冰冻圈"大数据+AI+模型"耦合研究范式探索OA

[Objective]As a vital component of Earth's system,the cryosphere profoundly impacts global climate,hydrolog-ical cycles,and ecological security through multi-element nonlinear interactions,cross-sphere couplings,and long disaster chains.Traditional physical models exhibit limitations in characterizing multi-scale cryospheric changes,failing to meet practical needs for large-scale monitoring,rapid evolution analysis,and hazard early-warning.[Methods]This paper proposes a"Big Data+AI+Model"research paradigm for cryospheric studies and develops the Global Cryosphere research Engine(GCE)to support this framework.The GCE forms an integrated platform encompassing data management,model construction,AI algorithm application,and task workflow or-chestration,with capabilities for multi-source heterogeneous data fusion,cross-language model collaboration,high-performance computing scheduling,and intelligent analysis.[Conclusions]Based on the GCE platform,model construction approaches such as"embedding AI into physical models","embedding physical models into AI",and"data-driven parameter optimization coupled with AI"have been implemented.Focusing on soil mois-ture simulation experiments over the Tibetan Plateau,a novel multi-element interaction framework integrating"observation data+deep learning(hierarchical LSTM)+physical model(Noah-MP)"was developed.The average correlation between simulated and observed layered soil moisture exceeded 0.8,significantly outperforming the Noah model.The results demonstrate the feasibility of a research paradigm that combines data-driven approach-es,physical model support,and artificial intelligence to effectively characterize cryospheric processes,while also validating the utility of the GCE platform.This framework can provide a supportive environment for new re-search paradigms in ecological environments and cryospheric disasters,promoting intelligent cryospheric re-search with cross-regional multi-element interactions.