‘From a drop of water,’ said the writer, ‘a logician could infer the possibility of an Atlantic or a Niagara without having seen or heard of one or the other. So all life is a great chain, the nature of which is known whenever we are shown a single link of it.’
Suppose you're looking for patterns or relationships in your data. For example, you may be trying to quantify the presence and distribution of certain water masses in a hydrographic section, or you may be looking for evidence and patterns of nitrogen fixation or denitrification in some nutrient data. Perhaps you're trying to find the best way to account for interferences from other elements (“matrix effects”) in your ICPMS data. You've gathered your data, maybe obtained from a cleverly designed experiment, or extracted from a hydrographic atlas or a collection of cruise data. The information you require lies within the relationships or correlations between the different properties or variables in your data set. But where (and how) do you look? If instinct leads you to look at the data covariance matrix, then your instinct is right! In this chapter we'll show you some techniques for extracting and analyzing this structure. We will start with some underlying basics that you'll need to understand these techniques, and we'll mention a few relatively intuitive approaches for analyzing data structure.
Autumn to winter, winter into spring,
Spring into summer, summer into fall, –
So rolls the changing year, and so we change;
Motion so swift, we know not that we move.
Scope, background, and purpose
So far in our models the physical characteristics of velocity and diffusivity have been specified or “hard-wired” into the calculations. The next step to consider is allowing them to respond to changing conditions. In this chapter, we will be developing and exploring a class of models aimed at simulating the seasonal behavior of the upper ocean in response to changing atmospheric forcing. We subsequently will extend this model to simulate the response of dissolved gases in the upper ocean. This approach can be more generally applied to other shallow water column properties (including bio-optical modeling, particle dynamics, etc.) with very minor modifications. What we're trying to show you here is not just how to design, build, and extend the model, but more importantly how to figure out what the model is actually doing, and how to compare its performance quantitatively with actual observations.
There are two general types of upper ocean models (although there are hybrids of these two as well). There are the bulk mixed layer models which, as the name suggests, treat the mixed layer as a homogeneous, well-mixed box, within which properties including chemical species, temperature, salinity, and physical momentum are uniformly distributed.
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