Solar-Cycle Modulation and Photospheric Magnetic Control of Turbulence in the Young Solar Wind

This paper investigates the solar-cycle modulation and photospheric magnetic control of turbulence in the young solar wind, using observations from 25 solar orbits of NASA's Parker Solar Probe (PSP) mission combined with a global MHD model, photospheric magnetograms, and extreme ultraviolet maps of the low corona.

Key findings:

1. Solar-cycle variations in the young solar wind: The authors analyzed PSP observations from 2018 October 31 to 2025 November 1, dividing data into non-overlapping 2-hour intervals and using daily sunspot number (SSN) as a reference for solar activity. They found that Higher-SSN intervals generally have faster speeds, stronger magnetic fields, and greater Z2, σc, and energy transfer rates. Specifically, magnetic-field strength, wind speed, turbulent energy density, cross helicity, residual energy, and the turbulent energy-transfer rate are enhanced with increasing solar activity, while proton density is diminished. The turbulence correlation length shows weaker and less monotonic ordering. The paper notes that the magnitude of variability produced by solar activity is of a level comparable to the variation with r.

2. Source-region control of turbulence: By tracing PSP-connected magnetic flux tubes to their solar sources using the global MHD model, the authors demonstrated that "high turbulent-energy intervals are preferentially connected to coronal-hole sources with unipolar magnetic topology, whereas low turbulent-energy intervals are associated with multipolar topology and active-region environments. The analysis of two representative encounters (E6 during solar minimum and E22 during solar maximum) revealed that wind streams with large turbulence-amplitudes and Alfvénicity tend to originate from the deep interiors of unipolar coronal holes. Multipolar magnetic topology at the source (including active-region environments) tends to produce streams with smaller turbulence amplitudes and low cross helicity, and the boundaries of CHs are associated with an intermediate turbulence level."

3. Magnetic field strength is not the controlling factor: "A comparison of the surface BR values at the traced footpoints shows strongly overlapping distributions between the upper and lower RZ2 subsets in both cases, indicating that the high and low turbulence-amplitude intervals are not simply ordered by the footpoint magnetic-field strength. Instead, magnetic topology and complexity is the controlling factor."

4. Modeled turbulence evolution: The modeled turbulent energy density evolves non-monotonically along traced flux tubes, increases rapidly above the solar surface, reaches a maximum in the middle-outer corona, and subsequently decreases with r. The location of the modeled maximum of Z2 changes substantially with solar activity: During low activity, Z2max is concentrated within a relatively narrow radial range near 0.025-0.03 AU. During solar maximum, the distribution is considerably broader and displaced outward to approximately 0.05-0.06 AU. The maximum occurs in sub-Alfvénic flow in both cases, but the solar-max distribution extends to substantially larger MA and lies closer to the modeled Alfvén surface.

5. Interpretation: The equator-ward extension of coronal holes during solar maximum produces a greater filling fraction of CH-sourced wind streams in the ecliptic heliolatitudes sampled by PSP, which can be directly accounted for by this global shift in photospheric magnetic topology.

The study concludes that turbulence properties exhibit strong solar-cycle dependence in the very inner heliosphere, indicating trends that are broadly consistent with the variability observed near Earth in previous studies and provides a clear demonstration of the influence exerted by solar-cycle-dependent source regions on turbulence in the young solar wind.

Improvements to AI Systems:

1. Solar Wind Forecasting with Source-Region Awareness: An AI system can be enhanced to predict turbulence levels (e.g., Z2, cross helicity, energy-transfer rate) in the inner heliosphere by incorporating photospheric magnetic topology (unipolar vs. multipolar) and coronal-hole boundaries, rather than relying solely on magnetic-field strength or solar wind speed. This would improve space-weather forecasting accuracy, especially during solar maximum when source regions shift.

2. Data-Driven Turbulence Model Calibration: The AI can be trained to map solar-cycle phase (e.g., sunspot number) and source-region magnetic complexity to the radial profile of turbulent energy density (Z2), including the location and magnitude of its maximum. This enables real-time adjustment of solar wind turbulence models in heliospheric simulations, improving predictions of Alfvén surface position and sub-Alfvénic flow regions.

3. Automatic Classification of Solar Wind Source Regions: An AI system can be built to classify PSP or other spacecraft-connected flux tubes into coronal-hole interiors, boundaries, or active-region/multipolar environments using MHD tracing outputs. This would automate the identification of high- vs. low-Alfvénicity streams, enabling faster analysis of new encounters and better interpretation of in-situ measurements without manual tracing.

4. Solar-Cycle-Aware Anomaly Detection: The AI can be trained to distinguish solar-cycle-driven variability from transient events (e.g., coronal mass ejections) in young solar wind data by using the documented relationships (e.g., higher SSN → faster speed, stronger B, higher Z2, lower density). This improves event detection and removes false positives in automated data pipelines.

5. Transfer Learning for Other Stars or Missions: The demonstrated relationship between source-region magnetic topology and turbulence can be encoded into an AI model that predicts turbulence properties for other heliospheric missions (e.g., Solar Orbiter) or even stellar winds, by using photospheric magnetograms and a proxy for magnetic complexity (e.g., multipolarity index). This extends the model’s applicability beyond PSP’s orbital range and solar cycle coverage.

6. Uncertainty Quantification in Turbulence Predictions: The overlapping footpoint B R distributions between high- and low-turbulence subsets can be used to train an AI that explicitly outputs uncertainty bounds when predicting turbulence from magnetic-field strength alone, flagging cases where topology (not field strength) is the dominant driver. This improves reliability in operational forecasting where magnetic field data are incomplete.