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Effects of Version 2 of the International Sunspot Number on Naïve https://ntrs.nasa.gov/search.jsp?R=20190001636 2019-08-30T11:15:09+00:00Z View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by NASA Technical Reports Server Space Weather RESEARCH ARTICLE Effects of Version 2 of the International Sunspot Number on 10.1029/2018SW002080 Naïve Predictions of Solar Cycle 25 Key Points: • A new calibration of sunspot W. Dean Pesnell1 number must be used in solar cycle predictions 1Solar Physics Laboratory, NASA/Goddard Space Flight Center, Greenbelt, MD, USA • The climatological average is used to assess other predictions • Other simple predictions are not very accurate or useful Abstract The recalibration of the International Sunspot Number brings new challenges to predictions of Solar Cycle 25. One is that the list of extrema for the original series is no longer usable because the values of all maxima and minima are different for the new version of the sunspot number. Timings of extrema are Correspondence to: W. D. Pesnell, less sensitive to the recalibration but are a natural result of the calculation. Predictions of Solar Cycle 25 [email protected] published before 2016 must be converted to the new version of the sunspot number. Any prediction method that looks across the entire time span will have to be reconsidered because values in the nineteenth Citation: century were corrected by a larger factor than those in the twentieth century. We report a list of solar Pesnell, W. D. (2018). Effects of maxima and minima values and timings based on the recalibrated sunspot number. Naïve forecasts that version 2 of the International Sunspot depend only on the current values of the time series are common in economic studies. Several naïve Number on naïve predictions of Solar Cycle 25. Space Weather, 16. predictions of Solar Cycle 25, the climatological average (180 ± 60), two versions of the inertial forecast, and https://doi.org/10.1029/2018SW002080 two versions of the even-odd forecast, are derived from that table. The climatological average forecast is the baseline for more accurate predictions and the initial forecast in assimilative models of the Sun. It also Received 16 SEP 2018 provides the error estimate for Monte Carlo techniques that anticipate the long-term effects on the Accepted 15 NOV 2018 terrestrial environment. The other four predictions are shown to be statistically insignificant. Accepted article online 23 NOV 2018 Plain Language Summary We use predictions of the next sunspot cycle to plan satellite missions. The simplest prediction is the average of all previous peaks in the sunspot number. A new version of the sunspot number has been released, and this average must be recalculated. The new value predicts a peak sunspot number of 180 in Solar Cycle 25. 1. Introduction to Solar Cycle Predictions Long-term predictions of solar activity are an essential part of space weather forecasts. Two of the main sources of space weather effects at the Earth tend to track the sunspot number, solar flares (e.g., Temmer et al., 2001), and coronal mass ejections (Wang & Colaninno, 2014). A third source, high-speed streams from coro- nal holes, is not as well correlated with sunspot number, especially during the decline from solar maximum. This requires that a second quantity, such as a geomagnetic index, also be predicted. However, long-term pre- dictions of the geomagnetic indices can be created from the predictions of sunspot number and a stochastic component (Naasz et al., 2008). Satellite operators need to plan their missions years, if not decades, in advance. As a result, forecasters are asking for usable predictions of upcoming solar cycle(s) long before the anticipated maxima and leading to the development of many types of solar cycle predictions. Up through Solar Cycle 24, all solar cycle amplitude predictions had been made or reported using the International Sunspot Number, RZ. On 1 July 2015 the Solar Influences Data Center (SIDC) began serving version 2 of the International Sunspot Number, SN. It reconciled numerous inconsistencies in earlier calibrations of RZ, such as RZ being too small in the nineteenth century, that can be repaired by comparing the raw observations and deducing biases among the observers or by comparison with the geomagnetic measurements (Clette et al., 2014; Pevtsov et al., 2014; Svalgaard, 2012). Solar Cycle 24 was a below-average amplitude, peaking at an annual sunspot number of about 80 in RZ.A set of 105 predictions of the amplitude of Solar Cycle 24 ranged from zero to unprecedented levels of solar activity (Pesnell, 2016). The unweighted average of that compilation⟨ of⟩ predictions anticipated the amplitude Published 2018. This article is a US of the annual-averaged sunspot number for Solar Cycle 24 as R = 106 ± 31, slightly lower than the Government work and is in the public Z,24 domain in the USA. average value of previous cycles, but higher than the actual amplitude. There were peaks in the daily- and PESNELL 1 Space Weather 10.1029/2018SW002080 monthly-averaged sunspot number in the Northern Hemisphere in 2011 and in the Southern Hemisphere in 2014, another complication in the prediction of the amplitude that we do not consider here. At least 13 predictions of the amplitude of Solar Cycle 25 already exist. Some were from before 2016 and use RZ (Dumitrache, 2011; Helal & Galal, 2013; Javaraiah, 2015; Kakad, 2011; Lampropoulos et al., 2016; Li et al., 2015; Miao et al., 2015; Yoshida, 2014; Zachilas & Gkana, 2015). Others report their results with SN (Gkana & Zachilas, 2016; Pesnell & Schatten, 2018) or as fractions of previous activity (De Jager & Duhau, 2012; Figure 1. Version 2 of the International Sunspot Number as a function of time since 1750. The solid blue line is the 13-month running average of the Shepherd et al., 2014). The Solar Cycle 25 prediction of Helal and Galal values (sampled monthly) and the “+” symbols show the monthly values. (2013) used RZ to derive a correlation between the number of spotless days and the rise time. But Pesnell (2012) showed that the number of spotless days in SN was a more accurate precursor. Because RZ is no longer officially produced, this lack of consistency in what is reported in a prediction complicates the comparison and use of those predictions. Dynamo models rarely predict the sunspot number. They often report the factor by which the next cycle will increase (Cameron et al., 2016; Hathaway & Upton, 2016) or decrease (Upton & Hathaway, 2018) over the current one. Therefore, they are less sensitive to the nominal values of SN —unless they calibrated the model on the sunspot number. Naïve forecasts use information from the near past without further adjustment or attempting to determine causality. They provide an easily calculated benchmark to compare with more sophisticated predictions. Naïve predictions are used by businesses as well as scientists. Examples include saying that tomorrow’s weather will be the same as today’s and next month’s sales of winter coats will be the same as this month’s. Both of these forecasts may ignore possible trends and seasonal effects but provide the information needed to judge other forecasts. We will describe the impact of SN on several simple solar cycle predictions. A brief description of SN will be presented, followed by a table of extrema to be used in solar cycle predictions. Five naïve predictions that can be derived from that table will then be discussed. 2. The New Calibration of the International Sunspot Number The sunspot number is a unique reference for solar dynamo models, climate studies, and solar activity predic- tions as well as a direct proxy of magnetic flux emergence (Stenflo, 2012). The original International Sunspot Number (RZ, also called version 1) was measured, derived, and recorded for over 300 years. It was created in 1849 by R. Wolf of the Zurich Observatory as an index that combined the total number of observed spots (S) and the number of groups of spots (G)∶RZ = 10G + S. Observer-dependent correction factors were intro- duced, as successive observers became the curators of the data series. Since 1981, RZ has been produced at the World Data Center for Sunspot Index and Long-term Solar Observations in Brussels using a worldwide network of over 85 stations. Daily values of RZ exist since 1818, monthly means since 1749, and annual aver- ages since 1700. The time dependences of the 13-month running mean, sampled at monthly intervals, and the monthly-averaged SN are shown in Figure 1. The reliability of RZ had long been questioned because a reanalysis of the data gave differing long-term trends (Hoyt & Schatten, 1998a, 1998b). From 2011 to 2015 a community effort led to the first recalibration of the entire series (Clette et al., 2014; Pevtsov et al., 2014; Svalgaard, 2012). Version 2 of the International Sunspot Number (SN) has been produced since 1 July 2015 and is the recommended version of the International Sunspot Number. The largest change is the renormalization of new version that makes SN 40–70% larger than RZ. This increase is particularly problematic when predicting the next solar cycle because the renormalization changes with time. The largest change was to drop the 0.6 normalization, effectively multiplying the entire series by 1∕0.6 ≈ 1.67. After 1947 the scaling factor changes from 1.67 to 1.42. Details of these and other changes are in Clette and Lefèvre (2016). The RZ data set was the basis of the many predictions of the amplitudes of Solar Cycles 21–24 (Pesnell, 2016). A recalibration of such a well-analyzed data set requires any existing prediction algorithm be thoroughly reconsidered for Solar Cycle 25.
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