New Framework to Accurately Predict the Effect of Abrupt Climate Changes

What is it about?

A sudden transition from one climatic state to another is rare but not unheard of, and how it happens is not very clear yet. One explanation is that it can be caused by single extreme events on Earth, generally induced by ‘non-Gaussian Lévy noise’. Identifying climate states and accurately determining the transition path between them could help us predict abrupt climatic transitions. Existing approaches for studying transition paths work well for small noise-induced or finite noise-induced transitions, in systems that rely on Gaussian dynamics. However, they are not very accurate in predicting shifts caused by non-Gaussian Lévy noise. Therefore, the current approaches are undesirable for abrupt climate changes which occur intermittently with high probability. To overcome this problem, the authors of this study have developed a probabilistic framework, based on the Fokker-Planck equation, to deter-mine maximum likely climate change for a global warming of 1.5ºC, under a stable non-Gaussian Lévy noise.

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Photo by Michal Matlon on Unsplash

Photo by Michal Matlon on Unsplash

Why is it important?

The proposed framework overcomes the challenges of previous approaches and is applicable to systems with both Gaussian and non-Gaussian noise. Moreover, it verifies that a relatively stable climate can be interrupted by sudden transitions to the warmer state, triggered by large a stable Lévy noise jumps with low frequency. The framework reveals another important finding: a global rise of 1.5ºC coupled with the greenhouse effect generates a step like growth process, resulting in incremental increases in the global surface temperature. This insight can potentially contribute to the Paris Agreement targets, which aim to limit the average rise in global warming to 1.5ºC. KEY TAKEAWAY The maximum likelihood framework sheds light on the combined effect of the greenhouse effect and Lévy noise on abrupt climate shifts and can act as an effective research tool to quantitatively analyze climate change. Predicting and preventing climate change can contribute to a sustainable future for the coming generations.