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Spatial Attribution of Temporal Variability in Global Land-Atmosphere CO2 Exchange Using a model-data integration Framework

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JOURNAL OF ADVANCES IN modelING EARTH SYSTEMS 2025年第3期17卷

作者： Lee, H. Jung, M. Carvalhais, N. Reichstein, M. Forkel, M. Bloom, A. A. Pacheco-Labrador, J. Koirala, S. Max Planck Inst Biogeochem Jena Germany Tech Univ Dresden Inst Photogrammetry & Remote Sensing Dresden Germany Univ Nova Lisboa Fac Ciencias & Tecnol Dept Ciencias & Engn Ambiente Costa Capar Portugal Michael Stifel Ctr Jena Data Driven & Simulat Sci ELLIS Unit Jena Jena Germany CALTECH Jet Prop Lab Pasadena CA USA CSIC Environm Remote Sensing & Spect Lab SpecLab Madrid Spain

The spatial contribution to the global land-atmosphere carbon dioxide (CO2) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the parameter and structural uncertainties and advance our understanding. Here, we develop a parsimonious diagnostic process-based model of land carbon cycles, constraining parameters with observation-based products. We compare CO2 flux estimates from our model with observational constraints and Trends in Net Land-Atmosphere Carbon Exchange (TRENDY) model ensemble to show that our model reasonably reproduces the seasonality of net ecosystem exchange (NEE) and gross primary productivity (GPP) and interannual variability (IAV) of NEE. Finally, we use the developed model, TRENDY models, and observational constraints to attribute variability in global NEE and GPP to regional variability. The attribution analysis confirms the dominance of Northern temperate and boreal regions in the seasonality of CO2 fluxes. Regarding NEE IAV, we identify a significant contribution from tropical savanna regions as previously perceived. Furthermore, we highlight that tropical humid regions are also identified as at least equally relevant contributors as semi-arid regions. At the same time, the largest uncertainty among ensemble members of NEE constraint and TRENDY models in the tropical humid regions underscore the necessity of better process understanding and more observations in these regions. Overall, our study identifies tropical humid regions as key regions for global land-atmosphere CO2 exchanges and the inter-model spread of its modeling.

关键词： carbon dioxide land-atmosphere interaction model-data integration interannual variability attribution

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Balancing multiple constraints in model-data integration: Weights and the parameter block approach

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JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 2014年第11期119卷 2112-2129页

作者： Wutzler, T. Carvalhais, N. Max Planck Inst Biogeochem D-07745 Jena Germany Univ Nova Lisboa FCT DCEA Dept Ciencias & Engn Ambiente Caparica Portugal

model data integration (MDI) studies are key to parameterize ecosystem models that synthesize our knowledge about ecosystem function. The use of diverse data sets, however, results in strongly imbalanced contributions of data streams with model fits favoring the largest data stream. This imbalance poses new challenges in the identification of model deficiencies. A standard approach for balancing is to attribute weights to different data streams in the cost function. However, this may result in overestimation of posterior uncertainty. In this study, we propose an alternative: the parameter block approach. The proposed method enables joint optimization of different blocks, i.e., subsets of the parameters, against particular data streams. This method is applicable when specific parameter blocks are related to processes that are more strongly associated with specific observations, i.e., data streams. A comparison of different approaches using simple artificial examples and the DALEC ecosystem model is presented. The unweighted inversion of a DALEC model variant, where artificial structural errors in photosynthesis calculation had been introduced, failed to reveal the resulting biases in fast processes (e.g., turnover). The posterior bias emerged only in parameters related to slower processes (e.g., carbon allocation) constrained by fewer data sets. On the other hand, when weighted or blocked approaches were used, the introduced biases were revealed, as expected, in parameters of fast processes. Ultimately, with the parameter block approach, the transfer of model error was diminished and at the same time the overestimation of posterior uncertainty associated with weighting was prevented.

关键词： model-data integration imbalanced data streams weighting uncertainty MCMC MDI

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LGM hosing approach to Heinrich Event 1: results and perspectives from data-model integration using water isotopes

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QUATERNARY SCIENCE REVIEWS 2014年 106卷 247-261页

作者： Roche, Didier M. Paillard, Didier Caley, Thibaut Waelbroeck, Claire Vrije Univ Amsterdam Earth & Climate Cluster Fac Earth & Life Sci Amsterdam Netherlands CEA CNRS INSU UVSQ Lab Sci Climat & Environm Gif Sur Yvette France

Freshwater hosing has long been used in climate models to represent the perturbation associated with the release of icebergs during Heinrich Events. However, freshwater hosing ignores some of the icebergs-induced feedbacks on climate. It therefore bears the question whether the perturbed climate obtained through classical freshwater hosing experiments is realistic enough to be used as a proxy for a Heinrich Event. Using the fully coupled, water isotope enabled, climate model iLOVECUM, we conducted a series of freshwater hosing experiments, accounting for the depleted signature of ice-sheet sourced water, under LGM conditions. Using paleoceanographic data, we integrated simulated and measured 6180calcite at the surface and within the ocean column to evaluate whether (i) LGM freshwater hosing is a good proxy for Heinrich Event 1 and whether (ii) different sources for the freshwater in the North Atlantic result in different spatial anomalies that could be used to fingerprint the source of melt water. Results obtained with two sets of experiments indicate that first order simulated delta(18)Ocalcite anomalies are broadly consistent with the measured anomalies and that a complete shutdown of the thermohaline circulation is not consistent with the reconstructed patterns from foraminiferal calcite. A more detailed statistical analysis of the planktic and benthic delta(18)Ocalcite anomalies shows that the best experiment is obtained for a severely disturbed thermohaline circulation but not a complete shutdown. With our model sensitivity, this corresponds to a freshwater flux between 0.18 Sv and 03 Sv. Our results also indicate a better agreement when the freshwater flux is applied in the Labrador Sea than when directly applied to the Ruddiman Belt. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Heinrich Event 1 Oxygen isotopes modelling model-data integration

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Soil respiration across scales: towards an integration of patterns and processes

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NEW PHYTOLOGIST 2010年第2期186卷 292-296页

作者： Bahn, Michael Janssens, Ivan A. Reichstein, Markus Smith, Pete Trumbore, Susan E. Univ Innsbruck Inst Ecol A-6020 Innsbruck Austria Univ Instelling Antwerp Dept Biol B-2610 Antwerp Belgium Max Planck Inst Biogeochem D-07745 Jena Germany Univ Aberdeen Sch Biol Sci Inst Biol & Environm Sci Aberdeen AB24 3UU Scotland

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Reactive Transport modeling of Microbial Dynamics

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ELEMENTS 2019年第2期15卷 111-116页

作者： Meile, Christof Scheibe, Timothy D. Univ Georgia Dept Marine Sci Athens GA 30602 USA Pacific Northwest Natl Lab 902 Battelle BlvdPOB 999MSIN K8-83 Richland WA 99352 USA

Reactive transport modeling of microbially mediated processes has contributed significantly to an improved understanding of elemental cycling in Earth's near-surface environments. We describe key characteristics of microbial reactive transport models, recent advances in modeling approaches, and the application of such models to terrestrial and marine environmental problems. We introduce relevant case studies and discuss ways to integrate omics data (e.g., genomics, proteomics, metabolomics) that can inform and validate microbial reactive transport models, thereby improving our ability to address some of the grand challenges in a changing world.

关键词： reactive transport modeling microbial dynamics omics data model-data integration biogeochemistry

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Formulation of the Evolutionary-Based data Assimilation, and its Implementation in Hydrological Forecasting

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WATER RESOURCES MANAGEMENT 2012年第13期26卷 3853-3870页

作者： Dumedah, Gift Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia

Multi-objective evolutionary algorithms (MOEAs) have gained popularity for their capability to handle complex and nonlinear problems. MOEAs are population-based search tools which employ the concept of biological evolution and natural selection. While MOEAs have been applied in numerous hydrological studies for parameter estimation, their formulation for solving data assimilation (DA) problems has not been completely formalized in the literature. This study presents the evolutionary-based data assimilation (EDA) where it formulates the MOEA strategy into an applied DA procedure. The study outlines the stochastic and adaptive capabilities of MOEAs, and shows how MOEA operators including Pareto dominance, crossover, and random variation are naturally suited to handle DA problems. The EDA employs the cost function from variational DA to approximate the least squares estimate between ensemble simulations and perturbed observation. The EDA uses the MOEA strategy to evolve a population of competing members through several cycles of evolution at each assimilation step. The EDA determines several non-dominated members for each assimilation time step, allows these members to evolve, and evaluate updated members for subsequent time steps. Several ensemble members are evaluated for each assimilation time step but the updated ensembles are determined as a subset of the final evolved population which comprise the Pareto-optimal set. The EDA has been illustrated in a practical implementation to assimilate daily streamflow into the Sacramento Soil Moisture Accounting model.

关键词： Multi-objective evolutionary algorithms model-data integration Pareto-optimality

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The landscape of soil carbon data: emerging questions, synergies and databases

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PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT 2019年第5期43卷 707-719页

作者： Malhotra, Avni Todd-Brown, Katherine Nave, Lucas E. Batjes, Niels H. Holmquist, James R. Hoyt, Alison M. Iversen, Colleen M. Jackson, Robert B. Lajtha, Kate Lawrence, Corey Vinduskova, Olga Wieder, William Williams, Mathew Hugelius, Gustaf Harden, Jennifer Stanford Univ Stanford CA 94305 USA Wilfrid Laurier Univ Waterloo ON Canada Univ Michigan Ann Arbor MI 48109 USA ISRIC World Soil Informat Wageningen Netherlands Smithsonian Environm Res Ctr Edgewater MD USA Max Planck Inst Biogeochem Jena Germany Oak Ridge Natl Lab Environm Sci Div Climate Change Sci Inst Oak Ridge TN USA Oregon State Univ Corvallis OR 97331 USA US Geol Survey Geol & Environm Change Sci Ctr 959 Natl Ctr Reston VA 22092 USA Natl Ctr Atmospher Res POB 3000 Boulder CO 80307 USA Univ Colorado Boulder CO 80309 USA Univ Edinburgh Edinburgh Midlothian Scotland Stockholm Univ Stockholm Sweden Bolin Ctr Climate Res Stockholm Sweden

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model-data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.

关键词： Soil carbon data soil database wetland carbon soil chronosequence soil radiocarbon long-term ecological research root traits soil carbon stabilization model-data integration

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Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints

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GLOBAL CHANGE BIOLOGY 2010年第10期16卷 2813-2829页

作者： Carvalhais, Nuno Reichstein, Markus Ciais, Philippe Collatz, G. James Mahecha, Miguel D. Montagnani, Leonardo Papale, Dario Rambal, Serge Seixas, Julia Univ Nova Lisboa FCT DCEA P-2829516 Caparica Portugal Max Planck Inst Biogeochem D-07745 Jena Germany CEA CNRS UVSQ Lab Sci Climate & Environm F-91191 Gif Sur Yvette France NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA Free Univ Bolzano Bozen Fac Sci & Technol I-39100 Bozen Bolzano Italy Univ Tuscia DISAFRI I-01100 Viterbo Italy CNRS UMR 5175 CEFE DREAM F-34293 Montpellier 5 France

Assumptions of steady-state conditions in biogeochemical modelling are often invoked because knowledge on the development status of the modelling domain is generally unavailable. Here, we investigate the role of vegetation pool sizes on nonequilibrium conditions through model-data integration approaches for a set of sites using eddy covariance CO2 flux data. The study is based on the Carnegie-Ames-Stanford Approach (CASA) model, modified (CASA(G)) in order to evaluate the sensitivity of simulated net ecosystem production (NEP) fluxes to vegetation pool sizes. The experimental design is based on the inverse model optimization of different parameter vectors performed at the measurement site level. Each parameter vector prescribes different simulation dynamics that embody different model structural assumptions concerning (non)steady-state conditions in vegetation and soil carbon pools. We further explore the potential of assimilating biometric constraints through the cost function for sites where in situ information on aboveground biomass or wood pools is available. The integration of biometric data yields marked improvements in the simulation of vegetation C pools compared to single constraints with eddy flux data. Overall, it is necessary to relax both vegetation and soil carbon pools for consistency with the observed data streams. Multiple constraints approaches also leads to variable model performance among the different experimental setups and model structures. We identify and assess the limitations of various model structures and the role of multiple constraints approaches for tackling issues of equifinality. These studies emphasize the need for establishing consistent data sets of fluxes and biometric data for successful model-data fusion.

关键词： carbon pools CASA model equifinality model-data integration multiple constraints net ecosystem production steady-state assumption

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Impact of anomalous climates on carbon allocation to biomass production of leaves, woody components, and fine roots in a cool-temperate deciduous forest

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AGRICULTURAL AND FOREST METEOROLOGY 2015年 201卷 38-50页

作者： Kondo, Masayuki Ichii, Kazuhito Ueyama, Masahito Fukushima Univ Fac Symbiot Syst Sci Fukushima 9601296 Japan Japan Agcy Marine Earth Sci & Technol Dept Environm Geochem Cycle Res Kanazawa Ku Yokohama Kanagawa 2360001 Japan Osaka Prefecture Univ Grad Sch Life & Environm Sci Naka Ku Sakai Osaka 5998531 Japan

We investigated carbon allocation in a cool-temperate forest in central Japan in years of contrasting climate anomalies: an early spring warming induced by the El Nino Southern Oscillation in 2002 and a low summer photosynthetic photon flux density (PPFD) induced by a stationary rain front in 2003. Observations of eddy flux, biometric variables, and chamber measurements from 1999 to 2006 and interannual variations in fine root net primary production (frNPP) were analyzed in conjunction with a terrestrial biosphere model simulation with multiple biometric constraints. Compared to the annual means (excluding 2002 and 2003), the low summer PPFD in 2003 reduced the annual gross primary productivity (GPP;6%), soil respiration (SR;-11%), and ecosystem respiration (RE;-5%). Under the low summer PPFD, CO2 fluxes commonly decreased but components of the NPP were not affected. The low variation in NPP is explained by previous findings that NPP is more sensitive to climate conditions before or during the early stage of the growing season. The early spring warming in 2002 increased the GPP (8%) and woody tissue NPP (wNPP;55%) and decreased the frNPP (-33%) and SR (-6%). Although early spring warming prolonged the growing season, the foliage NPP (fNPP) and litterfall were invariant. The increase in wNPP and the decrease in frNPP imply that the forest decreased frNPP in favor of wNPP under the high spring temperature. Although the frNPP was estimated by model-data integration, we argue that the decrease in frNPP is plausible because the decrease in SR cannot be explained without the contribution from fine root respiration. These results suggest that increasing or decreasing patterns of component fluxes cannot necessarily be inferred from the GPP. Factors such as the nature and duration of climate anomalies and allocation shift between components of the NPP may need to be considered when characterizing carbon allocation under anomalous climate events. (C) 2014 Elsevier B.V. All ri

关键词： Carbon allocation Climate anomaly Fine root model-data integration Terrestrial

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A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations

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ECOLOGICAL INFORMATICS 2017年 42卷 148-158页

作者： Christianson, Danielle S. Varadharajan, Charuleka Christoffersen, Bradley Detto, Matteo Faybishenko, Boris Gimenez, Bruno O. Hendrix, Val Jardine, Kolby J. Negron-Juarez, Robinson Pastorello, Gilberto Z. Powell, Thomas L. Sandesh, Megha Warren, Jeffrey M. Wolfe, Brett T. Chambers, Jeffrey Q. Kueppers, Lara M. McDowell, Nathan G. Agarwal, Deborah A. Lawrence Berkeley Natl Lab Earth & Environm Sci Area 1 Cyclotron Rd Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div 1 Cyclotron Rd Berkeley CA 94720 USA Los Alamos Natl Lab Earth & Environm Sci Los Alamos NM 87545 USA Smithsonian Trop Res Inst Ctr Trop Forest Sci Panama City Panama Princeton Univ Dept Ecol & Evolutionary Biol 106A Guyot Hall Princeton NJ 08554 USA Natl Inst Amazonian Res INPA Ave Andre Araujo 2936Campus IIBldg LEA BR-6908097 Manaus Amazonas Brazil Oak Ridge Natl Lab Climate Change Sci Inst Oak Ridge TN 37831 USA Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN 37831 USA Univ Calif Berkeley Energy & Resources Grp 310 Barrows Hall Berkeley CA 94720 USA Pacific Northwest Natl Lab Richland WA 99352 USA

Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development.

关键词： Metadata data management system model-data integration data synthesis data preservation Informatics

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