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TOSCP

Financiado

Towards Operational Supermodel Climate Prediction

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... ver más

30/06/2024

HVL

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

HOGSKULEN PA VESTLANDET No se ha especificado una descripción o un objeto social para esta compañía.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-06-30

ERC-2022-POC2: ERC PROOF OF CONCEPT GRANTS2 Objective:Objectives:

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

HVL

150.00K€ | Lider

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Duración del proyecto: 20 meses Fecha Inicio: 2022-10-07
Fecha Fin: 2024-06-30

Líder del proyecto

HOGSKULEN PA VESTLANDET No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 150K€

Descripción del proyecto 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. 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 under representation of the predictable dynamics, compared to unpredictable atmospheric weather patterns. 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. 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 so as to produce a superior model. The approach is extremely effective in mitigating long-standing model errors, and can control the ratio between predictable and unpredictable dynamics. 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. 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. 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. This will set the stage for the wider exploitation of supermodel climate prediction, leading to improved climate services for the benefit of society.

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