Assimilation of data into coupled-atmosphere-ocean-shelf models

点击翻译按钮获取中文摘要

Our scientific goal is to improve our understanding of the processes that control predictability of the atmoshere, the ocean and the shelf seas that border them. On a more practical note, we want to make more accurate predictions. The funding will support two graduate students to work on the following research topics.1. Global Atmosphere-Ocean Assimilation and Prediction: The most effective way of predicting future states of the ocean and atmosphere is based on assimilating observations into dynamic models. Without assimilation the models quickly drift away from reality. We will focus on the assimilation of new types of ocean data (e.g. sea levels measured by satellites, vertical profiles of temperature and salinity from freely-drifting floats) into coupled atmosphere-ocean models. New assimilation methods will be devised that allow observations from one fluid (e.g. upper ocean temperatures) to influence both fluids in a dynamically-consistent way. The research should lead to better forecasts of the coupled system on time scales of days to seasons, and a deeper understanding of the physical processes that control predictability, both globally and regionally.2. Predictability of the Coastal Ocean: Storms surges along the coast of Atlantic Canada can be forecast two-days into the future to within about 10 cm.  More research is needed to make accurate forecasts of currents, temperature and salinity. We will improve the Dalhousie coastal ocean forecast system by coupling it to an eddy-resolving model of the adjacent North Atlantic. This will allow changes in deep ocean "weather" to influence coastal circulation. The research should improve short-term forecasts of currents and water properties of the shelf seas of Atlantic Canada, and deepen our understanding of the physical causes of the dramatic changes (about 3C) in sea surface temperature that have occurred in this region over the last 50 years.