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).
Verifying the prediction skill of the system model from April 2011 to December 2012, the accuracy of the most likely forecast (solid red line) remains at a high level of 75%, and the accuracy relative to prediction uncertainty (pink area) is an exceptional 98%. Given the noise in the data (presumably incomplete set of system variables considered, noise added during measurement and preprocessing of raw observation data, or random events, for example), this clearly confirms the validity of the system model and its forecast.
In comparison, the IPCC AR4 A1B projection currently shows a prediction accuracy of 23% (September 2007 – December 2012, 64 months) and just 7% accuracy for the same forecast horizon as applied for the system model (April 2011 – December 2012, 21 months).
The two models, IPCC model and atmospheric system model, use two very different modeling approaches: theory-driven vs data-driven modeling. The IPCC model is based essentially on AGW theory by emission of greenhouse gases, namely CO2, the presented atmospheric system model on the other hand is a CO2-free prediction model. It is described by 5 other variables. The IPCC model shows a prediction accuracy of 7% and the atmospheric system model an accuracy of 75% for the same most recent 21 months of time…
The climate system is a complex system that consists of a number of variables, which are connected interdependently, nonlinearly and dynamically and where it is not clear, which are the causes and which are the effects. The simplistic linear cause-effect relationship “more atmospheric CO2 = higher temperatures” the IPCC model is based on is not an adequate tool to describe the complexity of the atmosphere sufficiently.
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