A multi-model ensemble method that combines imperfect models through learning

Bergé, Louis-Pierre; Selten, Frank; Wiegerinck, Wim; Duane, Gregory S.

doi:10.5194/esd-2-161-2011

Cited by 37 publications

(26 citation statements)

References 24 publications

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“…And finally, along that same vein, (Van den Berge et al 2011) have considered connectivity among nodes when modeling climate. They invoke the influential work done by (Pecora and Carroll 1990) on non-linear systems, applying principles of synchronized theory to modeling climate.…”

Section: Summary and Discussionmentioning

confidence: 99%

A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base

Wyatt

Peters

2012

SpringerPlus

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Results of previous studies support the existence of a spatially coherent, secularly varying climate signal, propagating through a network of synchronized climate indices across the Northern Hemisphere during the 20th century. The signal was identified in both instrumental and proxy data sets. In this present study, we seek to detect this same low-frequency signal propagating hemispherically through networks of model-simulated climate indices. These simulated climate indices were reconstructed from a data set generated by models of the third Coupled Model Intercomparison Project (CMIP3). Methods used in the earlier studies on climate-signal propagation guide the strategy for this companion study, for which 60 network analyses were performed. Most analyses focused on 20th century behavior, several on pre-industrial conditions. None succeeded in reproducing a hemispherically propagating signal. In light of previous results, we offer possible reasons for this finding. Among them is speculation on whether mechanisms relevant to signal propagation might be missing from this suite of general circulation models.

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Section: Summary and Discussionmentioning

confidence: 99%

A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base

Wyatt

Peters

2012

SpringerPlus

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“…The optimization of the coefficients is achieved by minimizing a cost function. In Wiegerinck et al (2013), it is noted that the size of these coefficients is typically very large. If the connection coefficients are large enough, the system will quickly syn- chronize into a joint state.…”

Section: Introductionmentioning

confidence: 99%

An efficient training scheme for supermodels

Schevenhoven

Selten

2017

Earth Syst. Dynam.

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Abstract.Weather and climate models have improved steadily over time as witnessed by objective skill scores, although significant model errors remain. Given these imperfect models, predictions might be improved by combining them dynamically into a so-called "supermodel". In this paper a new training scheme to construct such a supermodel is explored using a technique called cross pollination in time (CPT). In the CPT approach the models exchange states during the prediction. The number of possible predictions grows quickly with time, and a strategy to retain only a small number of predictions, called pruning, needs to be developed. The method is explored using low-order dynamical systems and applied to a global atmospheric model. The results indicate that the CPT training is efficient and leads to a supermodel with improved forecast quality as compared to the individual models. Due to its computational efficiency, the technique is suited for application to state-of-the art high-dimensional weather and climate models.

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“…However, our goal was to minimize the actual number of connections. Fewer connections makes it easier for learning algorithms to learn how these connections should be defined in a super-modeling approach (van den Berge et al, 2011). In addition, fewer connected state variables reduces the amount of data exchange (in bytes) between the models.…”

Section: Efficiency Of Eofsmentioning

confidence: 99%

“…Such teleconnections can also lead to the weaker form of phase synchronization, as shown for ENSO and Indian Monsoon (Maraun and Kurths, 2005;Mokhov et al, 2011). A third example is synchronization in a multi-model approach known as super modeling (van den Berge et al, 2011). Here the ensemble of connected imperfect models synchronize on a common solution that more accurately follows the dynamics of the real system than each of the individual models separately.…”

Section: Introductionmentioning

confidence: 99%

“…This form of data assimilation performs well when the models show highly non-linear behavior. In this case other linear data assimilation methods, such as the ensemble Kalman filter (Evensen, 1994), do not perform well (Simon, 2006). As a second example, long-range teleconnections can be seen as synchronization between connected systems within larger atmospheric systems.…”

Section: Introductionmentioning

confidence: 99%

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Complete synchronization of chaotic atmospheric models by connecting only a subset of state space

Hiemstra

Fujiwara

Selten

et al. 2012

Nonlin. Processes Geophys.

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Abstract. Connected chaotic systems can, under some circumstances, synchronize their states with an exchange of matter and energy between the systems. This is the case for toy models like the Lorenz 63, and more complex models. In this study we perform synchronization experiments with two connected quasi-geostrophic (QG) models of the atmosphere with 1449 degrees of freedom. The purpose is to determine whether connecting only a subset of the model state space can still lead to complete synchronization (CS). In addition, we evaluated whether empirical orthogonal functions (EOF) form efficient basis functions for synchronization in order to limit the number of connections. In this paper, we show that only the intermediate spectral wavenumbers (5-12) need to be connected in order to achieve CS. In addition, the minimum connection timescale needed for CS is 7.3 days. Both the connection subset and the connection timescale, or strength, are consistent with the time and spatial scales of the baroclinic instabilities in the model. This is in line with the fact that the baroclinic instabilities are the largest source of divergence between the two connected models. Using the Lorenz 63 model, we show that EOFs are nearly optimal basis functions for synchronization. The QG model results show that the minimum number of EOFs that need to be connected for CS is a factor of three smaller than when connecting the original state variables.

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A multi-model ensemble method that combines imperfect models through learning

Cited by 37 publications

References 24 publications

A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base

A secularly varying hemispheric climate-signal propagation previously detected in instrumental and proxy data not detected in CMIP3 data base

An efficient training scheme for supermodels

Complete synchronization of chaotic atmospheric models by connecting only a subset of state space

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