Role of Ocean-Atmosphere Interface in Annual and Semiannual SST Cycles in the Indian Ocean

Abstract

A long-term analysis of temperature can be used to describe the main mechanisms that operate at the surface of the ocean. The average sea surface temperature (SST) contour plots for the Indian Ocean are produced based on the World Ocean Atlas Data Set (1994). SST, together with the independent variables wind (Wx, zonal component of pseudo-stress wind and Wy, meridional component of pseudo-stress wind), net-down-fresh-water-flow (NDFF), and Ekman pumping, are included in a multiple regression analysis to define the relative importance of each one of these variables in the physical processes at the surface of the Indian Ocean. The NDFF data set is based on COADS (Comprehensive Ocean-Atmosphere Data Set). The wind data is obtained from the Florida State University (FSU). The harmonic terms of the variables are calculated, which is considered to be stationary and expressed by a Fourier series as a cosine function. The harmonic terms are multiplied by the maximum amplitude of the variables and then added to their mean annual values. The isotherms are mainly meridional along the western boundary, but zonal in the southern Indian Ocean. The annual component is seen to have a maximum in July, Summer Monsoon (SW Monsoon) and a minimum in January, during the Winter Monsoon (NE Monsoon). The amplitude of the semiannual component is smaller, with two maxima in May and October and two minima in February and August. The small magnitude of these residuals errors is an indication that the temperature variability during this period and for this area can be explained reasonably well by the two harmonic terms. In the Arabian Sea, the final regression equations for SST variability show that it is mainly affected by the Wx, Ekman pumping and NDFF. For most of the areas of the Bay of Bengal, as well as for most of the locations in the southern tropical Indian Ocean, the entered independent variables can explain SST. Two components fit to observation can be used to predict SST together with the regression equations. Although harmonic analysis can be used to study SST variability, a multiple regression analysis is required to identify and quantify the variables related to areas of large annual and semiannual variability. Different techniques are therefore used together to provide more reliable results in SST configuration in the Indian Ocean.