基于工作流的陆地生态系统碳循环实时同化预测系统OA

[Objective]The feedback effects of terrestrial ecosystem carbon cycling on climate change are central to global change research.Currently,there is a lack of real-time,efficient,and automated carbon source-sink assessment and prediction systems,making it difficult to quickly and accurately quantify the carbon sink size,stability,and sustainability.This limitation affects the formulation of carbon sequestration strategies and the implementation of carbon neutrality initiatives.[Methods]This study develops a real-time assimilation and prediction system for ter-restrial ecosystem carbon cycling,comprising multiple core modules such as data collection,transmission,analy-sis,workflow management,scheduling,prediction,and visualization.By integrating deep learning-based meteoro-logical models,carbon cycle process models,data assimilation algorithms,and ecological iterative prediction methods,the system continuously assimilates real-time station observation data to enable short-term carbon sink predictions,which serves as a paradigm for transitioning from observation to prediction in field station research.[Results]Since its deployment in February 2023,the system has successfully integrated with four stations,includ-ing Dinghushan,Qianyanzhou,and Huitong,accumulating over 110,000 data records to date.[Conclusion]The system significantly improves the timeliness and efficiency of carbon cycle prediction,providing reliable data support and observable real-time retrieval services for ecological research and environmental management deci-sion-making.