Zhang et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-021-00426-7(2021) 8:56

REVIEW

Progress in Earth andPlanetary Science

Open Access

Earth-affecting solar transients: a review ofprogresses in solar cycle 24
Jie Zhang1* , Manuela Temmer2, Nat Gopalswamy3, Olga Malandraki4, Nariaki V. Nitta5, Spiros Patsourakos6,
Fang Shen7, Bojan Vršnak8, Yuming Wang9, David Webb10, Mihir I. Desai11,12, Karin Dissauer2,13, Nina Dresing14,15,
Mateja Dumbović8, Xueshang Feng7, Stephan G. Heinemann2,16, Monica Laurenza17, Noé Lugaz18 and
Bin Zhuang18

Abstract
This review article summarizes the advancement in the studies of Earth-affecting solar transients in the last decadethat encompasses most of solar cycle 24. It is a part of the effort of the International Study of Earth-affecting Solar
Transients (ISEST) project, sponsored by the SCOSTEP/VarSITI program (2014–2018). The Sun-Earth is an integrated
physical system in which the space environment of the Earth sustains continuous influence from mass, magnetic
field, and radiation energy output of the Sun in varying timescales from minutes to millennium. This article
addresses short timescale events, from minutes to days that directly cause transient disturbances in the Earth’s
space environment and generate intense adverse effects on advanced technological systems of human society.
Such transient events largely fall into the following four types: (1) solar flares, (2) coronal mass ejections (CMEs)
including their interplanetary counterparts ICMEs, (3) solar energetic particle (SEP) events, and (4) stream interaction
regions (SIRs) including corotating interaction regions (CIRs). In the last decade, the unprecedented multi-viewpoint
observations of the Sun from space, enabled by STEREO Ahead/Behind spacecraft in combination with a suite ofobservatories along the Sun-Earth lines, have provided much more accurate and global measurements of the size,
speed, propagation direction, and morphology of CMEs in both 3D and over a large volume in the heliosphere.
Many CMEs, fast ones, in particular, can be clearly characterized as a two-front (shock front plus ejecta front) andthree-part (bright ejecta front, dark cavity, and bright core) structure. Drag-based kinematic models of CMEs aredeveloped to interpret CME propagation in the heliosphere and are applied to predict their arrival times at 1 AU in
an efficient manner. Several advanced MHD models have been developed to simulate realistic CME events from theinitiation on the Sun until their arrival at 1 AU. Much progress has been made on detailed kinematic and dynamic
behaviors of CMEs, including non-radial motion, rotation and deformation of CMEs, CME-CME interaction, andstealth CMEs and problematic ICMEs. The knowledge about SEPs has also been significantly improved. An outlookof how to address critical issues related to Earth-affecting solar transients concludes this article.
Keywords: Coronal mass ejection, Interplanetary coronal mass ejection, Solar energetic particle, Corotatinginteraction region, Flare, Corona, Sun, Geomagnetic storms, Space weather* Correspondence: jzhang7@gmu.edu
1
Department of Physics and Astronomy, George Mason University, 4400
University Dr., MSN 3F3, Fairfax, VA 22030, USA
Full list of author information is available at the end of the article© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

1 Introduction
Earth-affecting solar transients refer to a broad range ofenergetic and/or eruptive events occurring on the Sunthat have direct effects on the space environment near
the Earth and cause adverse space weather impact on
advanced technological systems of human society. They
occur near the Sun on timescales of minutes to hours,
and the resulting effects on the Earth can take place inminutes to days. These transient events are commonly
categorized into four different types: (1) solar flares, (2)coronal mass ejections (CMEs) and their interplanetary
counterparts, Interplanetary CMEs (ICMEs), (3) solar
energetic particle (SEP) events, and (4) stream interaction regions (SIRS) including corotating interaction
regions (CIRs). These four types of Earth-affecting transient events differ in their observational appearances,
physical origin or processes, as well as the geoeffectiveness in their own unique ways (Table 1). Other energetic
events on the Sun, such as filament eruptions, coronaldimmings, waves, etc., can be usually treated as an associated phenomenon with solar flares and/or CMEs. In
the following, we briefly introduce the definition of these
phenomena and their possible geoeffectiveness, alongwith selected review articles that discuss in depth these
phenomena. The detailed review of these phenomena,
including theoretical interpretations and numerical modelings, is given in the subsequent sections of this article.
1.1 Solar flares

Solar flares are probably the oldest transientphenomenon ever observed on the Sun. They were first
discovered as a flash in white light by Carrington (1859)and Hodgson (1859) when observing sunspots. In themodern era, solar flares are observed as sudden enhancement in electromagnetic radiation over a broad

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range of wavelengths including radio, visible light, EUV,
X-rays, and gamma rays. The radiation energy released
during a flare is about 1028 to 1032 ergs during a timescale of minutes to hours. It is well accepted that magnetic reconnection in a configuration of current sheet isthe central mechanism that converts free magnetic
energy in the corona into particle acceleration andplasma heating, producing a solar flare. Shibata andMagara (2011) provided a review on solar flares with afocus on the theoretical magnetohydrodynamic process.
Fletcher et al. (2011) made a review on solar flares froman observational point of view. Hudson (2011) discussed
flares from the perspective of their global properties.
Note that it is now well known that the process of flaresis strongly coupled with that of CMEs (Harrison 1995;
Zhang et al. 2001; Temmer et al. 2008). Therefore, any
further discussion on the origin of flares will be includedin the discussions on the origin of CMEs, which will be
extensively reviewed in this article. Very often, the term
solar eruptions is used to refer to transient and largescale energy release on the Sun, and a solar eruption
contains both flare and CME, along with other associated phenomena, such as coronal dimmings and global
coronal waves, etc.
While the total electromagnetic radiation, or irradiance from the Sun, is nearly a constant with an amplitude of approximately 0.1% over the 11-year solar cyclethat affects the long-term climate of the Earth (Lean
1991), the EUV and X-ray irradiances during solar flares
can increase by many folds to orders of magnitude. One
space weather effect of solar flares is from EUV radiationin particular the Lyman-alpha radiation at 121.6 nm
wavelength absorbed in the Earth’s upper atmospherecausing its instantaneous heating and expansion, which
results in a sudden drag and lowering of low-orbiting

Table 1 Four types of Earth-affecting solar transients and their key physical processes and geoeffectiveness
Earthaffecting
solartransients

Key physical processes

Effects on near-earth spaceenvironment

Effects on technological system and life

Solar flares Magnetic reconnection; ideal MHD instability;
particle acceleration; plasma heating

Disturbances in the ionosphere;
heating and expansion of upper
atmosphereHigh-frequency radio communication; satellite
drag (Earth climate from long-term variationof solar irradiance)

CMEs and
ICMEs

Ideal MHD instability; flux rope formation; shock
formation; particle acceleration; aerodynamic drag;CME-CME interaction; magnetic reconnection

Geomagnetic storms; substorms;
disturbances in the ionosphere;
ionosphere scintillations; radiation
belt storms

GPS systems and navigation; satellite
communication; high-frequency radio communication; electric power transmission; satellite degradation and failure (single event
upset; dielectric material charging and discharging; surface charging); radiation hazards toastronauts; radiation hazards to aircraft crew
and passengers

SEPs

Particle acceleration; injection; propagation;
turbulence

Particle radiation storms

Satellite degradation and failure; highfrequency radio communication; radiation
hazards to astronauts; atmospheric chemistry

SIRs/CIRs

Stream interaction; particle acceleration

Substorms; geomagnetic storms

Similar to CMEs to a lesser extent

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

satellites (Schwenn 2006). Enhanced X-ray emissions
from solar flares can penetrate to the bottom of theionosphere and create an enhancement in the electron
content, which may affect high-frequency radio
communication.
1.2 CMEs and ICMEs

Since the discovery of solar flares by Carrington in 1859,
it had long been conceived that there was a cause andeffect relation between solar flares and geomagnetic activities on the Earth. Only starting from the 1980s, it became clear that the only type of solar transients that hasa clear cause-effect relation to geomagnetic activity lies
in CMEs, not in flares (e.g., Schwenn 1983; Sheeley Jr etal. 1985; Gosling 1993; Reames 1999; Zhang et al. 2007).
It is now well accepted that CMEs are the solar transients that have the most profound effect on the spaceenvironment and inflict the most adverse space weather
effect (Gopalswamy 2016).
CMEs are transient and energetic expulsion of massand magnetic flux on a large scale from the low corona into interplanetary space. While the basic configuration of shock-driven magnetic structures from theSun has been proposed to explain geomagnetic storm
sudden commencement (Gold 1962) and various typesof non-thermal solar radio bursts (Fokker 1963),
CMEs were first directly imaged in white light fromspace by the OSO-7 coronagraph in the early 1970s(Tousey 1973). The speed of CMEs in the outer corona ranges from ~ 100 km/s to ~ 3000 km/s at maximum with an average speed from 300 to 500 km/s
depending on the phase of the solar cycle (Yashiro etal. 2004). Mass of CMEs is mostly in the range from1013 to 1016 g with a peak value at 3.4 × 1014 g, andtheir kinetic energy is mostly between 1027 to 1032
ergs with a peak value at 8.5 × 1029 ergs (Vourlidas
et al. 2010). CMEs reach their peak speed in the time
range from minutes to hours with a median value at~ 54 min (Zhang and Dere 2006).
The following is a list of review articles on CMEs,
or more generally in solar eruptions, in the last decade. Schrijver (2009) reviewed the drivers of major
solar flares and eruptions with a focus on flux emergence and its interaction with the ambient field. Chen
(2011) provided a comprehensive overview of theoretical models and their observational basis of CMEs.
The review of Webb and Howard (2012) focused theobservational aspects of CMEs. Schmieder et al. (2015)
further reviewed on observational perspectives of flareCME models. Gopalswamy et al. (2016) reviewed major
discoveries on CMEs observed by spaceborne coronagraphs that show the growing significance of CMEs as theprimary source of severe space weather. More recently,
reviews were made focusing on magnetic structures ofPage 3 of 102

solar eruptions, from the perspective of magnetic flux
rope (Cheng et al. 2017) and modeling of magnetic field(Guo et al. 2017). Chen (2017) made a review on thephysics of erupting solar flux ropes in the aspects of boththeory and observation. The origin, early evolution andpredictability of solar eruptions were recently reviewed by
Green et al. (2018).
The recent review of Patsourakos et al. (2020) discusses the formation and the nature of the pre-eruptive
magnetic configuration of CMEs. We would also like topoint out the following earlier reviews on CME models(Forbes 2000; Klimchuk 2001; Lin et al. 2003). Most recently, Lamy et al. (2019) made an extensive review on
statistical properties of CMEs covering two complete
solar cycles 23 and 24. Gopalswamy et al. (2020b) also
reviewed how CME properties varied with the solar
cycle, taking advantage of the availability of uniform andextensive observations made over two complete solar
cycles.
Following the ejection from the corona, a CMElargely maintains its magnetic configuration or topology that is well organized by a twisted magnetic flux
rope, thus is able to continuously propagate outward
through the heliosphere to a large distance, interactingwith the ambient solar wind and impacting planets
along its path. Its counterpart in the heliosphere iscalled interplanetary CME (ICME). Howard and Tappin(2009) reviewed the theory of ICMEs observed in the
heliosphere. Rouillard (2011) provided a short review
relating white light CMEs near the Sun and in situ
ICMEs. Zhao and Dryer (2014) summarized the status of
CME/shock arrival time prediction to that date. The
physical processes of CME/ICME evolution are reviewed
in Manchester et al. (2017). Lugaz et al.’s (2017) review
focused on the interaction of successive CMEs. Shenet al. (2017) also reviewed on CME interaction with afocus on analyzing the physical nature of the interaction. More recently, Vourlidas et al. (2019) made an
overview of predicting the geoeffectiveness properties
of CMEs, including current status, open issues, and apath forward. The review by Kilpua et al. (2019) focusedon the forecasting of magnetic structure and orientationof CMEs. The most recent review on ICMEs was by
Luhmann et al. (2020).
Besides the ejecta or magnetic flux rope component,a fast CME is capable of driving a wider shock ahead
and forming a thick sheath region between the eject
front and the shock front. While the shock is the main
source of solar energetic particles (SEPs), the sheath,like the magnetic flux rope, is also an important transient structure for causing geomagnetic storms. The
properties and importance of shock, sheath regions, aswell as CME ejecta, are viewed in (Kilpua et al. 2017;2017a).

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

ICMEs passing the Earth can significantly distort andenergize the Earth’s magnetosphere and generate a cascade of effects in the different layers of the Earth’s spaceenvironment, including in the magnetosphere, radiation
belt, ionosphere and upper atmosphere, and even in thelithosphere. These are collectively defined as space weather. The term space weather refers to conditions on the
Sun and in the solar wind, magnetosphere, ionosphere,
and thermosphere that can influence the performanceand reliability of space-borne and ground-based technological systems and that can affect human life and health(Schwenn 2006). The terrestrial perspective and impactsof space weather are summarized in Pulkkinen (2007).
Space weather effects in the Earth’s radiation belts wererecently reviewed in Baker et al. (2018). CMEs can cause
extensive ionospheric anomalies (e.g., Wang et al. 2016a,b, c, d), and disturbances in the atmosphere-ionosphere
coupling system (Yiğit et al. 2016). The cradle to grave
process of some extreme space weather events is outlined in Riley et al. (2018a). Bocchialini et al. (2018)studied a large number of storm sudden commencement(SSCs), traced their origin to solar sources, identified
their effects in the interplanetary medium, and investigated the corresponding response of the terrestrial
ionized and neutral environment.
The impacts due to severe space weather storms
caused by CMEs/ICMEs on technological systems areprofound (Lanzerotti 2017). These impacts include
hazards to astronauts, satellite degradation, and failure
through single event upset. dielectric material charging and discharging and surface charging, error andfailure in GPS navigation, effects on satellite communication, effects on high-frequency communication,effects on power grids and aviation, etc. The potential
catastrophic societal effect of the May 1967 greatstorm is revisited by Knipp et al. (2016). The economic
impact of space weather is reviewed and analyzed in
Eastwood et al. (2017).
1.3 SEPs

Solar energetic particle events are enhancements of electrons, protons and heavy ion fluxes observed in the
heliosphere related to both solar flares and CMEs. SEP
events present energy spectra that span more than six
orders of magnitude, from a few keV superthermal to
GeV relativistic energies.
High energy particles from the Sun were first observed
as a sudden increase in intensity in ground-based ion
chambers and neutron monitors during large solar flares(Forbush 1946). Such ground-level enhancements (GLEs)
consist of the strongest set of SEPs events that are mostly
detected from space. For half a century following its discovery, it was generally assumed that energetic particles
originated from solar flares, i.e., the point-like source inPage 4 of 102

time and space. However, in the 1990s, it became clearthat there are two types of SEPs: impulsive type and gradual type, whose source is of impulsive flares on the Sun
and of large-scale long-lasting shocks driven by CMEs, respectively (Reames 1999). The gradual SEPs typically lastfor several days, while the impulsive events only last for a
few hours. It is now believed that CME-driven coronaland interplanetary shocks are the most prolific producersof SEPs that pose radiation hazards for our environmentand our assets on Earth and in space. Particle enhancements accompanying CME-driven interplanetary shocks
that are passing near the Earth are known as energetic
storm particles or ESP events, because they are often
associated with “Sudden Storm Commencements.”
Besides the seminal review paper that summarized theparadigm shift on the origin of SEPs in (Reames 1999), a
series of review papers were also published in the last
decade (Reames 2013, 2015, 2018, 2020). Reames (2013)provided a comprehensive account on the two sourcesof solar energetic particles, which highlighted the early
evidence from fast-drifting type III and slow-drifting
type II solar radio emissions (Wild et al. 1963). Reames(2015) focused on element abundances and source
plasma temperatures of SEPs. Reames (2018) extended
the topics including abundances, ionization states, temperatures and FIP (First Ionization Potential) in SEPs.
Most recently, Reames (2020) categorized SEPs into fourbasic populations and discussed the four distinct pathways, to account for the mixture of SEPs from pure impulsive and pure gradual events. Desai and Giacalone(2016) provided a comprehensive review on large gradual SEP events to date. Klein and Dalla (2017) also madea review on the acceleration and propagation of SEPs.
More recently, a set of 10 review papers on SEPs are collected in a published book (Malandraki and Crosby
2018b), which built upon the 2-year HESPERIA (High
Energy Solar Particle Events Forecasting and Analysis)
project of the EU HORIZON 2020 program.
Like flares and CMEs, it is apparent that SEPs pose athreat to modern technology strongly relying on spacecraft and are a serious radiation hazard to humans inspace (Jiggens et al. 2014; Malandraki and Crosby
2018a). High energy charged particles have been foundto have damaging impacts on various components ofspacecraft, including instruments, electronic components, solar arrays etc. SEPs also effect signal propagation between Earth and satellites due to Polar Cap
Absorption (PCA) which results from intense ionizationof the D-layer of the polar ionosphere. In the instances
when SEP events reach aviation latitudes, they are also a
concern for human health as the radiation dose received
can increase. This applies specifically to high latitude
flights and polar routes for commercial aviation. It canbe a risk for frequent flyers and for aircrew. SEP

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

forecasting is relied upon to mitigate against the effects
SEP events.
1.4 SIRs/CIRs

The solar wind reveals long-term, and most often
periodic, variations in terms of high speed solar windstreams that may lead to geomagnetic storms. The
solar wind also transports short-term disturbances,such as CMEs. Investigating the solar wind is therefore of crucial interest for Space Weather forecasting.
The interplay between fast and slow solar wind causes
stream interaction regions (SIRs). SIRs are related tocoronal holes (CHs), long-lived regions on the Sun
with predominantly open magnetic field. Due to thequasi-stationary location of low-latitude CHs, theinteraction of high and slow speed solar wind streams
results in a compression of plasma and magnetic fieldthat occurs at certain distance from the Sun. As the
Sun rotates, recurring SIRs are referred to as corotating interaction regions (CIRs).
Recent reviews on solar wind high speed streams are
given by Cranmer, Gibson, and Riley et al. (2017); Living
review by Richardson (2018) on SIRs and corona andsolar wind by Cranmer and Winebarger (2019). Cranmer
et al. (2017) gives an overview of the community’s recent
progress and understanding of two major problems associated with HSSs: the coronal heating and the acceleration of the fast and slow solar wind. They discuss
recent observational, theoretical, model and forecasting
techniques with a positive forecast to the future. The
review by Richardson (2018) focuses entirely on theinteraction of slow and fast solar wind leading to the formation of SIRs and discusses the acceleration processesof energetic particles in stream-stream interaction regions, modulation of the galactic cosmic ray count,
resulting geomagnetic disturbances as well as MHD
modeling results. The very recent review by Cranmer
and Winebarger (2019) gives detailed insight into highresolution observations of the solar corona as well as 3D
numerical simulations which hint towards small-scaleentangled, twisted, and braided magnetic fields. These
processes, which may lead to reconnection, are, despite
their limitations, thought to be a main source of heating
the solar corona.
The geoeffectiveness and Space Weather impact of
SIRs/CIRs can be observed as variations in the Earth’s
magnetosphere, ionosphere, and even neutral density
in the thermosphere. The solar wind delivers significant energy that causes various Space Weather phenomena. CIR-related storms are more hazardous tospace-based assets, particularly at geosynchronous
orbit compared to CMEs, because CIRs are of longer
duration and have hotter plasma sheets causing astronger spacecraft charging (Borovsky and Denton

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2006). Details on the geoeffectiveness of SIRs/CIRs
can be found in recent reviews and statistical papers(Kilpua et al. 2017; Vršnak et al. 2017; Yermolaev etal. 2018).
1.5 ISEST projectThis review article is part of the collective effort madeby the International Study of Earth-affecting Solar Transients (ISEST) project, which is one of the four research
projects of the Variability of the Sun and Its Terrestrial
Impact (VarSITI)) program, sponsored by the ScientificCommittee on Solar-Terrestrial Physics (SCOSTEP) forthe period of 2014 – 2018. The VarSITI program issummarized in a companion article (Shiokawa and Katya
2020 to be added). The stated overarching goal of the
ISEST project is to understand the origin, propagation,and evolution of solar transients through the space between the Sun and the Earth, and develop the prediction
capability of space weather. Toward this goal, the ISEST
project has organized four dedicated workshops in threedifferent geographic locations across the globe: 17 – 20
June 2013 in Hvar, Croatia, 26 – 30 October 2015 inMexico City, Mexico, 18 – 22 September 2017 in Jeju,
South Korea, and 24 – 28 September 2018 again in
Hvar, Croatia. The ISEST project maintains a standing
website for hosting event catalogs, data, and presentations, and offers a forum for discussion at http://solar.
gmu.edu/heliophysics/index.php/Main_Page.
The ISEST project has resulted in a Topic Issue inthe journal of Solar Physics with a collection of 32
articles (Zhang et al. 2018a); this collection is thenconverted to a published book (Zhang et al. 2018b).
A similar but earlier project in the CAWSES-II era,
Climate and Weather of the Sun-Earth System of
SCOSTEP (2010-2014), is the project of “Short-term
variability of the Sun-Earth system”; the summary of
the activity from 2010-2014 is in Gopalswamy et al.
(2015c).
The implementation of the ISEST project is centeredaround several working groups, which are (1) data, (2)theory, (3) simulation, (4) campaign study, (5) SEP, (6)Bs challenge, and (7) MiniMax24 campaign. The sections of this article largely contain the contribution ofthese seven working groups, respectively. We organize
the articles as follows. Section 2 is on observational progress on CMEs and ICMEs. Section 3 is on theoreticalprogress on CMEs and ICMEs, while Section 4 summarizes the progress in simulation studies of CMEs and
ICMEs. Campaign-style studies are reviewed in Section
5. SEP studies are reviewed in Section 6. Section 7 reviews stream interaction regions. Forecasting CMEs arereviewed in Section 8. Section 9 summarizes the activityof MiniMax24 campaign. The conclusion and outlook
are in Section 10.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

2 Progress in observations of CMEs/ICMEs
2.1 Introduction

The capacity of observing and studying the solarterrestrial system has increased dramatically during solar
cycle 24. We can observe and track solar eruptions
nearly continuously in time and space from Sun to
Earth, thanks to a large set of sensitive remote-sensing
and in situ instruments onboard a fleet of spacecraft.
These include the Solar Terrestrial Relations Observatory Ahead/Behind (STEREO A/B) (twin spacecraft
launched in 2006; drifting along the Earth orbit) (Kaiser
et al. 2008), the Solar Dynamics Observatory (SDO)(geosynchronous orbit; launched in 2010) (Pesnell et al.
2012), the Solar and Heliospheric Observatory (SOHO)(L1 point; launched in 1995) (Domingo et al. 1995),
Hinode mission (polar orbit of Earth; launched in 2006)(Kosugi et al. 2007), the Advance Composition Explorer(ACE) (L1 point; launched in 1997) (Smith et al. 1998),
Wind spacecraft (L1 point; launched in 1994) (Ogilvie etal. 1995), and other space-based spacecraft and groundbased observatories.
In particular, the SECCHI (Sun Earth Connection Coronal and Heliospheric Investigation) suite onboard STER
EO comprised five imaging telescopes, which together
observe the solar corona from the solar disk to beyond 1
AU; these telescopes are EUVI (Extreme Ultraviolet
Imager: 1–1.7 Rs), COR-1 (Coronagraph 1: 1.5–4 Rs),
COR-2 (Coronagraph 2: 2.5–15 Rs), HI-1 (Heliospheric
Imager 1: 15–84 Rs, or 4–24° in elongation angle) andHI-2 (Heliospheric Imager 2: 66–318 Rs, or 19–89° in
elongation angle ) (Howard et al. 2008). Two recent missions dedicated to solar and heliospheric physics are theParker Solar Probe (PSP) (varying elliptical orbit aroundthe Sun with perihelia < 10 Rs; launched in 2018) (Foxet al. 2016) and the Solar Orbiter (highly elliptical andinclined orbit around the Sun; launched in 2020) (Müller
et al. 2020). These missions will provide observations of
the unexplored territories of the Sun-heliosphere system,
but results from these two spacecraft will not be included in this review.
The global and long-lasting nature of CMEs makes theso-called Sun-Earth connection truly meaningful. Erupting from the low corona of the Sun and propagating into
the outer corona and interplanetary space, a typical fast
CME largely contains two volumetric components thatare persistent in time and space: the magnetic ejecta
component and the shock sheath component. Each of
the two volumetric components has its own uniquefront: the ejecta front and the shock front, respectively.
This is evident in both remote-sensing imaging observations in white light as well as from in situ one-point
time-series sampling when the CME passes through thespacecraft, as illustrated in Fig. 1. The ejecta contains
the erupted magnetic field and plasma originating in the

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low corona, while the sheath region contains the magnetic field and plasma corresponding to the ambient
solar wind that is disturbed and compressed by the forward shock (or forward compressing waves for slowerCMEs). Through its propagation from the Sun to Earth,a CME ejecta is believed to maintain its curved flux-rope
shape in a quasi-self-similar manner and keep its twolegs remaining rooted on the surface of the Sun for days
and even longer.
In light of the fact that the behavior of CMEs is dominated by different kinematics and dynamics at different
distances within the vast space between the Sun andEarth, we loosely divide the whole Sun-Earth domain
into three sub-domains: (1) in the corona where CME
evolution is dominated by its internal magnetic force;
this is also the region imaged by coronagraphic instruments, (2) farther from the Sun in the interplanetary
space where CME evolution is mostly dominated by the
aerodynamic drag force, i.e., momentum transfer between the CME and the ambient solar wind flow; practically, this is the area observed by Heliospheric Imagers
onboard STEREO, and (3) near the Earth (or other locations near 1 AU) where most in situ sampling data aretaken. These in situ data provide detailed diagnostics ofplasma, magnetic, and abundance properties of CMEejecta and driven shock, albeit limited at one particularpoint in space or a particular sampling line for a traveling ICME. Remote-sensing of the CME-driven shock has
been enabled by tracking type II radio bursts with theradio instruments on board the Wind and STEREO missions. A CME in these three sub-domains can be conveniently called as CME in the traditional sense, an
ICME in the interplanetary space and in situ ICME, respectively. Note that there is certainly no boundary or
barrier between the aforementioned corona and interplanetary space, which can be anywhere between 4 Rs
and 30 Rs. For the sake of simplicity only, one could arbitrarily adopt a value of 20 Rs (roughly coinciding with
the Alfvenic critical point) to separate domains 1 and 2.
In this Section, we review the basic morphology andgeometry (Section 2.2) as well as kinematic behavior of
CMEs (Section 2.3) in the corona and in the interplanetary space. The properties of source regions in the low
corona where CMEs originate are reviewed in Section
2.4. Section 2.5 reviews statistical properties and solar
cycle variation of CMEs and ICMEs. A summary is given
in Section 2.6
2.2 CME morphology, geometry and their evolution
2.2.1 Basic morphology of CMEs

One of the fundamental properties of CMEs is its
morphology near the Sun, as obtained from the directinterpretation of outer corona images made by whitelight coronagraphs. Prior to the SOHO era, theZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 1 A schematic of a CME and its interplanetary counterpart ICME. Top left: the CME image in white light near the Sun (an event on July 12,2012, adopted from Hess and Zhang (2014). The red and blue curves outline the shock front and CME ejecta front, respectively. The Sun isindicated by the white circle in the center. Top right: the in situ data of the resulting ICME near the Earth (adopted from Hess and Zhang 2014).
From top to bottom, the five panels show the Dst index, solar wind magnetic field, velocity, and density. The vertical red line indicates the arrival
time of the shock, and two vertical blue lines indicate the beginning and ending time of the CME ejecta. Bottom: A schematic of CME/ICME
illustrates its geometry and internal components including the shock front, turbulent sheath and draped ambient magnetic field, twisted
magnetic field in the CME ejecta and electron heat flux along magnetic fields. (adopted from Zurbuchen and Richardson 2006)

morphology of CMEs had been characterized by theso-called three-part structure: a bright frontal shell,
followed by a relatively dark cavity surrounding abright core (Illing and Hundhausen 1986). The expected shock fronts missing in this traditional structure were later routinely identified, thanks to theimproved sensitivity of coronagraphs on the SOHOand STEREO spacecraft. The shock front appears as
an outline or boundary of a weakly brightened region
that contains displaced or kinked coronal streamersand rays (Sheeley et al. 2000; Wood and Howard
2009; Ontiveros and Vourlidas 2009; Hess and Zhang

2014; Liu et al. 2017a; Gopalswamy et al. 2009a,
2009b; Gopalswamy and Yashiro 2011). A shock
fronts is expected to form when the speed of the
CME ejecta in the frame of the ambient solar wind isfaster than the fast mode wave speed. Figure 2 shows
one example of the identification and geometrical fitting of the fronts of the ejecta (green wireframe) andthe shock (red wireframe). Thus, the overall morphology of a typically large and fast CME can be characterized by two fronts: a large fuzzy shock/wave
front followed by a bright loop-like ejecta front which
can be interpreted as the plasma pileup at the boundary ofZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 2 Forward model fitting of CME ejecta front and CME-driven shock front of July 12, 2012 event based on STEREO-A COR2 (left) and STEREOB COR (right) images, along with (bottom) and without the raytrace mesh. The green mesh shows the GCS fitting to the eject front, whereas thered mesh shows the spheroid fitting to the shock front (adapted from Hess and Zhang 2014)

the expanding magnetic flux rope, irrespective of whether
a three-part structure can be identified following the looplike front (Vourlidas et al. 2013).
To quantitatively capture the 3D morphology (i.e.,shape and size) and geometry (i.e., location and orientation) of a CME ejecta, the graduated cylindricalshell (GCS) model has been widely used (Thernisienet al. 2006; Thernisien et al. 2009; Thernisien 2011).
This 3D geometric model, meant to reproduce fluxrope-like CMEs, consists of a tubular section forming
the main body of the flux rope attached to two conesthat correspond to the “legs” of the flux rope that areconnected to the surface of the Sun. In this model,
the bright frontal shell of the ejecta corresponds tothe surface of the flux rope, and the cavity as the
body of the flux rope, being consonant with the common view (e.g., Chen et al. 1997a; Cremades et al.
2006). This model contains a central axis that threads
through the center of the curved tube and the conicallegs. The shell surface of the flux rope exhibits rotational symmetry around the central axis at each crosssection perpendicular to the axis. This axis also defines the geometric plane of the flux rope. The GCSmodel, as the one implemented in Solar Software(IDL), has the following six free parameters, (1)propagation longitude, (2) propagation latitude, (3) tilt
angle of the curved central axis (the plane of the fluxrope), (4) height of the leading edge of the front, (5)half angle between the axes of the legs and (6) aspectratio between the radius of the circular cross sectionof the tubular shell and the distance to the outeredge of the shell from the Sun center; for details of
the geometry and the parameters, refer to Fig. 1 in
Thernisien et al. (2009). The first three parameters
define the geometry, while the later three parameters
define the morphology or sizes of the CME. As will be discussed below, CME geometry changes significantly close
to the Sun, but is assumed to remains largely constant in
the interplanetary space. On the contrary, CME morphology remains self-similar in the corona, but distorts
significantly in the interplanetary space.
To capture both the ejecta and shock fronts of CMEs,
Kwon et al. (2014) developed a compound model, in
which the shock front is modeled as an ellipsoid, which
can be spherical or ellipsoidal depending on events aswell as on the evolution stage of the event of study;
the ejecta front simply follows the GCS model. The
ellipsoid model of the shock front also has six freeZhang et al. Progress in Earth and Planetary Science(2021) 8:56

parameters that define the geometry and morphology
in 3D. Using this model, Kwon et al. (2014) demonstrated that the footprints of expanding shock waves
seen in the outer corona correspond well to the EUV
wave front observed on the solar disk in the early
development of CMEs. Similar results of reconciling
CME-driven shock and EUV coronal waves are obtained in other studies (Cheng et al. 2012; Veronig
et al. 2018), revealing the global behavior of CMEdriven shocks.
The reconstruction of shock fronts in 3D also reveals the global properties of halo CMEs. It had been
widely believed that the halo appearance of a CME iscaused by the geometric projection effect, i.e., a CME
moves along the Sun-Earth line and project in all directions on the plane of the sky surrounding the occulter. However, Kwon et al. (2015) found that 66% of
halo CMEs from 2010 to 2012 are seen as halos in all
three spacecraft, SOHO, STEREO-A and STREO-B
when they are in quadrature configuration. They concluded that the halo structure largely represents theshock/wave that propagates in all directions, with alesser dependence on the projection effect of the CME
ejecta that has a limited size. Shen et al. (2013a, b, c)also found that very fast (> 900 km/s) full-halo CMEs
originating far from the vicinity of solar disk center
have a small projection effect. This global reach of
CME-driven shock, even having a component propagating in the opposite direction of the CME ejecta,
helps explain that some SEP events have a wide rangeof helio-longitude distribution, even allowing particle
intensity increase at poorly connected spacecraft (Larioet al. 2014; Liu et al. 2017a).
While the physical properties of CME-driven shocks
were refined in the last decade, as discussed above, thephysical nature of the core of CMEs has been recently
questioned in several studies (Howard et al. 2017; Song etal. 2017; Veronig et al. 2018; Song et al. 2019). It has long
been believed that the bright core inside the CME cavity
originates from entrained erupting filament/prominencematerial which has a high density. However, throughinvestigating source region of CMEs on the solar disk andtracking eruptions continuously into the coronagraph
FOV from multiple viewpoints in space, unambiguous
observational evidence shows that many “classical” threepart CMEs do not contain an erupting filament/prominence (Howard et al. 2017; Song et al. 2017). Howard et al.
(2017) suggested that the core could be the result of amathematical caustic produced by the geometric projection of a twisted/writhed flux rope, implying the same flux
rope produces both the cavity and core; they also suggested another possible cause that could arise spontaneously from the eruption of a flux rope. Throughinvestigating the well-observed highly structured CME on

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2017 September 10, Veronig et al. (2018) argued that thebright core rises from the hot plasma generated throughmagnetic reconnection but adds onto the rim of the risingflux rope, implying that the core is the flux rope.
Song et al. (2019) suggested that the core might correspond to the entirety of the flux rope in the earlyphase, but expand continuously and fill-in the entire
cavity at a later time. The physical nature of theobserved CME core and cavity remains to be an open
question.
2.2.2 CME morphological evolution in the corona: selfsimilar expansion

How does the morphology of CMEs evolve in the corona(as well as in the interplanetary space; see next subsection)? This issue is far from settled. One simple questionis whether such evolution is self-similar or not. Since it
is a structured 3D entity, a CME evolves along threeprincipal directions in a 3D space. Thus, to properly
answer this question, one has to define which direction
the self-similarity refers to. For the clarity of discussion
hereafter, we define three principal directions in theframe of the flux rope with the apex of the central axisof the flux rope at the origin: toroidal direction (T),poloidal direction (P) and radial direction (R). The radial
direction is the vector line connecting the Sun center
toward the apex of the central axis, the toroidal direction, which is on the plane of the flux rope, is along thedirection of the central axis at the apex, while the poloidal direction is perpendicular to the plane of the flux
rope. Both toroidal and poloidal directions are perpendicular to the radial direction. If the tilt angle of the flux
rope is zero, the toroidal direction will be exactly along
the heliographic longitude, while the poloidal direction
will be along the heliographic latitude. The linear sizesof the flux ropes can be characterized by LT, LP and LR,
respectively. Similarly, one can define three aspect ratios:
κT = LT/dA, κP = LP/dA, κR = LR/dA, respectively, wheredA is the distance of the apex of the flux rope central
axis. A constant aspect ratio along one particular direction defines the self-similar evolution in that direction.
Since the advent of multi-viewpoint observations ofCMEs, the aforementioned GCS model is widely used todetermine CME morphology in 3D for a large numberof CMEs (e.g., Poomvises et al. 2010; Kilpua et al. 2012;
Colaninno et al. 2013; Subramanian et al. 2014; Hess
and Zhang 2014; Veronig et al. 2018; Chi et al. 2018b).
These studies found good agreement between GCSgenerated flux rope shells and the observed CME
appearances. One particular interesting result, relevantto the morphology, is that the constant aspect ratio andangular width can be adopted for a particular CME
observed in different times, implying a self-similar evolution of the morphology in all three principle directions.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

Note that the aspect ratio in the GCS model is thesame as κP and κR defined above, and κP = κR since
the GCS flux rope has a circular cross section perpendicular to the central axis. The angular width in the
GCS model is equivalent to κT defined above. In otherwords, CME angular widths along both toroidal andpoloidal directions remain constant as it evolves, andin the meantime, the CME expands radially at thesame rate as along the two lateral directions, maintaining a circular cross section, or constant κR. However,the constant aspect ratio along the radial direction will
not be true in the interplanetary space as discussed in
the next subsection.
A more robust examination of self-similarity can be
carried out by comparing expansion speed and bulk
speed of CMEs, and a constant ratio with time indicatesthe self-similarity. Through a statistical study of 475
CMEs from 2007 -2014 that are geometrically wellstructured and whose geometric centroid and boundary can be well determined from single-viewpoint images (Vourlidas et al. 2017; Balmaceda et al. 2018),
Balmaceda et al. (2020) found that (1) the relationshipbetween lateral expansion and radial expansion speeds
is linear and does not change with height, and (2) the
ratio of the bulk propagation speed to the lateral expansion speed is a function of the angular width that follows
the description of self-similar evolution. They also foundthat most CMEs achieve a self-similar evolution above
4 Rs, which is especially applicable to impulsively accelerated events.
However, in the inner corona (e.g., < 4 Rs), CMEs
would not evolve in a self-similar manner and experience the so-called over-expansion, i.e., the aspect ratio
increases with height and consequently the angularwidth also increases with height (Patsourakos et al.
2010; Balmaceda et al. 2020; Cremades et al. 2020).
Studying a sizeable number of CMEs that could betracked from their inception in the EUV low corona
to the outer corona from multiple viewpoints, Cremades
et al. (2020) found that CME angular widths, along
both toroidal and poloidal directions, increase considerably with height below ~ 3 Rs, and the growthrate along the toroidal direction is higher than thatalong the poloidal direction. They also found that theratio of the two expansion speeds is nearly constantafter ~ 4 Rs, implying that CMEs there reach a stateof self-similar expansion.
2.2.3 CME geometric change in the corona: deflection androtationOn the top of morphological expansion of CMEs discussed above, the geometry of CMEs evolves in a manner that deviates from the simplest behavior of straightradial motion near the Sun: (1) deflection or non-radial

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motion, (2) rotation resulting in the change of the tilt
angle of the CME. Both deviations pose a challenge forpredicting hits or misses for Earth-directed CMEs andeventually whether a given CME would be geoeffective(Kay et al. 2017).
CME deflection has long been noticed (MacQueenet al. 1986; Gopalswamy et al. 2003; Cremades andBothmer 2004). The deflection found in these observations was restricted along the latitude only, since they
were made from single viewpoint observations fromspacecraft along the Sun-Earth line. The deflection has
a tendency that changes the direction of motion of
CMEs from high latitude toward the low latitude
equator during the solar minimum. This tendency
implies that the deflection during solar minimum isrelated to the large-scale magnetic field from polar
coronal holes (Gopalswamy et al. 2003; Cremades andBothmer 2004). However, during the solar maximum,
the directions of deflection can be complex, i.e., towardboth higher and lower latitudes from the original position
angle of CMEs.
Multiple viewpoint observations from STEREO provide much improved diagnostics of CME deflections,
including the time evolution of deflections along both
latitudinal and longitudinal directions. (Gopalswamy
et al. 2003) found that a slow CME during solar
minimum was deflected toward a lower latitude region by ~30°, and demonstrated that such a deflection
is caused by a non-uniform distribution of the background magnetic field, and the CME tended to propagate to the region with lower magnetic-energy
density. A follow-up study on a larger sample ofevents further confirmed that the background magnetic field quantitatively described by the magnetic
energy density control the deflection of CMEs along
both longitude and latitude (Gui et al. 2011). Kilpua
et al. (2009) showed that a CME originating in a highlatitude crown prominence was guided by polar coronal hole fields to the equator and produced a clear
ICME in the near-ecliptic solar wind at in situ. Sucha scenario of large latitude deflection (e.g., >30°) of ahigh-latitude CME moving toward the equator andintercepting the Earth was also reported in Byrne etal. (2010). Besides being influenced by coronal holes,
Liewer et al. (2015) attributed the rapid initial asymmetric expansion or deflection of some CMEs in the
inner corona (< 1.5 Rs in EUVI FOV) to the magnetic
pressure of active regions fields in the immediate
vicinity of the eruption.
CME deflections in longitude were also recognized
and studied, but to a lesser extent than in latitude. Oneof the earlier clues came from the fact that there was aneast-west asymmetry of solar source regions of geoeffective CMEs, i.e., more geoeffective CMEs originated from

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

the western hemisphere than from the eastern hemisphere (Zhang et al. 2003; Wang et al. 2004), thus
favoring an interpretation of longitudinal deflection(Wang et al. 2004). Another line of evidence is related to
the finding of “driverless” shocks found at 1 AU whose
solar sources were near the solar disk center, indicatingthat the CME ejecta were deflected away from the
Sun-Earth line (Gopalswamy et al. 2009b).
Direct measurements of longitudinal deflection only
became possible with the advent of STEREO (Isavnin
et al. 2013, 2014; Möstl et al. 2015; Mays et al.
2015). Based on a sample of 14 events, Isavnin et al.
(2014) showed that most longitudinal and latitudinal
deflections happened within 30 Rs, and a large partof the latitudinal deflection occurred within a few Rs.
Möstl et al. (2015) studied a particularly interestingcase of the January 7, 2014, CME, which originatednear the disk center but was deflected toward thewest by ~ 37° in longitude. Thus, this major CME(projected speed of ~ 2400 km/s and associated withan X1.2 flare) almost entirely missed the Earth andcausing a false alarm of prediction by various space
weather prediction centers. They also found that sucha large longitudinal direction was attained very close
to the Sun (< 2.1 Rs), likely caused in this particularcase by the channeling of nearby active region magnetic fields rather than coronal holes. Such a surprising geomagnetic non-event from a major disk center
eruption highlights the importance of knowing thetrue directionality of CMEs for space weather prediction(Mays et al. 2015).
Another very relevant geometric evolution of CMEs isthe rotation, or the change of the tilt angle of theentrained magnetic flux rope. The tilt angle is zero if thetoroidal axis of CMEs lies on the equatorial plane of theSun, and 90° if perpendicular to the equatorial plane.
The tilt angle is critically important in deciding how
much of the southward magnetic field will encounterthe Earth for a given impacting CME, thus determining
its expected intensity of geoeffectiveness (e.g., Bothmer
and Schwenn 1998). Prior to the STEREO era, the evidence of rotation resided on the observations of erupting
filaments in EUV coronal images (Ji et al. 2003; Zhou etal. 2006; Green et al. 2007). Using the orientation of theelongation of halo CMEs from single viewpoint LASCO
observations as a proxy and assuming that the orientation of the post-eruption arcade in the source region is
the CME orientation at the beginning of the eruption,
Yurchyshyn et al. (2009) found that most CMEs appeared to rotate by 10°, but up to 30–50° in someevents.
Multiple viewpoint STEREO observations provide direct measurements of CME rotations in the coronagraphic field of view (Vourlidas et al. 2011; Isavnin et al.

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2013, 2014; Liu et al. 2018d; Chen et al. 2019). Using the
GCS model to define and track the 3D geometry of aslow CME on 2010 June 16, Vourlidas et al. (2011)
found that the CME had an initial tilt of about 30° at 2–
3 Rs, very similar to the orientation of the neutral lineon the surface source, but later rotated by about 60°
when the CME traveled from 2 to 15 Rs. Liu et al.
(2018d) studied a CME on 2015 December 16 and foundthat the tilt from the GCS model rotated by almost 95°
compared with the orientation on the source region; thesame CME was also deflected by 45° in longitude and
35° in latitude. Such an extremely large rotation of themain structural axis was also found in an erupting filament based on STEREO observations in Song et al.
(2018), who reported a counter-clockwise rotation of
about 135° of the filament in ~ 26 min and then reversed tothe clockwise rotation of 45° in about 15 min. Based on a
statistical study of geometry of CMEs, Isavnin et al. (2014)
noted that the rotation largely occurred below 5 Rs, but
continued in the outer corona and the interplanetary
space.
2.2.4 Geometry of ICMEs in the interplanetary space

As discussed above, in the inner corona (~ < 4 Rs), a
CME usually undergoes a super-expansion or non-selfsimilar increase of sizes in comparison with its distancefrom the Sun, and also experiences most geometricchanges as defined by radial deflection and rotation oftilt angles. In the outer corona (e.g., from ~ 4 Rs to 20
Rs), on the other hand, a CME usually undergoes a selfsimilar expansion in all three principle directions andsome relatively smaller changes in propagation direction
and tilt angle. What about morphological and geometricevolution of CMEs in the interplanetary space, i.e., from~ 20 Rs to 1 AU? Much progress has been made in thelast one and half decades, thanks to the Heliospheric
Imager (HI) (Eyles et al. 2009) onboard STEREO A/B.
Nevertheless, the knowledge that has been gathered islargely limited, as discussed below.
Studies of using HI images from a single spacecraft
usually assume that CMEs have a constant propagation
direction and speed in HI FOVs. Having expanded intoa huge volume thus becoming extremely faint, CMEs in
HI images have a much lower signal-to-noise ratio thanin coronagraphic images. Instead of forward-fitting CME
appearances using a 3D geometric model such as the
GCS model, HI studies often make use of timeelongation maps, or so-called J-maps (Sheeley et al.
1999), which are stack plots of slices taken along a given
position angle (often along the ecliptic plane) fromconsecutive images of a single STEREO spacecraft(Lugaz et al. 2009; Davies et al. 2012). The slice provides
a direct measure of elongation angles from the inner tothe outer edges of HI FOV (Fig. 3). Such time-elongation

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 3 An example of measuring CME (July 12–14, 2012 CME)
leading fronts using slice-stacking plot or J-map and geometricmodels. a The density track of the CME viable in a J-map from STER
EO-A. b Fits of the extracted CME track with the SSEF (Self-Similar
Expansion Fitting) model. c The resulting geometry of the event,with propagation directions derived from FP (dot-dashed red line, 0°
full width), SSEF (solid green line, 90° full width), and HM (dottedblue line, 180° full width) geometric assumption of the CME.
Adopted from Möstl et al. (2014)

maps show enhanced tracks of the leading edges of a
CME, but at the expense of its 3D geometry such as aspectratios and tilt angles. The time-elongation curve of the
tracked feature in the map is then used to determine thepropagation longitude and speed of the feature along
the selected latitude/slice. This assumption of constantpropagation longitude and speed, imposed by this timeelongation map method from a single spacecraft, is notunrealistic, since it is known that most changes have occurred near the Sun in coronagraphic FOVs.
The orbital configuration of STEREO A/B is such thatthe degree to which the same CMEs are imaged by the
HI cameras on both spacecraft critically depends on the
mission phase (Harrison et al. 2018). The percentage ofsuch so-called coincident events by HI-1 ranges between
40% and 90%. Note that the percentage of coincident
events by COR2 is about 80% in total (Vourlidas et al.
2017). For coincident events of HI observations, one canapply a geometric triangulation technique on the timeelongation data from two spacecraft to extract instantaneous propagation longitude and distance at each time ofthe observation, thus allowing the time variation of
propagation longitude and velocity of CMEs (Liu et al.
2010; Lugaz 2010; Davies et al. 2013). Using stereoscopic
time-elongation methods and theoretical arguments, several studied suggested possible non-radial motions of
CMEs over a large distance in the heliosphere (Lugaz et
al. 2010a; Wang et al. 2014; Isavnin et al. 2014).
Nevertheless, caution is needed as the time-elongation
methods do not provide a converging result on the
propagation longitude when different geometric assumption of CMEs are made (Liu et al. 2013; Davies et al.
2013) and different spacecraft are used (Barnes et al.
2020). Note that a set of commonly used timeelongation methods have been developed, which differ in
the assumption of CME geometry on the plane containing the selected slice and the observing spacecraft: as apoint or compact source (the fixed-φ, or FP method)(Sheeley et al. 1999; Rouillard et al. 2008), as a circle
with the feature at the tangent front and the bottom attached to Sun-center (harmonic mean, or HM method)(Lugaz 2010), or as a generalized circle of certain half
angle λ (generalized self-expansion, or SEE method)(Davies et al. 2012); the FP and HM geometries form theZhang et al. Progress in Earth and Planetary Science(2021) 8:56

limiting cases with λ equals 0° and 90° respectively, while
λ can be chosen between 0° and 90° in the SEE method(Fig. 3). Davies et al. (2013) found that the derived CMElongitude is a function depending on the choice of λ,and the disparity in longitudes can be significant between the two limiting cases. In a statistical study of 273
coincident events, Barnes et al. (2020) noted that thelongitude derived from single-spacecraft are in fairly
poor agreement with each other, and moreover, neither
agree well with the results from stereoscopic analysis.
Such systematic disparity may indicate the incorrectnessof the underlying assumption, i.e., the assumed circular
front of CMEs may deviate significantly from the actual
morphology, which will be discussed below.
2.2.5 Morphology of ICMEs in the interplanetary space

In contrast to the largely self-similar expansion patternof CMEs in the corona, CMEs may undergo significant
deformation in the interplanetary space, thanks to theenhanced effect of structured solar wind flows on CMEs
(Odstrčil and Pizzo 1999a; Riley and Crooker 2004). It is
understood that, in the regime of high plasma beta
where plasma pressure dominates magnetic pressure, themagnetic structure of CMEs will be strongly modulatedby the pattern of plasma flow. In the interplanetary
space, the solar wind plasma flows along the radial direction but in a spherically diverging geometry. Consequently, such a flow pattern introduces the following
kinematic effects on the structure of CMEs: (1) selfsimilar expansion along lateral directions, or directions
on the spherical surface (2) no expansion at all along theradial direction that is perpendicular to the sphericalsurface, leading to the thinning or pancaking of theoverall CME morphology (Riley and Crooker 2004). In
the following, we discuss the self-similarity of CME evolution along lateral and radial directions respectively.
Observations in HI FOVs show that CMEs maintain anearly constant angular width, indicating a self-similar
expansion or constant aspect ratios along lateral directions with respect to the distance of CMEs (Wood et al.
2009; Wood et al. 2017). Note that the two principal lateral directions for a flux rope CME are along the toroidal and poloidal directions, respectively. To reproduce
the observed two-dimensional loop of CME leading
fronts in a flexible way, Wood et al. (2009) adopted ageometric shape described by a quasi-Gaussian equationin polar coordinates with a variable power index α regulating the shape of the loop; the shape is a perfect
Gaussian for α = 2, while higher values of α result inloops with flatter tops. Using a statistical survey study of48 events, Wood et al. (2017) noted that self-similar expansion is a decent, albeit not perfect, approximation for
CMEs expanding into the interplanetary space. Suchself-similar expansion along lateral directions should

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have continued from coronagraphic FOVs into HI
FOVs.
Nevertheless, the self-similar evolution breaks down
for the dimension along the radial direction. The crosssection of the CME flux rope can be initially well described by a circle, as in the highly successful GCS
models. Into the interplanetary space, the circular shape
may evolve into a highly flattened and distorted shape,
which has been described as a convex-outward pancake
shape (Riley and Crooker 2004), elliptical shape (Savani
et al. 2011), or even a concave shape (Savani et al. 2010).
As an example of one extreme case, (Savani et al. 2010)
clearly showed the observation that a circular-shaped
CME in the coronagraphic FOV evolved into a concaved
structure in the HI FOV, and suggested that the kinematic effect of a bimodal speed solar wind caused such
distortion. Therefore, the shape of the leading front of a
CME can deviate significantly from a circular shape, andcaution needs to be taken when a circular-shapeassumption is assumed in modeling ICMEs.
Note that, besides the studies based on coronal andheliospheric imaging observations mentioned above, thegeometry and morphology of CMEs can also be inferredfrom in situ observations. There is a vast amount ofwork of fitting in situ data to infer the structure ofshocks and magnetic flux ropes, and such studies are
partially reviewed in Section 3.5. In the next sub-section,
we provide a review on the studies of kinematic properties of CMEs and ICMEs, which are mostly based on thetime tracking of the leading fronts of the CME, insteadof the 3D extension of the structure.
2.3 Kinematics of CMEs and ICMEs

Our knowledge about the whole kinematic evolution of
CMEs from the Sun to the Earth has improved significantly in the last decade, largely thanks to the wideangle observations of STEREO. Rising from locations
above magnetic polarity inversion lines near the surfaceof the Sun, CMEs accelerate and reach speeds in theouter corona with a wide range of values from tens of
km/s up to ~ 4000 km/s. The subsequent evolution of
CMEs depends on their initial speeds in the outer corona relative to the speed of ambient solar wind: faster
CMEs decelerate, while slower CMEs accelerate. As
CMEs propagate further into the interplanetary space,
their speeds tend to equalize with that of the solar wind
due to the effect of aerodynamic drag (or more precisely,
magnetohydrodynamic drag). For a large fraction of
CMEs, the balance in speed and pressure is not established at the distance of 1 AU. The speed of ICMEs at 1
AU ranges from ~ 300 km/s to ~ 1000 km/s, meaningthat it can be much faster than the ambient solar wind
at 1 AU. In the following, we provide a review on the
Sun-to-Earth kinematic evolution, including the phases

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

of evolution, peak velocity, terminal velocity, cessation
distance and others. The topic on the prediction of CMETime of Arrival at 1 AU will be given in Section 8.2.
Based on tens of thousands of CMEs observed, it isfound that CME speed (i.e., average projected speed
measured in LASCO FOV) has a very broad distribution
ranging from ~ 10 km/s to ~ 3000 km/s (Yashiro et al.
2004; Robbrecht et al. 2009a; Olmedo and Zhang 2010;
Webb and Howard 2012; Lamy et al. 2019). The average
speed of all CMEs in the various observed periods isabout 300 km/s during the solar minimum and about
500 km/s during the solar maximum. Further, halo
CMEs, which are the ones likely hitting the Earth, havean average speed of about 950 km/s, or about twice ofthat of all CMEs. Slow CMEs are quite common, as
about half of CMEs in the LASCO FOV are slower ornear the speed of the ambient solar wind. On the other
hand, fast CMEs are equally common. Nevertheless, extremely fast CMEs, i.e., > 1500 km/s, are rather rare, occupying ~0.5% of all CMEs (Wang and Zhang 2007).
The highest CME speed on the record is ~4400 km/s
(Gopalswamy et al. 2018a, b, c, d, e).
Recently, Barnes et al. (2019) made a statistical studyof CME kinematics in the STEREO HI-1 FOV and compared with that in the LASCO FOV. They found thatthe velocity distributions are similar in both areas: asharp peak at the low end of the distribution and a longtail of high-speed CMEs. The yearly mean speeds in
HI-1 FOV are consistently higher than that in
LASCO; however, the two types of speeds are very similar
after projecting HI speeds onto the plane of the sky. In the
HI FOV, the range of CME speeds is from ~ 100 km/s to~ 2000 km/s. It is noticed that there are very few CMEs
with speeds less than 200 km/s in the HI FOV, which
is of a distinct contrast with that of LASCO CMEs.
This difference is certainly not surprising, as slow
CMEs in the corona are picked up by the drag of
ambient solar wind.
A large number of studies on individual events haveprovided detailed kinematic evolution of CMEs fromcorona and far into the inner heliosphere (Wood andHoward 2009; Poomvises et al. 2010; Liu et al. 2010;
Colaninno et al. 2013; Hess and Zhang 2014; Liu et al.
2016; Wang et al. 2016a, b, c, d; Wood et al. 2017). The
observed speed profiles of three typical CMEs, which areof slow, intermediate and fast initial speeds respectively,
are shown in Fig. 4 (adopted from Liu et al. 2016).
Clearly, faster CMEs decelerate and slower CMEs accelerate, as also shown in earlier studies (Sheeley et al.
1999; Gopalswamy et al. 2000). One of the interesting
results from observational studies is that there appears
the existence of a cessation distance, at which a CMEreaches its terminal velocity; after this distance, theCME moves at a nearly constant speed, or too small toPage 14 of 102

be measured by existing imaging instruments. Note that
we are cautious on the usage of the term “terminal
speed,” as CME speeds will continue to change, albeit ina relatively small rate (e.g., < 1 m/s2). Poomvises et al.
(2010) showed that this cessation distance was at about
50 Rs for several events including very fast ones. Using akinematic model that divides the CME evolution into 2–
4 phases of constant acceleration and constant velocity(Wood and Howard 2009), a recent statistical study by
Wood et al. (2017) showed that the cessation distance
ranged from ~ 10 Rs to ~ 100 Rs, and the terminal velocity ranged from ~ 300 km/s to ~ 1200 km/s. Similar
result was found in an earlier study based on Type II
radio observations (Reiner et al. 2007).
CMEs reach their peak velocity at varying heights fromthe Sun. In general, fast CMEs reach their peak velocity
at a low height, thanks to strong and impulsive acceleration, while slow CMEs reach their peak velocity at a
relatively high height (Zhang and Dere 2006; Bein et al.
2011, 2012; Wood et al. 2017). Based on a statistical
study of 95 events, Bein et al. (2011) found that theheights of peak velocity distribute from a very low heightof 1.17 Rs (from disk center) to ~ 10.5 Rs (close to theborder height of STEREO COR2 used in this study). A
continued study by Bein et al. (2012) found that CMEs
associated with flares, in comparison with CMEs associated with filaments, have on average significantly higher
peak acceleration and lower height of peak velocities.
Wood et al. (2017) found that the average peak velocity
height was ~ 3.2 Rs for fast CMEs that were associatedwith flares, ~ 13.9 Rs for intermediate velocity CME
associated with erupting filaments, and ~ 29.4 Rs forslow CMEs that were not associated with any apparent
surface source regions.
The full Sun-to-Earth evolution of CMEs can belargely divided into four phases, each of which depends
how the velocity varies and what forces drive the velocitychange. Near the surface and in the corona, the fullkinematic evolution of a CME can be characterized bythree distinct phases: (1) a slow rise phase, or initiation
phase, (2) a fast acceleration phase, or main phase, (3) apropagation phase with no or small variation of velocity(Zhang et al. 2001); this third phase is called residual acceleration phase in Zhang and Dere (2006). During thefirst two phases, a CME should be mainly driven by the
Lorentz force. However, following the main acceleration,
the Lorentz force may become significantly weaker, andthe aerodynamic drag force sets in and become important. During this third phase, a CME likely experiences a
combined effect of both Lorentz force and aerodynamic
drag force, leading to the observed residual acceleration
which can be either positive or negative (Zhang and
Dere 2006). Further moving out, the Lorentz force eventually diminishes and the aerodynamic drag force will

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Fig. 4 Sun to Earth velocity profiles of a typical fast CME (upper), a typical intermediate-speed one (middle), and a typical slow one (lower). The
horizontal dashed line indicates the observed speed at the Earth. Adopted from Liu et al. (2016)

dominate; this phase can be considered the 4th phase of
the full evolution, or the drag phase. When only the
aerodynamic drag force is considered, the kinematic evolution of a CME can be modeled in a relatively straightforward way (Cargill 2004; Vršnak et al. 2013). As the
aerodynamic drag force is proportional to the squareof the difference between the CME velocity and theambient solar wind velocity, the CME velocity will
asymptotically approach the velocity of the ambient
solar wind. In other words, a faster CME decelerates
and a slower CME accelerates, and the acceleration
rate is not a constant but asymptotically approaches
zero. For a slow CME, the full evolution may be
reduced to only two phases, a gradual accelerationout to about 20-30 Rs, followed by a nearly constant
speed near the solar wind level (Liu et al. 2016). A detailed
review on theories of CMEs propagation is given in
Section 3.

2.4 Coronal sources of solar eruptions

The initiation and early evolution of CMEs cannot be
observed using traditional coronagraphic observations,due to the blockage of the eruption region by the occulting disk. Therefore, various associated phenomena inH⍺, extreme-ultraviolet (EUV), X-rays, and microwaves
on solar disk are linked to specific properties of theeruption and used to infer the origin of CMEs (Gopalswamy et al. 1999; Hudson and Cliver 2001; Harra 2009;
Webb and Howard 2012). Over the course of theeruption, the associated activities can be a combinationof filament eruptions, solar flares, large-scale coronal
EIT waves, post-eruptive arcades, and coronal dimmings.
For example, the CME onset is often accompanied withthe eruption of filaments/prominences that later form
the inner bright core of CMEs observed in coronagraphic data (Gopalswamy et al. 2003; Parenti 2014).
The relationship between eruptive prominences andZhang et al. Progress in Earth and Planetary Science(2021) 8:56

CMEs was investigated in several statistical studies(Munro et al. 1979; Webb and Hundhausen 1987; Horiand Culhane 2002; Gopalswamy et al. 2003), where an
association rate of up to 90% was found.
Low coronal observations also revealed the close relationship between solar flares and CMEs
(Schmieder et al. 2015; Vršnak 2016). Strong andpowerful flares tend to be associated with fast andmassive CMEs (Moon et al. 2002; Burkepile et al. 2004;
Vršnak et al. 2005; Bein et al. 2012), which results in a
90% correspondence for flares above X-class (Yashiro
et al. 2006). However, there exist flares without CMEs
(i.e., confined flares (e.g., Pallavicini et al. 1977; Wang
and Zhang 2007; Sun et al. 2015) and vice versa, CMEs
without flares (e.g., stealth CMEs) (Robbrecht et al.
2009b; Ma et al. 2010; Howard and Harrison 2013;
D’Huys et al. 2014). A recent study by Nitta and Mulligan (2017) showed that stealth CMEs can result in significant geoeffective disturbances at 1 AU, highlighting
their importance in space weather research.
If CMEs and flares occur together, they are interpreted
to be different parts of the same magnetically driven
event (Harrison 1995; Priest and Forbes 2002; Webb andHoward 2012; Green et al. 2018).
Over the past years, it was shown that CMEs andflares are closely related in time; i.e., the SXR peak andthe main acceleration phase of the CME are nearly synchronized (Zhang et al. 2001; Neupert et al. 2001; Shanmugaraju et al. 2003; Maričić et al. 2004; Vršnak et al.
2004; Zhang et al. 2004; Zhang and Dere 2006; Cheng etal. 2020). The main acceleration phase of the CME iscorrelated with the time evolution of the flare-related
hard X-ray burst (Temmer et al. 2008; Gou et al. 2020)
and a close relationship between their onset times wasfound in statistical studies (Maričić et al. 2007; Bein etal. 2012). Further evidence for a close flare/CME relationship is provided by the strong correlation between
characteristic CME parameters, such as the velocity, theacceleration, and its kinetic energy with the SXR peak
flux, indicating the flare strength, or the integrated fluxof the associated flare (Vršnak et al. 2005; Maričić et al.
2007; Yashiro and Gopalswamy 2009).
Since the flare energy release rate is closely related tothe magnetic reconnection rate (Miklenic et al. 2009), a
feedback relationship between the CME and its associatedflare is established (Zhang et al. 2001; Vršnak 2008; Temmer et al. 2010; Welsch 2018). Increasing reconnection
rates enhance CME acceleration, and vice versa, enhanced
acceleration provides more efficient reconnection. Studies
showed the correlation between CME velocities and thetotal reconnection flux supporting this interpretation (Qiu
and Yurchyshyn 2005; Miklenic et al. 2009; Tschernitz etal. 2018; Gopalswamy et al. 2018a, b, c, d; Pal et al. 2018).
The most recent study by Zhu et al. (2020) even directly

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proves this interpretation observationally by reporting ona strong correlation between the reconnection rates, estimated by flare ribbons and CME accelerations (c > 0.7).
Interestingly, they also report on a positive correlation between the maximum speed of CMEs and the total reconnection flux but only for fast CMEs (v > 600 km/s). For
slow CMEs with weak reconnection, other physical processes may play a more important role during acceleration
than magnetic reconnection.
The initial lateral expansion of the CME also drives
fast-mode magneto-sonic waves observed as large-scale
perturbations of enhanced EUV emission, so-called EIT
waves (Thompson et al. 1999; Patsourakos and Vourlidas
2009; Long et al. 2016). Their speeds typically range
from 200 to 400 km/s (Klassen et al. 2000; Thompson
and Myers 2009; Muhr et al. 2014), but also EIT waves
with speeds up to 1000 km/s have been reported (Nitta
et al. 2013; Seaton and Darnel 2018). Statistical studies
revealed that fast and wide CMEs are in general accompanied with well-observed EIT waves often associatedwith shocks and therefore also related with type-II radio
bursts (Biesecker et al. 2002; Cliver et al. 2005; Nitta etal. 2013, 2014; Muhr et al. 2014; Warmuth 2015). Combining type II radio burst observations with EUV waves
observed by SOHO and STEREO, Gopalswamy et al.
(2013b) found that the EUV waves are shocks forming
very close to the Sun - as low as 0.2 Rs above the solar
surface. For the physical mechanisms leading to theshock wave formation and coronal and chromospheric
response, see, e.g., Vršnak et al. (2016) and references
therein.
After the CME has erupted, bright post-eruptive
arcades or post-flare loops appear in soft X-ray and EUV(Kahler 1977; McAllister and Hundhausen 1996;
Tripathi et al. 2004) as a consequence of magnetic reconnection processes (Kopp and Pneuman 1976). Tripathi
et al. (2004) statistically analyzed post-eruptive arcades
using data from SOHO/EIT. They found that the majority of post-eruptive arcades (92%) were associatedwith CMEs identified in SOHO/LASCO.
Due to the expansion of the CME volume andevacuation of plasma during the eruption, regionsof decreased emission in soft X-rays and EUV are
formed, so-called coronal dimmings (Hudson et al.
1996; Thompson et al. 2000; Harra and Sterling
2001; Vanninathan et al. 2018). As they represent
the lower footprint of CMEs in the low corona,
their properties are closely related to the initialproperties of the observed CME later on. For instance,
several studies tried to relate the mass loss within coronaldimming regions to the CME mass measured fromcoronagraphic observations (Harrison and Lyons 2000;
Zhukov and Auchère 2004; Aschwanden et al. 2009;
López et al. 2019).

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

Recently performed statistical studies confirm theclose connection between coronal dimmings and CMEs
and found that the dimming area, its total magnetic flux,and its brightness are strongly correlated with the CME
mass (Dissauer et al. 2018, 2019; Sindhuja and Gopalswamy 2020). Dimming parameters, describing its dynamics, such as the area growth rate, brightness change
rate, and magnetic flux change rate, are tightly related to
the CME speed. This is in agreement with the results of
Mason et al. (2016) who studied coronal dimmings extracted from full-disk irradiance light curves of SDO/
EVE (EUV Variability Experiment).
A number of studies also successfully compared magnetic flux rope properties, such as the magnetic flux, thechirality, and its helicity sign determined from posteruptive arcades, flare ribbons, and coronal dimmings
measured close to the Sun with magnetic cloud properties at 1 AU (Qiu et al. 2007; Yurchyshyn 2008; Hu et al.
2014; Marubashi et al. 2015; Gopalswamy et al. 2017b;
Palmerio et al. 2017, 2018; James et al. 2017; Aparna andMartens 2020). The total amount of magnetic flux
ejected during an eruption is estimated by the total reconnection flux in the wake of the CME or sometimes
also by the magnetic flux involved in coronal dimming
regions, which form the footprint of CMEs in the low
corona (Mandrini et al. 2005; Attrill et al. 2006; Qiu etal. 2007; Hu et al. 2014). Especially the total reconnection flux strongly correlates with the magnetic flux ofmagnetic clouds (Qiu et al. 2007; Hu et al. 2014).
The helicity sign and the total amount of helicity ofmagnetic clouds at 1 AU seem to be strongly controlled
by the location and properties of the solar source region(Cho et al. 2013; Hu et al. 2014; Marubashi et al. 2015).
CMEs erupting in the southern (northern) hemispheretend to have a positive (negative) helicity sign (hemispheric helicity rule, e.g., Pevtsov et al. 2003). Recently,
Aparna and Martens (2020) investigated the directionality (chirality) of 86 CMEs-ICME pairs by comparing theorientation of their flux rope axes close to the Sun withthe direction of the interplanetary magnetic field near
Earth at L1. An agreement between the northward/
southward orientation of Bz between ICMEs and their
CME source regions was found in 85% of the cases,
which is comparable to earlier results by Palmerio et al.
(2018) and Yurchyshyn (2008), which found agreementfor 55% and 77% of their cases.
In recent years, several studies also focused on Sun-toEarth analysis of CMEs by linking the low coronal behavior and properties of the eruption with its observedin situ signature (Möstl et al. 2015; Patsourakos et al.
2016; D’Huys et al. 2017; Temmer et al. 2017a). A number of studies also compared magnetic flux rope properties, such as the magnetic flux, the chirality, and its
helicity sign determined from post-eruptive arcades, flare

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ribbons, and coronal dimmings measured close to the
Sun with magnetic cloud properties at 1 AU (Qiu et al.
2007; Gopalswamy et al. 2017b; Palmerio et al. 2017;
James et al. 2017). Scolini et al. (2019b) used proxies ofmagnetic flux estimates determined from post-flare arcades (Gopalswamy et al. 2017b), flare ribbons (Kazachenko et al. 2017; Tschernitz et al. 2018) as well as
coronal dimmings (Dissauer et al. 2018), as initial input
for the global heliospheric EUHFORIA model, to study
the geoeffectiveness of the famous 2017 September
events. Good agreement with the observed Dst profile
was found for simulations using the optimized input andincluding CME-CME interactions.
2.5 Solar cycle variations of CMEs and ICMEs

Solar cycle 24 is known to be weaker than previous several solar cycles, which is the focus of many studies during the VarSITI program. A weak solar cycle 24 isunderstood to be due to the weak polar magnetic fieldin the preceding solar minimum according to the Babcock Leighton Mechanism of the solar cycle (see e.g.,
Petrovay 2010). A weak cycle implies mild space weather
that helps satellites in Earth orbit live longer. A weakcycle also means less total solar irradiance reaching
Earth (e.g., Krivova and Solanki 2008). Here, we focus
on the effect of a weak solar cycle on solar wind magnetic structures originating from the Sun and their space
weather consequences.
Both solar source and impact of CMEs showed significant variations in cycle 24. The overall rate of CMEs increased in solar cycle 24 relative to cycle 23, although
the rate of fast and wide CMEs decreased. Accordingly,
the phenomena that are linked to fast and wide (FW)CMEs appeared subdued in cycle 24. The rate of occurrence of CMEs is known to be correlated with the sunspot number (SSN) for a long time. However, the slopeof the regression line is significantly different in cycle 24.
The relation between CME width and speed is also different in cycle 24: for a given speed, cycle-24 CMEs are
significantly wider. CMEs are the main source of severe
space weather. Weakened solar activity is reflected in
the weak heliospheric state in terms of magnetic fieldstrength, temperature, density, speed, and consequently
the total pressure. The backreaction of the weakened
heliosphere had led to the changed properties of CMEs
and hence affected the space weather consequences.
Marked reductions are observed in the number of intense (Dst ≤ − 100 nT) geomagnetic storms and highenergy (≥ 500 MeV) solar energetic particle (SEP) events.
The number of halo CMEs in cycle 24 did not decreasesignificantly. In fact, the number of halo CMEs normalized to the sunspot number is larger in cycle 24. One
would have expected enhanced geomagnetic activity incycle 24 because of the higher abundance of halo CMEs,

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

but it did not occur. In this section, we summarize someof the key observational results that describe the compound effect of the weak solar activity and heliosphericbackreaction on CME properties.
2.5.1 Solar activity and eruption properties

Figure 5 shows the solar-cycle variation of CMEs andflares compared to SSN cycles 23 and 24 updated from
(Gopalswamy et al. 2020a; Gopalswamy et al. 2020b). We
can readily infer the following: (i) the daily rate of the general population of CMEs (width ≥ 30°) and that of the soft
X-ray flares (size ≥ C1.0) did not decline in cycle 24, (ii)the FW CME rate declines significantly in cycle 24 (as opposed to the general population), (iii) the CME daily rates
have a different relationship with SSN in the two cycles,(iv) the variation in the number of FW CMEs is similar tothat of major soft X-ray flares (M- and X-class flares), and(v) the CME rate increases more rapidly as the activity increased, indicated by the steeper slope in the cycle-23
CME rate—SSN scatter plot. The reduction in FW CMEs
is significant because they are the ones that are relevant
for space weather consequences (geomagnetic storms and
SEP events).
Petrie (2015) studied SOHO/LASCO CMEs with angular widths > 30° listed in the manual (CDAW) andautomatic (CACTus and SEEDS) catalogs. He found that
the CME rate relative to the sunspot number began an
upward divergence with respect to the SSN in 2004 after
the polar field reversal, while the interplanetary magnetic
field decreased by ~ 30% around the same time (seeFig. 3a). These results are consistent with the enhanced
halo CME detections due to the increased CME expansion in a heliosphere with diminished total pressure(Gopalswamy et al. 2015a). Petrie (2015) also showedthat the increased CME rate in cycle 24 is not due to the

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LASCO cadence change that occurred in August 2010
(Wang and Colaninno 2014; Hess and Colaninno 2017).
The cadence change was also found to be not importantfor halo CME rates (Gopalswamy et al. 2015a). Michalek
et al. (2019) showed that the higher rate of the narrow
CMEs can be attributed to the global magnetic structure
in cycle 24 coupled with the reduced total pressure in
the heliosphere, in agreement with Petrie (2015).
2.5.2 Phenomena associated with energetic CMEs

Figure 6 shows the solar cycle variation of the numbers of
halo CMEs, intense geomagnetic storms, large SEP events,
and decameter-hectometric (DH) type II bursts. All thenumbers are summed over Carrington rotation periods.
The properties of halo CMEs have a good overlap withFW CMEs, although some halos are wide, but not fast.
Halo CMEs, when front-sided, are important because they
can affect space weather. Intense geomagnetic storms aremostly due to energetic CMEs heading toward Earth containing southward magnetic field components either inthe ejecta part or in the shock sheath. On the other hand,the shock at the leading edge of CMEs accelerates electrons and ions. The accelerated electrons produce type II
radio bursts that are a good indicator of shock-driving
CMEs near the Sun and in the interplanetary medium.
Accelerated particles traveling along interplanetary fieldlines are detected as SEP events. We see that the numberof events in each case generally follows the solar cycle withmore events occurring during solar maxima. A notable exception is the number of intense geomagnetic storms incycle 24 that remained flat.
2.5.2.1 Halo CMEs in solar cycle 24 As reported in(Gopalswamy et al. 2015a; Gopalswamy et al. 2020b;
Gopalswamy et al. 2020), the halo CME rate in cycle 24

Fig. 5 Solar cycle variation of eruptive phenomena (flares and CMEs): a daily CME rate for the general population (width ≥30°) in red and thenumber of soft X-ray flares with size ≥C1.0 in blue, b fast and wide CMEs (speed ≥ 900 km/s; width ≥ 60°) in red and major (M- and X-class) soft
X-ray flares in blue, and c scatter plots between the sunspot number (SSN, V2.0) and the daily CME rate (general population) for cycle 23 (red)and 24 (blue). In (a) and (b), SSN is shown in gray background. The CME and flare rates are averaged over the Carrington rotation (CR) periods.
The flare rates are multiplied by 0.5 to fit the scale. Updated from Gopalswamy et al. (2020a); Gopalswamy et al. (2020b)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 6 Solar cycle variation of the key CME-related phenomena during cycles 23 and 24 (from 1996 to the end of 2019): a SOHO/LASCO halo
CME number from https://cdaw.gsfc.nasa.gov/CME_list/halo/halo.html. b the number of large geomagnetic storms (Dst ≤ − 100 nT), c thenumber of large solar energetic particle events (SEPEs) detected in the > 10 MeV GOES channel with intensity exceeding 10 pfu, and d thenumber of decameter-hectometric (DH) type II radio bursts from Wind/WAVES. For comparison with the solar cycle, the sunspot number
averaged over Carrington rotation (CR) periods is also shown. Updated from Gopalswamy et al. (2015a, b, c)

did not decline commensurate with the SSN. There areroughly the same number of halos in the two cycles.
When normalized to SSN, cycle 24 has ~ 30% more
halos (per SSN). As with the general population of
CMEs, the halo CMEs are slower in cycle 24. Furthermore, CMEs in cycle 24 become halos at shorter heliocentric distances than in cycle 23. These authors
attributed the peculiar behavior of halo CMEs to the diminished heliospheric total pressure in cycle 24 thatmade relatively slower CMEs and those originating atlarger central meridian distances become halos. Solarwind parameters measured at Sun-Earth L1 confirm
that most of the parameters have lower values incycle 24. In particular, the decline in the heliosphericfield strength resulted in the diminished total pressure
and the Alfven speed (Gopalswamy et al. 2020a).
2.5.2.2 Interplanetary CMEs in solar cycle 24 The reduction in the number of FW CMEs in cycle 24 is expected to be reflected in the number of interplanetary

CMEs (ICMEs) observed at 1 AU because the latter are
known to be associated with energetic CMEs. The annual number of ICMEs in Fig. 7 shows clear solar-cycle
variation, similar to other phenomena. The total number
of ICMEs declined from 307 in cycle 23 to 208 in cycle
24, representing a reduction of 32%. The decline issmaller than that in SSN. One possible explanation isthat ICMEs also originate from non-spot regions wherequiescent filaments erupt and the associated CMEs become ICMEs. When normalized to the cycle-averaged
SSN in each cycle (81 in cycle 23 dropping to 49 in cycle24), we see a 12% increase in the number of ICMEs. If
we separate the ICMEs into magnetic clouds (MCs) andnon-cloud ejecta (EJ), we see a similar trend, but the decline in EJ (37%) is steeper than in MCs (25%). Whennormalized to SSN, we see an increase of 4% and 25%
for EJs and MCs, respectively. This behavior was noted
previously for MCs detected during the rise and maximum phases of cycles 23 and 24 (Gopalswamy et al.
2015a, b, c). The fraction of MCs is also slightly higher

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Fig. 7 a Solar cycle variation of the annual number of ICMEs (magnetic clouds—MCs and non-cloud ejecta—EJs) observed near Earth ascompiled in http://www.srl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm. The total number of ICMEs in cycles 23 (307) and 24 (208) arenoted on the plot. b Annual number of EJs and MCs shown separately. The number of EJs and MCs is noted on the plotin cycle 24: 37% (114 out of 397 ICMEs) vs. 41% (86 out
of 208 ICMEs) in cycle 23.
2.5.3 Stream interaction regions

While CMEs cause most of the intense geomagnetic
storms, stream interaction regions (SIRs) and especiallyco-rotating interaction regions (CIR) can cause relatively
weaker but more frequent geomagnetic storms. The
solar sources of SIRs and CIRs also have been shown tobe different in cycles 23 and 24. Nakagawa et al. (2019)studied the temporal and spatial variations of the lowlatitude coronal hole (CH) area related to high-speed
solar (HSS) wind during solar cycles 23 (1996–2008) and24 (2009–2016). They found that (i) the CHs in solar
cycle 24 appeared over a wider latitude range than insolar cycle 23, and (ii) the maximum values of the CH
area and the solar wind speed in solar cycle 24 were
smaller than those in solar cycle 23. Jian et al. (2019)
compared the annual occurrence rates of SIRs and CIRs
using Wind/ACE and STEREO in situ observations from1995 to 2016 as displayed in Fig. 8. They found a higher
occurrence rate of SIRs in cycle 24, which they attributeto the presence of persistent equatorial coronal holes aswell as weaker CMEs in that cycle. The fraction of CIRs
is higher in the declining minimum phase of each cycle.

Grandin et al. (2019) developed a catalog of 588 HSS
and SIR events that occurred during the interval 1995 to2017. Their list is largely in agreement with the list of
SIRs identified manually by Jian et al. (2019).
2.5.4 GeoeffectivenessA severe reduction in the geoeffectiveness of CMEs incycle 24 as measured by the number of intense (Dst ≤ −100 nT) geomagnetic storms has been reported (Gopalswamy et al. 2014a, b; Gopalswamy et al. 2015a, b, c) in
the rise to the maximum phases. In the updated version
covering two full cycles shown in Fig. 4b, we see that
there are 86 intense storms in cycle 23 compared to just
22 in cycle 24. These include 11 CIR storms in cycle 23
and 2 in cycle 24 (an 82% reduction). The first intense
geomagnetic storm occurred during the maximum phaseof cycle 24 on 2013 June 1 due to a low-latitude coronalhole that was at the central meridian on 2013 May 30(Gopalswamy et al. 2015c). The second storm occurred
on 2015 October 6 due to a coronal hole that waspresent at the central meridian on 2015 October 5(Watari 2018). The number of intense storms due to
CMEs declined by ~ 73% from 75 to 20. Both these reductions can be attributed to the dilution of the CME
and CIR magnetic content.

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Fig. 8 Monthly sunspot number (top), number of SIR/CIR (middle), and CIR rate (bottom) in solar cycles 23 and 24. Corresponding epochs incycles 23 and 24 are indicated by the cyan bars at the top (adapted from Jian et al. 2019)

Considering MCs that occurred during the first 6 years
in each of cycles 23 and 24, it was found that the MCassociated storms were weaker in cycle 24: the averagevalue of Dst increased from − 66 to − 55 nT. Scolini etal. (2018b) investigated the geoeffectiveness of halo
CMEs in cycles 23 and 24. They found that during thefirst 85 months of Cycle 23 the geoeffectiveness rate ofthe disk-center full-halo CMEs was 58% compared to
35% in cycle 24. The average minimum value of the Dst
index was − 146 nT in cycle 23 compared to − 97 nT incycle 24. These results are consistent with the reduced
geoeffectiveness of cycle-24 MCs (Gopalswamy et al.
2015a, b, c). Hess and Zhang (2017) studied 70 Earthaffecting ICMEs in Solar Cycle 24 and found the lack ofevents resulting in extreme geomagnetic storms.
Selvakumaran et al. (2016) considered the moderate
and intense geomagnetic storms that occurred during
the first 77 months of solar cycles 23 and 24. While they
confirmed an 80% reduction in the occurrence of intensestorms, they found that the number of moderate storms
decreased by only ~ 40%. When moderate storms from
CIRs are considered separately, these authors found thatthe reduction in the number of geomagnetic storms ismore drastic (see also Grandin et al. 2019). In cycle 23,
there were 43 CIR storms during the first 77 months ofcycle 23 compared to 15 in cycle 24, amounting to a reduction of 63%. This is about the same when the CIR
storms are separated into moderate and intense storms.
Chi et al. (2018a) studied the geoeffectiveness of SIRs
from 1995 to 2016 and found that about 52% of the
SIRs caused geomagnetic storms with Dst ≤ − 30 nT,
but only 3% of them caused intense geomagnetic
storms (Dst ≤ − 100 nT). They also reported that thepossibility of SIR-ICME interaction structures causing
geomagnetic storms is significantly higher than that ofisolated SIRs or isolated ICMEs.

2.5.5 Solar cycle variation of large SEP events

As of December 2019, there were 46 large SEP events(> 10 MeV proton intensity ≥ 10 pfu) in cycle 24compared to 102 in cycle 23 (see Fig. 4c). This is a
55% reduction, more than the reduction in the SSN.
Considering the period up to the middle of the maximum phase, the reduction reported previously was
smaller (~ 26%, Gopalswamy et al. 2015c), most likely
due to the lower Alfven speed in the rising phase ofcycle 24. If we consider the highest energy particle
events, viz., the ground level enhancement (GLE)events, the reduction is very drastic: there were 16
GLE events in cycle 23 compared to just 2 in cycle
24, amounting to a reduction of 88%. All large SEP
events are associated with DH Type II bursts because
the same CME-driven shock accelerates electrons(producing type II bursts) and ions (observed in spaceas SEP events). CMEs associated with SEP events andDH type II bursts are typically fast and wide (Gopalswamy et al. 2019). Not all DH type II bursts are associated with SEP events because of connectivity issues,high particle background, and < 10 pfu events. The
number of DH type II bursts decreased from 339 incycle 23 to 181 in cycle 24 (see Fig. 6d), indicating adecline by 47%, very similar to the reduction in FW
CMEs (50%) because most of the type II bursts are
due to FW CMEs.
2.5.6 Summary of solar cycle variation

Table 2 summarizes the Earth-affecting events in solar
cycles 23 and 24 in comparison with the SSN. In
addition to SSN, we have shown major soft X-ray flares,
regular CMEs, fast and wide CMEs, halo CMEs, andICMEs followed by the heliospheric consequences (DHtype II bursts, large SEP events, and major geomagnetic
storms). The decline in solar activity in cycle 24 is(2021) 8:56

Zhang et al. Progress in Earth and Planetary Science

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Table 2 Earth-affecting events in solar cycles 23 and 24 in comparison with the SSN
Property

Cycle 23

Cycle 24

ChangeChange/SSN

SSN

81

49

− 39%

0%

Major flares (M&X)

1584

809

− 49%

− 16%

X-class flares

128

49

− 62%

− 37%

M-class flares

1456

760

− 48%

− 14%

8429c

8470

+ 1%

+ 66%

501c

253

− 50%

− 17%

All CMEs (width ≥ 30°)aa

Fast & wide CMEs
Halo CMEs

a

ICMEsb

c

409

323

− 21%

+ 30%

307

208

− 32%

+ 12%

Magnetic clouds (MC)

114

86

− 25%

+ 25%

Ejecta (EJ)

193

122

− 37%

+ 4%

DH Type II bursts

339

181

− 47%

− 12%

Large SEP events

102

46

− 55%

− 26%

GLE events

16

2

− 88%

− 79%

Magnetic storms (Dst < − 100 nT)

86

22

− 74%

− 58%

CIR storms

11

2

− 82%

− 70%

CME storms

75

20

− 73%

− 56%

a

From the search engine in the CDAW catalog (http://www.lmsal.com/solarsoft/www_getcme_list.html)
From http://www.srl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm
Includes 223 Width ≥ 30° CMEs, 16 FW CMEs, and 13 halo CMEs estimated to have occurred during the 4-month SOHO/LASCO data gapbc

represented by the 39% drop in the cycle-averaged SSN.
Most events declined more than the SSN did in cycle 24,
except for ICMEs (declined by 32%), halo CMEs (declined by 21%), and the general population (W ≥ 30°) of
CMEs (increased by 1%). When the numbers are normalized to SSN, these three types of events show a clear
increase in cycle 24. Fast and wide CMEs declined incycle 24 by ~ 47%, slightly more than the SSN did.
When normalized to SSN, the number of FW CMEs per
SSN declined only by 17% in cycle 24, somewhat similar
to the decline in major soft X-ray flares and DH type II
bursts. The general population of CMEs with width > 30°
clearly showed no decrease in number; when normalized
to SSN, the average number of CMEs per SSN increased
by 66%. The two phenomena that showed the deepest
decline are the major geomagnetic storms (74%) andGLE events (88%). Both these events are related tothe heliospheric magnetic field strength, which significantly declined in cycle 24. The reduced heliosphericmagnetic field results in the weaker heliospheric pressure leading to the anomalous expansion of CMEs
and the attendant magnetic dilution in CMEs. In thecase of SEP events, the particle acceleration efficiency
depends on the strength of the heliospheric magnetic
field, hence a reduction in the latter results in lessefficient acceleration, and hence, particles do notattain high energies (Gopalswamy et al. 2014a, b).
The reduction in the heliospheric magnetic field isalso likely to be responsible for weaker CIR storms:

the decreased MHD compression of the weaker field
does not increase it to high levels.
2.6 Summary

To conclude this section, we provide a list of catalogs ofEarth-affecting transient events that have been compiled
and maintained online by many workers in the past
(Table 3). The catalogs include lists of observed solar
flares, CMEs, ICMEs in the inner heliosphere, ICMEs atin situ, interplanetary shocks, ICMEs with solar sources,
SIRs, and SEPs. These event catalogs are useful resources for facilitating research for the wide community.

3 Progress in theories of CMEs and ICMEs
3.1 Introduction

It is generally accepted that CMEs are driven by the freemagnetic energy stored in the non-potential magnetic
fields. There is a general consensus that the erupted
structure is twisted, where the most common magnetic
structure employed in modeling is a flux rope, i.e., acylindrical plasma structure with a magnetic field draped
around the central axis (Lepping et al. 1990). The
eruption of the twisted magnetic structure is interrelatedwith the magnetic reconnection of the surrounding
coronal magnetic field, releasing both thermal and nonthermal energy and producing a number of effects(Priest and Forbes 2002). The magnetic dips of the flux
rope can support cool plasma, in which case also an
eruptive filament can be observed. The whole process isZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Table 3 Catalogs of Earth-affecting solar transients, including flares, CMEs, ICMEs-IH (inner heliosphere), ICMEs-IS (in situ), shocks, SIRs
and SEPsType

AcronymDescription and link

Reference(s)Flares–Solarsoft latest events on solar flares
https://www.lmsal.com/solarsoft/latest_events/

–

CMEs

CDAW

SOHO CME catalog
https://cdaw.gsfc.nasa.gov/CME_list/(Yashiro et al. 2004)

CMEs

SEEDS

SOHO and STEREO CME catalogs based on automated method.
http://spaceweather.gmu.edu/seeds/(Olmedo et al. 2008)

CMEs

CACTUSSOHO and STEREO CME catalogs based on automated method
http://sidc.oma.be/cactus/(Robbrecht and Berghmans 2004)

CMEs

ARTEMISSOHO CME catalog based on automated method
http://cesam.lam.fr/lascomission/ARTEMIS/index.html(Boursier et al. 2005)

CMEs

CORIMPSOHO CME catalog based on automated method
http://alshamess.ifa.hawaii.edu/CORIMP/(Byrne et al. 2012)

CMEs

–

STEREO COR1 catalog, including CMEs and other events
https://cor1.gsfc.nasa.gov/catalog/

–

CMEs

MVCC

STEREO Dual-viewpoint CME catalog
http://solar.jhuapl.edu/Data-Products/COR-CME-Catalog.php(Angelos Vourlidas et al. 2017)

CMEs

KINCAT

STEREO COR2 CMEs (2007-2013) with GCS model results
http://www.affects-fp7.eu/cme-database/index.php(Bosman et al. 2012)

ICMEs-IH

HELCATSSTEREO HI event catalogs including HICAT, HIJoinCAT, HIGeoCAT
http://www.helcats-fp7.eu/(Harrison et al. 2018)

ICMEs-IS–

ACE ICMEs since 1996 complied by Richardson & Cane
http://www.srl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm(Richardson and Cane 2010)

ICMEs-IS–

WIND ICME catalog (1995-2015)
https://wind.nasa.gov/ICME_catalog/ICME_catalog_viewer.php(Nieves-Chinchilla et al. 2018a, b)

ICMEs-IS–

WIND Magnetic Cloud list (1995-2006)
https://wind.nasa.gov/mfi/mag_cloud_pub1.html(Lepping and Wu 2007)

ICMEs-IS–

WIND ICME catalog (1995-2015)
http://space.ustc.edu.cn/dreams/wind_icmes/(Chi et al. 2016)

ICMEs-IS–ICMEs and other large scale structures in solar wind
ftp://www.iki.rssi.ru/pub/omni/(Yermolaev et al. 2009)

Shocks–CfA Interplanetary Shock Database
https://www.cfa.harvard.edu/shocks/–

Shocks–Heliospheric shock database at the University of Helsinki
http://ipshocks.fi/(Kilpua et al. 2015)

ICMEs-CMEs–

ICMEs and their solar sources in solar cycle 24 from GMU
http://solar.gmu.edu/heliophysics/index.php/GMU_CME/ICME_List(Hess and Zhang 2017)

Coronal Holes–

Coronal holes during SDO era list
https://cdsarc.unistra.fr/viz-bin/cat/J/other/SoPh/294.144(Heinemann et al. 2019b)

SIRs

–

STEREO SIR list
http://www-ssc.igpp.ucla.edu/forms/stereo/stereo_level_3.html(Jian et al. 2019)

SEPs

–

Solar Proton Events from SWPC
https://umbra.nascom.nasa.gov/SEP/

–

SEPs

–

> 25 MeV Proton Events Observed by the High Energy Telescopes on
the STEREO A and B Spacecraft and/or at Earth(Richardson et al. 2014)

SEPs

–

Catalog of 55–80 MeV solar proton events extending through solar cycles 23 and 24(Paassilta et al. 2017)

SEPs

–

STEREO/SEPT Solar Energetic Electron Event List
http://www2.physik.uni-kiel.de/stereo/downloads/sept_electron_events.pdf(Nina Dresing et al. 2020)

known as “the standard flare-CME model” and is
sketched in the left panel of Fig. 9. In “the standard magnetic cloud model,” the erupting flux rope propagatesaway from the Sun, expanding at the same time, but

stays attached to the Sun, i.e., remains a closed structure,
as shown in the right panel of Fig. 9.
In order to understand the full picture of the CMEs,i.e., the magnetic structure of CMEs and processes

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

Page 24 of 102

Fig. 9 Cartoon describing three different stages of CME evolution based on “the standard CME-flare model” and “the standard magnetic cloudmodel”: the onset (left), post-eruption phase (middle), and interplanetary propagation (right)

involved in its initiation, evolution and propagation, we
need theories to explain the observed properties of
CMEs. Both numerical and analytical models need to be
employed to reach deeper understanding of the origin
and evolution of CMEs and provide a theoretical basis to
the CME/ICME observations. The numerical modelingwill be addressed in Section 4, while this section is devoted to analytical models and divided into subsections
related to CME initiation, propagation, forward modeling, fitting to in situ measurements and finally interactions with other magnetic structures.
3.2 Mechanisms, processes, and forces governing the
CME take-off

Comprehensive theoretical work on solar eruptive events
started in the 1970s and early 1980s of the past century(Green et al. 2018). Many models were focused on theflux-rope configurations and their instabilities (see references in Chen 1989; Vršnak 1990), identifying Lorentz
force as the main driving element of the eruption, and emphasizing the importance of the amount of the magnetic
field twist. For example, using the analytical approach,Chen (1989) and Vršnak (1990), independently consideredin their semi-toroidal flux-rope models a number ofeffects that were before taken only partially or were
neglected. The flux-rope models progressively developed,
becoming more sophisticated. A particular important step
was the analytical model presented by Titov and Demoulin (1999), which took into account relevant effects andprovides an idealized, but quite realistic configuration of asemi-toroidal flux rope embedded in a coronal arcade.
This model was later on included in a number of analytical studies, or numerical simulations of the loss ofequilibrium in general, and in particular, in studies of kinkand torus instabilities (Török and Kliem 2003, 2005;
Török et al. 2004; Török et al. 2010; Kliem and Török
2006; Kliem et al. 2014).
More recently in the past decade, which includes also
the VarSITI/ISEST era, there was significant progress inphysical understanding of solar eruptions. A number of
papers were published considering the pre-eruptive storage of free energy and helicity, processes that cause theevolution of the system towards an unstable state, criteria defining stable/unstable configurations, initiation of
the eruption, the dynamics of the eruption itself, as wellas the analysis of the effects of the eruption in the ambient corona (see recent reviews by Aulanier 2013;
Schmieder et al. 2015; Green et al. 2018; Patsourakos etal. 2020). Although it was clearly demonstrated that theactive region magnetic complexity and its dynamics,
free-energy, and helicity content are essential parameters, a number of open questions appeared. All of themcan be summarized in the fact that we still do notunderstand why active regions that are quite similar according to the mentioned basic characteristics sometimes produce an eruption and sometimes not.
Obviously, some key parameters are still missing, andconsequently, this makes the eruption forecasting still
highly unreliable.
The aforementioned standard CME-flare model was
realized and investigated in detail in 3D using a zero-β
MHD simulation of an initially potential bipolar field,
which evolves by means of line-tied shearing motions in
the photosphere (Aulanier et al. 2010; Aulanier et al.
2012). This 3D standard model has been exploited to investigate the properties of 3D slip-running reconnection

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

in eruption regions (Janvier et al. 2013a, b), evolution of
localized currents in flare ribbons (Janvier et al. 2014;
Barczynski et al. 2020), vortex and sink flows related tocoronal implosion (Zuccarello et al. 2017), and downward Lorentz force in the Sun’s photosphere during
eruptions (Barczynski et al. 2019). (2018) made a review
on the electric currents in the pre-eruption state and in
the course of the eruption of solar magnetic structures.
Apart from general theoretical, numerical, or observational considerations regarding the mentioned preeruption configurations, a number of papers were published being focused more on specific processes that leadto the eruption than to general properties of active regions. For example, Török et al. (2013) analyzed in detailthe consequences of rotational motions at the footpointsof a flux rope in one well-observed event. The analysis
provided a very detailed physical interpretation of characteristics of the pre-eruptive evolution of the magnetic
configuration, the initiation of the eruption, the dynamics of the eruption, as well as the evolution of an ambient magnetic system. On the other hand, Vršnak (2019)considered the rotating flux-rope leg process in moregeneral terms and showed that except in quite extreme
cases, such as considered by Török et al. (2013), thetwisting motion does not allow for poloidal flux injection
sufficient to explain the observed speeds of the slow riseof the flux rope in the gradual pre-eruptive stage. On
the other hand, it was shown that the emerging flux
process is viable to cause rising speed close to that observed in the pre-eruptive phase. Let us note that bothprocesses eventually lead to a loss of equilibrium of thesystem if persistent long enough (Green et al. 2018).
In the last decade, special attention was paid to therole of the reconnection below the erupting flux rope inthe dynamics of the eruption, primarily based on a number of observational results directly relating theeruption-related flare energy release and the eruption acceleration. This type of reconnection has three very important consequences. First, it reduces the tension of theoverlying field, which tries to prevent the eruption and,in some cases, can cause a failed eruption. Second, it
supplies the flux rope with a fresh poloidal flux that has
a twofold effect. One is a direct enhancement of the
upward-directed component of the hoop-force. The
other is weakening of the self-inductive effect of the
expanding structure that should lead to fast electriccurrent decrease, and thus a weakening of the Lorentz
force. In this respect, it is worth mentioning the paper
by Vršnak et al. (2016), where it was clearly demonstrated that the peak acceleration of the flux rope isdependent on the reconnection rate and the peak velocity is proportional to the total reconnected flux. Moreover, it was shown that the flux-rope acceleration time
profile, as well as the velocity time profile, are closely

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synchronized with the time evolution of the reconnection rate. These theoretical results are able to fully explain the effects observed in the CME-flare relationship,as discussed in Section 2.4.

3.3 CME propagation from corona through interplanetaryspace

The kinematical evolution of CMEs can be divided
into three phases: (1) slow rising phase during the
CME initiation; (2) impulsive or main acceleration
phase; (3) interplanetary propagation phase (Zhang
and Dere 2006; Temmer 2016). There are three competing forces governing CME kinematical evolution,
the gravitational force, Lorentz force and the MHD
drag.
The Lorentz force mainly provided by the magnetic
field in the corona is introduced in Section 3.2, while the
MHD drag can be well represented by the aerodynamic
drag equation and is believed to be dominant in theinterplanetary propagation phase (Cargill 2004; Vršnak
and Žic 2007). The latter is supported by observations
showing deceleration of fast CMEs and acceleration ofslow CMEs (Sheeley et al. 1999; Gopalswamy et al. 2020;
Sachdeva et al. 2015). This concept was previously introduced into a simple Empirical Shock Arrival (ESA)model (Gopalswamy et al. 2001a, b), Expansion Speed
Model (ESM) (Schwenn et al. 2005), and more recently
in an Effective Acceleration Model (EAM) (Paouris andMavromichalaki 2017), an analytical observation-driven
model by (Liu et al. 2017a) and an empirically driven
piston shock model by (Corona-Romero et al. 2017).
Based on the concept of CME propagation being governed by MHD drag, an analytical drag-based model(DBM) was introduced by (Vršnak et al. 2013). DBM
was found to very successfully describe heliosphericpropagation of CMEs (Vršnak et al. 2014; Hess andZhang 2014), therefore it was expanded to different
geometries in recent years: most notably using a 2DCone geometry (Žic et al. 2015) and 2D ellipse front(Möstl et al. 2015). Note that the interplanetary acceleration obtained by Gopalswamy et al. (2001a, b) is proportional to CME speed and hence related to the Stokes
drag, as opposed to the aerodynamic drag proportional
to the square of the CME speed.
Observations by STEREO-HI instrument lent support
to empirical models. The elongation conversion models
were previously typically combined with fitting algorithms to determine CME arrival, assuming constant
speed, as discussed in Section 2.2.4. However, the constant speed assumption was recently substituted with
DBM and combined with a newly developed Ellipse
Conversion method for the HI observations into the
ElEvoHI model (Rollett et al. 2016).

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

CMEs typically propagate radially, although deviations
were found, namely rotations (Lynch et al. 2009; Vourlidas et al. 2011) and deflections (Wang et al. 2004; Lugaz
et al. 2010b; Isavnin et al. 2014). Deflections by coronalholes have been previously modeled empirically, where adeflection from the original direction by a certain angle
was quantified by the so-called coronal hole influenceparameter (CHIP), assuming that CME continues topropagate radially (Gopalswamy et al., 2009; Mohamed
et al. 2012). Interplanetary deflections were regarded
previously as well, in a kinematic model assuming interplanetary spiral magnetic field and the background solar
wind. As a result, CMEs faster than background solar
wind are deflected to the east, whereas CMEs slower
than background solar wind are deflected to the west
(Wang et al. 2004, 2006). Recently, both deflections androtations were included in a 3D CME propagation
model, where the CME flux rope is represented with arigid, un-deformable torus and deflections and rotations
are calculated using the magnetic tension and magnetic
pressure gradients calculated from magnetometer input(Kay et al. 2013, 2020b; Kay and Opher 2015).
Analytical models are easy to run and are not time
consuming and therefore ideal for ensemble modeling(for details on ensemble modeling see Section 4). Therefore, it is not surprising that several recently developed
analytical models have their ensemble version (seeTable 4). Finally, recently a novel approach was adapted
in semi-empirical CME propagation modeling, machinelearning (Sudar et al. 2016; Liu et al. 2018d), which in
the future might prove to be a powerful predictive tool.

3.4 Forward modeling of CMEs

Recently, there has been quite a development in the forward modeling of the magnetic structures using physicsbased empirical models. The forward models typicallyassume a specific morphology of a magnetic structure,i.e., flux rope, and evolve it, assuming specific propagation and expansion. For instance, Wood et al. (2017) use
the croissant-like morphology described by Wood andHoward (2009), apply self-similar expansion and propagate it to Earth using either the Harmonic mean orFixed Phi method as a kinematic model. With thismethod, it is possible to obtain a global shape of thestructure, as well as its local size and orientation.
A step further is to include a specific flux rope magnetic field topology. Möstl et al. (2018) in their 3Dimensional COronal Rope Ejection (3DCORE) model
used a tapered torus geometry and a Gold-Hoyle magnetic field topology (Gold and Hoyle 1960) and evolve itusing DBM and self-similar expansion. They constrain
the magnetic field values using measurements at Mercury. In their Flux Rope from Eruption Data (FRED)

Page 26 of 102

method, Gopalswamy et al. (2018c) evolve self-similarlya croissant shaped flux rope obtained by the GraduatedCylindrical Shell (GCS) model and assume a Lundquisttype magnetic field topology (Lundquist 1951) constrained using reconnection flux computed from thearea under post-eruption arcades (Gopalswamy et al.
2017b; Pal et al. 2018; Sarkar et al. 2020). It should be
noted that a similar approach was studied previously by
Savani et al. (2015) and Temmer et al. (2017), where several options were regarded to constrain the magnetic
field (reconnection flux computed from H-alpha ribbons,dimming flux, and non-linear force-free modeling). Kay
et al. (2017, 2020b) also use a Lundquist-type magnetic
structure along with the shape and size represented witha rigid, un-deformable torus, which is the output of their
ForeCAT model. Their ForeCAT In situ Data Observer(FIDO) model primarily assumes self-similar expansion
and constant time-shift; however, with the addition ofsome free parameters, they are able to also simulate distortion, i.e., pancaking. They also extensively study thesensitivity of the model using ensembles (Kay and
Gopalswamy 2018), although in its current form themagnetic structure is free-fitted to the in situ measurements. Recently, the FIDO model is improved by incorporating the forward model of the CME-driven shock andsheath, as FIDO-SIT (Sheath Induced by Transient) (Kayet al. 2020a, b). Flux rope in the 3D (FRi3D) model by
Isavnin (2016) uses a croissant-like shape, but allows it
to deform while expanding due to front flattening, pancaking, and skewing. The assumed magnetic structure isof Gold-Hoyle type and the whole FR is free-fitted to thein situ measurements. Finally, probably the most extensive forward modeling study was performed by Patsourakos et al. (2016), who used GCS croissant for the CME
shape, propagated it using DBM, and then applied several different combinations for estimating the magnetic
structure topology (including non-force free magnetic
structures by Hidalgo et al. 2000 and Cid et al. 2002), as
well as the initial helicity and expansion.
Although most of the forward modeling proceduresare focused on reproducing the magnetic structure at a
certain heliospheric distance, other applications might
be noteworthy as well. A similar procedure was recently
adopted in the Forbush decrease model (ForbMod) by(Dumbović et al. 2018a, b), who expanded the GCS
croissant using empirical power-law relations for size
and magnetic field, assuming constant-speed propagation and homogeneous magnetic field in order to derivegalactic cosmic ray counts. The evolution of the internal
properties (e.g., the plasma temperature, density, velocity, and heating) of CMEs is also one of the importantresearch aspects, but it is limited to a certain position ora certain time by using remote sensing and in situ observations (e.g., Wang et al. 2005; Liu et al. 2006; Bemporad

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Table 4 List of recent analytical and semi-empirical models of CMEs/ICMEs
Type Acronym Short description

ReferenceP

EAM

Empirical; shock propagation

Paouris and Mavromichalaki (2017)

P–

Empirical; shock propagation

Liu et al. (2017b)

P–

Empirical; shock propagation

Corona-Romero et al. (2017)

P

ElEvo

DBM+2D ellipse; shock propagation

Möstl et al. (2015)

P

DBM/
DBEM

DBM + 2D Cone; CME propagation(Žic et al. 2015)(Dumbović et al. 2018a, b) (ensemble)

P

PDBM

1D DBM; probabilistic; CME propagation(Napoletano et al. 2018) (ensemble)

P

ElEvoHI

DBM+ElEvo+HI fitting; shock propagation

Rollett et al. 2016 (Amerstorfer et al. 2018)(ensemble)

P

ForeCATCME propagation; deflection; rotation(Kay et al. 2013)(Kay and Opher 2015)

P–

Machine learning; CME propagation(Sudar et al. 2016)P

CATPUMA

Machine learning, shock propagation

Liu et al. 2018aP–

Oblique collision in 3D + constrain conservation of momentum

Mishra et al. (2016)
Mishra et al. (2017)

EC–

triangulation

Liu et al. (2017a)

FM–

Croissant + self-similar expansion + HM/fixed Phi≥ size, orientation

Wood et al. (2017)

FM

3DCORE

Torus + self-similar expansion + DBM + Gold-Hoyle ≥ magnetic structure

Möstl et al. (2018)

FM

FRED

GCS + self-similar expansion + Lundquist ≥ magnetic structure

Gopalswamy et al. (2017c)

FM

FIDO

Torus + self-similar expansion + Lundquist ≥ magnetic structure

Kay et al. 2017; Kay and Gopalswamy (2018)(ensemble)

FM

FIDO-SIT

FIDO + forward model of shock and sheath

Kay et al. (2020a)

FM

FRi3D

Deformable croissant + Gold-Hoyle ≥ magnetic structure

Isavnin (2016)FM–

GCS + power-law expansion + DBM +(non) force free FR ≥ magnetic
structure(Patsourakos et al. 2016)FM

ForbMod GCS + power-law expansion ≥ Forbush decrease(Dumbović et al. 2018a, b)

FM

FRIS

CME internal properties

Mishra and Wang 2018; Mishra et al. (2020)

FIT–

Force free + circular-cylindrical + velocity modified;

Wang et al. (2015)
Wang et al. (2016d)

FIT–

Force free + circular-cylindrical + B modified

Lepping et al. (2018)

FIT–

Force free + circular-cylindrical + boundary pitch-angle treatments

Nishimura et al. (2019)

FIT–

Force free; circular-cylindrical + varying prescribed twist

Vandas and Romashets (2019)

FIT–

Non-force free; torus + non-uniform cross-section; plasma pressure + proton
current density

Hidalgo and Nieves-Chinchilla 2012; Hidalgo(2014a, b)
Hidalgo (2016)

FIT–

Non-force free; force distribution + circular-cylindrical; elliptic-cylindrical

Nieves-Chinchilla et al. 2016; Nieves-Chinchilla et al.
(2018a, b)

GS–

GS reconstruction + toroidal geometry

Hu et al. (2017)

and Mancuso 2010; Susino et al. 2013; Susino and Bemporad 2016). In a novel approach, GCS is used to calculate the CME volume and density to derive the evolutionof the magnetic ejecta and sheath density from Sun to
Earth (M. Temmer et al. 2021). To continuously figureout the internal state of an individual CME during its
heliospheric propagation, a self-similar flux rope internal
state (FRIS) model was proposed by Wang, et al. (2009),
providing the variations of the polytropic index of the

CME plasma, the average Lorentz force and the thermalpressure force inside a CME with heliocentric distance.
Recently, Mishra and Wang (2018) improved the FRIS
model, by constraining it with the observed propagationand expansion behavior of a CME and deriving a few
additional parameters (absorbed heat, entropy, heating
rate, and entropy changing rate). This model was thenimplemented to a slow (Mishra and Wang 2018) and fast
(Mishra et al. 2020) CME respectively, which showedZhang et al. Progress in Earth and Planetary Science(2021) 8:56

that during the propagation (1) the expansion was driven
by the thermal force inside the CME but prevented by
the Lorentz force, (2) the slow CME released heat before
it reached an adiabatic state and then absorbed heat, and(3) the fast CME was in the heat-releasing state throughout its journey.

3.5 In situ fitting of ICMEs

ICMEs can be identified by a number of typical properties that differ from those of the ambient solar wind
(Gosling 1990; Wimmer-Schweingruber et al. 2006; Zurbuchen and Richardson 2006; Rouillard 2011; Kilpua etal. 2017). Among ICMEs there is a subset, called magnetic clouds (MCs), which were first identified by Burlaga et al. (1981), and then studied widely in the past
decades. MCs exhibit a smooth rotation of the magnetic
field direction through a large angle, an enhanced
magnetic field strength, and a low proton temperature(Burlaga et al. 1981). MCs play an important role in understanding the evolution of CMEs in the heliosphereand are one of the main drives of space weather events(Wilson 1987; Gopalswamy et al. 2008; Gopalswamy etal. 2015a, b, c; Tsurutani et al. 1988; Huttunen et al.
2002; Wu and Lepping 2002; Cane and Richardson
2003; Zhang et al. 2007; Hidalgo 2011; Li et al. 2018).
MCs are believed to have a magnetic flux rope structure with two ends rooting on the Sun (Burlaga et al.
1981; Larson et al. 1997; Janvier et al. 2013a, b). So far,the observations about MCs could only rely on the insitu data along the MC pass path. To reconstruct the
global configuration of MCs in 2D or 3D space, a variety
of techniques has been developed. Based on the idea offorce-free (∇ × B = αB) magnetic configuration of MCs(Goldstein 1983), the symmetric-cylindrical models, withlinear (Burlaga 1988; Lepping et al. 1990; Lepping 2003)
and non-linear (Farrugia et al. 1999) force-free fields,
were proposed. Other models were also developed byadopting different assumptions, e.g., (1) the expanding
model (Farrugia et al. 1993; Vandas et al. 2006), (2) themodels with distorted cross section (Romashets andVandas 2003; Vandas et al. 2006; Démoulin and Dasso2009), and (3) the torus model (Romashets and Vandas
2003; Marubashi and Lepping 2007). Based on the observations of pressure gradients inside MCs, some nonforce-free models were proposed and then improved(Hidalgo 2002; Mulligan and Russell 2001; Hidalgo et al.
2002; Cid et al. 2002; Hidalgo 2003).
Nowadays, with our deeper understanding about MCs,
several new models have emerged, which are still basedon the well-developed description of (non-)force-freemagnetic configuration, but can provide extra information about MCs.

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3.5.1 Force-free magnetic field

The assumptions of the cross section and the symmetry
along the MC axis are two keys in the models. Keeping
the cylindrical symmetry and the circular cross section,
Wang et al., (Wang et al. 2015; Wang et al. 2016a, b, c,d) developed the velocity-modified force-free flux ropemodels, which can provide the MC kinetic information
by incorporating the linear propagating motion awayfrom the Sun, the expanding, and the poloidal motion of
the plasma inside MCs with respect to the MC axis. Recently, Lepping et al. (2018) improved their previous
model (Lepping et al. 1990) by modifying the magnetic
field magnitude based on a B-modification scheme presented by Lepping et al. (2017). This model improves
the fitted B-profile, but is applicable for use with data
originating only at/near 1 AU. The models of linear
force-free field with different boundary pitch-angle treatments and of non-linear force-free field with varying
prescribed twist were derived by Nishimura et al. andVandas and Romashets, respectively. Discarding the assumption of locally straight MC axis, the non-cylindricalmodels were developed. For example, Owens et al.
(2012) developed a curved flux rope (CFR) model, assuming a circular cross section but bending the axial
field in a similar manner to a Parker spiral magnetic
field. This allows the radius of curvature of the axis andthe cross-sectional extent of the MC to vary along thelength of the axis. To model irregularities in MCs,
Romashets and Vandas (2013) added a local irregularity
in the form of a compact toroid into a cylindrical linear
force-free magnetic structure. Furthermore, MCs in toroidal geometry can also be constructed by the constantalpha force-free magnetic field with elliptical cross sections (Vandas and Romashets 2017a) or uniform-twist
force-free field with circular cross sections (Vandas andRomashets 2017b).

3.5.2 Non-force-free magnetic field

Inherited from Hidalgo (2003), a much more complicated model was improved in series (Hidalgo and NievesChinchilla 2012; Hidalgo 2013). Hidalgo and NievesChinchilla described MC topology with torus geometry
and a non-uniform cross section. They established an intrinsic coordinate system for that topology, and then analytically solved the Maxwell equations in terms of it. The
model was tested by applying it to the observations ofmultiple spacecraft by Hidalgo (2013). The model was further improved with inclusion of the plasma pressure (Hidalgo) and the proton current density (Hidalgo).
Extending the concept of Hidalgo et al. (2002), NievesChinchilla et al. (2016) presented a circular-cylindricalflux-rope model by introducing a general form for theZhang et al. Progress in Earth and Planetary Science(2021) 8:56

radial dependence of the current density, which can givethe information on the force distribution inside MCs. The
circular cross section was later improved to an ellipse(new elliptic-cylindrical flux rope model, Nieves-Chinchilla et al. 2018b), which, for the first time, allows us tocompletely describe MCs by nonorthogonal geometry.
Different from the above introduced techniques, theGrad-Shafranov (GS) reconstruction technique (Hu andSonnerup 2001, 2002) doesn’t presume a specific magnetic structure or cross section of MCs. It assumes thatan asymmetric-cylindrical MC is in an approximately
magnetostatic equilibrium with an invariant direction,and uses the GS equation to recover the magnetic field
as well as the plasma pressure. With this technique, theboundaries of the MC need not first be identified in thedata. Recently, trying to approach a more real flux rope
topology, the GS reconstruction of MCs in toroidal
geometry with rotational symmetry was developed (Hu2017; Hu et al. 2017).
The development of all the introduced techniques provides invaluable tools for extracting information about
the properties of MCs and leads to our understanding of
the underlying physics of MCs. However, it was shownthat assessing their accuracy based on in situ data ischallenging. Different comparisons of the MC properties
have been performed between: (1) MHD simulations
and fitting techniques (Riley et al. 2004; Vandas et al.
2010; Al-Haddad et al. 2011, 2019) and (2) different fitting models (Al-Haddad et al. 2013; Démoulin et al.
2013; Hu et al. 2013; Vandas et al. 2015; Vandas andRomashets 2015; Lepping et al.; Nishimura et al. 2019).
While these studies largely support the applicability of
examined techniques and methods to gain insight into
the MC structure, they also reveal their limitations aswell as reliability issues, which should be tackled in the
future.
3.6 CME-CME interactions

Erupting from the Sun, coronal mass ejections (CMEs)
will interact with different structures. The interaction
between a CME and the magnetic fields in the corona
and interplanetary space, and the solar wind govern the
propagation and evolution of the CME itself (see Section
3.3 and 3.4). Magnetic reconnection between CMEs andambient solar wind can lead to the peeling-off of themagnetic field lines and the erosion of the axial magnetic flux from the CMEs (Dasso et al. 2006; Gosling
2012; Ruffenach et al. 2012, 2015; Manchester et al.
2014; Lavraud et al. 2014; Wang et al. 2018). Furthermore, CMEs were found to be interacting with other
CMEs by many observations (e.g., Gopalswamy et al.
2001a; Lugaz et al. 2012; Shen et al. 2012; Harrison et al.
2012; Liu et al. 2012; Liu et al. 2014; Oliveros et al. 2012;
Möstl et al. 2012; Temmer et al. 2012, 2014; Webb et al.

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2013; Shanmugaraju et al. 2014; Colaninno and Vourlidas 2015; Mishra et al. 2014, 2015, 2017), involving complicated physical processes, resulting in the changes of
CME properties, forming complex structures, and playing an important role in leading to large solar energetic
particle events (see Section 6) and intense geomagnetic
storms. CME-CME interaction can result in the changes
in CME speed, propagation direction (Xiong et al. 2009;
Lugaz et al. 2012; Shen et al. 2012; Mishra et al.), radialextent as well as internal magnetic strength (Schmidt
and Cargill 2004; Xiong et al. 2006; Lugaz et al. 2005,
2012, 2013). The speed change, or more physical, the natures of the collision of one CME by another, were
widely studied, namely, inelastic (Lugaz et al. 2012; Maričić et al. 2004; Mishra et al. 2015) vs. elastic (Mishra etal. 2014, 2015) vs. super-elastic collision (Shen et al.
2012; Shen et al. 2013a, b, c; Colaninno and Vourlidas
2015). Note that the term of collision used here refers tothat the main bodies of two CMEs are touching.
In determining the nature of collisions, most of theearlier studies have used a simplistic approach that
CMEs are propagating exactly in the same direction, i.e.,
1D head-on collision (with momentum conservation
constraint, Mishra and Srivastava 2014). However, arelatively more precise analysis should be performed in
3D space. For the first time, Shen et al. (2012) studied
an oblique collision of CMEs in 3D using imaging observations (Fig. 10a) and took several uncertainties into
consideration, but did not constrain the conservation of
momentum instead of indirectly evaluating it by analyzing the effect of the solar wind on the acceleration of thefirst CME. Recently, Mishra et al. addressed previous
limitation and proposed a method for the 3D obliquecollision (Fig. 10b), in which the post-collision directions
and speeds are theoretically measured with different preset restitution coefficients, together satisfying the momentum conservation law, and the best-matched parameters with the observations are extracted. Based on this,Mishra et al. (2017) calculated the uncertainties in determining the nature of collisions quantitively, with different CME observed parameters considered, which thenemphasized the possibility of a large uncertainty (seeone example in Fig. 10c). Furthermore, Shen et al. (2017)presented four definitions of different types of collisions,
i.e., a classical Newtonian definition, an energy definition,
Poissons definition, and Stronges definition, helping witha deeper understanding of the determination of collisions
in theory. They focused on the first two used in observational and numerical studies, and found out that these two
definitions are not equivalent to each other when thecolliding objects are expanding or contracting with achanging rate.
Including collision, CME-CME interaction could be
involved in four forms: (1) the two-CME driven shockZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 10 a The super-elastic collision of two interacting CMEs observed in STEREO/HI (Shen et al. 2012). b Oblique collision of two CMEs assumed
as spherical bubbles (Mishra et al.,). c The variation of the restitution coefficient with the uncertainties of pre-collision longitudes of CMEs
considered (Mishra et al.,)waves interact without the ejecta interacting, (2) one
shock wave interacts with preceding magnetic ejecta,(3) the direct interaction between two ejecta, and (4)the reconnection between successive magnetic ejecta.
The last three may result in a variety of complex
structures at 1 AU: from 1) partial ongoing interaction of the preceding ejecta with a shock wave inside (Lugaz et al. 2015a, b; Wang 2003; Liu et al.
2018c; Wang et al. 2018; Xu et al. 2019), to full interaction of 2) the multiple magnetic cloud (MC) events(Wang et al. 2002; Wang 2003; Shen et al. 2011c;
Lugaz et al. 2013), 3) a complex ejecta or compound
stream (Burlaga et al. 2003), and 4) long-duration
events (Dasso et al. 2009; Lugaz et al. 2013), leading
to a hard understanding of the undisturbed conditions
by in situ observations. Wang et al. (2018) proposeda recovery model based on Rankine-Hugoniot jumpconditions to recover the shocked structure back to
the uncompressed state in the first type of the interacted structure, which was later used to estimate therelated interacting effects in causing geoeffectiveness(Shen et al. 2018a; Xu et al. 2019). The resulting
structure from CME-CME interaction was discussed
in detail in the review of Lugaz et al. (2017).
3.7 Summary of recent analytical and semi-empiricalmodelsTable 4 shows the analytical and semi-empirical modelsof CMEs/ICMEs introduced above, which were proposedroughly during the VarSITI period (ca. 2014-2018).
Models are listed according to their type: P = propagation, EC = elongation conversion, FM = forward
modeling, FIT = in situ fitting, GS = Grad-Shafranov
reconstruction.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

4 Progress in numerical modeling of CMEs/ICMEs
Magnetohydrodynamic (MHD) simulations have proved
to be one of the most important tools to study the evolution of a coronal mass ejection (CME) in both corona
and interplanetary space, and the modeled results can beused to analyze the initiation and propagation characteristics observed by ground-based and space-based instruments. Lugaz and Roussev (2011) gave a detailed review
and discussion on the efforts to use numerical simulations of ICMEs to investigate the magnetic topology,
density structure, energetics, and kinematics of ICMEs
in the interplanetary space. In the book of Feng (2020),
the author has provided a recent in-depth review of the
field focusing primarily on the current status of MHD
modeling for space weather with a thorough collection
at the time of writing the book. Here, the review is devoted to recent progress of time-dependent MHD space
weather modeling with the focus on such topics: the ambient solar wind, CME initiation and CME propagation,
CME-solar-wind interaction, and CME-CME interaction,
especially on the work performed in the second half ofthe 2010s.
4.1 Modeling the background solar wind

Numerical simulations have shown the importance of an
accurate modeling of the background medium in which
the disturbances propagate (Odstrčil and Pizzo 1999a,
1999b; Chané et al. 2005; Shen et al. 2007). In the past
decade, due to the vast improvement in computational
resources, the usage of 3D MHD models for reconstructing the solar corona and interplanetary solar wind has
become almost routine. Moreover, based on much improved observations, it is possible to produce more realistic simulations, e.g., by the inclusion of theobservational data through the boundary conditions or
through data assimilation. Hayashi (2012) presented atreatment of observation-based time-dependent boundary conditions for the inner boundary sphere in the 3D
MHD simulations of the global co-rotating solar windstructures. In order to adjust the model to the timevarying magnetic field on the bottom boundary, developed the model of the confined differential potential
field (CDPF) to prescribe the bottom boundary condition, In addition, a modified version of this model was
adopted as the module of the time-dependent 3D MHD
simulation at the Joint Science Operation Center (JSOC)
of SDO (Hayashi et al. 2015). The module could
routinely generate 3D data of the time-relaxed
minimum-energy state of the solar corona using the fulldisk magnetogram data from HMI/SDO.
In parallel, data assimilation has been included in the
WSA model through ADAPT (Hickmann et al. 2015),
which allows photospheric simulations to agree betterwith available observations from magnetograms. This

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model has been coupled to the 3D MHD LFM-Helio(Merkin et al. 2016a) to perform time-dependent simulations of the background solar wind. These simulations
are able to reproduce more accurate details of smallscale of the heliospheric current sheet and corotatinginteraction regions.
Recently, by using magnetogram synoptic map images
from GONG and theoretical/empirical models such as
the PFSS model and WSA model, Shen et al. (2018a, b)
applied a new boundary treatment to the 3D MHD
simulation of solar wind and established the 3D IN(INterplanetary)-TVD-MHD model. The boundary conditions depend on five tunable parameters when simulating the solar wind for different phases of the solar cycle,
and the simulated solar wind parameters are in good
agreement with the observations most of the time. However, we know that models tend to fail when solar activity increases. The comparison of their modeled resultswith the in situ data throughout 2007 is shown in Fig. 11,
which demonstrates that the simulation retrieves mostof the high-speed streams (HSSs), and the duration time
and the magnitude of the HSSs are largely consistent
with those of the observations. Later, Yang and Shen
(2019) presented a new method to construct the global
distribution of solar wind parameters at the source surface using multiple observations and the ANN (Artificial
Neural Network) technique, which could be used to provide a more realistic boundary condition for 3D MHD
solar wind modeling.
By using CORHEL (CORona-HELiosphere), Linker etal. (2016) further developed a time-dependent study ofthe solar wind empirically driven by magnetic maps at adaily cadence using ADAPT. Their simulation showedboth classic features of stream structure in the interplanetary medium often seen in steady-state models and evolutionary features unable to be captured in a steady-state
approach. Their model results also compared reasonably
well with 1 AU OMNI observations. As a rather mature
space weather model, CORHEL is a coupled suite ofmodels for simulating the solar corona and solar wind in
3D space, which provides three solar coronal models atpresent, including the Magnetohydrodynamic Algorithm
outside a Sphere (MAS) polytropic model, MAS thermodynamic model, and the potential field source surface
and Wang-Sheeley-Arge (PFSS-WSA) model. The
heliospheric models involved in CORHEL are ENLIL(Odstrcil et al. 2004), the MAS-Heliosphere (MAS-H)
models (Lionello et al. 2013), and the LFM-Helio(Merkin et al. 2016b).
Based on the SWMF (Space Weather Modeling
Framework), van der Holst et al. (2014) developed the
Alfvén wave solar model named AWSoM, which is a 3D
MHD model that considers the anisotropy of ion
temperature in the solar corona and the innerZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 11 Modeled (red lines) and observed (blue lines) time profiles of solar wind parameters at 1.0 au through all of 2007. From the top to thebottom, the panels show the speed Vr, number density N, temperature T, and total magnetic field strength B (From Shen et al. 2018b). Besides
the improvement in the treatment of the inner boundary condition, there have been significant improvements in other aspects of backgroundsolar wind simulations in recent years. Some of these are detailed below

heliosphere. In Meng et al. (2015), the AWSoM model
has been applied to simulate the steady solar wind fromthe solar corona to 1 AU of CRs 2107 and 2123.
Figure 12 a shows the simulated proton pressure anisotropy ratio and the simulated solar wind speed in the Y =0 and Z = 0 planes for CR2107. The simulation results
also show a reasonable agreement with in situ observations at 1 AU, as shown in Fig. 12b. Together with a coronal charge state evolution model, the Michigan
Ionization Code (MIC, see Landi et al. 2012), Oran et al.
(2015) employed the AWSoM model to calculate theelemental charge state evolution along the modeled open
magnetic field lines. The charge state evolution model
was initiated with the electron density, temperature, andspeed simulated by the AWSoM wind model, and provided the first charge state calculation covering all latitudes in a realistic magnetic field. Evans et al. (2012)self-consistently coupled the Alfvén wave energy transport with the MHD equations. In this solar wind model,
they introduced an additional dissipation mechanism:
surface Alfvén wave (SAW) damping, which was weak in
the polar regions, and strong in subpolar latitudes andthe boundaries of open and closed magnetic fields. Theirsimulated results showed that SAW damping could reproduce regions of enhanced temperature at the boundaries of open and closed magnetic fields seen in both

tomographic reconstructions in the low corona andUlysses data in the heliosphere. Sokolov et al. (2013)presented a combined global model of the solar corona,
the low corona, the transition region, and the top of thechromosphere. Their model used MHD Alfvén wave turbulence as the only momentum and energy source toheat the coronal plasma and drive the solar wind withdifferent turbulence dissipation efficiencies in coronalholes and closed field regions. Recent developments include further validations of the AWSoM model (Gombosi et al. 2018; Sachdeva et al. 2019). It is known that
the SWMF couples the models of Lower Corona (LC),Solar Corona (SC), Inner Heliosphere (IH), and other integrated components (Tóth et al. 2012). The models ofSC, IH, and several other components are modeled by
the BATS-R-US code. In the SC model, van der Holst etal. (2010) solved the two-temperature MHD equations
with Alfvén wave heating and heat conduction on either
Cartesian or spherical grid in a frame corotating with
the Sun.
Feng et al. (2010) employed the SIP-CESE MHD
model within a six-component overset grid for solar
wind simulation. They numerically investigated thelarge-scale structures of interplanetary solar wind andthe evolution of the heliospheric magnetic field. Feng etal. (2012) carried out the numerical studies for the solar

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 12 a The simulated proton pressure anisotropy ratio p⊥/p_ and the simulated solar wind speed in the Y = 0 and Z = 0 planes for CR2107 by
the AWSoM model, and the plot on the right bottom panel also shows the trajectories of the Earth, STEREO-A and B satellites projected to the Z= 0 plane; b The simulated solar wind properties along the WIND orbit and the WIND data during CR2107 (From Meng et al. 2015)

wind background of different solar-activity phases byusing the SIP-AMR-CESE MHD model, and their modeled results could reproduce many features near the Sun
and in interplanetary space, e.g., the changing trends ofthe solar-wind parameters for the selected CRs, and the
IMF polarities and their changes. Furthermore, Feng etal. (2015) investigated the solar wind evolution between
the solar surface to the Earth’s orbit from 1 July to 11
August 2008 with the SIP-AMR-CESE MHD model
driven by the consecutive synoptic maps from GONG.
Similarly, Li and Feng (2018) simulated the evolution ofsolar wind from the solar surface to the Earth’s orbit
during the year 2008, and evaluated simulated results
quantitatively by comparison with in situ measurements.
Merkin et al. (2011) adapted the Lyon-FedderMobarry (LFM) model for the inner heliosphere, which
was referred to as the LFM-helio model. They simulated
the solar wind and heliospheric magnetic field between
0.1 and 2 AU to study the disruption of a heliosphericcurrent sheet fold during CR 1892 in the decline phaseof solar cycle 22. (Merkin et al. 2016a) also presented asimulation study exploring heliospheric consequences oftime-dependent changes at the Sun during a 2-month
period in the beginning of the year 2008, in which theyused the Air Force Data Assimilate Photospheric Flux
Transport (ADAPT) model to obtain daily updated
photospheric magnetograms and drive the WSA modelof the corona. The results of the WSA model were used

as a time-dependent boundary condition for the LFMhelio model. They compared the simulation results with
ACE, STEREO-A and B near 1 AU, and MESSENGER
spacecraft orbiting between 0.35 and 0.6 AU and theirsimulations showed that time-dependent simulations
could reproduce the gross-scale structure of theheliosphere.
The CRONOS model was employed to solve the equations of ideal MHD in a one-fluid model and to obtainrealistic modeling solar wind conditions from 0.1 AU to
2 AU (Wiengarten et al. 2013, 2014). Additionally,
Wiengarten et al. (2015) incorporated turbulence transport into the Reynolds-averaged MHD equations in theframework of the CRONOS, which was used to investigate the effects on the turbulence evolution for transient
events from 0.1 AU to 1 AU by injecting a CME fromthe inner boundary.
Shiota et al. (2014) developed the SUSANOO model(also see Shiota and Kataoka 2016), and they used it tosimulate the ambient solar wind structure from 25 to
425 Rs covering 3 years (2007–2009). Their numericalresults were in reasonable agreement with in situ measurements at Venus and Mars by Venus Express andMars Express, respectively (Shiota et al. 2014).
The “European heliospheric forecasting information
asset” (EUHFORIA) is another space weather forecastingmodel focus on the inner heliosphere, which is capableto provide MHD modeling of the ambient solar windZhang et al. Progress in Earth and Planetary Science(2021) 8:56

and the CME eruptions from 0.1 AU to 2 AU (Moschou
et al. 2017; Pomoell and Poedts 2018). EUHFORIA
consists of an empirical coronal model and an MHD
heliosphere model. The coronal model provides
plasma and magnetic field parameters at Rb = 0.1AU,
which was then used as a boundary condition to drive
the heliospheric model. The performance of the solar
wind model was recently performed in the study ofHinterreiter et al. (2019).
Usmanov et al. (2018) presented a fully 3D MHD
model of the solar corona and solar wind by coupling
Reynolds-averaged solar wind equations with transport
equations for turbulence energy, cross helicity, and correlation scale, from the coronal base to 5 AU. Theirsimulation results showed that the model could reproduce most of the solar wind parameters compared with
Ulysses data during its first and third fast latitudetransits.
Piantschitsch et al. (2017) developed a 2.5D MHD
code to simulate the corona wave propagation and its
interaction with a low-density region, such as the coronal hole (CH). By using this new code, they also madea comprehensive analysis on the dependence on different Alfvén speeds inside the CH and initial amplitude of
the incoming wave (Piantschitsch et al. 2018a, b). Theirresults depicted that the density value inside the CH influenced the phase speed and the amplitude values ofdensity and magnetic field for all different secondary
waves and there existed a correlation between the initial
amplitude of the incoming wave and the amplitudes ofthe secondary waves as well as the peak values of thestationary features.
4.2 Modeling CME initiation and propagation

Jacobs and Poedts (2011) gave a detailed review about
the state-of-the-art models for CME simulation before
2011. Here, we focus on the progress on the CME initiation, propagation, CME-CME interaction, and CMEsolar wind interaction, mainly after 2011, especially fromyears 2014 to 2019. The review by Manchester et al.
(2017) includes a section about simulations andadditional information about the main physical processes
occurring during CME evolution in the innerheliosphere.
4.2.1 Modeling CME initialization

Most of the existing CME models are candidates for
mimicking the morphology near the Sun, with the purpose of reproducing the plasma parameters comparablewith 1 AU observations, with the stated goal for many ofthem to move towards real-time space weather forecasting simulations. Presently, significant progress has been
made towards improving the performance of the existing
CME initialization models.

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4.2.1.1 Cone model The cone model ((Fisher andMunro 1984; Zhao et al. 2002; Zhao et al. 2016a; Xie etal. 2004) is one of the popular CME initiation models
because of its simplicity and relatively good match with
CME arrival time observations. In most implementationsof the model, the CME does not possess an internal
magnetic field, but the input size, speed, and location
are determined from coronal observations, typically fromcoronagraphs. In addition, due to its geometry and lackof internal magnetic field, the initiation does not include
parameters related to CME orientation. By using conemodel as CME initialization, Odstrcil et al. (2005) applied the 3D MHD simulation to the 12 May 1997 interplanetary event to analyze possible interactions of the
ICME propagating in various steady-state and evolving
configurations of the background solar wind.
Taktakishvili et al. (2011) reported the simulated results of selected well-observed halo CME events using a
combined model of the WSA/ENLIL and the conemodels. Their simulation results demonstrated that the
combination of numerical models with the observations
from coronagraph as input could give reasonably goodresults for the CMEs’ arrival times for the selected set of“geoeffective” CME events. Bain et al. (2016) also combined the WSA-ENLIL and cone models to discuss
shock connectivity in the August 2010 and July 2012
events. Dewey et al. (2015) integrated the cone model
into the WSA-ENLIL model to study the CME-related
solar wind perturbations on the Mercury system. Theirsimulation results demonstrated that the modeled results
could be compared with the observations by the
spacecraft of MErcury Surface, Space ENvironment,
GEochemistry and Ranging (MESSENGER) during theperiod from March 2011 to December 2012.
Pomoell and Poedts (2018) integrated the cone model
into the EUHRORIA model to simulate the CME eventsin the inner heliosphere during 17–29 July 2015. Also by
combining the cone model and the EUHRORIA model,
Scolini et al. (2018a, b) tested the effect of different
CME shapes on the simulation results, and theirsimulation results showed that all the parameters specifying the CME shape in the model significantly affectsimulation results at 1 AU as well as the predicted CME
geoeffectiveness.
4.2.1.2 Flux rope models Flux rope models have beenshown to self-consistently reproduce many observed
properties of CMEs, including the three-part density
structure (Manchester et al. 2017). Contrary to the conemodels, they include an internal magnetic field and may
therefore reproduce not only the arrival time but also
the magnetic field components when a CME impacts
Earth. Due to their more complex nature, additionalparameters are required to initialize such models,Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

including the internal magnetic field strength or flux
and the orientation of the flux rope. These models
were first implemented in 3D MHD simulations by
Roussev et al. (2003).
Lionello et al. (2013) improved the MAS-ENLIL modelby inserting an out-of-equilibrium flux rope in the coronal model within 7 Rs as CME initiation model, andthey simulated the propagation of an interplanetary
CME (ICME) from 18 Rs to 1.1 AU. Their simulation results showed that the improved model could follow the
propagation of the CME accurately. By using the MAS/
MAS-H model combined the modified Titov-Démoulin(TDm) model (Titov et al. 2014), Török et al. (2018)
inserted a magnetically stable flux rope to generate a
CME close to the observed properties of the 2000 July
14 “Bastille Day” eruption. The properties of the CME as
it propagates were studied based on MHD simulationsof solar eruptions from near the Sun to the Earth.
Figure 13 a shows the initial flux-rope field lines, (b) depicts the field lines of the flux-rope core at t = 164.10,
shortly after eruption onset, and (c) and (d) demonstrates the interplanetary magnetic field and ICME flux
rope at t = 256, shortly before it reached 1 AU.
Using the Titov-Démoulin (TD) flux-rope model toinitiate the CME, Jin et al. (2013) simulated a fast CME
erupted from active region NOAA AR 11164 during
CR2107. Simulations of this CME event were conducted

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with 1T (one-temperature) and 2T (two-temperature:coupled electron and proton) MHD models. The authors
compared the propagation of this fast CME and thethermodynamics of CME-driven shocks in both the 1T
and 2T CME simulations, and their results demonstrated
the importance of the electron heat conduction in conjunction with proton shock heating in order to produce
the physically correct CME structures and CME-driven
shocks.
Based on the solar wind background constructed by
the AWSoM SC model (van der Holst et al. 2014), Jin etal. (2016) presented a numerical simulation on the CME
which occurred at 00:04 UT on 15 February 2011, which
was initiated by using the analytical Gibson-Low (GL)flux rope model (Gibson and Low 1998) with different
parameters. Their simulation results showed that a
CME’s impact on the surrounding solar wind structures
would be influenced by the magnetic strength of thesestructures, their distance to the source region, and theinteraction between the CME with the large-scale magnetic field. Jin et al. (2017a) developed a new data-driven
tool called Eruptive Event Generator Gibson-Low(EEGGL) to automatically determine the GL flux ropeparameters using synoptic magnetogram data fromGONG and CME speed derived from the observationsof SOHO/LASCO. By combining the EEGGL model andthe AWSoM solar wind model, Jin et al. (2017a)

Fig. 13 a Initial flux-rope field lines with zero-β relaxation; b field lines of the flux-rope core at t = 164.10; c Interplanetary magnetic field and
ICME flux rope at t = 256; d close-up view on (c), showing two flux bundles at the core of the flux rope (From Török et al. 2018)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

conducted a comprehensive study of CME propagationon 7 March 2011 by performing a simulation from thechromosphere to 1 AU. Their simulated results could reproduce many of the observed features both near the
Sun and in the heliosphere. Figure 14 a depicts the initial
GL flux rope configuration for 7 March 2011 CME eventwith the central plane showing the radial velocity, andFig. 14 b compares EUV waves in the simulation and in
the SDO/AIA observation.
The Versatile Advection Code (VAC) is a general tool
for solving MHD and hydrodynamical problems withastrophysical applications. A variety of numerical
schemes are available for users to solve hyperbolic differential equations, including, e.g., TVD-Roe, TVDLF, andPage 36 of 102

flux correction of transport (FCT) method. By using the
VAC model, Jacobs and Poedts (2012) solved the MHD
equations with the inner boundary of the domain locating at the low solar corona. They investigated the effectof new flux emergence on a magnetic system that possessed a 3D topology favorable for the breakout scenario,
which was suitable for the ‘breakout’ CME scenario towork. Keppens et al. (2012) implemented a block-based
AMR on the parallel VAC model using the Message
Passing Interface library (MPI-AMRVAC), which has
been used to provide interplanetary space weather forecasting models with relative accurate time-dependent
boundary conditions of erupting magnetic flux ropes in
the upper solar corona. Pagano et al. (2015) performed a

Fig. 14 a Initial GL flux rope configuration for 7 March 2011 CME; b EUV waves in the simulation (left) and in the SDO/AIA observation (right)(From Jin et al. 2017a)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

3D MHD simulation of a flux rope ejection where a
CME was produced by using the MPI-AMRVAC. Theirresults showed that the polarization ratio techniquecould reproduce the position of the center of mass along
the line of sight with a relative high accuracy and studied
the propagation of the CME in the real 3D direction.
Singh et al. (2019, 2020) modified the Gibson-Low flux
rope by constraining the poloidal and toroidal fluxes of
the initial flux rope using eruption data such as total
reconnected flux (Gopalswamy et al. 2018c) and flux inthe core dimming regions (Webb et al. 2000; Dissauer et
al. 2018; Kay and Gopalswamy 2018). The modified
spheromak has the option to control the helicity sign offlux ropes, which can be derived from line-of-sight magnetograms. Singh et al. (2020) simulated the 2012 July
12 CME and showed that they can reproduce the properties of the CME in the coronagraph FOV. They created a solar wind background from 1.03 Rs to 30 Rs
using solar synoptic magnetograms. Then the flux ropemodel is inserted into the domain, allowing it to erupt
as a CME due to pressure imbalance. They used theMulti-Scale Fluid Kinetic Simulation Suite (MS-FLUKSS,
Yalim et al. 2017), which is a highly parallelized code
suitable for MHD treatment of plasma and fluid.
Wu et al. (2016c) presented a 3D MHD simulation
based on an observed eruptive twisted flux rope deduced
from solar vector magnetograms. They combined a datadriven flux rope model for the CME initiation and a global coronal-heliosphere evolution model to track the
propagation of the CME. They selected the CME eventon 6 September 2011 to test this model, and their simulation results suggested that the flux rope evolution
model produced the physical properties of a CME, andthe morphology resembled the observations made by
STEREO/COR1.
By using the 3D IN-TVD-MHD model (Shen et al.
2018b), Liu et al. (2019a) established a CME flux ropemodel based on the graduated cylindrical shell (GCS)
model and applied it into the numerical simulation on
the propagation and deflection of the fast CMEs in theinterplanetary space from 0.1 AU to 1 AU.
4.2.1.3 Spherical plasmoid/magnetized plasma blob
Besides the cone model and the flux rope models, thespherical plasmoid model and the magnetized plasma
blob are also popular CME initialization models used inrecent years (Shen et al. 2011a; Shen et al. 2011b; Zhou
et al. 2012; Shen et al. 2012a; Zhou and Feng 2013; Shenet al. 2014; Zhou et al. 2014; Kataoka et al. 2009; Shiota
and Kataoka 2016; Chané et al. 2006). Similar to the flux
rope model, they incorporate an internal magnetic field
and require the associated parameters. Initial 3D MHD
simulations were performed by Groth et al. (2000) andManchester et al. (2004).

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By using the 3D SIP-CESE MHD model with thespherical plasmoid mimicking CME initiation model, thetime-dependent propagation of the Sun-Earth connection CME events, such as 4 November 1997, 12 May
1997, and 2010 April 3 CME events were investigated(Zhou et al. 2012; Zhou and Feng 2013; Zhou et al.
2014). And their simulated results provided a relatively
satisfactory comparison with the Wind spacecraft
observations.
By using the 3D COIN-TVD MHD model with themagnetized plasma blob as CME initialization model,
Shen et al. (2011b) and Shen et al. (2014) simulated thetime-dependent propagation of single CME events, andthe interaction of two CMEs events, such as 4 April
2000 and 12 July 2012 CME events, and 28 March 2001
CME-CME interaction event. Their simulation could reproduce relatively well the real 3D nature of the CME inmorphology and their evolution from the Sun to the Earth.
A spheromak-type magnetic flux rope was also taken
as the magnetic field structure of the initial CME modelby Kataoka et al. (2009) and Shiota and Kataoka (2016),
to simulate the propagation of the CME by using theSUSANOO model.
Recently, a spheromak model was included in
EUFHORIA (Verbeke et al. 2019a) and shown to workin term of comparison with in situ measurements for
specific case studies for Sun-to-Earth propagation of
CMEs (Scolini et al. 2019c; Palmerio et al. 2019).
4.2.1.4 Reconstructions of coronal magnetic fields Inorder to extrapolate the coronal magnetic field fromphotospheric vector magnetograms based on the nonlinear force-free method, Jiang et al. (2011) and Jiang andFeng (2012) Jiang and Feng (2012) exploited the CESEMHD model to solve the zero-beta MHD equations witha fictitious frictional force and make reconstructions ofcoronal magnetic field, which was called as CESE-MHD
nonlinear force-free field (CESE-MHD-NLFFF) model(also see (Jiang et al. 2013a, ; Jiang et al. 2014; Jiang andFeng 2014). By using the CESE-MHD-NLFFF model
combing the vector magnetograms observations, a seriesof simulations were carried out to investigate, among
others, the 3D magnetic field of NOAA AR 11117 on 25
October 2010, formation and eruption of the active region sigmoid in AR 11283, a large-scale pre-flare current
sheet in NOAA AR 11967, and the evolving magnetic
topology for an X9.3 eruptive flare from geoeffective AR
12673 that occurred on September 6, 2017 (Jiang et al.
2012; Jiang et al. 2013b; Jiang et al. 2016, 2017, 2018).
Their simulations could qualitatively reproduce the basic
structures of the 3D magnetic field; the current sheet in
the corona as well as providing insight into the magnetic
mechanism of solar flares; the spatial location, the temporal separation of the observed flare ribbons, as well asZhang et al. Progress in Earth and Planetary Science(2021) 8:56

the dynamic boundary of the flux rope’s feet by mapping
footpoints of the newly reconnected field lines. Figure 15
presents the comparison of the modeled magnetic fieldwith the observed features of the solar corona prior to
the flare field lines (Jiang et al. 2018). Several other studies also reconstructed the 3D coronal magnetic field of
AR 12673 and addressed the possible magnetic processes
responsible for the X9.3 eruptive flares (Inoue et al.
2018; Liu et al. 2018b; Yan et al. 2018; Mitra et al. 2018;
Hou et al. 2018).
By using MPI-AMRVAC MHD code to the reduced
MHD equations with only the density, velocity, andthe magnetic field, and without the gradient of gas
pressure and gravity, and the energy equation, Guo etal. (2019) developed a data-driven MHD model withthe zero-β approximation. The initial condition isprovided by a nonlinear force-free field derived fromthe magnetofrictional method based on vector magnetic field observation from SDO. Their MHD simulation was carried out for AR 11123 observed on 11
November 2010 and could reproduce the eruption
process of the magnetic flux rope.
Other works that have more realistic magnetic fieldevolution models into the CME model include Price etal. (2019), Pomoell et al. (2019), Hayashi et al. (2018)
and Hayashi et al. (2019) among others.

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4.2.1.5 Other CME models The HAFv.2+3D MHD
model has been used to study a variety of solar eruptive
events, such as the interplanetary evolution of the observed geoeffective CME during 1–4 August 2010 (Wuet al. 2011), and the effects of the coronal hole on CME/
shock morphology in the inner heliosphere with 7
March 2011 solar events (Wood et al. 2012). Liou et al.
(2014) employed the model to investigate the propagation of the extremely fast backside CME event on 23 July2012 and the modeled results were in agreement with
the in situ measurement from STEREO-A. Specially, Wu
et al. (2017) investigated the CME encountered by theWind spacecraft on 9 September 2011 in detail and verified the association of the short-duration (∼ 35 min) extremely dense pulse (with a peak of ∼ 94 cm-3) with the
heliospheric plasma sheet compressed by the interplanetary shock.
By injecting a CME from the bottom boundary, Wiengarten et al. (2015) incorporated turbulence transport
into the CRONOS model and investigated the effects on
the turbulence evolution for transient events from 0.1
AU to 1 AU. Their study found that the CME-associatedshock increased the turbulence levels and inhibited thecross helicity. They also indicated that researches on thelarge-scale structures associated with CMEs did not needto consider the turbulence transport effects due to theFig. 15 Comparison of the reconstructed magnetic field with the observed features of the solar corona prior to the flare. a SDO view of sampled
magnetic field lines of the CESE-MHD-NLFFF reconstruction. b and c SDO/AIA 171 Å and 304 Å images of the pre-flare corona. d The low-lying
magnetic field lines in the core region. The field lines are color-coded by the value of height z. e Locations of dips in the magnetic field lines; thecolor indicates the value of height z. f GONG Hα image of the active region. The dashed curve denotes the location of a long filament (From
Jiang et al. 2018)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

absence of strong back-reaction of the turbulence on thelarge-scale structures.
4.2.2 Modeling the interaction between CMEs and solar
wind structure (CIR, HCS)

Previous numerical studies have shown that both the
corotating interaction region (CIR) and heliosphericcurrent sheet (HCS) structures of the background solar
wind could play a substantial role in the propagation of
CMEs and their geoeffectiveness (Odstrčil et al. 1996;
Odstrcil et al. 2004). Therefore, the MHD simulation on
the interaction between CMEs and the solar wind structure (e.g., CIR, HCS) is one of the important aspects in
the CME simulations and has achieved a lot of progressin recent years.
By using the 3D SIP-CESE MHD model, Zhou andFeng (2017) simulated the propagation characteristics ofCMEs launched at different positions in a realistic structured ambient solar wind. By using the HAFv.2+
3DMHD model, a time series of synoptic photosphericmagnetic maps, and the recording of CMEs from STER
EO/COR2, Wu et al. (2016c) simulated the Sun-to-Earthpropagation of multiple CMEs and their associatedshocks in September 2011. Their simulation found thatthe evolution of the CME-driven shock and its interaction with the HCS and the non-uniform solar wind
could explain time-intensity profile of the high-energy(> 10 MeV) solar energetic particles (SEPs), and thesector boundary acted as an obstacle to the propagation of
SEPs. Further, Wu et al. (2016c) employed the model tostudy 12 CMEs and their associated shocks in September
2011. The results demonstrated that the backgroundsolar wind speed was an important controlling parameter in the propagation of interplanetary shocks andCMEs.
Using 2.5D version of VAC MHD model, Zuccarello
et al. (2012) and (Bemporad et al. 2012) numericallystudied the role of streamers in the deflection of CMEs
or multiple CMEs. Their results showed that the CME
deflected toward the current sheet of the larger northern
helmet streamer due to an imbalance in the magnetic
pressure and tension forces and finally gets into thestreamer. As pointed out by (Zuccarello et al. 2012),
during solar minima, even CMEs originating from highlatitude could be easily deflected toward the HCS, eventually resulting in geoeffective events, and that this latitudinal migration depended on both the strength of thelarge-scale coronal magnetic field and the magnetic fluxof the erupting filament.
Zhuang et al. (2019) simulated the deflection of CMEs
with different speeds in the interplanetary space using a
2.5D MHD simulation. Their simulation confirmed theexistence of the CME deflection in the interplanetary
space, which was related to the difference between the

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CME speed and the solar wind speed. They found that a
CME, which traveled slower or faster than the solar
wind medium, would be deflected to the west or east;and the greater the difference was, the larger the deflection angle would be. Liu et al. (2019b) simulated the
propagation and deflection of the fast CMEs interacting with CIR in the interplanetary space by using the
3D IN-TVD-MHD model and the CME flux ropemodel based on the GCS reconstruction. Their simulation results showed that when the fast CME hit the
CIR on its west side, it would deflect eastward, andthe deflection angle would increase compared withthe situation without CIR.
4.3 Modeling CME-CME interactions

Observational and numerical studies have shown thatthe kinematic characteristics of two or more CMEs maychange significantly after the CMEs interaction. The
CME-CME interaction is always associated with complex phenomena, including magnetic reconnection, momentum exchange, energy transfer, the propagation of afast magnetosonic shock through magnetic ejecta, andchanges in the CME expansion, and so on (Lugaz et al.
2017; Shen et al. 2017). Numerical modeling, which
always yields the observed complexity, has been provento be a useful tool to understand and determine thedynamical evolutionary processes of the CME-CME
interaction.
Webb et al. (2013) tracked the propagation of multiple
CMEs of late July to early August 2010 in the innerheliosphere by comparing the results from the ENLIL
model, 3D reconstruction techniques based on a kinematic solar wind model, and in situ results from multiple
spacecraft. By using WSA-ENLIL+Cone model based oncoronagraph image observations, Werner et al. (2019)modeled the multiple CME interaction event on 6–9
September 2017. The predicted arrival time of the firstinterplanetary shock was drastically improved, while thebackground solar wind preconditioned by the passage ofthe first interplanetary shock likely caused the last CME
to experience insignificant deceleration and led to theearly arrival of the second interplanetary shock.
Using the COIN-TVD MHD model, Shen et al.
(2011c) and Shen et al. (2012) simulated the interaction of two CMEs in interplanetary space, analyzed
variations of different forces during the interaction,and found that the momentum exchange during thecollision of two CMEs was very important for the
deceleration and acceleration of the CMEs. The 3D
COIN-TVD model was also used to study the superelastic collisions of CMEs in the heliosphere (Shen etal. 2013a). Results showed that the collision led to
extra kinetic energy gain by 3–4% of the initial kinetic energy of the two CMEs, which suggested that

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

the collision of CMEs could be superelastic. Shen etal. (2016) furthered the dependence of CMEs’ collision type on the ratio of the CME’s kinetic energy to
the CME’s total energy.
By employing the SUSANDOO model, Shiota andKataoka (2016) reproduced the propagation and interaction process of multiple CMEs associated with thehighly complex active region NOAA AR 10486 from
30 Rs to 430 Rs in October to November 2003. Theirsimulation results could successfully provide reasonably good results for velocity and the profile of southward magnetic field component of the Halloween
Event on 29 October 2003. The simulation also indicated that the propagation of the following CME wassignificantly affected by the trails of the preceding
CMEs. Lugaz et al. (2013) used the SWMF to study
the influence of the relative orientation of the twointeracting CMEs on their interaction and the resulting structure. In addition to the well-studied
multiple-MC event, they described other potential
structures, further compared with actual CME measurements in Lugaz and Farrugia (2014).
The September 2017 series of events, which resulted
in one of the largest geomagnetic storms of solar cycle
24, has been investigated by means of numerical simulations, focusing on the CME-CME interaction by a number of groups. Scolini et al. (2019a, b, c) used
EUHFORIA to study how complex interactions between
multiple interacting CMEs on their way to Earth may result in an intensification of the geo-effectiveness potential of such multiple-CME events. Werner et al. (2019)used the ENLIL model to investigate how the successionof CMEs in early September 2017 resulted in one of thelargest geo-effective periods of solar cycle 24. They specifically focused on the importance of pre-conditioningof previous, non-interacting CMEs on the propagationof the following fast CMEs, confirming past work, both
based on simulations (Lugaz et al. 2005; Tóth et al.
2007) and measurements Liu et al. (2019a).
4.4 Conclusions and future prospects

In the past five years, the main developments in the investigation of CME propagation and the backgroundsolar wind by means of numerical simulation have been
as follows.
There has been a significant increase in the number of
3D MHD codes that have been successfully used tosimulate the Sun-to-Earth propagation of CMEs as wellas the background solar wind; this has been the casemost notably in Europe with EUHFORIA, Japan withSUSANOO, and China with IN-TVD MHD. In addition,
a number of existing MHD codes have been adapted toinvestigate the heliospheric propagation of CMEs, including LFM into LFM-Helio and MAS into MAS-Helio.

Page 40 of 102

Heliospheric codes (starting typically at 0.1 AU) havebeen used with spheromak and/or flux rope CMEs,
which bridges the gap between computationally intensive Sun-to-Earth simulations and heliospheric simulations with cone models. These types of simulations may
be used to investigate the magnetic field configuration
inside CMEs as well as their arrival time and are more
physically consistent when investigating CME-CME
interaction than simulations where the CMEs do nothave internal magnetic fields.
In parallel, there has been an effort to make the CME
initialization quicker and easier to perform in coronal
codes using out-of-equilibrium flux ropes, especiallywith EEGL in the SWMF and within the MAS code.
This paves the way for future, real-time Sun-to-Earthsimulations with magnetized CMEs initiated based onmagnetograms, EUV images, and early coronagraphic
images. It is well known that major changes in the CME
properties, including its speed and orientation, may
occur below 0.1 AU where heliospheric models are initiated. At this time, it is however unclear whether simulations with magnetized CMEs initiated at 0.1 AU using
multi-viewpoints coronagraphic measurements (as described in point 2) will perform worse than simulations
with magnetized CMEs initiated at the solar surface interm of space weather forecasting capabilities. The number of Sun-to-Earth simulations of CMEs initiated at thesolar surface with a realistic model is still relatively low,even though there has been effort in presenting the results near 1 AU of more complex initiation mechanisms,
as done for example in Török et al. (2018).
The solar wind background plays an important role inconstraining CME propagation, in particular, in predicting the arrival times of CMEs. There has been significant
new physics included in the solar wind models, including
more advanced thermodynamics treatment, the inclusionof Alfvén waves and the new treatment of the innerboundary. However, there has not been significant work
quantifying how these new additions affect the CME
propagation and the resulting structure near 1 AU.
Lastly, there has been progress towards coupling timedependent magnetic field models with coronal models
and heliospheric models. This is already the case for the
background coronal and interplanetary magnetic fieldwith ADAPT coupled to a number of MHD models,
which has been shown to result in more accurately modeled heliospheric current sheets. Initiating CMEs bymeans of magnetofrictional or other self-consistentmodels based on solar observations or flux emergence
may lead the way for a better physical understanding of
CMEs and is probably the only way space weather forecasting could provide information before the launch of a
CME. We expect further improvements towards thiscoupling in the next few years.

(2021) 8:56

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5 Campaign study of Sun-Earth connection events

Only the first 11 events were included at the time ofwriting of the WN17 paper. The 5 events studied by
WN17 are highlighted in red. Three of the events, aswell as others, were described by NM17 and are marked
in purple. The October 2012 problem event was discussed in both papers.
This Section is organized as follows. The next subsection provides a summary of the Campaign events WG 4
studied during the rise of Solar Cycle 24 that highlight
forecasting problems. Subsection 5.3 summarizes the results from the Nitta and Mulligan study of stealth CMEs.
The results are discussed in subsection 5.4.

Zhang et al. Progress in Earth and Planetary Science

5.1 Introduction

The task of ISEST Working Group 4 (Campaign Events)
was to integrate theory, simulations, and observations tobetter understand the chain of cause-effect activity fromthe Sun to Earth for carefully selected events. ISEST
provided “textbook,” or well-understood, Sun to Earthcases to the community, but WG 4 also examined more
controversial events, such as stealth CMEs and problem
ICMEs, to enhance our understanding. This includes
analyzing the difficulties in linking CMEs to ICMEs,
which are usually observed only in situ.
WG 4 classified the studied events into three generalcategories: (1) Possible “textbook” cases in which thecomplete chain of a well-observed event is relatively wellunderstood from its solar source, through its heliospheric propagation, to its geo-effects. These cases involve forecasts that are successful in a general way. (2)Cases in which there were problems understanding thecomplete chain, but which we think we now understand.
Thus, something was missing in the chain of a wellobserved event but, in retrospect after analysis, we now
understand why. These cases usually involve forecaststhat failed because they were not geoeffective, or wereotherwise not accurate. (3) Finally, there are problem
cases in which the chain is not complete and we still do
not understand why. In the next two sub-sections, we
briefly summarize the results for each type of the events,
which are discussed in detail in Webb and Nitta (2017)(hereafter WN17) and Nitta and Mulligan (2017) (hereafter NM17). WN17 studied six cases during the rise of
Solar Cycle 24 that highlight forecasting problems. The
six events were chosen to illustrate some key problems
in understanding the chain from solar cause to geoeffect.
NM17 studied stealth, or problem CMEs that have noclear Low Coronal Signatures (LCS).
Table 5 is a summary of the 14 campaign events thatwere discussed and analyzed by WG 4. These studies
have resulted in many presentations and papers in theliterature. The first column group gives the Event Number and the range of dates from the solar source to any
geo-effect. The second column group summarizes thesource activity, the third the geo-response, followed by
the storm peak Dst, if any, and the peak Kp and G indices (see below). Finally, at the right is given our estimateof the degree of forecast success. The first six eventswere chosen as VarSITI-wide Campaign Study Events
because they had certain space weather effects of interest
to one or more of the other three VarSITI projects. A
focus of the WG 4 studies was to understand the Sunto-Earth cause-effect chain for five of these 6 campaign
events. The other 8 of the 14 events were chosen because of particular aspects of interest to ISEST WG 4
that help elucidate the Sun-to-Earth chain.

5.2 Understanding problem forecasts

WN17’s six events were selected to illustrate the rangeof problems that can occur in understanding thecomplete chain of activity from its source region(s) at
the Sun, its propagation through the heliosphere, to its
effects at Earth. Likely source CMEs were identified inall six cases, but related solar surface activity ranged
from uncertain or weak to X-class flares. The geoeffects
ranged from no effects to severe effects, such as the two
Sun-Earth events in 2015 that caused “superstorms.” Foreach event, they noted the official NOAA forecast thatwas issued after the solar source eruption but before its
arrival at Earth, and whether the forecast was successful
or was problematic in some important manner. Summaries of these forecasts are available under Reports ofSolar and Geophysical Activity (RSGA) through theSpace Weather Prediction Center (SWPC) site: ftp://ftp.
swpc.noaa.gov/pub/warehouse/.
Event #1, 12–14 July 2012, was considered a classic
textbook event, in that we observed the complete chainof a well-observed Sun-to-Earth event, from its solar
source, through heliospheric propagation, to its geoeffects (Figs. 1 and 16). The propagation kinematics, flux
rope eruption, and MHD modeling for this event were
well studied by WG 4 members (Gopalswamy et al.
2013a; Hess and Zhang 2014; Shen et al. 2014; Möstl etal. 2014; Cheng et al. 2014; Hu et al. 2016; Marubashi etal. 2017) and others. On 12 July 2012, an eruptive X1.4
flare occurred in AR 11520 (S17 W08) with an X-ray
peak ~ 16:45 UT. Later during its rotation, this same active region produced several strong flares and CMEs.
One was the 23-24 July CME (#8), aimed at the STER
EO-A and one of the fastest, most energetic CME everobserved (e.g., Baker et al. 2013; Liu et al. 2014). On 14
July the CME arrived at L1 with a shock observed by theWind spacecraft at 17:38 UT, followed by the shock
sheath and a 2-day long magnetic cloud. This ICME
drove a moderate, long-lived geomagnetic storm withpeak Dst = −127 nT on 15 July and with a duration ofseveral days. The NOAA forecast was mostly successfulZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Table 5 ISEST/MiniMax WG 4 Campaign Events

for this event, but the storm was only moderate level soslightly under-predicted.
The 21–24 June 2015 case, #6, was also possibly a
textbook event, but it was a compound in situ event at 1
AU resulting from a series of four shocks arriving over a

3-day span, and one likely ICME on 23-24 June. The
third shock and ICME were likely produced by a symmetric halo CME on 21 June. Southward field in multiple shock sheaths and the ICME drove a powerfulmulti-step geomagnetic storm reaching Kp = 8+, G4 andFig. 16 Images showing the solar source region of Event #1 on 12 July 2012. Left: SOHO/LASCO running difference image showing the full halo
CME on 12 July, 17:24 UT. Superimposed within the occulting disk area is a near-simultaneous SDO/AIA 193 Å image with an arrow pointing to
the near-disk center flare and dimming in AR 11520. This image is from the SOHO/LASCO CDAW CME catalog. Right: Enlarged AIA basedifference image of the source region

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

Dst = -204 nT on 22–23 June. The NOAA forecast of asevere storm was accurate, but that level was reached a
day later than predicted. This severe level was reachedbecause there were multiple shocks and sheaths, strong
southward MC fields, and high speed solar wind that
acted to compress and enhance the wind structures.
Publications of this event include the following (Ying
D. Liu et al. 2015; Manoharan et al. 2016; Lugaz et
al. 2016; Marubashi et al. 2017; Gopalswamy et al.
2018c).
The 15–18 March 2015 case, #5, was initially a problem event, but we now understand why. A slow (350 kms-1) CME occurred to the south-southwest late on 14
March likely associated with a small C2.6 flare from AR
12297 at S21° W20° with a small filament eruption. Thenon 15 March, ~ 01:36 UT, a fast (1120 km s-1), asymmetric halo CME associated with a C9.1 flare erupted
from the same active region but was brightest over thewest limb. In situ L1 observations showed a strong shockat Wind on 17 March at 04:01 UT, followed by an extended sheath then an ICME and a MC later on 17
March. Behind the cloud were a corotating interaction
region (CIR) and its high-speed stream (HSS) that likely
enhanced the solar wind parameters. This most severe
storm of Solar Cycle 24 was very much under-predicted,
in terms of both its magnitude and early time of arrival.
Thus, this was a problem forecast. The CMEs may or
may not have interacted near the Sun. There were two
detailed papers by Liu et al. (2015) and Wang et al.
(2004) arguing either side of that dispute. It is likely thatduring transport to Earth there was interaction with a
CIR and deflection toward Earth. Other papers analyzing
the MC/flux rope at 1 AU include (Marubashi et al.
2016; Marubashi et al. 2017; Wu et al. 2016a).
Although not one of WN17’s primary study events,
Event #10, 7-9 January 2014, was at the time considered
a problem event, but is now understood. It was a problem because a large storm was predicted but none occurred! Unlike the March 2015 case in which the CME
was deflected toward Earth, in this case, the source flareand EUV wave were Sun-centered but the CME was offset to southwest, possibly deflected by a CH and/or
channeled by strong AR magnetic fields, and thus
missed the Earth (Gopalswamy et al. 2014a, b; Möstl etal. 2015; Wang et al. 2015; Mays et al. 2015a).
The 10–12 September 2014 case, #11, was initially aproblem event, but we now understand why. On 10
September 2014, an X1.6 flare erupted in AR 12158, associated with a fast (1400 km s-1) symmetric halo CME.
The event was centered on the disk and had a large dimming region and a rapidly expanding coronal wave. This
event seemed like a textbook example, with a major
storm predicted, followed by a strong shock and longduration MC hitting Earth on 12-13 September.

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However, the storm was minor because the sheath andMC magnetic fields were northward (+BZ), so the storm
was over-predicted.
WG 4 members tried to use polarity inversion line
data to predict the expected flux rope orientation at 1
AU, but no consensus was reached. It was found thatthe flux rope fit better to a later, smaller flare in thesame AR (Marubashi et al. 2017; Cho et al. 2017). Thus,
the effects at L1/Earth were likely due to the interactionof the large and small eruptions; see Webb and Nitta(2017) for details. An et al. (2019) used observations of
the large event in a 3D MHD simulation to comparewith a set of the ICME parameters at 1 AU. However,they did not consider any interaction of the solar eventsor forecasting problems per se.
The 4–9 October 2012 case, #2, was initially a problem
event but we now understand why. The source CME
and resulting ICME that drove a small, two-step geostorm (Kp = 6+, G2) were identified, but the storm wasslightly under-predicted. There were weak and multiple
surface signatures and the CME was initially very slow,
leading to uncertainties in the arrival time. Marubashi etal. (2017) studied the ICME for this event and fit a portion of it as a flux rope.
Finally, the 27 May–1 June 2013 case #4, was a problem event, which we still do not fully understand. During
this entire period, NOAA/SWPC did not forecast any
important geoactivity. But on 1 June, there was abrief but strong storm (G3) that reached Kp = 7 and
Dst = − 119 nT. A possible source was a slow CME
on 27 May, but the associated surface features were
unclear. There was also likely influence from a large
coronal hole that led to interaction with a CIR or
HSS at Earth (Gopalswamy et al. 2015a, b, c), with alikely embedded ICME and flux rope (Marubashi etal. 2017; Nitta and Mulligan 2017). The ultimate
cause of the strong storm remains unclear.
Events #12 and 13 in the WG 4 table are discussed as
stealth CMEs in the next section. The last “event,” #14,
was actually a series of major flare (M and X-class)CME events from 4-10 September 2017 resulting inshocks, MCs, and a Forbush decrease at 1 AU. X flares
occurred on 6, 7, and 10 September. One notable featureof this series of events was the timing of the 1 AU arrivalof the shock from the second CME (associated with a
X9.3 flare) that was nearly simultaneous with when themagnetic field of the ICME from the earlier CME(associated with a M5.5 flare) turned southward. Thisapparently enhanced the net geoeffectiveness of these individual events, but it is presently very challenging toforecast the timings of the successive phenomena,
let alone individually. This event period occurred toolate in the ISEST interval to be extensively studied by
WG 4, but WG 4 members contributed to several papers

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

mostly analyzing the X flares and CMEs at the Sun. Recently, some WG4 members (Schmieder et al. 2020)studied the geoeffectiveness of a set of X-class flares in
2002. Most had near-limb solar sources but half also had
fast, halo CMEs. They conclude that these were “problem” events in that the usual solar proxies were notsufficient to forecast the rather weak geoeffects.
5.3 Study of stealth CMEs; those without clear low
coronal signatures

A related study of the Campaign group was of the origin
of CMEs that were not accompanied by obvious low coronal signatures (LCSs), but produced appreciable geoeffects at 1 AU. These CMEs characteristically startslowly. In several examples, extreme ultraviolet (EUV)
images taken by the Solar Dynamics Observatory(SDO)/Atmospheric Imaging Assembly (AIA) revealed
coronal dimmings and post-eruption arcades using difference images with sufficiently long temporal separations, which are commensurate with the slow initialdevelopment of the CME. Images from SECCHI EUVI
and COR coronagraphs provided limb views of Earthbound CMEs. Combined with SOHO observations, thesehelped limit the time interval in which the CME forms
and undergoes initial acceleration. For other CMEs, wefound similar dimming, although with lower confidenceof its link to the CME. We note that even these unclear
events can result in unambiguous magnetic cloud/flux
rope signatures in in situ data at 1 AU. In addition, there
was a tendency for the CME source regions to be located near coronal holes. i.e., open field regions. Thismay have implications for both the initiation of a stealth
CME in the corona and its outcome in the heliosphere.
The fact that some events without obvious LCSs produce appreciable geostorms was one of the motivationsof the work by NM17, who not only discussed Event #2
in detail but also described Events #12 and #13 and also
included Event #4 in their event list. It is notable thatfour of the 14 events dealt with by WG 4 were in the
category of geomagnetic storms without obvious LCSs.
Stealth CMEs are of great scientific interest because they
may represent a different class of eruptions than normal
CMEs, whose LCSs are unambiguous. Howard and Harrison (2013), however, cautioned that stealth CMEs may
be due largely to observational effects (such as limited
sensitivity and temperature coverage). Indeed it has beenshown that initially unseen LCSs may be revealed afterimage enhancement or processing (Alzate and Morgan
2017; Nitta and Mulligan 2017). Therefore, it may be
more appropriate to use the adjective “stealthy” whenthe LCSs of the CME are not clearly identified. Regardless of whether they are fundamentally different fromnormal CMEs or simply represent the low-energy end of
a continuous spectrum of events triggered in similar

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ways (Lynch et al. 2016), stealthy events pose challenges
to space weather prediction.
In Event #2, NM17 found a post-eruption arcade(PEA) sandwiched by coronal dimming regions in AIA
images around the time of the first appearance of the
CME in LASCO C2 images, using difference image
cubes with long (~ several hours) temporal separations(Fig. 17). It was found that the PEA and dimming regions delineated a polarity inversion line that looked likea filament channel without a filament. Apart from thebase difference images that needed compensation ofsolar rotation as a result of the long temporal separations, another important point was to use COR-1 datathat allowed the determination of the CME lift-off timeby observing the eruption from the side. Using AIA basedifference images and COR-1 data for several stealth
events, NM17 identified similar patterns of a PEA withdimming regions on either side. Figure 18 shows the
ICME with MC and fitted flux rope at 1 AU.
However, Events #12 and #13 were more problematic.
In Event #12, a partial halo CME was linked to the
ICME responsible for the strong (Dst = 99 nT) geostorm. This CME was both very slow and diffuse. Even
though one dimming region was clear not only in basedifference images but also in intensity images in AIA’s
193Å and 211Å channels as an augmentation of thesouth polar coronal hole, its mapping to the CME was
not straightforward. Assuming that the dimming regions
represent the legs of the erupting flux rope responsible
for the CME, we would expect two dimming regions in
opposite magnetic polarities. However, no second
dimming region was found. Moreover, multiple regions became brighter in base difference images, and
it is not possible to determine which ones may represent
PEAs. This event also lacked STEREO observations,making it difficult to know the time of the CME
liftoff.
Event #13 involved a full halo CME, which wasagain slow and diffuse. Without STEREO observations, this could have easily been taken as a backside
event because there were no clear changes in the low
corona around the time of the CME. NM17 showed
two marginal dimming regions in AIA difference images taken 14 h apart, but did not attach high confidence to them. Event #4 is controversial as towhether the strong (Dst = 119 nT) geomagnetic
storm was purely CIR-related or enhanced by a small
ICME embedded in the solar wind. The most likely
CME in the time range in question was observed tohead northward, giving an impression that it was not
Earth-directed, but it could have deflected equatorward as indicated in STEREO HI data. Marubashi etal. (2017) showed a flux rope fitting, possibly
supporting the latter possibility. NM17 located aZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 17 AIA 211 Å intensity and percent difference images in the upper and lower panels, respectively, taken before and during the CME on 5
October 2012. Differences are made relative to an image 5 h earlier. The dimming regions found in difference images are encircled in cyan. From
Nitta and Mulligan (2017)

dimming region next to the coronal hole as was thecase for Event #12.
5.4 Discussion

The goal of ISEST WG 4 was to integrate observations,
theory, and simulations to understand the chain of causeeffect dynamics from the Sun to Earth for a few carefully
selected (Campaign) events. This should help us develop
and/or improve the prediction capability for the arrival ofthese transient and their potential impacts at Earth.
WG 4 also examined controversial events, such as
stealth or “silent” CMEs and problem ICMEs to enhance
our understanding. One focus of WG 4 was on why doforecasts fail and how can we improve our predictions.
This included analyzing the complications in linking
CMEs to ICMEs, usually observed only in situ at 1 AU.
Our July 2012 (#1) and June 2015 (#6) cases were considered “textbook,” but the forecasts were not fully
accurate. The June 2015 case involved a compound
event that likely enhanced the level to a severe storm.
The next three cases, March 2015, #5, September 2014,#1, and October 2012, #2, were all considered problem

events that we now understand. In March 2015 two
CMEs possibly interacted near the Sun and were
deflected by a CIR. In September 2014 the storm wasmuch over-predicted because the shock sheath and MC
fields were almost entirely northward (+BZ). For October
2012 a CME was identified but the surface signatures
were multiple and weak leading to uncertainties in thearrival time. Finally, the last case in May-June 2013 (#4)
was a problem event that we still do not fully understand. No storm was forecast but a brief, strong storm
occurred. The surface activities associated with a slow
CME were unclear as was the cause of the southward
field (-BZ) at 1 AU.
As in several of our cases, we note that about 20% of
important geomagnetic storms have identified ICMEs
but no compelling solar signatures. Likewise, Earthaffecting CMEs are sometimes “stealthily” launchedwithout clear LCSs. In our stealth CME study, we demonstrated the need to compare AIA images with longtemporal separations to find weak LCSs, especially coronal dimmings and PEAs, in stealthy eruptions or slow
CMEs. In addition, STEREO COR data provided theZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 18 Solar wind data from Wind and the Dst variation for three days, 8–10 October 2012. From top to bottom are plotted the IMF intensity (B),X-, Y-, Z-components in GSE coordinates (Bx, By, Bz), the degree of field fluctuations defined by the standard deviation divided by averaged
intensity obtained from higher time resolution data (Sb/B), solar wind speed (Vsw), proton number density (N), number density ratio of He++ to
H+ (He/H), proton temperature, plasma β, and the Dst index. The vertical dashed line indicates the shock arrival time (on 8 October at 04:12 UT)
and the two vertical lines indicate the flux rope (from 8 October at 17:20 UT to 9 October at 18:30 UT). The red curves show the model values
obtained from the fitting with a toroidal flux rope model. Adapted from Marubashi et al. (2017)

time range to examine AIA data that matches CME formation and acceleration. We found a tendency for the
CME source regions to be located near coronal holes, oropen field regions.
Finally, about 10% of intense storms are due to thecompression of fields and plasma by CIRs and their
HSSs (Zhang et al. 2007). CIRs played a role in two of

our cases. The shock sheath region can also be very important for driving storms as was the case in at least twoof our events. A problem is that the sheath fields consistof swept-up coronal and heliospheric material which are
hard to predict in advance. Thus, studies of sheathregions are an important, but not poorly understoodaspect of space weather forecasting.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

6 Solar energetic particle events
6.1 Introduction

Solar Energetic Particles (SEPs) from suprathermal (fewkeV) up to relativistic (few GeV) energies constitute animportant contributor to the characterization of thespace environment. They are emitted from the Sun inassociation with solar flares and Coronal Mass Ejection(CME)-driven shock waves. SEP radiation storms may
have durations from a period of hours to days or evenweeks and have a large range of energy spectrum profiles. These events pose a threat to modern technology
strongly relying on spacecraft and are a serious radiation
hazard to humans in space, and additionally of concern
for avionics and commercial aviation in extreme circumstances (Malandraki and Crosby 2018a, b). This section
is divided into subsections, devoted to the progress of
SEP research from both the observational as well as thetheoretical and modeling perspective.
6.2 State of SEP observations and theory until 2014

By the end of the 1990s, a two-class paradigm (seeFig. 19) for SEP events was generally accepted (Reames
1999, 2013; Desai and Giacalone 2016). In this paradigm,
the gradual events occurred as a result of diffusive acceleration at CME-driven coronal and interplanetary (IP)shocks, while the impulsive events were attributed to acceleration during magnetic reconnection in solar flares.
The gradual or CME-related events typically lasted several days and had larger fluences, while the impulsive or
flare-related events lasted a few hours and had smaller
fluences. Impulsive events were typically observed when
the observer was magnetically connected to the flare site,while ions accelerated at the expanding large-scale
CME-driven shocks can populate magnetic field lines
over a significantly broad range of longitudes (Cane etal. 1988). The distinction between impulsive and gradual
SEP events was further justified on the basis of the energetic particle composition and radio observations (Caneet al. 1986). For instance, the flare-related impulsive SEP
events were electron-rich and associated with type III
radio bursts. These events also had 3He/4He ratios enhanced between factors of ~ 103–104, Fe/O ratios enhanced by up to a factor of 10 over the corresponding
SW values and had Fe with ionization states up to ∼ 20.
In contrast, the gradual events were proton-rich, hadaverage Fe/O ratios of ∼ 0.1 with Fe ionization states of∼ 14, had no measurable enhancements in the 3He/4He
ratio, and were associated with type II bursts (Reames
1999; Cliver 2000).
Based on these early measurements, most researchers
accepted the notion that CME-shock associated large
gradual SEP and ESP events result from the diffusive
shock acceleration (DSA) of thermal solar wind (Lee1983; Tan et al. 1989), although others pointed out that

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the suprathermal tail of the solar wind may be thesource (Gosling et al. 1981; Tsurutani and Lin 1985; Tanet al. 1989). Indeed, based on the correlation between
the particle intensities, abundances, and energy spectra
during ESP events and pre-event ion populations, Tsurutani and Lin (1985) and Tan et al. (1989) had suggested
that the concomitant solar flares might provide thesuprathermal seed particles accelerated at the IP shocks.
Since the mid-1990s, instruments with greater sensitivity and resolution on board Wind (Russell et al. 1995)
and ACE (Stone et al. 1998) have provided major observational advances in terms of measuring the solar wind
ion composition and its variations (Von Steiger et al.
2000) and comparing them with the energy-dependence
and event-to-event variability of the ionic charge state,
and elemental and isotopic composition in ESP and SEP
events over a broad energy range (Oetliker et al. 1997;
Mazur et al. 1999; Möbius et al. 1999; Cohen et al. 2005;
Desai et al. 2006; Klecker et al. 2007). These new observations have made it possible to re-examine questions
about the origin of the seed populations and improveunderstanding of how SEPs are accelerated and transported to 1 AU. The following subsections highlightmajor advances and insights into the origin, acceleration,and propagation of SEPs that have resulted from two decades of research. In particular, we present the state ofknowledge of SEP studies by the end of 2014 and discussopen questions that are yet to be fully resolved.
6.2.1 SEP origin

Observations of extremely rare elements and rare tracer
ions like impulsive SEP-associated 3He and interstellar
pickup He+ ions in SEP and ESP events have provided
compelling evidence that CME-driven shocks acceleratematerial preferentially out of a suprathermal “seed”
population that comprises contributions from the heatedsolar wind, coronal material, and remnants of solar transient events (Mason et al. 1999; Gloeckler 2003; Desai etal. 2006; Mason et al. 2004; Allegrini et al. 2008; Dayehet al. 2009). Other studies have shown that the abundances of heavy ions accelerated in SEP and ESP events
are not well organized by any physical quantity such as
the ion’s Q/M ratio or its First Ionization Potential (FIP)
when compared with the corresponding SW abundances(Mewaldt et al. 2002; Desai et al. 2003, 2006; Kahler et
al. 2009). Further, Mewaldt et al. (2012a, b, c) found thatthe suprathermal Fe densities at 1 AU are generally significantly greater one day before the occurrence of theselarge SEP events compared to all other days, perhaps indicating that the presence of high-density suprathermal
Fe is necessary for SEP events with large Fe fluences tooccur. Finally, Desai et al. (2003) found that the IP shock
abundances were well correlated with the average abundances measured at the same energy (∼ 1 MeV/nucleon)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

Page 48 of 102

Fig. 19 The two-class paradigm for SEP events in which (a) gradual SEP events are produced as a result of diffusive acceleration by large-scale
CME-driven coronal and interplanetary (IP) shock waves. The accelerated SEPs populate Interplanetary Magnetic Field (IMF) lines over a wide
range of longitudes. (b) Impulsive SEP events are attributed to acceleration during magnetic reconnection in solar flares and observed when theobserver is magnetically connected to the flare site. Intensity-time profiles of electrons and protons in (c) gradual and (d) impulsive SEP events.
(Reproduced from Desai and Giacalone 2016, after Reames 1999)

in the interplanetary medium prior to the arrival of the
IP shocks. In particular, elements with higher M/Q ratios are systematically depleted, which is consistent withshock acceleration models wherein ions with higher M/
Q ratios are accelerated less efficiently than those withlower M/Q values (Lee 2005). Collectively, these results
indicate that the material accelerated in large SEP eventsis quite distinct from that measured in the solar wind.
Therefore, the SEP heavy ions are unlikely to originate
from the bulk solar wind, but rather from a suprathermal tail that comprises ions from multiple sources, including 3He and Fe-enriched material accelerated inflares and suprathermal material accelerated at previous
CME shocks (Mason et al. 1999; Desai et al. 2006;
Mewaldt et al. 2012a; Mewaldt et al. 2012b).
6.2.2 SEP acceleration

DSA comprises two main mechanisms, namely, shockdrift mechanism at quasi-perpendicular shocks (Decker1981), and first-order Fermi mechanism at quasi-parallelshocks (Lee 1983). DSA theory successfully predicts
some SEP observations, but fails to prevail as a universal
theory in explaining most SEP events, partly because of
external drivers that simultaneously affect the observed
properties at 1 AU, including species-dependent escapefrom the IP shock, ambient turbulence, and shock finite
size and geometry. For instance, SEP studies have shownthat the differential energy spectra of H-Fe nuclei inlarge SEP events exhibit a distinct form of a broken (i.e.,
double) power-law (hereafter PL) with a characteristic
break-energy (hereafter Eo) (Tylka et al. 2005). In contrast, DSA theory predicts a single power-law. The location of Eo was found to typically decrease for the heavier
ion species as a power-law function of ion’s charge-tomass ratio (Zank et al. 2000; Tylka et al. 2010; Mewaldt
et al. 2012a). This systematic Q/M dependence occurs
because the energy spectra roll-over or break at thesame value of the diffusion coefficient for different species, which depends on ion rigidity or the Q/M ratio(Tylka et al. 2000; Cohen et al. 2005; Mewaldt et al.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

2005). Li et al. 2009a) generalized an SEP acceleration
model by including varying levels of turbulence nearshocks of different obliquity and predicted that α could
range between ~ 0.2 for weaker scattering near quasiperpendicular shocks and ~ 2 for stronger scattering
near quasi-parallel shocks. Finally, Alfvén waves generated by energetic protons streaming upstream of ICME
shocks could trap particles locally near the shock (Larioet al. 2005). This indicates that particle scattering andtrapping near the shock, in some cases, could be dominated by a dynamic wave spectrum rather than a more
universal background Kolmogorov-like wave spectrum(Tylka et al. 2005; Ng et al. 2003).
6.2.3 SEP transport

Effects of interplanetary transport on the temporal evolution of the heavy ion abundances and spectra are also
believed to play a critical role in determining SEP observations at 1 AU. Tylka et al. (1999) and Ng et al. 1999,
modeled the energy spectra and systematic temporal
evolution of the elemental abundances of ∼ 5–10 MeV/
nucleon He, C, O, Ne, Si, and Fe ions in two large SEP
events in terms of rigidity-dependent trapping and scattering by Alfvén waves generated by streaming energetic
protons accelerated at CME-driven shocks. These observations were successfully modeled using self-consistentnumerical calculations of wave generation or amplification by shock-accelerated protons escaping or streamingaway from the near-Sun CME shock (Lee 2005; Ng et al.
2003; Ng et al. 2012). Such self-excited Alfvén waves canscatter and trap particles near the shock and increase its
acceleration efficiency. This, in turn, throttles the proton
intensities near ∼ few MeV/nucleon resulting in energydependent upper bounds or plateaus, known as streaming limits (Reames 1990). Independently, Mason et al.
(2006) pointed out that the dramatic variations in thetemporal behavior of Fe/O ratio at all energies between∼ 0.1 and 60 MeV/nucleon vanish in > 70% of theprompt western hemisphere SEP events if the Fe intensities are compared to O intensities at ∼twice the Fe kinetic energy-per-nucleon. To explore the physical process
involved, Mason et al. (2012) modeled the rise phases inlarge SEP events and showed that the temporal evolution
of Fe/O can be reasonably fitted by a state-of-the-art
model where the differences in the transport of Fe versus O are due to the slope of the turbulence spectrum of
the IMF. Another effect of turbulence and waves which
scatter SEPs can be a significant amount of transport
perpendicular to the average magnetic field leading towider angular particle spreads than the corresponding
extent of their acceleration region (Dresing et al. 2012;
Dröge et al. 2014; Dröge et al. 2016). However, the very
widespread SEP events observed with the STEREO mission and close to Earth spacecraft (Gómez-Herrero et al.

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2015; Lario et al. 2014, 2016) challenged state-of-the-art
transport models based on the longitudinal distributionof electron anisotropies; Dresing et al. (2014) suggested
that there exist different types of widespread events, on
one hand, related to efficient perpendicular diffusion in
the IP medium and, on the other hand, caused by anextended injection region close to the Sun.
In summary, observations from ACE, Wind, and STER
EO during the 1995-2014 epoch have shown that large
gradual SEP events are governed by a confluence of multiple processes and effects by the time they are observed
at 1 AU. These include: (1) origin and variability of thesuprathermal seed populations (Mason et al. 1999;
Mason et al. 2005; Desai et al. 2003, 2004, 2006;
Mewaldt et al. 2012c); (2) the efficiency with which populations from different sources and with distinct distribution functions are injected into the shock acceleration
mechanisms; (3) factors that control the efficiency withwhich particles are accelerated (e.g., CME speed and kinetic energy, shock strength and obliquity (Kahler 2001;
Kahler and Vourlidas 2013; Tylka et al. 2005; Mewaldt
et al. 2008); (4) the presence or absence of multiple,
interacting CMEs (Gopalswamy et al. 2004; Li et al.
2012) (5) the type, level, and characteristics of the waves
and turbulence present near the shock and in the interplanetary medium (Tylka et al. 1999; Tylka et al. 2005;
Ng et al. 1999; Cohen et al. 2003; Li et al. 2009a, b) and(6) the charge-to-mass (Q/M)-dependence of scatteringand transport through the turbulent interplanetarymedium (G. M. Mason et al. 2006; Tylka et al. 2013).
The relative roles of these effects continue to be topics
of hot scientific debates, and unraveling their influence
remains a major focus of SEP research. In addition,
several studies claim a direct flare acceleration of thehigh-energy (>25 MeV) proton component in large
SEP events to augment that produced by coronal/
interplanetary shock waves driven by CMEs (Cane andRichardson 2003; Cane et al. 2006; Klein and Posner 2005;
Aschwanden 2012).
In the following sections (6.3 and 6.4), we will highlightthe progress made during the 2014-2019 timeframe andpoint out the key areas in which critical observations in the
inner heliosphere from Parker Solar Probe and Solar Orbiter will advance our understanding of the physics of SEP
events. These efforts are essential for developing models
that can reliably forecast and mitigate radiation risks
from extreme SEPs and are essential for deep space
exploration.
6.3 Progress in SEP observations during the VarSITI era(2015–2019)
6.3.1 SEP origin

Among the more unusual solar phenomena are thelong-duration gamma-ray flares (LDGRFs). The prime

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

characteristic of these events is delayed and prolonged
γ-ray (>100 MeV) emission after the impulsive phase(Ryan 2000). Recently, the Fermi Large Area Telescope(LAT) observed dozens of LDGRFs, with the most intense and longest duration example the 2012 March 7
event, for which >100 MeV emission was observed fornearly 20 hr (Ajello et al. 2014). Share et al. (2018) characterized and cataloged 30 solar eruptive events observed by Fermi/LAT from 2008-2016, referring to thisemission as “late-phase gamma-ray emission” (LPGRE).
These authors produced and presented “light-bucket”
time profiles for all the events, obtaining an estimate ofthe > 100 MeV γ-ray flux from within about 10° of theSun. Figure 20 presents an example of one of these time
histories for the 2011 March 7 event, in which the
LPGRE lasted ~ 14 h. GBM and RHESSI observed impulsive hard X-rays up to only 100-300 keV with no evidence for nuclear-line emission. The > 100 MeV fluxes
plotted in the inset reveal that the LPGRE started withinminutes of the hard X-ray peak. It is clear from therising LPGRE flux that it is due to a distinct particle
acceleration phase and is not just the tail of emission
from the impulsive phase of the flare.
In all the events studied, Share et al. (2018) found that
the LPGRE is temporally and spectrally distinct from theimpulsive phase emission. The spectra are consistent

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with the decay of pions produced by > 300 MeV protons
and are not consistent with primary electron bremsstrahlung with synchrotron losses. All but two of the
LPGRE events were accompanied by a fast and broad
CME. The LPGRE start times range from CME onset to
2 hr later whereas their durations range from ~ 0.1 to 20
hr and appear to be correlated with the durations of theaccompanying > 100 MeV SEP proton events. Comparison showed that the number of > 500 MeV protons producing the LPGRE is at least a factor of 10 larger than
the number producing the impulsive phase > 100 MeV
γ-ray emission during the associated flare. The numberof > 500 MeV protons needed to produce the LPGRE
ranges in nine events from 0.1% to 50% of the numberof protons observed in the accompanying SEP event ininterplanetary (IP) space (based on the observations by
the GOES/HEPAD experiment and neutron monitors).
There are significant systematic uncertainties in the SEP
estimates, however.
One of the features of the LPGRE events Share et al.
(2018) found is their association with fast CMEs and
SEP production, which points to acceleration of particles
by the shock produced by a fast CME as a clear candidate for the energy source for the >300 MeV protons
that produces LPGRE. Furthermore, (Gopalswamy et al.
2018e) presented strong quantitative evidence that

Fig. 20 Main plot: Time history of the > 100 MeV light-bucket fluxes from < 10° of the Sun, revealing LPGRE from the 2011 March 7 solar
eruptive event. Vertical dashed lines show the GOES 1–8 Å start and end times. Inset: 4 min accumulation light-bucket fluxes as detected byFermi/LAT are shown. The dashed curve shows the GOES time history. The blue shaded region denotes the estimated duration of the LPGRE. The
pink shaded region depicts where estimates of the flux of > 100 MeV impulsive-flare γ-ray emission were made (see text; after Share et al. 2018)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

interplanetary type II radio bursts and such sustained
gamma-ray emission (SGRE) events from the Sun areclosely related. Out of the 30 SGRE events reported by(Share et al. 2018), they considered 13 events that had aduration exceeding ~5 hr, thus excluding any flareimpulsive phase gamma-rays. These authors found that
the SGRE duration has a linear relation with the ending
frequency of the bursts (Fig. 21). The synchronism also
found between the ending times of SGRE and the type IIemission (Fig. 21) strongly supports the idea that thesame shock accelerates electrons to produce type II
bursts and protons (> 300 MeV) that propagate from theshock to the solar surface to produce SGRE via pion
decay. A CME-shock origin could also explain the wide
range of delays observed in LPGRE onset times: short
LPGRE onset delays represent shock acceleration low inthe corona (Gopalswamy et al. 2018c), while long
LPGRE onset delays indicate that the CME had to expand over several solar radii before accelerating >300
MeV protons that could return to the Sun. The smooth
time histories of the long duration LPGRE events can be
explained by the precipitation of particles that are magnetically trapped in a reservoir (Reames 2013) behindthe expanding CME.
Quantitative estimates, however, have indicated that
only a very small fraction of accelerated protons can return to interact in the chromosphere (Hudson 2018;
Klein et al. 2018) due to the transport of the protons
back to the Sun against the magnetic mirror force, wellafter the flare when the CME is many solar radii above
the surface with magnetic field strengths much lower
than at the solar surface. Share et al. (2018) go aroundthis difficulty by assuming that there is significant MHD
turbulence on the field lines connecting the CME to theSun, such as required in the model of (Ryan and Lee1991). Furthermore, Kocharov et al. (2015) presented a

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shock-wave model and their estimated ratio of the number of protons that return to the Sun and interact to thenumber that escape into IP space depending on theamount of turbulence is consistent with the range estimated by Share et al. (2018) in their comparison of thenumber of > 500 MeV protons producing LPGRE andthose detected as SEPs in space. A variant on the magnetic trap model was proposed by Hudson (2018) andcalled the ‘lasso’ model in which the SEP particle accelerator crossed both open (SEP) and closed (LPGRE) fieldlines, leaving energetic particles on both.
de Nolfo et al. (2019) compared the total number of >500 MeV protons at 1 AU by combining Payload for
Matter-Antimatter Exploration and Light Nuclei Astrophysics (PAMELA) and STEREO spacecraft data with
the number of high-energy protons at the Sun as deduced from Fermi/LAT (Share et al. 2018). Their analysis showed that the two proton numbers areuncorrelated such that their ratio spans more than 5 orders of magnitude, suggesting that the back precipitationof particles accelerated at CME-driven shocks is unlikely
to be the source of the LPGRE emission. They discussed
an alternative explanation for LPGREs based on continuous particle acceleration and trapping within large coronal structures that are not causally connected to the
CME shock, within the context of new remote observations of these loops available.
6.3.2 SEP acceleration

A lively debate has continued in recent years on thequestion of the principal source of high-energy protons
in large SEP events. Some studies have provided new
support for a significant contributory or dominant rolefor flare acceleration of high-energy protons in gradual
SEP events, contrary to the generally accepted scenario
favoring shock acceleration. This new evidence is mainly

Fig. 21 Scatter plots of SGRE duration with type II ending frequency (a) and type II duration (b). The best-fit lines (red) are obtained using the
Orthogonal Distance Regression method, which considers errors in both X and Y variables. The shaded area represents 95% confidence interval of
the fit (after Gopalswamy et al. (2018c))

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

based on correlations between the sizes of X-ray and/or
microwave bursts and the SEP fluence at different energies, e.g., for 15–40 MeV protons (Trottet et al. 2015), >30 MeV protons (Le and Zhang 2017), > 50 MeV protons (Dierckxsens et al. 2015), and > 100 MeV protons(Grechnev et al. 2015; Le et al. 2017). In order to assess
the above correlations, the technique of partial correlation coefficients (see Trottet et al. (2015) for a detailed
description) has been used along with the classical Pearson correlation coefficient, to remove the correlation effects between the solar parameters themselves. In
particular, Grechnev et al. (2015) addressed the relation
between the >100 MeV proton fluences measured by the
Geostationary operational Environmental Satellite(GOES) monitors and the associated flare microwave
fluences at 35 GHz, recorded by the Nobeyama Radio
Polarimeters, over the 1996–2014 time interval. Grechnev et al. (2015) found a partial correlation coefficient of
0.67, versus 0.001 for a corresponding comparison of the> 100 MeV proton fluence and the CME speed, concluding that these SEP events originated in the associatedflares. These results were criticized by Cliver (2016), according to which the exclusion in Grechnev et al. (2015)
of four outlying “abundant proton” events (black squares
in the orange rectangle in Fig. 22) is not justified if one
considers the associated CME speeds and widths, andelectron-to-proton ratios which are comparable to thosein the main sequence (black circles in Fig. 22). On thecontrary, inclusion of such events in the analysis reverses
the conclusion in favor of shock acceleration for the >100 MeV protons. Nevertheless, we point out these results are based on the assumptions that non-DH -associated SEPs are flare generated and those behind the limb
are shock generated, which although reasonable are notultimately proven.
Several other recent studies support the prevailingshock picture for gradual SEP events, such as the observation of a prompt SEP event at widespread locations inconjunction with the longitudinal propagation of awhite-light shock (Lario et al. 2016), the SEP source
temperatures (Reames 2015) and the hierarchical relationship found between the fluence spectra of gradual
SEP events and the kinematics of the CMEs (Gopalswamy et al. 2017a). Gopalswamy et al. (2017c) analyzed
the SEP fluence spectra of three classes of SEP events(Filament eruption (FE) SEP events, well-connected
regular ones, and SEP/GLEs) over cycles 23 and 24.
They found that: FE SEP events have the softest spectra
and lowest initial acceleration; SEP/GLE events have thehardest spectra and the largest initial acceleration; theregular SEP events have intermediate spectral indicesand acceleration. It has to be noted that the computed
spectral indices by considering a simple power law, without taking into account possible rollovers and breaks,

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Fig. 22 Scatterplot of longitude-corrected > 100 MeV proton
fluence (Φ100) vs. 35 GHz fluence (Φ35) for solar proton events from1996 to 2014; black circles and squares (W21–W90); gray circles(E30–W20); open circles (< E30). The orange rectangle isolates eventswith Φ100 > 2 × 105 pfu (from Cliver 2016)

thus mainly representing the spectrum behavior at lower
energies. The hierarchical relationship was shown to bepresent (Gopalswamy et al. 2017a) also in terms of theaverage starting frequencies of the associated type II
bursts and the shock formation height (as obtained bymatching the onset time of the type II bursts with the
CME leading edge height-time history). Such behavior
could be explained by considering that the rapid acceleration of CMEs leads to very high initial speeds andhence a shock formation close to the Sun, where the ambient magnetic field and density are high for an efficientparticle acceleration, resulting in harder spectra for GLE
events.
Understanding the origin of SEP/GLE events is especially challenging, as particles are accelerated up to GeV
energies and can reach the Earth’s atmosphere in about
10 min, while the various acceleration mechanisms may
be expected to exhibit different characteristic timescales.
The comparison between the solar particle release (SPR)
time and the onset time of the SEP event associatedsolar phenomena is generally used to pose temporal constraints that any putative acceleration process must
meet. Nevertheless, the different methodologies used toperform such studies and underlying assumptions, e.g.,Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

the constant SPR at all energies and the particle’s
scatter-free propagation in the interplanetary space, e.g.,questioned by Wang and Qin (2015), can lead to contrasting results about the principal source of particle acceleration. The most recent SEP/GLE event, which
occurred on 2017 September 10, has been attributed toboth flare and shock acceleration, e.g., by Zhao et al.
(2018) and Gopalswamy et al. (2018c), respectively, due
to their different evaluations of the SPR and the Type II
bursts onset time. In particular Gopalswamy et al.
(2018c), confirmed the parabolic relationship between
the eruption longitude and the CME height at SPR,
which is considered to be the key to the understandingof particle acceleration by shocks and the magnetic connectivity to the observer. Those authors also showed thatthe 2017 September 10 GLE did not have an unusually
hard spectrum (Schwadron et al. 2018), but a softerthan-average spectrum for a GLE event, having the 10–
100 MeV fluence spectral index of 3.17 with respect tothe average one of 2.68 for SEP/GLEs (Gopalswamy etal. 2016). They suggested this to be due to the poor longitudinal and latitudinal connectivity of the shock nosewith the Earth, possibly compounded by the weak ambient magnetic field reducing the shock acceleration efficiency. Similarly, it is suggested that some gradual SEP
events with high initial speeds did not produce a GLE,
as only the shock flanks were magnetically connectedwith the Earth (Gopalswamy et al. 2018c). Nevertheless,this has to be reconciled with the hypothesis that highenergy protons are accelerated preferentially by quasiperpendicular shocks which could be located at theshock flanks (Schwadron et al. 2015; Kong et al. 2019),
as will be discussed in sub-section 6.3.2.

6.3.3 SEP transport

The unprecedented orbits of the two-spacecraft STER
EO mission provided well-separated observations at 1
AU and allowed to study the longitudinal distribution of
SEPs and especially events with extraordinarily wide particle spreads in great detail (Lario et al. 2014, 2016;
Gómez-Herrero et al. 2015; Nina Dresing et al. 2018).
Based on Gaussian functions applied to multi-spacecraft
events, Lario et al. (2013) and Richardson et al. (2014)
determined their mean widths to be between 36° (27–37
MeV protons) and 49° (71–112 keV electrons). However,a large event to event variation was observed which also
limited the determination of the average displacement of
the Gaussian center with respect to the longitude of theassociated flare site. However, the displacements towards
the west of the flare site found in the above studies may
be caused by the associated CME-driven shocks thatwould shift the Gaussian distributions towards thecentral meridian as viewed from the spacecraft.

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In a similar manner, Cohen et al. (2017) have systematically investigated the energy and Q/M dependence ofthe longitudinal distributions for large ion events using
STEREO and close-to-Earth spacecraft reporting comparable values for widths and mean Gaussian-center displacements. While the widths were found to show anenergy dependence with distributions narrowing with increasing energy, no Q/M dependence to the widths ofGaussian centers were found for the 41 ion event distributions studied (see Fig. 23). This suggested that lower
energy ions might experience more field line co-rotation,or are accelerated over a larger portion of the CMEdriven shock or for longer times as the shock expands.
Rigidity-related processes seemed, however, not to beimportant in terms of longitudinal spreading of theparticles.
The STEREO mission has also enabled the identification and study of some extreme cases of widespread
events with distributions up to 360° around the Sun(Dresing et al. 2012; Dresing et al. 2014; Lario et al.
2014, 2016; Gómez-Herrero et al. 2015). However, evenwhen the application of interplanetary transport models(see Section 6.3.2) suggests the presence of strong perpendicular transport (Dröge et al. 2014; Dröge et al.
2016; Strauss et al. 2017), it seems that all widespread
events need at least a somewhat extended injection region of e.g., 25° width (Strauss et al. 2017). The natureof this extended injection region may be an extended
shock which accelerates and injects the particles over awide longitudinal range. Modern reconstruction techniques of the coronal and interplanetary shock using
EUV and white-light data (see section 2.2) have shown agood agreement between the inferred SEP injection
times and the times of the expanding shock intersecting
the magnetic footpoint of the spacecraft (Kouloumvakos
et al. 2016; Lario et al. 2016). For well-connected observers, similar correlations were found also for the time
when the associated EUV wave intersects the spacecraft
magnetic footpoint in the low corona and the role ofthese waves in terms of particle acceleration and coronaltransport were discussed controversially (Park et al.
2013; Lario et al. 2014; Miteva et al. 2014). In the case of
the farthest separated spacecraft in extreme widespread
events, however, the event can usually not solely be explained by the established magnetic connection to theshock or an EUV wave but transport effects are likely toplay a role in spreading the SEPs to the farthest observers (Lario et al. 2014; A Kouloumvakos et al. 2016).
While shocks are accepted to be the main source oflarge and gradual solar ion events, their role for efficientelectron acceleration is still under debate (Dresing et al.
2020 and references therein). Alternative scenarios providing wide injection regions involved in widespreadelectron events (Dresing et al. 2014) are the presence ofZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 23 Peak intensities of He, O, and Fe as a function of flare-spacecraft footpoint separations (Δ Longitude) of two-spacecraft events (Cohen etal. 2017). Negative values of Δ Longitude correspond to footpoints west of the flare location, and positive values correspond to locations east of
the flare. The different panels show different energies with (a) 0.3, (b) 1, and (c) 10 MeV/n. Panel (d) shows protons at all three energies.
Individual events are connected by lines, the thick curves show periodic Gaussian fits

fan-shaped magnetic field lines (Klein et al. 2008), but
non-uniform or non-radial spatial injections (Klassen etal. 2016, 2018) may play a role as well.
The 26 Dec 2013 widespread electron event (Dresing
et al. 2018) suggested to be caused by a shock, at a firstglance, because of its wide SEP spread and very longlasting proton and electron anisotropies pointing to atime-extended injection different to a flare. However,other features of the event, like an additional highenergy SEP component, arriving four hours later than
the first one, could not be explained solely by the presence of a shock and these authors suggested a trapping
scenario to be involved in forming the characteristics ofthe event. Additionally, to accelerating SEPs, CMEs canalso play an important role in SEP transport when they
have propagated into interplanetary space. If a previous
CME, which is still magnetically anchored at the Sun, isconvected over the observer just at the time whenanother eruptive event occurs at the Sun, new SEPs may
be injected into this ICME and propagate through it.

The magnetic connection of an observer inside thisstructure to the Sun is dramatically changed and one
loop leg may even provide otherwise unlikely magnetic
connections to eastern longitudes so that SEPs fromthese source regions can be observed even if the source
extent is small (Richardson et al. 1991). First arriving
SEPs may then also arrive in anti-sunward pointing
telescopes (Gómez-Herrero et al. 2017).
Measuring an SEP event inside an ICME confirms not
only that the structure is still magnetically anchored at
the Sun (Malandraki et al. 2002, 2005) but also that solar
energetic electron observations constitute a tool to probe
the magnetic structure, e.g., the winding of the magnetic
field inside the ICME (Kahler et al. 2011; Tan et al.
2012). If combined with reconstruction of the early
CME based on coronagraph observations, one can thendetermine the dimension of the large-scale structure,such as its loop length, at 1 AU, i.e., for times when it
propagated far out of the fields of view of coronagraphs(Dresing et al. 2016).

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

6.3.4 SEP compositional results

Measurements of relative abundances of heavy elements,
their isotopic and ionic charge state composition in SEP
events have been used in a wide variety of ways to infer
critical information about the origins of the seed populations and the physical conditions under which these
populations are produced, and the manner in whichthese seed particles are accelerated by CME shocks or insolar jets. In addition, we can infer the conditions that
affect their transport through the solar corona, the interplanetary medium, and out into the heliosphere (Reames
2016; Desai et al. 2016a). For instance, it is wellestablished that elemental abundances in SEPs exhibit
the so-called first ionization potential or FIP effect whencompared with corresponding photospheric abundances,as shown in Fig. 24. Based on well-documented differences between the average SEP abundances of elements
such as C, S, and P that have intermediate FIP values(Desai et al. 2006; Reames 2018) and those measured in
the slow solar wind (Bochsler 2008), Reames (2018) suggested that these differences could be due to the actionof ponderomotive forces of Alfvén waves on C, S, and P
ions on open field lines and their corresponding absenceon neutral C, S, and P atoms on closed field lines in the

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chromosphere (Laming 2015). According to this hypothesis, coronal material accelerated in SEPs differs fromthat found in the solar wind (see Fig. 25), because thechromospheric plasma that later becomes SEPs enters
the corona on closed field lines in active regions, while
the corresponding plasma that later becomes the solar
wind appears on open field lines, thus resulting in theobserved differences between the C, S, and P abundances in SEPs and the SW (Reames 2018, 2020).
Numerous SEP studies have also shown that the differential energy spectra of H-Fe nuclei in large SEP events
exhibit a distinct form of a broken (i.e., double) powerlaw (hereafter PL) with a characteristic break-energy(hereafter Eo). Desai et al. 2016a, b surveyed the heavy
ion spectra in 46 isolated, large gradual SEP events observed in solar cycles 23 and 24, and found that the Fe
spectra had lower Eo owing to the lower Q/M ratio or
higher rigidity of Fe when compared with O. Figure 26,
taken from (Desai et al., b), shows an example of this relation. This systematic Q/M dependence occurs because
the energy spectra roll-over or break at the same valueof the diffusion coefficient for different species, which
depends on ion rigidity or the Q/M ratio (Tylka et al.
2000; Cohen et al. 2005; Mewaldt et al. 2005; Desai et al.

Fig. 24 The upper panel shows the SEP/photospheric and 1.2xslow solar wind (SSW)/photospheric abundance ratios as a function of FIP. Curves
help show the trends of each data set. The lower panel shows the ratio of the “coronal” abundances from SEPs to those of the slow SW (Bochsler2008), as a function of FIP. The dashed line suggests the preferred normalization factor of 1.2. Adopted from Reames (2018)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 25 SEP abundances (from Reames 2014) divided by photospheric abundances (from Reames 2014) divided by photospheric abundances(from Caffau et al. 2011, Lodders et al. 2009), normalized at O (blue). Abundances of He in three SEP events are also shown (taken from Reames 2018)2016a, b). The authors also found that α varies between~ 0.2 and 3, where extreme SEP events associated with
Ground Level Enhancements (GLE) often exceeded theupper limit of 2 (see Fig. 26), as expected from theoretical predictions (Li et al. 2009a).
Later, Zhao et al. 2016a, b performed a comprehensive
numerical simulation study and found that the single
power law spectrum near the Sun transitions into a double
power law near 1 AU. The authors found that the spectral
indices above and below Eo, along with the value of Eo,
are related to the Kolmogorov-like interplanetary

magnetic turbulence spectrum. Zhao et al. 2016a, b alsostudied the proton energy spectra in the decay phase (reservoir) of selected SEP events, where transport effects are
expected to be minimal. They found that some events (seeFig. 27) in which the event-integrated spectra were described by double power-laws transitioned into a single
power-law during the reservoir or decay phase. This behavior was interpreted in terms of scattering, streaming,and diffusion effects for lower particles below Eo
Finally, Zelina et al. (2017) and Doran et al. (2019)suggested that the observed temporal evolution of heavy

Fig. 26 a Event-integrated differential fluences vs. energy of ~ 0.1–500 MeV/nucleon for H-Fe nuclei during a large SEP event, taken from Desaiet al. (2016b). The energy spectra for different species are offset for clarity. Solid lines are Band function fits to the spectra. b Spectral breakenergy EX of species X normalized to EH—break energy of H vs. the ion’s charge-to-mass (Q/M) ratio. Solid lines are fits to the data EX/EH = no
(Qx/Mx)α; dashed line: same equation with α = 2; dotted line: same equation with α = 0.2

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

Page 57 of 102

CME-driven shock waves predominantly acceleratematerial from the ambient ~ 1–2 MK coronal plasma
at ~2–3 RS. These events have negligible amounts ofimpulsive suprathermal ions.
6.4 Progress in SEP theory and modeling during the
VarSITI era (2015–2019)
6.4.1 SEP acceleration

Fig. 27 Event integrated and reservoir spectra during the 11/22/
1977 SEP event. The spectrum shows a double power-law feature,
while the reservoir spectrum exhibits a single power law (from Zhao
et al. 2017). This effect was interpreted in terms of particle streaming
and rigidity-dependent scattering effects that modify the spectrum
at lower energies

ion elemental abundances and energy spectra duringsome large SEP events observed at ACE and STEREO
could be accounted either by rigidity-dependent scattering (Parker 1965; Mason et al. 2006; Mason et al. 2012;
Tylka et al. 2013) (or via drift-associated Q/Mdependent deceleration during transport through theinterplanetary medium (Kota 1979; Dalla et al. 2015).
To reconcile the puzzling SEP observations from
Wind, ACE, and STEREO with the two-class paradigmof the mid-1990’s, Reames (2020) has recently proposedthat the SEPs observed at 1 AU (see Fig. 28) can begrouped into four sub-classes based on where and how
they are accelerated, namely (1) Pure impulsive SEP
events are produced when the ~ 3 MK coronal plasma isaccelerated by magnetic reconnection islands in solar
jets around ~ 1.5 Rs. These events have large M/Qdependent enhancements for H through ultra-heavy ionsas well as the 3He/4He ratio; (2) Impulsive SEP eventsare produced when CMEs from the same jets are fastenough to drive shocks. These shocks re-accelerate the~ 3 MK impulsive suprathermal material mixed with
ambient plasma mainly comprising protons; (3) Weak
gradual SEP events occur when wide, moderately fast(> 500 km/s) CME-driven shock waves accelerate theambient coronal plasma, including protons, but alsopreferentially accelerate the faster impulsive ~3 MK
suprathermal heavy ion material left over in the corona from many small jets; and (4) Strong gradual SEP
events are produced when wide, fast (> 1000 km/s)

Valuable insight on particle acceleration at coronalshock waves has been recently obtained by studying theevolution of CME and shocks low in the corona, as wellas their interaction with underlying magnetic fields andcoronal plasma, both through data-driven or analyticalmodeling, as well as MHD simulations and combinationsof different approaches. New information has been gathered about the relevant parameters for efficient shock
acceleration (such as the Mach number, compression ratios, geometry of shock waves), the primary acceleration
regions along the shock, the role of coronal magnetic
field configuration and how these factors are related tothe particle spectra observed in space.
Forward modeling techniques (Rouillard et al. 2016;
Salas-Matamoros et al. 2016; Kouloumvakos et al. 2019)
have been used to perform the geometrical fitting ofshock waves or CMEs with different geometrical models(i.e., the spheroid or the graduated cylindrical model),based on multipoint imaging. Rouillard et al. 2016
performed a triangulation of the three-dimensional (3D)
expansion of high-pressure fronts using three simultaneous viewpoints from SOHO, SDO, and STEREO observations for the 17 May 2012 GLE event. In conjunction,
they inverted remote-sensing observations to derive thebackground coronal conditions through which the pressure front propagates and modeled the topology of the
background magnetic field. To this end, they employedboth the PFSS model, based on the line of sight component measured by HMI, and the MHD MAST (Magnetohydrodynamic Around a Sphere Thermodynamic)
model with improved thermodynamics including realistic energy equations with thermal conduction parallel tothe magnetic field, radiative losses, and coronal heating(Lionello et al. 2008). They derived the normal speed
and the Mach number (Mfm) over the entire surface of
the CME front, as well as the shock geometry and themagnetic connectivity of the near-Earth environment
with the shock. A band of high Mfm values was found to
be co-located with the region of quasi-perpendiculargeometry, which evolved within 10 min into a quasiparallel geometry, reaching its highest values near thenose of the CME. In addition, a super-critical shock(Mfm values in excess of (3) had formed at the releasetime of high-energy particles, suggesting that delayed release times of GeV protons could be related with thetime needed for the shock to become super-critical.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 28 Possible reconciliation between the two-class paradigm of the early 1900s and the puzzling SEP observations from Wind, ACE, and STER
EO. The SEPs are grouped into four sub-classes according to their acceleration locations and the contributions of relevant physical processes(taken from Reames 2020). a Elements experience different FIP processing on open and closed field lines. b SEPs are accelerated in magnetic
reconnection regions or by CME shocks. c SEPs enriched in 3He and heavy ions escape along open field lines associated with solar jets, and notfrom closed field lines associated with solar flares. d Weaker CME-driven shocks preferentially accelerate residual suprathermal material left overfrom multiple jets, while the stronger CME shocks accelerate ambient coronal material. e Fast CMEs associated with jets also accelerate
suprathermal jet-accelerated material. f Rigidity-dependent trapping near CME shocks or scattering in the corona and the IP medium modifies
heavy ion abundances and energy spectra in large gradual SEP events. In some large events, self-generated waves can enhance particle trappingand increase the acceleration efficiencies of the CME shocks

Moreover, the presence of very high Mfm values alongopen field lines crossing the shock in the vicinity of theneutral line, corresponding to the heliospheric plasma
sheet, suggests that the neutral line could be a favorable
region for particle acceleration, although spatially limited. Thus, it is necessary a good connectivity between
the shock regions crossing the vicinity of the tip ofstreamers and the associated neutral line, e.g., through alarge-scale magnetic flux rope or any complex magnetic
field structure.
By using the same approach, Plotnikov et al. (2017) reconstructed the evolving shock front and its properties
for three far-side CME events (2013 Oct. 11, 2014, Jan.
06 and Sep. 01) which were associated with LPGREs, as
observed by the Fermi/LAT (Large Area Telescope) atenergy > 100 MeV–300 GeV, and SEP events measured
in situ at 1 AU (by SOHO and STEREO A, B). They obtained that for all the three events, the shock became
super-magnetosonic (Mfm > 1) and magnetically connected to the visible solar surface within 10–15 min of
the start of the flare and just before the onset of the> 100 MeV-ray emission, showing a quasi-perpendiculargeometry at the flanks during the bulk of the gamma-ray
emission. Moreover, by comparing the SEP electron andgamma-ray onset times and the computed electron to
proton ratios at the Sun and in space (within a factor 5),
they concluded that the same shock processes are responsible for both LPGREs and the production of SEPs. Nevertheless, they found no clear correlation between the shock
Mach number levels and the intensity of the gamma-ray

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

flux measured by LAT, suggesting that a more complicated physics might be at work, which requires further
investigation.
Furthermore, Kouloumvakos et al. (2019) derived theshock parameters from 3D modeling of the coronal pressure waves and compared them with properties of SEP
events over an extended dataset. They analyzed a number of 33 SEP events with energy > 50 MeV clearly observed at least at two interplanetary locations by SOHOand STEREO, and computed the correlation between
the peak intensity during the prompt phase and several
shock parameters at the shock regions magneticallywell-connected with the observers. Correlations withshock speed, compression ratios, and Mach numbers
were found to be significant for well-connected fieldlines, having Pearson’s correlation coefficients of 65%,
56%, and 72%, respectively, supporting previous results(Rouillard et al. 2016; Plotnikov et al. 2017; Afanasiev etal. 2018). On the other hand, no significant correlation
was found between the SEP peak intensity and the shock
angle as well as no energy dependence on any performed
correlation. Finally, shock waves were found to becomesuper-critical at a median distance of 3 RS, while solar
particles to be released ∼ 15–20 min after the shockwaves become super-critical and have connected magnetically with the SEP observing point.
Kozarev et al. (2015) analyzed the initial phase of a
CME on 2011 May 11 by combining observations fromthe SDO/AIA images in the time interval 02:12–02:31
UT and the following models: 1) the Coronal ShockGeometric Surface (CSGS) model which produces athree-dimensional spherical dome surface, propagating
through the corona with speed and radius based on theobserved time-dependent position of the EUV coronalbright front (describing the shock front) in the radial direction; 2) the Potential Field Source Surface (PFSS) magnetic field model, providing a global coronal vector
magnetic field solution for a 3D grid of polar coordinates.
They estimated the time-dependent orientation of thewave/shock with respect to the coronal magnetic fields
between 1.1 and 1.5 Rs, the part of the coronal surface
which shock-accelerated particles during the initial stages
as well as heliospheric connectivity during the shockpassage in the low corona. They found that the fieldshock angle changes significantly throughout the evolution of the shock surface, with higher values preferentiallynear the flanks, although they reached almost 90° near theshock nose as well in the last phase of the event. Moreover, the open field lines crossed by the shock in thesecond half of the event were likely both related to highangle values and magnetically connected to the Earth.
Thus, the authors concluded that the shock acceleration
efficiency and particle release is considerably higher in thesecond part of the shock evolution in the AIA FOV.

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Recent MHD simulations of the CME expansion haveshown the formation of shocks or strong compression
regions at low coronal heights (< 2 solar radii). As a result of the CME’s rapid acceleration, shocks and strong
compressions appear on the flank of the CME, showing
a large negative velocity divergence and creating theconditions that lead to rapid particle acceleration. By
analytically solving the Parker equation in the presenceof size-limited acceleration regions as obtained by MAS(Magnetohydrodynamic Algorithm outside a Sphere)
simulations, Schwadron et al. 2015) showed that broken
power laws can be naturally obtained due to pronounced
effects of particle diffusion and particle escape. As amatter of fact, the finite size of the shock or compression limits the maximum energy gain of the particles,
because the magnetic field line (or flux bundle), nearwhich these particles are accelerated, moves off of theaccelerator, i.e., the shock or compression, leading to theformation of a broken power-law in the particle distribution. Moreover, the break energy and the spectral index
of the second power law increase with the shock angle,
the size of the shock, the CME driver speed, and with
reductions in the rigidity dependence of the scattering
mean free path.
The effect of large-scale streamer-like magnetic configuration on particle acceleration at coronal shocks has
been investigated by considering a CME-driven shockpropagating through a streamer-like magnetic field(Kong et al. 2017) or from its flank (Kong et al. 2019),
i.e., when the streamer is rotated with respect to the
CME propagation direction. By numerically solving theParker transport equation with both parallel and perpendicular diffusion in such configuration, Kong et al.
(2019) found that the primary sources for particle acceleration are located at different regions and vary significantly as the shock propagates and expands, dependingon the particle energy and on time. In particular, theacceleration of particles to more than 100 MeV mainly
occurs in the shock-streamer interaction region close to
the shock flank, where the shock is quasi-perpendicularand closed magnetic fields are present, favoring particle
trapping upstream of the shock, both conditions increasing the acceleration efficiency. They also obtained
broken power laws for the particle spectra up to 100
MeV integrated over the simulation domain. Thus, thestreamer-like magnetic field can play a critical role inproducing gradual solar energetic particle events andthey may be a mixture of two distinct populations accelerated in the streamer and open field regions, having
different acceleration rates. Nevertheless, these results
might be affected by the two-dimensional treatment,
leading to non-zero divergence of the magnetic field at theshock front and possibly to mismatch parallel diffusionand perpendicular diffusion.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

Some previous models attribute the generation of
double power-laws and Q/M-dependent spectral breaks
to the trapping and subsequent rigidity-dependent escape of ions from the shock during the SEP acceleration
processes via DSA at CME-driven shocks in the solar
corona (Li et al. 2009a; Tylka and Lee 2006). In the
Schwadron et al. (2015) lower coronal SEP acceleration
model, the finite size of the ICME shock and stronger
Q/M-dependence of the diffusion coefficient facilitates
particle escape from the acceleration region, which reduces the Eo and steepens or softens the higher energy
spectral slope. Conversely, in the Schwadron et al.
(2015) model, the weaker Q/M dependence inhibits particle escape, which increases the break energy and flattens the higher energy spectral slope. Indeed, Desai et al.
(2016b) were able to infer key properties such as thestrength, obliquity, and turbulence conditions associatedwith the corresponding near-Sun CME shocks using thespectral properties of those 1 AU SEP events where theobserved Q/M-dependent spectral breaks were interpreted to be consistent with DSA shock acceleration rather than transport-dominated processes. Recently, Tanet al. (2017) also found that the high-energy Ne/O ratio
is well correlated with the source plasma temperature of
SEPs, and used the variability of Ne/O and Fe/O ratios
to investigate the accelerating shock properties.
In contrast, Li and Lee (2015), Zhao et al. (2016a, b),
and Zhao et al. (2016b) argue that, in some large SEP
events, single power-law spectra injected by near-Sun
ICME shocks can exhibit spectral breaks at 1 AU due to
Q/M-dependent scatter-dominated transport in the IP
medium. Specifically, Li and Lee (2015) showed that particle scattering and diffusion from the Sun en route to 1
AU could alter a single proton power law into three
distinct power laws by the time the shock arrives at 1
AU, suggesting that scatter-induced particle propagationin the IP medium can also result in spectral breaks at 1
AU even if the ICME shock-accelerated spectra are pure
power laws; in this case, the Q/M dependence (α parameter) has an upper limit of ~1.4. Thus, in cases where
the spectral breaks are interpreted to be caused primarily
by interplanetary transport effects, the observed temporal evolution of the heavy ion abundances and spectral
breaks can provide insights into the interplanetary turbulence conditions encountered by the SEPs during theirtransit to 1 AU (Li and Lee 2015; Zhao et al. 2016a;
Zhao et al. 2016b). On the other hand, another functional form was also found to reproduce the proton energy spectrum during SEP events (Laurenza et al. 2013,2015), being the Weibull distribution the best fit for the
event-integrated spectrum and separately, during theprompt and energetic storm particle phases. A theoretical derivation of the Weibull spectrum was provided(Laurenza et al. 2016; Pallocchia et al. 2017) in the

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framework of the acceleration by “killed” stochastic processes exhibiting power-law growth in time of the velocity expectation, such as the classical Fermi process, oralternatively, by the shock-surfing acceleration. Thus,
those authors suggested that a scenario in which different mechanisms could account for particle acceleration
at shocks in different energy ranges, the stochastic orshock surfing acceleration contributing significantly tothe acceleration of high energetic particles, in addition
to the DSA at lower energies.
6.4.2 SEP transport

The well-separated SEP observations with the STEREO
mission have also lent themselves to study the transportof SEPs in more detail. The comparison of multispacecraft observations with results of 2D or 3D models
solving the focused transport equation allowed to study
not only transport along the mean magnetic field but
also perpendicular to it. It was found that the role of efficient transport perpendicular to the mean magnetic
field can eventually be much stronger than expected(Dresing et al. 2012) and might play an important role,
among extended injection and acceleration regions, inthe longitudinal spreading of SEPs (Dröge et al. 2014;
Dröge et al. 2016) but also in creating asymmetries intheir longitudinal distribution at 1 AU (He and Wan
2015; Strauss et al. 2017).
The main theories attempting to describe perpendicular transport are diffusion, i.e., by particle scattering at
magnetic field irregularities resonant with the particles’
gyro radius (Zhang et al. 2009; Giacalone and Jokipii
2012; Dröge et al. 2010; Dröge et al. 2014; Dröge et al.
2016) and field line meandering. Laitinen et al. (2016)
and references therein propose a combined scenario
with particles remaining on turbulently meandering fieldlines early in the event which turns over into diffusivetransport at a later phase, both leading to particles
spreading perpendicular to the mean field. While a solely
diffusive approximation is not able to explain efficientperpendicular transport at the same time like the presence of sharp SEP drop out events (Mazur et al. 2000),
the model of field line meandering is. Furthermore, it isable to explain too early SEP onsets caused by a propagation path length shorter than the nominal Parker
spiral length (Laitinen and Dalla 2019). However, both
diffusion and field line meandering approaches are
struggling to explain the extreme widespread events observed with the STEREO and close-to-Earth spacecraft.
This suggests that such events are not only caused by
efficient perpendicular transport but by a combinationof transport and an extended injection and/or acceleration region. The limited number of well-separated observers during the STEREO era was not fully sufficient
to constrain the injection size and the transportZhang et al. Progress in Earth and Planetary Science(2021) 8:56

conditions at the same time which is expected to improve significantly during the next solar cycle with thepresence of new missions like Parker Solar Probe andSolar Orbiter.
An important step taken in SEP transport modeling is
the inclusion of a realistic solar wind background. Wijsen
et al. (2019a) use the data-driven EUropean Heliospheric
FORecasting Information Asset (EUHFORIA, Pomoell
and Poedts 2018) to generate a background solar wind fortheir SEP transport code. This allows, on the one hand,the study of the effect of solar wind streams on the SEP
propagation and also on adiabatic energy changes (Wijsen
et al. 2019b). On the other hand, case studies with arealistic background field will be possible accounting forthe effects of transient structures leading to non-Parker
field configurations (Leske et al. 2012)
Pacheco et al. (2019) have re-visited 15 relativistic
electron events observed with the Helios spacecraft in
the 1970s and 1980s using the 1D transport code by
Agueda et al. (2008), which allows to infer the solar release time profiles and the values of the radial mean freepath. Short injection duration (< 30min) events were
only found in 30% of the cases compared to longduration events (> 30min) otherwise. The radial mean
free paths, observed at spacecraft locations between 0.31
and 0.94 AU, vary between 0.02 and 0.27 AU. Agueda
and Lario (2016) found indications that the strength of
the interplanetary scattering varies with the size of thesolar parent event suggesting that the energetic particle
population itself generates waves. However, Pacheco etal. (2019) did not find such a dependency in theirsample. While self-generated waves are an accepted
phenomenon for ions, which plays also an importantrole in proton acceleration at shocks (Bell 1978) (see also
section 6.3.1), their presence in relation to solar energetic electrons remains elusive. The four consecutive
Helios events studied by Agueda and Lario et al. (2016)
are also a famous example used to illustrate the radialeffect of SEP transport: the same SEP events were observed by the close-to-Earth spacecraft IMP-8, but thefour distinct impulsive increases seemed to have merged
into only one gradual event. However, as discussed andmodeled by Agueda and Lario et al. (2016) and Strauss
et al. (2017), not only radial scattering alone but most
likely the contribution of perpendicular diffusion is responsible for the loss of the detailed SEP event structure
at 1 AU.
6.5 Conclusion

In conclusion, for a full understanding of the SEP acceleration, injection, and transport processes which
altogether determine the variable particle observations
discussed so far, additional systematic, multi-spacecraft
studies are needed. SEP observations provided by the

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Parker Solar Probe (McComas et al. 2016) and Solar
Orbiter (Rodríguez-Pacheco et al. 2020) missions in the
inner heliosphere in conjunction with modeling efforts
will be the basis of upcoming advancements in our
understanding.

7 Stream interaction regions/co-rotatinginteraction regions
7.1 Introduction

The solar corona is structured by open and closed magnetic field regions that transition at a certain distance as
open field into interplanetary space (see Fig. 29). The
solar atmosphere is permanently in a state of dynamic
energy release and renewal, e.g., structures constantlyinteract with each other. Flares and CMEs are mostly related to active regions and filament eruptions. Other
solar features that can cause strong geomagnetic events
are stream interaction regions (SIRs) which are related
to coronal holes, known as areas on the Sun with predominantly open magnetic field. In that respect, theknowledge about the 360° structuring of interplanetary
space is a key input parameter for Space Weather forecasting. As CMEs are affected by the MHD drag whichis in quadratic dependence on the ambient solar windspeed, fast streams may strongly influence the predictions of arrival times and impact speeds of CMEs. The
structured interplanetary space also actively affects SEP
propagation and causes by itself geomagnetic storms.
During the solar minimum phase, the energy input into
Earth’s magnetosphere by CIRs is similar to that of
CMEs (Richardson et al. 2001; Tsurutani et al. 2006).
SIRs are related to the high-speed streams originatingfrom regions of open magnetic fields along whichplasma is accelerated and may easily escape from theSun. Primarily open magnetic fields (and flux) are observed within coronal holes, dark regions of lower
temperature and density as observed in EUV and SXR.
Harvey and Recely (2002) describe the evolution of CHs
based on observations during solar cycles 22 and 23. Isolated high-latitude CHs evolve into polar CHs, where
they appear as stable and rigidly rotating objects, which
may exist for several years. Approaching the descending
phase of a solar activity cycle, polar CHs extends tolower latitudes that consequently change the solar windin the ecliptic (i.e., encompassing the planets) quite dramatically. This evolutionary process is revealed in EUV
data from the clear change of morphology (area, shape)and location of CHs. Wang and Sheeley Jr 1990 describe
the magnetic structure of CHs and find that there is a
close relation between the flux tube expansion and theunderlying photospheric magnetic field. Recent results
show that the evolution of the CH area is not correlated
to the evolution of the underlying magnetic field, indicating that the magnetic field inside a CH does not driveZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 29 Sketch of the open-closed magnetic field structures on the Sun. Adapted from Fig. 4.8 of the book “Introduction to Space Physics” editedby Kivelson and Russell et al. (1995)

its evolution (Heinemann et al. 2020). SIRs have been
observed throughout the heliosphere by a large varietyof spacecraft, such as Helios in the inner heliosphere, by
ACE, Wind, DSCOVR near Earth at 1 au, by Voyager
and Pioneer in the outer heliosphere, and also out of theecliptic by Ulysses.
Due to the quasi-stationary location of low-latitudeCHs, the interaction of high- and slow-speed solar windstreams results in compression of plasma and magnetic
fields. As the plasma is of enhanced speed compared toneighboring regions, interactions between fast and slow
solar wind streams occur at certain distances from theSun (cf. Fig. 30). Here the leading edge of the interaction
region forms a forward pressure wave that propagates
into the slower plasma ahead. Likewise, the trailing edge
is a reverse pressure wave (see review by Gosling andPizzo 1999). Since CHs are slowly evolving but longlived structures, these interaction regions recur withevery solar rotation and are then called co-rotating interaction regions (CIRs). On the large scale, it is assumed
that their interplanetary dynamics can be described byideal MHD equations.
One of the characteristics of SIRs, measured at 1AU
distance, is the so-called stream interface, characterized
by an abrupt drop in density, simultaneous rise in proton
temperature and gradual increase in speed, and an eastwest flow deflection, i.e., the region separating the originally slow, dense plasma from originally fast thin plasmaback at the Sun (e.g., Wimmer-Schweingruber et al. 1997).
Furthermore, the stream interface is usually preceded by adensity increase due to the compression (compression region) and is often associated with a sudden change in themagnetic polarity (sector boundary), and a gradually risingsolar wind speed profile (Jian et al. 2009). The change inthe magnetic polarity is related to the alternation of magnetic sectors, referring to the neutral line (heliosphericcurrent sheet). The number of magnetic sectors changes
with the solar cycle, typically there are 4 sectors, but it can
get more complex during solar maximum. Shocks on bothsides of the interaction region develop more strongly
further out in the heliosphere at 2–3 AU (forward andreverse shock).
Because SIRs/CIRs rotate in the direction of planetary
motion, the high-speed solar wind streams emanatingfrom a centrally located coronal hole arrives at Earthabout 4 days later (see Fig. 31). Typical HSS intervals
following CIRs, may drive prolonged geomagnetic activity and cause strong high energy particle enhancementsin the Earth’s radiation belts (Reeves et al. 2003; Miyoshi
et al. 2013; Kilpua et al. 2015). In detail, the strength andimpact of geomagnetic storms depends, beside the impact speed, most importantly on the north-south component, Bz, of the magnetic field of the solar windstream (see also Krauss et al. 2015). In the case of CIRs,
the cause of the disturbances is often Alfvenic waves inthe HSS (Tsurutani et al. 2018).

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 30 Sketch of an SIR as seen in the ecliptic plane showing the fast (slow) solar wind in red (blue). The frozen-in magnetic field lines becomealigned with the stream interface by the reverse (forward) wave. Taken from Owens and Forsyth (2013) adapted from Gosling et al. (1981)

7.2 Progress in observations

The evolution of long-lived coronal holes, closely related
to SIRs/CIRs, can be studied in detail using multiple
views on the Sun from combined STEREO and Earthimagery. Using spacecraft separated by 120°, studies ofchanges in the large-scale EUV structures over time andtheir relation to the in situ measured solar wind can be
performed. It was found that coronal holes undergo evolutionary patterns revealing a growing and declining
phase where area increases and decreases again over several solar rotations (Heinemann et al. 2018a; Heinemannet al. 2018b; Heinemann et al. 2020). The slow evolutionof coronal hole areas and the steady-state of the related
solar wind streams are found to be well related to thesolar wind speed measured in situ at 1 AU (Nolte et al.
1976; Veronig 2007a; Abramenko et al. 2009; Karachik
and Pevtsov 2011; Rotter et al. 2012; Tokumaru et al.
2017). With this well-known area-speed relation, empirical forecasting tools for the “pure” background solar
wind on the basis of coronal hole area measurements
are performed on a regular basis as depicted in Fig. 32(Vršnak et al. 2007a; Rotter et al. 2012; Reiss et al. 2016).
By understanding the photospheric and coronal evolutionary characteristics of CH, one can aim to gain a better understanding and in turn improve the forecast of
CIRs (see Heinemann et al. 2018a, b). Temmer et al.
(2017) showed in a case study that the evolutionary
trend visible in the CH area is matched by the trend of
the peak velocity of the associated HSS over the lifetimeof the CH. This is an important indicator that the highspeed peak velocity to CH area relation also persists over

a CH’s evolution. They also found that the total perpendicular pressure at the stream interface and the in situ
magnetic field at the B- peak and the v- peak do notshow the same evolutionary profile. The forecasts arefound to be most successful for periods of low solar activity, as during increased solar activity transient events,
such as CMEs, strongly disturb the rather stable solar
wind outflow for several days (up to 5 days; see Temmer
et al. 2017); cf. also Janvier et al. (2019).
As the high-speed solar wind streams emanate fromCHs which are low dynamic structures, CIRs can beforecasted with long lead times. Based on that a varietyof persistence models were developed. Under the assumption of persistence, in situ measurements of thesolar wind plasma flow from L1 and varying STEREO
vantage points provide a forecast for Earth position withlead times of up to 27 days (depending on the exact
STEREO spacecraft position). The closer the measuring
spacecraft is ahead or behind Earth, the less the effect of
the temporal evolution of the solar wind profile (Opitz
et al. 2009). Implementing the actual changes of CH
areas (from EUV data) into such simple forecasting tools
can improve the forecast quality (Temmer et al. 2018).
Lead times with about 4.5 days could be achieved whenusing data from an instrument permanently located at
the Lagrangian point L5 (60° behind Earth; ESA preparation for the future L5 mission “Lagrange”). However, it
is pointed out that the latitudinal offset between themeasuring spacecraft is limiting the accuracy of persistence modeling (Owens et al. 2019) as the streams
flow speed profile is rather depending on theZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 31 Left: SDO/AIA composite image of the wavelength channels 211-193-171A from June 30, 2012, showing the reduced density region of acoronal hole (shaded area). At the time t0, the central position of the coronal hole is extracted from remote data. At about t0 + 1 day, themaximum in the density/magnetic field and at about t0 + 4d the maximum in the speed/temperature is observed from in situ data at 1 AU.
Right: Cartoon by Pizzo (1978) to illustrate the fundamental processes involved in the 3D dynamics of stream evolution

Fig. 32 Magnetogram (scaled to ± 30 G) of a large coronal holeobserved on 2013 May 29. The coronal hole boundaries are outlined
in black. Regions with an absolute magnetic field density of morethan 10 G are outlined in blue and of more than 50 G in red. Taken
from Hofmeister et al. (2017)

latitudinal range of the center of mass (Hofmeister et
al. 2018). Recent studies also found latitudinal differences in the geoeffectiveness of events which arecaused by variations in the interplanetary magnetic
field due to closed flux ropes (CME magnetic structure) and compression regions - CME shock-sheathor SIR (Huttunen et al. 2008; Yermolaev et al. 2017).
Together with the warping of the heliospheric current
sheet, the forecasting of high-speed solar wind structures
is rather complex, but considering the uncertainties,
nevertheless, a good estimation of the flow approaching Earth can be given.
Forecasting solar wind structures in interplanetary
space serves also as important information for analytical
CME propagation models and Space Weather models.
However, SIR/CIRs may not only influence the propagation of CMEs but also the evolution of its internal magnetic structure, as the compression region may represent
an obstacle which can hamper the CME expansion (seee.g., Dumbović et al. 2019). Therefore, under the Space
Situational Awareness Program of the European Space
Agency forecasting services using empirical, and numerical models for the solar wind are available (see http://swe.ssa.esa.int). In that respect, a new four-plasma
categorization scheme for the solar wind is given by Xu

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

and Borovsky (2015) that can be used for the automatized detection scheme for solar wind and CMEs (private
communication with S. Vennerstroem, 2015). As theforecasting/nowcasting quality is still not sufficient for
producing reliable Space Weather alerts, we need to better understand and closely monitor SIRs/CIRs. Main
aims for the near future are to (1) verify and evaluate
background solar wind models and with that improve
the input for CME propagation models (as discussed in
Section 4.1 and 4.2) and more accurately predict periodic and recurrent geomagnetic effects from CIRs. In
general, single events are easier to forecast compared tomultiple events featuring CME-CME interactions, or interactions of CMEs with CIRs. In order to better understand the fast flow plasma in interplanetary space,research on their sources, CHs, is of utmost importance(Wilcox and Howard 1968). By better understanding thephysics behind CH evolution we may improve their forecasting capability. With that, we will also gain moreinsight into the ejection and acceleration processes thatdefine high-speed streams. This is of timeliness as we
can exploit data from the NASA mission Parker Solar
Probe (PSP) (Fox et al. 2016) measuring the near-Sun
space and with that regions where the solar wind
actually gets accelerated.
Hofmeister et al. (2017) in a statistical study of 288
low-latitude coronal holes during the time range ofJanuary 01, 2011–December 31, 2013, and Hofmeister etal. (2019) in a statistical study of 98 coronal holes shedlight on the magnetic fine structure in photosphere
underlying the projected CH boundary. Using SDO/
HMI line-of-sight magnetograms, they showed that themagnetic field is made up of a very weak slightly asymmetrically skewed background field (|BBG| = 0.2–1.2 G)
and small unipolar magnetic elements. These small unipolar magnetic elements contain most of the signed
magnetic flux that arises from coronal holes. It was
found that the area that these unipolar, usually longliving (lifetimes > 40 h), magnetic elements cover determines the total signed flux of a coronal hole (see Fig. 32).
These magnetic elements are important in the context
of solar wind acceleration, propagation and forecasting
as they are suspected to be the footpoints of flux tubes
or magnetic funnels. Flux tubes, or clusters of magnetic
fibers open towards interplanetary space, have beenfound to be the small-scale source regions of the plasma
outflows within CHs (Hassler et al. 1999; Tu et al. 2005).
Wiegelmann et al. (2014) showed, using SUMER data
and magnetic field extrapolations, that the regions showing high outflow velocities correspond to strong unipolar
flux concentrations within CHs (see Figure 36 in Section
7.3 (“Progress in theory and simulations”)). The substructure of these flux tubes, e.g., a bundle of magnetic
fibers, is represented by the abundance of magnetic

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bright points (MBPs) within magnetic elements as observed in high-resolution HINODE/SOT G-band filtergrams (Hofmeister et al. 2019). This shows the highly
structured magnetic field configuration of CHs in comparison to the previously often assumed rather uniform
configuration. This structuring also carries out into thesolar wind structure in interplanetary space as shown by
the first PSP observations of HSS that were found toshow a strongly structured and perturbed speed profile(Bale et al. 2019).
Though harder to evaluate and forecast, interaction
events between SIR/CIR and CMEs are of special interest. Interaction events can lead to significant increase ingeomagnetic effects when compared to individual eventsof similar strength (He et al. 2018; Dumbović et al.
2019). Heinemann et al. (2019a) showed in a case studyof a CME interacting and propagating within a HSS on
June 22, 2011, that the dynamic pressure of the SIR/CIR
followed by the CME shock signature within the HSS induces wave-like flaring-motion into the Earth’s magnetopause and causes, due to enhanced magnetopause
currents, a much stronger Sym-H value than would havebeen expected. Due to the rather small and weak CME
the effects were still only moderate. Also, enhanced substorm activity was recorded.
In a recent paper by Jian et al. (2019), physical properties of a large sample of slow-to-fast SIRs were investigated using STEREO-A and -B data. They identified 518
pristine SIRs, of which more than 50% are associated withcrossings of the heliospheric current sheet (HCS) and areof slow speeds but higher densities, and of increased
dynamic and total pressure compared to those without
HCS (see Fig. 33). In that respect, HCS related SIRs can
be classified as more effective in terms of Space Weather.
The detection and extraction of reliable CH areas fromoperational solar observations is extremely important,
not only for solar wind forecasting using the area-speed
relation, but also for investigating the magnetic open
flux on the Sun. At the present, most extraction
methods focus on EUV observation taken by SDO/AIA,SOHO/EIT, GOES/SUVI and/or STEREO/EUVI. Due tothe optimal filter sensitivity and high contrast, wavelengths of highly ionized iron (e.g., Fe XII: 193/195 Å) areoften used. Methodologically, intensity-based methods are
the go-to choice, with some form of intensity thresholding
being the preferred choice. Intensity threshold methods
include the CHARM algorithm (L. Krista PhD Thesis,
Krista 2012), which uses local intensity histograms todetermine a fitting threshold; a fixed threshold based on
the median solar disk intensity (Rotter et al. 2012, 2015;
Reiss et al. 2015; Boucheron et al. 2016; Hofmeister et al.
2017; Heinemann et al. 2018a); a dual-threshold growing
algorithm (Caplan et al. 2016) and a supervised intensity
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Fig. 33 The comparison of occurrence probabilities between SIRs with an HCS crossing (blue bars) and without any HCS crossing (orange bars)
for the following parameters: a maximum Np, b minimum speed, c maximum speed, d maximum B, e peak Pt, and f peak dynamic pressure. The
brown shaded regions are the overlapping regions between SIRs with an HCS and the SIRs without any HCS crossing. Taken from Jian et al.
(2019)

perpendicular to the CH boundary (CATCH; Heinemann et al. 2019a, b). A multi-wavelength approach
was developed by Garton et al. (2018) in the form of
the multi-thermal emission recognition algorithm
CHIMERA. A spatial possibilistic clustering approach
was taken by Verbeeck et al. (2014) which is available
as the SPOCA algorithm. Recently, with the dawn ofmachine learning, new methods, utilizing the increased computational performance have also emerged
to provide an additional tool to identify and extract
coronal holes (e.g., Illarionov and Tlatov 2018).
Using various techniques, several CH datasets weregathered (especially for CH areas) that are freely available. Automatically created SPOCA boundaries of CHs
are available via the Heliophysics Events Knowledgebase(HEK: https://www.lmsal.com/hek/index.html), the automated coronal hole detection and extraction using three
SDO/AIA wavelengths (171, 193, 211A) CHIMERA isavailable via SolarMonitor (https://www.solarmonitor.
org/) and an extensive, manually checked, CH catalog
covering the SDO-era (2010–2019) created using CATC
H is available via the VizieR catalog service (http://
cdsarc.u-strasbg.fr/viz-bin/cat/J/other/SoPh/294.144).
A recent chain of studies used data from HINODE,SOHO, and TRACE to study small-scale changes in the

CH boundary. In a first step, Madjarska and Wiegelmann (2009) showed that although CHs maintain theiroverall shape over short timescales, small loops that areabundant along the boundaries continuously reconnect,changing the small-scale magnetic structure (in theorder of 1–40″). Using XRT observations of coronalbright points within a CH, Subramanian et al. (2010)
showed that these small loops could be a source of slow
outflowing solar wind. These loops may erupt as X-ray
jets ejecting plasma along open field lines into interplanetary space. Madjarska et al. (2012) confirmed thesefindings using spectrograph data from SUMER/SOHO
and EIS/HINDOE. In the last paper of the series, Huang
et al. (2012) demonstrated that magnetic flux in CHs
undergoes constant reconnection processes. It is suggested that these constant restructuring processes of thesmall-scale magnetic field within CHs might be largely
involved with the overall magnetic flux formation within
CHs. The connection between these phenomena andtheir cause remains an open question.
7.3 Progress in theory and simulations

The lack of understanding in solar wind accelerationand solar wind structures is closely related to the problem of coronal heating. In that respect, the properties ofZhang et al. Progress in Earth and Planetary Science(2021) 8:56

the solar corona and its connection to the solar wind are
not well understood. As described above, recent studies
use high-resolution observations of coronal structuresand underlying magnetic field in order to gain a deeper
insight into the mechanisms of CH evolution andmorphology. Remote observations are coupled with insitu measurements for investigating the impact of thesolar wind structures at larger distances from the Sun.
However, due to the scarce in situ measurements ininterplanetary space, we need to rely on improved modeling to make more conclusive interpretations of thephysical processes underlying SIRs/CIRs and their Space
Weather effects.
As mentioned before, recent studies found that unipolar flux tubes, presumably the main outflow regions of
the fast solar wind, cover only a small percentage of theentire area of the CH (see Hofmeister et al. 2017). The
open flux problem in that respect is a topic on its own
that is tackled by combined observational and modeling
efforts.
To improve solar wind models and to ensure accurate
space weather forecasting the solar wind models have to
be tested and validated. The validation of solar windmodels is done by comparing the simulation results toin situ measurements.
The performance assessment of the EUHFORIA solarwind model was analyzed by Hinterreiter et al. (2019).
Within a thorough statistical investigation, a comparison
between modeled and in situ measured solar wind highspeed streams was made to identify possible caveats ofthe model results. The solar wind was modeled rather
well for times of solar minimum (see Fig. 34). However,during increased solar activity, complex solar-surface situations could be identified that stem from the interplay
between evolving and dissipating magnetic field.
In a study by Lee et al. (2009) the heliospheric models
ENLIL/MAS and ENLIL/WSA were compared with insitu measurements from ACE and Wind (time range:
2003-2006). They found that the model results givelower densities for faster solar wind fully agreeing withthe solar wind momentum flux conservation. They also
derived a general good agreement between the solar
wind models and the in situ measurements for largescale structures and for timescales of several days. The
results are in agreement with findings from Gressl et al.
(2014). Jian et al. (2015) performed a comparison of several models installed at CCMC (ENLIL , MAS, WSA,
SWMF) with solar wind in situ measurements and revealed strengths and weaknesses of each model. Common to all studies is the fact that different magnetogram
inputs have a huge impact on the model performance.
This result should be taken as basis to improve datadriven models or at least to add more observational parameters in order to better constrain the models and to

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identify input magnetograms that match better depending on the specific solar cycle condition.
Interesting recent studies for a better understanding ofsolar wind evolution are given by Jian et al. (2016) who
compared ENLIL model results with observations at
ACE and at Ulysses for times when ACE and Ulysses
were in latitudinal alignment. The alignment made it
possible to compare the model results for the same latitude due to the different radial distances (1 AU and 5.4
AU) the evolution of the solar wind could be wellobserved and interpreted.
For simulating the solar wind magnetic field close to
the Sun, EUHFORIA uses an adaption of the semiempirical Wang-Sheeley-Arge (WSA) model from Arge
& Pizzo (2000). WSA is composed of the Potential Field
Source Surface (PFSS) (Altschuler and Newkirk 1969;
Schatten et al. 1969) and the Schatten Current Sheet(SCS) (Schatten 1971). The PFSS outer boundary, called“source surface,” divides the corona into an inner andouter sector. At the transition from PFSS to SCS modeling domains, artificial kinks appear in the magnetic fieldlines, which can be improved by putting the innerboundary of the SCS model at a distance below thesource surface (McGregor et al. 2008). Presently, no
exact heights for those boundaries exist, and actuallyvarying them leads to different results in the computed
open magnetic flux (Asvestari et al. 2019) (cf. Fig. 35).
Moreover, as height variations of those boundaries affectthe modeled magnetic field topology, e.g., the bending(spatial gradients) of the magnetic field lines, this has
further effects on the computation of the expected
propagation and extension of high-speed solar windstreams, and how they interact with Earth.
The magnetic topology is thought to be key, not only
for the shape and morphology of CHs but also for theprocess of solar wind acceleration. In modeling, the flux
tube expansion factor plays an important role in empirically determining the outflowing plasma velocities inCHs. Wiegelmann and Solanki (2004) showed that themagnetic configuration of CHs does not only exist ofopen field but is dominated by small-scale low-lying
loops and few high and long. The small loops that seemto confine the expansion of the flux tubes in the transition region and lower corona were found to be on average flatter than their equivalent in the quiet sun. In that
respect, the modeling of flux tubes within a CH is achallenge due to the much weaker magnetic field, but
can be achieved as given in Fig. 36.
7.4 Impact at Earth

The solar wind couples the interplanetary space with the
Earth’s magnetosphere. Hence, the upper atmosphere reacts to the energy input into the system depending on
the speed and magnetic field of the solar wind stream.

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Fig. 34 Snapshot of the background solar-wind radial speed modeled by EUHFORIA. The top-left panel shows the MHD solution in the
heliographic equatorial plane, and the right panel shows the meridional plane cut that includes the Earth (blue circle). The lower panel shows
comparison of the modeled and observed solar wind by EUHFORIA and ACE, respectively. Taken from Hinterreiter et al. 2019

As such, especially the stream interaction regions andtheir compressed plasma (sometimes associated toshocks) put energy into the magnetosphere that has consequences for the upper atmosphere (coupled throughPoynting flux). With the arrival of CIRs there is a persistent evolution from slow to fast solar wind and theEarth’s plasmasphere significantly changes accordingly(Denton and Borovsky 2017). Spatial and temporal variations in the magnetic field are found to be most relevant
for the amount of energy input into the system. Moreover, speed and density give the ram-pressure, which iswell correlated to the amplitude of sudden storm commencements caused by the rapid compression of the
Earth's magnetic field (Gonzalez et al. 1989). In that respect, the faster and stronger the compression, the larger
the Space Weather effects. Further, the preconditioningof the magnetosphere plays an important role as pointed
out in a well-cited study by Borovsky and Denton(2006). They further concluded that CIR-related storms
are more hazardous to space-based assets, particularly at
geosynchronous orbit compared to CMEs. The reason isthat CIRs are of longer duration and have hotter plasma
sheets causing a stronger spacecraft charging. Denton
et al. (2016) gave an overview on unsolved problems ofthe magnetosphere. The atmospheric layers of the Earth
all react on CIRs, and effects are measurable down to
the neutral atmosphere. Variations in the thermospheredensity occur in relation to the arrival of CIRs and
CMEs. In that respect, CIRs and CME sheath regions
have a similar impact on the amount of density increase
whereas most strong variations come from the magnetic
structure of the CME (e.g., Krauss et al. 2015; Krauss etal. 2018).
7.5 Conclusions

Studying solar wind streams and their solar sources,CHs, is of utmost importance. The streams highly structure interplanetary space and as such interact with disturbances propagating in the flow.
Consequences for CMEs are strong changes in theirpropagation behavior (speed, direction) which is the primary cause of large uncertainties in the CME forecast.

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 35 Maps of open-closed flux generated for the same CH by two different model runs. The one on the left resulted by EUHFORIA running forthe default pair of heights [2.3, 2.6] Rs, while the one on the right for the pair [1.3, 1.8] Rs. It is clear that the CH is not present in the model resulton the left but is present and well captured by the run setup based on lower heights. This example highlights the impact the heights of thesource surface and the inner boundary of the SCS model have in the modeling result. Taken from Asvestari et al. 2019

In view of this, a reliable interpretation of observedchanges in the kinematical profiles of CMEs is only possible when we properly understand the variation of theambient solar wind flow. Besides influencing near-Earthspace, high-speed solar wind streams themselves are
sources of geomagnetic effects and especially during
times of low solar activity put with their recurrent
characteristics a comparable amount of energy into the
Earth atmosphere as CMEs do over short timescales.
The different atmospheric layers of the Earth arecoupled through dynamical, electromagnetic, and photochemical processes. With that, geomagnetic effects due
to SIRs/CIRs and CMEs may cause a cascade of impacts
down to ground-level enhancements and induced currents. Long and short-term effects were studied during
CAWSES that led to a significant improvement in our
understanding of the solar influence on our Earth system(see special issue devoted to CAWSES-II (http://
progearthplanetsci.org/collection/001.html). SCOSTEP

endorsed the continuation of the CAWSES program as
CAWSES II during 2009–2013. With the success of the
VarSITI program (2014–2018), the path of interdisciplinary studies will be continued in the recently approved
SCOSTEP program PRESTO (Predictability of thevariable solar-terrestrial coupling) that will act during
2020–2024.

8 Forecasting CMEs
Having discussed the observational properties, theory,and modeling of CMEs in Section 2, 3, and 4, respectively, in this section we review the efforts addressing
forecasting CME occurrence (Section 8.1), time-ofarrival at 1 AU (section 8.2), coronal and heliosphericmodulations (Section 8.3) and magnetic fields (section
8.4). Recent reviews on these topics can be found in
Vourlidas et al. (2019) and Kilpua et al. (2019). We will
hereby mainly focus on empirical or semi-empiricalFig. 36 Magnetic structures in a CH. The gray-coding shows the field strength in the photosphere. The black line gives roughly the boundary of
the CH. The field of view for SUMER is marked as a red rectangle in both panels. The magnetic field was constructed from a MDI magnetogram.
Left figure: mostly closed loops at various scales. Only closed magnetic field lines with B ≥ 30 G are shown. Right figure: only open fields withlarge photospheric values, B ≥ 100 G. The open flux is bundled in narrow uniform filaments and originates in stronger fields concentrated atsmall-scale footpoints. The flux tubes expand as they extend into the corona. Taken from Wiegelmann, Xia and Marsch (2005)

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

physics-based approaches, which are more amenable tooperational purposes in the current stage.
8.1 Predicting CME occurrence

The lack of critical observations (e.g., no routine observations of the magnetic field in the corona), and limitations in theory and models (e.g., idealized initial andboundary conditions), are currently not allowing topredict when a CME would occur. However, thanks toadvances in our observational capacity (e.g., new observations from STEREO, Hinode, SDO, IRIS), and inmodeling (e.g., increase of realism in models, dataconstrained and data-driven models) and in analysis andforecasting techniques (e.g., use of advanced statistical
tools and machine learning methods) significant progress in our understanding and eventual prediction of
CMEs has been achieved over the last decade.
Identifying and understanding the physical mechanism(s) behind CME onsets would be a key element indeveloping the capability to predict them on a regularbasis. While there exists no doubt about the magnetic
origin of CMEs (Forbes 2000; Vourlidas et al. 2000),
there is currently no consensus regarding the specifics ofthe eruption process (Chen 2011; Schmieder et al. 2015;
Cheng et al. 2017; Green et al. 2018; Georgoulis et al.
2019; Patsourakos et al. 2020) and sections 2 and 3.
A first approach to the prediction of the most powerful
CMEs, which are in general the most geoeffective, is to
use the observational finding of a rapidly increasing probability of eruption with associated flare magnitude, withflares above X of the GOES classification approaching 100% (Andrews 2003; Yashiro et al. 2005; Wang and Zhang
2007). Therefore, by assessing conditions/forecasts for
major flares, one could also infer whether major CMEs
could occur (Anastasiadis et al. 2017). However, the flaremagnitude-CME occurrence relationship is statistical, andtherefore, exceptions should be anticipated (e.g., thesuper-active AR 12192 which hosted 6 confined X-class(e.g., Thalmann et al. 2019). In addition, this approach
excludes CMEs associated with weaker flares as wellas CMEs originating from quiet Sun regions.
Eruption predictors based purely on imaging observations include SXR and EUV sigmoids (Canfield et al.
1999; Green and Kliem 2009), EUV and WL cavities(Gibson et al. 2006), EUV hot channels (Zhang et al.
2012; Patsourakos et al. 2013; Nindos et al. 2015; Chenget al. 2017). For instance, the statistical analysis of Canfield et al. (1999) found that sigmoidal ARs are more
likely to erupt than non-sigmoidal ARs. Recently, thestatistical study of Nindos et al. (2020) showed that asignificant fraction (i.e., two-thirds) of the erupting hot
flux ropes in their sample was formed well before theactual eruptions (from 51 min to more than 8 h) duringconfined flares.

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These are discussed in the recent reviews by Green etal. (2018), Cheng et al. (2017), and Patsourakos et al.
(2020).
Given the magnetic nature of CMEs, several magnetic
metrics have been considered in the literature as
eruption diagnostics. The definition of these metrics ismotivated by various proposed eruption models/mechanisms and/or by physical intuition. Pertinent studies
calculate a given metric for a set of eruptive and noneruptive ARs and then search whether there exist specific thresholds, or region of values of the consideredmetrics, segregating eruptive and non-eruptive cases.
Note here, that since a small fraction of ARs gives riseto eruptive flares, this large disparity between eruptive
and non-eruptive cases should be properly accounted forin the corresponding analysis.
A group of CME predictors refers to properties calculated within or around polarity inversion lines (PILs),i.e., regions in the photosphere where the vertical magnetic field changes sign. This is motivated by the factthat CMEs, and large-scale eruptive phenomena in general, originate from intense and complex PILs (Webb etal. 1997; Schrijver 2007). The corresponding studies employ line-of-sight or vector photospheric magnetograms.
Various metrics are then calculated for each traced PIL
and we hereby discuss a sample thereof (see also Fig. 37).
Falconer et al. (2006, 2008) introduced several PILrelated non-potentiality measures related to for example
the length of the strong shear PIL(s), the integral of theshear angle along the PIL(s), the magnetic gradientweighted integral length of the PIL(s). Considering pairsof these measures for a set of ARs, and designating suitable thresholds for each measure, supplied success rates
of above 75% for an AR to give rise to a CME in thenext few days from the corresponding measurements.
Schrijver (2007) calculated the R metric, i.e., the total
unsigned magnetic flux within 15 Mm of strong-field
and high-gradient PILs. He found that when an R > 2 ×
1021 Mx is recorded, there is a high probability of amajor (> M class) flare, and hence of a CME as well,within 24 h from the measurement. Georgoulis and Rust(2007) calculated the Beff metric, which essentially calculates the total magnetic field of connected magnetic partitions in strong PILs. They found that the conditional
probability for the occurrence of M- and X-class flareswithin a 12 h window of the measurement exceeds 0.95
for Beff above 1600 G and 2100 G, respectively.
Recently, Kusano et al. (2020) proposed a new physicsbased scheme (k-scheme) to predict large imminent
solar flares. This scheme is based on the calculationfrom HMI magnetic field data and NLFF extrapolationsof the critical length scale (rc), for the region of the trigger reconnection along the PIL to become unstable tothe double-arc instability (Ishiguro and Kusano 2017) asZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 37 Examples of magnetic metrics applied to major flares/CMEs. Panel (a): the integral of the shear angle along the PILs versus the gradientweighted integral length of the PILs for 56 bipolar and multipolar ARs. The darker crosses correspond to ARs which gave rise to a CME within the
72 hr search window from the corresponding measurement whereas the lighter crosses correspond to those ARs without a CME with the samesearch window. From Falconer et al. (2008). Panel (b): fraction of ARs with an M- or X-class flare as a function of R, within a 24-hour window fromthe R recording. 2500 ARs were employed. From Schrijver (2007). Panel (c): ARs giving rise to M- or X-class flares (blue and red symbols) and nonflaring ARs (black symbols) versus their peak Beff in a 24-hour search window. 298 ARs were employed. From Georgoulis and Rust (2007)

well as the magnetic field energy (Er) of the field overlying the double-arc loop. Kusano et al. (2020) showed forsolar cycle 24 that in 6 out 7 ARs giving rise to major
eruptive flares (X2 class and above) had rc < 1 Mm and
Er > 4 × 1031 ergs along their PILs and these conditions
were satisfied for periods ranging from several hours up
to one day before the onset and indeed k-scheme represent a showcase of how MHD theory could be utilized
in the prediction of eruptive flares. Kontogiannis et al.
(2019) used a number of properties of eruptive ARs andfound that the total amount of their non-neutralized
electric currents and the length of their main polarityinversion lines have the strongest correlation with theassociated CME properties, and therefore these two parameters better reflect the potential of an AR to produce
eruptions.
Magnetic helicity (e.g., review by Pevtsov et al. 2014), ameasure of the twist and linkage of magnetic field lines,
is an extensively employed quantity given also its conservative nature (e.g., Berger and Field 1984) which allows
to draw links between CMEs in the Sun and in the interplanetary medium (Kumar and Rust 1996; Green et al.
2002; Démoulin et al. 2002). In addition, it plays a central role in models of filament channel formation suchas the helicity condensation model (Antiochos 2013).
There exist several methods to calculate the magnetic
helicity in the corona or its injection rate in the photosphere. Statistical surveys supplied important clues on
the importance of magnetic helicity in CMEs. Park et al.
(2008) found that the accumulated magnetic helicity
prior to a set of 11 X-class flares over intervals half a

day up to a few days was significant and in the range
1.8–32 × 1042 Mx2. Nindos and Andrews (2004) studied
the pre-flare helicity of active regions which gave rise to
big flares, and showed, that statistically speaking active
regions with eruptive flares are associated with larger
magnetic helicity compared to active regions with confined (i.e., non-eruptive) flares LaBonte et al. 2007. foundthat active regions giving rise to X-class flares, hencewith a high probability of being eruptive, exhibit peakhelicity flux injection rates above 6 × 1036 Mx2/s in 24hour windows prior to the considered flares. Tziotziou
et al. (2012 found that helicity and magnetic free energy
thresholds of ~ 2 × 1042 Mx2 and ~ 4 × 1031 ergs separate eruptive with non-eruptive ARs. Using MHD simulations, Pariat et al. (2017) suggested that the ratio
between the current-carrying part of magnetic helicity to
the total (volume-integrated) helicity could be used as adiscriminator between eruptive and non-eruptive cases.
This has been recently tested in a handful of cases(Moraitis et al. 2019; Thalmann et al. 2019), where it
was found that this ratio increases significantly prior to
eruptive flares while it does not significantly change
prior to confined flares. The recent study of Pagano etal. (2019) showed that a metric based on the calculationof the Lorentz force from data-driven nonlinear forcefree field models could help in discriminating between
eruptive and non-eruptive active regions.
Another important physical parameter pertinent toeruptivity is the decay index (n) of the overlying horizontal magnetic field. This essentially measures how fast
the strapping magnetic field declines with height above

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

the erupting flux. Its value assumes a key role in certain
models such as the torus instability (Kliem and Török
2006). Depending on the properties of the magnetic
setup (e.g., bipolar, multipolar, aspect ratio and shape offlux rope, etc.), n should be at least as steep as ~ [0.5, 2]
for an eruption to take place (Kliem and Török 2006;
Fan and Gibson 2007; Démoulin and Aulanier 2010;
Olmedo and Zhang 2010; Zuccarello et al. 2016; Syntelis
et al. 2017). Note that stability against the torus instability could become more involved for complex systems
like multiple flux ropes (Inoue et al. 2016). Magnetic
field extrapolations are normally employed in the calculation of the coronal magnetic field in regions that
erupted (or not). In some cases, the starting height ofthe eruption, as inferred from height-time single ormulti-viewpoint measurements, is used as the bottom
boundary for the decay-index analysis.
The statistical study by Liu (2008) showed that the difference of n between eruptive (> 1.74) and non-eruptive
cases (< 1.71) is statistically significant. Analysis of n, fora set of confined and eruptive flares which took place inthe same active region, showed that the eruptive flares
were associated with steeper n in the low corona compared to the confined ones (Cheng et al. 2011). The statistical studies of Wang et al. (2017) and Baumgartner etal. (2018) showed that the critical n (i.e., 1.5), as per
Kliem and Török (2006), is achieved at somehow larger
heights above active regions which gave rise to confinedflares as compared to cases associated with eruptive
flares. Surveys of eruptive prominences by McCauley etal. (2015) and Vasantharaju et al. (2019) found n in therange [0.8, 1.3] above the starting heights of the eruptions. Duan et al. (2019) calculated n along inclined
paths, reflecting non-radial CME propagation rather
along the local vertical, and found that all eruptive flares
they considered had n > 1.3. Recently, Cheng et al.
(2020) found that the average n at the onset heights ofthe main acceleration was close to the torus-instability
threshold for the 12 CMEs they analyzed. The segregation between eruptive and non-eruptive cases in termsof n is also reported in several case studies as discussed
in the review of Cheng et al. (2017). However, taking
into account the temporal evolution of n seems that asteep decay index of the overlying magnetic field is a necessary but not a sufficient condition for eruptions totake place (Nindos et al. 2012; Suzuki et al. 2012; Chintzoglou et al. 2015). In addition, cases of high-lying QS
filaments with overlying magnetic fields with n > 1.5 associated with confined eruptions could be also found(Zhou et al. 2019). Strong overlying fields seem to prevent CMEs as vividly illustrated in the case of superactive AR 12192 which exhibited a multitude of confinedflares, including X-class (Chen et al. 2015; Sun et al.
2015).

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The twist number (Tw) of magnetic field lines is another parameter that is extensively used in CME onset
studies (e.g., Inoue et al. 2011). The magnetic twist is an
integral part of magnetic helicity discussed above, and acomparison of its properties/distribution in the preeruptive/eruptive configurations in the solar atmosphereand at 1 AU supplies important physical clues about
CMEs (Wang et al. 2016d). In addition, magnetic twist
plays a key role in the triggering of the helical kink instability, and related Tw thresholds in the range ~ [1.25,
2.0], depending on the specifics of the implementation(e.g., twist profile, employed geometry, etc.), were derived (Hood and Priest 1981; Fan and Gibson 2003;
Török and Kliem 2003; Török et al. 2004; Hassanin andKliem 2016). Jing et al. (2018) calculated the spatial average of Tw for a sample of 38 eruptive and confinedflares. They found that Tw was not playing a role in separating between confined and eruptive flares, by indeed
all eruptive cases having Tw smaller than the lower
bound of the Tw kink instability threshold (i.e., 1.25) discussed above. On the other hand, a similar survey of 45
eruptive and confined flares by Duan et al. (2019), calculating this time the maximum Tw per flux rope, showedthat Tw above (below) 2, i.e., close to the upper boundof the kink-instability thresholds discussed above, wererelevant to the majority of the considered eruptive (confined) flares, therefore allowing to segregate between
confined and eruptive cases. The differences between
these two studies could be possibly attributed to the different approaches used to calculate the coronal magnetic
field and Tw, i.e., NLFFF extrapolations and average Tw
in Jing et al. (2018) and magnetic relaxation and maximum Tw in Duan et al. (2019). Recently, Lin et al.
(2020) proposed a new parameter (rm) that could be
used in eruptive flare prediction. It is based on the ratioof the magnetic flux of twist higher than a thresholdover the overlying magnetic flux and discriminant function analysis showed that rm is moderately able to tellARs which are capable of producing eruptive flares. Irrespectively of whether kink instability assumes the mainrole in setting CMEs, it may still, in cases of confined
eruptions triggered by this instability, lift flux ropes toheights where the torus instability could take over.
The existence of coronal null points (i.e., points ofvanishing magnetic field) has a central role in CMEmodels such as the breakout model (Antiochos et al.
1999). Therefore, magnetic field extrapolations areused to investigate whether coronal null points exist
above erupting ARs, e.g., Aulanier et al. (2000) for
the first such application. Searches of coronal nullpoints and their association with CMEs have been extended to larger statistical samples by Ugarte-Urra
et al. (2007) and Barnes (2007). These studies foundthat a significant fraction of the analyzed ARs with

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

coronal null points were eruptive, 73% and 26%, respectively. Both studies reported that for the majorityof the considered eruptive cases (~ 75%), no preeruptive coronal null points were found.
Syntelis et al. (2016) found a rise in the emission
measure for hot plasmas (T > 6 MK) before two eruptive
flares and this was extended to 16 ARs by Gontikakis etal. (2020) who showed that the positive temporal derivative of the total emission measure could conceivably be
used as a credible precursor, or short-term predictor, of
an imminent major flare.
As discussed in the introduction of this section, theproblem of predicting major CMEs could be mitigatedby predicting major flares. Flare forecasting is a field thathas really boomed over the last decade because of thequantum increase of vector magnetic field data, thanks
to HMI on SDO, and the advent of machine-learning(ML) in heliophysics. ML schemes allow to digest in autonomous or semi-autonomous means large volumes ofdata, explore multi-dimensional parameter spaces, andare particularly suited for identification and classification
tasks (e.g., review of Camporeale (2019) of ML applications in heliophysics. Recent reviews on flare forecasting,including ML schemes, could be found in Leka et al.
(2019a), Leka et al. (2019b), Park et al. (2020)). Major
conclusions from their extensive benchmarking of alarge number of methods currently used in flare forecasting are that such numerous methods do better thanclimatology, no method clearly outperforms the others,
and consideration of prior flare history improves thecorresponding skill scores.
ML has been used directly in CME predictions. Bobra
and Ilonidis (2016) applied a Support Vector Machineclassification scheme to 18 parameters derived from
HMI vector magnetograms for more than 3000 ARs andfound that only a handful (i.e., 6) among these parameters is sufficient to separate erupting and non-erupting
ARs within 24 hours from the corresponding measurements. These parameters (e.g., mean gradient of thehorizontal magnetic field, mean current helicity, mean
twist parameter) are intensive (i.e., do not depend on theAR size but are spatial averages) and not extensive (i.e.,
depend on the AR size and correspond to spatial sums).
Interestingly, extensive measures seem more appropriate
for the prediction of any flare, irrespectively of its eruptivity (e.g., Bobra and Couvidat 2015).
MHD simulations are invaluable in evaluating existing
metrics as well as for supplying physical insight into new
metrics. For example, Guennou et al. (2017) analyzed aset of eruptive and non-eruptive MHD simulations andfound, in agreement with the ML work discussed in theprevious paragraph, that intensive parameters are more
relevant to eruptivity. Another example is the study of
Moraitis et al. (2014) who validated the existence ofPage 73 of 102

particular magnetic helicity-free magnetic energy regimes pertinent to eruptivity as reported in the observational study of Tziotziou et al. (2012). Routine
observations of the AR vector magnetic field at several
layers above the photosphere may be instrumental inpredicting CMEs (e.g., Patsourakos et al. 2020).
8.2 Predicting CME time of arrival

The essential questions regarding the CME forecast are
if and when it will hit Earth. Therefore, various models
and methods have been proposed in the past decades togive (a reliable) answer to these questions. Recently,
Vourlidas et al. (2019) gave an extensive overview of thetime-of-arrival (ToA) forecast models, whereas earlier
reviews include that of Zhao and Dryer (2014) and Siscoe and Schwenn (2006). In this review, ToA models arenoted and described in Sections 3 (analytical and semiempirical models) and Section 4 (numerical models),
whereas in this section, we will primarily focus on theirperformance.
Vourlidas et al. (2019) made a comprehensive summary of the mean absolute error (MAE) reported by numerous studies and found that the unweighted mean of
all MAE is 9.8 ± 2 h, representing the value for thecurrent state of accuracy of ToA studies. However, as
noted by Vourlidas et al. (2019) and Verbeke et al.
(2019), most of the studies on the ToA prediction does
not report their performance validation consistently, andmoreover, a comparison of the ToA performance between different methods/models is generally missing.
Not only should different methods be compared on thesame sample and under the same conditions, but also
community-agreed metrics and validation methods
should be used in order to assess the current state of
CME modeling capabilities unbiasedly. An effort in that
direction was recently made by Riley et al. (2018b), who
compared the performance of different methods andmodels that performed predictions on the CME scoreboard (https://kauai.ccmc.gsfc.nasa.gov/CMEscoreboard/). The CME scoreboard, facilitated by the Community
Coordinated Modeling Center (CCMC), is one aspect of
the efforts of the CME Arrival Time and Impact Working Team that started in 2017 in the scope of thecommunity-wide International Forum for Space Weather Capabilities Assessment. Riley et al. (2018b) explored the accuracy and precision of the predictions
made by 32 teams 2013–2017 and found that the models
on average predict arrival times to within ± 10 hours
with the precision around the average of ± 20 hours. In
addition, they found that the “Average of all Methods”
forecasts generally perform as well as, or outperforms,
the other models, thus acting as a simple superensemble approach. It should be noted that the ensemble approach was implemented in several ToA models/Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

methods in the past years (Mays et al., 2015a; Dumbović
et al., 2018a, b; Amerstorfer et al. 2018; Kay and Gopalswamy 2018; Napoletano et al. 2018), as it can provide aprobabilistic forecast of CME arrival time as well as an
estimation of arrival time uncertainty from the spread.
The current state of the accuracy of ToA prediction,
regardless of the method used, seems to revolve around10 hours. This resemblance in the performance of very
different propagation models indicates that the major
drawback lies in the lack of reliable observation-based
input. This includes the CME input parameters as wellas the input of the heliospheric background in which itpropagates, which on their own contain errors. Namely,
to obtain CME input parameters, such as the CME velocity and angular width, different methods and models
have to be used to go past the problems related to theprojection effects (for observational properties of CMEs
see Section 2). Different methods can present a rather
wide spread in the obtained CME parameters on a caseby case basis (Mierla et al. 2010). Therefore, it is important to keep track of the CME parameter measurement
metadata and to test the CME input errors. On the other
hand, the model performance also depends on the inputof the heliospheric background, which hugely relies on
our current capabilities of solar wind modeling (seeSection 4). Recently, Kay et al. (2020a) performed ananalysis of the ToA sensitivity of arrival times to various
input parameters for drag-based models. They foundthat the ToA tends to be more sensitive to CME parameters than solar wind parameters and that different precisions on the input parameters are needed for different“strength” CMEs. We note that improvements in the accuracy of CME ToA prediction may be achieved usingnovel approaches, e.g., assimilation of interplanetary
scintillation observations demonstrated by Iwai et al.
(2021). We expect more such studies will be performedin the near future and especially in the scope of the new
SCOSTEP program PRESTO.
8.3 Predicting the magnetic field of CMEs

Predicting the magnetic field distribution within CMEs/
ICMEs represents a holy grail in heliophysics, given thefact that extended intervals of intense southward magnetic fields, typically associated with ICMEs, spawn thestronger geomagnetic storms (e.g., Gonzalez et al. 1999).
A thorough account of the state-of-the-art in this important problem was recently given in (Vourlidas et al.
2019). Currently, we are able to routinely observe themagnetic field of CMEs only at the “end of the road” togeospace, via in situ observations at 1 AU by the WIND,
ACE, etc. spacecraft (e.g., Chi et al. 2016; Nieves-Chinchilla et al. 2019 and Section 3). Occasionally we can observe it also in the inner heliosphere beyond ~ 0.3 AU,
with HELIOS (e.g., Bothmer and Schwenn 1998), andPage 74 of 102

more recently with Messenger and VEX (Miho Janvier et
al. 2019; Good et al. 2019). Much closer to the Sun, inthe corona, we have only a few reported cases of direct
observations of the magnetic field of CMEs in the radio
domain. These observations exploit gyrosynchrotron
emissions from mildly relativistic electrons spiraling in
CMEs (Bastian et al. 2001; Tun and Vourlidas 2013; Carley et al. 2017; Mondal et al. 2020) and Faraday rotationof the electromagnetic radiation of either natural (e.g.,pulsars) or artificial sources going through the CME
body (Jensen and Russell 2008; Howard et al. 2016; Kooi
et al. 2017, 2021). The lack of continuous monitoring of
the Sun with solar-dedicated instruments and the low
sensitivity of the existing instrumentation is behind thescarcity of radio diagnostics of CME magnetic fields.
Therefore, we have to mainly rely on modeling, either
MHD or empirical/semi-empirical, to remedy this critical deficiency. MHD simulations of CMEs covering thedomain spanning the lower solar atmosphere, the outercorona, and the inner heliosphere out to 1 AU, could in
principle deal with the problem in a self-consistent manner, since they simultaneously treat CME initiation, evolution, and propagation in a realistic background coronaand solar wind (e.g., Jin et al. 2017b; Török et al. 2018
and review by Manchester et al. 2017). However, such
simulations, given the huge resources they require, arecurrently used almost exclusively for research purposes
and not for forecasting.
A more tractable approach and recently developed
capability is to use heliospheric models of magnetized
CMEs (see Section 4.2 for details). These models represent a major step over heliospheric CME models which
treat CMEs as purely hydrodynamic disturbances suchas the widely-used ENLIL model (Odstrcil et al. 2004),
which is indeed the standard operational space weathermodel (Mays et al. 2015a). Heliospheric CME models
launch CMEs in their inner boundary, typically in therange 10–20 Rs, and follow their evolution in the innerheliosphere (e.g., Odstrcil et al. 2004; Shen et al. 2011a,b, c; Shiota and Kataoka 2016; Verbeke et al. 2019a;
Scolini et al. 2019b; see also Section 4). The prescribed
CMEs are empirically constrained by STEREO observations supplying their speed, size, and orientation. Magnetized CME models require also inputs for the CME
magnetic field in the inner boundary of their computational domain such as the axial magnetic field. The lesscomputing resources that these models require allows to
run them in almost real time, with ensemble studies
testing the influence of uncertainties of the input parameters on the ICME properties upon impact at 1 AU.
To deal with the lack of direct routine observations of
the near-Sun magnetic field of CMEs, an ever-growing
number of empirical or semi-empirical models to infer
this vital parameter has appeared over the last decadeZhang et al. Progress in Earth and Planetary Science(2021) 8:56

(Vourlidas et al. 2000; Kunkel and Chen 2010; Savani etal. 2015, 2017; Isavnin 2016; Patsourakos et al. 2016; Patsourakos and Georgoulis 2017; Möstl et al. 2018; Sarkar
et al. 2020). These models can be used to supply inputs
to the heliospheric magnetized CMEs’ models, and to
predict on their own CME magnetic profiles at 1 AU.
Various empirical inputs (e.g., CME positional, orientation, and size information, reconnected magnetic flux
and SXR light-curve of the associated flare, eruptionrelated magnetic helicity, height-time measurements of
the CME, CME energetics, etc.) and underlying assumptions (conservation of energy, magnetic flux, and magnetic helicity, self-similar expansion, force-free/nonforce-free magnetic fields, etc.) are used. The handedness of the near-Sun CME flux ropes is derived fromempirical schemes based on, for example, the CME
source region hemisphere, the sense of winding of observed features, etc. (Bothmer and Schwenn 1998; Palmerio et al. 2017). Given that the inputs to these models
could be retrieved from easy-to-obtain observations
from magnetographs, imagers, and coronagraphs, andtheir analytical or semi-analytical nature, they could be
used to routinely infer the near-Sun magnetic field of
CMEs and to predict it upon impact at 1 AU. In
addition, ensemble studies to account for uncertainties
in the inputs are feasible at a minimal computationalcost. Meaningful and rigorous comparisons between
forecasts and observations could benefit from tools/concepts developed for terrestrial weather (Austin and
Savani 2018). A major conclusion was that while existingmodels supply encouraging results, they nevertheless require further development and validation. For instance,
there exist models that lack the ability to derive beforehand the near-Sun CME magnetic field and have to relyon the in situ observations upon the corresponding
ICME arrival at 1 AU in order to properly scale the predicted CME magnetic field vectors at 1 AU. In addition,
there is no benchmarking of these models. A zero-ordercomparison of the near-Sun CME magnetic fields fromtwo of these models seems encouraging (see Fig. 38).
Another major limiting factor in our ability to forecast
CME magnetic fields upon impact at 1 AU is related tothe uncertainties of the input parameters of theemployed models. This includes uncertainty related tothe deflection and rotation that CMEs undergo, in thedetermination of the initial (i.e., source region) and coronal CME location and orientation. In addition, models
also rely upon the properties of the background coronaand solar wind, which are derived from different
models/approaches, which constitutes another source of
uncertainty. A common outcome of these studies,
whether based on Sun-to-Earth MHD simulations (e.g.,
Török et al. 2018), heliospheric MHD models (e.g., Verbeke et al. 2019a; Scolini et al. 2019c), or semi-analytical

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physics-based models (e.g., Kay et al. 2017, 2020b) is thatrather small changes in the CME positional and orientation parameters in the range 2–20°, could have a significant impact on the predicted CME magnetic field
profiles at 1 AU, and particularly on the field components (e.g., Fig. 39). Pattern recognition applied to in situ
observations of incoming ICMEs at 1 AU could be also
used to predict CME magnetic fields with however much
shorter lead times of a few hours only (Chen et al.
1997b; Riley et al. 2017; Salman et al. 2018; Camporeale2019).
Very recently, important results regarding the natureof young CMEs started to emerge from the first observations of the recently launched PSP mission (NievesChinchilla et al. 2020; Hess et al. 2020). As more CMEs
are observed by PSP, as well as by the SoLO mission, it
should be possible to validate and to eventually increase
the physical realism of models of CME magnetic field
forecasting. In situ monitoring of ICMEs at Venus
orbit tied with either empirical scaling laws and/or
propagation models could supply predictions ofEarth-bound CMEs with a lead-in of ~ 1 day prior to
impact (Kubicka et al. 2016). Recent or upcoming facilities observing in radio like LoFAR, MWA, andSKA will finally supply more systematic observationsof CME magnetic fields in the corona (e.g., Nindos etal. 2019).

9 Minimax24 project
The ISEST/MiniMax24 non-flare target is an email alert
service about non-flare-related, but possibly geoeffective, phenomena provided daily by the observer on
duty. The observation overview is shown in Fig. 40. The
service was first established in the scope of the SCOSTEP/CAWSES “MiniMax24 Campaign” in 2013, which
was declared as the year of “MiniMax24” to note that,even though the Sun is going through activity maximum
conditions, the activity is rather low. The goal of the action was to understand and explain the current behaviorof the Sun and its potential impact on human society
and Earth’s space environment through yearlong scientific and outreach activities. The campaign team counted
37 institutions from 17 countries focused on the solarterrestrial observations of solar eruptive events throughthe MaxMillenium program of solar flare research(http://solar.physics.montana.edu/max_millennium/) as
well as CHs, filaments, and CIRs (i.e., non-flare related
phenomena) through the newly established email alert
service. By the end of the year of “MiniMax24,” MiniMax24 email list reached more than 140 participantsfrom more than 30 countries. As the MiniMax24 has
shown to be a very useful and successful hub for the scientific community, the action transcended from its original 1-year-campaign scheme and was included in theZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 38 Histograms of near-Sun CME axial magnetic fields at 10 Rs from two different methods. Panel A. From Patsourakos & Georgoulis (2017)(blue histogram corresponds to eruptive cases). Panel B. From Gopalswamy et al. (2018c) (results from CMEs corresponding to magnetic clouds
upon impact at 1 AU)

Fig. 39 Predicted (non-black curves and shaded areas) and observed (black curves) CME magnetic field profiles at 1 AU from two different
models. Panel (a): from Kay and Gopalswamy 2018; panel (b): from Scolini et al. 2019b. The predicted profiles consider uncertainties in input
parameters. In both panels from top to bottom the magnetic field magnitude, and its Bx, By, and Bz components in the GSE system are plottedZhang et al. Progress in Earth and Planetary Science(2021) 8:56

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Fig. 40 On the top: archived non-flare target alerts 2014–2020; Bottom left: number of archived alerts (observation days) vs number of nonarchived alerts (observation days); bottom right: number of quiet days, filament, and CH observations in the archived alerts

new SCOSTEP program VarSITI, as one of the working
groups of the ISEST project.
The aim of the ISEST/MiniMax24 non-flare target is
to monitor and warn against potentially geoeffective
phenomena that are not related to solar flares. This includes CMEs that are not accompanied by solar flares,
but rather eruptive filaments, as well as stream interaction regions associated with coronal holes.
9.1 The scientific outline

CMEs may either originate from strong active regions,large-scale relatively weak magnetic fields, or filaments(Green et al. 2018). The production of CMEs is highly
likely when associated with eruptions of filaments Schmieder et al. 2013 and eruptive filaments areoften used as on-disk signatures of the non-flare related
CMEs. These do not show significantly different properties from the flare-related CMEs (Vršnak et al. 2005;
Chmielewska et al. 2016), unlike, e.g., “stealth CMEs,” i.e.,
CMEs without obvious on-disk signatures, which areslower and therefore potentially less geoeffective(Robbrecht, Patsourakos and Vourlidas 2009; Kilpua et al.
2014; Nitta and Mulligan 2017). Therefore, eruptive
filaments can be regarded as potential sources of significant geoeffective events. Since filaments are regarded ascool plasma suspended in the magnetic dips of the flux
rope (Gibson 2018), the scale of the filament is directly
related to the scale of the flux rope, and therefore, by that
logic, large and dark filaments are indicative of large flux
ropes, i.e., more massive/energetic CMEs, which are thenmore likely to be significantly geoeffective (Gopalswamy
et al. 2007). Thus, the ISEST/MiniMax24 focuses on
detecting and monitoring only large and dark filaments.
Since the CMEs are largely propagating radially, thoseoriginating from sources close to the center of the solar
disc are more likely to arrive at Earth and, therefore, more
likely to be geoeffective (Srivastava and Venkatakrishnan
2004; Gopalswamy et al. 2007; Zhang et al. 2007). Therefore, the ISEST/MiniMax24 focuses on filaments locatedclose around the central meridian. It should be noted
though, that not all filaments erupt and that filament
eruptions are not necessarily related to CMEs and might

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

be triggered by, e.g., magnetic flux emergence or local andlarge-scale photospheric motions (Parenti 2014). Fromthat perspective, ISEST/MiniMax24 only alerts on thepossibly geoeffective filament targets.
Another significant source of non-flare-related geoeffectiveness, as pointed out in Section 7, are SIRs formedby the interaction of the high-speed solar wind originating from a CH with the preceding slower solar wind(Richardson 2018). CHs are the darkest and least active
regions of the Sun, associated with rapidly expanding
open magnetic fields and acceleration of the solar wind
(Cranmer 2009) and can be easily outlined automaticallyin EUV images using the threshold technique (Rotter et
al. 2012). Moreover, the area of the CH was found to behighly correlated with the speed of the corresponding
HSS at Earth (Nolte et al. 1976; Tokumaru et al. 2017)
and was found to typically need about 4 days to arrive at
Earth (Vršnak et al. 2007a, b; Temmer et al. 2007). Since
generally geoeffectiveness is related to the dawn-to-dusk
electric field and therefore solar wind flow speed (Richardson and Cane 2011), the ISEST/MiniMax24 relies on
the premise that the potentially geoeffective HSS emanate from large CHs close to the central meridian. Empirical relations between coronal holes and HSS have beenutilized to produce a tool for automatic detection of the
CH area in a meridional slice around the center of thesolar disc to predict solar wind speed near Earth 4 daysin advance (Vršnak et al. 2007a, b; Rotter et al. 2012;
Reiss et al. 2016). This HSS forecast algorithm is called“Empirical Solar Wind Forecasting” (ESWF) tool and it
operates automatically using near real-time SDO/AIA
193 Å images to forecast solar wind speed 4 days inadvance (http://cesar.kso.ac.at/programme/minimax.php).
9.2 Non-flare target alert description

The non-flare target alerts are sent on a daily basis, witha “NO non-flare targets” descriptor for quiet times andbrief notification and description of the activity for nonquiet times. The non-quiet times are defined based on
the following criteria:
Criterion #1. There is a CH located within +/−7.5° inlongitude exceeding a ratio area of 0.2, or predicted SW
speed at Earth exceeding 500km/s as observed/forecasted by the ESWF tool.
Criterion #2. There is a prominent (i.e., dark andwide) filament located in longitude within +/−30°
around the central meridian as detected in H-alphaimages provided by Kanzelhöhe Observatory or GONG
H-alpha network (http://halpha.nso.edu). If one or more
CHs are identified as non-flare targets based on criterion
1, the observer notifies in the alert the total ratio area(calculated cumulatively for all CHs across the whole
meridional slice), approximate position of each CH, andthe arrival time and speed of the corresponding HSS. If

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one or more filaments are identified as non-flare targets
based on criterion 2, the observer notifies in the alert
the position, E-W and N-S spread of each filament. In
both cases, the reference to the observing image is provided. The email alerts were not systematically stored
until 2018, although some of the observers archived asignificant part of the alerts. Since the beginning of
2018, the alerts are systematically archived and together
with previously stored alerts compiled into a single nonflare target catalog maintained by the ISEST/MiniMax
24 team and available upon request (contact email:mateja.dumbovic@geof.unizg.hr).
9.3 Summary

The ISEST/MiniMax24 non-flare Target activity started
as a 1-year SCOSTEP/CAWSES activity and continued
throughout the SCOSTEP/VarSITI program in the scopeof the ISEST project. It has been shown that it is a usefulscientific community service that provides a daily overview of the non-flare solar activity. Moreover, as an
added value we highlight that this activity has significantly helped to improve the visibility of the young scientists acting as the daily observers. Therefore, theMiniMax24 activity is planned to continue within the
new SCOSTEP program PRESTO, in the scope of which
it is in addition planned to archive and compile all thealerts into a non-flare target catalog to be used for futurestatistical analysis.

10 Conclusion and outlook
The aim of the ISEST project is to understand the origin, propagation, and evolution of solar transients
through the space between the Sun and the Earth anddevelop the prediction capability for space weather withparticular emphasis on the weak solar activity prevailingin solar cycle 24. The ISEST project dealt with shortterm solar variability in the form of flares, CMEs, andSIRs and the associated phenomena such as interplanetary shocks and SEP events by forming several working
groups to focus on specific problems. The MiniMax24
program, which started during the previous SCOSTEP
program, has proven to be extremely useful. This program will be continued and become a permanent feature. The program has developed an extensive databaseon Earth-affecting transients that is available to the scientific community (Fig. 40). The ISEST program alsobrought together hundreds of scientists from all over theworld to focus on specific problems that resulted in
rapid progress. In the following, we discuss the implications of the current status and suggestions for future
directions.
CMEs are the most recently (1971) discovered phenomena compared to other disturbances. The discovery
of solar wind and interplanetary shocks preceded theZhang et al. Progress in Earth and Planetary Science(2021) 8:56

CME discovery by a decade, while the discovery of SEPs
was even earlier (1940s). Observationally, our understanding of CMEs has progressed significantly over the
next four decades: a well-defined magnetic structure
ejected along with the coronal plasma. While the vast
majority of CMEs near the Sun do not drive a shock, thesmall number of shock-driving CMEs have the most intense consequences in the heliosphere. Although wetreated SEP events as a transient, they are closely related
to CMEs via their shocks. SEPs not related to shocks are
generally the weak and short-lived impulsive events
thought to be accelerated in the flare reconnection region. Once SEPs are released from the shock or flaresite, their further propagation and evolution depends on
the wave/plasma/magnetic properties of the backgroundsolar wind through which they propagate before reaching the observer. Shocks are also readily inferred fromtype II radio bursts, providing information on shocks
from their origin close to the surface to 1 AU and beyond. Interplanetary scintillation is another techniquethat can be used to track the turbulent sheath region ofinterplanetary shocks. SIRs/CIRs also drive shocks andaccelerate particles, but generally at large distances fromthe Sun. Only about a fifth of IP shocks detected at
Earth are due to CIRs. Particle acceleration in CIRs is
significant beyond 1 AU. Geospace phenomena such asenergetic storm particle events, ultra relativistic electron
events, storm sudden commencement, and the onset ofgeomagnetically induced currents. The magnetic structure of the shock sheath and the driving CME determine
the onset of a geomagnetic storm following shock arrival. The primary requirement for a storm is the presence of intense and prolonged southward pointing
magnetic field in the sheath and/or the CME. This is the
motivation behind the attempts to assess the internal
magnetic field of CMEs when they are still near the Sun.
For example, if the magnetic structure of the CME canbe determined near the Sun, it should be possible to predict the structure in the heliosphere taking into accountthe environmental conditions. Both CIRs and shock
sheaths are compressed heliospheric plasmas and hence
have a similar impact on the magnetosphere in causing
geomagnetic storms.
A wealth of observational information on CMEs and
SIRs has accumulated over the past two decades, thanks
to the fleet of space missions observing the Sun-Earthsystem. These data have contributed greatly to thecurrent understanding of CMEs and SIRs. SOHO has
provided extensive data over two solar cycles (23 and24). In cycle 24, STEREO has provided multiview observations, enabling the determination of three-dimensional
morphology of CMEs. The extended STEREO field ofview allowed CMEs to be tracked from the coronal baseto beyond Earth orbit, significantly enhancing

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observational knowledge of CME propagation. SOHO
observations have shown that a magnetograph to measure the photospheric magnetic field, EUV imager to observe eruption signatures, white-light coronagraph toimage CMEs, heliospheric imager for tracking CMEs to
1 AU, and a low-frequency radio telescope to detect
shock signatures from close to the Sun to 1 AU. In
addition to these, we also have instruments for in situ
measurements of plasma, magnetic field, and energetic
particles to complete the data set needed for investigating earth-affecting solar transients. The STEREO mission’s twin spacecraft transited through the Sun-Earth
Lagrange points L4 and L5 and demonstrated that these
are ideal locations for placing these instruments to better observe Earth-directed CMEs. STEREO did not havea magnetograph to observe the photospheric magnetic
field; SOHO did not have a magnetometer for detect
CMEs and CIRs in the solar wind. Multiview magnetograms are important not only to track potentially eruptive active regions from behind the east limb before theyrotate onto the disk but also to build global magnetic
field distribution used as input to background solar windmodels. While both L4 and L5 observations help
characterize CMEs near the Sun, L5 vantage is useful in
identifying active regions before they rotate into Earthview. On the other hand, L4 can observe Earth-directed
CMEs without being affected by a “snowstorm” of secondary particles created by SEPs hitting the spacecraft.
Earth-directed energetic CMEs are magnetically wellconnected to L5, so coronagraph images are vulnerable
to such “snowstorms.” Another advantage of the L5 location is that CIRs arrive at L5 a few days before they arrive at Earth, so one can predict the nature of CIRs
arriving at Earth. Placing similar instruments at L4 andL5 will be ideal for a better characterization of CMEs
near the Sun. Ideally, one should have multiple spacecraft at various locations in Earth orbit to provide space
weather–relevant information on transients as well as
the global magnetic field. Future efforts should also be
directed toward using other techniques such as Faraday
rotation to measure the magnetic content of CME flux
ropes.
There has also been a rapid development of several
MHD models with sophisticated simulation techniques
to describe most or all stages of CMEs, i.e., pre-eruptive
stage, destabilization and eruption, and propagation. In
particular, models involving flux rope are growing in
number so that the currently used hydrodynamic pulse
representing a CME can be eventually replaced by a flux
rope, which is more realistic and consistent with in situ
observations. Such a transition would have the potential
to predict the magnetic field vectors in the heliosphererather than predicting just the CME arrival time. MHD
models have also started considering the simulation

Zhang et al. Progress in Earth and Planetary Science(2021) 8:56

boundaries closer to the Sun to account for forces thatsignificantly affect the propagation of CMEs. The ultimate goal is to predict the magnetic field vectors at any
point in the inner heliosphere, soon after the eruption at
the Sun. Recent work on deriving the magnetic properties of CMEs near the Sun using source properties anderuption data will help test and improve global MHD
modeling of CME propagation. However, we still have along way to go in understanding when a magnetic region
on the Sun hosts an eruption, but significant progressover the recent years in both terms of our observationaland modeling capabilities is seamlessly contributing towards an eventual resolution of this cornerstone challenge. We still do not have a reliable set of active region
parameters that would indicate an eruption. This is acommon problem to both flares and CMEs because they
are manifestations of a common energy release in thesource magnetic region. Observations of the magnetic
field at several layers above the photosphere and all
around the Sun tied with advances in our capability tomodel magnetic fields from emergence to eruption are
lending reasonable optimism.
The VarSITI program was launched at the peak phaseof solar cycle 24, which turned out to be only half asstrong as cycle 23. VarSITI investigations dealt with
Earth-affecting solar transients in the background of thediminished solar activity and the related changes in the
heliosphere into which solar disturbances propagated.
The space weather consequences of CMEs and CIRs
have proven to be mild in solar cycle 24. This is the
combined effect of the diminished number of energetic
CMEs and the weakened heliospheric state. This was thesmallest cycle in the space age, so we are able to expand
our knowledge of extended parameter space of Earthaffecting phenomena. Current predictions of thestrength of solar cycle 25 point to a weak cycle as well,and one can expect another cycle with mild space weather. The extended and uniform data set from SOHO
have helped us characterize the solar cycles from thepoint of view of CME evolution and particle acceleration. The Parker Solar Probe and the Solar Orbiter are
sampling the weak heliosphere and we expect to learn a
lot on the behavior of Earth-affecting transients in the
heliosphere.
Abbreviations
ACE: Advance Composition Explorer; ADAPT: Air Force Data Assimilate
Photospheric Flux Transport; AWSoM: Alfvén wave solar model; CAWS
ES: Climate And Weather of the Sun-Earth System; CME: Coronal massejection; CIR: Corotating interaction region; CORHEL: Coronal-heliosphere;
CH: Coronal hole; EVE: EUV Variability Experiment; DSA: Diffusive shock
acceleration; EEGGL: Eruptive Event Generator Gibson-Low;
EUHFORIA: European Heliospheric Forecasting Information Asset;
ESP: Energetic storm particle; FIP: First ionization potential;
ICME: Interplanetary coronal mass ejection; IP: Interplanetary; LASCO: Large
Angle and Spectrometric Coronagraph Experiment; LAT: Large area
telescope; LDGRF: Long-duration gamma-ray flare; PFSS: Potential field

Page 80 of 102

source surface; PSP: Parker Solar Probe; ISEST: International Study of Earthaffecting Solar Transients; LFM: Lyon-Fedder-Mobarry;
MAS: Magnetohydrodynamic Algorithm Outside a Sphere; MESSENGE
R: Mercury Surface, Space Environment, Geochemistry and Ranging;
MHD: Magnetohydrodnamics; PCA: Polar cap absorption; SCOSTEP: ScientificCommittee on Solar-Terrestrial Physics; SDO: Solar Dynamic Observatory;
SECCHI: Sun Earth Connection Coronal and Heliospheric Investigation;
SEP: Solar energetic particle; SIR: Stream interaction region; SO: Solar Orbiter;
SOHO: Solar and Heliospheric Observatory; SPR: Solar particle release;
SSN: Sunspot number; STEREO: Solar Terrestrial Relations Observatory; STER
EO/A: STEREO Ahead; STEREO/B: STEREO Behind; SWMF: Space Weather
Modeling Framework; VarSITI: Variability of the Sun and Its Terrestrial Impact
Acknowledgements
JZ, NG, SF, and MD gratefully acknowledge the travel grant from Nagoya
University to attend the workshop held in Nagoya, Japan, in November 2019,
for initiating this review work. JZ thanks Prof. Kazuo Shiokawa for his
assistance in guiding the process of this review work. SP thanks Dr. M. K.
Georgoulis for useful discussions.
Authors’ contributions
JZ provided the overall management of writing this review paper, which wasa group effort of 19 individual authors. JZ, NG, and MT conceived thestructure of the paper. JZ (leading author) contributed to S1 (Section 1). JZ(leading author), NG, and KD contributed to S2. BV (leading author), YW(leading author), BZ, and MD contributed to S3. FS (leading author), NL, and
XF contributed to S4. DW (leading author) and NVN (leading author)
contributed to S5. OM (leading author), NG, MID, ND, and ML contributed to
S6. MT (leading author), KD, MD, and SGH contributed to S7. SP (leading
author), MD, and JZ contributed to S8. MT (leading author), MD, and KD
contributed to S9. NG (leading author) contributed to S10. The author(s) readand approved the final manuscript.
Funding
The ISEST project, which results in this review, is supported by the SCOSTEP/VARSITI program. JZ was funded by NASA grants NNH17ZDA001N-HSWO2R,
80NSSC20K1274, and 80NSSC19K0082. SF is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences Grant No. XDB
41000000, the National Natural Science Foundation of China (41774184 and41974202), and the Specialized Research Fund for State Key Laboratories.
NVN’s work was supported by NASA contract NNX17AB73G. MID acknowledges the support of NASA grants 80NSSC18K0520, 80NSSC19K0079,
80NSSC20K1255, and 80NSSC18K1366. NL was supported by the NASA grant
80NSSC20K0700. XF is supported by the Natural Science Foundation of China
under grants 41531073,41861164026, 42030204.
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated oranalyzed during the current study.

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Physics and Astronomy, George Mason University, 4400
University Dr., MSN 3F3, Fairfax, VA 22030, USA. 2Institute of Physics,
University of Graz, Graz, Austria. 3Goddard Space Flight Center, Greenbelt,
MD, USA. 4National Observatory of Athens, Institute for Astronomy,
Astrophysics, Space Applications and Remote Sensing, Penteli, Athens,
Greece. 5Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA,
USA. 6Department of Physics, University of Ioannina, 45110 Ioannina, Greece.
7
SIGMA Weather Group, State Key Laboratory of Space Weather, National
Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
8
Hvar Observatory, Faculty of Geodesy, University of Zagreb, Kaciceva 26,
HR-10000 Zagreb, Croatia. 9CAS Key Laboratory of Geospace Environment,
Department of Geophysics and Planetary Sciences, University of Science andTechnology of China, Hefei, Anhui 230026, PR China. 10ISR, Boston College,
140 Commonwealth Ave., Chestnut Hill, MA 02467, USA. 11Southwest
Research Institute, 6220 Culebra Road, San Antonia, TX 78023, USA.

(2021) 8:56

Page 81 of 102

Department of Physics and Astronomy, University of Texas at San Antonio,
San Antonio, TX 78249, USA. 13NorthWest Research Association, Boulder, CO,USA. 14Institut fuer Experimentelle und Angewandte Physik, University of Kiel,
Kiel, Germany. 15Department of Physics and Astronomy, University of Turku,
Turku, Finland. 16Max-Planck-Institut für Sonnensystemforschung,
Justus-von-Liebig-Weg 3, 37077 Göttingen, Germany. 17INAF-Istituto di
Astrofisica e Planetologia Spaziali, Via del Fosso del Cavaliere, 100, I-00133
Rome, Italy. 18Space Science Center and Department of Physics, University of
New Hampshire, Durham, NH, USA.

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Received: 19 November 2020 Accepted: 26 April 2021

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