Preprints
https://doi.org/10.5194/egusphere-2022-523
https://doi.org/10.5194/egusphere-2022-523
22 Jul 2022
 | 22 Jul 2022

On the interaction of stochastic forcing and regime dynamics

Joshua Dorrington and Tim Palmer

Abstract. In this paper we investigate the curious ability of stochastic forcing to increase the persistence of regimes, in a low-order, stochastically forced system. In recent years, evidence from both simple models and climate simulations have suggested that stochastic forcing can act as a stabilising force to increase regime persistence, but the mechanisms driving this potential reinforcement are unclear. Using a six-mode truncation of a barotropic β-plane model, featuring transitions between analogues of zonal and blocked flow conditions, we show that moderate levels of fast-varying stochastic forcing can increase the low-frequency variability of the system, and act asymmetrically to increase the persistence of certain regimes. We show that the presence of a deterministically-inaccessible unstable fixed point, and the low-dimensionality of the flow during blocking, are vital dynamical components that allow this stochastic persistence to occur. We present a simple geometric argument that explains how stochastic forcing can slow the growth of instabilities, which may have more general applicability in understanding stochastic chaotic systems.

Journal article(s) based on this preprint

07 Feb 2023
On the interaction of stochastic forcing and regime dynamics
Joshua Dorrington and Tim Palmer
Nonlin. Processes Geophys., 30, 49–62, https://doi.org/10.5194/npg-30-49-2023,https://doi.org/10.5194/npg-30-49-2023, 2023
Short summary

Joshua Dorrington and Tim Palmer

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-523', Paul PUKITE, 22 Jul 2022
  • RC1: 'Comment on egusphere-2022-523', Anonymous Referee #1, 19 Aug 2022
  • RC2: 'Comment on egusphere-2022-523', Tamas Bodai, 06 Nov 2022
  • AC1: 'Comment on egusphere-2022-523', Joshua Dorrington, 20 Dec 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-523', Paul PUKITE, 22 Jul 2022
  • RC1: 'Comment on egusphere-2022-523', Anonymous Referee #1, 19 Aug 2022
  • RC2: 'Comment on egusphere-2022-523', Tamas Bodai, 06 Nov 2022
  • AC1: 'Comment on egusphere-2022-523', Joshua Dorrington, 20 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Joshua Dorrington on behalf of the Authors (20 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Jan 2023) by Lesley De Cruz
RR by Tamas Bodai (06 Jan 2023)
RR by Anonymous Referee #1 (19 Jan 2023)
ED: Publish subject to technical corrections (27 Jan 2023) by Lesley De Cruz
AR by Joshua Dorrington on behalf of the Authors (27 Jan 2023)  Manuscript 

Journal article(s) based on this preprint

07 Feb 2023
On the interaction of stochastic forcing and regime dynamics
Joshua Dorrington and Tim Palmer
Nonlin. Processes Geophys., 30, 49–62, https://doi.org/10.5194/npg-30-49-2023,https://doi.org/10.5194/npg-30-49-2023, 2023
Short summary

Joshua Dorrington and Tim Palmer

Joshua Dorrington and Tim Palmer

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Atmospheric models often include random forcings, which aim to replicate the impact of processes too small to be resolved. Recent results in simple atmospheric models suggest that this random forcing can actually stabilise certain slow-varying aspects of the system, which could provide a path for resolving known errors in our models. We use randomly-forced simulations of a 'toy' chaotic system and theoretical arguments to explain why this strange effect occurs: at least in simple models.