Climate-induced changes in temperature, salinity, and sound speed in the global ocean

Abstract

Oceans play a significant role in climate change by absorbing the earth's excess heat and transporting heat from one location to another. Melting and reducing ice coverage, rising sea levels, ocean acidification, and changes in underwater sound propagation are a few impacts of global warming on oceans. Marine lives have been threatened by many of these alterations, such as interference of their communications and reduction of their habitable places, resulting in the ocean ecosystem imbalance. Ocean datasets containing temperature and salinity are necessary to measure the warming rates and places, access the magnitude and extent of the impacts, and consider mitigation policies. Data collected by traditional in situ measurement techniques are insufficient for this purpose because of the scarcity of the measurements in the vast ocean. Here, an idea to overcome the limitation is proposed by using machine learning to infer the ocean temperature and salinity from satellite-observed sea surface temperature and salinity data that are broadly available. Following the predicted temperature and salinity values, global warming indicators, such as ocean heat content and mixed layer depth, are examined. Also examined are the underwater sound speed variations.