Sunday, November 19, 2017

Pat Frank and Error Propagation in GCMs

This post reviews recent controversy over some strange theories of Pat Frank. It reviews his WUWT posts, blog discussion, some obvious errors, and more substantively, how error propagation really works in solution of numerical partial differential equations (pde, as GCM climate models are), why it is important, and why it is well understood. It's a bit long so I'll start with a TOC.

Contents:


Friday, November 17, 2017

GISS October global up 0.1°C from September, now 0.9°C.

GISS warmed, going from 0.80°C in September to 0.90°C in October (GISS report here). That is very similar to the increase (now 0.105°C) in TempLS mesh. It was the second warmest October on record, after 2015. It is interesting to reflect on that month two years ago, which at 1.04°C was at the time by some margin the warmest month ever.

The overall pattern was similar to that in TempLS. Even warmer in Antarctica, cool in W US, and E Mediterranean, but the band of cool through to China is less continuous.

As usual here, I will compare the GISS and previous TempLS plots below the jump.

Wednesday, November 8, 2017

October TempLS global surface temperature up 0.11°C

TempLS mesh anomaly (1961-90 base) was up from 0.618°C in September to 0.73°C in October. This compares with the smaller rise of 0.055°C in the NCEP/NCAR index, and a similar rise (0.09) in the UAH LT satellite index.

The anomaly pattern was similar to the NCEP/NCAR picture. No great heat, except in Antarctica, but warm almost everywhere. A band of cool between the Sahara and Far East Russia, cool in the SE Pacific, and some cool in the western US. The change toward warmth at the poles will again lead to varying results in the major indices, with GISS likely to rise strongly compared with NOAA and HADCRUT. In fact, TempLS grid, which also has less coverage at the poles, fell slightly to October. Overall, sea temperatures rose a little, after dropping last month.

Roy Spencer noted the recent marked rise in the satellite records, which are now relatively a lot higher than the surface. I'll show (from here) the graph of the last four years on a common 1981-2010 base. Even UAH, which had been a low outlier for most of the time, is now well above the surface measures, and is not far below the 1998 peak.



Here is the temperature map: :


Friday, November 3, 2017

October NCEP/NCAR global anomaly up 0.055°C from September

In the Moyhu NCEP/NCAR index, the monthly reanalysis average rose from 0.317°C in September to 0.372°C in October, 2017, making it the warmest month since May It was again a very varied month; starting cool, them a warm spike, and cool again at the end.

The main feature was a big cool band from the Sahara through Siberia to China. Also cold in the SE Pacific, with some La Nina like pattern. Mixed at the poles, but more warm than cold. Since Antarctica was cold last month, this suggests the warming will be reflected more strongly in GISS/TempLS rather than NOAA/HADCRUT.







Wednesday, November 1, 2017

Penguins - more fantasy about a Time cover

There was a really crazy article at WUWT by Leo Goldstein on fact checkers and allegations of fakery. Leo G regularly publishes really paranoid stuff on Google conspiracies etc. But this one was a doozy, titled "How Google and MSM Use “Fact Checkers” to Flood Us with Fake Claims". Here is an extract:
An example is a global cooling scare of the 70s. In 1977, Time magazine published an issue under the following cover:



That cover is a seriously inconvenient truth for climate alarmists and their media accessories. So, Time attempted to re-write a history. It published a forged version of its own cover, the left one on the following picture (the “Time-2013-version-of-1977”):



…and then easily debunked it as a photoshopped version of its April 2007 cover (3). As I will explain below, Time magazine knew it was launching a hoax. The rest of the liberal media popularized it, although it could have easily recognized it. Snopes adopted it (4), invented additional details that were not present in the Time article, and angrily condemned “climate deniers.”
...
And there is lots more about how "notorious Greg Laden" exposed the hoax etc
The “original source” of the fake cover is hard to trace. It is almost certainly somebody in the climate alarmism camp: the real cover from 1977 was very clearly making a point against climate alarmism. But the point of entry of the forgery into mass circulation was Time magazine, June 6 of 2013. Good job, motherf*ckers.
My initial commentary was a bit confused, mainly because we have recently had NBN (a new optic fibre system, much complained about) installed, and it kept disconnecting from the internet. However, I loooked into it a bit more and found quite a lot of history.

Firstly, some links.

Goldstein is of course talking nonsense about the forgery being a plant designed to pick up on the 1977 Big Freeze cover. That cover wasn't about global cooling at all. It was a straight forward factual article about a very snowy winter in 1976/7 in North America. There was in fact a 1974 Time article on global cooling that people might have wanted to look up, as the Time link describes. But there was no cover associated with that, although the contrary is widely believed.

But I did some more searching. First some notable occurrences of the hoax, which is actually ten years old:
  • As mentioned, it apparently got to President Trump via K.T.MacFarland
  • David Rose at Daily Mail, "Great Green Con". See the blue box which says "In the Seventies, scientists and policymakers were just as concerned about a looming ‘ice age’ as they have been lately about global warming – as the Time magazine cover pictured here illustrates. The picture has now been removed without explanation, which doesn't help the clarity of the text.
  • WUWT, 2017
  • Roy Spencer, 2013

There clearly was more history, since most of these don't have the 1977 pasted over as in the Time/Kirtley versions. So I did a bit more searching.

Steven Goddard, 2011 is an old reliable. This predates 2013, so clearly debunks the Goldstein fantasy about Time forging its own cover in 2013.

Neocon Exresss is the earliest nominal date, at Feb 12, 2007. But that predates the real cover, so I presume the image was added later. Of interest is a Jan 2011 comment, drawing attention to the fakery.

But the most interesting early occurrence was in August 2007, in Free Republic. That was soon after the genuine cover in April 2007. But this one is an animated GIF, and shows alternately the fake and the real. I'm not sure what the point is, but it must be getting close to the source, where it was somehow, I suppose, seen as parody. The url links to this site, which seems to be for prank pictures, but I couldn't find an original there. Update: The picture numbering on the StrangePolitics site isn't entirely consistent, but seems to place the original in April 2007, the month of the genuine cover. <



Here is Goldstein's summary:
In this example, multiple entities are involved: Google, Snopes, Time magazine, and ScienceBlogs. They are independent entities, but each of them knowingly plays its own well-defined role in the chain of injection, amplification, propagation, and utilization of a lie. Thus, they might be referred to as a single body.




Sunday, October 22, 2017

Averaging and error propagation - random walk - math

I have been arguing again at WUWT. There is a persistent belief there which crops up over and over, that averages of large numbers of temperatures must have error estimates comparable to those for their individual components. The usual statement is that you can't make a bad measure good just by repeating over and over. I try to point out that the usually criticised climate averages are not reached by repeating one measure many times, and have invited people to identify the occurrence of such a problem that concerns them, without success.

I dealt with a rather similar occurrence of this issue last year. There I showed an example where Melbourne daily maxima given to 1 decimal (dp) were averaged over a month, for several months, and then averaged again after rounding to nearest integer. As expected, the errors in averaging were much less than 1°C. The theoretical is the standard deviation of the unit uniform distribution (sqrt(1/12) approx 0.29, divided by the sqrt of the number in the average, and the results were close. This time I did a more elaborate averaging with a century of data for each month. As expected this reduced the error (discrepancy between the 1dp mean and the 0dp mean) by a factor of 10.

I also showed here that for the whole process of averaging over time and globally over space, adding white noise to all monthly averages of amplitude 1°C made almost no difference to the global anomaly time series.



The general response that there is something special about the measurement errors which would make them behave differently to the rounding change. And there are usually arguments about whether the original data was really as accurate as claimed. But if one could somehow have perfect data, it would just be a set of rather similar numbers distributed over a similar range, and there is no reason to expect rounding to have a different effect. Nor is there any kind of variation that could be expected to have different effect to rounding, as long as there is no bias; that is, as long as the errors are equally likely to be up or down. If there is bias, then it will be propagated. That is why bias should be the focus.

Here is a table of contents for what is below the fold:

Wednesday, October 18, 2017

GISS September down 0.04°C from August.

GISS showed a small decrease, going from 0.84°C in August to 0.80°C in September (GISS report here). It was the fourth warmest September in the record. That decrease is very similar to the 0.06°C fall in TempLS mesh.

The overall pattern was similar to that in TempLS. Warm almost everywhere, especially across N America, and S America and Middle/near East. Cool spots in W Europe and N central Siberia.

As usual here, I will compare the GISS and previous TempLS plots below the jump.