Investigating climate tipping points under various emission reduction and carbon capture scenarios with a stochastic climate model

Abstract

We study the mitigation of climate tipping point transitions using an energy balance model. The evolution of the global mean surface temperature is coupled with the CO2 concentration through the green house effect. We model the CO2 concentration with a stochastic delay differential equation (SDDE), accounting for various carbon emission and capture scenarios. The resulting coupled system of SDDEs exhibits a tipping point phenomena: if CO2 concentration exceeds a critical threshold (around 478ppm), the temperature experiences an abrupt increase of about six degrees Celsius. We show that the CO2 concentration exhibits a transient growth which may cause a climate tipping point, even if the concentration decays asymptotically. We derive a rigorous upper bound for the CO2 evolution which quantifies its transient and asymptotic growths, and provides sufficient conditions for evading the climate tipping point. Combining this upper bound with Monte Carlo simulations of the stochastic climate model, we investigate the emission reduction and carbon capture scenarios that would avert the tipping point.

arXiv commentAccepted for publication in Proc. Roy. Soc. A

References in corpus (10)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Extreme events in excitable systems and mechanisms of their generation
• Rogue Waves and Large Deviations in Deep Sea
• A variational approach to probing extreme events in turbulent dynamical systems
• Reduced-order prediction of rogue waves in two-dimensional deep-water waves
• A probabilistic decomposition-synthesis method for the quantification of rare events due to internal instabilities
• Data-driven prediction and prevention of extreme events in a spatially extended excitable system
• Extreme sensitivity and climate tipping points
• Closed-loop adaptive control of extreme events in a turbulent flow
• Mitigation of tipping point transitions by time-delay feedback control

Cited by in corpus (2)

• Model-assisted deep learning of rare extreme events from partial observations
• Stochastic models of ventilation driven by opposing wind and buoyancy