Machine-learning prediction of tipping and collapse of the Atlantic
  Meridional Overturning Circulation
Shirin Panahi Ling-Wei Kong Mohammadamin Moradi Zheng-Meng Zhai Bryan Glaz Mulugeta Haile Ying-Cheng Lai
Recent research on the Atlantic Meridional Overturning Circulation (AMOC)
raised concern about its potential collapse through a tipping point due to the
climate-change caused increase in the freshwater input into the North Atlantic.
The predicted time window of collapse is centered about the middle of the
century and the earliest possible start is approximately two years from now.
More generally, anticipating a tipping point at which the system transitions
from one stable steady state to another is relevant to a broad range of fields.
We develop a machine-learning approach to predicting tipping in noisy dynamical
systems with a time-varying parameter and test it on a number of systems
including the AMOC, ecological networks, an electrical power system, and a
climate model. For the AMOC, our prediction based on simulated fingerprint data
and real data of the sea surface temperature places the time window of a
potential collapse between the years 2040 and 2065.