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Optimum initialization of South Asian seasonal forecast using climatological relevant singular vectors

Banff International Research Station for Mathematical Innovation and Discovery

Designing an efficient seasonal forecasting system is ensuring that the uncertainty in the initial conditions is sampled optimally. Perturbation in the initial condition and the methodology used for sampling perturbation optimally plays a key role in the improvement of the current seasonal climate forecast. In this study the error growth properties of initial perturbation are investigated using climatically relevant singular vectors (CSVs). The Community Climate System Model version 4 (CCSM4) is used as a simulation tool to examine the growth of optimal perturbations with different lead times over the South Asian Monsoon region. It is found that reliable set of CSVs can be estimated by running an ensemble of model forecasts. Amplification of the optimal perturbations occurs for more than 1 month and possibly up to 6 months. The results show the growth rates of the singular vectors are very sensitive to the variable of perturbation, number of perturbations and the error norm. When the SV is used as an initial perturbation, the forecast skill of key atmospheric variables over South Asian Monsoon region is significantly improved. Further, it is demonstrated that the predictions with the singular vector have a more reliable ensemble spread, suggesting a potential merit for a probabilistic forecast. The promising results reported here should hopefully encourage further investigation of the methodology at different timescales.

Mathematics; Partial differential equations; Dynamical systems and ergodic theory

video/mp4

Author affiliation: University of Northern British Columbia

2018-05-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66018

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/