Leap in modelling human impact on climate may lead to early warning of climate disasters

An international collaboration between University of Leicester mathematician Valerio Lucarini and scientist Mickaël Chekroun applies the principles of statistical mechanics to climate science to frame how to distinguish climate change signals from the “background noise” of natural climate variability and the signals that mark “tipping points” Approaching, for example, tipping points related to the Atlantic circulation or the collapse of the Amazon forest.

This theoretical advance paves the way for the development of innovative methods to study climate change and its associated risks, thanks to a deeper understanding of the underlying mechanisms driving climate change.

published in journal Physical Review Lettersit will give scientists the confidence to make climate change attributions and determine when we are in the midst of a potential climate tilt, and to take preventive measures to mitigate climate change. It would provide policymakers with much-needed certainty in the procedures used to assess climate change.

Tipping points are thresholds in our climate system that can lead to large-scale changes and environmental damage. Events such as the collapse of the Atlantic Meridional Overturning Circulation (its slowdown will lead to relative cooling of the region) or the ecological collapse of the Amazon rainforest will have catastrophic effects on life on our planet. However, it is difficult to predict based on climate data when we will reach potential tipping points.

The challenge is to distinguish evidence of climate change, especially upcoming tipping points, from existing natural climate changes. The “signal” of man-made climate change is obscured by the “noise” of natural changes in the environment. The team led by Lester found that existing methods were based on purely statistical methods and provided limited information about the dynamic processes that influence climate. It provides a snapshot of our climate but no insight into how it got to be what it is.

By applying the principles of statistical mechanics (the physics behind stochastic dynamic processes), their research allows us to turn back the clock on the snapshot and understand how the image came to be. They created a mathematical model that was able to dynamically recreate the processes at play and identify the causes of changes. In this way, they can “identify” the signals of man-made climate change and determine its impact, significantly improving the ability to detect early warnings of climate tipping points.

Lead author Professor Valerio Lucarini from the School of Computational and Mathematical Sciences at the University of Leicester said: “The question of how we attribute anthropogenic forcing in climate data has far-reaching implications. Climate change skeptics question how Linking forcings in the system The climate has always changed a lot for specific reasons, how do you counter this argument and prove that the phenomena we are observing now are due to human intervention, based on statistical arguments and not? Kinetic argument.

“The breakthrough we made was to connect the physics of the system, the laws that govern its evolution, with what you can observe. It became clear that the best way to study change is to study the evolutionary laws that affect us. We are observing that this change is happening is the climate forcing we’re looking for.

Dr. Mickaël Chekroun of UCLA and the Weizmann Institute of Science added: “This is a pretty big step because it tells us that the detection and attribution methods we have used for many years indicate that climate change is indeed happening. We show How this method can be improved, and we can see its potential flaws, has greatly advanced the theory of climate dynamics and the relationship between climate change and climate change.