If you live within sight of the sea, as we do, then your life is probably influenced, to some degree, by the rise and fall of tides. In Liverpool, we are lucky to have a particularly long historical record of tidal heights and one of my colleagues, an oceanographer, Professor Ric Williams has used this record to discuss climate variability. The record was started and maintained between 1768 and 1793 by Captain William Hutchinson whose achievement is commemorated with a fountain in Liverpool’s historic docks, which are a UNESCO World Heritage Site.
A few weeks ago I listened to a talk by Prof Williams, in which he described how there is a rather simple relationship between surface warming and the effect of future emissions of greenhouse gases. If the predictions of surface warming are plotted as a function of how much carbon is emitted to the atmosphere, rather than time, then a simple response emerges: the more carbon we emit, the warmer it will get. Associated with the surface warming, there is an expected sea level rise from the expansion of the water column augmented by the effect of addition of freshwater from melting of land ice. Watch Prof Williams’ Youtube video to find out more.
Many of us will be familiar with the concept of the carbon cycle, but what about the silicon cycle? Silicon is the second most abundant element in the Earth’s crust. As a consequence of erosion, it is carried by rivers into the sea where organisms, such as sponges and diatoms (photosynthetic algae), convert the silicon in seawater into opal that ends up in ocean sediment when these organisms die. This marine silicon cycle can be incorporated into climate models, since each step is influenced by climatic conditions, and the opal sediment distribution from deep sea sediment cores can be used for model validation.
There are many examples in engineering where we tend to shy away from comprehensive validation of computational models because the acquisition of measured data seems too difficult and, or expensive. We should take inspiration from sponges – by looking for data that is not necessarily the objective of the modelling but that nevertheless characterises the model’s behaviour.