| one publication added to basket [391375] | Simulating AMOC tipping driven by internal climate variability with a rare event algorithm
Cini, M.; Zappa, G.; Ragone, F.; Corti, S. (2024). Simulating AMOC tipping driven by internal climate variability with a rare event algorithm. npj Climate and Atmospheric Science 7(1): 31. https://dx.doi.org/10.1038/s41612-024-00568-7
In: npj Climate and Atmospheric Science. Nature Portfolio: London. e-ISSN 2397-3722, meer
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| Auteurs | | Top |
- Cini, M.
- Zappa, G.
- Ragone, F., meer
- Corti, S.
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| Abstract |
This study investigates the possibility of Atlantic Meridional Overturning Circulation (AMOC) noise-induced tipping solely driven by internal climate variability without applying external forcing that alter the radiative forcing or the North Atlantic freshwater budget. We address this hypothesis by applying a rare event algorithm to ensemble simulations of present-day climate with an intermediate complexity climate model. The algorithm successfully identifies trajectories leading to abrupt AMOC slowdowns, which are unprecedented in a 2000-year control run. Part of these AMOC weakened states lead to collapsed state without evidence of AMOC recovery on multi-centennial time scales. The temperature and Northern Hemisphere jet stream responses to these internally-induced AMOC slowdowns show strong similarities with those found in externally forced AMOC slowdowns in state-of-the-art climate models. The AMOC slowdown seems to be initially driven by Ekman transport due to westerly wind stress anomalies in the North Atlantic and subsequently sustained by a complete collapse of the oceanic convection in the Labrador Sea. These results demonstrate that transitions to a collapsed AMOC state purely due to internal variability in a model simulation of present-day climate are rare but theoretically possible. Additionally, these results show that rare event algorithms are a tool of valuable and general interest to study tipping points since they introduce the possibility of collecting a large number of tipping events that cannot be sampled using traditional approaches. This opens the possibility of identifying the mechanisms driving tipping events in complex systems in which little a-priori knowledge is available. |
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