EGU25-20396, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-20396
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Dynamics-informed deep learning for tipping point forecasting
Carla Roesch and Christina Last
Carla Roesch and Christina Last
  • TipplyAI Ltd, Nottingham, United Kingdom of Great Britain – England, Scotland, Wales (carla.roesch@tipply.ai)

As the world enters a period of accelerated climate change, we require the rapid development of an early warning system (EWS) that identifies whether climatic conditions will result in reaching a tipping point. Tipping points represent critical thresholds where a small disturbance can cause a significant, qualitative shift in a system's state that can have crucial effects on human livelihoods. The impacts of political developments on future emission pathways, highlights the need for warning systems focused on climate risk communication that can be deployed and updated easily by policy teams with data pertaining to representative emission profiles. We are developing an early warning system to detect tipping points using a combination of observational and model data. In this abstract, we introduce the Tipsy-API platform; a dynamics-informed deep learning model to forecast relevant thresholds of the Greenland ice sheet and Atlantic Meridional Ocean Circulation. Following the objective of a “real time” warning system, our framework  iteratively updates forecasts with new observations to adjust the tipping point prediction accordingly. Finally, the framework will be deployed online and be available as an API, which we aim to be interactive and iteratively updated once new information about future warming becomes available. This ongoing work attempts to understand and address the requirements of a UK Government R&D funding agency, with the remit of engaging in high risk and high reward climate research. Thus, our project aims to both reduce uncertainty about tipping points and to allow for necessary open communication with policy makers and other relevant stakeholders.

How to cite: Roesch, C. and Last, C.: Dynamics-informed deep learning for tipping point forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20396, https://doi.org/10.5194/egusphere-egu25-20396, 2025.