资源环境科技发展态势分析平台

In the present study the association between mean and interannual subsurface temperature bias over the equatorial Indian Ocean (EIO) is investigated during boreal summer (June through September; JJAS) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. Anomalously high subsurface warm bias (greater than 3 A degrees C) over the eastern EIO (EEIO) region is noted in CFSv2 during summer, which is higher compared to other parts of the tropical Indian Ocean. Prominent eastward current bias in the upper 100 m over the EIO region induced by anomalous westerly winds is primarily responsible for subsurface temperature bias. The eastward currents transport warm water to the EEIO and is pushed down to subsurface due to downwelling. Thus biases in both horizontal and vertical currents over the EIO region support subsurface warm bias. The evolution of systematic subsurface warm bias in the model shows strong interannual variability. These maximum subsurface warming episodes over the EEIO are mainly associated with La Nia like forcing. Strong convergence of low level winds over the EEIO and Maritime continent enhanced the westerly wind bias over the EIO during maximum warming years. This low level convergence of wind is induced by the bias in the gradient in the mean sea level pressure with positive bias over western EIO and negative bias over EEIO and parts of western Pacific. Consequently, changes in the atmospheric circulation associated with La Nia like conditions affected the ocean dynamics by modulating the current bias thereby enhancing the subsurface warm bias over the EEIO. It is identified that EEIO subsurface warming is stronger when La Nia co-occurred with negative Indian Ocean Dipole events as compared to La Nia only years in the model. Ocean general circulation model (OGCM) experiments forced with CFSv2 winds clearly support our hypothesis that ocean dynamics influenced by westerly winds bias is primarily responsible for the strong subsurface warm bias over the EEIO. This study advocates the importance of understanding the ability of the models in representing the large scale air-sea interactions over the tropics and their impact on ocean biases for better monsoon forecast.