SLC Fingerprints R Us

Fig. 1 Juneau, AK

I am moving along with the sea level change (SLC) "fingerprint" (FP) algorithms in the Dredd Blog software model.

Specifically, it may be the first model to FP individual PSMSL stations en masse, since it goes through all 487 PSMSL "good" tide gauge stations in the SQL database I built, and while doing so it FP's them ("good" stations are those that have been active for at least 30 years, and are still active).

Not only that, part of the FP mode gives some detail as to how much each ice source (Greenland, Antarctica, and non-ice-sheet glaciers) has/have contributed to sea level rise (SLR) and sea level fall (SLF) at each individual station.

I have provided two beta version graphs to illustrate the way it is working now (but it is going to work more robustly when it gets out of beta).

Fig. 2  New York, NY

 I chose a station that experiences SLF (Juneau, Fig. 1), and another that experiences SLR (NY, Fig. 2).

The SQL database contains records for the distances from each station to the various ice sources.

The SQL database also contains a percentage representing how much each ice source contributes to influencing each station's current sea level.

I use proximity to sources-of-influence, as the first analysis, to rough-frame the initial picture.

Fig. 3 Juneau sans fingerprint data

The vertical numerical-column on the left side of each of the four depictions in each graph (Fig. 1, Fig. 2) indicates the strength of influence (the "combined" is the sum of the other three - each value point of the three add up to the value point in the Combined view).

The horizontal numbers at the bottom of each of the four sections in each graph indicates the years involved in that SLC FP section.

The span of years and the pattern in each of the four sections of each of the two graphs has the same "look and feel", i.e., only the intensity of each influence source varies (IOW "same DNA").

Fig. 4  New York sans fingerprint data

For example, in Fig. 1 you can seen that the left column numbers indicate that glaciers (Glacier Bay in this case) are the most influential players in this SLF part of the world, but in Fig. 2,  you see Greenland being the most influential.

The essence of the SLC FP view is to identify the players that are influencing the SLC.

If the influence changes these numbers will likewise change, and thereby tell us in turn which ice sheet changed (Fig. 3 and Fig. 4 show the same stations without the FP data).

Super FP work involves processing all good individual tide gauge stations, and telling how much each of those SLC sources is contributing to that one location's FP.

As of now, as you can see by the graphs, I am into the preliminary algebra, and so forth, for doing that.

At the moment I have not plugged in the two modules that will supplement the FP mode and fine tune it.

More on that later.

But you get the picture:

It is important to consider the separate fingerprints of RSL from the major sources to investigate their individual gravitationally-consistent “fingerprints”, but for present-day and future trends in sea level, it is the combined signal that is important. To first order, this can be approximated as the sum of the individual sources.

(Cryosphere, cf. Oceanography). One cannot have a "combined" without having the individual parts to combine into one.

Stay tuned.

The next post in this series is here.

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