Towards Operational Supermodel Climate Prediction
TOSCP aims to enhance climate prediction by reconfiguring a supermodel that combines multiple models to reduce errors, ultimately improving climate services for societal resilience.
Projectdetails
Introduction
TOSCP will proof-of-concept a radically new approach to climate prediction based on supermodelling. Climate prediction promises reliable information on climate and its extremes for the coming seasons and years. This information is critical to providing climate services that are needed to build a resilient and sustainable society.
Challenges in Climate Prediction
Unfortunately, predicting climate in the extra-tropics remains a major challenge. Model systematic error is the major limitation. In the North Atlantic-European sector, it leads to the strong underrepresentation of the predictable dynamics compared to unpredictable atmospheric weather patterns.
Current Approaches
The current approach to account for such errors is to perform a vast number of independent simulations with different models. This is computationally expensive and impractical in an operational context.
Supermodel Approach
The supermodel approach developed in the ERC-STERCP project is aptly suited to improve climate prediction. A supermodel combines a set of different models in runtime so that the individual model errors compensate to produce a superior model.
Benefits of the Supermodel
- The approach is extremely effective in mitigating long-standing model errors.
- It can control the ratio between predictable and unpredictable dynamics.
Project Goals
TOSCP will reconfigure a supermodel developed in the STERCP project for climate prediction. The supermodel is based on three state-of-the-art climate models.
Development Plans
We will develop new ensemble generation and data assimilation schemes. We aim to demonstrate that supermodel climate predictions greatly outperform the standard approach to climate prediction that is currently used for climate services.
User Engagement
Dialogue with users and providers of climate services will ensure the development of an optimal configuration of the new prediction system and its use in operational climate services.
Conclusion
This will set the stage for the wider exploitation of supermodel climate prediction, leading to improved climate services for the benefit of society.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 150.000 |
Totale projectbegroting | € 150.000 |
Tijdlijn
Startdatum | 1-1-2023 |
Einddatum | 30-6-2024 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- UNIVERSITETET I BERGENpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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Resolving and understanding the contributions of mesoscale eddies to climate prediction
PREDDYCT aims to enhance climate predictability in the North Atlantic by accurately simulating mesoscale eddies, improving atmospheric circulation models for better socio-economic decision-making.
Forecasting climate surprises on longer timescales
Develop a novel probabilistic methodology and Fast Earth System Model to forecast climate surprises from ice-sheet and AMOC collapse over centuries to millennia, enhancing long-term climate projections.
Stratospheric cOmposition in a changing CLIMate: drivers and mechanisms
The SOCLIM project aims to enhance weather and climate predictions by analyzing stratospheric ozone and water vapor's role in atmospheric circulation and climate change impacts.
Advancing Subseasonal PredIctions at Reduced computational Effort
ASPIRE aims to enhance subseasonal weather predictions by leveraging tropical convective variability and machine learning to reduce computational costs while improving forecast accuracy.
Unlocking the mesoscale frontier of cloud-climate uncertainty
The project aims to develop a novel framework for predicting mesoscale cloudiness using satellite imagery to reduce climate projection uncertainties and enhance future cloud research.
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