Project

EarlyWarning - Generic Early Warning Signals for Critical Transitions

The project is aimed at developing an alternative way to predict critical transitions and at finding early warning signals for such transitions that are generic in the sense that they work irrespective of the (often poorly known) mechanisms responsible for the tipping points.

Abrupt shifts occasionally reshape complex systems in nature ranging in scale from lakes and reefs to regional climate systems. Such shifts sometimes represent critical transitions in the sense that they happen at tipping points where runaway change propels the system towards an alterative contrasting state. Although the mechanism of critical transitions can often be reconstructed in the hindsight, we are virtually unable to predict when they will happen in advance. Simulation models for complex environmental systems are simply not good enough to predict tipping points, and there is little hope that this will change over the coming decades.

Aim

The proposed project is aimed at developing an alternative way to predict critical transitions. We aim at finding early warning signals for such transitions that are generic in the sense that they work irrespective of the (often poorly known) mechanisms responsible for the tipping points. Mathematical theory indicates that this might be possible. However, although excitement about these ideas is emerging, we are far from having a cohesive theory, let alone practical approaches for predicting critical transitions in large complex systems like lakes, coral reefs or the climate. 

Three lines

  • Develop a comprehensive theory of early warning signals using analytical mathematical techniques as well as models ranging in character from simple and transparent to elaborate and realistic.
  • Test the theory on experimental plankton systems kept in controlled microcosms.
  • Analyze data from real systems that go through catastrophic transitions.

Expected results

The anticipated results would imply a major breakthrough in a field of research that is exiting as well as highly relevant to society. If we are successful, it would allow us to anticipate critical transitions even in large complex systems where we have little hope of predicting tipping points on the basis of mechanistic models.

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