By Dr. Gregory Young, a neuroscientist and physicist, a doctoral graduate of the University of Oxford, Oxford, England, whilst previously completing postgraduate work at King's College, University of Aberdeen, Scotland, and having taught graduate-level Statistical Analysis and Mathematical Modeling. He currently chairs a privately funded think-tank engaged in experimental biophysics
What's in a Model:
Mathematical Modeling is used throughout our world to help forecast the future in many arenas of life, including economics, biology, medicine, and yes, climate change. Like all modeling, one attempts to study the past through scientific observation, accurately and unbiasedly collect the data, and then fit the data to a dynamic computer model that is meant to predict, to some degree of accuracy, some measure of tomorrow. In this way scientists hope to discover trends that not only document the past, but could forecast the future.
Virtually all climate models are basically mathematical models, built upon a series of mathematical equations. Change just one equation, or the number of variables in an equation, or how they relate to one another, and the results of the model can change dramatically. Unfortunately, unlike many other forms of modeling, climate models have yet to prove their wanted accuracy.