Cross-helicity is a measure of the correlation between the velocity and magnetic fluctuations in a turbulent conducting fluid. Unlike the more familiar magnetic helicity, which quantifies the knottedness and linkage of magnetic field lines and has been studied since the foundational work of H.K. Moffatt in the late 1960s, cross-helicity captures how intimately the swirling plasma motion is coupled to the magnetic structures it carries. When velocity and magnetic fluctuations are strongly correlated, cross-helicity becomes large, and its effects on transport, diffusion, and dynamo action can be profound. Recent reviews by Nobumitsu Yokoi have highlighted what he calls unappreciated cross-helicity effects in plasma physics, including anti-diffusion effects in both dynamo action and momentum transport, hinting that this quantity could reshape how physicists model magnetized turbulence.

The new analysis is grounded in a Kolmogorov-like phenomenology developed within the framework of what Bershadskii terms distributed chaos. In classical turbulence theory, the energy spectrum of a turbulent flow follows well-known power-law scaling relations, first articulated by Andrei Kolmogorov in 1941. But chaotic systems, including turbulent magnetohydrodynamic flows, can produce frequency power spectra whose decaying parts follow exponential rather than power-law forms. Exponential power spectra have been observed in a striking variety of chaotic settings, from magnetically confined laboratory plasmas studied by J.E. Maggs and G.J. Morales, to time series of sunspot numbers examined as far back as R.N. Bracewell’s pioneering analyses in the 1950s. In the distributed chaos picture, the exponential decay of the spectrum encodes the underlying deterministic but unpredictable dynamics of the system.

Bershadskii demonstrates, using results from laboratory dynamo experiments and direct numerical simulations of magnetohydrodynamic turbulence, that when the magnetohydrodynamic turbulence is sufficiently strong in chaotic, swirling flows, cross-helicity comes to dominate the decaying portion of the frequency power spectra of the generated magnetic field. The simulations draw on sparse-mode spectral methods of the kind developed by M. Meneguzzi and colleagues, while the laboratory context connects to experiments such as the Taylor-Green dynamo, in which bistability and chaos have already been documented by R.K. Yadav and collaborators. The essential message is that the interaction between swirl and field, quantified by cross-helicity, is not a minor correction to solar dynamo theory but a leading-order feature when turbulence is vigorous.

To test whether this picture applies to the real Sun, the study turns to observational records of the full-disc solar magnetic field and to the international sunspot number series maintained by the Solar Influences Data Analysis Center in Belgium, along with mean-field measurements from Stanford’s Wilcox Solar Observatory and vector observations from the SOLIS facility. The past two solar cycles have been notably weak in magnetic activity, a period that researchers such as K. Mursula and colleagues have described as a transition away from the Modern Maximum toward a weaker Sun. Bershadskii finds that the full-disc magnetic field during these cycles exhibits deterministic chaotic behavior, with the chaotic dynamics concentrated around the solar equator, precisely the region where sunspot activity emerges and migrates in the familiar butterfly diagram.

The finding that the field’s dynamics are concentrated near the equator resonates with independent evidence about the character of solar interior turbulence. Helioseismic analysis by S. Hanasoge, H. Hotta, and K.R. Sreenivasan has shown that turbulence in the Sun is suppressed on large scales and confined to equatorial regions, a result that surprised much of the community. A dynamo operating in a chaotic, swirling, equatorially concentrated flow environment is exactly the kind of system in which cross-helicity dominance of the spectral decay would be expected, according to the theoretical framework of the new paper. The consistency between theory, simulation, experiment, and observation strengthens the case that the distributed chaos approach captures something real about the solar dynamo.

Deterministic chaos is a concept with a long scientific lineage, tracing back to Edward Lorenz’s celebrated 1963 discovery of deterministic nonperiodic flow in atmospheric convection. A chaotic system obeys definite physical laws, yet its future evolution becomes practically unpredictable beyond a certain horizon because tiny uncertainties grow exponentially. The solar cycle, with its roughly eleven-year rhythm, its irregular amplitudes, and its occasional deep minima, has long tempted scientists to search for such behavior. Studies by C. Letellier and colleagues found evidence for low-dimensional chaos in sunspot cycles, while N. Platt, E.A. Spiegel, and C. Tresser modeled the intermittent character of the cycle, and P. Mininni and collaborators developed stochastic relaxation oscillator models. The new work adds a spectral fingerprint, the exponential decay associated with distributed chaos and cross-helicity, to this body of evidence.

The technical core of the argument lies in how the decaying parts of frequency power spectra distinguish between different underlying regimes. Purely stochastic processes tend to produce power-law spectra, whereas deterministic chaos in turbulent flows produces exponential spectral decay whose slope can be predicted by the Kolmogorov-like phenomenology of distributed chaos. When Bershadskii applies this diagnostic to the full-disc solar magnetic field data from the last two weak cycles, the observed spectra match the theoretical expectations for a system in which cross-helicity dominates. In other words, the correlation between the Sun’s plasma motions and its magnetic fluctuations is strong enough that it governs how magnetic energy is distributed across frequencies, a conclusion with direct implications for how dynamo models should be formulated and how the limits of solar activity prediction should be understood.

The implications extend beyond academic curiosity. Solar magnetic activity drives space weather that can damage satellites, disrupt radio communications, and stress terrestrial power grids. Forecasting the amplitude and timing of future cycles is a major practical goal, and approaches based on mean-field dynamo theory continue to be refined by groups such as V. Obridko and colleagues. If the full-disc magnetic field is genuinely governed by deterministic chaos with cross-helicity-dominated spectral dynamics, then there are hard, quantifiable limits to how far ahead reliable prediction can reach, however good the models become. At the same time, recognizing the system as chaotic rather than purely stochastic opens the possibility of short-term predictability techniques borrowed from nonlinear dynamics, exploiting the deterministic structure that chaos still preserves.

There is also a broader astrophysical payoff. Magnetic cycles and dynamos are observed on stars throughout the galaxy, and the physics uncovered in the solar context applies to magnetized plasmas everywhere, from planetary interiors to accretion disks and the interstellar medium. The study’s synthesis, linking swirling MHD turbulence, laboratory dynamo experiments, numerical simulation, and a half-century of solar monitoring, illustrates how a single theoretical lens, the distributed chaos framework enriched by cross-helicity, can bring coherence to phenomena that have often been treated separately. As the Sun continues its journey away from the Modern Maximum into a period of weaker activity, the chaotic, equator-concentrated dynamics identified in this work offer both a warning about the limits of prediction and an invitation to look more closely at the turbulent, helical plasma churning beneath the solar surface.

Subject of Research: Cross-helicity and deterministic chaotic dynamics of the full-disc solar magnetic field generated by a turbulent magnetohydrodynamic dynamo.

Article Title: Cross-Helicity and Chaotic Dynamics of Full-Disc Solar Magnetic Field

Article References: Bershadskii, A. (2026). Cross-Helicity and Chaotic Dynamics of Full-Disc Solar Magnetic Field. Solar Physics, 301(9), Article 141. https://doi.org/10.1007/s11207-026-02725-y

Image Credits: AI Generated

DOI: 10.1007/s11207-026-02725-y

Keywords: solar dynamo, cross-helicity, magnetohydrodynamic turbulence, distributed chaos, full-disc magnetic field, sunspot number, solar cycle, deterministic chaos, power spectra, Kolmogorov phenomenology, swirling flows, solar physics

The discovery was made by researchers from the NSF National Solar Observatory, Germany’s Max Planck Institute for Solar System Research and the High Altitude Observatory in the United States. Their study, published in Nature, describes swirling plasma features positioned along the boundaries of solar granules. These granules are constantly shifting cellular patterns that cover the Sun’s visible surface, or photosphere. Each granule is typically between 500 and 2,000 kilometers across and forms as hot plasma rises from deeper layers, cools near the surface and sinks again.

The new images reveal delicate, fringed structures lining the edges of these granules. Some are less than 20 kilometers wide, placing them at the limit of what current solar imaging technology can resolve. The researchers compare the challenge to seeing a one-euro coin from a distance of 180 kilometers. To reach this level of detail, the team used a broad-band imaging camera supplied by the Max Planck Institute for Solar System Research, together with sophisticated image-restoration techniques that compensate for atmospheric distortion and instrumental effects.

Over time, the fringes were seen to develop swirling motions that resemble breaking ocean waves. The researchers interpret these motions as Kelvin–Helmholtz instabilities, a fluid-dynamic process generated when adjacent layers of fluid or plasma move at different speeds. The velocity difference creates shear at their interface. Small disturbances can then amplify into rolling waves and vortices, much as wind passing over water produces ripples that eventually curl and break.

Kelvin–Helmholtz instabilities are not unique to the Sun. They appear in terrestrial oceans and lakes, in cloud systems, in the atmospheres of Jupiter and Saturn, and where the solar wind encounters planetary magnetic fields. On the Sun, however, the process unfolds in electrically charged plasma governed by both fluid motion and magnetic forces. At the borders of granules, neighboring plasma streams can move in different directions or at different velocities, creating the conditions required for the instability to grow.

The discovery could alter scientists’ understanding of how solar magnetic fields become energized. Magnetic field lines emerging through the photosphere can be stretched, twisted and coiled by moving plasma. In this state, the field stores magnetic energy, much like a tightly wound spring. When the configuration becomes unstable, magnetic reconnection can abruptly rearrange the field lines and convert stored energy into heat, particle acceleration and radiation.

One possible consequence is the production of nanoflares, extremely small bursts of energy that are individually far weaker than major solar flares but may occur in enormous numbers. Nanoflares have long been considered a possible contributor to the Sun’s extraordinarily hot corona, yet the physical processes that supply and release their energy remain debated. If the newly observed vortices continually twist magnetic field lines, they could provide a persistent mechanism for loading energy into the solar atmosphere and initiating small-scale reconnection events.

The vortices may also solve part of a long-standing problem involving the transport of magnetic flux. The observations and simulations indicate that these miniature whirlpools efficiently mix magnetized and non-magnetized plasma at the solar surface. Such mixing could help magnetic fields spread upward into the atmosphere more rapidly than existing models predict. This matters because the Sun’s magnetic activity rises and falls over an approximately 11-year cycle, a remarkably fast transformation that requires magnetic flux to be redistributed efficiently through the solar surface and atmosphere.

Researchers caution that the images represent only a narrow window into a highly dynamic environment, and further observations will be needed to determine how often the vortices occur, how strongly they are shaped by magnetic fields and how much energy they transport. Nevertheless, their apparent ubiquity wherever the magnetic field is sufficiently strong suggests that they may be a fundamental part of solar surface physics rather than an unusual phenomenon. The result demonstrates how processes occurring across just a few dozen kilometers can influence the evolution of the Sun’s magnetic architecture—and potentially the bursts of radiation that affect the space environment around Earth.

Subject of Research: Not applicable

Article Title: Ubiquitous Kelvin–Helmholtz instabilities driving plasma mixing on the Sun

News Publication Date: 5-Aug-2026

Web References: https://doi.org/10.1038/s41586-026-10871-3

References: Nature, DOI: 10.1038/s41586-026-10871-3

Image Credits: NSF/NSO/AURA/MPS

Keywords

Sun, solar physics, plasma vortices, Kelvin–Helmholtz instability, solar granulation, magnetic reconnection, nanoflares, solar magnetic fields, Daniel K. Inouye Solar Telescope, space weather

The challenge of forecasting solar active regions has long been a complex puzzle. Active regions on the Sun, characterized by intense magnetic fields, are the epicenters of volatile phenomena such as solar flares and coronal mass ejections (CMEs). These explosive events can unleash clouds of charged particles and electromagnetic radiation that disrupt satellites, GPS systems, power grids, and even threaten astronaut safety during space missions. Historically, predicting the emergence of these regions has been constrained by limited observational windows and the complexity of the Sun’s magnetic dynamics.

Central to this breakthrough is the recognition that solar active regions do not simply appear at random but instead form along large-scale, undulating magnetic structures known as toroidal bands. These bands represent deep-seated magnetic flux that migrates and twists beneath the Sun’s visible surface layers. Utilizing state-of-the-art data from NASA’s Solar Dynamics Observatory (SDO), specifically from the Helioseismic and Magnetic Imager (HMI), the research team successfully mapped these surface magnetic signatures and developed methods to invert them, revealing the hidden subsurface magnetic states that precede active region emergence.

The cornerstone of this innovative forecasting tool is a physics-informed neural network called PINNBARDS (Physics-Informed Neural Network-Based Active Region Distribution Simulator). This model integrates the physics of solar magnetohydrodynamics (MHD) with advanced machine learning techniques to bridge observations from the solar surface to the enigmatic tachocline—a critical transition zone embedded deep within the solar interior between the radiative core and the convective outer layer. The tachocline plays a vital role in the Sun’s magnetic dynamo, making insights into its behavior essential for understanding solar magnetic activity cycles.

Traditional forecasting approaches rely heavily on surface magnetic details that appear shortly before a flare or eruption, offering limited warning times. By contrast, PINNBARDS offers a transformative leap by extracting the global magnetic environment and connecting it to subsurface dynamics, thus laying the groundwork for long-range predictions. The neural network is designed to respect the fundamental physical laws governing solar plasma and magnetic fields, ensuring that its predictions are not merely statistical correlations but rooted in solar physics principles.

By reconstructing the subsurface magnetic environment, PINNBARDS supplies critical initial conditions for subsequent forward simulations modeling the evolution of solar magnetic fields. This innovation paves the way for identifying the latitude and longitude where large, flare-producing active regions are likely to emerge weeks in advance. Such spatial precision is crucial because it determines whether the resulting bursts of solar particles will be Earth-directed or dissipated harmlessly into space, thus enabling more targeted and effective mitigation strategies.

The potential operational benefits of this extended forecast capacity are immense. Satellite operators could prepare to shield sensitive electronics, power grid managers could implement protective measures to fend off geomagnetically induced currents, and space agencies could make informed decisions to safeguard crewed space missions. As our society becomes increasingly reliant on technology vulnerable to solar disturbances, the ability to forecast space weather well in advance is no longer a scientific curiosity but a strategic imperative.

The success of PINNBARDS results from an interdisciplinary collaboration melding expertise in heliophysics, computational modeling, and artificial intelligence. This synergy reflects the future of scientific discovery, where AI tools informed by rigorous physics can extract meaningful signals from complex datasets that were previously inscrutable. The researchers emphasize that this approach could inspire similar methodologies for understanding other stellar magnetic phenomena, enhancing our comprehension of magnetic activity beyond our Sun.

Underpinning this advance are the continuous, high-fidelity observations furnished by the SDO/HMI instrument, which captures detailed magnetograms at the solar surface. These observations provide the baseline data for PINNBARDS to perform its inversion techniques, a process akin to seismic tomography but applied to solar magnetism. The ability to perceive the “hidden” magnetic undercurrents equips scientists with a novel view not accessible through direct observation alone.

Furthermore, the research highlights the importance of the tachocline region in the solar dynamo process. The transition layer between the Sun’s internal radiative zone and outer convection zone is where differential rotation acts on magnetic fields, twisting and amplifying them. PINNBARDS’ capacity to infer magnetic state vectors within this elusive layer represents a milestone, as direct measurement of conditions at these depths is currently unattainable with existing instrumentation.

The study, recently published in The Astrophysical Journal, was supported by NASA’s Heliophysics Guest Investigator Open (HGIO) program and NSF-NCAR, signifying robust institutional backing for cutting-edge heliophysics research. Stanford University’s center focusing on the consequences of magnetic fields and plasma flows inside and outside the Sun also contributed, underscoring the project’s standing at the nexus of observational astrophysics, computational science, and applied mathematics.

Looking ahead, the researchers anticipate that integrating PINNBARDS with operational forecasting frameworks will usher in a new era of space weather prediction. This integration will leverage continuous solar monitoring, real-time data assimilation, and physics-informed AI to provide decision-makers with timely, actionable insights. Protecting Earth’s technological assets from the volatile temperament of our star is an achievable goal, thanks to these pioneering efforts.

In sum, this research not only deepens our understanding of solar magnetic processes but ushers in a paradigm shift in our approach to forecasting space weather. The capacity to anticipate large-scale solar eruptions weeks in advance will transform how humanity prepares for and responds to the Sun’s tempestuous behavior, securing technological systems and expanding the frontiers of space exploration with newfound confidence.

Subject of Research: Not applicable
Article Title: A Physics Informed Neural Network for Deriving MHD State Vectors from Global Active Regions Observations
News Publication Date: February 19, 2026
Web References:
– https://iopscience.iop.org/article/10.3847/1538-4357/ae30de
– https://www.swri.org/markets/earth-space/space-research-technology/space-science/heliophysics
References: The Astrophysical Journal, DOI: 10.3847/1538-4357/ae30de
Image Credits: NASA/SDO HMI/SwRI/NCAR

Keywords

Solar active regions, space weather forecasting, solar flares, coronal mass ejections, magnetohydrodynamics, neural networks, tachocline, heliophysics, Solar Dynamics Observatory, physics-informed AI, solar magnetic fields, PINNBARDS