Sifting Out the Noise to Find the Signal

Imagine you are sitting in a coffee shop, reading a book. This is a horrible place to find peace and quiet, but you are determined.  The story heated up a couple of chapters ago when someone in the library shouted, “Murder!” You are on the edge of your seat to know which character is the killer, and whether it was with the candlestick or rope.  You suspect Colonel Mustard, but you can’t be sure. Just then, the barista begins grinding a fresh batch of coffee beans. The new barista, still in training, is shouting completed drink orders across the whole room.  Your telephone rings.  It’s your mother so you must pick up.  It’s been a few days, so there is a lot to reassure her about how you’ve been getting in all your food groups. The subtle jazz music to which you had enjoyed tapping your foot has become dominated by saxophone squeaking in your ear.  A little gray-haired woman asks you if you’d mind if she sat next to you. You’re trying to determine Colonel Mustard’s integrity but the author has employed all sorts of literary tools like foreshadowing and analogy, which make the author a bestseller but now just annoy you.  The gray-haired woman is discouraged at your lack of response and begins inquiring more loudly and you’ve completely forgotten what you were telling your mother about.  Solving the murder mystery seems far afield.

After talking with Roger this afternoon for an hour, this is how I imagine analyzing a time-series might feel. It started with a question of how HOT fits in anthropogenic climate change research. The answer will not fit in this blog but the short version of the short version that he summarized for me is that it all depends on sifting out the noise to get the signals. The noise can range widely spatially, from turbulence on the scale of a molecule to global ocean circulation, and temporally from internal tides on the scale of tens of minutes to the earth’s motion on a scale of tens of thousands of years. The signals could be telling us how our climate is changing.  Identifying the patterns of noise is hard enough, and when those patterns begin to change – due to climate change or otherwise – it becomes harder. That is where a time-series becomes useful.  They attempt to give consistent data with high enough frequency over a long period of time in order to start picking out the patterns so that the signal becomes clear: Professor Plum in the library with the candlestick. Troubles, of course, include limited ranges.  You can’t measure the whole ocean and data is only available a few decades back, not thousands of years.  It is becoming clearer to me, however, that this work is ever more pressing and ever more complicated in light of those limitations.