Science China Earth Sciences

, Volume 60, Issue 1, pp 156–172 | Cite as

IOD-related optimal initial errors and optimal precursors for IOD predictions from reanalysis data

Research Paper
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Abstract

This study explored the spatial patterns of winter predictability barrier (WPB)-related optimal initial errors and optimal precursors for positive Indian Ocean dipole (IOD) events, and the associated physical mechanisms for their developments were analyzed using the Simple Ocean Data Assimilation dataset. Without consideration of the effects of model errors on “predictions,” it was assumed that different “predictions” are caused by different initial conditions. The two types of WPB-related optimal initial errors are almost opposite for the start months of July (–1) and July (0), although they both present a west-east dipole pattern in the tropical Indian Ocean, with the maximum errors located at the thermocline depth. Bjerknes feedback and ocean waves play important roles in the growth of prediction errors. These two physical mechanisms compete during July–December and ocean waves dominate during January–June. The spatial patterns of optimal precursors and the physical mechanisms for their developments are similar to those of WPB-related optimal initial errors. It is worth noting that large values of WPB-related optimal initial errors and optimal precursors are concentrated within a few locations, which probably represent the sensitive areas of targeted observations for positive IOD events. The great similarities between WPB-related optimal initial errors and optimal precursors suggest that were intensive observations performed over these areas, this would not only reduce initial errors and thus, prediction errors, but it would also permit the detection of the signal of IOD events in advance, greatly improving the forecast skill of positive IOD events.

Keywords

Optimal precursors Initial errors Winter predictability barrier Indian Ocean dipole 

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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

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