| Summary: | The emission of greenhouse gases (GHGs) from human economy and social activity, such as burning of fossil fuel, cement industry, deforestation and so on, contribute to continuously increasing of GHGs, CO2 for example. The increased GHGs concentration leads to the energy imbalance at the top of atmosphere and heat cumulating within the earth climate system. Understanding the ocean’s role in Earth’s energy budget is fundamental to evaluate climate variability and change, including the rate of global warming and the recent 18-years’ so-called Global Surface Warming Hiatus (GSWH). Progress has been made continuously on this topic: studies show that the GSWH is related to external forcing changes, internal variability induced by trade wind and wind-driven circulation adjustment with the resulting heat redistribution within the climate system, and the warming up in subsurface and deeper ocean. There are a wide range of opinions among climate scientists and no unanimous conclusion has been drawn about the mechanism of the global warming hiatus. In this dissertation, the spatio-temporal variations of the Ocean Heat Content (OHC) were investigated to reveal the physical mechanisms of the global warming hiatus and the regional climate response in East Asia. Firstly, methods were developed to estimate temperature anomaly for subsurface and deeper layer from sea surface parameters provided by remote sensing, to generate new data sets for decadal climate variability research. A Self-Organizing Map Neural Network (SOM) was developed from Argo gridded data sets in order to estimate subsurface temperature anomaly (STA) from remote sensing data. The SOM maps were trained using anomalies of sea surface temperature (SST), height (SSH) and salinity(SSS) data from Argo gridded monthly anomaly data sets, labeled with Argo STA data for 2005∼2010 and then used to estimate the STAs at different depths in the North Atlantic from the sea surface data. The estimated STA maps and time series were compared with Argo STAs including ...
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