Climate models with delay differential equations
| dc.contributor.author | Keane, Andrew | en |
| dc.contributor.author | Krauskopf, Bernd | en |
| dc.contributor.author | Postlethwaite, Claire M. | en |
| dc.date.accessioned | 2025-04-15T15:38:51Z | |
| dc.date.available | 2025-04-15T15:38:51Z | |
| dc.date.issued | 2017-10-17 | en |
| dc.description.abstract | A fundamental challenge in mathematical modelling is to find a model that embodies the essential underlying physics of a system, while at the same time being simple enough to allow for mathematical analysis. Delay differential equations (DDEs) can often assist in this goal because, in some cases, only the delayed effects of complex processes need to be described and not the processes themselves. This is true for some climate systems, whose dynamics are driven in part by delayed feedback loops associated with transport times of mass or energy from one location of the globe to another. The infinite-dimensional nature of DDEs allows them to be sufficiently complex to reproduce realistic dynamics accurately with a small number of variables and parameters. In this paper, we review how DDEs have been used to model climate systems at a conceptual level. Most studies of DDE climate models have focused on gaining insights into either the global energy balance or the fundamental workings of the El Niño Southern Oscillation (ENSO) system. For example, studies of DDEs have led to proposed mechanisms for the interannual oscillations in sea-surface temperature that is characteristic of ENSO, the irregular behaviour that makes ENSO difficult to forecast and the tendency of El Niño events to occur near Christmas. We also discuss the tools used to analyse such DDE models. In particular, the recent development of continuation software for DDEs makes it possible to explore large regions of parameter space in an efficient manner in order to provide a “global picture” of the possible dynamics. We also point out some directions for future research, including the incorporation of non-constant delays, which we believe could improve the descriptive power of DDE climate models. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.articleid | 114309 | en |
| dc.identifier.citation | Keane, A., Krauskopf, B. and Postlethwaite, C. M. (2017) 'Climate models with delay differential equations', Chaos, 27(11), 114309 (15pp). https://doi.org/10.1063/1.5006923 | en |
| dc.identifier.doi | 10.1063/1.5006923 | en |
| dc.identifier.eissn | 1089-7682 | en |
| dc.identifier.endpage | 15 | en |
| dc.identifier.issn | 1054-1500 | en |
| dc.identifier.issued | 11 | en |
| dc.identifier.journaltitle | Chaos | en |
| dc.identifier.startpage | 1 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/17271 | |
| dc.identifier.volume | 27 | en |
| dc.language.iso | en | en |
| dc.publisher | AIP Publishing | en |
| dc.relation.ispartof | Chaos: An Interdisciplinary Journal of Nonlinear Science | en |
| dc.rights | © 2017, the Authors. Published by AIP Publishing. | en |
| dc.subject | Dynamical systems | en |
| dc.subject | Thermodynamic states and processes | en |
| dc.subject | Oceanography | en |
| dc.subject | Oceans | en |
| dc.subject | Climatology | en |
| dc.subject | Environmental economics | en |
| dc.subject | Mathematical modeling | en |
| dc.subject | Phase space methods | en |
| dc.subject | Recurrence relations | en |
| dc.subject | Stochastic processes | en |
| dc.title | Climate models with delay differential equations | en |
| dc.type | Article (peer-reviewed) | en |
| dc.type | journal-article | en |
| oaire.citation.issue | 11 | en |
| oaire.citation.volume | 27 | en |
