Fusion of Alternative Climate Models By Dynamical Synchronization

Información del proyecto

COCLIMAT

Identificador del acuerdo de subvención: 658602

DOI

10.3030/658602

Proyecto cerrado

Fecha de la firma de la CE 6 Marzo 2015

Fecha de inicio 1 Mayo 2015

Fecha de finalización 28 Octubre 2017

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 196 400,40

Aportación de la UE

€ 196 400,40

Coordinado por

UNIVERSITETET I BERGEN
Norway

Objetivo

Climate models of the sort used by the Intergovernmental Panel on Climate Change (IPCC) all predict global warming over the next century, but differ widely in their detailed predictions for any specific region of the globe. The state of the art is just to run the models separately and form a weighted average of their outputs.
A new approach put forward by the applicant is that of “supermodeling”: instead of just averaging the outputs of the models, the models are allowed to influence each other in run time. One must specify how much weight a given model gives to corresponding data in each other model. In a supermodel, the weights, or “connection coefficients” are given by a machine learning algorithm. That is one would use a collection of historical data to train the connections in the supermodel, so that the most reliable dynamical features of each model would be combined. Supermodeling is an instance of “chaos synchronization”, the phenomenon wherein chaotic systems can be made to follow corresponding trajectories by exchanging surprisingly little information.
In prior investigations with supermodels, it was determined that they are particularly useful for predicting variability, like that in the El Nino cycle in the Pacific. The proposed project would use a supermodel to predict variability in the Atlantic sector due to changes in the Atlantic Meridional Overturning Circulation (AMOC), which has a large effect on climate in the surrounding region on multi-decadal time scales. Existing climate models differ widely in their predictions for AMOC.
The proposed application will require changes in the way supermodels are formed and trained so as to focus on the positions and gross characteristics of coherent structures such as ocean currents. The models that will be used to build the supermodel will be a) a collection of European models, and b) a combination of U.S. and European models from which a supermodel is already being built.

Ámbito científico (EuroSciVoc)

• ciencias naturales › ciencias de la tierra y ciencias ambientales conexas › ciencias de la atmósfera › meteorología
• ciencias naturales › informática y ciencias de la información › ciencias de la computación
• ciencias naturales › ciencias de la tierra y ciencias ambientales conexas › ciencias de la atmósfera › climatología › cambios climáticos › el niño
• ciencias naturales › informática y ciencias de la información › inteligencia artificial › aprendizaje automático
• ciencias naturales › informática y ciencias de la información › inteligencia artificial › inteligencia computacional

Programa(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions PROGRAMA PRINCIPAL
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Tema(s)

• MSCA-IF-2014-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF)
  

Régimen de financiación

MSCA-IF-EF-ST - Standard EF

Convocatoria de propuestas

H2020-MSCA-IF-2014

Coordinador

UNIVERSITETET I BERGEN

• Aportación neta de la UEn: € 196 400,40
• Dirección: MUSEPLASSEN 1 ; 5020 Bergen ; Noruega
• Región: Norge › Vestlandet › Vestland
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: € 196 400,40