资源环境科技发展态势分析平台

This study investigates inter-dependency between various wave parameters. It includes the mean wave direction (MWD), swell MWD, wind direction, maximum significant wave height (MSWH), resultant of wind direction and swell MWD, mean wave period (MWP), and peak wave period (PWP) using daily data from altimeter measurements, European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim), and ERA-20C datasets in a changing climate for the past two decades in Indian Ocean region. It also examined the variability in monthly anomalies using linear circular correlation maps. The analysis used quality checked and well calibrated altimeter data from eight satellite missions for MSWH, and WAM (wave model) output from ERA-Interim and ERA-20C datasets for other variables. Spatial variability of correlation coefficient maps explained their inter-dependency. Directional statistics methods explain the directional wave characteristics. Correlation maps signify that regions over the western sectors of Indian Ocean and Bay of Bengal are swell dominant. Over these regions the swell MWD clearly dominated the total MWD (combined effects from wind-sea and swells). For regions in Arabian Sea, the resultant of wind direction and swell MWD contributed about 91%, whereas contribution from swell MWD alone is about 86%. The variability in monthly anomaly MSWH exhibited a specific pattern that resembles the movement of synoptic systems from Southern Ocean propagating north-eastwards reaching various destinations in the Bay of Bengal basin, having a strong positive correlation with MWD and swell MWD. Over the tropical South Indian Ocean (TSIO) region, the obtained feature correlates with the surface current pattern of Agulhas retroflection. These patterns are distinctly visible in the correlation maps between MSWH and resultant of wind direction and swell MWD as well wind direction monthly anomaly maps. For the North Indian Ocean an out of phase relation exists between the variability of MWSH with PWP monthly anomalies.