Assessment of Abrupt Shifts in CMIP6 Models using Edge Detection

TL;DR

This study uses edge detection to identify and analyze the timing and spatial extent of abrupt climate shifts in CMIP6 models under increased CO2 scenarios, revealing early risks at 1.5°C warming.

Contribution

It introduces a novel application of edge detection to identify abrupt climate shifts in CMIP6 models, providing insights into their timing and spatial patterns.

Findings

Abrupt shifts occur in most climate subsystems analyzed.

Higher global warming levels increase the risk of abrupt shifts.

Six of ten subsystems show large-scale shifts at 1.5°C warming.

Abstract

Past research has shown that multiple climate subsystems might undergo abrupt shifts, such as the Arctic Winter sea ice or the Amazon rainforest, but there are large uncertainties regarding their timing and spatial extent. In this study we investigated when and where abrupt shifts occur in the latest generation of earth system models (CMIP6) under a scenario of 1\% annual increase in CO$_2$. We considered 82 ocean, atmosphere, and land variables across 57 models. We used a Canny edge detection method to identify abrupt shifts occurring on yearly to decadal timescales, and performed a connected component analysis to quantify the spatial extent of these shifts. The systems analyzed include the North Atlantic subpolar gyre, Tibetan Plateau, land permafrost, Amazon rainforest, Antarctic sea ice, monsoon systems, Arctic summer sea ice, Arctic winter sea ice, and Barents sea ice. Except for the monsoon systems, we found abrupt shifts in all of these across multiple models. Despite large inter-model variations, higher levels of global warming consistently increase the risk of abrupt shifts in CMIP6 models. At a global warming of 1.5 $^\circ \text{C}$, six out of ten studied climate subsystems already show large-scale abrupt shifts across multiple models.