Supermodeling by Combining Imperfect Models

SUMO - Supermodeling by combining imperfect models

Supermodeling: Combining Imperfect Models through Learning

At a dozen or so institutes around the world, comprehensive climate models are being developed and improved. Each model provides reasonable simulations of the observed climate, each with its own strengths and weaknesses. In the current multi-model ensemble approach model simulations are combined a posteriori. As an alternative, [1] proposed to dynamically connect the models into one supermodel....

Supermodeling dynamics and learning mechanisms

At a dozen or so institutes around the world, comprehensive climate models are being developed and improved. Each model provides reasonable simulations of the observed climate, each with its own strengths and weaknesses. In the current multi-model ensemble approach model simulations are combined a posteriori. Recently, it has been proposed to dynamically combine the models and so construct one ...

On the limit of large couplings and weighted averaged dynamics

We consider a network of deterministic non-linear oscillators with nonidentical parameters. Interactions between the different oscillators are linear, but the coupling coefficients for each interaction may differ. We consider the case where coupling coefficients are sufficiently large, so that the different oscillators will have their state variables strongly tied together and variables of the ...

Dynamically combining climate models to “supermodel” the tropical Pacific

We construct an interactive ensemble of two different climate models to improve simulation of key aspects of tropical Pacific climate. Our so-called supermodel is based on two atmospheric general circulation models (AGCMs) coupled to a single ocean GCM, which is driven by a weighted average of the air-sea fluxes. Optimal weights are determined using a machine learning algorithm to minimize sea ...