Articles | Volume 11, issue 7
https://doi.org/10.5194/gmd-11-3027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-11-3027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
27 Jul 2018
Development and technical paper |
| 27 Jul 2018
Parameter calibration in global soil carbon models using surrogate-based optimization
Haoyu Xu
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Tao Zhang
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
Yiqi Luo
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
Center for Ecosystem Science and Society, Northern Arizona
University, Flagstaff, AZ, USA
Xin Huang
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
Wei Xue
CORRESPONDING AUTHOR
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
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R. Rafique, J. Xia, O. Hararuk, and Y. Luo
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Revised manuscript not accepted
Y. P. Wang, B. C. Chen, W. R. Wieder, M. Leite, B. E. Medlyn, M. Rasmussen, M. J. Smith, F. B. Agusto, F. Hoffman, and Y. Q. Luo
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P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
O. Hararuk, D. Obrist, and Y. Luo
Biogeosciences, 10, 2393–2407, https://doi.org/10.5194/bg-10-2393-2013, https://doi.org/10.5194/bg-10-2393-2013, 2013
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The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
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Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
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We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
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Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
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The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
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Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
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Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
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We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Cited articles
Aleman, D. M., Romeijn, H. E., and Dempsey, J. F.: A response surface approach to beam
orientation optimization in intensity-modulated radiation therapy treatment
planning, INFORMS J. Comput., 21, 62–76, 2009.
Allison, S. D., Wallenstein, M. D., and Bradford, M. A.: Soil-carbon response to warming
dependent on microbial physiology, Nat. Geosci., 3,
336–340, 2010.
Behzad, M., Asghari, K., Eazi, M., and Palhang, M.: Generalization
performance of support vector machines and neural networks in runoff
modeling, Expert Syst. Appl., 36, 7624–7629, 2009.
Booker, A. J., Dennis Jr., J. E., Frank, P. D., Serafini, D. B., Torczon, V., and Trosset, M. W.: A rigorous framework for
optimization of expensive functions by surrogates, Struct.
optimization, 17, 1–13, 1999.
Breiman, L.: Statistical modeling: The two cultures (with comments and a
rejoinder by the author), Stat. Sci., 16, 199–231, 2001.
Davis, E. and Ierapetritou, M.: A kriging based method for the solution of
mixed-integer nonlinear programs containing black-box functions, J. Global
Optim., 43, 191–205, 2009.
Fontaine, S., Bardoux, G., Abbadie, L., and Mariotti, A.: Carbon input to soil may decrease
soil carbon content, Ecol. Lett., 7, 314–320, 2004.
Fontaine, S., Barot, S., Barré, P., Bdioui, N., Mary, B., and Rumpel, C.: Stability of organic carbon in deep
soil layers controlled by fresh carbon supply, Nature,
450, 277–280, 2007.
Forrester A. I. J., Sóbester, A., and Keane, A. J.: Engineering Design via
Surrogate Modelling: A Practical Guide, John Wiley & Sons, Chichester, 2008.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V., and Bala, G.: Climate-carbon cycle feedback analysis: Results
from the C4MIP model intercomparison, Climate, 19, 3337–3353, 2006.
Friedman, J. H.: Multivariate adaptive regression splines, Ann.
Stat., 19, 1–67, 1991.
German, D. P., Marcelo, K. R. B., Stone, M. M., and Allison, S. D.: The Michaelis–Menten kinetics
of soil extracellular enzymes in response to temperature: a
cross-latitudinal study, Glob. Change Biol., 18, 1468–1479, 2012.
Giunta, A. A.: Aircraft multidisciplinary design optimization using design of
experiments theory and response surface modeling methods, Virginia
polytechnic institute and state university, 1997.
Global Soil Data Task Group: Global Gridded Surfaces of Selected Soil
Characteris- tics (IGBP-DIS), [Global Gridded Surfaces of Selected Soil
Characteristics (International Geosphere-Biosphere Programme – Data and
Information System)], Data set, Oak Ridge National Laboratory
Distributed Active Archive Center, Oak Ridge, TN, 2000.
Gutmann, H. M.: A radial basis function method for global optimization,
J. Global Optim., 19, 201–227, 2001.
Haario, H., Saksman, E., and Tamminen, J.: An adaptive Metropolis algorithm,
Bernoulli, 7, 223–242, 2001.
Hansen, N.: Benchmarking the Nelder-Mead downhill simplex algorithm with many
local restarts, Proceedings of the 11th Annual Conference Companion on
Genetic and Evolutionary Computation Conference: Late Breaking Papers, ACM,
2403–2408, 2009.
Hansen, N. and Kern, S.: Evaluating the CMA evolution strategy on multimodal test
functions, Proceedings of International Conference on Parallel Problem
Solving from Nature, Springer Berlin Heidelberg, 282–291, 2004.
Hansen, N. and Ostermeier, A.: Completely de-randomized self-adaptation in
evolution strategies, Evol. Comput., 9, 159–195, 2001.
Hapuarachchi, H. A. P., Li, Z., and Wang, S.: Application of SCE-UA method for
calibrating the Xinanjiang watershed model, Journal of Lake Sciences,
13, 304–314, 2001.
Hararuk, O., Xia, J., and Luo, Y.: Evaluation and improvement of a global land model
against soil carbon data using a Bayesian Markov chain Monte Carlo
method, J. Geophys. Res.-Biogeo., 119, 403–417, 2014.
Hararuk, O., Smith, M. J., and Luo, Y.: Microbial models with data-driven parameters
predict stronger soil carbon responses to climate change, Glob. Change
Biol., 21, 2439–2453, 2015.
Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J.,
Dai, X., Maskell, K., and Johnson, C.: Climate Change 2001: the scientific
basis, Cambridge University Press, Cambridge, 2001.
Huang, Y. Y., Lu, X. J., Shi, Z., Lawrence, D., Koven, C., Xia, J. Y., Du, Z. G., Kluzek, E., and Luo, Y.
Q.: Matrix approach to land carbon cycle modeling: A case study with
Community Land Model, Glob. Change Biol., 24, 1394–1404, 2018.
Iman, R. L., Campbell, J., and Helton, J.: An approach to sensitivity analysis of
computer models, J. Qual. Technol., 13, 174–183, 1981.
Jones, D. R.: A taxonomy of global optimization methods based on response
surfaces, J. Global Optim., 21, 345–383, 2001.
Jones, D. R., Schonlau, M., and Welch, W. J.: Efficient global optimization of expensive
black-box functions, J. Global Optim., 13
455–492, 1998.
Kennedy, J.: Particle swarm optimization, Encyclopedia of Machine
Learning, Springer US, 760–766, 2011.
Kuzyakov, Y., Friedel, J. K., and Stahr, K.: Review of mechanisms and quantification of
priming effects, Soil Biol. Biochem.,
32, 1485–1498, 2000.
Li, G., Cheng, C. T., Lin, J. Y., and Zeng, Y.: Short-term load forecasting using support
vector machine with SCE-UA algorithm, Third International Conference on
Natural Computation (ICNC 2007), IEEE, 1, 290–294, 2007.
Luo, Y.: Terrestrial carbon-cycle feedback to climate warming,
Annu.
Rev. Ecol. Evol. S., 38, 683–712, 2007.
Luo, Y. and Weng, E.: Dynamic disequilibrium of the terrestrial carbon cycle
under global change, Trends Ecol. Evol., 26, 96–104, 2011.
Luo, Y., Wu, L., Andrews, J. A., White, L., Matamala, R.,
Schäfer, K. V. R., and Schlesinger, W. H.: Elevated CO2 differentiates ecosystem carbon
processes: Deconvolution analysis of Duke Forest FACE data, Ecol.
Monogr., 71, 357–376, 2001.
Luo, Y., White, L. W., Canadell, J. G., DeLucia, E. H., Ellsworth, D. S.,
Finzi, A., Lichter, J., and Schlesinger, W. H.: Sustainability of terrestrial
carbon sequestration: A case study in Duke Forest with inversion approach,
Global Biogeochem. Cy., 17, 1021, https://doi.org/10.1029/2002gb001923 , 2003.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V., Carvalhais, N.,
Chappell, A., Ciais, P., Davidson, E. A., Finzi, A., Georgiou, K., Guenet, B., Hararuk, O.,
Harden, J. W., He, Y. J., Hopkins, F., Jiang, L. F., Koven, C., Jackson, R. B., Jones, C. D.,
Lara, M. J., Liang, J. Y., McGuire, A. D., Parton, W., Peng, C. H., Randerson, J. T., Salazar, A.,
Sierra, C. A., Smith, M. J., Tian, H. Q., Todd-Brown, K. E. O., Torn, M., van Groenigen, K. J., Wang, Y. P.,
West, T. O., Wei, Y. X., Wieder, W. R., Xia, J. Y., Xu, X., Xu, X. F., and Zhou, T.: Towards More
Realistic Projections of Soil Carbon Dynamics by Earth System Models,
Global Biogeochem. Cy., 30, 40–56, 2016.
Luo, Y. Q., Keenan, T. F., and Smith, M.: Predictability of the terrestrial carbon
cycle, Glob. Change Biol., 21, 1737–1751, 2015.
Ma, H., Dong, Z., Zhang, W. M., and Liang, Z. M.: Application of SCE-UA algorithm to
optimization of TOPMODEL parameters, Journal of Hohai University
4, 2006.
Marshall, L., Nott, D., and Sharma, A.: A comparative study of Markov
chain Monte Carlo methods for conceptual rainfall-runoff modeling, Water
Resour. Res., 40, W02501, https://doi.org/10.1029/2003WR002378, 2004.
McKay, M. D., Beckman, R. J., and Conover, W. J.: Comparison of three methods
for selecting values of input variables in the analysis of output from a
computer code, Technometrics, 21, 239–245, 1979.
Melillo, J. M., Steudler, P. A., Aber, J. D., Newkirk, K., Lux, H.,
Bowles, F. P., Catricala, C., Magill, A., Ahrens, T., and Morrisseau, S.:
Soil warming and carbon-cycle feedbacks to the climate system, Science, 298,
2173–2176, 2002.
Müller, J.: MATSuMoTo: The MATLAB Surrogate Model Toolbox For
Computationally Expensive Black-Box Global Optimization Problems,
arXiv:1404.4261, 2014.
Müller, J. and Shoemaker, C. A.: Influence of ensemble surrogate models and
sampling strategy on the solution quality of algorithms for computationally
expensive black-box global optimization problems, J. Global
Optim., 60, 123–144, 2014.
Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M.: Response surface methodology:
process and product optimization using designed experiments, John Wiley &
Sons, 2016.
Oleson, K. W., Dai, Y., Bonan, G., Bosilovich, M., Dickinson, R.,
Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., and Niu, G.-Y.: Technical description of the
community land model (CLM), NCAR Tech., 2004.
Oleson, K. W., Niu, G., Yang, Z., Lawrence, D., Thornton, P., Lawrence, P.,
Stockli, R., Dickinson, R., Bonan, G., and Levis, S.: Improvements to the Community
Land Model and their impact on the hydrological cycle, J. Geophys. Res.,
113, https://doi.org/10.1029/2007jg000563, 2008.
Peng, S., Piao, S., Wang, T., Sun, J., and Shen, Z.: Temperature sensitivity of soil respiration
in different ecosystems in China, Soil Biol. Biochem., 41, 1008–1014, 2009.
Picheny, V., Ginsbourger, D., Richet, Y., and Caplin, G.: Quantile-Based Optimization of
Noisy Computer Experiments With Tunable Precision, Technometrics, 55, 2–9,
2012.
Powell, M. J. D.: The theory of radial basis function approximation in 1990,
in: Advances in Numerical Analysis II: Wavelets, Subdivision Algorithms and
Radial Functions, edited by: Light, W. A., Oxford University Press (Oxford),
105–210, 1992.
Price, K., Storn, R. M., and Lampinen, J. A.: Differential evolution: a practical
approach to global optimization, Springer Science & Business Media, 2006.
Regis, R. G.: Stochastic radial basis function algorithms for large-scale
optimization involving expensive black-box objective and constraint
functions, Comput. Oper. Res., 38, 837–853, 2011.
Regis, R. G. and Shoemaker, C. A.: A stochastic radial basis function method for the
global optimization of expensive functions, INFORMS J. Comput.,
19, 497–509, 2007.
Regis, R. G. and Shoemaker, C. A.: Parallel stochastic global optimization using
radial basis functions, INFORMS J. Comput., 21,
411–426, 2009.
Rocha, H.: Model parameter tuning by cross validation and global
optimization: application to the wing weight fitting problem, Structural and
Multidisciplinary Optimization, 37, 197 pp., 2008.
Schimel, J. P. and Weintraub, M. N.: The implications of exoenzyme activity on
microbial carbon and nitrogen limitation in soil: a theoretical model,
Soil Biol. Biochem., 35, 549–563, 2003.
Schonlau, M., Welch, W. J., and Jones, D. R.: Global versus local search in constrained
optimization of computer models, Lecture Notes-Monograph Series, 11–25,
https://doi.org/10.1214/lnms/1215456182, 1998.
Shi, Y. and Eberhart, R. C.: Empirical study of particle swarm optimization,
Front. Comput. Sci. Chi., 3, 31–37, 2009.
Shi, Z., Crowell, S., Luo, Y. Q., and Moore III, B.: Uncertainty in soil carbon
projection constrained by data but amplified by model structures, Nat.
Commun., https://doi.org/10.1038/s41467-018-04526-9, 2018.
Simpson, T. W., Mauery, T. M., Korte, J. J., and Mistree, F.: Kriging models for global
approximation in simulation-based multidisciplinary design optimization, AIAA
J., 39, 2233–2241, 2001.
Smith, M. J., Purves, D. W., Vanderwel, M. C., Lyutsarev, V., and Emmott, S.: The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties, Biogeosciences,
10, 583–606, https://doi.org/10.5194/bg-10-583-2013, 2013.
Storn, R. and Price, K.: Differential evolution – a simple and efficient heuristic
for global optimization over continuous spaces, J. Global
Optim., 11, 341–359, 1997.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M.,
Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in
soil carbon simulations from CMIP5 Earth system models and comparison with
observations, Biogeosciences, 10, 1717–1736,
https://doi.org/10.5194/bg-10-1717-2013, 2013.
Vu, K. K., D'Ambrosio, C., Hamadi, Y., and Liberti, L.: Surrogate-based methods for
black-box optimization, International Transactions in Operational Research,
24, 393–424, 2016.
Wang, C., Duan, Q., Gong, W., Ye, A., Di, Z., and Miao, C.: An evaluation of adaptive surrogate modelling based optimization with two
benchmark problems, Environ. Modell. Softw., 60, 167–179, 2014.
Weng, E. and Luo, Y.: Relative information contributions of model vs. data
to short- and long-term forecasts of forest carbon dynamics, Ecol. Appl., 21,
1490–1505, 2011.
Wild, S. M. and Shoemaker, C.: Global convergence of radial basis function
trust-region algorithms for derivative-free optimization, SIAM Rev., 55,
349–371, 2013.
Xia, J., Luo, Y., Wang, Y. P., and Hararuk, O.: Traceable components of
terrestrial carbon storage capacity in biogeochemical models, Glob. Change
Biol., 19, 2104–2116, 2013.
Xia, J. Y., Luo, Y. Q., Wang, Y.-P., Weng, E. S., and Hararuk, O.: A
semi-analytical solution to accelerate spin-up of a coupled carbon and
nitrogen land model to steady state, Geosci. Model Dev., 5, 1259–1271,
https://doi.org/10.5194/gmd-5-1259-2012, 2012.
Xu, T., White, L., Hui, D., and Luo, Y.: Probabilistic inversion of a
terrestrial ecosystem model: Analysis of uncertainty in parameter estimation
and model prediction, Global Biogeochem. Cy., 20, GB2007,
https://doi.org/10.1029/2005gb002468, 2006.
Zhang, X., Srinivasan, R., and Van Liew, M.: Approximating SWAT model using
artificial neural network and support vector machine, J. Am. Water Resour.
As., 45, 460–474, 2009.
Zhou, T., Shi, P., Hui, D., and Luo, Y.: Global pattern of temperature
sensitivity of soil heterotrophic respiration (Q10) and its implications for
carbon-climate feedback, J. Geophys. Res., 114, https://doi.org/10.1029/2008jg000850,
2009.
Short summary
This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of soil organic carbon. Experiments on three popular global soil carbon cycle models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. This research would help develop and improve the parameterization schemes of Earth climate systems.
This study proposes a new parameter calibration method based on surrogate optimization...
