Many conventional General Circulation Models (GCMs) in the Global Land‐Atmosphere Coupling Experiment (GLACE) tend to produce what is now recognized as overly strong land‐atmosphere (L‐A) coupling. We investigate the effects of cloud Superparameterization (SP) on L‐A coupling on timescales beyond diurnal where it has been recently shown to have a favorable muting effect hydrologically. Using the Community Atmosphere Model v3.5 (CAM3.5) and its Superparameterized counterpart SPCAM3.5, we conducted soil moisture interference experiments following the GLACE and Atmospheric Model Intercomparison Project (AMIP) protocols. The results show that, on weekly‐to‐subseasonal timescales, SP also mutes hydrologic L‐A coupling. This is detectable globally, and happens through the evapotranspiration‐precipitation segment. But on seasonal timescales, SP does not exhibit detectable effects on hydrologic L‐A coupling. Two robust regional effects of SP on thermal L‐A coupling have also been explored. Over the Arabian Peninsula, SP reduces thermal L‐A coupling through a straightforward control by mean rainfall reduction. More counterintuitively, over the Southwestern US and Northern Mexico, SP enhances the thermal L‐A coupling in a way that is independent of rainfall and soil moisture. This signal is associated with a systematic and previously unrecognized effect of SP that produces an amplified Bowen ratio, and is detectable in multiple SP model versions and experiment designs. In addition to amplifying the present‐day Bowen ratio, SP is found to amplify the climate sensitivity of Bowen ratio as well, which likely plays a role in influencing climate change predictions at the L‐A interface.

For the purpose of establishing vertical knowledge graph and auxiliary applications in the agricultural field, a set of agricultural knowledge graph construction methods, calculation frameworks and practical application systems are proposed. Firstly, the existing storage form and knowledge representation of knowledge in the agricultural field are integrated and regularized. On the basis of this data processing, the intelligent construction method of automatic and manual dual mode of knowledge graph in the agricultural field is proposed, and the key technology of entity relationship joint model to extract entity relationship and intelligent retrieval of irregular data. Then, similarity calculation will be used to perform entity knowledge fusion on knowledge graph in the agricultural field, making the graph more standardized, accurate and complete. A good graph is visualized and applied to the mainstream functions of intelligent question answering, which makes the whole system sort out the messy agricultural knowledge and apply it better to better assist learning and research.