[h/t to amirlach] Note: Short term predictions are relatively easy, it remains to be seen if this holds up over the long term. I have my doubts. – Anthony
Guest post by Frank Lemke
The Global Warming Prediction Project is an impartial, transparent, and independent project where no public, private or corporate funding is involved. It is about original concepts and results of inductive self-organizing modeling and prediction of global warming and related problems.
In September 2011, we presented a medium-term (79 months) quantitative prediction of monthly global mean temperatures based on an interdependent system model of the atmosphere developed by KnowledgeMiner, which was also discussed at Climate Etc. in October 2011. This model describes a non-linear dynamic system of the atmosphere consisting of 5 major climate drivers: Ozone concentration, aerosols, radiative cloud fraction, and global mean temperature as endogenous variables and sun activity (sunspot numbers) as exogenous variable of the system. This system model was obtained from monthly observation data of the past 33 years (6 variables in total: the 5 variables the system is actually composed of (see above) plus CO2, which, however, has not been identified as relevant system variable), exclusively, by unique self-organizing knowledge extraction technologies.
Now, more than a year has passed, and we can verify what has been predicted relative to the temperatures, which have really been measured (fig. 1).