Year: 2026
Type: preprint
Abstract: This study presents an automated algorithm for detecting Moderate Non-linear Surprises (MNS) in the outputs of climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), building upon and relaxing existing methodologies for detection of critical transitions in climate models. The approach combines four statistical criteria — standard deviation, maximum 10-year jump, a multimodality test, and deviation over the last 30 years relative to the preindustrial distribution — with a density-based spatial clustering step to objectively identify regions undergoing non-linear transitions, without requiring expert judgment. The algorithm is applied across sea-ice, ocean, land, and atmospheric variables from up to 37 CMIP6 models under four Shared Socioeconomic Pathways (ssp) scenarios. Results highlight multiple MNS in high-latitude ocean and ice regions, and a recurrent signal of hydrological decline over tropical South America, including the Amazon Basin, detected in large fraction of the models with land-surface output. Some models also exhibit significant shifts in the extratropical atmospheric circulation, most notably in the blocking frequency, particularly under the strong ssp585 forcing. These examples demonstrate that the tool offers a systematic, reproducible framework for screening emerging non-linear climate responses in large model ensembles that may not qualify as tipping points, but still challenge the adaptation of human societies and ecosystems
DOI: 10.31223/x5hn6w
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7223178910