Improved Prediction of the Indian Ocean Dipole Mode by Use of Subsurface Ocean Observations
Author(s) -
Takeshi Doi,
Andrea Storto,
Swadhin K. Behera,
Antonio Navarra,
Toshio Yamagata
Publication year - 2017
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/jcli-d-16-0915.1
Subject(s) - indian ocean dipole , climatology , initialization , data assimilation , ocean heat content , sea surface temperature , ocean observations , mode (computer interface) , environmental science , temperature salinity diagrams , predictability , thermocline , ocean current , subtropical indian ocean dipole , meteorology , salinity , oceanography , geology , geography , computer science , mathematics , statistics , programming language , operating system
The numerical seasonal prediction system using the Scale Interaction Experiment–Frontier version 1 (SINTEX-F) ocean–atmosphere coupled model has so far demonstrated a good performance for prediction of the Indian Ocean dipole mode (IOD) despite the fact that the system adopts a relatively simple initialization scheme based on nudging only the sea surface temperature (SST). However, it is to be expected that the system is not sufficient to capture in detail the subsurface oceanic precondition. Therefore, the authors have introduced a new three-dimensional variational ocean data assimilation (3DVAR) method that takes three-dimensional observed ocean temperature and salinity into account. Since the new system has successfully improved IOD predictions, the present study is showing that the ocean observational efforts in the tropical Indian Ocean are decisive for improvement of the IOD predictions and may have a large impact on important socioeconomic activities, particularly in the Indian Ocean rim co...
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