An Assessment of Sunspot Number Data Composites Over 1845–2014

Abstract

New sunspot data composites, some of which are radically different in the character of their long-term variation, are evaluated over the interval 18452014. The method commonly used to calibrate historic sunspot data, relative to modern-day data, is "daisy-chaining", whereby calibration is passed from one data subset to the neighbouring one, usually using regressions of the data subsets for the intervals of their overlap. Recent studies have illustrated serious pitfalls in these regressions and the resulting errors can be compounded by their repeated use as the data sequence is extended back in time. Hence the recent composite data series by Usoskin et al. (2016), R UEA , is a very important advance because it avoids regressions, daisy-chaining and other common, but invalid, assumptions: this is achieved by comparing the statistics of "active day" fractions to those for a single reference dataset. We study six sunspot data series including R UEA and the new "backbone" data series (R BB , recently generated by Svalgaard and Schatten, (2016) by employing both regression and daisychaining). We show that all six can be used with a continuity model to reproduce the main features of the open solar flux variation for 18452014, as reconstructed from geomagnetic activity data. However, some differences can be identified that are consistent with tests using a basket of other proxies for solar magnetic fields. Using data from a variety of sunspot observers, we illustrate problems with the method employed in generating R BB which cause it to increasingly overestimate sunspot numbers going back in time and we recommend using R UEA because it employs more robust procedures that avoid such problems.

Sunspot number recalibration: The ~1840–1920 anomaly in the observer normalization factors of the group sunspot number

“…In the Hoyt & Schatten (1998a, 1998b observer normalization scheme, k 0 -factors are formed by dividing the total number of sunspot groups observed by RGO by the corresponding total of the secondary observer, limiting the comparison to those days when both observers reported a non-zero group count. The comparison is made over the full period of overlap of the two observers, be it a fraction of (a) (b) (Svalgaard & Schatten 2016); and (2) R WL (Lockwood et al 2014a(Lockwood et al , 2014b(Lockwood et al , 2016b, R GU , and R WF (Friedli, personal communication, 2016). Cycle numbers are given at the bottom of the plot.…”

“…From the beginning of the RGO sunspot patrol in 1874, we posit that RGO observers followed an effective institutional ''learning curve'' in counting groups due to presumed improvements in instrumentation and technique before stabilizing circa 1915. In a recent paper, Chatzistergos et al (2017) note that that the time span over which the early RGO group count record is inhomogeneous is under debate and cite Sarychev & Roshchina (2009) and Lockwood et al (2016b) who presented evidence for much shorter intervals of nonuniformity of 1875-1880 and 1875-1877 (and 1892-1895), respectively. Neither Sarychev & Roshchina (2009) nor Lockwood et al (2016b), however, considered data for individual observers, other than Greenwich, beyond 1900.…”

“…In addition to the criticism of the Hoyt & Schatten (1998a, 1998b observer normalization procedure presented here, Lockwood, Usoskin, and colleagues (Lockwood et al , 2016bUsoskin et al 2016aUsoskin et al , 2016b have pointed out several potential problems in the methodology of the Svalgaard & Schatten (2016) time series, some of which (linear regression, direct proportionality, comparison of observers over entire intervals of overlap, daisy-chaining) apply more generally to all of the time series in Figures 1 and 2, including the Hoyt and Schatten series, but excepting the Usoskin et al (2016) series which employs a novel normalization procedure based on a synthetic primary observer and non-linear normalization of secondary observers. The main concern in the above papers of Lockwood et al and Usoskin et al, however, has been that the methods in question can lead to inflation of the resulting sunspot number (e.g., Usoskin et al 2016b) while the problems we identify for the Hoyt and Schatten time series lead to an underestimate of group counts.…”

“…Recently, much attention has been focused on the sunspot number time series (e.g., Clette et al 2014;Lockwood et al 2014aLockwood et al , 2014bUsoskin et al 2016aUsoskin et al , 2016bCliver & Ling 2016;Svalgaard & Schatten 2016;Cliver 2016;Lockwood et al 2016aLockwood et al , 2016b. The original sunspot number was developed by Rudolf Wolf in the second half of the 19th century (Wolf 1851(Wolf , 1856.…”

“…Both S N and R GS hew closer to the original WSN than they do to the Hoyt & Schatten (1998a, 1998b GSN. The Sunspot Number Workshops and the publication of these two series fostered much interest and activity regarding the sunspot number, culminating in the proposal of three independently derived alternative time series: R WL (Lockwood et al 2014a(Lockwood et al , 2014b(Lockwood et al , 2016b, R GU , and R WF (Friedli, personal communication, 2016), 1 that more closely resemble GSN than S N . Thus, in the present situation, we have a variant of the discrepancy that motivated the 2011-2015 examination of the discordant WSN and GSN time series.…”