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.
Read the complete post here:
http://climateprediction.eu/cc/Main/Entries/2013/1/21_What_Drives_Global_Warming_-_Update.html
Phil.:
Moe was wrong.
You have come here to try to say he was right.
You have failed because he was wrong.
The trends I cited to you (at February 1, 2013 at 10:34 am) are correct at 95% confidence. And, yes, I know that is an inconvenient truth for warmunists like you.
If you want to know how to conduct regression analysis and how to assess its statistical significance then see my explanation (above) for Moe at January 27, 2013 at 11:42 am with corrigendum at January 27, 2013 at 12:19 pm.
And that is all I will bother to say in response to any more of your trolling.
Richard
PS Although your knowledge of statistics is as low as Moe’s, I congratulate you that your intended insults are more subtle than his.
Richard, I had been away for a few days on assignment and I see that Phil has also point put your error, which you seem unable to comprehend.
I had left a challenge for you to provide a time period so I could work put a statistically significant trend for you, but it was snipped. At any rate, I am bored with this issue, but at least your ability to comprehend basic statistics is now on record.
richardscourtney says:
February 2, 2013 at 10:41 am
Phil.:
Moe was wrong.
You have come here to try to say he was right.
You have failed because he was wrong.
Given your demonstrated knowledge of statistics you’re not competent to judge that!
You don’t understand the Pearson Significance test and the importance of sample size in it despite being given a link to a description of how it is used.
The trends I cited to you (at February 1, 2013 at 10:34 am) are correct at 95% confidence. And, yes, I know that is an inconvenient truth for warmunists like you.
Really, you say that:
“RSS
Warming is NOT significant for over the most recent 23 years.
Trend: +0.126 +/-0.136 C/decade at the two sigma level from 1990”
Based on that data there is ~3% probability of the trend being zero or less!
If you want to know how to conduct regression analysis and how to assess its statistical significance then see my explanation (above) for Moe at January 27, 2013 at 11:42 am with corrigendum at January 27, 2013 at 12:19 pm.
Yes I know you think that’s how it’s done, try reading the link I gave which describes how to determine significance.
And that is all I will bother to say in response to any more of your trolling.
Richard
PS Although your knowledge of statistics is as low as Moe’s, I congratulate you that your intended insults are more subtle than his.
As stated above you don’t know enough to judge that. I make no insults when I describe your knowledge of stats as weak, that’s based on your statements, it’s a statement of fact not an insult. Your posts on the other hand are full of insults, calling anyone who disagrees with you a ‘troll’, a ‘warmunista’ (whatever that’s supposed to mean?)