Wei, H. orcid.org/0000-0002-4704-7346, Bigg, G.R. and Hanna, E. (Accepted: 2023) Predicting the Atlantic Meridional Overturning Circulation using nonlinear system identification methods and NARMAX models. In: IOP Conference Series: Earth and Environmental Science. 8th World Multidisciplinary Earth Sciences Symposium (WMESS 2023), 28 Aug - 01 Sep 2023, Prague, Czech Republic. IOP Publishing . (In Press)
Abstract
The Atlantic Meridional Overturning Circulation (AMOC) plays an important role in the coupled ocean-climate system and in global climate change. The analysis of its own behaviour and the understanding its links to other climate dynamics is of paramount importance today as we encounter an increasing pressure to adapt to climate change. Due to the enormous complexity, it is almost impossible to establish accurate models, purely based on first-principle modelling approaches, that can perfectly represent the relationships between the AMOC and other dynamic climate parameters. Data-based or data-driven modelling methods, can therefore provide an attractive alternative solution. Systematic regular and continuous measurement of the AMOC time series began in April 2004. The main objective of the paper is to use the monthly data of the AMOC measured during April 2004-Febuary 2017, together with the North Atlantic Oscillation (NAO) index, and density anomalies of the Gulf of Mexico, Labrador Sea and Norwegian Sea, measured during the same period, to investigate and understand the quantitative relationship between the AMOC and four drivers (NAO and the three density anomaly variables). In doing so, nonlinear system identification methods and the Nonlinear AutoRegressive Moving Average with Exogenous input (NARMAX) method are employed to develop a quantitative model that relates the AMOC to the four drivers. Experimental results show that the derived nonlinear model skillfully captures and represents the dynamics of the AMOC based on the other four variables. One of the findings from this study is that the use of autoregressive variables can help improve the prediction of the AMOC.
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). | ||||||||
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Institution: | The University of Sheffield | ||||||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) | ||||||||
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Depositing User: | Symplectic Sheffield | ||||||||
Date Deposited: | 26 Sep 2023 14:35 | ||||||||
Last Modified: | 26 Sep 2023 14:36 | ||||||||
Status: | In Press | ||||||||
Publisher: | IOP Publishing | ||||||||
Refereed: | Yes | ||||||||
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Filename: Predicting the AMOC Using NARMAX Models (Accepted Manuscript).pdf