High Resolution Coupled Modeling and Data Assimilation for Improved Understanding of Transition Layer Processes in the Arabian Sea Warm Pool
Abstract
The Arabian Sea along with the Indian Ocean warms up for a few months (Jan-Apr) prior to the onset of the South Asian monsoon. The Arabian Sea mini warm pool (ASMWP, Kurian and Vinayachandran, 2007) is the warmest part of the world ocean at that point in time andis characterized by high SST (> 30�C) in the southeastern Arabian Sea. These warm waters modulate air-sea fluxes over the region and can sustain atmospheric convection over the monsoon region, thereby playing an important role in the formation of the monsoon onset vortex (Vinayachandran et al., 2007). The mini warm pool and the wider tropical Indian Ocean has experienced a pronounced warming trend during the last century (Roxy et al., 2014) suggesting an enhancement of the role played by the mini warm pool in monsoon onset. It is thus important to identify the mechanisms governing the anomalous warming in the region, and the processes that cause the formation of the warm pool, its maintenance and collapse. Despite the importance of the mini warm pool, coupled models still suffer from a cool bias in the Arabian Sea. Processes relevant to ASMWP simulation such as the coupled feedbacks in the southeastern ArabianSea, interactions with the Findlater jet, and the large-scale Indian monsoon circulation are inadequately represented in regional and global coupled models (Vinayachandran et al., 2007; Centurioni et al., 2017). These inaccuracies could be related to either poor parameterization of model physics or insufficient model resolution to resolve the critical processes. New efforts in observations, process understanding, and translation to weather and climate models are necessary for improvements in simulation and prediction of the ASMWP and monsoon onset. There is also a need to identify sources of predictability for subseasonal phenomena such as Monsoon Intra-Seasonal Oscillations (MISO) events that strongly modulate rainfall over the Indian subcontinent. Targeted modeling experiments to help identify these sources of predictability will help improve MISO prediction capabilities.We propose to address the key goals of the ASTraL DRI by designing a high resolution regional coupled ocean-atmosphere forecasting tool with coupled data assimilation for improved process understanding, identifying sources of predictability in combination with NCAR#s global coupled integrations, as well as the better design of observation networks in the region. We will collaborate with other scientists in the DRI to optimally use observations as well as global modeling studies for better physical understanding and prediction of the ASMWP events. We have discussed a possible collaboration with Dr. Verena Hormann on the use of surface drifter data for data assimilation or validation purposes. We will also collaborate with Dr. Mani Mathur (IIT Madras) and the Geophysical Fluids Laboratory at IIT Madras to use their Autonomous Unmanned Aerial Systems# (UAS) observations of the atmospheric boundary layer for data assimilation experiments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 12, 2023
- Source ID
- N000142312092
Entities
People
- Aneesh Subramanian
Organizations
- Office of Naval Research
- Regents of the University of Colorado
- United States Navy