Notes and Musings
Paul's thoughts on developing and using WoodForTrees, interesting results, and what they might mean...
UAH6.0 vs UAH5.6
I was holding off adding UAH6.0 while it was still in 'Beta', but that seems like a fairly permanent state of affairs now, so I've added it, while keeping UAH5.6 as the default for now, and still using 5.6 as a component in WTI.
You can see a comparison of UAH6.0Beta and UAH5.6 here:
One notable difference can be seen in the trends - the decadal trend is reduced from 0.15°C per decade to 0.12°C per decade (data to July16). More details on the differences can be found at Dr. Roy Spencer's site.
BEST and other land-only temperature data
BEST is a land-only dataset, so for fair comparison I've also added a whole bunch of other land-only data (*) from GISS, CRU, RSS and UAH. To compare these properly I did the same alignment to a common baseline as I did with the global baselines, fetching the means within the 1981-2011 UAH baseline:
Using these gives us the following comparison plot, rebased to UAH with 30 year trends:
The trends from the data dump are as follows:
So BEST has the highest trend here, even compared to other land-only datasets that show bigger trends than the land-ocean ones, which cluster around 1.5°C per century on a 30 year trend.
* UPDATE: It's been pointed out to me by a number of people that GISS dTs is not a land-only dataset but a land-ocean one extrapolated from land stations. I've left it in the comparison above but renamed it and reclassified it. Previous URLs quoting 'gistemp-land' will still work but will be renamed on plotting to 'gistemp-dts'.
UPDATE 2: I also added the BEST lower and upper uncertainty intervals. Initially I thought the uncertainty value in the analysis data was the width of the confidence interval, and hence added/subtracted half of it to the BEST data to get the upper and lower bounds. However, I've been convinced (not least by EFS_Junior) that I should be adding/subtracting the full amount. As of 5th Nov 2011 (analyse 0.8.4) this is now the case.
Satellite data updates in 2011
After considerable prompting (thanks everyone who pointed out the satellite data feeds had stalled), I've finally updated the data fetching scripts to use the new versions of UAH (5.4) and RSS (3.3). All the temperature feeds are now up to date.
However UAH v5.4 has a new baseline period, which has introduced a marked baseline shift relative to RSS (approximately 0.1°C) and this means the baseline differences and the calculation of WTI has changed. WTI has been updated to include UAH v5.4 and now uses the UAH baseline. This means its anomalies are roughly 0.1°C lower than before. But remember, baseline shifts don't affect trends!
WTI: The WoodForTrees Temperature Index
When playing around with temperature graphs, I always found myself having to choose which of the four global temperature sources - HADCRUT4, GISTEMP, UAH, RSS - to use. Since they all have their differences, particularly around short-term responses to extreme events like the 1998 El Nino, I thought it would be nice to have an average of all four...
Hence I've created the WoodForTrees Temperature Index (WTI). This is created from the mean of HADCRUT4GL, GISTEMP, RSS and UAH, offset by their baseline differences. It covers only the time period where all four series are valid, so begins in 1979 and will only contain the latest month's values when all four sources are in. It is updated from the master sources at 3am GMT/BST each night.
Technically, the series is:
WTI = mean(GISTEMP-0.43, HADCRUT4GL-0.29, RSS-0.10, UAH)
WTI = mean(GISTEMP, HADCRUT4GL, RSS, UAH)-0.205
The adjustment brings down the baseline, so the series is expressed as anomalies from 1981-2011 monthly averages (same as UAH).
Of course, adjusting the baseline doesn't make any difference when you're looking for trends or cycles.
Choice of sources
The four sources used are the four global sources most often quoted in climate studies, plus there are two land/sea-based (HADCRUT4, GISTEMP) and two from satellite air measurements (RSS, UAH). Hopefully the combination gives better accuracy than any one of them individually.
See the credits page for source information.
WTI compared to original sources
Here is WTI compared to the other four series with baseline adjustments over the full range:
and here is the latest year's data:
Temperature trends - pick a timescale, any timescale!
After many requests, I finally added trend-lines (linear least-squares regression) to the graph generator. I hope this is useful, but I would also like to point out that it can be fairly dangerous...
Depending on your preconceptions, by picking your start and end times carefully, you can now 'prove' that:
- Temperature is falling!
- Temperature is static!
- Temperature is rising!
- Temperature is rising really fast!
Here are all four of the above trendlines plotted together:
What you find can depend on where (or when) you look!
Personally, I prefer the long view, and now we have trendlines, and adjusted anomaly baselines, we can throw it all together into one monster plot:
If you look at the trend data, you can see the current trends in °C, between 0.13-0.17°C/decade, or, if it continues at the same rate, between 1.3 and 1.7°C per century.
Comparing temperature anomalies - getting the baselines right
The main temperature series we have on this site - HADCRUT4, GISTEMP, UAH and RSS - are all expressed as monthly temperature anomalies from a defined baseline period. This means that the average temperature for each similar month (all Januaries, all Februaries, etc.) is subtracted from the monthly value to remove any seasonal cycle, and (in theory) any difference between the absolute starting positions of the series.
Why 'in theory'? Well, the problem arises because the four series use three different baseline periods. Here are the baseline periods as reported by each source:
|GISTEMP||Jan 1951 - Dec 1980 (30 years)|
|HADCRUT4||Jan 1961 - Dec 1990 (30 years)|
|RSS||Jan 1979 - Dec 1998 (20 years)|
|UAH||Jan 1981 - Dec 2010 (30 years)|
Now take a look at all four series from 1979 (the period in common), unadjusted, but slightly smoothed:
Clearly they are very similar, but there is an offset between them.
If you think about the different baseline periods, the reason for this is obvious. GISTEMP has the earliest baseline period, when temperatures were cooler, so its anomalies from this baseline are always higher. HADCRUT and RSS are somewhere in the middle, and UAH has the most recent, warmest baseline, so its anomalies are lowest now.
Estimating the offset
To make a fairer comparison between the series, we need to know what these offsets are, and we can then correct for them. As UAH is the lowest and newest baseline, let's use their baseline period - Jan 1981 to Dec 2010 - as the period of comparison. We will then calculate the average anomaly of each series during this period to get an approximate offset between them.
Conveniently (in fact, just for this purpose!), the data output for all four series for 1981-2011 will give us the mean values at the end of each data set (look for lines beginning "#Mean:"). Note that we quote 2011 as the end month, because it is "up to but not including", so the last month is Dec 2010.
Here are the means for 1981-2011 for each source, to 2 decimal places:
The mean for UAH is zero, as we would expect, since this is their defined baseline period.
Shifting the sources
So, now we can shift the sources to the same baseline period, using an 'offset' step:
This clearly removes the overall offset, but there are still differences in range, particularly in the peaks around the 1998 El Nino. My guess is the satellite air temperature sources (UAH & RSS) are more sensitive to short-term influences like this than the land and sea temperature ones (HADCRUT4 & GISTEMP).
Armed with our magic offsets, we can now do a fair comparison of recent history:
This graph will stay up to date with the latest year's values, so feel free to copy the image link to your own site, but please link back to these notes so people can understand it and play with it themselves.