of record, there is considerable deviation of this ratio among the different reanalysis products (i.e., the reference wind speed data uncertainty is high). As the length of the period of record increases, this ratio tends to converge to 1.0, and the spread between the three reanalysis products decreases (i.e., the reference wind speed data uncertainty is low).\
Finally, the (weak) negative correlation between linear regression and wind resource IAV uncertainties is linked to the fact that they respond differently to the $R^2$ coefficient between the reanalysis wind speed and the energy production data (Figure \[dep\_r2\]).

![image](f10){width="16cm"}

Predictably, the linear regression uncertainty is inversely proportional to the coefficient of determination because a stronger correlation between winds and energy production will lead to a reduced uncertainty of the regression between the two variables. On the other hand, wind resource IAV uncertainty shows a positive correlation with the regression $R^2$ coefficient. This dependence can be explained because both quantities are positively correlated with the total variance of wind speed or, equivalently, produced energy. Figure \[dep\_iav\_spread\] shows the relationship between IAV uncertainty and the total sum of squares $SS_{\text{tot, WS}}$ of reanalysis wind speed (here, using MERRA-2 monthly data), which is proportional to the 