For paleo sea-level studies, a key challenge is to partition physical signals operating on multiple spatio-temporal scales. For example, paleo relative sea-level (RSL) data record a combined signal from global ice-ocean mass exchange-induced global mean sea-level change and gravitational, rotational, and deformational effects, along with regional and local RSL change caused by changing ocean density, groundwater storage, and sediment redistribution. Spatiotemporal hierarchical modeling provides a theoretically straightforward framework for investigating this problem by separating the underlying phenomenon of interest and its variability from the noisy mechanisms by which this underlying process is observed. Here we present an open-source spatio-temporal hierarchical model framework (PaleoSTeHM), which is specifically designed for paleo-environmental studies. We will provide a demonstration example of using this framework to decompose different process-related sea-level change signals across China during the Holocene. We seek feedback from potential users in order to further co-develop this framework and allow a wide range of paleo-sea level and -climate researchers to easily incorporate spatiotemporal statistical modeling into their work.
 

Sea level,paleocliamte,climate change,Machine learning techniques