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Scafetta

The Scafetta Saga

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It has taken 17 months to get a comment published pointing out the obvious errors in the [cite ref=”Scafetta (2022)”]10.1029/2022GL097716[/cite] paper in GRL.

Back in March 2022, Nicola Scafetta published a short paper in Geophysical Research Letters (GRL) purporting to show through ‘advanced’ means that ‘all models with ECS > 3.0°C overestimate the observed global surface warming’ (as defined by ERA5). We (me, Gareth Jones and John Kennedy) wrote a note up within a couple of days pointing out how wrongheaded the reasoning was and how the results did not stand up to scrutiny.

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Scafetta comes back for more

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A new paper from Scafetta and it’s almost as bad as the last one.

Back in March, we outlined how a model-observations comparison paper in GRL by Nicola Scafetta ([cite ref=”Scafetta, 2022a”]10.1029/2022GL097716[/cite]) got wrong basically everything that one could get wrong (the uncertainty in the observations, the internal variability in the models, the statistical basis for comparisons – the lot!). Now he’s back with a new paper in a different journal [cite ref=”(Scafetta, 2022b)”]10.1007/s00382-022-06493-w[/cite] that could be seen as trying to patch the holes in the first one, but while he makes some progress, he now adds some new errors while attempting CPR on his original conclusions.

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Issues and Errors in a new Scafetta paper

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Earlier this week, a new paper appeared in GRL by Nicola Scafetta [cite ref=”(Scafetta, 2022)”]10.1029/2022GL097716[/cite] which purported to conclude that the CMIP6 models with medium or high climate sensitivity (higher than 3ºC) were not consistent with recent historical temperature changes. Since there have been a number of papers already on this topic, notably [cite ref=”Tokarska et al (2020)”]10.1126/sciadv.aaz9549[/cite], which did not come to such a conclusion, it is worthwhile to investigate where Scafetta’s result comes from. Unfortunately, it appears to emerge from a mis-appreciation of what is in the CMIP6 archive, an inappropriate statistical test, and a total neglect of observational uncertainty and internal variability.

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