在数据同化后,通过结构上不同的模型模拟全球土壤有机碳的趋同
Feng Tao1,2, Benjamin Z Houlton1,3, Yuanyuan Huang4
1Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA.
Global change biology
|May 13, 2024
概括
使用普通土壤有机碳 (SOC) 数据可以减少全球碳储存模型的不确定性. 数据同化有助于不同模型在相似的SOC分布预测上趋同,改善气候反预测.
科学领域:
- 环境科学环境科学
- 地球系统科学 地球系统科学
- 计算建模计算建模
背景情况:
- 生物地化学模型在碳气候反预测中表现出显著的不确定性.
- 模拟土壤有机碳 (SOC) 动态的模型结构差异是这种不确定性的主要原因.
- 模型参数化也导致模拟土壤碳循环过程的不确定性.
研究的目的:
- 证明观察数据在减少全球SOC存储估计模型不确定性的关键作用.
- 调查数据同化如何影响结构不同模型的SOC模拟.
- 评估参数值与模型结构对SOC动态的影响.
主要方法:
- 采用数据同化方法,使用一个共同的全球SOC数据库.
- 利用了两个结构上不同的生物地化学模型,具有不同的碳池和分解动力学.
- 在数据同化之前和之后比较模型模拟.
主要成果:
- 两个不同的模型,最初预测相反的全球SOC分布,在数据同化后汇聚到类似的结果.
- 数据同化导致对碳转移效率和分解率等关键组件进行可比的模拟.
- 第一个阶级和迈凯利斯-门动力学模型都显示了在全球范围内有效的SOC模拟.
结论:
- 观测数据对于告知模型开发和限制全球SOC动态预测至关重要.
- 数据同化有效降低模型不确定性,无论模型结构或动态方法.
- 需要进一步的高质量数据,包括微生物基因组学,以改进SOC模型并减少参数不确定性.
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