3:30 PM - 3:45 PM
[MGI29-13] Data-driven Nonlinear Dynamical Models for Forecast of Climate Variability
Keywords:Nonlinear Dynamical Model, Forecast of Climate Variability, Nonlinear Data Decomposition
The methodology abilities are demonstrated by modeling and forecast of ENSO system variability. Three monthly data sets are used: global sea surface temperature anomalies, troposphere zonal wind speed, and thermocline depth; all data sets are limited by 30 S, 30 N and have horizontal resolution 10x10 .
We compare results of optimal data decomposition as well as prognostic skill of the constructed models for different combinations of involved data sets. We also present comparative analysis of ENSO indices forecasts fulfilled by our models and by IRI/CPC ENSO Predictions Plume.
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