About 12 years ago, a local theater group put on a production of 1976. Jim was cast as James Wilson one of the three representatives from Pennsylvania. The part has very, very few lines. Nearly all the lines start around minute 6:55 in this scene from the movie.
Happy 4th of July!
I well remember the opening song:
“It’s hot as hell in / Phili-del-phia”.
“Someone ought to open up a win-dow”.
Was Senator Wirth at the proceedings, perchance?
Happy Independence day, anyway, from a Brit. 🙂
Lucia: Appolgies to intrude on your holdays but it is only fair, given that I first posted this here, to also post heare what I have posted elsewhere. Happy holidays!
Ideal digital sampling series for a bandpass splitter in Climate research.
next = rounded(previous*1.3371) starting from 3 or 1.
It precisely ‘nulls’ the 12 month signal whilst leaving all of its harmonics and all other frequencies intact.
(The same applies to any sample frequency that has one of the later poles as its direct multiple.)
This is a digital implementation of a ‘brick wall’ cascaded low pass/bandpass splitter circuit (approx 1/3rd octave).
As it is a digital average, it has a ‘square wave’ sampling methodology on the source data.
The well known side effects of this ‘square wave’ sampling are cancelled out by using the 1.3371 inter-stage multiplier.
On the plus side, it has a infinite(?) roll off per octave between stages so the bands are precise, though not tunable.
Also, as it is completely de-tuned in the passbands, it is completely insensitive to internal data distribution ‘in band’.
On the digital side, this is just the well know 3 pole filter arrangement which running means require, extended to the full cascaded filter set.
It can be used on either normalised (at 12 months) or non-normalised data.
When used as a simple 3 stage filter on daily data it can provide a 28 day smooth which is more scientifically accurate than a human Monthly one.
When looking at temperature data, this series and circuit should be used rather than Normals of any period.
Because of the 4 year period it discovers in temperature series, Yearly Normals should be discarded.
When used in Splitter mode (each stage subracted from the previous stage) it provides a ‘DC’/zero referenced view of each passband.
The final stage can be set to ‘DC’/zero or a ramp as required for any very long term trends (such as CO2).
Nodes can be extracted by using, -previous*next between each stage, or other such similar arrangements.
RMS power can be extracted from each passband in the normal way.
The maximum period that can be discovered by this method is limited by the record length and this is a power series.
Getting very long cycles will require correspondingly much longer input data!
Fig 1
http://i1291.photobucket.com/albums/b550/RichardLH/UAH-Comparisonofcascadedlowpassfilteroutputmonthsrunningaverage_zpsf1883e44.png
Fig 2
http://i1291.photobucket.com/albums/b550/RichardLH/CET-4YearAverageDailyAnomolyfromAnnual_zpsa1bbe38c.png
Fig 3
http://i1291.photobucket.com/albums/b550/RichardLH/NINO34-Bandpass_zpsce85f520.png
A ‘spreadsheet’ verification model exists as, by definition, the sum of all of the stages outputs will always equal the input.
The ‘power’ left to be assigned to any long term, yet to be discovered cycles, ramps or theories, can be determined and/or accounted for.
Happy 237th, old girl! 🙂
Also worth noting, the period length limit appears to be the reciprocal of Nyquist as 1/period = frequency.
That video wasn’t a joke? I gave up after a couple of minutes, when I couldn’t see a punch-line coming.
James Evan–
No. It’s a very good film with great songs etc. That particular scene isn’t ordinarily highlighted, but it happens to be the scence my husband would have been featured in had the production he was in been on youtube.
It is the case that James Wilson was caught between Ben Franklin and John Dickinson, gave in the Dickinson through weeks and weeks, and then, when push came to shove, made a surprise move to Franklins side, thereby getting the Declration signed. (The representative from NY was a prisoner of war and could not make it to convention. That’s why they didn’t vote.)
I lived 1776. Did they make a sequel taking place two hundred years later?
While making a joke about a typo, I made a typo. Go figure.
I mean, that was on purpose.
Happy 4th to my rebellious cousins to the south 🙂
This is the deleted passage on slavery:
“He has waged cruel war against human nature itself, violating its most sacred rights of life and liberty in the persons of a distant people who never offended him, captivating & carrying them into slavery in another hemisphere or to incur miserable death in their transportation thither. This piratical warfare, the opprobrium of infidel powers, is the warfare of the Christian King of Great Britain. Determined to keep open a market where Men should be bought & sold, he has prostituted his negative for suppressing every legislative attempt to prohibit or restrain this execrable commerce. And that this assemblage of horrors might want no fact of distinguished die, he is now exciting those very people to rise in arms among us, and to purchase that liberty of which he has deprived them, by murdering the people on whom he has obtruded them: thus paying off former crimes committed again the Liberties of one people, with crimes which he urges them to commit against the lives of another.”
Bear in mind Jefferson wrote these lines and that the whole basis for his wealth was through slave-labor.
Hi Lucia,
Sorry for the OT, but I’d like someone the review the following mini-analysis I put together:
https://sites.google.com/site/climateadj/multiscale-trend-analysis—hadcrut4
I think I must have reinvented the wheel, so any feedback on any similar existing analysis would be welcomed. It’s basically just another way of visualizing the 60 and 20 year cycles in HadCRUT that Paul_K described a few weeks ago. Perhaps this comment belongs over at his post?
Thanks, AJ
Bored. 🙁
That’s a pretty picture AJ.
With no new articles here I may as well show off my own analysis here as well. With the focus on the pause I started wondering what had happened to some of the ‘fingerprints’ of AGW. My suspicion was that these fingerprints may have melted away along with the warming. I started with the warming rate for NH winters and summers. The theory (which I’d suspected was over-simplistic anyway) is outlined in this SkS posting
http://www.skepticalscience.com/news.php?p=4&t=177&&n=474
and basically goes winter should warm faster than summer over land.
So here is what I get.
http://oi41.tinypic.com/2nicieh.jpg
Interestingly what I get is summers have continued to warm at the same rate but winters have slowed so that we now have summers warming faster. Oops no more fingerprint. I guess if you’re going to continue with the simplistic approach outlined by SkS then you have to look for a cooling process that only affects winter. I don’t know of anything.
Its hard to take the model output seriously when confronted with that analysis. The pattern is striking. Nick Stokes does some pretty good visualisations too.
re: AJ (Comment #117654)
Sweet AJ! Paul_K will love this. And thanks for posting the code. This could be a great tool for comparing observations with model runs, or even doing your own simulations. Have you given that any thought?
Layman… I’ve done my own projections for 21st century warming. Although I have used these to form an opinion, I don’t give this opinion a lot of weight. I haven’t attempted to incorporate quasi-cycles into my model. It was simply curve fitting forcings and responses.
AJ, wrt to simulations, I was thinking more along the lines of plugging model runs and/or your own simulations into your code and comparing the simulated visual patterns with observations.
Layman… my apologies for being so dense. I hadn’t previously run any model output through my code, but I just did so for the CMIP5 RCP4.5 multimodel mean “hindcast” (pre-2005) from Climate Explorer. Aside from the most recent warming (post-1970), the model output doesn’t really pass the eyeball test for low frequency cycles. There are some higher frequency signals which I assume are related to volcanoes.
No problem AJ. I just appreciate you putting that compelling graphic up. It really strikes home the patterns of natural variability which IMO need to be reconciled with any climate model or simulation. It might also provide some cool insights into lots of other processes. When I get time I would really like to play with your code. All kinds of ideas for comparison come to mind. Ocean vs land. NH vs SH. tropics vs higher latitudes. Etc. Unfortunately I have less time for this now then I ever have.
re:AJ (Comment #117654)
July 12th, 2013 at 10:44 am
AJ,
A very nice way of presenting the data. You (and Layman Lurker) may be interested in this paper http://oceanrep.geomar.de/1616/1/3209.pdf
Pohlmann et al made a serious attempt to get an AOGCM to yield spectral characteristics of SST similar to observed data – partially successful in some – but not all – ocean regions.
One of the things that surprises me is that there is still a strong mainstream resistance to accepting the importance of multi-decadal oscillations, despite the evidence for their presence in long-term records. I spent a little time looking for a statistical test which would allow me to test whether a certain oscillation was “real” or “accidental” in a time series, but I have not succeeded in finding (nor inventing) such a test as yet.
Paul_K,
Doesn’t surprise me at all. To accept that long term variation is important automatically means 1) assumed historical aerosol offsets used by GCM’s are wrong, and 2) there is a high probability of less causal/secular warming (GHG driven!) since about 1975 than climate models have been suggesting. These are not things that the main stream climate folks are going to happily embrace.
.
Which is not to suggest that I think they are right; on the contrary, there appears to be an overwhelming resistance to ANY data from any source which suggests ‘it’s not as bad as we thought’…. even as the evidence mounts that it is, well, not as bad as they thought. 😉
Thanks Paul_K… I’ll have a look at the paper. I added a similar sea level plot to my page last night, probably before you read it. You might find that interesting as well.
I think I could call my temperature plot the “Cherry Pickers Guide to The Universe”. It shows what start and end point you should use, depending on the argument you want to make.
Paul_K (Comment #117765)
July 17th, 2013 at 7:34 pm
Did you link the correct paper?
Quote from the paper: “This study is idealized, since no observational data for the initialization of the experiments or the veriï¬cation of the results are used. Instead, the upper predictability limit of ECHAM5/MPI-OM…”
Doesn’t seem like they tried “to get an AOGCM to yield spectral characteristics of SST similar to observed data…”
No matter. If you happen to look at my plot again, I think it might help in determining if a signal is “accidental”. Note the influence of Krakatoa in 1883. Deep blue before and somewhat redder after. You might even be able to pick out Pinatubo, perhaps even Mt. Saint Helens. Tambora is obvious using the BEST data. Much of the cyclical structure, however, remains unexplained to me.
https://sites.google.com/site/climateadj/multiscale-trend-analysis—hadcrut4