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Tao F, Houlton BZ, Huang Y, Wang YP, Manzoni S, Ahrens B, Mishra U, Jiang L, Huang X, Luo Y. Convergence in simulating global soil organic carbon by structurally different models after data assimilation. GLOBAL CHANGE BIOLOGY 2024; 30:e17297. [PMID: 38738805 DOI: 10.1111/gcb.17297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 05/14/2024]
Abstract
Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.
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Affiliation(s)
- Feng Tao
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Institute for Global Change Studies, Tsinghua University, Beijing, China
| | - Benjamin Z Houlton
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA
- Department of Global Development, Cornell University, Ithaca, New York, USA
| | - Yuanyuan Huang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | | | - Stefano Manzoni
- Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | | | - Umakant Mishra
- Computational Biology and Biophysics, Sandia National Laboratories, Livermore, California, USA
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, California, USA
| | - Lifen Jiang
- Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Xiaomeng Huang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Institute for Global Change Studies, Tsinghua University, Beijing, China
| | - Yiqi Luo
- Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
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Tao F, Zhou Z, Huang Y, Li Q, Lu X, Ma S, Huang X, Liang Y, Hugelius G, Jiang L, Doughty R, Ren Z, Luo Y. Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States. Front Big Data 2020; 3:17. [PMID: 33693391 PMCID: PMC7931903 DOI: 10.3389/fdata.2020.00017] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 04/21/2020] [Indexed: 11/13/2022] Open
Abstract
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 (R 2 = 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.
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Affiliation(s)
- Feng Tao
- 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
| | - Zhenghu Zhou
- Center for Ecological Research, Northeast Forestry University, Harbin, China
| | - Yuanyuan Huang
- Le Laboratoire des Sciences du Climat et de l'Environnement, IPSL-LSCECEA/CNRS/UVSQ Saclay, Gif-sur-Yvette, France
| | - Qianyu Li
- 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
| | - Xingjie Lu
- School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China.,Department of Biological Sciences, Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, United States
| | - Shuang Ma
- Department of Biological Sciences, Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, United States
| | - Xiaomeng Huang
- 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
| | - Yishuang Liang
- 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
| | - Gustaf Hugelius
- Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Lifen Jiang
- Department of Biological Sciences, Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, United States
| | - Russell Doughty
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, United States
| | - Zhehao Ren
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yiqi Luo
- Department of Biological Sciences, Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, United States
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3
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Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation. SUSTAINABILITY 2020. [DOI: 10.3390/su12072584] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters’ sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( f C i ) , canopy quantum efficiency of photon conversion ( α q ) , maximum carboxylation rate at 25 ° C ( V m 25 ) were the most sensitive parameters for the GPP. It was also indicated that α q , E V m and Q 10 were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as f c i , α q , E V m , V m 25 strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters’ SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.
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Luo Y, Schuur EAG. Model parameterization to represent processes at unresolved scales and changing properties of evolving systems. GLOBAL CHANGE BIOLOGY 2020; 26:1109-1117. [PMID: 31782216 DOI: 10.1111/gcb.14939] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/21/2019] [Accepted: 11/22/2019] [Indexed: 06/10/2023]
Abstract
Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.
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Affiliation(s)
- Yiqi Luo
- Department of Biological Sciences, Center for Ecosystem Sciences and Society, Northern Arizona University, Flagstaff, AZ, USA
| | - Edward A G Schuur
- Department of Biological Sciences, Center for Ecosystem Sciences and Society, Northern Arizona University, Flagstaff, AZ, USA
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Temperature Sensitivity in Individual Components of Ecosystem Respiration Increases along the Vertical Gradient of Leaf–Stem–Soil in Three Subtropical Forests. FORESTS 2020. [DOI: 10.3390/f11020140] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Temperature sensitivity (Q10) of ecosystem respiration (ER) is a crucial parameter for predicting the fate of CO2 in terrestrial e cosystems under global warming. Most studies focus their attention in the variation of Q10 in one or two components of ER, but not in the integration or comparison among Q10 in major components of ER. Vertical and seasonal variations in individual components, including leaf respiration, stem respiration and soil respiration, of ER were observed synchronously along the gradient of leaf–stem–soil over a 2 year period in three forest stands dominated by masson pine, loblolly pine and oak, respectively, in a subtropical forest ecosystem of central China. We found that Q10 in individual components of ER increased along the vertical gradient of leaf–stem–soil. The vertical pattern of Q10 in individual components of ER was ascribed to variations of diurnal temperature range (DTR) and activation energy (ΔHa). These results suggest that a vertical pattern of Q10 in individual components of ER along the gradient of leaf–stem–soil should be taken into consideration in process-based models that simulate respiratory carbon flux in terrestrial ecosystems.
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Fu Z, Stoy PC, Poulter B, Gerken T, Zhang Z, Wakbulcho G, Niu S. Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange. GLOBAL CHANGE BIOLOGY 2019; 25:3381-3394. [PMID: 31197940 DOI: 10.1111/gcb.14731] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Revised: 06/05/2019] [Accepted: 06/06/2019] [Indexed: 06/09/2023]
Abstract
Terrestrial ecosystems contribute most of the interannual variability (IAV) in atmospheric carbon dioxide (CO2 ) concentrations, but processes driving the IAV of net ecosystem CO2 exchange (NEE) remain elusive. For a predictive understanding of the global C cycle, it is imperative to identify indicators associated with ecological processes that determine the IAV of NEE. Here, we decompose the annual NEE of global terrestrial ecosystems into their phenological and physiological components, namely maximum carbon uptake (MCU) and release (MCR), the carbon uptake period (CUP), and two parameters, α and β, that describe the ratio between actual versus hypothetical maximum C sink and source, respectively. Using long-term observed NEE from 66 eddy covariance sites and global products derived from FLUXNET observations, we found that the IAV of NEE is determined predominately by MCU at the global scale, which explains 48% of the IAV of NEE on average while α, CUP, β, and MCR explain 14%, 25%, 2%, and 8%, respectively. These patterns differ in water-limited ecosystems versus temperature- and radiation-limited ecosystems; 31% of the IAV of NEE is determined by the IAV of CUP in water-limited ecosystems, and 60% of the IAV of NEE is determined by the IAV of MCU in temperature- and radiation-limited ecosystems. The Lund-Potsdam-Jena (LPJ) model and the Multi-scale Synthesis and Terrestrial Model Inter-comparison Project (MsTMIP) models underestimate the contribution of MCU to the IAV of NEE by about 18% on average, and overestimate the contribution of CUP by about 25%. This study provides a new perspective on the proximate causes of the IAV of NEE, which suggest that capturing the variability of MCU is critical for modeling the IAV of NEE across most of the global land surface.
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Affiliation(s)
- Zheng Fu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Paul C Stoy
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana
| | - Benjamin Poulter
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
| | - Tobias Gerken
- Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania
| | - Zhen Zhang
- Department of Geographical Sciences, University of Maryland, College Park, Maryland
| | - Guta Wakbulcho
- Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana
| | - Shuli Niu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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7
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Liang J, Xia J, Shi Z, Jiang L, Ma S, Lu X, Mauritz M, Natali SM, Pegoraro E, Penton CR, Plaza C, Salmon VG, Celis G, Cole JR, Konstantinidis KT, Tiedje JM, Zhou J, Schuur EAG, Luo Y. Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra. GLOBAL CHANGE BIOLOGY 2018; 24:4946-4959. [PMID: 29802797 DOI: 10.1111/gcb.14325] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 05/10/2018] [Indexed: 05/06/2023]
Abstract
Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5 years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g/m2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.
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Affiliation(s)
- Junyi Liang
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Jiangyang Xia
- Tiantong National Station of Forest Ecosystem, Research Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
- Institute of Eco-Chongming (IEC), Shanghai, China
| | - Zheng Shi
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
| | - Lifen Jiang
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Shuang Ma
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Xingjie Lu
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Marguerite Mauritz
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | | | - Elaine Pegoraro
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Christopher Ryan Penton
- College of Integrative Sciences and Arts, Arizona State University, Mesa, Arizona
- Center for Fundamental and Applied Microbiomics, Biodesign Institute, Arizona State University, Tempe, Arizona
| | - César Plaza
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
- Departamento de Biología y Geología, Física y Química Inorgánica, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, Móstoles, Spain
- Instituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Verity G Salmon
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Gerardo Celis
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - James R Cole
- Department of Plant, Soil and Microbial Sciences, Center for Microbial Ecology, Michigan State University, East Lansing, Michigan
| | - Konstantinos T Konstantinidis
- School of Civil and Environmental Engineering and School of Biology, Georgia Institute of Technology, Atlanta, Georgia
| | - James M Tiedje
- Department of Plant, Soil and Microbial Sciences, Center for Microbial Ecology, Michigan State University, East Lansing, Michigan
| | - Jizhong Zhou
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
- Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
- Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Edward A G Schuur
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Yiqi Luo
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
- Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
- Department of Earth System Science, Tsinghua University, Beijing, China
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