A comparison of adjusted -vs- unadjusted surface data
Posted on August 1, 2012 by Anthony Watts
Frank Lansner sends word of this paper in progress, relevant to the current discussions on the U.S. surface temperature record, and writes:
In the attached article of mine I make an estimate of Hadcrut’s adjustments to us temperature data over the years.
They do not really resemble NOAA´s adjustments:
- As if Hadcru finds reason to step-change in 1946 while NOAA don’t. And NOAAs (mostly TOBS) issues are supposed to take place around 1970-90, but this is not confirmed by Hadcrut. – Frank
Here’s the full paper in progress:
USA TEMPERATURE DEVELOPMENT 1880-2010 FROM UNADJUSTED GHCN V2 DATA
By Frank Lansner, Civil engineer Chemistry
Software engineer (SAP), Novo Nordic IT, Bagsvaerd, Denmark
This paper is part of the RUTI project, described further: http://hidethedecline.eu/pages/ruti.php
ABSTRACT
Temperature records worldwide are used to estimate a warming signal due to increase of CO2 and are thus key parameters when deciding an appropriate climate policy. Despite the fact that the contiguous USA has the best availability of temperature data in the world, there is a large difference between recent [2] and earlier [1] published temperature data from GISS. This raises questions about the robustness of data.
The USA temperature trend calculated from unadjusted Global Historical Climate Network (GHCN) data shows the 1930s as the warmest decade, around 0,2 K warmer than 2000-2009. The calculated temperature trend is based on data from 826 stations, and is virtually identical to that of Hansen et al. 1999 [1].
The calculated temperature trend 1930-2010 is around 0,5 K colder than the updated 2011 GISS US temperature trend [2], and around 0,4 K Colder than temperature trend calculated from Hadcrut stations in USA.
The increased warming trend found in the Hadcrut data appears to originate more due to the choice of temperature stations used than due to adjustments of station data.
Keywords: temperature adjustments, temperature stations, anthropogenic climate impacts
INTRODUCTION
The role of CO2 as a warming agent in the atmosphere is the suspected driver of basically all unfortunate developments in the climate as described in the recent IPCC paper: “Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation”.
The “extreme events and disasters” in connection with changes in ice cover, sea levels, precipitation and frequency of large tropical storms and more mentioned in the above IPCC paper [9-10] is based on the magnitude of the recent warming supposed to be caused mostly by CO2 in the Atmosphere. Thus, the “extreme events and disasters” mentioned in the above IPCC writing is dependent on the correctness of temperature data.
This is why credible analyses of temperature data are vital for any estimation of the potential hazards of CO2 and thus the policy to be carried out.
In this analysis, estimates of USA temperature trend 1880-2010 were made and the results compared to USA data published by NASA in 1999 [1] and 2008 [2]. Results were also compared to USA temperature stations used by Hadcrut.
For the Hadcrut data set, other issues are investigated. For example, the selection of temperature stations in the Hadcrut subset of those available suggests a bias towards stations from large urban areas. The temperature data from stations used by Hadcrut was compared to the bulk of the stations from the same data source, unadjusted GHCN. This was done to evaluate how much of the warming trend in Hadcrut data originated simply from the choice of temperature stations done by Hadcrut. The temperature trends for Hadcrut stations should resemble the general temperature trend using unadjusted GHCN v2 data.
To learn further about the increased heat trend in Hadcrut data, data from the unadjusted GHCN dataset – as used by Hadcrut were compared with the of temperature data in Hadcrut adjusted version. This way an estimate of the actual adjustments was made.
The nature of the adjustments in Hadcrut data was then compared to the adjustments for the USA temperature data described by NCDC for the USHCN dataset [3-4]. If adjustments used by Hadcrut and NCDC to US station data are not similar, this will not support the robustness in the adjustments of US temperature data. USHCN is a collection of temperature stations from the USA with longer periods of data available. USHCN was developed by Thomas Karl et al. 1990 [4].
Due to the impact of geography on temperature trends, a method “two-zones-averaging method” was used to improve the estimate of what land area is best covered by what stations. For example coastal temperature stations often show a significantly different trend than nearby non-coastal stations [5]. This information was used in determining sub-regional classifications of temperature stations. Furthermore, to avoid the introduction of possible non-climatic factors, data from large urban areas were not included. Additionally, stations with lack of data before 1940 or after 1980 were not used. In total, 826 stations were used from unadjusted GHCN.
METHODS
USA temperature data used were unadjusted monthly GHCN v2 and it was obtained from Appinsys.com. At the time of data retrieval, GHCN data was latest updated March 2011, years 1880-2010 are used. The Hadcrut data were obtained also from Appinsys.com, latest updated December 2009. Years 1880-2008 are used. All graphs shown have a base period of 1961-90 unless otherwise noted.
Temperature trend was calculated for 5×5 grids, and grid results where averaged by weighting for the area of US land territory for each grid using a “two-zone-averaging” method.
“Two-zone-averaging method”:
Figure 1. For each grid, the area of two zones where estimated: One with temperature stations showing colder temperatures 1998-2008 than 1930-1940 (blue areas on fig 1), and another zone showing colder temperatures 1998-2008 than 1930-40 (red areas on fig 1). (Black stars shown on fig 1 are temperature stations included in the Hadcrut dataset. Temperature trends on the graph are 5 year averaged).
On the graph, The legend text “Colder 13 st 48%” explained:
1) temperature trend within the areas with colder trend
2) 13 such temperature stations found for the 5×5 grid
3) the area covered by cold trended temperature stations is estimated to cover 48% of the US land area within the 5×5 grid.
Temperature stations are unevenly distributed and thus a raw average of all temperature stations belonging to one grid may be misleading. Using 2 zones reduces this source of error.
Figure 2. As discussed in “RUTI: Coastal temperature stations” [5-6], land areas zone 1 and 3 are most likely to be affected by marine air temperatures. On the other hand low land non-coastal areas, zone 2, and higher elevated areas not facing marine air, zone 4, both seem to share a non-coastal temperature trend.
Figure 3. Example from NE US from “RUTI: Coastal temperature stations [5]. Although figure 2 shows 4 zones, often the zones influenced by marine air (zone 1 and 3) show similar temperature trends, just as non-coastal zones (2 and 4) show similar trends. Thus, it makes sense to work with just 2 zones: Cold and warm trended, shown as blue and red respectively hereafter.
There are other ways that geography locally can affect temperature – changing snow/ice cover, rivers etc. – and such locations will be part of either the cold or warm trended zones too. Graphs on fig 3 are 5 year averages.
Also, a Hadcrut dataset calculated on basis of 87 temperature stations located in the USA was made. (the Station Key west located far from the mainland was not used). Hadcrut use numerous strongly urban located stations, but all was included when calculating data from the Hadcrut stations. Dataset for Hadcrut stations where calculated for each 5×5 grid, and each grid was weighted with respect to area of US land territory – just like the RUTI USA data.
Finally a dataset of “Unadjusted GHCN dataset for stations used by Hadcrut was calculated just like the Hadcrut dataset. Of the 87 Hadcrut stations used for USA, 77 has data publicly available from Unadjusted GHCN, and thus it is possible to analyse temperature trend of these unadjusted stations used by Hadcrut.
RESULTS
Figure 4. The distribution of GHCN stations in the USA . Blue: 1998-2008 colder than 1930-1940. Red: 1998-2008 colder than 1930-40 based on unadjusted GHCN data. The coastal and mountain locations of warm trended stations are visible.
In many areas, the cold and warm trended stations can co-exist within short distances, and thus in such areas adjustments of temperature stations based on results from other nearby temperature stations should normally be avoided. This also questions the still lower number of temperature stations used by GHCN, Hadcrut and GISS, especially after 1990. This approach of low number of temperature stations lowers the accuracy of the resulting overall temperature trend.
In the Eastern USA, one can spot a number of warm trended temperature stations located on the Atlantic side of the Appalachian Highlands, but for the Rocky Mountains the terrain is more nuanced and so is the distribution of warm trended and cold trended stations. For more details on this, see [6].
Figure 5. Average temperature trends of the 2 zones shown (red and blue graphs) for each grid. The unit on the graphs is shown in fig 1. Data is 5 year averaged. For some grids, the difference between temperature trend of the 2 zones approaches 1 K when comparing recent decade with temperatures before 1940.
Figure 6. On this illustration, the cold and warm averages has been calculated into one temperature trend for each grid weighted with respect to estimated area coverage by warm and cold trended areas. The red/blue numbers shown on the graphs are the temperature average 1998-2008 compared to 1930-40.
Figure 7. Temperature trends for all grids (48 contiguous states of USA) where then combined, producing a single temperature anomaly dataset here after referred to as “RUTI USA Contiguous 48 states”. This resulting graph shows a remarkable agreement with that published by Hansen 1999 [1]:
Figure 8. The similarity RUTI vs. Hansen 1999 is obvious. Visible differences in 5 year averaged temperatures between Hansen et al. 1999 and RUTI USA, occurs mostly before 1895.
Figure 9. RUTI USA, warmest decade found in unadjusted GHCN data for USA was the 1930´ies, around 0,2 K warmer than the decade 2000-09. Warmest year found was 1934, followed by 1921 and 2006.
Figure 10. Comparison of US temperature datasets: Temperature trends from Hansen et al. [1] and RUTI USA datasets are shown, and now in addition, the resulting adjusted temperature dataset from Hadcrut US stations (red) and adjusted temperature dataset from GISS updated US temperatures [2] (green) are shown. On this graphic, base period of datasets where set equal with RUTI USA for the period 1930-39.
The GISS updated US dataset shows the last 10-15 years to be around 0,5 K warmer than the RUTI USA dataset (and thus the original Hansen et al. 1999 [1] dataset ). Hadcrut stations shows recent decade around 0,4 K warmer than RUTI USA (and thus Hansen et al. 1999 [1] ).
This difference is shown again in fig 11 now with base period 1895-1905 where it amounts to around 0,42 K:
Figure 11. Most of the temperature stations used by Hadcrut have unadjusted temperature data publicly available from GHCN (black graph)
The similarity between the red and black graphs (stations chosen by Hadcrut, adjusted vs. unadjusted) suggests, that adjustments to Hadcrut US temperature data have a relatively small impact: 0,12 K of warming of trend.
This indicates, that most of the difference between the 826 stations from Unadjusted GHCN and then the adjusted Hadcrut temperature trends, approx. 0,42K, is due to the Hadcrut choice of temperature stations, approx. 0,3K.
From unadjusted GHCN, the 77 stations used by Hadcrut has around 0,3K more heat trend 1900-2010 than the temperature trend calculated in this writing using 826 stations.
Figure 12. Locations and populations in thousands for the Hadcrut US temperature stations [€€€€]. The raw average population around Hadcrut temperature stations is 1.3 mio people. The typical Hadcrut station is placed in a town with more than hundred thousand inhabitants.
Figure 13. The trend of Hadcrut adjustments of the US temperature data can be estimated by comparing unadjusted GHCN temperature data with Hadcrut adjusted temperature data for the US temperature stations chosen by Hadcrut (black graph fig 13)
The trend of Hadcrut adjustments shows a larger step change around 1946, of 0.12 K.
The larger step change in 1946 for Hadcrut stations was not expected, since specifically for the USA, adjustments announced by NCDC for USHCN temperature trends [3-4] shows a gradual adjustment with the bulk of adjustments done for mostly the period 1970-90, not at all a step change mostly around 1946 (see red graph fig 13).
It seems that NCDC does not agree with Hadcrut on when adjustments should take place. And vice versa: The Hadcrut adjustments for 1970-1990 do not support the adjustments done by NCDC.
CONCLUSION
Calculation of USA temperature trend (“RUTI USA”) from Unadjusted GHCN confirmed temperature trend published by Hansen et al. 1999 [1]. Fig 10 shows that the updated GISS temperature trend for USA [2] since the 1930´ies has around 0.5 K more heat trend after recent adjustments. Considering that the USA temperature data has the highest quality in the world, the large adjustments of 0.5 K may question the quality of temperature data world wide.
The extra heat found for Hadcrut US stations in comparison with both Hansen et al. 1999 [1] and the results from the present paper amounts to approx. 0.42 K since the 1930´ies. Far most of the Hadcrut temperature stations (77 out of 87 stations) are available unadjusted from GHCN, and thus it was possible to estimate that only approx. 0.12 K of the extra roughly 0.42 K heat trend in Hadcrut US temperature trend originates from adjustments. The remaining roughly 0.3 K of extra heat trend for Hadcrut US temperature stations seems to originate from the choice of temperature stations from GHCN included in the Hadcrut USA subset.
Thus the unadjusted temperature stations used by Hadcrut generally has more heat trend than neighbouring unadjusted temperature stations, but even so, Hadcrut add heat trend when they adjust their temperature data.
In addition, the trend of adjustments done to Hadcrut US temperature stations shows one larger step change in 1946, other wise flat trend. This does not at all resemble the NCDC adjustments to USHCN temperature data as one would expect (see fig 13). This mismatch in the nature of adjustments between Hadcrut and NCDC USA temperature data lowers confidence to temperature adjustments done by both.
Hadcrut temperature stations are often located in medium to large urban areas. Hadcrut only use 12 rural temperature stations, but there are many more than 12 useful rural temperature stations in the USA available. A bias towards using more warm trended temperature stations was also questioned by the Russian IEA [7-8] for the Russian land temperatures estimated by Hadcrut.
Does temperature data turn out to be credible and solid as basis for policy making and solid input to models that predicts the future development of temperatures and consequences?
USA temperature data has the highest quality in the world, so if there is an area where temperature data should be robust it is USA. None the less, for each stone turned, hardly any support of robust modern versions of adjusted temperature data turns up.
The most robust result was found when analysing USA temperature data is the good match between RUTI (Unadjusted GHCN) and Hansen 99. The conflict between RUTI (Unadjusted GHCN) and Hansen 99 on one side and Hadcrut + Hansen 2008 temperature trends on the other side speaks against credibility of temperature data to be used for policy making.
ACKNOWLEDGEMENTS
Thank you: Verity Jones, Joanne Nova, Alan Cheetham and Oguzhan Tandogac.
REFERENCES
[1] Hansen et al. 1999: “GISS analysis of surface temperature change “
http://pubs.giss.nasa.gov/docs/1999/1999_Hansen_etal.pdf
[2] Data for GISS USA are updated online at http://data.giss.nasa.gov/gistemp/graphs/Fig.D.txt
[3] USCHN online description: http://www.ncdc.noaa.gov/oa/climate/research/ushcn/ushcn.html
[4] Karl, T.R., C.N. Williams, Jr., F.T. Quinlan, and T.A. Boden, 1990: United States Historical Climatology Network (HCN) Serial Temperature and Precipitation Data, Environmental Science Division, Publication No. 3404, Carbon Dioxide Information and Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, 389 pp.
[5] RUTI: Coastal temperature stations, online : http://hidethedecline.eu/pages/ruti/coastal-temperature-stations.php or
http://joannenova.com.au/2011/10/messages-from-the-global-raw-rural-data-warnings-gotchas-and-tree-ring-divergence-explained/
[6] RUTI: USA, online http://hidethedecline.eu/pages/ruti/north-america/usa-part-1.php
[7] Russian complaint orig pdf in Russian: http://www.iea.ru/article/kioto_order/15.12.2009.pdf
[8] RiaNovosti: http://en.rian.ru/papers/20091216/157260660.html
[9] IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, http://ipcc-wg2.gov/SREX/
[10] IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, PDF overview: http://www.ipcc.ch/pdf/press/ipcc_leaflets_2010/ipcc_srex_leaflet.pdf
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