Dynamical analysis and solutions of coupled 4D fractional differential systems with applications to predictability limit quantification in ocean-atmosphere models

For coupled two-dimensional fractional differential systems, a new two-step fractional-order Runge–Kutta method is proposed in this paper, which can reach a convergence order of 2α, with α being the fractional-order number. Further, we extend the two-step fractional-order Runge–Kutta algorithm to any coupled n-dimensional fractional differential system while maintaining convergence and consistency. To demonstrate the validity of the proposed method, numerical experiments are given for a four-dimensional fractional Lorenz system, and the dynamics of the four-dimensional fractional Lorenz system is analyzed using Lyapunov characteristic exponents, bifurcation diagrams, chaos diagrams, and C0 complexity. The results demonstrate that the system exhibits a diverse dynamical behavior and a broader range of fractional orders [0.43, 1] is accessible to the periodic orbit at the same parameter, compared to previous findings [He et al., Math. Methods Appl. Sci. 39, 2965–2973 (2016)]. Finally, we use global attractor radius and attractor radius to investigate the predictability of the coupled fractional-order ocean-atmosphere system [Li et al., Clim. Dyn. 51, 2359–2374 (2018)]. The results show that both global attractor radius and attractor radius decrease with decreasing fractional order, and the smaller the initial perturbation, the longer it takes to reach the attractor radius, but the attractor radius to the global attractor radius is not significantly correlated with the initial perturbation. These findings suggest that the predictability of the coupled fractional ocean-atmosphere system is limited by the presence of long-range memory effects captured in the fractional-order differential equations. By offering quantitative assessments of predictability, these approaches enhance our understanding of the intricate dynamics within such systems and can support informed decision-making in addressing and mitigating the effects of climate change on the global atmosphere and oceans.