Climate Response and Sensitivity: Timescales and Late Tipping Points

TL;DR

This paper examines the limitations of climate response metrics like ECS, especially their assumptions of linearity, and highlights how slow timescales can cause late tipping points and estimation challenges in climate models.

Contribution

It demonstrates potential issues in estimating ECS from transient simulations due to nonlinear responses and slow timescales, with implications for climate model assessments.

Findings

Slow timescales can lead to poor ECS estimates.

Nonlinear responses may cause late abrupt climate changes.

Estimating equilibrium states in climate models remains challenging.

Abstract

Climate response metrics are used to quantify the Earth's climate response to anthropogenic changes of atmospheric CO2. Equilibrium Climate Sensitivity (ECS) is one such metric that measures the equilibrium response to CO2 doubling. However, both in their estimation and their usage, such metrics make assumptions on the linearity of climate response, although it is known that, especially for larger forcing levels, response can be nonlinear. Such nonlinear responses may become visible immediately in response to a larger perturbation, or may only become apparent after a long transient. In this paper, we illustrate some potential problems and caveats when estimating ECS from transient simulations. We highlight ways that very slow timescales may lead to poor estimation of ECS even if there is seemingly good fit to linear response over moderate timescales. Moreover, such slow timescale might lead to late abrupt responses ("late tipping points") associated with a system's nonlinearities. We illustrate these ideas using simulations on a global energy balance model with dynamic albedo. We also discuss the implications for estimating ECS for global climate models, highlighting that it is likely to remain difficult to make definitive statements about the simulation times needed to reach an equilibrium.