Abstract
We analyse the bias present in the Indian Summer Monsoon Rainfall (ISMR), as simulated by Climate Forecast System Model 2 (CFSv2), the operational model used for monsoon forecasts in India. In the simulations, the precipitation intensity is redistributed within the ITCZ band with southward shifts of precipitation maxima. We observe weakening of maximum intensity of precipitation over the region between 20°N and 14°N. In the simulations by CFSv2, there exists two rain bands: the northern one located slightly southward compared to reanalysis dataset and the southern one over the equator with intensified precipitation. This results in dry bias over land and wet bias over the ocean. We use a Dynamic Recycling Model, based on Lagrangian approach, to investigate the role of various moisture sources in generating these biases. We find that, the dry bias during June exists due to the delayed monsoon onset and reduced moisture flow from the Arabian Sea. As the monsoon progresses, deficiency in the simulated contributions from South Indian Ocean becomes the key source of bias. The reduced supply of moisture from oceanic sources is primarily attributed to the weaker northward transport of moisture flux from the Southern Ocean, associated with a weaker southward energy flux. Inefficiency of the model in simulating the heating in Tibetan plateau during the pre-monsoon period leads to this reduced cross equatorial energy flow. We also find that, towards the end of monsoon season, moisture contributions from land sources namely, Ganga Basin and North-Eastern forests become significant and underestimations of the same in the simulations by CFSv2 result into biases over Central and Eastern India.
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The authors sincerely thank the financial support provided by Ministry of Earth Sciences through project no MoES/PAMC/H&C/35/2013-PC-II. The authors also thank the editor and anonymous reviewer for providing useful suggestions.
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Sahana, A.S., Pathak, A., Roxy, M.K. et al. Understanding the role of moisture transport on the dry bias in indian monsoon simulations by CFSv2. Clim Dyn 52, 637–651 (2019). https://doi.org/10.1007/s00382-018-4154-y
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DOI: https://doi.org/10.1007/s00382-018-4154-y