A few weeks ago I put up a post on how the homogeneity adjustments applied by GISS to raw surface temperature records increase warming at the hemispheric and global scale. In this post I extend the review to include the homogeneity adjustments applied by NCDC, CRU and BEST and re-evaluate GISS using a series which is more relevant than the “meteorological station only” series I used last time. Here is a summary of results:
- BEST applies homogeneity adjustments to the raw Northern Hemisphere land surface air temperature records that add 0.3-0.4C of warming since 1890. The adjustments applied by NCDC, CRU and GISS, seem to be limited to a few countries such as Iceland and the USA and add no significant warming.
- All series apply homogeneity adjustments to the Southern Hemisphere raw records before about 1970 and each series gets different results, adding anywhere from 0.3 to 0.6C of warming. There is little doubt that this warming is introduced by the adjustments. The scatter between the series further demonstrates that in practice homogeneity adjustments do not homogenize the raw data; they simply introduce additional distortions.
- However, the impacts of Southern Hemisphere homogeneity adjustments on global warming estimates, which are commonly quantified from “surface temperature” series such as HadCRUT4, are diluted by a factor of about ten because HadCRUT4 is an area-weighted average of air temperatures over land and SSTs over the oceans, and land in the Southern Hemisphere covers only about 10% of the Earth’s surface. Because of this dilution I estimate that the homogeneity adjustments applied to CRUTEM4, HadCRUT4’s component “land” series, add only ~0.03C of global warming since 1890. (BEST adds the most, at ~0.1C)
- Of the series reviewed BEST appears to be the least representative of actual land surface air temperature trends.
Five published surface air temperature series are considered here. All of them have been subjected to some degree of homogeneity adjustment:
- The NOAA/NCDC land series, which uses several thousand records and which covers land areas only.
- The UEA/CRU CRUTEM4 land series, which also uses several thousand records and covers land areas only.
- The Berkeley Earth BEST land series, which covers land areas only but reportedly uses some tens of thousands of records.
- The GISS 250 series, which projects data up to 250km out over the oceans and which also uses several thousand records. GISS does not publish an official “land” series but I made GISS250 into one by applying a land mask.
- The GISS met (meteorological station only) series, which uses the same set of records as GISS250 but is not a land series because it projects temperatures well out over the oceans. I include this series because it was the one used in the previous post.
These series are compared with two hemispheric and global series I constructed from scratch some years ago using unadjusted GHCN v2 records, which reviews show are very similar to the current GHCN v3.2 unadjusted records. The series are:
The Verified series runs from 1890 to 2010. It uses 801 unadjusted GHCN v2 records (460 in the Northern Hemisphere) that I verified as correct to within acceptable limits by matching them against adjacent records. This series is not strictly a “land” series because it projects temperatures over the oceans, although except for a few short records from moored weather ships all the data are from land stations.
The RA series runs from 1880 to 2005. It adds 71 records of questionable quality to fill in gaps in the Verified series and estimates annual means by a first-difference approach that is effectively the equivalent of taking a mathematical average of all the records. This should make it a reasonably close approximation to a “land” series:
Figure 1 compares these two series in the Northern Hemisphere. The series are plotted as anomalies relative to 1990-2005 means, as are all the series presented here unless otherwise specified. Using 1990-2005 as the baseline period causes differences between the series to appear in the early rather than the later years:
Figure 1: Unadjusted RA and Verified Series, North Hemisphere
The two series are effectively the same. Changing the estimation approach and adding 71 dubious records makes no significant difference.
The question now arises, are these two series reliable? Or not to put too fine a point on it, do I know what I’m doing? Figure 2 superimposes four of the five published homogeneity-adjusted NH land surface air temperature series on the RA series. The Verified series is omitted to improve clarity and the BEST series is shown later:
Figure 2: Unadjusted RA vs. NCDC, CRU, GISS 250 and GISS Met homogeneity-adjusted land surface air temperature series, North Hemisphere
The four adjusted series show substantially the same trends and substantially the same amount of overall warming as the unadjusted RA series, indicating:
a) that the adjustments these series apply to the raw records do not add any appreciable amount of warming in the North Hemisphere.
b) that the number of records used, which ranges from a few hundred to several thousand, makes no significant difference.
and hopefully confirming:
c) that I know how to construct temperature time series.
Figure 3 now plots BEST against the average of the other published series. BEST is the odd man out, showing 0.3-0.4C more warming in the Northern Hemisphere since 1890:
Figure 3: BEST versus average of NCDC, CRU, GISS 250 and GISS Met series
Now on to the Southern Hemisphere. Figure 4 compares the verified and RA series. Verified uses 341 unadjusted records and RA adds another 27, including some that show urban warming signatures, such as Buenos Aires, Rio, Sao Paulo, Sydney and Melbourne. Verified also area-weights the results while RA gives disproportionate weighting to Australia, where most of the long-term SH records are. Yet there is still a reasonably good match between the two series after 1900, with neither showing any appreciable warming between 1900 and 1975. (The mismatches before 1900 are a result of a shortage of good long-term SH records. Results depend on which records you accept and which you reject.)
Figure 4: Unadjusted RA and Verified Series, South Hemisphere
Figure 5 now superimposes the RA series on the five published and homogeneity-adjusted SH land series (Verified is again omitted but BEST is included):
Figure 5: Unadjusted RA vs. NCDC, CRU, GISS 250, GISS Met and BEST homogeneity-adjusted land surface air temperature series, South Hemisphere
We see that the best efforts of NOAA, CRU, GISS and BEST to homogenize the Southern Hemisphere raw records have created a hodgepodge of conflicting series that frustrates all attempts to make a robust estimate of Southern Hemisphere warming before about 1970. Clearly the homogenization algorithms have not homogenized. They’ve added noise instead.
However, one thing the homogenization algorithms do agree on is that the RA series is cooling-biased (they all add warming). But how do they arrive at this conclusion? It’s hard to find supporting evidence. RA matches the published Northern Hemisphere series since 1880 (Figure 2) and the published Southern Hemisphere series after about 1970 (Figure 5), so why should it be cooling-biased in the Southern Hemisphere before 1970, considering that it was constructed using the same procedures? Some of the 368 records RA uses over this period, such as Cape Town, Anatananarivo and Adelaide Airport, may indeed be cooling-biased, but some are warming-biased, such as Buenos Aires, Sao Paulo and Low Head, and deleting suspect records of this type makes no significant difference. And in specific examples discussed in previous posts, such as Alice Springs and the Paraguayan records, the GISS and NCDC homogenization algorithms have unquestionably added non-existent warming. The conclusion has to be that the warming added in the Southern Hemisphere is an artifact of warming-biased homogenization algorithms, although exactly how they do this remains unclear.
Now on to the global series. Figure 6 compares the RA and Verified global series. Again the match is good. (To analog a land series RA is calculated as Northern Hemisphere times 2/3 + Southern Hemisphere times 1/3 to allow for the fact that there is approximately twice as much land area in the Northern Hemisphere:)
Figure 6: Unadjusted RA and Verified Series, Global
Figure 7 now compares the global RA series against the published global series. The larger NH landmass mutes the impacts of the SH homogeneity adjustments when the hemispheres are combined:
Figure 7: Unadjusted RA vs. NCDC, CRU, GISS 250 and GISS Met homogeneity-adjusted land surface air temperature series, Global
We will now assume that the RA global series is correct and proceed to make some quantitative estimates of how much “global warming” homogeneity adjustments have added. The table below lists how much surface air temperature warming the different series show over different periods, calculated as the difference between ten-year means (trend lines can be misleading when the trend is not linear, as is the case here). All series show similar amounts of global warming between 1970 and 2000 (calculated as the 1996-2005 mean minus the 1966-1975 mean) but not between 1895 and 1970 (calculated as the 1966-1975 mean minus the 1890-1899 mean):
The “excess warming” column at right shows how much global warming the adjusted series have added since 1895 over and above the 0.88C shown by RA. Since this excess warming applies only to land areas, however, only 30% of it contributes to the total warming shown by the land and ocean series that are commonly used to measure global warming, and subtracting this 30% from these series makes little difference. Quantitative estimates are:
- NCDC Merged Land-Ocean shows 0.08C less overall global warming since 1880
- HadCRUT4 shows 0.03C less overall global warming since 1880
- The GISS Land-Ocean Temperature Index (LOTI) shows 0.04C less overall global warming since 1880
- BEST Land + Ocean shows 0.10C less overall global warming since 1880.
So there you have it. Homogeneity adjustments applied to surface air temperature records add only 0.03C of warming to HadCRUT4, the series the IPCC uses and the one most commonly used to quantify global warming. So we have all been waving our arms about nothing, right?
Well, not exactly.
First comes the question of why NCDC, GISS, CRU and BEST continue to claim in the face of a mounting body of evidence that their homogeneity adjustments are valid, but I don’t intend to go into that here.
Second is the question of how to measure global warming. At the risk of repeating myself yet again, HadCRUT4 and the other series that combine air temperatures over land with SSTs in the oceans do not give meaningful estimates (I use HadCRUT4 as an example in this post only to conform with common usage). Surface air temperatures and SSTs exhibit different trends and must be considered separately, which makes the lack of surface air warming in the SH before 1970 a matter of importance because climate models can’t replicate it.
Third is the fact that the emphasis on surface air temperature adjustments has diverted attention from the main problem – the “bias” adjustments applied to the SST data, which are at least as suspect as the air temperature homogeneity adjustments and have a much larger impact on our perceptions of “global warming”, but that is a separate issue.
Finally a word on BEST. The results shown above indicate that BEST is overall the farthest from reality of the four adjusted surface air temperature series considered. I don’t know exactly why this should be but suspect it has something to do with the fact that in addition to homogenizing the data BEST also uses all records within a radius of 2,000km (representing an area of 12.6 million sq km, not that much smaller than Russia) to estimate mean temperatures in a grid block. This approach does not make much difference when there are stations in or close to the grid block because the temperatures are distance-weighted (I believe using kriging weights), but when there are none temperatures may be projected into the block from many miles away. A common result is to find grid blocks in the middle of nowhere with temperature time series that go back well into the 19th century. Some BEST grid blocks in the middle of the Sahara Desert, for example, have scattered data going back almost to 1790. I have yet to find which station these data came from, but it certainly isn’t in the Sahara Desert.