TY - JOUR
T1 - PaleoSTeHM v1.0
T2 - a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
AU - Lin, Yucheng
AU - Kopp, Robert E.
AU - Reedy, Alexander
AU - Turilli, Matteo
AU - Jha, Shantenu
AU - Ashe, Erica L.
N1 - Publisher Copyright: © Copyright: 2025 Yucheng Lin et al.
PY - 2025/5/14
Y1 - 2025/5/14
N2 - Geological records of past environmental change provide crucial insights into long-term climate variability, trends, non-stationarity, and nonlinear feedback mechanisms. However, reconstructing spatiotemporal fields from these records is statistically challenging due to their sparse, indirect, and noisy nature. Here, we present PaleoSTeHM, a scalable and modern framework for spatiotemporal hierarchical modeling of paleo-environmental data. This framework enables the implementation of flexible statistical models that rigorously quantify spatial and temporal variability from geological data while clearly distinguishing measurement and inferential uncertainty from process variability. We illustrate its application by reconstructing temporal and spatiotemporal paleo-sea-level changes across multiple locations. Using various modeling and analysis choices, PaleoSTeHM demonstrates the impact of different methods on inference results and computational efficiency. Our results highlight the critical role of model selection in addressing specific paleo-environmental questions, showcasing the PaleoSTeHM framework's potential to enhance the robustness and transparency of paleo-environmental reconstructions.
AB - Geological records of past environmental change provide crucial insights into long-term climate variability, trends, non-stationarity, and nonlinear feedback mechanisms. However, reconstructing spatiotemporal fields from these records is statistically challenging due to their sparse, indirect, and noisy nature. Here, we present PaleoSTeHM, a scalable and modern framework for spatiotemporal hierarchical modeling of paleo-environmental data. This framework enables the implementation of flexible statistical models that rigorously quantify spatial and temporal variability from geological data while clearly distinguishing measurement and inferential uncertainty from process variability. We illustrate its application by reconstructing temporal and spatiotemporal paleo-sea-level changes across multiple locations. Using various modeling and analysis choices, PaleoSTeHM demonstrates the impact of different methods on inference results and computational efficiency. Our results highlight the critical role of model selection in addressing specific paleo-environmental questions, showcasing the PaleoSTeHM framework's potential to enhance the robustness and transparency of paleo-environmental reconstructions.
UR - https://www.scopus.com/pages/publications/105005064400
UR - https://www.scopus.com/pages/publications/105005064400#tab=citedBy
U2 - 10.5194/gmd-18-2609-2025
DO - 10.5194/gmd-18-2609-2025
M3 - Article
SN - 1991-959X
VL - 18
SP - 2609
EP - 2637
JO - Geoscientific Model Development
JF - Geoscientific Model Development
IS - 9
ER -