Imagine the Earth has a few 'tripwires' that, if crossed, cause a sudden and permanent change, like a giant domino effect. This work identifies where those wires are and how to spot the warning signs before they snap. It creates a detailed map of the fallout to help people prepare for the worst-case scenarios.
Current climate models are unreliable in predicting 'tipping points'—sudden, irreversible changes in the Earth system. This creates a massive blind spot for businesses and governments trying to assess long-term economic and ecological risks.
A methodological framework for identifying tipping points and high-resolution, bias-corrected climate fields based on Earth system model storylines.
If you are a crop insurance provider dealing with unpredictable harvest failures — this project developed high-resolution climate fields that predict large-scale tipping events. This allows for more accurate risk pricing and better crop adaptation strategies.
If you are an investment fund dealing with long-term asset devaluation due to climate instability — this project developed a knowledge basis for tipping-aware risk assessment. This helps in quantifying the economic impacts of irreversible climate shifts on portfolios.
If you are a government agency dealing with the need to meet the 2015 Paris Agreement — this project developed a method to identify safe operating spaces for a stable Earth system. This provides the data needed to create effective national mitigation and adaptation laws.
Based on available project data, no pricing or cost structure is mentioned as the project is funded under a HORIZON-RIA scheme.
The project focuses on providing a knowledge basis and climate services via models; it is currently in the research and quantification phase rather than a commercial product scale.
Based on available project data, specific licensing terms are not provided, though results are intended for dissemination to the scientific community and bodies like the IPCC.
The project period runs from 2024-03-01 to 2028-02-29, suggesting deliverables will be finalized by early 2028.
ClimTip improves the representation of tipping elements within ESMs to make their predictions of tipping events more reliable.
The consortium is heavily academic, consisting of 22 partners from 12 countries, with 15 universities and 4 research organizations. There is a 0% industry ratio, meaning the project is driven by scientific discovery and theoretical modeling rather than immediate commercial application.
Contact the Technical University of Munich (TUM) regarding the ClimTip project.
Contact us to track the transition of these climate models into commercial risk-assessment tools.
