Reconstruction of the North Atlantic Oscillation variability and its response to anthropogenic forcing using data-driven stochastic models based on artificial neural networks

Aleksei F. Seleznev; Andrey S. Gavrilov; Dmitry N. Mukhin; Alexander M. Feigin

doi:10.1117/12.2601900

15 December 2021 Reconstruction of the North Atlantic Oscillation variability and its response to anthropogenic forcing using data-driven stochastic models based on artificial neural networks

Aleksei F. Seleznev, Andrey S. Gavrilov, Dmitry N. Mukhin, Alexander M. Feigin

Author Affiliations +

Proceedings Volume 11916, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics; 119164Y (2021) https://doi.org/10.1117/12.2601900
Event: 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics, 2021, Moscow, Russian Federation

Abstract

We use the data-driven stochastic model for reconstruction of variability of the North Atlantic Oscillation (NAO) and its response to anthropogenic forcing. We apply the data-driven model to both the data produced by INM RAS Climate Model and NCEP / NCAR reanalysis data. The data-driven model reproduces well the characteristic statistical properties of the NAO index, such as skewness. We predict the NAO variability in the 21st century under various scenarios of anthropogenic CO₂ emissions using data-driven model. The study was supported by the Russian Science Foundation (grant No. 9-42-04121).

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Aleksei F. Seleznev, Andrey S. Gavrilov, Dmitry N. Mukhin, and Alexander M. Feigin "Reconstruction of the North Atlantic Oscillation variability and its response to anthropogenic forcing using data-driven stochastic models based on artificial neural networks", Proc. SPIE 11916, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics, 119164Y (15 December 2021); https://doi.org/10.1117/12.2601900

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