title: Predicting stochastic systems by noise sampling, and application to the El Niño-Southern Oscillation.
abstract: Interannual and interdecadal prediction are major challenges of climate dynamics. In this article we develop a prediction method for climate processes that exhibit low-frequency variability (LFV). The method constructs a nonlinear stochastic model from past observations and estimates a path of the "weather" noise that drives this model over previous finite-time windows. The method has two steps: (i) select noise samples--or "snippets"--from the past noise, which have forced the system during short-time intervals that resemble the LFV phase just preceding the currently observed state; and (ii) use these snippets to drive the system from the current state into the future. The method is placed in the framework of pathwise linear-response theory and is then applied to an El Niño-Southern Oscillation (ENSO) model derived by the empirical model reduction (EMR) methodology; this nonlinear model has 40 coupled, slow, and fast variables. The domain of validity of this forecasting procedure depends on the nature of the system's pathwise response; it is shown numerically that the ENSO model's response is linear on interannual time scales. As a result, the method's skill at a 6- to 16-month lead is highly competitive when compared with currently used dynamic and statistic prediction methods for the Niño-3 index and the global sea surface temperature field.
authors: Environmental Research and Teaching Institute, École Normale Supérieure, F-75230 Paris Cedex 05, France. mchekroun@atmos.ucla.edu
authors: Mickaël David
authors: MD
authors: Chekroun
authors: Dmitri
authors: D
authors: Kondrashov
authors: Michael
authors: M
authors: Ghil
mesh_terms: D003198:Computer Simulation; D059000:El Nino-Southern Oscillation; D005544:Forecasting / Q000379:methods*; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D013269:Stochastic Processes