Assessing the impact of parametric uncertainty on tipping points of the Atlantic meridional overturning circulation
Datos Bibliográficos
| ID | 15546751 |
|---|---|
| Autores | Kerstin Lux (0000-0001-8412-2707, Technical University of Munich, autor de correspondencia), Peter Ashwin (0000-0001-7330-4951, University of Exeter), Richard Wood (0000-0002-7906-3324, Met Office), Christian Kuehn (0000-0002-7063-6173, Complexity Science Hub Vienna) |
| Año | 2022 |
| Volumen | 17 |
| Número | 7 |
| Páginas | 075002-075002 |
| Fecha de publicación | 2022-06-16 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Environmental Research Letters (JOURNAL) |
| Identificadores de la revista | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Editorial | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ac7602 |
| OpenAlex | W4282982470 |
| Idioma | EN |
| Citas recibidas | 2 |
| Referencias citadas | 30 |
Various elements of the Earth system have the potential to undergo critical transitions to a radically different state, under sustained changes to climate forcing. The Atlantic meridional overturning circulation (AMOC) is of particular importance for North Atlantic heat transport and is thought to be potentially at risk of passing such a tipping point (TP). In climate models, the location and likelihood of such TPs depends on model parameters that may be poorly known. Reducing this parametric uncertainty is important to understand the likelihood of tipping behaviour. In this letter, we develop estimates for parametric uncertainty in a simple model of AMOC tipping, using a Bayesian inversion technique. When applied using synthetic (‘perfect model’) salinity timeseries data, the technique drastically reduces the uncertainty in model parameters, compared to prior estimates derived from previous literature, resulting in tighter constraints on the AMOC TPs. To visualise the impact of parametric uncertainty on TPs, we extend classical tipping diagrams by showing probabilistic bifurcation curves according to the inferred distribution of the model parameter, allowing the uncertain locations of TPs along the probabilistic bifurcation curves to be highlighted. Our results show that suitable palaeo-proxy timeseries may contain enough information to assess the likely position of AMOC (and potentially other Earth system) TPs, even in cases where no tipping occurred during the period of the proxy data
Bayesian probability · Climatology · Econometrics · Forcing (mathematics · Meteorology · Parametric statistics · Physics · Probabilistic logic · Proxy (statistics · Statistics · Climate variability and models · Computer Science · Ecosystem dynamics and resilience · Environmental Science · Geology and Paleoclimatology Research · Mathematics · Geology
| Obras citantes distintas | 2 |
|---|---|
| Citas por año | 0,5 |
| Intervalo de citas | 2022 - 2024 (3) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 2 |