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COCLIMAT

Fusion of Alternative Climate Models By Dynamical Synchronization

Total Cost €

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EC-Contrib. €

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Partnership

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Project "COCLIMAT" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITETET I BERGEN 

Organization address
address: MUSEPLASSEN 1
city: BERGEN
postcode: 5020
website: www.uib.no

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Norway [NO]
 Project website http://www.uib.no/en/persons/Gregory.Duane
 Total cost 196˙400 €
 EC max contribution 196˙400 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2015
 Duration (year-month-day) from 2015-05-01   to  2017-10-28

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITETET I BERGEN NO (BERGEN) coordinator 196˙400.00

Map

 Project objective

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.

 Publications

year authors and title journal last update
List of publications.
2017 Gregory S. Duane, Carsten Grabow, Frank Selten, Michael Ghil
Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels
published pages: 126601, ISSN: 1054-1500, DOI: 10.1063/1.5018728
Chaos: An Interdisciplinary Journal of Nonlinear Science 27/12 2019-06-14
2017 Frank M. Selten, Francine J. Schevenhoven, Gregory S. Duane
Simulating climate with a synchronization-based supermodel
published pages: 126903, ISSN: 1054-1500, DOI: 10.1063/1.4990721
Chaos: An Interdisciplinary Journal of Nonlinear Science 27/12 2019-06-14
2017 Gregory S. Duane
“FORCE” learning in recurrent neural networks as data assimilation
published pages: 126804, ISSN: 1054-1500, DOI: 10.1063/1.4990730
Chaos: An Interdisciplinary Journal of Nonlinear Science 27/12 2019-06-14
2018 Gregory S. Duane, Wim Wiegerinck, Frank Selten, Mao-Lin Shen, Noel Keenlyside
Supermodeling: Synchronization of Alternative Dynamical Models of a Single Objective Process
published pages: 101-121, ISSN: , DOI: 10.1007/978-3-319-58895-7_5
Advances in Nonlinear Geosciences 2019-06-14
2017 Mao-Lin Shen, Noel Keenlyside, Bhuwan C. Bhatt, Gregory S. Duane
Role of atmosphere-ocean interactions in supermodeling the tropical Pacific climate
published pages: 126704, ISSN: 1054-1500, DOI: 10.1063/1.4990713
Chaos: An Interdisciplinary Journal of Nonlinear Science 27/12 2019-06-14

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The information about "COCLIMAT" are provided by the European Opendata Portal: CORDIS opendata.

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