Investigating Sea Surface Temperature Diurnal Variation over the Tropical Warm Pool Region

Abstract

This thesis investigates the sea surface temperature (SST) diurnal variation (DV) signals over the tropical warm pool region (TWP, defined in this study as 90°E-170°E, 25°S-15°N). First, the general DV features and DV cycles are investigated using the v3 (version 3) geostationary MTSAT-1R (Multi-functional Transport Satellites – 1R) satellite SST data set. This data set was produced by the Australian Bureau of Meteorology (the Bureau) over the TWP spatial domain and for four months from January to April 2010. Together with surface wind speed and solar shortwave insolation (SSI) outputs from the Australian Community Climate and Earth-System Simulator-Regional (ACCESS-R) numerical weather prediction model, statistical analyses of DV signals are conducted. Good correlation is found between DV events and low wind and high SSI conditions. The dominant role of wind speed in SST DV events over SSI is also revealed. Second, we investigate the seasonal patterns of DV using the Bureau’s Integrated Marine Observing System (IMOS) five-year (2010-2014), version fv02 AVHRR (Advanced Very High Resolution Radiometer) SST data. A double-peak seasonal pattern of SST DV is observed: the strongest DVs are found in February/March and October/November, and the weakest in June/July. The more important role of the late morning and early afternoon (07-14 local time) winds in determining the DV amplitude is also shown. Then we look into the physical phenomena behind the double-peak/trough DV/wind seasonal pattern over the Australian North-Western Shelf (NWS, defined as 105°E-125°E, 25°S-10°S). It is found that this seasonal DV/wind pattern over the NWS is largely regulated by the westerly Australian summer monsoon, which reduces the easterly trade winds during the monsoonal period. As a result, wind speed over the region reaches a minimum at the beginning and end of the monsoonal period with the weakest wind mixing, favouring SST DV development. Finally, a group of DV models, comprising one empirical model, two physical models, and one air-sea coupled model with DV schemes implemented, are validated against the v3 MTSAT-1R data set and inter-compared with each other. Results indicate that all models are able to capture the general DV patterns but with differing accuracies and features.