Tipping in an adaptive climate network model

TL;DR

This paper extends a climate network model by including adaptation mechanisms, revealing that adaptation slightly stabilizes the system and alters the roles of tipping elements in cascade risks, especially highlighting the Amazon rainforest's potential as a trigger.

Contribution

It introduces an adaptive feedback into a climate tipping network model, analyzing its effects on cascade risks and the roles of different tipping elements.

Findings

Large tipping cascades are less probable with adaptation due to negative feedback.

Adaptation slightly stabilizes the climate network.

The Amazon rainforest can act as a trigger in cascades.

Abstract

With rising global temperatures Earth's tipping elements are becoming increasingly more vulnerable to crossing their critical thresholds. The reaching of such tipping points does not only impact other tipping elements through their connections, but can also have further effect on the global mean surface temperature (GMT) itself, either increasing or decreasing the probability of further tipping points being reached. Recently, a numerical study analyzing the risk of tipping cascades has been conducted, using a conceptual model describing the dynamics of a tipping element with its interactions with other tipping elements taken into account [1]. Here, we extend the model substantially by including adaptation, so that the GMT-feedback induced by the crossing of a tipping point is incorporated as well. We find that although the adaptive mechanism does not impact the risk for the occurrence of tipping events, large tipping cascades are less probable due to the negative GMT-feedback of the ocean circulation systems. Furthermore, several tipping elements can play a different role in cascades in the adaptive model. In particular, the Amazon rainforest could be a trigger in a tipping cascade. Overall, the adaptation mechanism tends to slightly stabilize the network.