Search from over 60,000 research works

Advanced Search

Predictors and predictands of linear response in spatially extended systems

[thumbnail of 2011.04800.pdf]
2011.04800.pdf - Accepted Version (1MB)
Restricted to Repository staff only
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Tomasini, U. M. and Lucarini, V. orcid id iconORCID: https://orcid.org/0000-0001-9392-1471 (2021) Predictors and predictands of linear response in spatially extended systems. European Physical Journal Special Topics, 230. pp. 2813-2832. ISSN 1951-6355 doi: 10.1140/epjs/s11734-021-00158-1

Abstract/Summary

The goal of response theory, in each of its many statistical mechanical formulations, is to predict the perturbed response of a system from the knowledge of the unperturbed state and of the applied perturbation. A new recent angle on the problem focuses on providing a method to perform predictions of the change in one observable of the system using the change in a second observable as a surrogate for the actual forcing. Such a viewpoint tries to address the very relevant problem of causal links within complex system when only incomplete information is available. We present here a method for quantifying and ranking the predictive ability of observables and use it to investigate the response of a paradigmatic spatially extended system, the Lorenz ’96 model. We perturb locally the system and we then study to what extent a given local observable can predict the behaviour of a separate local observable. We show that this approach can reveal insights on the way a signal propagates inside the system. We also show that the procedure becomes more efficient if one considers multiple acting forcings and, correspondingly, multiple observables as predictors of the observable of interest.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/97621
Item Type Article
Refereed Yes
Divisions Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE)
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher Springer
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar