Leading Nonlinear Mode of Inter-Annual and Decadal Climate Behavior
Abstract
We analyze long sea surface temperature time series (from the end of nineteenth century to present) by the new method of data expansion which takes into account nonlinear couplings in data. Actually, the method is aimed to reveal a few hidden dynamical signals which explain an essential part of data and are interpreted as dominant internal modes driving the observed dynamics. This is achieved by the projecting of data on parametrically defined low-dimensional manifolds embedded in data space. Special prior restrictions are applied to the reconstructed time series adjusting its autocorrelation time to the dominant time scales of the dynamics. It is important that the structure of the data transformation used for the mode construction is data-adapted: both nonlinearity degree and mode's dimension are estimated by Bayesian optimality criterion. We obtain leading nonlinear mode underlying the time series, which captures the climate shifts connected with PDO phase changes during 20th century. It is shown that the obtained components reduce efficiently dimensionality of the studied climate field. It is important that they allow to reveal the long-term evolution of both dominant patterns of variability and their teleconnections over the Globe. In particular, ENSO-related interannual and decadal variability in various regions can be described by scalar signal, including teleconnections to Indian and South and North Pacific regions. These results show the increasing ENSO impact on South Pacific in the last decades. Also, we demonstrate that substantial parts of multidecadal variability in Pacific ocean (PDO) and North Atlantic (AMO) are coupled by this single component.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFMNG24A..03M
- Keywords:
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- 3325 Monte Carlo technique;
- ATMOSPHERIC PROCESSESDE: 3265 Stochastic processes;
- MATHEMATICAL GEOPHYSICSDE: 3275 Uncertainty quantification;
- MATHEMATICAL GEOPHYSICSDE: 4468 Probability distributions;
- heavy and fat-tailed;
- NONLINEAR GEOPHYSICS