Failing Computer Models

Failing Computer Models

JANUARY 21, 2021tags: modelsRSSsatellite temperatures

By Paul Homewood

If anybody tries to tell you that the computer models are accurately predicting global warming, show them this:


 It comes from RSS, who monitor atmospheric temperatures via satellite observation. They are ardent warmists, and here us what they have to say:

Over the past decade, we have been collaborating with Ben Santer at LLNL (along with numerous other investigators) to compare our tropospheric results with the predictions of climate models.  Our results can be summarized as follows:

  • Over the past 35 years, the troposphere has warmed significantly.  The global average temperature has risen at an average rate of about 0.18 degrees Kelvin per decade (0.32 degrees F per decade).
  • Climate models cannot explain this warming if human-caused increases in greenhouse gases are not included as input to the model simulation.
  • The spatial pattern of warming is consistent with human-induced warming.  See Santer et al 2008, 2009, 2011, and 2012 for more about the detection and attribution of human induced changes in atmospheric temperature using MSU/AMSU data.


  • The troposphere has not warmed quite as fast as most climate models predict.  Note that this problem has been reduced by the large 2015-2106 El Nino Event, and the updated version of the RSS tropospheric datasets.

It is of course nonsensical to argue that a record El Nino, which raised global temperatures by 0.7C, should in any way be factored into the appraisal of model accuracy.

Take that out, and the models are clearly failing. Actual temperatures have consistently been trundling along the bottom of that yellow band, and more often than not below it, for the last decade or more.

What makes this failure even more damning is that there was a reasonable fit up to 2005, when the models were forced with historical values of greenhouse gases, volcanic aerosols, and solar output ( a process known as backcasting). There is of course nothing clever about tweaking your models until they give you the result you are looking for. But that does not mean the models have any value in forecasting, as subsequent events have shown.

For the actual data to have diverged so much in the space of just a decade suggests the models are worthless.

And we must not forget this comment by RSS:

Note that this problem has been reduced by the large 2015-2106 El Nino Event, and the updated version of the RSS tropospheric datasets”

You may recall that in 2016 RSS suddenly dropped their highly inconvenient dataset, below, which inconveniently showed that global temperatures had fallen since 1998:


In favour of the new version, which showed the opposite:


When the models don’t even agree with tampered data, you have big problems!!

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