Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data
Rui Ma
CORRESPONDING AUTHOR
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Jingfeng Xiao
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Department of Geography, University of Hong Kong, Hong Kong SAR 999077,
China
Department of Geography, University of Hong Kong, Hong Kong SAR 999077,
China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
College of Resources and Environment, University of Chinese Academy
of Sciences, Beijing 100049, China
Xiaobang Liu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Haibo Lu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai
519082, China
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The Global Change Analysis Model (GCAM) simulates the world’s climate–land–energy–water system interactions , but its reservoir representation is limited. We developed the GLObal Reservoir Yield (GLORY) model to provide GCAM with information on the cost of supplying water based on reservoir construction costs, climate and demand conditions, and reservoir expansion potential. GLORY enhances our understanding of future reservoir capacity needs to meet human demands in a changing climate.
Lara Welder, Neil Grant, and Matthew J. Gidden
EGUsphere, https://doi.org/10.5194/egusphere-2024-761, https://doi.org/10.5194/egusphere-2024-761, 2024
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Pathways investigating the link between emissions and global warming have been continuously used to inform climate policy. We have developed a tool that can facilitate the systematic and robust analysis of ensembles of such pathways. We describe the structure of this tool and then show an illustrative application of it. The application indicates the usefulness of the tool to the research community and shows how it can be used to establish best-practices.
Tommi Ekholm, Nadine-Cyra Freistetter, Aapo Rautiainen, and Laura Thölix
Geosci. Model Dev., 17, 3041–3062, https://doi.org/10.5194/gmd-17-3041-2024, https://doi.org/10.5194/gmd-17-3041-2024, 2024
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CLASH is a numerical model that portrays land allocation between different uses, land carbon stocks, and agricultural and forestry production globally. CLASH can help in examining the role of land use in mitigating climate change, providing food and biogenic raw materials for the economy, and conserving primary ecosystems. Our demonstration with CLASH confirms that reduction of animal-based food, shifting croplands and storing carbon in forests are effective ways to mitigate climate change.
Léna Gurriaran, Yannig Goude, Katsumasa Tanaka, Biqing Zhu, Zhu Deng, Xuanren Song, and Philippe Ciais
Geosci. Model Dev., 17, 2663–2682, https://doi.org/10.5194/gmd-17-2663-2024, https://doi.org/10.5194/gmd-17-2663-2024, 2024
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We developed a data-driven model simulating daily regional power demand based on climate and socioeconomic variables. Our model was applied to eight countries or regions (Australia, Brazil, China, EU, India, Russia, South Africa, US), identifying influential factors and their relationship with power demand. Our findings highlight the significance of economic indicators in addition to temperature, showcasing country-specific variations. This research aids energy planning and emission reduction.
Chao Gao, Xuelei Zhang, Aijun Xiu, Qingqing Tong, Hongmei Zhao, Shichun Zhang, Guangyi Yang, Mengduo Zhang, and Shengjin Xie
Geosci. Model Dev., 17, 2471–2492, https://doi.org/10.5194/gmd-17-2471-2024, https://doi.org/10.5194/gmd-17-2471-2024, 2024
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A comprehensive comparison study is conducted targeting the performances of three two-way coupled meteorology and air quality models (WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) for eastern China during 2017. The impacts of aerosol–radiation–cloud interactions on these models’ results are evaluated against satellite and surface observations. Further improvements to the calculation of aerosol–cloud interactions in these models are crucial to ensure more accurate and timely air quality forecasts.
Muhammad Awais, Adriano Vinca, Edward Byers, Stefan Frank, Oliver Fricko, Esther Boere, Peter Burek, Miguel Poblete Cazenave, Paul Natsuo Kishimoto, Alessio Mastrucci, Yusuke Satoh, Amanda Palazzo, Madeleine McPherson, Keywan Riahi, and Volker Krey
Geosci. Model Dev., 17, 2447–2469, https://doi.org/10.5194/gmd-17-2447-2024, https://doi.org/10.5194/gmd-17-2447-2024, 2024
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Climate change, population growth, and depletion of natural resources all pose complex and interconnected challenges. Our research offers a novel model that can help in understanding the interplay of these aspects, providing policymakers with a more robust tool for making informed future decisions. The study highlights the significance of incorporating climate impacts within large-scale global integrated assessments, which can help us in generating more climate-resilient scenarios.
Utkan M. Durdağ
Geosci. Model Dev., 17, 2187–2196, https://doi.org/10.5194/gmd-17-2187-2024, https://doi.org/10.5194/gmd-17-2187-2024, 2024
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This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss–Markov model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The mean success rate (MSR) comparisons highlight its effectiveness, improving the MSR by 40–45 % for multiple outliers.
Michaja Pehl, Felix Schreyer, and Gunnar Luderer
Geosci. Model Dev., 17, 2015–2038, https://doi.org/10.5194/gmd-17-2015-2024, https://doi.org/10.5194/gmd-17-2015-2024, 2024
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We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemical, steel, and other industries. We also present a method for deriving scenarios of industry subsector activity and energy demand, consistent with established socioeconomic scenarios, allowing us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of the entire energy–economy–climate system.
Chen Chris Gong, Falko Ueckerdt, Robert Pietzcker, Adrian Odenweller, Wolf-Peter Schill, Martin Kittel, and Gunnar Luderer
Geosci. Model Dev., 16, 4977–5033, https://doi.org/10.5194/gmd-16-4977-2023, https://doi.org/10.5194/gmd-16-4977-2023, 2023
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To mitigate climate change, the global economy must drastically reduce its greenhouse gas emissions, for which the power sector plays a key role. Until now, long-term models which simulate this transformation cannot always accurately depict the power sector due to a lack of resolution. Our work bridges this gap by linking a long-term model to an hourly model. The result is an almost full harmonization of the models in generating a power sector mix until 2100 with hourly resolution.
David R. Morrow, Raphael Apeaning, and Garrett Guard
Geosci. Model Dev., 16, 1105–1118, https://doi.org/10.5194/gmd-16-1105-2023, https://doi.org/10.5194/gmd-16-1105-2023, 2023
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GCAM-CDR is a variant of the Global Change Analysis Model that makes it easier to study the roles that carbon dioxide removal (CDR) might play in climate policy. Building on GCAM 5.4, GCAM-CDR adds several extra technologies to permanently remove carbon dioxide from the air and enables users to simulate a wider range of CDR-related policies and controls.
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
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Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
climate-assessmentworkflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.
Théo Le Guenedal, Philippe Drobinski, and Peter Tankov
Geosci. Model Dev., 15, 8001–8039, https://doi.org/10.5194/gmd-15-8001-2022, https://doi.org/10.5194/gmd-15-8001-2022, 2022
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The CATHERINA model produces simulations of cyclone-related annualized damage costs at a country level from climate data and open-source socioeconomic indicators. The framework couples statistical and physical modeling of tropical cyclones to bridge the gap between general circulation and integrated assessment models providing a precise description of tropical-cyclone-related damages.
William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, and Noelle E. Selin
Geosci. Model Dev., 15, 7767–7789, https://doi.org/10.5194/gmd-15-7767-2022, https://doi.org/10.5194/gmd-15-7767-2022, 2022
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Understanding policy effects on human-caused air pollutant emissions is key for assessing related health impacts. We develop a flexible scenario tool that combines updated emissions data sets, long-term economic modeling, and comprehensive technology pathways to clarify the impacts of climate and air quality policies. Results show the importance of both policy levers in the future to prevent long-term emission increases from offsetting near-term air quality improvements from existing policies.
Chengyong Wu, Kelong Chen, Chongyi E, Xiaoni You, Dongcai He, Liangbai Hu, Baokang Liu, Runke Wang, Yaya Shi, Chengxiu Li, and Fumei Liu
Geosci. Model Dev., 15, 6919–6933, https://doi.org/10.5194/gmd-15-6919-2022, https://doi.org/10.5194/gmd-15-6919-2022, 2022
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The traditional Carnegie–Ames–Stanford Approach (CASA) model driven by multisource data such as meteorology, soil, and remote sensing (RS) has notable disadvantages. We drove the CASA using RS data and conducted a case study of the Qinghai Lake basin alpine grassland. The simulated result is similar to published and measured net primary productivity (NPP). It may provide a reference for simulating vegetation NPP to satisfy the requirements of accounting carbon stocks and other applications.
Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté, Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie
Geosci. Model Dev., 15, 6115–6142, https://doi.org/10.5194/gmd-15-6115-2022, https://doi.org/10.5194/gmd-15-6115-2022, 2022
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CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.
Olexandr Balyk, James Glynn, Vahid Aryanpur, Ankita Gaur, Jason McGuire, Andrew Smith, Xiufeng Yue, and Hannah Daly
Geosci. Model Dev., 15, 4991–5019, https://doi.org/10.5194/gmd-15-4991-2022, https://doi.org/10.5194/gmd-15-4991-2022, 2022
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Ireland has significantly increased its climate mitigation ambition, with a recent commitment to reduce greenhouse gases by an average of 7 % yr-1 in the period to 2030 and a net-zero target for 2050. This article describes the TIMES-Ireland model (TIM) developed to inform Ireland's energy system decarbonisation challenge. The paper also outlines a priority list of future model developments to better meet the challenge, taking into account equity, cost-effectiveness, and technical feasibility.
Haiyan Jiang, Slobodan P. Simonovic, and Zhongbo Yu
Geosci. Model Dev., 15, 4503–4528, https://doi.org/10.5194/gmd-15-4503-2022, https://doi.org/10.5194/gmd-15-4503-2022, 2022
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The Yangtze Economic Belt is one of the most dynamic regions of China. The fast urbanization and strong economic growth in the region pose severe challenges for its sustainable development. To improve our understanding of the interactions among coupled human–natural systems in the Belt and to provide the foundation for science-based policy-making for the sustainable development of the Belt, we developed an integrated system-dynamics-based simulation model (ANEMI_Yangtze) for the Belt.
Thi Lan Anh Dinh and Filipe Aires
Geosci. Model Dev., 15, 3519–3535, https://doi.org/10.5194/gmd-15-3519-2022, https://doi.org/10.5194/gmd-15-3519-2022, 2022
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We proposed the leave-two-out method (i.e. one particular implementation of the nested cross-validation) to determine the optimal statistical crop model (using the validation dataset) and estimate its true generalization ability (using the testing dataset). This approach is applied to two examples (robusta coffee in Cu M'gar and grain maize in France). The results suggested that the simple models are more suitable in crop modelling where a limited number of samples is available.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
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GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Colm Duffy, Remi Prudhomme, Brian Duffy, James Gibbons, Cathal O'Donoghue, Mary Ryan, and David Styles
Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, https://doi.org/10.5194/gmd-15-2239-2022, 2022
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The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
bottom-upbiophysical land use model capable of identifying broad pathways towards climate neutrality in the agriculture, forestry, and other land use (AFOLU) sector. The model is intended to bridge the gap between hindsight representations of national emissions and much larger globally integrated assessment models.
Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
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In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
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This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
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This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
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Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413,
2013.
Alton, P. B.: From site-level to global simulation: Reconciling carbon,
water and energy fluxes over different spatial scales using a process-based
ecophysiological land-surface model, Agr. Forest Meteorol., 176, 111–124,
https://doi.org/10.1016/j.agrformet.2013.03.010, 2013.
Bacour, C., Peylin, P., MacBean, N., Rayner, P. J., Delage, F., Chevallier,
F., Weiss, M., Demarty, J., Santaren, D., Baret, F., Berveiller, D.,
Dufrêne, E., and Prunet, P.: Joint assimilation of eddy covariance flux
measurements and FAPAR products over temperate forests within a
process-oriented biosphere model, J. Geophys. Res.-Biogeo., 120,
1839–1857, https://doi.org/10.1002/2015jg002966, 2015.
Barman, R., Jain, A. K., and Liang, M.: Climate-driven uncertainties in
modeling terrestrial gross primary production: a site level to global-scale
analysis, Glob. Change Biol., 20, 1394–1411, https://doi.org/10.1111/gcb.12474, 2014.
Bloom, A. A., Exbrayat, J.-F., van der Velde, I. R., Feng, L., and Williams, M.: The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times, P. Natl. Acad. Sci. USA, 113, 1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016.
Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., Swenson, S. C., Bonan, C., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., and Swenson, S. C.: Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data, J. Geophys. Res.-Biogeo., 116, G02014, https://doi.org/10.1029/2010JG001593, 2011.
Cao, X., Zhou, Z., Chen, X., Shao, W., and Wang, Z.: Improving leaf area
index simulation of IBIS model and its effect on water carbon and energy – A
case study in Changbai Mountain broadleaved forest of China, Ecol. Model.,
303, 97–104, https://doi.org/10.1016/j.ecolmodel.2015.02.012, 2015.
Chaney, N. W., Herman, J. D., Ek, M. B., and Wood, E. F.: Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning, J. Geophys. Res.-Atmos., 121, 13–218, https://doi.org/10.1002/2016JD024821, 2016.
Chen, J. M., Ju, W., Ciais, P., Viovy, N., Liu, R., Liu, Y., and Lu, X.: Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink, Nat. Commun., 10, 4259, https://doi.org/10.1038/s41467-019-12257-8, 2019.
Chuter, A. M., Aston, P. J., Skeldon, A. C., and Roulstone, I.: A dynamical systems analysis of the data assimilation linked ecosystem carbon (DALEC) models, Chaos, 25, 036401, https://doi.org/10.1063/1.4897912, 2015.
Croft, H., Chen, J. M., Luo, X., Bartlett, P., Chen, B., and Staebler, R.
M.: Leaf chlorophyll content as a proxy for leaf photosynthetic capacity,
Global Change Biol., 23, 3513–3524, https://doi.org/10.1111/gcb.13599, 2017.
Cunha, A. P. M. A., Alvalá, R. C. S., Sampaio, G., Shimizu, M. H., and
Costa, M. H.: Calibration and Validation of the Integrated Biosphere
Simulator (IBIS) for a Brazilian Semiarid Region, J. Appl. Meteorol.
Climatol., 52, 2753–2770, https://doi.org/10.1175/jamc-d-12-0190.1, 2013.
Dagon, K., Sanderson, B. M., Fisher, R. A., and Lawrence, D. M.: A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, 2020.
Famiglietti, C. A., Smallman, T. L., Levine, P. A., Flack-Prain, S., Quetin, G. R., Meyer, V., Parazoo, N. C., Stettz, S. G., Yang, Y., Bonal, D., Bloom, A. A., Williams, M., and Konings, A. G.: Optimal model complexity for terrestrial carbon cycle prediction, Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, 2021.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149,
78–90, https://doi.org/10.1007/BF00386231, 1980.
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., and Dietze, M. C.: Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation, Biogeosciences, 15, 5801–5830, https://doi.org/10.5194/bg-15-5801-2018, 2018.
Fernández-Martínez, M., Sardans, J., Chevallier, F., Ciais, P.,
Obersteiner, M., Vicca, S., Canadell, J. G., Bastos, A., Friedlingstein, P.,
Sitch, S., Piao, S. L., Janssens, I. A., and Peñuelas, J.: Global trends
in carbon sinks and their relationships with CO2 and temperature, Nat.
Clim. Change, 9, 73–79, https://doi.org/10.1038/s41558-018-0367-7, 2018.
Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., and Haxeltine, A.: An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics, Global Biogeochem. Cy., 10, 603–628, https://doi.org/10.1029/96GB02692, 1996.
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., and Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model–data integration, Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, 2014.
Gong, W. and Duan, Q. Y.: An adaptive surrogate modelingbased sampling strategy for parameter optimization and distribution estimation (ASMO-PODE), Environ. Model. Softw., 95, 61–75, https://doi.org/10.1016/j.envsoft.2017.05.005, 2017.
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass,
L., Funk, M., Wickham, J., Stehman, S., Auch, R., and Riitters, K.:
Conterminous United States land cover change patterns 2001–2016 from the
2016 National Land Cover Database, ISPRS J. Photogramm. Remote Sens., 162,
184–199, https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020.
Hu, Z., Chen, X., Zhou, Q., Chen, D., and Li, J.: DISO: A rethink of Taylor
diagram, Int. J. Climatol., 39, 2825–2832, https://doi.org/10.1002/joc.5972, 2019.
Keenan, T. F., Davidson, E., Moffat, A. M., Munger, W., and Richardson, A.
D.: Using model-data fusion to interpret past trends, and quantify
uncertainties in future projections, of terrestrial ecosystem carbon
cycling, Glob. Change Biol., 18, 2555–2569,
10.1111/j.1365-2486.2012.02684.x, 2012.
Kucharik, C. J., Foley, J. A., Delire, C., Fisher, V. A., Coe, M. T., Lenters, J. D., Young-Molling, C., Ramankutty, N., Norman, J. M., and Gower, S. T.: Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure, Global Biogeochem. Cy., 14, 795–825, https://doi.org/10.1029/1999GB001138, 2000.
Kucharik, C. J., Barford, C. C., Maayar, M. E., Wofsy, S. C., Monson, R. K.,
and Baldocchi, D. D.: A multiyear evaluation of a Dynamic Global Vegetation
Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil
temperature, and CO2 and H2O vapor exchange, Ecol. Model., 196,
1–31, https://doi.org/10.1016/j.ecolmodel.2005.11.031, 2006.
Kumar, S. V., Holmes, T. R., Bindlish, R., de Jeu, R., and Peters-Lidard, C.: Assimilation of vegetation optical depth retrievals from passive microwave radiometry, Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, 2020.
Kuppel, S., Peylin, P., Maignan, F., Chevallier, F., Kiely, G., Montagnani, L., and Cescatti, A.: Model–data fusion across ecosystems: from multisite optimizations to global simulations, Geosci. Model Dev., 7, 2581–2597, https://doi.org/10.5194/gmd-7-2581-2014, 2014.
LDAS: NLDAS-2 Forcing Dataset, Land Data Assimilation Systems [data set], https://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing.php (last access: 9 January 2021), 2016.
Li, J., Duan, Q., Wang, Y.-P., Gong, W., Gan, Y., and Wang, C.: Parameter
optimization for carbon and water fluxes in two global land surface models
based on surrogate modelling, Int. J. Climatol., 38, e1016–e1031,
https://doi.org/10.1002/joc.5428, 2018.
Li, Y., Zhou, L., Wang, S., Chi, Y., and Chen, J.: Leaf Temperature and
Vapour Pressure Deficit (VPD) Driving Stomatal Conductance and Biochemical
Processes of Leaf Photosynthetic Rate in a Subtropical Evergreen Coniferous
Plantation, Sustainability, 10, 4063, https://doi.org/10.3390/su10114063, 2018.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan,
W., Zhang, X., Zhao, X., and Zhou, J.: The Global Land Surface Satellite
(GLASS) Product Suite, B. Am. Meteorol. Soc. 102, E323–E337,
https://doi.org/10.1175/bams-d-18-0341.1, 2021.
Liu, D., Cai, W., Xia, J., Dong, W., Zhou, G., Chen, Y., Zhang, H., and
Yuan, W.: Global validation of a process-based model on vegetation gross
primary production using eddy covariance observations, PLoS One, 9, e110407,
https://doi.org/10.1371/journal.pone.0110407, 2014.
Liu, M., He, H., Ren, X., Sun, X., Yu, G., Han, S., Wang, H., and Zhou, G.:
The effects of constraining variables on parameter optimization in carbon
and water flux modeling over different forest ecosystems, Ecol. Model., 303,
30–41, https://doi.org/10.1016/j.ecolmodel.2015.01.027, 2015.
Liu, X., Liang, S., Li, B., Ma, H., and He, T.: Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods, Remote Sens., 13, 2592, https://doi.org/10.3390/rs13132592, 2021.
Lu, D., Ricciuto, D., Walker, A., Safta, C., and Munger, W.: Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods, Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, 2017.
Ma, H. and Liang, S. L.: Development of the GLASS 250-m Leaf Area Index Product (Version 6) from MODIS data using the bidirectional LSTM deep learning model, Remote Sens. Environ., 273, 112985, https://doi.org/10.1016/j.rse.2022.112985, 2022.
Ma, R., Xiao, J., Liang, S., Ma, H., He, T., Guo, D., Liu, X., and Lu, H.: Modified adaptive surrogate modeling (MASM) (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.6953354, 2022.
MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.: Consistent assimilation of multiple data streams in a carbon cycle data assimilation system, Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, 2016.
Mäkelä, J., Knauer, J., Aurela, M., Black, A., Heimann, M., Kobayashi, H., Lohila, A., Mammarella, I., Margolis, H., Markkanen, T., Susiluoto, J., Thum, T., Viskari, T., Zaehle, S., and Aalto, T.: Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH, Geosci. Model Dev., 12, 4075–4098, https://doi.org/10.5194/gmd-12-4075-2019, 2019.
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational
Experiments, Technometrics, 33, 161–174, 1991.
NCEP: National Centers for Environmental Prediction (NCEP) North American
Regional Reanalysis (NARR), in: Research Data Archive at the
National Center for Atmospheric Research, Computational and Information
Systems Laboratory, Boulder, CO [data set],
https://psl.noaa.gov/data/gridded/data.narr.html (last access: 19 January 2021), 2005.
Owen, A. B.: Controlling Correlations in Latin Hypercube Samples, J. Am. Stat. Assoc., 89, 1517–1522,
https://doi.org/10.1080/01621459.1994.10476891, 1994.
Pastorello, G., Trotta, C., and Canfora, E., et al.: The FLUXNET2015 dataset
and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7,
225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Peaucelle, M., Bacour, C., Ciais, P., Vuichard, N., Kuppel, S., Peñuelas, J., Marchesini, L. B., Blanken, P. D., Buchmann, N., Chen, J., Delpierre, N., Desai, A. R., Dufrene, E., Gianelle, D., Gimeno-Colera, C., Gruening, C., Helfter, C., Hörtnagl, L., Ibrom, A., Joffre, R., Kato, T., Kolb, T. E., Law, B., Lindroth, A., Mammarella, I., Merbold, L., Minerbi, S., Montagnani, L., Šigut, L., Sutton, M., Varlagin, A., Vesala, T., Wohlfahrt, G., Wolf, S., Yakir, D., and Viovy, N.: Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model, Glob. Ecol. Biogeogr., 28, 1351–1365, https://doi.org/10.1111/geb.12937, 2019.
Piao, S., Wang, X., Wang, K., Li, X., Bastos, A., Canadell, J. G., Ciais,
P., Friedlingstein, P., and Sitch, S.: Interannual variation of terrestrial
carbon cycle: Issues and perspectives, Global Change Biol., 26, 300–318,
https://doi.org/10.1111/gcb.14884, 2020.
Pilaš, I., Gašparović, M., Novkinić, A., and Klobučar,
D.: Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2
Subpixel Level Using UAV and Machine Learning Approach, Remote Sens., 12,
3925, https://doi.org/10.3390/rs12233925, 2020.
Raoult, N. M., Jupp, T. E., Cox, P. M., and Luke, C. M.: Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0, Geosci. Model Dev., 9, 2833–2852, https://doi.org/10.5194/gmd-9-2833-2016, 2016.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rogers, A.: The use and misuse of Vc,max in Earth System Models, Photosyn.
Res., 119, 15–29, https://doi.org/10.1007/s11120-013-9818-1, 2014.
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992.
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M.: MODIS-derived global land
products of shortwave radiation and diffuse and total photosynthetically
active radiation at 5 km resolution from 2000, Remote Sens. Environ., 204,
812–825, https://doi.org/10.1016/j.rse.2017.09.021, 2018.
Safta, C., Ricciuto, D. M., Sargsyan, K., Debusschere, B., Najm, H. N., Williams, M., and Thornton, P. E.: Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model, Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, 2015.
Sawada, Y. and Koike, T.: Simultaneous estimation of both hydrological and ecological parameters in an ecohydrological model by assimilating microwave signal, J. Geophys. Res.-Atmos., 119, 8839–8857, https://doi.org/10.1002/2014JD021536, 2014.
Scholze, M., Kaminski, T., Knorr, W., Blessing, S., Vossbeck, M., Grant, J.
P., and Scipal, K.: Simultaneous assimilation of SMOS soil moisture and
atmospheric CO2 in-situ observations to constrain the global
terrestrial carbon cycle, Remote Sens. Environ., 180, 334–345,
https://doi.org/10.1016/j.rse.2016.02.058, 2016.
Schürmann, G. J., Kaminski, T., Köstler, C., Carvalhais, N., Voßbeck, M., Kattge, J., Giering, R., Rödenbeck, C., Heimann, M., and Zaehle, S.: Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0, Geosci. Model Dev., 9, 2999–3026, https://doi.org/10.5194/gmd-9-2999-2016, 2016.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data
set for earth system modeling, J. Adv. Model. Earth Syst., 6, 249–263,
https://doi.org/10.1002/2013ms000293, 2014.
Tao, F., Zhou, Z., Huang, Y., Li, Q., Lu, X., Ma, S., Huang, X., Liang, Y., Hugelius, G., Jiang, L., Doughty, R., Ren, Z., and Luo, Y.: Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States, Front. Big Data, 3, 17, https://doi.org/10.3389/fdata.2020.00017, 2020.
Thoning, K. W., Tans, P. P., and Komhyr, W. D.: Atmospheric carbon dioxide
at Mauna Loa Observatory: 2. Analysis of the NOAA GMCC data, 1974–1985, J.
Geophys. Res.-Atmospheres, 94, 8549–8565, 1989.
Thornton, M. M., Shrestha, R., Thornton, P. E., Kao, S., Wei, Y., and
Wilson, B. E.: Wilson: Daymet Version 4 Monthly Latency: Daily Surface
Weather Data. ORNL DAAC, Oak Ridge, Tennessee, USA [dataset],
https://doi.org/10.3334/ORNLDAAC/1904, 2021.
Varejão, C. G., Costa, M. H., and Camargos, C. C. S.: A multi-objective
hierarchical calibration procedure for land surface/ecosystem models,
Inverse Probl. Sci. Eng., 21, 357–386, https://doi.org/10.1080/17415977.2011.639453, 2013.
Walker, A. P., Beckerman, A. P., Gu, L., Kattge, J., Cernusak, L. A.,
Domingues, T. F., Scales, J. C., Wohlfahrt, G., Wullschleger, S. D., and
Woodward, F. I.: The relationship of leaf photosynthetic traits – Vcmax and Jmax – to leaf nitrogen, leaf phosphorus, and specific leaf area: a
meta-analysis and modeling study, Ecol. Evol., 4, 3218–3235,
https://doi.org/10.1002/ece3.1173, 2014.
Wang, C., Duan, Q., Gong, W., Ye, A., Di, Z., and Miao, C.: An evaluation of adaptive surrogate modeling based optimization with two benchmark problems, Environ. Modell. Softw., 60, 167–179, https://doi.org/10.1016/j.envsoft.2014.05.026, 2014.
Wang, J., Sun, J., Xia, J., He, N., Li, M., Niu, S., and Luo, Y.: Soil and
vegetation carbon turnover times from tropical to boreal forests, Funct.
Ecol., 32, 71–82, https://doi.org/10.1111/1365-2435.12914, 2017.
Wutzler, T. and Carvalhais, N.: Balancing multiple constraints in model-data
integration: Weights and the parameter block approach, J. Geophys. Res.-Biogeo., 119, 2112–2129, https://doi.org/10.1002/2014jg002650, 2014.
Xiao, J., Davis, K. J., Urban, N. M., and Keller, K.: Uncertainty in model
parameters and regional carbon fluxes: A model-data fusion approach, Agr.
Forest Meteorol., 189–190, 175–186, https://doi.org/10.1016/j.agrformet.2014.01.022, 2014.
Xu, H., Zhang, T., Luo, Y., Huang, X., and Xue, W.: Parameter calibration in global soil carbon models using surrogate-based optimization, Geosci. Model Dev., 11, 3027–3044, https://doi.org/10.5194/gmd-11-3027-2018, 2018.
Yuan, W., Liang, S., Liu, S., Weng, E., Luo, Y., Hollinger, D., and Zhang,
H.: Improving model parameter estimation using coupling relationships
between vegetation production and ecosystem respiration, Ecol. Model., 240,
29–40, https://doi.org/10.1016/j.ecolmodel.2012.04.027, 2012.
Yuan, W., Liu, D., Dong, W., Liu, S., Zhou, G., Yu, G., Zhao, T., Feng, J.,
Ma, Z., Chen, J., Chen, Y., Chen, S., Han, S., Huang, J., Li, L., Liu, H.,
Liu, S., Ma, M., Wang, Y., Xia, J., Xu, W., Zhang, Q., Zhao, X., and Zhao,
L.: Multiyear precipitation reduction strongly decreases carbon uptake over
northern China, J. Geophys. Res.-Biogeo., 119, 881–896,
https://doi.org/10.1002/2014jg002608, 2014.
Zhang, Q. R., Shi, L. S., Holzman, M., Ye, M., Wang, Y. K., Carmona, F., and Zha, Y. Y.: A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation, Adv. Water Resour., 132, 103407, https://doi.org/10.1016/j.advwatres.2019.103407, 2019.
Zheng, Y., Shen, R., Wang, Y., Li, X., Liu, S., Liang, S., Chen, J. M., Ju, W., Zhang, L., and Yuan, W.: Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017, Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, 2020.
Zhong, L., Hu, L., and Zhou, H.: Deep learning based multi-temporal crop
classification, Remote Sens. Environ., 221, 430–443,
https://doi.org/10.1016/j.rse.2018.11.032, 2019.
Zhou, Q., Chen, D., Hu, Z., and Chen, X.: Decompositions of Taylor diagram
and DISO performance criteria, Int. J. Climatol., 41, 5726–5732,
https://doi.org/10.1002/joc.7149, 2021.
Zhou, Y., Williams, C. A., Lauvaux, T., Davis, K. J., Feng, S., Baker, I., Denning, S., and Wei, Y.: A multiyear gridded data ensemble of surface biogenic carbon fluxes for North America: evaluation and analysis of results, J. Geophys. Res.-Biogeo., 125, e2019JG005314, https://doi.org/10.1029/2019jg005314, 2020.
Zobitz, J. M., Moore, D. J. P., Quaife, T., Braswell, B. H., Bergeson, A.,
Anthony, J. A., and Monson, R. K.: Joint data assimilation of satellite
reflectance and net ecosystem exchange data constrains ecosystem carbon
fluxes at a high-elevation subalpine forest, Agr. Forest Meteorol., 195–196,
73–88, https://doi.org/10.1016/j.agrformet.2014.04.011, 2014.
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted...