Global Temperature Update – May 14
July 3, 2014
By Paul Homewood
| RSS | UAH | HADCRUT4 | GISS | NCDC | |
| May 2014 | 0.29 | 0.33 | 0.59 | 0.76 | 0.74 |
| Change from last month | +0.04 | +0.14 | -0.06 | +0.03 | -0.01 |
| 12 month running average | 0.22 | 0.24 | 0.51 | 0.65 | 0.66 |
| Average 2004-13 | 0.23 | 0.19 | 0.47 | 0.59 | 0.59 |
| 12 month average – 1981-2010 Baseline | 0.12 | 0.24 | 0.22 | 0.25 | 0.24 |
HADCRUT numbers only just out for May, while RSS and UAH are already out for June!!
I might have to have a rethink how I present this next month.
10 Comments
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NOT A LOT OF PEOPLE KNOW THAT 
I know, why not use a computer model to estimate for missing data
Thanks for adding NCDC, Paul.
If you’re thinking of changing your presentation of the monthly data then I believe that it might be worthwhile presenting all data sets against the same anomaly base period. This provides values that can be readily and fairly compared against one another.
For monthly updates you just need to offset the monthly values for each data set against previous values ‘for that month’.
For example, re the May 2014 table above: let’s say you want to compare UAH and RSS against the same base period; say 1981-2010. UAH is already at that base period, so it remains 0.33. For RSS, the average temperature ‘for May’ between 1981 and 2010 was 0.08; so this is the amount you deduct from the May RSS value.
That gives you a value of 0.21 for RSS, versus a value of 0.33 for UAH. Expanding the same principle to all the data sets you mention gives the following for May 2014:
UAH: 0.33
RSS: 0.21
NCDC: 0.32
GISS: 0.38
HadCRUT4: 0.32
We can already apply this system to June 2014 for the satellite producers:
UAH: 0.30
RSS: 0.35
David.
Sounds sensible.
Paul
I thought RSS shouldn’t have been higher than UAH!
Is the RSS figure for June correct?
The actual figure was only 0.345c and the June average is about 0.064c.
Which makes the figure wrt 1981-2010 only 0.281c
QV
“Is the RSS figure for June correct?”
No. You’re right; I just went and disobeyed my own rule. The correct figure for RSS base lined to 1981-2010 is 0.28 C; not 0.35 C (which is the RSS base line anomaly.)
Sorry about that, and well spotted QV.
I hope the general principle still holds true.
I don’t know how my last comment got there!
Much was made of the “record” May temperature in a number of the datasets.
A large part of the higher temperature in May was due to the S. Polar region, but coverage of that area varies between datasets with RSS coverage being the least because of the unreliable nature of the data. I think this accounts for much of the differences in the datasets.
RSS S.Pole only covers from – 60 to -70 degrees whereas UAH is ostensibly -60 to -85, but much of that is extrapolated.
Interestingly the RSS S.Pole has cooled by about 0.8c in June whereas the SH has warmed by 0.17c and the tropics by 0.27c so despite cooling at the S. Pole it was one of the warmest RSS June figures globally.
June UAH fell globally and in the SH, despite a large rise in the tropics, suggesting a big fall in their S.Pole figure, although the detailed data file is not yet available.
Paul, I wonder if all these adjustment are also smoothing-out the extremes of variablity of the past (say before 1980) and so making the current period look unusually variable.
How about a statistical analysis of land surface temperatures where each site is treated as a distinct microclimate. I have always been uncomfortable with the adjusting, anomalizing and homogenizing of land surface temperature readings in order to get global mean temperatures and trends. Years ago I came upon Richard Wakefield’s work on Canadian stations in which he analyzed the trend longitudinally in each station, and then compared the trends. This approach respects the reality of distinct microclimates and reveals any more global patterns based upon similarities in the individual trends. It is actually the differences between microclimates that inform, so IMO averaging and homogenizing is the wrong way to go.
In Richard’s study he found that in most locations over the last 100 years, extreme Tmaxs (>+30C) were less frequent and extreme Tmins <-20C) were less frequent. Monthly Tmax was in a mild lower trend, while Tmin was strongly trending higher , resulting in a warming monthly average in most locations. Also, Winters were milder, Springs earlier and Autumns later. His conclusion: What's not to like?
Now I have found that in July 2011, Lubos Motl did a similar analysis of HADCRUT3. He worked with the raw data from 5000+ stations with an average history of 77 years. He calculated for each station the trend for each month of the year over the station lifetime. The results are revealing. The average station had a warming trend of +0.75C/century +/- 2.35C/century. That value is similar to other GMT calculations, but the variability shows how much homogenization there has been. In fact 30% of the 5000+ locations experienced cooling trends.
Conclusions:
"If the rate of the warming in the coming 77 years or so were analogous to the previous 77 years, a given place XY would still have a 30% probability that it will cool down – judging by the linear regression – in those future 77 years! However, it's also conceivable that the noise is so substantial and the sensitivity is so low that once the weather stations add 100 years to their record, 70% of them will actually show a cooling trend.
Isn't it remarkable? There is nothing "global" about the warming we have seen in the recent century or so.The warming vs cooling depends on the place (as well as the month, as I mentioned) and the warming places only have a 2-to-1 majority while the cooling places are a sizable minority.
Of course, if you calculate the change of the global mean temperature, you get a positive sign – you had to get one of the signs because the exact zero result is infinitely unlikely. But the actual change of the global mean temperature in the last 77 years (in average) is so tiny that the place-dependent noise still safely beats the "global warming trend", yielding an ambiguous sign of the temperature trend that depends on the place."
http://motls.blogspot.ca/2011/07/hadcrut3-30-of-stations-recorded.html
Paul, it looks to me like Motl has done a world-wide analysis similar to your sampling of USHCN in Alabama, Kansas and Texas. His approach avoids all the homogenizing because he compares slopes without averaging or anomalizing.