| Image may be NSFW. Clik here to view.  |
| Fig. 1 Latitude Layers |
Each dataset type (APB, CTD, DRB, GLD, DBT, MBT, MRB, OSD, PFL, SUR, UOR, and XBT) has been placed into its own SQL table.
| Image may be NSFW. Clik here to view.  |
| Fig 2a (T) APB, Layer 5 |
| Image may be NSFW. Clik here to view.  |
| Fig 2b (SP) APB, Layer 5 |
| Image may be NSFW. Clik here to view.  |
| Fig 3a (T) CTD, Layer 5 |
| Image may be NSFW. Clik here to view.  |
| Fig 3b (SP) CTD, Layer 5 |
For example, some of them do not have salinity measurements to go along with the temperature measurements.
| Image may be NSFW. Clik here to view.  |
| Fig 4a (T) GLD, Layer 5 |
| Image may be NSFW. Clik here to view.  |
| Fig 4b (SP) GLD, Layer 5 |
The DRB, DBT, MBT, MRB, OSD, SUR, UOR, and XBT datasets contain plenty of in situ measurements for temperature (T) but are not adequate for salinity (SP).
At least not in the WOD layer (5) depicted in these graphs (Fig. 1).
| Image may be NSFW. Clik here to view.  |
| Fig 5a (T) PFL, Layer 5 |
| Image may be NSFW. Clik here to view.  |
| Fig 5b (SP) PFL, Layer 5 |
Any accurate world-view of the ocean forms in our minds according to measurements we have recorded.
Notice that the measurement streams recorded in the WOD, which I used to make today's graphs, vary according to several factors.
One factor shown in today's graphs is that the type of instruments used can have a huge effect on the results.
Other factors are the time of year, depth, and quantity of measurements.
Putting them all together into one mean average can reveal a trend when the individual ones by themselves can't (more about that below in this post).
The subject is vast (The World Ocean Database) but it can be more easily handled one step at a time (An inventory of Arctic Ocean data in the World Ocean Database).
The first step is to analyze the data types:
"Expendable bathythermograph (XBT) data provide one of the longest available records of upper-ocean temperature."(The Impact of Improved Thermistor Calibration on the Expendable Bathythermograph Profile Data). Sounds good but read the rest of the story in that paper to see that tons of work has been done to bring a lot of those measurements back to reality.
In the present situation I am going to take some time to move through all of the Latitude Layers (Fig. 1) to check out each data type one dataset at a time.
Just like I did today on Latitude Layer 5 (Fig. 2a - Fig. 5b).
Even though the WOD XBT data have no SP data, that dataset can be used for some temperature research if I figure out the best way to use that paper's suggestions to nullify any bias in that dataset.
When all of the datasets are used without careful consideration and selection (including especially the XBT dataset), they produce an inaccurate picture.
| Image may be NSFW. Clik here to view.  |
| Fig. 6a (T) All layers, all datasets |
| Image may be NSFW. Clik here to view.  |
| Fig. 6b (SP) All layers, all datasets |
show the data gyrations when all of the datasets are used without any adjustments.
Notice that before the WW II years there is considerable gyration.
After the war years, say mid-forties, the data pattern stabilizes.
A glaring error is the in situ temperatures recorded.
They indicate that the temperature at 250m deep is in the vicinity of 10 degrees C (~50 deg. F).
I suspect the XBT type data (all T, no SP) because the graph at Fig. 6b presents a more accurate picture after the WW II years.
The XBT has no SP measurements, so its influence is not present in the Fig. 6b graph as it is in the Fig. 6a graph.
I am going to do some more research using different combos of the various datasets to reach a best-use scenario.
For instance, what will Fig. 6a look like without the XBT (and other T only , no SP) datasets left out?
My expectation is that it will be more accurate.
The previous post in this series is here.