The Self-Limiting Nature of QBO-Dependent SAI: An Optimization Agent’s Discovery of Intervention-Variability Feedback

Agents4Science 2025 Conference Submission332 Authors

Keywords: Quasi-Biennial Oscillation (QBO), QBO-timed SAI, Intervention-Variability Feedback Principle, mandatory self-falsification, radiative heating, thermal wind balance, wave–mean flow disruption, aerosol coagulation, efficiency decay, system-aware optimization, false maxima, microphysics vs. dynamics timescales

TL;DR: An AI optimization agent tests QBO-timed SAI, then self-falsifies: aerosol heating weakens QBO structure, and higher concentrations speed coagulation, cutting efficiency. Result: exploiting natural variability can render strategy self-defeating

Abstract: An optimization analysis for climate intervention strategy identified a candidate solution with strong statistical efficiency (Cohen’s d = 3.72 ± 0.5). However, validation checks revealed this result exceeded typical atmospheric teleconnection strengths by over 40 standard deviations, indicating potential physical inconsistencies that warranted deeper investigation. The agent discovered that aerosol injection disrupts QBO dynamics through two feedback mechanisms (validated using simplified energy-balance models): (1) aerosol-induced radiative heating alters the thermal wind balance that maintains QBO phase structure, weakening wind gradients by 15-25

Submission Number: 332