Targeted Calibration to Adjust Stability Biases in Complex Dynamical System Models

TL;DR

This paper introduces a targeted calibration method to efficiently adjust stability biases in complex dynamical system models, such as climate models, to better identify potential instabilities and tipping points.

Contribution

The paper presents a novel calibration approach that adjusts stability biases in complex models without requiring differentiability, aiding in the detection of hidden instabilities.

Findings

The method effectively calibrates stability biases in Earth system models.

Calibration reveals potential hidden instabilities and tipping points.

Application to climate models demonstrates improved stability assessment.

Abstract

Models of complex dynamical systems like the Earth's climate often involve large numbers of uncertain parameters. Comprehensive exploration of the parameter space is typically prohibitive due to excessive computational costs. Systematic gradient-based parameter optimization is not feasible because such models are typically not differentiable. This is especially problematic in cases where the models describe highly nonlinear and possibly abrupt dynamics, where sensitivity to parameter changes is high. Components of Earth's climate system, such as the North Atlantic Overturning Circulation or the polar ice sheets, are at risk of undergoing critical transitions in response to anthropogenic climate change. Concerns have been raised that these Earth system components are too stable in state-of-the-art models. In my presentation, we will see examples how new scenario simulations allow studying the possibility and the consequences of tipping events in Earth system models. Also, we will discuss a method for efficient, systematic, and objective calibration of dynamical complex system models, targeted at adjusting system stability. Given a number of physical or observational constraints, the method can efficiently adjust stability biases in a range of complex system models and help reveal potentially hidden instabilities, with important implications for Earth system modelling.