Tao Feng, Zhou Zhenghu, Huang Yuanyuan, Li Qianyu, Lu Xingjie, Ma Shuang, Huang Xiaomeng, Liang Yishuang, Hugelius Gustaf, Jiang Lifen, Doughty Russell, Ren Zhehao, Luo Yiqi
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China.
National Supercomputing Center in Wuxi, Wuxi, China.
Front Big Data. 2020 Jun 3;3:17. doi: 10.3389/fdata.2020.00017. eCollection 2020.
Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and the Community Land Model version 5 (CLM5) to optimize the model representation of SOC over the conterminous United States. We firstly constrained parameters in CLM5 using observations of vertical profiles of SOC in both a batch mode (using all individual soil layers in one batch) and at individual sites (site-by-site). The estimated parameter values from the site-by-site data assimilation were then either randomly sampled (random-sampling) to generate continentally homogeneous (constant) parameter values or maximally preserved for their spatially heterogeneous distributions (varying parameter values to match the spatial patterns from the site-by-site data assimilation) so as to optimize spatial representation of SOC in CLM5 through a deep learning technique (neural networking) over the conterminous United States. Comparing modeled spatial distributions of SOC by CLM5 to observations yielded increasing predictive accuracy from default CLM5 settings ( = 0.32) to randomly sampled (0.36), one-batch estimated (0.43), and deep learning optimized (0.62) parameter values. While CLM5 with parameter values derived from random-sampling and one-batch methods substantially corrected the overestimated SOC storage by that with default model parameters, there were still considerable geographical biases. CLM5 with the spatially heterogeneous parameter values optimized from the neural networking method had the least estimation error and less geographical biases across the conterminous United States. Our study indicated that deep learning in combination with data assimilation can significantly improve the representation of SOC by complex land biogeochemical models.
土壤有机碳（SOC）是全球碳循环的关键组成部分，但在地球系统模型中，它并未得到很好的体现，难以准确预测全球碳动态对气候变化的响应。这项新研究整合了深度学习、数据同化、25444个垂直土壤剖面以及社区土地模型第5版（CLM5），以优化美国本土SOC的模型表示。我们首先使用SOC垂直剖面观测数据，以批处理模式（将所有单个土壤层作为一批）和逐个站点的方式对CLM5中的参数进行约束。然后，将逐个站点数据同化得到的估计参数值进行随机抽样（随机抽样），以生成大陆范围内均匀的（恒定的）参数值，或者最大程度地保留其空间异质分布（变化的参数值以匹配逐个站点数据同化的空间模式），从而通过深度学习技术（神经网络）在美国本土优化CLM5中SOC的空间表示。将CLM5模拟的SOC空间分布与观测值进行比较，结果显示，从CLM5的默认设置（ = 0.32）到随机抽样（0.36）、一批估计（0.43）和深度学习优化（0.62）的参数值，预测精度不断提高。虽然采用随机抽样和一批方法得出参数值的CLM5大大校正了默认模型参数下SOC储量的高估情况，但仍存在相当大的地理偏差。通过神经网络方法优化得到空间异质参数值的CLM5在美国本土的估计误差最小，地理偏差也较小。我们的研究表明，深度学习与数据同化相结合可以显著改善复杂陆地生物地球化学模型对SOC的表示。
