This continues a look at commonality between dataset with a clear linkage on the annual cycle.
Figure 1 is part of the result of rework based on better knowledge.
Previously I posted about the annual cycle and LoD/sea ice. Some of what I wrote was wrong in detail, a risk with actually doing things rather than copying others.
I’m not sure on how much detail to show.
Recently Paul Vaughan suggested via email it would be interesting to look at AAM (Atmospheric Angular Momentum) but this time he referenced an actual dataset, which helps. I tend to be reluctant over artificial datasets, in this case with very dubious underpinnings, including models which are assumptions, all based on sparse and inconsistent data.
I was aware there is a common view in the sciences involved that AAM part drives LoD (earth Length of Day, spin variation) but I have never seen more than opinion.
I am also sceptical for several reasons, a situation not helped by a lack of a coherent mechanism for the linkage, as well as ignoring the huge and moving mass of the oceans.
So I want to see for myself. I’ve split this article with the discussion about data processing in bottom part.
Figure 1 is troubling. When I looked at lead/lag AAM/LoD, using what ought to be accurate daily data it shows a good correlation very close to in phase.
I’d already looked at monthly, using daily Figure 2 confirms what it indicated, where according to this LoD lags AAM by about 10 days, very close to in phase and it seems regardless of a fixed time constant. I expected an elastic effect if one really does drive the other but this is essentially completely the same.
Alternatively I completely misinterpret what the AAM data really is. What I also need is a mechanism, how does one drive the other?
Simple investigative technique, what does the product (point by point) say, Figure 3
This is overwhelmingly unipolar, strange if one drives the other, pointing at a fixed time offset. Given the extensive computations needed for each dataset I am disinclined to take them as accurate.
Spectra from unfiltered daily data, figure 4, are essentially the same. Most noticeable difference is on the relative 0.5y. Given LoD is quantised to little more than 4000:1 some difference is expected.
Figure 5 shows a known major difference which is specifically excluded from most of the compare plots, such as monthly derived. (I am not looking at lunar)
LoD has very clear lunar spectra which I very clearly and precisely showed in another work elsewhere by splitting the doublets (invisible here) and correctly they show an 18.6 year repeat. There is no lunar component in AAM data. If this does exist it is not in the global average, (perfectly possible) it poses a need by those chasing lunar effects to explain this away.
The strong ~13 day strangely has a spectra null for AAM, suggesting a curiosity, an absence can be telling.
This also poses the question of why if LoD/AAM are so tightly linked at a short timescale there is no lunar effect in both?
Bringing in the sea ice from the previous article I am still of the opinion this is closely linked to LoD and as a driver, with the mechanism of latitudinal mass transfer. An error I made was mentioning the difference in north/south ice when actually it is the sum, ie. total.
Someone remarked they would like the see the effect of variable proportions of north/south. I agree, had a play but from what I could see this is of interest without changing much in this context. Here is an opportunity for others to explore. (showing anything here would take time)
A useful addition here is an article I published last year on my own blog where this attempts to show why the annual Arctic extent shape is very tightly a simple function driven by the sun except in hindsight that is not strictly correct. It has more to do with the tilted earth axis, which in current times is close in antiphase to sun/earth distance variation.
This gives me an idea for a future avenue to explore: as the earth orbital parameters drift so will the shape of any polar ice extent. This could be modelled. A change in seasonal shape might be illuminating re ice ages. Extending thought further for a moment, this will impact paleo proxy results which will vary in their behaviour, eg. rock deposits from iceberg melt.
Food for thought in there in the implications for fauna and flora in history, coping with different environments for the reproduction cycle. Darwin has spoken many times leading to what we have today.
Discussion is welcome, as is help.
This describes what I have done with the data.
Assume LoD and sea ice are similar to this recent article.
Data is sampled 4x daily, 1948 through end 2009. Documentation/description is very poor so I played around to guess my way to a reasonable solution.
There are 9 data columns, 3 triplets of xyz, with z a dominant figure, not clear which axis is which or whether this is linear or rotational. Units are not known, figures in the 1e24 range.
What is stated is mass with or without barometric compensation, plus motion, whatever that is.
I chose to use uncompensated, don’t want yet another dubious hack, however there is barely any difference barometer or not.
The xyz is awkward so I decided to simply do |xyz|, alternative looks too close to quaternions, don’t go there.
Laws of inertia/motion, compute ((motion^2/mass)/2 which ought to give inertia/momentum, ie. energy. Curiously this gives a result very close to 1.00e24 as though it is centred on that value. This was a worry, the signals are unipolar but variation in momentum is bipolar. The result looks sensible.
From that I get 1948 onwards at 4 samples per day, awkwardly large.
Accurately decimate to daily 1962 onward to match LoD dataset.
Output runs 1st Jan 1962 to 31st Dec 2009, daily samples. Few climatic or meteorological (or science for that matter) datasets are validly sampled.
Data for plotting and some compares was further decimated to monthly
Long term variations vary widely with these datasets but the primary interest here the annual cycle, so a high pass was applied, for consistency an identical filter to monthly data. (since sea ice is monthly data)
As a second effect this kills dataset offset from zero.
Filters use automatic end compensation, don’t like it, don’t use the results or ignore a few months at the ends.
Not provided at the moment, too much. Ask for specific things.
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