Regression models with memory for the linear response of turbulent dynamical systems

Emily L. Kang, John Harlim, Andrew J. Majda

Research output: Contribution to journalArticle

Abstract

Calculating the statistical linear response of turbulent dynamical systems to the change in external forcing is a problem of wide contemporary interest. Here the authors apply linear regression models with memory, AR(p) models, to approximate this statistical linear response by directly fitting the autocorrelations of the underlying turbulent dynamical system without further computational experiments. For highly nontrivial energy conserving turbulent dynamical systems like the Kruskal-Zabusky (KZ) or Truncated Burgers-Hopf (TBH) models, these AR(p) models exactly recover the mean linear statistical response to the change in external forcing at all response times with negligible errors. For a forced turbulent dynamical system like the Lorenz-96 (L-96) model, these approximations have improved skill comparable to the mean response with the quasi-Gaussian approximation for weakly chaotic turbulent dynamical systems. These AR(p) models also give new insight into the memory depth of the mean linear response operator for turbulent dynamical systems.

Original languageEnglish (US)
Pages (from-to)481-498
Number of pages18
JournalCommunications in Mathematical Sciences
Volume11
Issue number2
DOIs
StatePublished - Jan 1 2013

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Keywords

  • Autoregressive models
  • Climate change
  • Fluctuation-dissipation theory
  • Linear response

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

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