https://www.sciencedirect.com/science/article/pii/S0273117723006828
Abstract
The Committee on Space Research (COSPAR) is updating its Roadmap on Space Weather. As input for this update, the COSPAR International Space Weather Action Teams (ISWAT) were asked to provide an overview of the current state-of-the-art and advancements since the last Roadmap (Schrijver et al., 2015), identifying gaps and opportunities for moving forward within the next 5 years — based on ongoing and planned missions, available modeling, and observational capabilities — and presenting an outlook beyond 5 years and recommendations on reaching long-term goals. While space weather is typically associated with short-term solar activity, knowledge of past solar variability observed and recorded through various parameters, including historical space weather events, informs us about the range of possible solar fluctuations. This long-term solar variability, belonging to the domain of space climate, is the prime focus of the ISWAT S1 Cluster. The goal of this paper is to describe the key objectives of the three S1 Action Teams, summarize the current state of knowledge of the topic that each team is focusing on, and identify the key science gaps that need to be addressed in each area.
Keywords
1. Introduction
Life on Earth has been in existence from about 3.7–3.8 billion years ago (e.g., Dodd et al., 2017). Over that time period, the solar total luminosity has increased by about 25% as compared to the modern-day Sun (e.g., Ribas, 2009). By comparison, in modern days the total solar luminosity changes by about 0.1% over the 11-year sunspot cycle, indicating a very stable energy flow from the Sun to Earth — a prerequisite for a stable climate. Recent studies of stellar evolution also indicate that the early Sun may have exhibited cycle variability similar to that of modern times, albeit with larger amplitudes and different periods. Studies of solar analogs also suggest that after a star (Sun) enters the main sequence its magnetic activity gradually decreases until it is 6–7 Myr old (e.g., Lorenzo-Oliveira et al., 2018). Thus, the magnetic activity on the Sun will continue similar to what we see today for another 1–2 Myr. Occasionally, this regular cycle variability is interrupted by extended periods of grand minima, the nature of which is still poorly understood. Thus, it is important to understand how solar activity is changing over long periods of time, what conditions affect the long-term variability and trigger the grand minima, and whether these changes could be predicted. The leading polarity of sunspot groups reverses between two consecutive cycles and, in relation to this, the polar fields change polarity shortly after the maximum of each sunspot cycle. This results in a 22-year magnetic (Hale) solar cycle. An analysis of solar activity reveals several other long-term periodicities, the strongest of which are the 90–100 years Gleissberg cycle (Gleissberg, 1939; see Hathaway, 2015 for a review) seen in sunspots and a quasi-210-year variability (sometimes referred to as Suess or de Vries cycle — see Usoskin, 2023) observed in cosmogenic isotopes time series (Suess, 1980, Hathaway, 2015). A 210-year variability may play a role in the recurrence of grand minima (e.g., Tlatov and Pevtsov, 2017, Usoskin, 2023). The signature of all these periodicities is evident in many astrophysical channels, such as galactic cosmic rays (GCRs) and gamma-rays. Hale cycles directly affect the transport of GCRs in the heliosphere by changing the large-scale drift patterns (see, e.g., Rankin et al., 2022 and Engelbrecht et al., 2022), producing a second-order effect in GCR modulation. Interactions of modulated GCRs with the solar photosphere and corona produce high-energy gamma-rays from the solar disk and halo, whose intensity is thus modulated over the solar cycle (see, e.g., Linden et al., 2022 and Gutiérrez et al., 2022). The Sun also blocks high-energy GCRs, generating the so-called Sun’s shadow, a depletion in the intensity of high-energy GCRs coming from the direction of the Sun. This shadow can be used to constrain coronal magnetic field models and it has been shown to vary with the 11-year and 22-year solar cycles (see, e.g., Amenomori et al., 2000, Tjus et al., 2020, Aartsen et al., 2021). Most likely, the modulation of the Sun’s shadow and high-energy gamma-rays is also present for longer periodicities. However, observations of these channels started only in the last couple of decades, so they have not been yet detected.
The Committee on Space Research (COSPAR) International Space Weather Action Teams (ISWAT) is a global center for community-coordinated collaborations to address challenges across the field of space weather. ISWAT activities are organized by Clusters and the Action Teams within Clusters. Cluster S1 focuses on reconstructing and establishing the ranges of variations of past solar activity, evaluating the extreme space weather and space climate episodes and, when possible, their impacts, helping to assess predictive models of solar activity ranging from solar dynamo and surface flux transport models to coronal and heliospheric field evolution models, and transition validated data-driven computational models to operational space weather (and space climate) forecasting tools. The Cluster is organized into three action teams: S1-01: Long-term solar variability; S1-02: Worst-case scenario for extreme solar events; and S1-03: Data sets of historical observations of solar and geomagnetic activity. In this paper, the leads of each Action Team and the research community were asked to provide a review of the current understanding of the research field, identifying the key outstanding questions and the current knowledge gaps, as well as the future research developments.
In Section 2 we review the observational and modeling aspects of sunspot time series, solar total and spectral irradiance, large-scale heliospheric parameters, and extreme solar events. Section 3 discusses the physical understanding of long-term variability, including solar cycle predictions and forcing on planetary environments. Section 4 addresses historical data preservation. Section 5 outlines the importance of continuity of long-term observations of the Sun, and Section 6 summarizes our review and high-level recommendations. Specific recommendations for each sub-field are provided at the end of each Section. A includes a list of acronyms used throughout the text.
2. Observational and modeling/theoretical aspects
Solar variability can be probed through the observation of various parameters available in different periods (length of the raw dataset) and timescales. The longest time series available is an index based on the number of spots and sunspot groups that appear on the Sun, called the sunspot number (Owens, 2013): it is available from the beginning of the 17th century. It enables the study of the long-term behavior of the Sun. Other solar-activity indices include, e.g., the radio flux F10.7 (Tapping and Charrois, 1994), coronal index, etc. The spectral solar irradiance and the total solar irradiance have been directly measured since 1978 (see, e.g., Ermolli et al., 2013, Kopp, 2016, DeLand et al., 2019 and references therein) and can be modeled before that using other long-term parameters, e.g., sunspot (areas, positions, sunspot number) and plage (from white-light or Ca II K images) observations or cosmogenic isotope data (Solanki et al., 2013). The solar wind and heliospheric magnetic field have been directly measured by satellites for only a few decades (since late 1950s – early 1960s, Obridko and Vaisberg, 2017), but can be modeled over centuries and millennia through proxy data sources, e.g., cosmogenic radionuclides. They are used, for example, to understand and anticipate solar eruptive events. Here we discuss only extreme solar events because of the focus on longer timescales of the ISWAT S1 Cluster.
In this section we present the available observations, their compilation, and different modeling approaches used to extend these observations back in time and/or through data gaps, and to estimate their uncertainties.
2.1. Sunspot number time series
2.1.1. Historical compilation of sunspot observations
The sunspot number (SN, Clette et al., 2014, Clette et al., 2015, Clette and Lefèvre, 2016) and group number (GN, Hoyt and Schatten, 1998a, Hoyt and Schatten, 1998b, Svalgaard and Schatten, 2016, Usoskin et al., 2016, Chatzistergos et al., 2017) are time series (1610 – present) that trace solar activity over more than 400 years. SN was computed in near real-time by Rudolf Wolf (1816–1893, Friedli, 2016) and his successors from 1849 onward and is a compilation of records from 1700 to 1848 (Friedli, 2016, Bhattacharya et al., 2023). Today it is produced and maintained by the WDC-SILSO1), since its transfer from Zürich to Brussels in 1981. For SN, the original formula of Rudolf Wolf for the daily sunspot number of a single observer is given by (Wolf, 1851, Wolf, 1856):where G is the number of sunspot groups on the solar disk on a given day, S denotes the total number of individual spots within those groups, and k is a normalization factor that brings different observers to a common scale (k is the time-averaged ratio of daily SN of the primary reference observer to that of a secondary observer). Because there were about 10 spots per group on average in the nineteenth century, i.e., (Waldmeier, 1968, Clette et al., 2014), the two parameters have about equal weight in SN. Despite its simplicity, historical SN time series may be non-uniform. Thus, for example, early observations by Wolf did not include small sunspots. In 1947, Waldmeier introduced unequal weights to sunspot counts to reflect their size and presence/absence of penumbra (Svalgaard et al., 2017). These non-uniformities are corrected in the most recent SN time series.
On the other hand, the group number series GN, built from the available raw source data, was compiled in 1995 (Hoyt and Schatten, 1998a, Hoyt and Schatten, 1998b). Its undeniable advantage is that it goes back to the first telescopic observations in 1610 (Vaquero and Vázquez, 2009, Arlt and Vaquero, 2020), and it is easier to compute with respect to SN. Because of the amount of work necessary to actually gather and compile all the data required for SN calculations, Hoyt and Schatten, 1998a, Hoyt and Schatten, 1998b created an index based on the number of groups which does not take the number of individual spots into account. In order for GN to be comparable with SN, Hoyt and Schatten, 1998a, Hoyt and Schatten, 1998b introduced a linear relationship where GN has to be multiplied by 12.08 to reach the level of SN. We note, however, that the approach of using SN as a proxy for GN needs to be taken with caution because the number of sunspots per group varies with phase of the solar cycle (e.g., Tlatov, 2013). Georgieva et al. (2017) devised a correction function for the GN series that takes into account the changes in the number of sunspots per sunspot group from 1700–2017.
2.1.2. Observational data compilation
SN is a time series built by aggregating data from a number of observers with different quality and/or methods over a very long period of time (hundreds of years). The factor k mentioned above is the key to assembling all data, but as the methods and understanding of the physics of the Sun evolved, so did the compilation methods.Table 1
Table 1. Key questions the S1 Action Teams are focused on.
| Question | Team | On the Web | Sections |
|---|---|---|---|
| What are the reliable estimates of long-term solar variability, based on proxy data, including uncertainty assessments? | S1-01 | Link | 2.1 Sunspot number time series, 2.2 Solar total and spectral irradiance time series, and 2.3 |
| Nature of the extreme events: What is an extreme event? How often can they occur? Does the Sun have a limit in producing extreme events? | S1-02 | Link | 2.4 |
| What is a comprehensive inventory of solar and geomagnetic datasets relevant for long-term space weather and space climate research; a standardized method for processing and preservation of historical data, their quality, and current state? What resources are needed to preserve these critical datasets? | S1-03 | Link | 4 |
| Physical understanding of long-term variability and its consequences | S1-01, S1-02 | 3 |
Table 2 presents the evolution of the compilation methods of SN over time in Zürich and Brussels. From 1700 to 1848, SN was compiled from historical sources that can be found in the journals compiled by Rudolf Wolf (Wolf, 1848). From 18482 to 1876, Wolf used only his observations with gaps filled in by secondary observers (Bhattacharya et al., 2021, Bhattacharya et al., 2023). From 1877, Wolf introduced averaging of several observers for each daily observation.
Table 2. Key dates in sunspot number observations from 1700. Before 1848, the number of observations per day was highly variable. For that period, information can be found in Hoyt and Schatten, 1998a, Hoyt and Schatten, 1998b, Vaquero et al., 2016, Svalgaard and Schatten, 2016, Cliver, 2016. Table adapted from Dudok de Wit et al. (2016).
continued at https://www.sciencedirect.com/science/article/pii/S0273117723006828
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