deny there is any effect of UHI on grows would simply require an UHI effect that appears at first time and stays all the time constant. Is this your hypothesis of how UHI works?
Furthermore you ignore my argument above: the fact that including cities shows a slower/different trend after 1950s is an important hint. The fact that demographic changes occured also after this moment – especially in the northern region where this UHI effect is more relevant (the other hypothesis) – is again pointing towards it. No further cities grows, less warming.
The simple, direct answer to the headline question is: Yes.
All climate data has been “fiddled with”. The more important questions appear to be: why, how and to what purpose.
“Enquiring minds want to know.” FOIA might eventually allow them to know, years from now.
Which data set should be used when analyzing these predictions versus reality? I was given the impression that CRUTEM3 was not good because it did not cover the polar regions well. Presumably, we now have the cream of the crop with both CRUTEM4 and BEST. Is one better than the other? I checked out the year 1996 with both and there are HUGE differences! For example, with BEST, January 1996 was 0.282 and August was 1.095 for a rise of 0.813 between January and August of the same year. However with CRUTEM4, January 1996 was 0.208 and August was 0.220 for a rise of only 0.012 between January and August of that same year. The net difference is 0.801 C, which is supposedly the total warming since 1750. See
http://www.woodfortrees.org/plot/best/from:1996/to:1997/plot/crutem4vgl/from:1996/to:1997
Werner.
which dataset should be used when comparing predictions versus reality.
1. understand what the prediction actually is. Typically people predict a decadel trend.
fully understanding that monthly and yearly figures are going to be noisy.
2. There is no need to pick ONE. standard practice would be to compare the prediction
(.2C per decade ) to ALL observation datasets. Note the results and proceed accordingly.
Monthly temps are noisy. that is why you look at longer periods.
Steven Mosher says:
May 5, 2012 at 2:36 pm
Monthly temps are noisy. that is why you look at longer periods.
Thank you. However my point was not so much about the monthly noise but rather the huge difference between two land data sets for the same months.
clivebest says:
May 6, 2012 at 6:43 am
“Stephen – taking your analogy for climate sensitivity to a 1 watt increase in solar energy further. We know that 3.5 billion years ago the Earth had liquid oceans while the solar radiation was about 80 watts less than today. The sun gradually brightened by ~0.02 watts/million years. The Earth’s temperature has changed very little since then, in fact it seems to have cooled – otherwise we would not be here able to discuss it”
clivebest, what people do not take into account here is that the earth has lost a quarter of its water. This gradual loss of water compensates for the increase brightness of the sun. The oceans define earth temperature.
http://sciencenordic.com/earth-has-lost-quarter-its-water
There are several attractors where the earth with its oceans reaches its thermal equilibrium.
These are defined by specific behaviour of the oceans, first would be the high capacity of the oceans to absorb heat – in a three dimensional volume (gradually in depth to 200 meters) but lose heat only at the surface.
Second would be the evaporation which is taking a lot of heat and does not allow the surface to reach higher temperature – it will not radiate much heat as a rock would do.
Then it is the ice at the surface which creates an isolation sheet over the ocean in the parts that are in the dark for too long time.
Of course then come the clouds which – if too much evaporations clouds form and shield the ocean from further heat intake, heat transfer through enthalpy and so on.
Only after all these do come the “greenhouse gases” with heat transfer through radiation where carbon dioxide is a small player.
With the earth losing some of its water, it can capture and redistribute less energy as the continents warm much more under the sun and radiate the heat away – radiation increases with T**4.
This is why globally the climate is getting slowly colder even if the sun brightened.
Of course how currents circulate the stored heat plays also a major role too.
Steven Mosher says:
May 5, 2012 at 12:28 am
I was discussing the atomic stage before even considering how it effects the opacity of the atmosphere. I do know about the ERL and longwave radiation can be measured above the top of the atmosphere to see if there has been any change in it.
Outgoing longwave radiation has not changed over recent decades, indicating that the ERL has not increased.
http://www1.ncdc.noaa.gov/pub/data/cmb/teleconnections/olr-s-pg.gif
Another piece of evidence that supports the sensitivity is low.
@Larry Plume.P
Interesting. The study supports water as being the Earth’s thermostat, with the main mechanism as being changing albedo. Too hot more evaporation and clouds lowering albedo. Too cold less cloud and higher albedo. It appears the earth was mostly covered in oceans 4billion years ago and now it is 70% water. The loss of water through methanogenesis implies 50 to 500 times more methane then , but still far below levels either of methane or CO2 for a greenhouse explanation. However water vapor content in the high atmosphere could be another thermostat at play. Either way it is a remarkable fact that Earth’s temperature has remained so constant , and the only constant factor has been a dominant water surface.
Ken B … you’ve found the document but a snapshot of page 11 re Australian capital city mean temperatures from “Climate and Weather of Australia” by Commonwealth Meteorologist H.A.Hunt, Griffith Taylor and E.T.Quayle, published in 1913, is at http://www.waclimate.net/imgs/temperatures-hunt.gif
I’m not sure what years are covered for the mean temps but I assume it’s from the earliest Australian recordings in the 1850s. These ancient temps can be compared with the modern BoM records …
PERTH pre 1910
Mean Max – 22.8C
Mean Min – 12.8C
There are two modern Perth stations for comparison, the first Perth Airport 9021 which is the city’s official ACORN-SAT (formerly High Quality) station …
Perth Airport 9021 1944>
Mean Max – 24.4C
Mean Min – 12.1C
… or Perth Metro 9221 which is closer to the pre-1910 station and used by the BoM for Perth’s historic comparisons …
Perth Metro 9221 1992>
Mean Max – 24.6C
Mean Min – 12.7C
ADELAIDE pre 1910
Mean Max – 22.8C
Mean Min – 11.7C
Adelaide Airport 23034 1955>
Mean Max – 21.5C
Mean Min – 11.4C
Adelaide Kent Town 23090 1977> (ACORN-SAT)
Mean Max – 22.3C
Mean Min – 12.2C
BRISBANE pre 1910
Mean Max – 25.6C
Mean Min – 15.6C
Brisbane Aero 40842 1996> (ACORN SAT)
Mean Max – 25.3C
Mean Min – 15.6C
Brisbane 40913 2000>
Mean Max – 26.4C
Mean Min – 16.2C
SYDNEY pre 1910
Mean Max – 21.1C
Mean Min – 13.3C
Sydney Observatory Hill 66062 1858> (ACORN-SAT)
Mean Max – 21.7C
Mean Min – 13.8C
Sydney Airport AMO 66037 1929>
Mean Max – 22.2C
Mean Min – 13.4C
MELBOURNE pre 1910
Mean Max – 19.4C
Mean Min – 9.4C
Melbourne Regional Office 86071 1908> (ACORN-SAT)
Mean Max – 19.8C
Mean Min – 10.2C
Essendon Airport 86038 1929>
Mean Max – 19.6C
Mean Min – 9.2C
Melbourne Airport 86282 1970>
Mean Max – 19.7C
Mean Min – 9.5C
HOBART pre 1910
Mean Max – 16.7C
Mean Min – 7.8C
Hobart (Ellerslie Rd) 94029 1882> (ACORN-SAT)
Mean Max – 16.9C
Mean Min – 8.3C
Hobart Airport 94008 1958>
Mean Max – 17.5C
Mean Min – 8.1C
All of which provides a pre-1910 average mean max for all six Australian capital cities of 21.4C compared to 13 nearby ACORN/airport/same stations with 21.7C for all years.
The pre-1910 mean min was 11.8C compared to 13 nearby ACORN/airport/same stations of 11.7C for all years.
There is the inevitable argument that many of the pre-1910 temps were recorded without a Stevenson Screen for part or all of their record whereas after 1910 the temps were screened. In turn, these are all capital city stations with abundant modern UHI and jumbo jet factors.
I also maintain that in Australia’s new ACORN-SAT dataset, between 30% and 50% of all Fahrenheit degrees recorded before 1972 metrication were truncated down to .0F, creating a downward bias up to .3C before that year. The BoM has already acknowledged but not adjusted for a .1C metrication bias in 1972. See http://www.waclimate.net/round/australia-acorn.html
Who could help?
The big change in are lower temps 1814 until 1895, producing a steep
increasing line….
HadCRUT sets the global warming century figure 1900-2000 at 0.74 C. Now,
who can tell the global warming figure for the preceding century, 1800-1900.
Would it be 0.74 C as well, or something more or less? Help appreciated…..
JS
What also is interesting that they admit the 20-year step increase of the 60 year
Jovian cycle of 0.4 C 1815-1835……This step increase was always missing and
one can clearly see this 0.4 C increase…..somethin good in the fiddling, after all….
JS
Gah. Shameless, relentless. Convinced the teflon shield will shed all filth that lands on it.
There must be a lot of still, quiet voices shrieking in agony.