印度洋海表盐度预报的线性马尔可夫模型及其改进办法

doi:10.11978/2022008

Abstract

Abstract:

Marine salinity plays a crucial role in the change of water circulation, ocean circulation, marine ecosystems, global weather and climate. However, restricted by observation, previous studies on marine salinity are relatively scarce, and the prediction involving marine salinity is even rarer. This study used the linear Markov model to forecast the Indian Ocean sea surface salinity (SSS). Based on the mixed layer salinity budget equation, the SSH (sea surface height), SST (sea surface temperature), and SSS were selected as the components of the model to forecast the Indian Ocean SSS. The Markov model makes a great prediction of the Indian Ocean SSS for nine months in advance. In addition, considering teleconnection, the addition of SSHA (sea surface height anomaly), SSTA (sea surface temperature anomaly) in the South Pacific and the Indian Ocean dipole (IOD) coefficient can improve the prediction skill by an average of 10% (correlation coefficient). Real-time prediction of the Indian Ocean SSS for 1-11 months using improved models shows that the predictions are roughly consistent with the observations. As mentioned above, the improved linear Markov model has a certain predictive skill for SSS in the Indian Ocean, and it can be further improved in the future.

Key words: linear Markov model, Indian Ocean, sea surface salinity, forecast

CLC Number:

P731.3

LYU Hongke, GONG Yuanfa, WANG Guihua. A linear Markov model for the forecast of sea surface salinity in the Indian Ocean and its improvement method[J].Journal of Tropical Oceanography, 2022, 41(6): 151-158.

Figures/Tables 10

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A linear Markov model for the forecast of sea surface salinity in the Indian Ocean and its improvement method

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