Early warning signal for a tipping point suggested by a millennial Atlantic Multidecadal Variability reconstruction

• Published in: Nature Communications
• Main Authors: Michel, Simon L. L., Swingedouw, Didier, Ortega Montilla, Pablo, Gastineau, Guillaume, Mignot, Juliette, McCarthy, Gerard, Khodri, Myriam
• Other Authors: Barcelona Supercomputing Center
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Research 2022
• Subjects: Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia; Climate variations; North Atlantic oscillation; Paleoclimate; Palaeoclimate; Climate change; Simulació per ordinador; Camelot; North Atlantic
• Online Access: http://hdl.handle.net/2117/374497; https://doi.org/10.1038/s41467-022-32704-3

Atlantic multidecadal variability is a coherent mode of natural climate variability occurring in the North Atlantic Ocean, with strong impacts on human societies and ecosystems worldwide. However, its periodicity and drivers are widely debated due to the short temporal extent of instrumental observations and competing effects of both internal and external climate factors acting on North Atlantic surface temperature variability. Here, we use a paleoclimate database and an advanced statistical framework to generate, evaluate, and compare 312 reconstructions of the Atlantic multidecadal variability over the past millennium, based on different indices and regression methods. From this process, the best reconstruction is obtained with the random forest method, and its robustness is checked using climate model outputs and independent oceanic paleoclimate data. This reconstruction shows that memory in variations of Atlantic multidecadal variability have strongly increased recently—a potential early warning signal for the approach of a North Atlantic tipping point. This study benefited from the IPSL Prodiguer-Ciclad and Camelot supercomputing facilities, supported by CNRS, UPMC Labex L-IPSL. This work was also sponsored by NWO Exact and Natural Sciences for the use of SurfSARA supercomputing facilities (Amsterdam) under project 2020.022. Authors were also funded by EU‐H2020 TiPES (Grant no. 820970, S.M., contribution no. 164), Blue Action (Grant Agreement no. 727852, D.S., G.G, G.M., and J.M.), EUCP (Grant Agreement no 776613, D.S. and J.M.), ROADMAP (Grant Agreement no. 116020, J.M., G.M., and G.G.) ARCHANGE (ANR‐18‐MPGA‐0001, J.M. and G.G.) research programmes. Peer Reviewed Postprint (published version)