Predicting the Atlantic Meridional Overturning Circulation Using Nonlinear System Identification Methods and the NARMAX Model

• Autorzy: Weil Hua-Liang , Bigg Grant R. , Hanna Edward

Identyfikatory

DOI

10.29227/IM-2024-01-58

Warianty tytułu

PL

Przewidywanie atlantyckiej południkowej cyrkulacji z wykorzystaniem nieliniowych metod identyfikacji systemu i modelu NARMAX

• Języki publikacji: EN

Abstrakty

EN

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 firstprinciple 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.

Słowa kluczowe

EN

atlantic meridional overturning circulation; north atlantic oscillation; nonlinear system identification methods; narmax model

PL

Atlantycka Południkowa Cyrkulacja Wymienna; oscylacja północnoatlantycka; metody identyfikacji systemów nieliniowych; model NARMAX

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2024
• Tom: Vol. 1, no. 1
• Strony: 521--528
• Opis fizyczny: Bibliogr. 20 poz., tab., zdj.

Twórcy

autor

Weil Hua-Liang

• Department Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK

• https://orcid.org/0000-0002-4704-7346

autor

Bigg Grant R.

• Department of Geography, University of Sheffield, Sheffield, UK

• https://orcid.org/0000-0002-1910-0349

autor

Hanna Edward

• Department of Geography and Lincoln Climate Research Group, College of Science, University of Lincoln, UK

• https://orcid.org/0000-0002-8683-182X

Bibliografia

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• 2. H. L. Bryden, H. R. Longworth and S. A. Cunningham, “Slowing of the Atlantic meridional overturning circulation at 25°N,” Nature 438, 655-657 (2005).
• 3. M. W. Buckley and J. Marshall, “Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review,” Rev. Geophys. 54, 5-63 (2016).
• 4. L. C. Jackson, K. A. Peterson, C. D. Roberts and R. A. Wood (2016), “Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening,” Nature Geosci. 9, 518-522 (2016).
• 5. D. Hodson and R. Sutton, “The impact of resolution on the adjustment and decadal variability of the Atlantic meridional overturning circulation in a coupled climate model,” Clim. Dyn. 39, 3057-3073 (2012).
• 6. D. Matei, J. Baehr, J. H. Jungclaus, H. Haak, W. A. Müller and J. Marotzk, “Multiyear Prediction of Monthly Mean Atlantic Meridional Overturning Circulation at 26.5°N,” Science 335, 76-79 (2012)
• 7. J. R. Ayala-Solares, H. L. Wei and G. R. Bigg, “The variability of the Atlantic meridional circulation since 1980, as hindcast by a data-driven nonlinear systems model,” Acta Geophysica 66, 683-695 (2018).
• 8. S. Mahajan, R. Zhang and T. L. Delworth, “Impact of the Atlantic meridional overturning circulation (AMOC) on Arctic surface air temperature and sea ice variability,” Journal of Climate 24, 6573–6581 (2011).
• 9. Y. Kostov, K. C. Armour and J. Marshall, “Impact of the Atlantic meridional overturning circulation on ocean heat storage and transient climate change,” Geophys. Res. Lett. 41, 2108–2116 (2014).
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• 11. S. Z. Leidman, A. K. Rennermalm, A. J. Broccoli, D. van As, M. R. van den Broeke, K. Steffen and A. Hubbard, “Methods for predicting the likelihood of Safe Fieldwork conditions in harsh environments,” Frontiers in Earth Science 8, 260 (2020).
• 12. S. A. Billings, Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and SpatioTemporal Domains, Wiley, 2013.
• 13. H. -L. Wei, S. A. Billings and J. Liu, “Term and variable selection for non-linear system identification,” International Journal of Control 77, 86–110 (2004).
• 14. H. -L. Wei and S. A. Billings, “Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information,” International Journal of Modelling, Identification and Control 3, 341–356 (2008).
• 15. G. R. Bigg, H. -L. Wei, D. J. Wilton, Y. Zhao, S. A. Billings, E. Hanna and V. Kadirkamanathan, “A century of variation in the dependence of Greenland iceberg calving on ice sheet surface mass balance and regional climate change,” Proc. Roy. Soc Ser. A 470, 20130662 (2014).
• 16. Y. Zhao, G. R. Bigg, S. A. Billings, E. Hanna, A. J. Sole, H.-L. Wei, V. Kadirkamanathan and D. J. Wilton, “Inferring the variation of climatic and glaciological contributions to West Greenland iceberg discharge in the Twentieth Century,” Cold Reg. Sci. Technol. 121, 167-178 (2016).
• 17. G. R. Bigg, Y. Zhao and E. Hanna, “Forecasting the severity of the Newfoundland iceberg season using a control systems model,” J. Operational Oceanogr. 14, 24-36 (2021).
• 18. A. Marshall, G. R. Bigg, S. M. van Leeuwen, J. K. Pinnegar, H.-L. Wei, T. J. Webb and J. L. Blanchard, “Quantifying heterogeneous responses of fish community size structure using novel combined statistical methods,” Glob. Change Biol. 22, 1755-1768 (2016).
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).