Speaker
Description
The group sunspot number is the longest direct record of solar activity.
However, cross-calibrating the available data from many individual observers poses a challenge.
Several reconstructions of group sunspot numbers exist, based on different cross-calibration strategies.
Beyond this, the methods also vary in how the data are linked across observers.
Some methods rely on sequential "daisy-chaining" or "backbones" of overlapping records, while others use statistical properties of sunspot groups to link non-overlapping datasets.
As a result, the series increasingly diverge prior to the 20th century.
Here, we present results from a sensitivity study evaluating the performance of existing cross-calibration methods, using synthetic datasets designed to mimic real observers with different acuity and temporal coverage.
Our analysis shows that simple linear scaling introduces systematic biases, overestimating strong cycles and underestimating weak ones, and therefore distorting long-term solar activity trends.
In contrast, non-parametric approaches (e.g., Chatzistergos et al. 2017; Usoskin et al. 2021), yield more consistent results with lower errors.
Based on our findings, we recommend direct non-linear calibration (e.g., Chatzistergos et al. 2017) when sufficient overlap exists between observers.
For periods requiring multi-step daisy-chain calibration, which renders the cumulative errors significant, statistical methods such as the active-day fraction approach (Usoskin et al. 2021) are better suited for bridging data gaps.
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