In the sea we used floats of parsnip because it is easily visible, and because it is almost completely immersed so as not to catch the wind which, moreover, was slight. The floats were about 2cm in diameter.
the true relationship between spectrum and color turns out to be rather useful here. We are used to interpreting small differences from white light (“red” noise) as colors, so if the sea level spectrum was exactly “red” noise, a translation into a light spectrum would render it as a gray scale: low‐amplitude variability would be black, and high‐amplitude white, with grays at intermediate values.
While a zipper makes a closure by the functioning of a single accurate mechanism, the same task can be accomplished by the closing of enough of the myriad tiny hooks and eyes that make up a strip of Velcro. Baker (1983) first used this analogy to suggest "the importance of many relatively cheap measurements that cover a broad area as opposed to expensive detailed measurements at a point." As an example, consider the merits of the plentiful satellite measurements of sea surface temperature (SST) compared with highly accurate but exceedingly sparse shipboard SST measurements. Satellite information on the spatial patterns of SST and the changes of these patterns in space and time have provided a revolutionary new view of the ocean—a view completely unattainable from SST measurements along ship tracks. In such "Velcro" techniques, the quantity of measurements allows a description of the spatial patterns associated with the underlying fluid flows, with detail impossible to obtain from sparse sampling. As has been the case with satellite SST, the quantitative accuracy of the remote sensing algorithm is determined and improved by comparison with "ground-truth" data from accurate but sparsely distributed sensors.
The above analogy is of direct relevance to present state-of-the-art measurement of ocean turbulence, a field in which the accurate "zippers" are microscale profilers. While such profilers have become somewhat more common during the last decade, they remain relatively rare; are expensive to maintain and run; and in operation are often restricted by adverse conditions of weather, surface waves, or oceanic velocity shears.
That turbulent mixing processes in the ocean are extremely important in determining the oceanic general circulation, and are major limiting factors in the ability to calculate future climate states, is a cliche in oceanography and climate dynamics. Unlike some other hackneyed statements, this one does retain much of its validity.
We have gone from the situation described in Wunsch & Ferrari (2004) of being unable to account for the dissipation of geostrophic turbulence to the opposite case—there are now too many candidates.
We are in effect bragging about the results of a high-resolution yet wide-angle (10°) telescope in the sense of an astronomer looking at the sky. We have only used the new telescope four times in one portion of the ocean.
V Canuto and M Dubovikov were included as coauthors during the review process, because they contributed to resolving some inconsistencies in the original expressions for the eddy-induced streamfunction and the residual fluxes.
The obliquity modulation of tidal mixing by a few percent and the associated modulation in the meridional overturning circulation (MOC) may play a role comparable to the obliquity modulation of the incoming solar radiation (insolation), a cornerstone of the Milanković theory of ice ages. This speculation involves even more than the usual number of uncertainties found in climate speculations.
John Lumley, quoted in (Leibovich and Warhaft 2018)
Turbulence is rent by factionalism. Traditional approaches in the field are under attack, and one hears intemperate statements against long time averaging, Reynolds decomposition, and so forth. Some of these are reminiscent of the Einstein-Heisenberg controversy over quantum mechanics, and smack of a mistrust of any statistical approach. Coherent structure people sound like The Emperor’s New Clothes when they say that all turbulent flows consist primarily of coherent structures, in the face of visual evidence to the contrary. Dynamical systems theory people are sure that turbulence is chaos. Simulators have convinced many that we will be able to compute anything within a decade. Modeling is thus attacked as unnecessary, or irrelevant because it starts with Reynolds stress averaging or ignores coherent structures. The card-carrying physicists dismiss everything that has been done on turbulence from Osborne Reynolds until the last decade. Cellular Automata were hailed on their appearance as the answer to a maiden’s prayer, so far as turbulence was concerned. It is no wonder that funding agencies are confused. (quoted in Cantwell 1990, p. 97)
(Penland and Sardeshmukh 1995) throw down a challenge:
A factor of 2 sometimes finds itself, rather arbitrarily, in the denominator.
(Flierl 1978) quotes Karl Marx:
A second and, in my opinion, more persuasive argument for our studies is that, in sensitive systems, a small quantitative error may lead to large qualitative errors1.
_1 "Quantitative changes suddenly become qualitative changes" (Karl Marx)
Most floats sampled at regular pressure levels, though the Canadian Argo floats often sampled at irregular pressures and required substantial editing.
(Sutyrin and Flierl 1994) note insufficient printer resolution in their Figure 2.
(MacKinnon et al. 2017) summarize
A frequently asked question related to this work is “Which mixing processes matter most for climate?”. As with many alluringly comprehensive sounding questions, the answer is “it depends”.
and then end on an inspiring note
At the same time, the epiphanies, new ideas and novel observations that fundamentally drive the field forward frequently come not from big science, but from a cornucopia of much smaller exploratory efforts and the continued small-scale development of innovative observing technology and numerical techniques. We must not lose the ability to be surprised.
Also note the author order:
(Wunsch 2016) cites the Bible
That which is far off, and exceeding deep, who can find it out? — Ecclesiastes 7:24 (King James Version)
Enrico Fermi quotes John von Neumann to Freeman Dyson, critizing the latter's model (Dyson 2004):
"I remember my friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk." — Enrico Fermi
(Mayer, Khairy, and Howard 2010) take on the challenge yielding:
The resulting shape is schematic and cartoonlike but is still recognizable as an elephant. Although the use of the Fourier coordinate expansion is not new, our approach clearly demonstrates its usefulness in reducing the number of parameters needed to describe a two-dimensional contour. In the special case of fitting an elephant, it is even possible to lower it to four complex parameters and therein implement a well-known saying.
With five, they give the elephant an eye and make it's trunk wiggle!
Python implementation here.
As it turns out, the observations provide a clue to the problem.