|Fig. 1 The pattern of global anomalies|
Many scientists today like to describe events in terms of an anomaly ("something that deviates from what is standard, normal, or expected" - dictionary).
I like that technique, because, if one says "the temperature today is ..." it does not connote as much as "the temperature anomaly today is ..." (at least in terms of giving a "heads up").
With that in mind, a thought came to my mind in terms of "how can I present a group of analogous climate factors into a simple line graph?"
II. The Graph of Climate Anomalies Emerges
The first attempt to accomplish the task is shown in Fig. 1 as a simple line graph.
It is plain enough to please the Amish, however, it is bound to please others when they consider what it is composed of and what it "says."
III. The Simple Complexity of The Anomaly Pattern
The graph is composed of the anomalies in the GISS, WOD, and PSMSL in situ measurements.
But, the difference here is that this is the anomaly pattern that all of these measurements of anomaly present --when combined into one pattern.
It shows that the realm of anomaly is on the rise.
IV. How It Is Done
Once the data is loaded into a software module (not model), the anomalies are derived by:
1) acquiring the first measurement (oldest in time);
2) subtracting that amount from all subsequent measurements (newer in time);
3) averaging all of the anomalies into one anomaly stream (pattern);
4) writing the results to a CSV file;
5) generating the graph that shows what is happening (Fig. 1).
V. What It Means
The story the pattern tells is clear, which is that it is not only atmospheric temperature alone, not ocean temperature and salinity alone, and not sea levels alone, that are getting increasingly anomalous ("something that deviates from what is standard, normal, or expected" - dictionary).
No, the story this pattern tells us is that our world is and has been and continues to become something that deviates from what is standard, normal, or expected.
In short, our world is out of whack.
Today's graph was the first time I have generated this graph from the vast data stores I have collected ("GISS, WOD, and PSMSL in situ measurements").
Note that I have added Fig. 2 and Fig. 3 for contrast, in the sense that one can view the anomaly component sources individually (Fig. 3), in combination with the timing of individual impacts (Fig. 2).
Any ideas for future expansion or application are welcome.
Have a good weekend.