BIO-07\INT-08 DS4MES
Big data, small satellites and deep ocean remote sensing in climate change studies  (Invited)
Xiao-Hai Yan* , University of Delaware
William Llovel, Ifremer - Centre de Bretagne, France
Xupu Geng, Xiamen University
Hua Su, Fuzhou University
Wei Zhuang, Xiamen University
Lingsheng Meng, University of Delaware

Observing the subsurface and deeper ocean is becoming extremely important since recent evidences suggest widespread warming in the ocean’s interior as a response to the significant global warming (Earth's Energy Imbalance –EEI) in recent decades. EEI presents larger value over 2010-2021 than previous decades. The ocean responses to this increase of EEI has to be understood to fully evaluate the impact of actual global warming. Satellite remote sensing has collected multiple sea surface observations at various spatial-temporal scales for several decades, but they are confined to the ocean surface and cannot directly detect information beneath the surface, yet many significant dynamic processes and features are located below the surface. Deeper ocean remote sensing has the ability to detect and depict the processes and features in the subsurface and deeper layer within the ocean, as well as their implications for climate systems. However, the lack of consistent long-term subsurface observation hinders the inference and recognition of subsurface and deeper ocean processes. Although deeper ocean remote sensing based on satellite sensors has developed to detect the ocean’s interior successfully, still many important ocean processes in the interior need to be observed and studied from the space, such as deeper ocean warming, climate variability, heat redistribution process, internal dynamics, mixed layer variability, ocean circulation, biogeochemical process/marine ecosystem and so on, which relate and impact greatly to recent global warming/climate change.

This talk will summarize our recent contributions in small satellites, big data, and deeper ocean remote sensing in climate change studies, and report some of the recent attempts to combine all kinds of satellite sensors and remote sensing big data with other observations and techniques by using empirical statistics, machine learning, deep learning, dynamic model and data assimilation techniques to retrieve and reconstruct multi-dimensional and multi-scale physical and biogeochemical parameters in ocean’s interior, and further applied to the climate change and variability study during recent decades. Furthermore, investigations on internal variability (from the coupled Earth-atmosphere system) and ocean intrinsic variability contributions that can blurred the interpretation of the ocean warming processes will also be discussed.