The Sun, our nearest star, is more than just a steady source of light and warmth. It's a dynamic, churning ball of plasma with a powerful magnetic field that drives everything from beautiful auroras to disruptive space weather events that can affect our technology here on Earth. You might have heard about the Sun's famous 11-year cycle, marked by the rise and fall in the number of dark spots on its surface, called sunspots. We're heading towards the peak of the latest cycle, which means more sunspots, solar flares, and potentially more space weather.
But scientists have long known that the Sun's activity isn't perfectly regular; the strength of these 11-year cycles varies over much longer periods, sometimes for decades or even centuries. This long-term change in the Sun's activity amplitude is a big mystery, and understanding what causes it is crucial for predicting solar behaviour and its effects on us.
Scientists call this longer-term variation supradecadal modulation, meaning changes over periods longer than the usual 11-year cycle. For decades, there's been a considerable debate in the scientific community about what drives this. Is it purely due to the Sun's incredibly complex internal workings, like a complicated machine with internal feedback loops that can sometimes behave in unpredictable, even chaotic ways? Or is it more like random noise or bumps from the turbulent, swirling plasma inside the Sun that perturbs the system, causing these longer-term fluctuations?
To try and settle this debate, a team of researchers from the Indian Institute of Science Education and Research (IISER) Kolkata used computer simulations of the Sun's magnetic engine, known as the solar dynamo. The solar dynamo is the process deep inside the Sun's convection zone where plasma motions generate and maintain the Sun's magnetic field. A key part of this is the Babcock-Leighton mechanism, which describes how magnetic fields from sunspots get transported towards the Sun's poles and help create the magnetic field for the next cycle. The researchers used two different dynamo models: a spatially extended 2D model that's more detailed and a simpler dimensionally reduced time-delay model. Both models are based on the fundamental physics of how magnetic fields and plasma interact.
Crucially, the researchers focused their simulations on the near-critical regime. This is the state where the Sun's dynamo is thought to be operating right now – it's strong enough to keep the cycle going but not so strong that it becomes wildly unstable or chaotic. The team ran these simulations for incredibly long periods, simulating over 9000 solar cycles, spanning 100 millennia (100,000 years). This allowed them to look for long-term patterns in the simulated solar activity, which they measured by tracking the strength of the magnetic field, similar to how we track sunspot numbers on the real Sun.
They ran the simulations in two main ways. First, they ran the models relying only on the internal complexity and feedback loops or the nonlinear mechanisms to see if they could produce the observed long-term variations. Second, they added a random element, simulating the effect of turbulent plasma motions by introducing stochastic fluctuations or random noise into the models, specifically in the part of the dynamo that generates the poloidal (pole-to-pole) magnetic field.
The findings were clear when the results were analysed using Fourier transforms, which help identify periodic patterns in data. In the simulations that relied only on the Sun's internal complexity (nonlinearity) and did not include the random noise, there was no significant evidence of the long-term, supradecadal modulation seen in the real Sun's historical record. The 11-year cycle was there, but the longer-term variations in its strength were missing. However, when they added the random noise (stochastic forcing) to the simulations, the long-term modulation appeared.
The team also reconstructed the Sun's past activity based on cosmogenic isotopes in ice cores and tree rings. These elements, like carbon-14, are produced on Earth when high-energy cosmic rays collide with atoms and fall onto the Earth, where they are trapped by ice and trees. Measuring the amount of these isotopes over time allows us to recreate historical solar activity. When the team compared the results of their simulations with historical records, they matched closely.
This strongly suggests that at least in the Sun's current state (the near-critical regime), the long-term variations in its activity aren't just a result of its complex internal dynamics behaving chaotically. Instead, random disturbances from the turbulent plasma inside are essential for these longer cycles to show up. It's like saying the long-term sputtering of the car engine isn't just its complex design; it needs those random bumps in the road or inconsistent fuel flows to create that specific pattern of sputtering over time.
This research builds on previous work by testing the near-critical regime, which is believed to be the Sun's current operating state. It supports findings from other independent studies suggesting the Sun's cycle isn't purely chaotic right now. While it's practically impossible to include every single possible nonlinear mechanism in any model, the researchers tested the robustness of their finding by increasing the strength of the nonlinear effects in their models, and the core result – that stochasticity is needed for supradecadal modulation in this regime – still held true. The study also notes that these long-term variations aren't perfectly sharp, clock-like periodicities but rather spread out over bands of frequencies, which fits the idea that random processes influence them.
Understanding the Sun's magnetic activity is vital because it directly impacts our activities in space and on Earth. Major solar flares and coronal mass ejections can disrupt satellites, power grids, and communication systems. By figuring out the fundamental drivers of the Sun's long-term behaviour, scientists can improve models for predicting solar activity for the next 11-year cycle and potentially for decades to come. This research provides a crucial piece of the puzzle, highlighting the essential role of randomness in shaping the long-term mood swings of our dynamic star and helping us better prepare for its effects on our technological world.
This research article was written with the help of generative AI and edited by an editor at Research Matters.