27 Mar 20
The worst-case forecasts of coronavirus infection- and death rates, which were initially offered, are now seen as being extremely high. With more experience and data coming in, the projections are becoming more realistic and reliable. But how could the initial forecasts have been so wrong?
The mathematical modelling of pandemics is fairly straightforward – to a mathematician. The model is pretty rigorous, so what could go wrong? Like all models that are solutions to differential equations, the models are a general solution and produce what is called a “family” of curves. Each individual member of the family is distinguished from the others by the values of the parameters and coefficients of the function, of the general solution. If you put in the wrong numbers, the curve you get does not correspond to physical reality, and the forecast is “wrong.”. As matters have progressed, better data is coming in and so the more realistic values being used and the pandemic forecasts are becoming more accurate – more realistic.
It is worth comparing what we are seeing with the mathematics and forecasts of the progress of the pandemic with the mathematics and forecasts of global warming. The mathematical modelling of pandemics is straightforward, but the modelling of global temperatures is not. We understand a lot about the spread of pandemics, and have numerous examples in the past to compare the models to. But we lack good, historical temperature data from around the world, and even today vast swaths of the globe report no temperature data at all. We have no historical thermometric data around the time of the medieval warm period or the little ice age to help study our models. We simply do not know why theese occurred, which means the climate models may not account for all the variables.
The mathematics of forecasting the progress of pandemics is reliable, while the mathematics of modelling climate temperatures is crude and empirical – to say nothing of the problem of picking the right coefficient and parametric values to get the curve that corresponds to physical reality.
When the climate models came out forecasting catastrophe, my first reaction was “that’s interesting, worth more research, but too crude to base large decisions upon.” However, politics took over from the science. It was as though a fragment of thought answered the prayers of a political movement. That fragment was seized upon as gospel truth; we simply couldn’t risk this forecast of the future being wrong, and therefore couldn’t be doubted. Immediately, the focus turned away from the models, however crude, to the economic prescriptions demanded by the movement - all of which tended to the destruction of western economies. Kind of like what we are seeing during the pandemic.
After twenty-five years of experience, we now see that all the climate models were extreme. They all forecasted temperature increases much higher than the actually observed measurements. Given the empirical nature of the models, we cannot tell if there is a fundamental flaw with the underlying mathematics or just with the selection of the values for the coefficients and parameters of the models. Because of the extreme politics surrounding global warming, it is impossible for a dispassionate review of the entire theoretical basis for modelling global temperatures on a long-term basis. Too many careers and reputations are at stake in the scientific community itself.
Looking back to 1995 and comparing the forecasts at that time to actual events, it is obvious that global warming skepticism was completely justified. And since nothing has changed on the climate alarmism side, skepticism is more amply justified today, having been proven right for the last quarter century.
Understanding the mathematical modelling of the progress of a pandemic proves informative on another major issue of today; the modelling of climate.
-30-
No comments:
Post a Comment