Díaz Emiliano, Adsuara Jose E, Martínez Álvaro Moreno, Piles María, Camps-Valls Gustau
Image Processing Laboratory (IPL), Universitat de València, Valencia, Spain.
Sci Rep. 2022 Jan 31;12(1):1610. doi: 10.1038/s41598-022-05377-7.
Land, atmosphere and climate interact constantly and at different spatial and temporal scales. In this paper we rely on causal discovery methods to infer spatial patterns of causal relations between several key variables of the carbon and water cycles: gross primary productivity, latent heat energy flux for evaporation, surface air temperature, precipitation, soil moisture and radiation. We introduce a methodology based on the convergent cross-mapping (CCM) technique. Despite its good performance in general, CCM is sensitive to (even moderate) noise levels and hyper-parameter selection. We present a robust CCM (RCCM) that relies on temporal bootstrapping decision scores and the derivation of more stringent cross-map skill scores. The RCCM method is combined with the information-geometric causal inference (IGCI) method to address the problem of strong and instantaneous variable coupling, another important and long-standing issue of CCM. The proposed methodology allows to derive spatially explicit global maps of causal relations between the involved variables and retrieve the underlying complexity of the interactions. Results are generally consistent with reported patterns and process understanding, and constitute a new way to quantify and understand carbon and water fluxes interactions.
陆地、大气和气候在不同的空间和时间尺度上持续相互作用。在本文中，我们依靠因果发现方法来推断碳循环和水循环的几个关键变量之间因果关系的空间模式：总初级生产力、蒸发潜热通量、地表气温、降水、土壤湿度和辐射。我们介绍了一种基于收敛交叉映射（CCM）技术的方法。尽管CCM总体表现良好，但它对（甚至适度的）噪声水平和超参数选择很敏感。我们提出了一种稳健的CCM（RCCM），它依赖于时间自抽样决策分数和更严格的交叉映射技能分数的推导。RCCM方法与信息几何因果推断（IGCI）方法相结合，以解决强瞬时变量耦合问题，这是CCM的另一个重要且长期存在的问题。所提出的方法能够得出所涉及变量之间因果关系的空间明确的全球地图，并揭示相互作用的潜在复杂性。结果总体上与报道的模式和过程理解一致，构成了一种量化和理解碳通量与水通量相互作用的新方法。
