Chenmin Yu, Laiming Zhang, Mingcai Hou, Jianghai Yang, Hanting Zhong, Chengshan Wang. Climate paleogeography knowledge graph and deep time paleoclimate classifications[J]. Geoscience Frontiers, 2023, 14(5): 101450. DOI: 10.1016/j.gsf.2022.101450
Citation: Chenmin Yu, Laiming Zhang, Mingcai Hou, Jianghai Yang, Hanting Zhong, Chengshan Wang. Climate paleogeography knowledge graph and deep time paleoclimate classifications[J]. Geoscience Frontiers, 2023, 14(5): 101450. DOI: 10.1016/j.gsf.2022.101450

Climate paleogeography knowledge graph and deep time paleoclimate classifications

  • The climate paleogeography, especially the climate classifications, helps to interpret the global and regional climate changes and intuitively compare the climate conditions in different regions. However, the application of climate classification in deep time (i.e., climate paleogeography) is prohibited due to the usually qualitatively constrained paleoclimate and the inconsistent descriptions and semantic heterogeneity of the climate types. In this study, a climate paleogeography knowledge graph is established under the framework of the Deep-Time Digital Earth program (DDE). The hierarchical knowledge graph consists of five paleoclimate classifications based on various strategies. The classifications are described and their strengths and weaknesses are fully evaluated in four aspects: "simplicity, applicability, quantifiability, and comparability". We also reconstruct the global climate distributions in the Late Cretaceous according to these classifications. The results are compared and the relationships among these climate types in different classifications are evaluated. Our study unifies scientific concepts from different paleoclimate classifications, which provides an important theoretical basis for the application of paleoclimate classifications in deep time.
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