Collaborative Research: MRA: Constraining the continental-scale terrestrial carbon cycle using NEON data

合作研究：MRA：使用 NEON 数据约束大陆尺度的陆地碳循环

• 批准号: 2242034
• 负责人: Yiqi Luo
• 金额: $ 44.87万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242034&HistoricalAwards=false
• 关键词: Collaborative; Research; MRA; Constraining; continental

Plant life in U.S. forests consume large volumes of carbon dioxide, playing an important role in regulating the concentrations of this important gas in the atmosphere. There is an urgent need for scientists to reduce uncertainties in future projections of atmospheric carbon dioxide concentrations and to more confidently assess whether plants in forests will continue to offset carbon dioxide emissions to the atmosphere from the burning of fossil fuels. The NSF-funded National Ecological Observatory Network (NEON) is a continental-scale ecological observation facility that collects and provides quality-controlled data from 81 field sites across the United States that characterize and quantify how our nation's ecosystems are changing. NEON's measurements of plant photosynthesis and respiration are providing the data needed for computer projections of continental scale patterns, year-to-year variations, and trends in the global carbon cycle. This project will develop more refined continental-scale carbon profiles by using a novel approach to improve complex computer models and delivering a widely-sought application of NEON data. The investigators will broaden impacts of this project by providing training to early-career scientists, broadening diversity, sharing products with the research community, and enhancing research and education infrastructure. The results are anticipated to be of high value for integrated assessments of global change and will be useful for federal, state and local agencies, and land managers who are making decisions on managing U.S. natural resources.The investigators will constrain the continental-scale terrestrial carbon cycle by integrating observations from NEON, satellite data, data-driven methods, and data-model integration techniques. The objectives are to: (1) use NEON data to develop continental-scale flux products that are need to help realize NSF's goals for NEON; (2) evaluate information content of NEON data sets for reducing model uncertainty; (3) assimilate multiple NEON data sets into complex land models to quantify the U.S. land carbon sink and its uncertainty; (4) understand the continental-scale carbon dynamics and underlying regulatory mechanisms. First, the investigators will utilize NEON data to develop an hourly, gridded Gross Primary Production(GPP) product with uncertainty estimates based on satellite observations and meteorological data. Second, they will use NEON data along with solar-induced chlorophyll fluorescence (SIF) data from satellites to develop gridded complimentary GPP products with uncertainty estimates. Third, they will utilize multiple heterogeneous datasets from NEON and the gridded GPP products for quantifying the US land carbon sink potential and assess the effectiveness of NEON data to constrain parameter estimation and model prediction. Fourth, they will develop a multiple model ensemble to understand constraints of flux- vs. pool-based data on structure uncertainty in model prediction. Fifth, they will apply data assimilation techniques to quantify parameter uncertainty and assess constraints of multiple vs. single NEON data sets on parameter estimation with three different models. Finally, they will assess the US land carbon sink, its uncertainty, and regulatory mechanisms with NEON data-based gridded flux products, NEON data-trained models, and the traceability framework. This project will ultimately provide feedback towards improvement of models and NEON observations via uncertainty analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国森林中的植物消耗大量的二氧化碳，在调节这种重要气体在大气中的浓度方面发挥着重要作用。科学家们迫切需要减少今后对大气层二氧化碳浓度预测的不确定性，并更有信心地评估森林中的植物是否将继续抵消燃烧化石燃料向大气层排放的二氧化碳。NSF资助的国家生态观测网络（氖）是一个大陆规模的生态观测设施，收集和提供来自美国81个实地站点的质量控制数据，这些数据描述和量化了我们国家的生态系统正在发生的变化。氖对植物光合作用和呼吸作用的测量提供了计算机预测大陆尺度模式、年度变化和全球碳循环趋势所需的数据。该项目将通过使用一种新的方法来改进复杂的计算机模型，并提供广泛寻求的氖数据应用，从而制定更精确的大陆尺度碳分布图。研究人员将通过为早期职业科学家提供培训，扩大多样性，与研究界分享产品以及加强研究和教育基础设施来扩大该项目的影响。研究结果对全球变化的综合评估具有重要价值，并将对美国自然资源管理决策的联邦、州和地方机构以及土地管理者有用。研究人员将通过整合氖观测数据、卫星数据、数据驱动方法和数据模型集成技术来限制大陆尺度的陆地碳循环。其目标是：（1）使用氖数据开发大陆尺度通量产品，以帮助实现NSF的氖目标;（2）评估氖数据集的信息内容，以减少模型的不确定性;（3）将多个氖数据集同化到复杂的陆地模型中，以量化美国陆地碳汇及其不确定性;（4）了解大陆尺度碳动力学和潜在的监管机制。首先，研究人员将利用氖数据开发每小时网格化的初级生产总值（GPP）产品，并根据卫星观测和气象数据进行不确定性估计。第二，他们将使用氖数据沿着来自卫星的太阳诱导叶绿素荧光（SIF）数据来开发具有不确定性估计的网格化互补GPP产品。第三，他们将利用来自氖和网格化GPP产品的多个异构数据集来量化美国土地碳汇潜力，并评估氖数据约束参数估计和模型预测的有效性。第四，他们将开发一个多模型集合，以了解通量与基于池的数据对模型预测中结构不确定性的约束。第五，他们将应用数据同化技术来量化参数不确定性，并评估多个与单个NEON数据集对三种不同模型参数估计的约束。多个与单个氖数据集对三种不同模型参数估计的约束。最后，他们将评估美国的土地碳汇，其不确定性和监管机制与氖数据为基础的网格通量产品，氖数据训练模型，和可追溯性框架。该项目最终将通过不确定性分析为改进模型和氖观测提供反馈。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Microbe-iron interactions control lignin decomposition in soil

• DOI: 10.1016/j.soilbio.2022.108803
• 发表时间: 2022-08
• 期刊: Soil Biology and Biochemistry
• 影响因子: 9.7
• 作者: Cuijuan Liao;Wenjuan Huang;Jon Wells;Ruiying Zhao;K. Allen;E. Hou;Xin Huang;Han Qiu;F. Ta

Microbial Models for Simulating Soil Carbon Dynamics: A Review

• DOI: 10.1029/2023jg007436
• 发表时间: 2023-07
• 期刊: Journal of Geophysical Research: Biogeosciences
• 作者: Aneesh Kumar Chandel;Lifen Jiang;Yiqi Luo

Across‐model spread and shrinking in predicting peatland carbon dynamics under global change

• DOI: 10.1111/gcb.16643
• 发表时间: 2023-02
• 期刊: Global Change Biology
• 影响因子: 11.6
• 作者: E. Hou;Shuang Ma;Yuanyuan Huang;Yu Zhou;Hyung-Sub Kim;E. López-Blanco;Lifen Jiang;J. Xia;F. Tao;Christopher Williams;M. Williams;D. Ricciuto;P. Hanson;Yiqi Luo

Matrix Approach to Accelerate Spin‐Up of CLM5

加速 CLM5 旋转的矩阵方法

• DOI: 10.1029/2023ms003625
• 发表时间: 2023
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Liao, Cuijuan;Lu, Xingjie;Huang, Yuanyuan;Tao, Feng;Lawrence, David M.;Koven, Charles D.;Oleson, Keith W.;Wieder, William R.;Kluzek, Erik;Huang, Xiaomeng
• 通讯作者: Huang, Xiaomeng