Global Warming Prediction Project
Global Warming Prediction Project
Still confirming forecast of Apr 2011 at 73% accuracy. IPCC forecast at 10%. What drives Global Warming? (Update 2)
07.10.2013
This is an actual vs predicted update of the medium-term (79 months) quantitative forecast of monthly global mean temperatures based on an interdependent system model of the atmosphere developed by KnowledgeMiner Software based on observational data, exclusively. No prior assumptions, expectations or other subjective adjustments were used in the models.
This model describes a non-linear dynamic system of the atmosphere for short- to medium-term forecasting consisting of 5 drivers: Ozone concentration, aerosol index, radiative cloud fraction, and global mean temperature as endogenous variables and sun activity as exogenous variable of the system. Note that CO2, though provided as input data, has not been selected as relevant climate driver by self-organizing, inductive modeling. This is a CO2-free prediction model. The model was built from observational data from October 1988 till April 2011 of up to 1000 input variables with time lags of up to 120 months, which is a typical input space dimension for complex dynamic systems modeling.
As of August 2013, the OUT-OF-SAMPLE prediction accuracy of the most likely prediction (solid red line) of the self-organized model is 73%. The accuracy relative to the prediction range (pink area) is 98% (fig. 1).
Fig. 1: Ex-ante forecast (most likely (red), high, low (pink); April 2011 - November 2017) of the system model (as of March 2011) vs observed values (black and white square dots; HADCRUT3) from April 2011 to August 2013. These 29 months are used for verification of the out-of-sample predictive power of the system model.
In comparison, the highly expensive General Circulation Models (GCMs) which the IPCC AR4 projections are based on and which simplistically rely on atmospheric CO2 (and other greenhouse gases) as the major climate driver show a prediction accuracy of only 10% for the time period 2007 (the year of publication) till today. The tight connection between IPCC temperature projection (yellow) and CO2 concentration projection (gray) is clearly visible for the forecast horizon, too, as well as the growing gap between IPCC projected and observed temperatures (fig. 2).
Fig. 2: Ex-ante most likely forecast (red; April 2011 - November 2017) of the self-organized system model (as of March 2011) vs observed values (black and white square dots; HADCRUT3) from April 2011 to August 2013 vs IPCC A1B projection (yellow; until November 2017) vs CO2 concentration (light gray; until November 2017).
This one-sided drift of the IPCC projection seen in fig. 2 is not common for predictive models. A similar drift situation is not observed for the time before 2007, for the data the GCMs were developed on. Here, over- and underestimation of observed values is balancing within time periods of few years, as expected. In modeling, such a drift is seen as clear evidence of low (or decreasing) predictive power of the model, the lack of skill to describe the underlying phenomenon sufficiently. This evidence, for the IPCC model, is not surprising since the built-in simplistic linear cause-effect relationship „growing atmospheric CO2 concentration leads to proportionally growing global temperatures“ does not adequately describe the complex and interdependent nature of the atmosphere-ocean system and therefore does not sufficiently satisfy the adequateness law of modeling.
Additionally, our climate system is essentially influenced by external, cosmic climate drivers such as the Earth Orbit Oscillation in centennial time frames, the multidecadal tri-synodic Jupiter/Saturn cycle, or the well-known orbit eccentricity Milankovitch cycle, which causes glacial and interglacial ages on Earth. These cosmic climate drivers are responsible for most of the variation of solar radiation received on Earth, resulting in medium- to long-term warming and cooling periods, independently from the sun‘s own rather small changing activity and radiation.
The considerably higher predictive power of the discussed self-organized system model is clearly a result of the unique ability of the applied, proven inductive modeling technology to automatically and reliably extract more relevant information from the noisy observational data for identifying and modeling the internal workings of the ill-defined climate system than IPCC theory-driven modeling approaches can achieve based on incomplete and uncertain human knowledge.
impartial, transparent, independent modeling and prediction of global warming and related problems.