Wavelet analysis, wiggle matching, what on the planet am I doing??! And of course WHY? WHY? WHY? I’ve been literally chained to my desk the last couple of weeks looking at coral trace element data, climate indices, multi proxy data and I’m inundated with data I can’t see straight. “What am I trying to do,” you may ask? At the moment – I have absolutely no clue. When I started out, I thought I might use these deep-sea corals to tell a great story of how climate has changed over the last 4,000 years. After all, these corals are supposed to record water mass properties in their skeletons. But as as exciting as climate (marine) science is at this point in time, it is quite problematic.
The problem with climate science is the “climate” and the “science”. Climate by definition is a long-term average of weather conditions in a region. This means it’s hard to predict/measure because it is affected by a number of things, from orbital forcing, to volcano eruptions, oscillations (e.g El Nino Southern Ocsillation – ENSO, Pacific Decal Oscillation – PDO). Science is a problem mainly because of the non-scientists. I’ve encountered lots of non-scientists but there are two groups I’d like to talk about. The ones who don’t agree with the scientific method (the argument is quantitative analysis is too objective), and the ones who think science tells us the gospel truth.
A friend of mine who is a biosecurity economist (I’ll put economists in the science boat just this once), has recently been in a series of meetings with the government (policy makers, etc) and academics (scientists and economists). It was quite interesting the kind of discussions they were having. There were many people present in favour of swapping economic valuation principles for more subjective ones. For example, instead of using cost benefit analyses to value ecosystems, many favoured a survey approach that they felt was more representative of individual opinions and beliefs. My problem with qualitative analysis is where is the reproducibility? Can we use the same method to value marine parks say in Vanuatu and Australia? Of course we need to be sensitive to the cultural settings, but we also need a benchmark.
Then there’s the case of those who think that science is the gospel truth. The discouraging story of the Italian seismologists is the best example of this. We need to understand the meaning of uncertainty is science. We’re only human beings who use ever-improving (far from perfect) equipment trying to look hundreds or thousands or millions of years into the past, or future (for which we don’t have much data – just inferences from proxies). So to expect the gospel truth out of that is to expect us to play God. In Canberra last month, there was an entire week where the weather forecast was way off for pretty much every single day – thunderstorms and high temperatures were apparently supposed to be the prevailing conditions. Not so. If this was Italy, I wonder if the weather forecasters would’ve been jailed too.
So finally back to me and my wiggle matching. The problem now comes in because everything is related or as we say, there are teleconnections. So the wiggles in my data might be telling me something related to El Nino, or the PDO, increased primary productivity or just the biochemistry of the coral itself! The task at hand is that any deductions I make must have a sound scientific and statistical backing. The uncertainty associated with it would imply simply this: with all the methods I’ve used (which are subject to flaws), and based on my data and previous studies, this is my interpretation (which is subject to change with more information). This is how science has evolved, and why science will only get better.