@Simpson2013b [hereafter, S13] attributed positive values of lagged correlations between the zonal index and the eddy forcing, when the zonal index leads eddy forcing by a few days, to a positive feedback, and proposed statistical methods to quantify the strength of eddy-jet feedback in observations and simulations to improve understanding of the persistence of the jet. [Even though]{} S13 validated their method using synthetic time series generated by a second-order autoregressive process, [their]{} statistical method[, as well as the statistical method proposed by LH01, would benefit from an assessment]{} with more realistic time series of zonal index and eddy forcing. Due to the chaotic nature of eddies, the mean-state-dependent eddy forcing cannot be separated from the mean-state-independent part in the reanalysis data, and as a result, it is difficult to validate the assumptions of these statistical methods. Furthermore, a recent study showed that the existence of an internal eddy feedback cannot be distinguished from the presence of external interannual forcing using only the statistical methods (Byrne et al. 2016).

In the present study a linear response function (LRF), following @Pedram2016a, is used to identify the anomalous eddy fluxes in response to mean state anomalies that match the spatial pattern of 