A new method to compute the resilience of climate systems

What is it about?

We study here a new method to compute the resilience of climate systems, taking the Atlantic Meridional Overturning Circulation (AMOC) as an example. Such systems used to be in equilibrium but are now forced out of their equilibrium by human activity and global warming. Resilience is the measure of the ability of a given system to resist this forcing. Resilience is maximal when the system can come back to its equilibrium despite the perturbation (like a tree that would ploy and come back to its original position without breaking). On the contrary, the system loses resilience when it cannot resist the perturbation and is brought by this perturbation to another state (the tree has broken). Measuring the resilience of a climate system therefore informs us about its robustness against current climate change. We can also quantify how endangered this system would be under different climate change scenarios. The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the climate system due to its role in meridional (South-North) heat transport. Theory and recent research suggest that this circulation may be able to collapse because of climate change, bringing dramatic change over Europe, the US East Coast, the Amazon rainforest... In this paper, we showcase in a simple model a new method to compute the resilience of such systems.

Featured Image

Photo by Arne Tho on Unsplash

Photo by Arne Tho on Unsplash

Why is it important?

Existing measures of resilience often rely on complex mathematical information that cannot be obtained from complex climate models (the very models that would most accurately describe the studied climate system). Here, the method we develop only relies on simulations of the climate system where this system effectively loses resilience. This way, our method is in principle applicable to any model. However, simulating such loss of resilience can be very difficult. For instance, in the case of the AMOC, a collapse of the circulation is a rare event that is only observed in a few simulations. So-called rare-event algorithms iteratively increase the chance that every simulation contains an AMOC collapse until we obtain an ensemble of simulations, each containing a collapse. A collapse of the AMOC corresponds to a loss of resilience in this system: so, what we obtain with this algorithm is an ensemble of simulations all losing resilience. This allows us to extract statistical footprints of what it means for the system to lose resilience. These footprints then allow us to interpret the behavior of the system while it is losing resilience; however unlikely such loss of resilience is. Moreover, this analysis can pinpoint critical boundaries that the system mustn't cross or the moment when it can be most efficiently acted upon. Finally, using a similar method, we are able to compare different climate change scenarios and quantify how likely is the system to lose resilience in different scenarios. We can even relate physical measures performed in the field (on the real system) to the likelihood that it will lose resilience in the future. In other words, we can track (indirectly) the resilience of the system through real data.