Global Warming Prediction Project
Global Warming Prediction Project
Self-organized Model of the Atmosphere
29.06.2011
Now as there are predictive models for key characteristics of the atmosphere - ozone concentration, reflectivity, aerosols, and atmospheric CO2 - and for sun activity as its major force, we can go a step further and put all pieces together and build a system model of the atmosphere from these variables.
The atmosphere is a complex system and there is no single simple (linear) cause-effect relationship, but the system variables are interconnected and interdependent in a not completely known way with unknown dynamics building a complex relationship pattern where it is hard to tell cause from effect. This missing a priori knowledge is a major problem for modeling the climate system which leads to a lot of assumptions and often non-holistic approaches which introduces subjectivity into modeling and results. On the other hand, essential information about the complex behavior of the atmosphere is hidden in the observational data. This knowledge about the system only needs to be extracted appropriately from the data. And this is where self-organizing knowledge mining comes in as already shown in previous posts. The idea is to let the data, only, tell us what‘s happening, objectively and autonomously.
A still debated question of major interest and importance is: „What drives Global Warming?“ Is it CO2 alone and in the supposed way or is it a mix of influences or an other single driver, the sun? Adding a sixth system variable - Global Temperature Anomalies - leads to the nonlinear dynamic system model of the atmosphere shown in the figure below. With this system model it is possible now to self-organize a predictive model of global temperature from ozone, reflectivity, aerosols, CO2, and sun activity data. The outcome of this model self-organization, however, is open at this moment...
Self-organized system model of the atmosphere as a nonlinear system of difference equations.
This system model cannot be seen as a complete description of the atmosphere. It is a starting point and it may be extended by additional system variables over time, although we think it‘s already a good and powerful initial solution. Note that this project is open and independent and there is not any financial support behind it. We would love to hear about your comments, opinions, thoughts.
The objective of this project is doing monthly modeling and prediction of global temperature anomalies through self-organizing knowledge extraction from public data. The project is impartial and has no hidden personal, financial, political or other interests. It is entirely independent, transparent, and open in results.