Over in Suggestions Tenuc is tickled by Roy Spencer and his for amusement only Excel spline, not that Tenuc is right, just that Roy being a gent doesn’t want to let on Phil Jones helped him. Err, oh, I’m definitely regressing. (any strangers reading this, is gentle in-house humour)
Figure 1
The data used here is computed from published gridded by me. (matches but more could be extracted)
10 year to the dataset ends, educational purposes only. (tends to flatten the ends slightly) There is no Met Office / Hadley / CRU hockey stick generator with me. (I assume readers know about that Met Office red face on being caught)
I’m showing RSS and UAH because they are noticeably different with UAH showing more recent warming. Perhaps this is a surprise to some readers.
Dataset differences: RSS omits poles, UAH covers, RSS omits a few high mountains, UAH covers, RSS has more high frequencies. I could plot the two as maps. (haven’t because I am tired, maybe later if people ask)
Here is Roy Spencer on the same data
Figure 2
This is a local copy. (original)
Over to you folks. I’ve included other data in following if anyone wants to have a play.
Datafile here including RSS and UAH tlt, tmt and tls datasets global, north and south hemisphere. This is an OpenOffice .odt, change to .ods if needed. (if necessary I will have to export to .xls and upload that as well)
[UPDATE]
Figure 3
Had couple of hours snooze so here is some more and I hope illuminating to a few. Only takes a few minutes to hurl the datasets at some of the software, twiddle and plot.
A couple of posts ago Nicola Scafetta ticked off Mosh over what many call regression, the matter of the old trick of withholding known data. In this case I just said do a quick 7 term model on automatic, but on data to end 2003/4 (forget which). r2 is circa 0.7 – 0.8 and makes a sane job over the period where it can see data. Won’t match fine detail, not intended.
As you can see this dataset is NOT predictive, extrapolate fails. I’ve added traces for the whole model and the long period term only (with offset).
I think rather nicely this demonstrates the simplistic carry on upwards vs. nature speaks. I add, the software is in two minds on whether to lock to 45 – 70 years but rejects this. (mentioned because I am aware things are not as simple as I show)
Above is just a high speed hack.
Interesting aside here. Probably before Nicola S. showed his recent temperature model I had done the same thing but I kept silent. Reason is finding very interesting prediction using Hadcrut3 *but* this only works on the gridded data *without* Met Office / CRU / Hadley statistics post processing. I also found it was ambiguous, either carries on level then another rise to another plateau circa 2060, or things fall. This is useless. It can give insights though. (talking extrapolate from as far back as 1960)
I’d better stop.
/UPDATE]
Posted by co-moderator
Thanks for the graphs, Tim, which illustrate how little warming has occurred over the decade or so, with any variation within the error bars i would think, if these were to be shown.
“…Dataset differences: RSS omits poles, UAH covers, RSS omits a few high mountains, UAH covers, RSS has more high frequencies…”
I don’t think it is just the different spatial coverages which account for the variance between RSS and UAH data. As you will be aware, satellites don’t measure actual temperature, rather they observe radiance over several wavelength bands and use a computer model to obtain indirect inferences of temperature – a proxy if you will.
Temperatures thus depend on details of the mathematical model used to obtain temperatures from radiance measurement. This means that different groups analysing the satellite data have produced differing temperature datasets. Also, full series have had to be constructed from a series of different satellites with similar, but not identical, instrumentation. Then add in the fact that over time sensors deteriorate, corrections are needed for orbital drift and the models are changed (sometimes without notification), so what we end up with must have big error bars.
As ever with things connected to climate science we again see a thin veneer of elegance stuck over foundations of chipboard. I want to see some nice solid oak before I shell out any more cash to the CO2 driven CAGW scam!
Ignoring the 1998 El Nino event, 2010 was the warmest year in the record. Yet this year, the summer really, the Hanesist press says this year is: because 2% of the world’s area, the contiguous US of A, has had a summer scorcher.
Ma always said the squeaky wheel gets the grease. The myopic squeaky-wheel watchers are the reason.
How much grease does a squeaky wheel get? We’ll never know, because the WWF handles the finances.
Hi Tenuc
“satellites don’t measure actual temperature, rather they observe radiance over several wavelength bands and use a computer model to obtain indirect inferences of temperature – a proxy if you will.”
Well, I do will. It’s a proxy.
Interestingly, there are only two published sets of temperature time series that aren’t based on proxies – the “land” series (such as BEST) and the “ocean” series (such as HadSST). I exclude HadCRUT3 and the other “combined land and ocean series” because they use SSTs as proxies for air temperatures over the oceans, which they aren’t.
Major update to post, have fun.
I believe UAH changed over to the Aqua satellite last year and has been running warmer than RSS since. The reason given is that Aqua does not have diurnal(?) drift and does not need the processing to adjust for it. The pole coverage must be recent with the change to Aqua as previously UAH and RSS shared the raw data and had slightly different drift processing.
As much as we all respect Dr. Spencer you have to wonder how well they can splice all these different satellite segments with differing coverages and problems.
The noticeable climb of the UAH dataset over the HADcruT land/sea dataset indicates to me that my hypothesis concerning energy coming out of the ocean into the atmosphere when the Sun goes quiet is correct. Of course, the OLR and reflected shortwave (albedo) has to be considered too, but the general principle holds I think.
Releases of stored ocean heat associated with ENSO events occurred in 1976, 1987-88 and 1998-2000. If history had repeated itself there should have been another release at or around the end of the recent La Niña, but the fact that there wasn’t one suggests that maybe the ocean has at last run out of stored heat.
And when you back out the stair-step temperature increases caused by the heat releases you don’t see much difference between ICOADS SST, GISS SAT and UAH/RSS LT temperature trends. (I don’t use HadCRUT because it’s corrupted).
The non-applicable extrapolation looks like a Hansen Scenario A or B. Any similarity mathematically to the IPCC models?
Meanwhile UAH continues to track close to last years figures, despite the La Nina having disappeared many months ago.
http://discover.itsc.uah.edu/amsutemps/
Interesting comment from Werner Brozek over at WUWT, who quotes Dr. Spencer saying on February 2…
“Progress continues on Version 6 of our global temperature dataset. You can anticipate a little cooler anomalies than recently reported, maybe by a few hundredths of a degree, due to a small warming drift we have identified in one of the satellites carrying the AMSU instruments.”
Without a doubt there are issues with satellite data, just as there are with the thermometer record. With the right adjustment/corrections any global temperature anomaly data can give you the result you want to see – or so it would seem.
Doug,
Data driven, only about shape.