Climate Models Wrong Again

The latest version of HadCRUT4 (a collaborative product of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia), one of the major ground-based global temperature sets, is significantly and obviously diverging from the projections made by the CMIP5 computer models, which are featured prominently in the IPCC AR5 published in 2013-14. ci_glb_tas_2016014
The grey area is the 5 to 95 percentile range of CMIP5 models for global surface temperature and the dark line in the middle is the average of those models.

While the conclusion that reality and models are significantly diverging whether we look at ground temperatures or lower troposphere temperatures, the divergence of the models from reality is even more stark in the lower troposphere (the lowest part of the atmosphere where all weather takes place). Remote Sensing Systems (RSS) is one of the two independent groups that produces a lower-troposphere dataset.


The one thing that is absolutely clear in climate science (and there are not many) is that the models are seriously flawed and inaccurate, which leads one to wonder why the IPCC and so many politicians have made almost a religion out of believing in them—and indeed are wagering trillions of dollars on a sure lost bet. The fact is that the models have been scientifically proven to be drastically flawed and inaccurate time and time again over the past three decades.

And from the RSS website itself comes the following graph:

Fig. 1.  Global (80S to 80N) Mean TLT Anomaly plotted as a function of time.  The thick black line is the observed time series from RSS V3.3 MSU/AMSU Temperatures.  The yellow band is the 5% to 95% range of output from CMIP-5 climate simulations.  The mean value of each time series average from 1979-1984 is set to zero so the changes over time can be more easily seen.  Note that after 1998, the observations are likely to be below the simulated values, indicating that the simulation as a whole are predicting too much warming.


For more technical details on the divergence of models from reality, see Steve McIntyre’s excellent post.


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