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![\"Figure](\"http:\/\/rankexploits.com\/musings\/wp-content\/uploads\/2008\/11\/octoberipcccomparison.jpg\" "\\"octoberipcccomparison\\"")
<\/a>The temperature for the observation and the individual runs are relative to their individual monthly averages from Jan 1970-Dec 1999. The \u00c2\u00b195% uncertainty intervals represent the spread of all weather in all models, based on the assumption that the deviations from mean are normally distributed.<\/p>\n
As you can see<\/p>\n
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- Using the simple eyeball test, the IPCC AR4 simulations were in quite good agreement with observations during the period between publication of the IPCC TAR and the AR4. This was a period when analysts would have the opportunity to make decisions such as tweaking model parameters, screening model runs, deciding whether to weight model runs when finalizing the projections in the AR4. <\/li>\n
- Using the simple eyeball test, we see temperatures dropped publication of the document, but remain within the \u00c2\u00b195% uncertainty intervals for all weather in all models. <\/li>\n<\/ol>\n
It is worth noting that the IPCC projections are, evidently, based on averaging over models<\/i> not runs<\/i>. The uncertainty bands in figures in the IPCC AR4 are evidently 1 standard deviation for the average trend for all models rather than all possible weather in all possible models. As \u00c2\u00b195% is roughly equal to two standard deviations, their uncertainty bands would be tighter. (I’ll eventually put together a graph using the weighting described verbally in the IPCC. I’m pondering how best to do it– no matter how I look at it, the temperature projections in my files always look much noisier than the ones in the IPCC AR4. I suspect smoothing was done. . . )<\/p>\n","protected":false},"excerpt":{"rendered":"
As many readers know, I like to compare observations to projections rotating through various methods. Some statistical methods have more power (meaning they correctly detect statistically significant differences between a theory and data using smaller amounts of data.) Other methods have less power. Simply comparing the observations of global mean surface temperature (GMST) to the … Continue reading Comparison of 12 Month Running Averages.<\/span>