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

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).