Variability and modeling of surface high frequency waves in the southwestern South Atlantic - Sasaki, Dalton Kei (2020)
The objective of this work was to describe the climate variability of the waves on the southwestern region of the South Atlantic based on the wind forcing. The main tool for wave simulations in this work was the University of Miami Wave model (umwm). In order to evaluate and improve the results of this model, we implemented a wind input source function built in the Australian Experiment of Shallow Water (Donelan et al., 2006) and also an algorithm that includes the lateral boundary in the model (Chapter 2). The results of the different algorithms are compared against observations of the American National Data Buoy Center (NDBC), the brazilian National Buoy Program (PNBOIAS) and also the brazilian Rede Ondas datasets. The wave simulation, the 10m wind components, the 850hPa geopotential height from the Climate Forecast Reanalysis System (CFSR) and a cyclone tracking dataset were used to study the interannual variability (Chapter 3) and also the seasonal and high frequency variability (Chapter 4) of the surface wind waves on the Southwestern Atlantic. The results indicate the main mechanisms that controls the interannual variability of waves are the meridional shifting and intensity of the South Atlantic Subtropical Anticyclone (SASA) indicated by the Empirical Orthogonal Function (EOF) 1 of the geopotential height in 850hPa and the EOF1 of the zonal wind, which act over the EOF2 of the significant wave height. In the high frequency case (processes with period lower than 7 days), the conditions that define the dominant wave direction on the South and Southeast coast of South America are linked to the position of transient cyclones, anticyclones and also the position of the SASA. The average significant wave height close to the coast in the western flanks of cyclones (anticyclones) present values of 2-4m (1-2m). The results also indicate the low interannual variability has an explained variance that represent only 2% in the model results.