本文分析了微生物活性在多年冻土碳反馈(PCF)中的重要性,并强调了增强建模工具以适当预测与多年冻土融化相关的碳通量的必要性。从实验结果的概述开始,在现场和实验室,它强调了微生物和植物在PCF中的关键作用。该研究调查了几种建模技术,从目前在多年冻土环境之外建立的土壤呼吸和植物-微生物相互作用的模型开始,然后转向专用于PCF的特定型号。对现有文献的回顾揭示了多年冻土生态系统的复杂性,各种地球物理因素对温室气体排放有相当大的影响。土壤性质,植物类型,和时间尺度都有助于碳动力学。基于过程的模型广泛用于模拟温室气体生产,运输,和排放。虽然这些模型有利于捕获土壤呼吸的复杂性,将它们调整到永久冻土环境的独特约束通常需要新颖的过程描述以进行适当的表示。了解表层土壤呼吸和地下碳生产之间的时间相干性和时间延迟,受土壤质地等众多参数控制,含水量,和温度,仍然是一个挑战。这篇评论强调了需要综合模型来整合热-水-生物地球化学过程,以在不断变化的气候环境中了解多年冻土系统动力学。此外,它强调需要严格的验证程序来减少模型复杂性偏差。
This review paper analyses the significance of microbial activity in permafrost carbon feedback (PCF) and emphasizes the necessity for enhanced modeling tools to appropriately predict carbon fluxes associated with permafrost thaw. Beginning with an overview of experimental findings, both in situ and laboratory, it stresses the key role of microbes and plants in PCF. The research investigates several modeling techniques, starting with current models of soil respiration and plant-microorganism interactions built outside of the context of permafrost, and then moving on to specific models dedicated to PCF. The review of the current literature reveals the complex nature of permafrost ecosystems, where various geophysical factors have considerable effects on greenhouse gas emissions. Soil properties, plant types, and time scales all contribute to carbon dynamics. Process-based models are widely used for simulating greenhouse gas production, transport, and emissions. While these models are beneficial at capturing soil respiration complexity, adjusting them to the unique constraints of permafrost environments often calls for novel process descriptions for proper representation. Understanding the temporal coherence and time delays between surface soil respiration and subsurface carbon production, which are controlled by numerous parameters such as soil texture, water content, and temperature, remains a challenge. This review highlights the need for comprehensive models that integrate thermo-hydro-biogeochemical processes to understand permafrost system dynamics in the context of changing climatic circumstances. Furthermore, it emphasizes the need for rigorous validation procedures to reduce model complexity biases.