to be one of the major causes of dry spell/break episodes in ISM. Additionally, the presence of cold air over Central India reduces the north-south thermal contrast over the monsoon region thereby modifying the local Hadley circulation over the region.
Exploratory analysis using empirical orthogonal function revealed the presence of a stationary zonal wavenumber-4 (W4) pattern in the sea surface temperature (SST) anomaly in the southern subtropics (20°S–55°S). The signal over the Southern subtropics is seasonally phase-locked to the austral summer and persists up to mid-autumn. Thermodynamic coupling of atmosphere and the upper ocean helps in generating the W4 pattern, which later terminates due to the breaking of that coupled feedback. It is found that the presence of anomalous SST due to W4 mode in the surrounding of Australia affects the rainfall over the continent by modulating the local atmospheric circulation. During positive phase of W4 event, the presence of cold SST anomaly over the south-eastern and -western side of Australia creates an anomalous divergence circulation. This favours the moisture transport towards south-eastern Australia, resulting in more rainfall in February. The scenario reverses in case of a negative W4 event. There is also a difference of one month between the occurrence of positive and negative W4 peaks. This asymmetry seems to be responsible for the weak SST signal to the South of Australia. Correlation analysis suggests that the W4 pattern in SST is independent of other natural variabilities such as Southern Annular Mode, and Indian Ocean Dipole as well as a rather weak relationship with El Niño/Southern Oscillation.
This study investigates the impact of wind‐induced sea ice drift on sea ice cover over the Indian Ocean sector of the Southern Ocean (IOS) in the contrasting Southern Annular Mode (SAM) years during a summer (February) and a winter (July) month. Analysis of reanalysis wind shows that during positive SAM events, westerlies show stronger and more zonal flow over the west IOS (west of 45°E), while a stronger northerly component is seen over the east IOS, to the south of 55°S during both February and July. This is attributed to the zonally asymmetric feature of sea level pressure over the IOS. A coupled ocean–sea ice model was forced with dynamical wind forcing for positive and negative SAM events during above months. The zonal contrast as seen in wind and surface current is transferred to the sea ice drift. A stronger zonal eastward sea ice drift is apparent over the west IOS, suggesting increased transport of sea ice from the Weddell Sea region in July. The eastward advection of sea ice results in piling of sea ice over west IOS and causes an increase in sea ice concentration and thickness. Over east IOS, the sea ice drift shows a strong southeastward anomaly from the sea ice edge towards the coast. This results in a piling of sea ice near the coast and a divergence of sea ice near the edge. This results in a negative anomaly in sea ice concentration and sea ice thickness over east IOS. Thus, the dynamical SAM forcing leads to a non‐annular response in sea ice cover over the IOS.
The present study was intended to develop a Water Quality Index (WQI) for the coastal water of Visakhapatnam, India from multiple measured water quality parameters using different multivariate statistical techniques. Cluster analysis was used to classify the data set into three major groups based on similar water quality characteristics. Discriminant analysis was used to generate a discriminant function for developing a WQI. Discriminant analysis gave the best result for analyzing the seasonal variation of water quality. It helped in data reduction and found the most discriminant parameters responsible for seasonal variation of water quality. Coastal water was classified into good, average, and poor quality considering WQI and the nutrient load. The predictive capacity of WQI was proved with random samples taken from coastal areas. High concentration of ammonia in surface water during winter was attributed to nitrogen fixation by the phytoplankton bloom which resulted due to East India Coastal Current. This study brings out the fact that water quality in the coastal region not only depends on the discharge from different pollution sources but also on the presence of different current patterns. It also illustrates the usefulness of WQI for analyzing the complex nutrient data for assessing the coastal water and identifying different pollution sources, considering reasons for seasonal variation of water quality.
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