Data-driven modeling decadal-to-centennial ENSO variability and its response to external forcing
- Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, Russian Federation (aseleznev@ipfran.ru)
We investigate the decadal-to-centennial ENSO variability based on nonlinear data-driven stochastic modeling. We construct data-driven model of yearly Niño-3.4 indices reconstructed from paleoclimate proxies based on three different sea-surface temperature (SST) databases at the time interval from 1150 to 1995 [1]. The data-driven model is forced by the solar activity and CO2 concentration signals. We find the persistent antiphasing relationship between the solar forcing and Niño-3.4 SST on the bicentennial time scale. The dynamical mechanism of such a response is discussed.
The work was supported by the Russian Science Foundation (Grant No. 20-62-46056)
1. Emile-Geay, J., Cobb, K. M., Mann, M. E., & Wittenberg, A. T. (2013). Estimating Central Equatorial Pacific SST Variability over the Past Millennium. Part II: Reconstructions and Implications, Journal of Climate, 26(7), 2329-2352.
How to cite: Seleznev, A., Mukhin, D., Gavrilov, A., and Feigin, A.: Data-driven modeling decadal-to-centennial ENSO variability and its response to external forcing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8370, https://doi.org/10.5194/egusphere-egu21-8370, 2021.
