H2020Индивидуална стипендия2015–2017

COCLIMAT · Fusion of Alternative Climate Models By Dynamical Synchronization

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2015-05-01 → 2017-10-28
Финансиране от ЕС: 196 400 €
Участници: 1
Схема: MSCA-IF-EF-ST

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Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Fusion of Alternative Climate Models By Dynamical Synchronization

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 first put forward by the PI 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 a supermodel, the constituent models synchronize, at least partially, with one another as well as with reality in the training phase. In the free-running phase, the models remain partially synchronized with one another, with a common attractor that is expected to resemble the true attractor, even as parameters such as greenhouse gas levels are changed both in the true system and in the supermodel. To further develop the supemodel method, it was proposed to A) focus on the representation of coherent structures, such as the Atlantic Meridional Overturning Circulation (AMOC) in the supermodel, rather than the raw field variables; B) develop algorithms to train the supermodel that resemble human learning and that do not depend on cost functions that are computationally expensive to compute; and C) apply the supermodel to predict modes of climate variability on inter-decadal time scales.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

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