Fuzzy rules for global climate system.

The global carbon–climate system is a highly complex and dynamic network characterized by multiple feedback loops between interconnected components. Addressing the risks of climate change requires active intervention across these components (Atmospheric level, Surface ocean, and Terrestrial biosphere). Consequently, this research introduces a new mathematical fuzzy control theory to explore how control mechanisms, incorporating both open and closed–loop, can help guide the carbon–climate system toward more stable and sustainable levels. First, a fuzzy mathematical generalization as a compartmental dynamical model is proposed for a formal analysis of closed–loop control strategies for climate regulation. Second, the challenge of managing carbon–climate dynamics is reframed as a network congestion control problem, incorporating critical concepts to highlight gaps in current scientific approaches to climate feedback management. Third, an algorithm based on an implicit open–loop control assumption, incorporating the need for continuous adjustments when discrepancies arise between targets and actual measurements, is introduced. Additionally, taking into account nonlinear behavior and feedback from an international carbon monitoring system, the authors show how the task of regulating the global carbon cycle may be viewed as an abstracted network congestion problem using a reduced complexity model. Finally, a simulation scenario demonstrating how closed–loop control could be developed to more effectively manage the carbon–climate structure is presented.