Cmocean
CM Ocean. It’s a programming software that is running MATLAB, in order to output the data which has then been run in the model. It is a visualization that would run alongside, and produce varying different scales of data via color. So there’s a lot of different programs which can turn ocean data into color, like heat stripes, water warming, sea warming, water level rise, salinity… lots of different kinds of data.
We started off this journey speaking about why visualization don’t produce effect when they have to do with existential questions like Climate Change. So it makes sense to talk about CM Ocean.
The data that is transformed into these visualizations are numerical, it’s quantity. And then they are translated into a scale that is absolutely not numerical, and are very subjective in terms of its reception. The aim of CM Ocean is to desubjectify and to make colour scientific. It is quite a task, which is surprising that a group would take it on. But CM Ocean is funded by BP, a multinational oil and gas company, and funded by George Bush. Its not that necessarily this has a one-on-one effect. But it’s obvious, and worth noting that an oil company and the Texas Government would like to have a regulated way of understanding the contents of the ocean.
The second thing is that the subjectivity of color is aimed for regulation, which bypasses things like taste. It bypasses any kind of physiological reception. I was thinking that perhaps the expectation that color can be reproducible, that it can be accurate, that it can correctly represent numerical data, that it can’t be divorced from numericizing color in the first place, the attributions of CMYK and RGB. If color is printed, it is different to if it’s on a screen. There are so many unworkables to this method, if you think about it. But the belief is that its good color usage carries the responsibility of honesty. So, to use colors in an honest way is the responsibility of the scientist. But what is honesty in color representation of data points? Its previous iteration, called JETS, is supposedly not so accurate, not so precise because it has the movements through the color scale with arbitrary weights. So, this has you thinking that there’s a density of whatever it is you’re looking for in the ocean because this particular part of the color scale is more dense to you, to the reception of the eye. Dark purple rather than light yellow might misrepresent the density of the object in question, but you would never know that, because this perceived symbolism is skewed. The gradient of the color has to accelerate and decelerate but it might not do that at the scale of the numerical values have on the back-end. It might be that it looks like it’s getting warmer quickly, but it depending on how this color scale is being applied, it could completely skew the numerical results that you’ve run your model for.
It’s also worth saying, that these models are hugely energy expensive, and take around forty days to run. The step from programming to output, massive amounts of electricity are used and the possibility for it to go wrong are quite large. If so, you would have to start again and try to recalculate. As I mentioned at the start of this conversation, if we look at these instances in the process of data collection to output, solely in a critical mode, then we fail in a remarkable way: the ocean, its inhabitants, what is life and what sustains us on this planet, is still and always our object of study. We need to propose other methods of working together, of offering feedback, which differently separate our object, or work with separability itself. The grid-not-monument we’re working with here, is a try towards this.
