Newsgroups: alt.astronomy
From: kymhorsell@gmail.com
Subject: Saturn and ocean plankton

[uploaded 59 times; last 27/10/2024]

I've posted things before showing the position of the planets, usually
Saturn, has a connection ("somehow") with cycles in earth atm gases.

In the lead-up to putting something more detailed on a website
somewhere I thought I'd share some preliminary things with people that
ostensibly may be interested.

We have data on the composition of atm gases going back almost a
mn years now thanks to air bubbles trapped in (mostly) Antarctic ice.

One interesting pattern is a 30y oscillation in N2O -- a gas
associated (among other things) with the growth and death of ocean
plankton.

"For some reason" there is a long-standing pattern that seems to
correspond with the orbital period of Saturn. Like the orbit of
Jupiter that has recently been linked with the oceanic ENSO (La
Nina/El Nino) cycle, it seems the orbit of Saturn is associated with
some cycle in plankton growth.

While I don't have or plan to create a data series that tracks the
position of Saturn over the last mn years I do have a fairly good
data-set for the past 100y. This also corresponds with data on atm N2O
to high precision -- measured daily or monthly rather than every now
and then from whatever gas bubbles are extract from some ice core
somewhere.

It turns out the ecliptic longitude of Saturn is indeed linked with
cycles in atm N2O as measured at different locations around the Earth.
The models that "best explain" the total data-set are as follows:


Lag	Station	Trans	R2
(y)
13	brw		0.22294363
13	nwr	x	0.20643046
13	Global	x	0.18150672
13	smo	y	0.17599325
13	mhd		0.16401025
13	NH		0.11851227
13	sum		0.09600689
13	spo		0.08695158
13	kum	y	0.07078827
13	SH	xy	0.05758684

The table shows how well the lonecl of Saturn lagged by 13y (approx
180deg of orbit) with a possible x or y log transformation matches up
against avg monthly N2O at each of the stations listed.

The stations shown have a statistically significant match at better
than 90% confidence in 2 different tests -- a T-test on the \beta and
a rank test comparing the ordering of data by x and by y.  Together
the prob the data could match as well as seen is between 1 in 1000 
("3 9s") and 1 and 10000 ("4 9s").

The R2 shows for each station how much of the observed data is
explained by the position of Saturn. The best match, above, is
22%. Meaning almost 1/4 of the relevant data is explained just by
where Saturn is in its orbit.

As an example of what the data looks like, the following is the "best"
model -- the lonecl of Saturn (in deg from perihelion) against the atm
N2O in ppb at Barrow Alaska adjusted to remove the influence of human farming:

(AUTO CORR CORRECTION; estimated rho = 0.779824)
y = -0.00441783*x + 301.156
beta in -0.00441783 +- 0.00184338  90% CI
alpha in 301.156 +- 0.120596 
T-test: P(beta<0.000000) = 0.999909
Rank test: Calculated Spearman corr = -0.469562
           Critical Spearman = 0.432000 2-sided at 1%; reject H0:not_connected
r2 = 0.22294363
Durbin-Watson d = 0.428064
d < dL (1.560526):  Positive auto-corr at 5%

year                      avg lonecl  avg N2O ppb   Model-est N2O
1963                      182.942      300.121      300.348
1964                      195.072      300.926      300.295*(est 1sd under obs)
1965                      206.956      300.757      300.242*
1966                      218.568      300.304      300.191
1967                      229.962      300.956      300.141*
1968                       241.18      299.925      300.091
1969                      252.292      301.045      300.042**(2sd under)
1970                      263.295      300.373      299.993
1971                      274.252       300.45      299.945*
1972                      285.215       300.09      299.896
1973                      296.258      300.535      299.848*
1974                      307.379      300.184      299.799
1975                      318.643      300.222      299.749*
1976                      330.097      300.159      299.698*
1979                      9.86195       302.42      301.113**
1980                      18.5206      301.315      301.075
1981                      31.3752      301.548      301.018*
1982                      44.4772      301.484       300.96*
1983                      57.8101      300.999      300.901
1984                      71.3245      300.714      300.841
1985                      84.9859      300.712      300.781
1986                      98.6583      301.315      300.721*
1987                      112.288      301.252       300.66*
1988                      125.804       301.21      300.601*
1989                      139.175      301.491      300.542**
1990                      152.288      301.494      300.484**
1991                       165.13      301.656      300.427**
1992                      177.686      301.163      300.372*
1993                       189.98      300.982      300.317*
1994                      201.967      300.882      300.264*
1995                      213.689      299.589      300.212*
1996                      225.181      299.352      300.162*
1997                      236.505      299.242      300.112*
1998                      247.653      299.348      300.062*
1999                      258.689      299.298      300.014*
2000                      269.662      299.844      299.965
2001                      280.644      299.578      299.917
2002                      291.636      299.424      299.868*
2003                      302.702      299.398      299.819
2004                      313.893      299.184       299.77*
2005                      325.281      299.264      299.719*
2006                      336.858      299.338      299.668
2007                      348.678      299.374      299.616
2008                      158.773      299.541      300.455**
2009                      13.1922      299.484      301.098**
2010                      25.8829      299.453      301.042**
2011                      38.8539      299.634      300.985**
2012                      52.0797      299.571      300.926**
2013                      65.5461      299.749      300.867**
2014                       79.132      300.208      300.807*
2015                      92.7882      300.389      300.747
2016                      106.442      300.438      300.686
2017                      120.051      300.601      300.626
2018                      133.482      300.598      300.567
2019                      146.703      299.911      300.508*
2020                      159.674      300.546      300.451
2021                      172.396      300.369      300.395
2020                      159.674      300.546      300.451

So it seems "for some reason" atm N2O wiggles a bit in correspondence
with the position of Saturn.

The relevance of this is -- N2O is produced by oceanic plankton.
While N2O these days is increasing above its historic 300 ppb value
due to industrial agriculture and probably other reasons, it has a
seasonal cycle related to the growth and die-back of plant-type
plankton, the major base of the oceanic food chain.

It has also been found that the sightings of certain objects over the
Americas also highly correlates with atm N2O.  It seems some types of
objects "disappear" when N2O is high, and some are more numerous when
N2O is high.

The possible link is obvious -- someone may be eating something
growing in "our" (possibly having to be re-written at some point as
"their"; contact your legal department for details) oceans.

I've recently collected the data from about 200 ocean robots that
swarm around the Southern Ocean. They've been measuring things down
there for the past 10-20y. One of the things they can measure is
chlorophyll concentration in the water. I.e. the presence of
phytoplankton.

And I can announce for the first time here that, yes, there is a
statistically strong correlation between the coming and going of
certain reported objects across N America and the daily average levels
of chlorophyll on the Southern Ocean.

A summary table looks like:

Object type	Lag	R2
		(d)
dusk		-70	0.02593874
dawn		-70	0.02461682
HI		-80	0.02277169
daylight	-30	0.01619446
boomerang	-80	0.00734693
silver		10	0.00646424
AK		10	0.00644920
pink		-80	0.00592110
purple		40	0.00563332
dark		-70	0.00486125
DE		-50	0.00312025

This table is created from time-series correlation of daily oceanic
plankton levels with daily sightings, possibly lagged by a number of
days. -ve lags mean the relevant objects are reacting BEFORE the
plankton changes were measured.  I.e. indicating foreknowledge or
habituation (they've apparently had 1 mn years+ of experience). Out of
approx 100 ways to classify sightings -- by shape, by color, by
location, by broad time-of-day -- only these ~10 show up as >=90%
significant according to the 2 usual stats tests.

The R2 are all small -- i.e. 2.5% or less -- which is to be expected
given the data is daily, sightings are relatively rare, and there is
large day to day variation possibly because many objects seem to hit
birds or buzz aircraft and that interrupts their fishing schedule.

The table seems to show some types of objects tend to disappear before
oceanic phytoplankton reach their peak (in some parts of the ocean
there is a major annual peak and 2 smaller peaks) and return
afterwards; and some types of object become more numerous across N
America before phytoplankton peaks and less numerous after.

This is consistent with possible seeding and harvesting of oceanic
phytoplankton as directly measured again levels of the relevant
plankton marker in the S Ocean.

As an added note: I've previously looked at a NASA sat data-set that
tries to distinguish between 4 broad types of plankton.  Over a period
of a decade the data shows the monthly rise and fall of
oxygen-generating plankton, edible plankton, and 2 non-edible or
poisonous types of plankton.

It seems sightings of certain objects over N America is linked with
edible plankton but not the other kinds.

It seems "someone" may not want to eat poison and also don't want to
harvest types of plankton that are responsible for a good part of the
oxygen in the atm.

--
"Nothing in life is to be feared, it is only to be understood.
Now is the time to understand more, so that we may fear less."
- Marie Curie

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