Solar and anthropogenic influences on climate: Regression analysis and tentative predictions

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Abstract

The paper aims to quantify solar and anthropogenic influences on climate change, and to make some tentative predictions for the next hundred years. By means of double regression, we evaluate linear combinations of the logarithm of the carbon dioxide concentration and the geomagnetic aa index as a proxy for solar activity. Thereby, we reproduce the sea surface temperature (HadSST) since the middle of the 19th century with an adjusted R2 value of around 87 percent for a climate sensitivity (of TCR type) in the range of 0.6 K until 1.6 K per doubling of CO2 . The solution of the double regression is quite sensitive: when including data from the last decade, the simultaneous occurrence of a strong El Niño and of low aa values leads to a preponderance of solutions with relatively high climate sensitivities around 1.6 K. If these later data are excluded, the regression delivers a significantly higher weight of the aa index and, correspondingly, a lower climate sensitivity going down to 0.6 K. The plausibility of such low values is discussed in view of recent experimental and satellite-borne measurements. We argue that a further decade of data collection will be needed to allow for a reliable distinction between low and high sensitivity values. In the second part, which builds on recent ideas about a quasi-deterministic planetary synchronization of the solar dynamo, we make a first attempt to predict the aa index and the resulting temperature anomaly for various typical CO2 scenarios. Even for the highest climate sensitivities, and an unabated linear CO2 increase, we predict only a mild additional temperature rise of around 1 K until the end of the century, while for the lower values an imminent temperature drop in the near future, followed by a rather flat temperature curve, is prognosticated.

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Stefani, F. (2021). Solar and anthropogenic influences on climate: Regression analysis and tentative predictions. Climate, 9(11). https://doi.org/10.3390/cli9110163

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