Observation-Based Iterative Map for Solar Cycles. I. Nature of Solar Cycle Variability

TL;DR

This study uses a simplified iterative map to analyze solar cycle variability, concluding stochastic processes dominate over chaos, which limits long-term predictability of solar activity.

Contribution

It introduces a one-dimensional iterative map incorporating nonlinearity and stochasticity, demonstrating the absence of chaos and emphasizing the role of stochasticity in solar cycle variability.

Findings

Solar cycles are primarily influenced by stochastic processes.

Deterministic chaos is absent in the modeled solar cycle dynamics.

The iterative map can predict cycle strength range and uncertainty.

Abstract

Inter-cycle variations in the series of 11-year solar activity cycles have a significant impact on both the space environment and climate. Whether solar cycle variability is dominated by deterministic chaos or stochastic perturbations remains an open question. Distinguishing between the two mechanisms is crucial for predicting solar cycles. Here we reduce the solar dynamo process responsible for the solar cycle to a one-dimensional iterative map, incorporating recent advance in the observed nonlinearity and stochasticity of the cycle. We demonstrate that deterministic chaos is absent in the nonlinear system, regardless of model parameters, if the generation of the poloidal field follows an increase-then-saturate pattern as the cycle strength increases. The synthesized solar cycles generated by the iterative map exhibit a probability density function (PDF) similar to that of observed normal cycles, supporting the dominant role of stochasticity in solar cycle variability. The parameters governing nonlinearity and stochasticity profoundly influence the PDF. The iterative map provides a quick and effective tool for predicting the range, including uncertainty of the subsequent cycle strength when an ongoing cycle amplitude is known. Due to stochasticity, a solar cycle loses almost all its original information within 1 or 2 cycles. Although the simplicity of the iterative map, the behaviors it exhibits are generic for the nonlinear system. Our results provide guidelines for analyzing solar dynamo models in terms of chaos and stochasticity, highlight the limitation in predicting solar cycle, and motivate further refinement of observational constraints on nonlinear and stochastic processes.