Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1601-2019
© Author(s) 2019. 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-12-1601-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
24 Apr 2019
Model evaluation paper |
| 24 Apr 2019
| 24 Apr 2019
Evaluating the E3SM land model version 0 (ELMv0) at a temperate forest site using flux and soil water measurements
Environmental Sciences Division and Climate Change Science
Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Environmental Sciences Division and Climate Change Science
Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of
Oklahoma, Norman, OK 73019, USA
Daniel M. Ricciuto
Environmental Sciences Division and Climate Change Science
Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Lianhong Gu
Environmental Sciences Division and Climate Change Science
Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Paul J. Hanson
Environmental Sciences Division and Climate Change Science
Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Jeffrey D. Wood
School of Natural Resources, University of Missouri,
Columbia, MO 65211, USA
Melanie A. Mayes
Environmental Sciences Division and Climate Change Science
Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Related authors
Jing Wang, Jianyang Xia, Xuhui Zhou, Kun Huang, Jian Zhou, Yuanyuan Huang, Lifen Jiang, Xia Xu, Junyi Liang, Ying-Ping Wang, Xiaoli Cheng, and Yiqi Luo
Biogeosciences, 16, 917–926, https://doi.org/10.5194/bg-16-917-2019, https://doi.org/10.5194/bg-16-917-2019, 2019
Short summary
Short summary
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
Short summary
Short summary
Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Junyi Liang, Xuan Qi, Lara Souza, and Yiqi Luo
Biogeosciences, 13, 2689–2699, https://doi.org/10.5194/bg-13-2689-2016, https://doi.org/10.5194/bg-13-2689-2016, 2016
Short summary
Short summary
It is unclear how the nitrogen (N) cycle regulates climate change through influencing carbon sequestration. By using meta-analysis, we tested a popular hypothesis, progressive N limitation (PNL), which postulates that greater N sequestration in organisms leads to declining N availability for further plant growth under elevated CO2. Our analyses suggest that extra nitrogen supply by increased biological N fixation and decreased leaching may potentially alleviate PNL.
Christian Mark Garcia Salvador, Jeffrey D. Wood, Emma Grace Cochran, Hunter A. Seubert, Bella D. Kamplain, Sam S. Overby, Kevin R. Birdwell, Lianhong Gu, and Melanie A. Mayes
EGUsphere, https://doi.org/10.5194/egusphere-2024-1808, https://doi.org/10.5194/egusphere-2024-1808, 2024
Short summary
Short summary
Critical volatile organic compounds were continuously measured in a temperate deciduous and juniper forest in the midwestern US using PTR-ToF-MS. The forest included several sources of biogenic compounds and was influenced by short- and long-range transport of anthropogenic emissions. Extreme heat and wildfire emissions impacted the atmospheric conditions of the forest during the field measurement; such emissions are vital phenomena that provide insights into future climate.
Patrick Neri, Lianhong Gu, and Yang Song
Biogeosciences, 21, 2731–2758, https://doi.org/10.5194/bg-21-2731-2024, https://doi.org/10.5194/bg-21-2731-2024, 2024
Short summary
Short summary
A first-of-its-kind global-scale model of temperature resilience and tolerance of photosystem II maximum quantum yield informs how plants maintain their efficiency of converting light energy to chemical energy for photosynthesis under temperature changes. Our finding explores this variation across plant functional types and habitat climatology, highlighting diverse temperature response strategies and a method to improve global-scale photosynthesis modeling under climate change.
Liyuan He, Jorge L. Mazza Rodrigues, Melanie A. Mayes, Chun-Ta Lai, David A. Lipson, and Xiaofeng Xu
Biogeosciences, 21, 2313–2333, https://doi.org/10.5194/bg-21-2313-2024, https://doi.org/10.5194/bg-21-2313-2024, 2024
Short summary
Short summary
Soil microbes are the driving engine for biogeochemical cycles of carbon and nutrients. This study applies a microbial-explicit model to quantify bacteria and fungal biomass carbon in soils from 1901 to 2016. Results showed substantial increases in bacterial and fungal biomass carbon over the past century, jointly influenced by vegetation growth and soil temperature and moisture. This pioneering century-long estimation offers crucial insights into soil microbial roles in global carbon cycling.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
Short summary
Short summary
We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Yujie Wang, Philipp Köhler, Liyin He, Russell Doughty, Renato K. Braghiere, Jeffrey D. Wood, and Christian Frankenberg
Geosci. Model Dev., 14, 6741–6763, https://doi.org/10.5194/gmd-14-6741-2021, https://doi.org/10.5194/gmd-14-6741-2021, 2021
Short summary
Short summary
We present the first step in testing a new land model as part of a new Earth system model. Our model links plant hydraulics, stomatal optimization theory, and a comprehensive canopy radiation scheme. We compared model-predicted carbon and water fluxes to flux tower observations and model-predicted sun-induced chlorophyll fluorescence to satellite retrievals. Our model quantitatively predicted the carbon and water fluxes as well as the canopy fluorescence yield.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
Preprint withdrawn
Short summary
Short summary
Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
Daniel M. Ricciuto, Xiaojuan Yang, Dali Wang, and Peter E. Thornton
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-163, https://doi.org/10.5194/bg-2021-163, 2021
Publication in BG not foreseen
Short summary
Short summary
This paper uses a novel approach to quantify the impacts of the choice of decomposition model on carbon and nitrogen cycling. We compare the models to experimental data that examined litter decomposition over five different biomes. Despite widely differing assumptions, the models produce similar patterns of decomposition when nutrients are limiting. This differs from past analyses that did not consider the impacts of changing environmental conditions or nutrients.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Debjani Sihi, Xiaofeng Xu, Mónica Salazar Ortiz, Christine S. O'Connell, Whendee L. Silver, Carla López-Lloreda, Julia M. Brenner, Ryan K. Quinn, Jana R. Phillips, Brent D. Newman, and Melanie A. Mayes
Biogeosciences, 18, 1769–1786, https://doi.org/10.5194/bg-18-1769-2021, https://doi.org/10.5194/bg-18-1769-2021, 2021
Short summary
Short summary
Humid tropical soils are important sources and sinks of methane. We used model simulation to understand how different kinds of microbes and observed soil moisture and oxygen dynamics contribute to production and consumption of methane along a wet tropical hillslope during normal and drought conditions. Drought alters the diffusion of oxygen and microbial substrates into and out of soil microsites, resulting in enhanced methane release from the entire hillslope during drought recovery.
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486, https://doi.org/10.5194/bg-18-467-2021, https://doi.org/10.5194/bg-18-467-2021, 2021
Short summary
Short summary
The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, https://doi.org/10.5194/gmd-12-4133-2019, 2019
Short summary
Short summary
We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
Dan Lu and Daniel Ricciuto
Geosci. Model Dev., 12, 1791–1807, https://doi.org/10.5194/gmd-12-1791-2019, https://doi.org/10.5194/gmd-12-1791-2019, 2019
Short summary
Short summary
This work uses machine-learning techniques to advance the predictive understanding of large-scale Earth systems.
Yuanyuan Huang, Mark Stacy, Jiang Jiang, Nilutpal Sundi, Shuang Ma, Volodymyr Saruta, Chang Gyo Jung, Zheng Shi, Jianyang Xia, Paul J. Hanson, Daniel Ricciuto, and Yiqi Luo
Geosci. Model Dev., 12, 1119–1137, https://doi.org/10.5194/gmd-12-1119-2019, https://doi.org/10.5194/gmd-12-1119-2019, 2019
Short summary
Short summary
Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD) into models. EcoPAD also has the potential to become an interactive tool for resource management, stimulate citizen science in ecology, and transform environmental education.
Jing Wang, Jianyang Xia, Xuhui Zhou, Kun Huang, Jian Zhou, Yuanyuan Huang, Lifen Jiang, Xia Xu, Junyi Liang, Ying-Ping Wang, Xiaoli Cheng, and Yiqi Luo
Biogeosciences, 16, 917–926, https://doi.org/10.5194/bg-16-917-2019, https://doi.org/10.5194/bg-16-917-2019, 2019
Short summary
Short summary
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Misha B. Krassovski, Glen E. Lyon, Jeffery S. Riggs, and Paul J. Hanson
Geosci. Instrum. Method. Data Syst., 7, 289–295, https://doi.org/10.5194/gi-7-289-2018, https://doi.org/10.5194/gi-7-289-2018, 2018
Short summary
Short summary
Climate change studies are growing and related experiments are getting bigger and more complex. They are often conducted in remote areas where communications are limited. In cases like that the data can be transferred via a satellite connection, but these types of connections are slow. We found that by using the little known possibilities of LoggerNet software (the most popular data logger software in environmental science) it is possible to transfer quite a large amount of data.
Anthony P. Walker, Ming Ye, Dan Lu, Martin G. De Kauwe, Lianhong Gu, Belinda E. Medlyn, Alistair Rogers, and Shawn P. Serbin
Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018, https://doi.org/10.5194/gmd-11-3159-2018, 2018
Short summary
Short summary
Large uncertainty is inherent in model predictions due to imperfect knowledge of how to describe the processes that a model is intended to represent. Yet methods to quantify and evaluate this model hypothesis uncertainty are limited. To address this, the multi-assumption architecture and testbed (MAAT) automates the generation of all possible models by combining multiple representations of multiple processes. MAAT provides a formal framework for quantification of model hypothesis uncertainty.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
Short summary
Short summary
Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Henrique F. Duarte, Brett M. Raczka, Daniel M. Ricciuto, John C. Lin, Charles D. Koven, Peter E. Thornton, David R. Bowling, Chun-Ta Lai, Kenneth J. Bible, and James R. Ehleringer
Biogeosciences, 14, 4315–4340, https://doi.org/10.5194/bg-14-4315-2017, https://doi.org/10.5194/bg-14-4315-2017, 2017
Short summary
Short summary
We evaluate the Community Land Model (CLM4.5) against observations at an old-growth coniferous forest site that is subjected to water stress each summer. We found that, after calibration, CLM was able to reasonably simulate the observed fluxes of energy and carbon, carbon stocks, carbon isotope ratios, and ecosystem response to water stress. This study demonstrates that carbon isotopes can expose structural weaknesses in CLM and provide a key constraint that may guide future model development.
Dan Lu, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger
Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, https://doi.org/10.5194/bg-14-4295-2017, 2017
Short summary
Short summary
Calibration of terrestrial ecosystem models (TEMs) is important but challenging. This study applies an advanced sampling technique for parameter estimation of a TEM. The results improve the model fit and predictive performance.
Francesc Montané, Andrew M. Fox, Avelino F. Arellano, Natasha MacBean, M. Ross Alexander, Alex Dye, Daniel A. Bishop, Valerie Trouet, Flurin Babst, Amy E. Hessl, Neil Pederson, Peter D. Blanken, Gil Bohrer, Christopher M. Gough, Marcy E. Litvak, Kimberly A. Novick, Richard P. Phillips, Jeffrey D. Wood, and David J. P. Moore
Geosci. Model Dev., 10, 3499–3517, https://doi.org/10.5194/gmd-10-3499-2017, https://doi.org/10.5194/gmd-10-3499-2017, 2017
Short summary
Short summary
How carbon is allocated to different plant tissues (leaves, stem, and roots) determines carbon residence time and thus remains a central challenge for understanding the global carbon cycle. In this paper, we compared standard and novel carbon allocation schemes in CLM4.5 and evaluated them using eddy covariance wood and leaf biomass. The dynamic scheme based on work by Litton improved model performance, but this was dependent on model assumptions about woody turnover.
Erik A. Hobbie, Janet Chen, Paul J. Hanson, Colleen M. Iversen, Karis J. McFarlane, Nathan R. Thorp, and Kirsten S. Hofmockel
Biogeosciences, 14, 2481–2494, https://doi.org/10.5194/bg-14-2481-2017, https://doi.org/10.5194/bg-14-2481-2017, 2017
Short summary
Short summary
We measured carbon and nitrogen isotope ratios (13C : 12C and 15N : 14N) in peat cores in a northern Minnesota bog to understand how climate, vegetation type, and decomposition affected C and N budgets over the last 9000 years. 13C : 12C patterns were primarily influenced by shifts in temperature, peatland vegetation and atmospheric CO2, whereas tree colonization and upland N influxes affected 15N : 14N ratios. Isotopic markers provided new insights into long-term patterns of CO2 and nitrogen losses.
Paul J. Hanson, Jeffery S. Riggs, W. Robert Nettles, Jana R. Phillips, Misha B. Krassovski, Leslie A. Hook, Lianhong Gu, Andrew D. Richardson, Donald M. Aubrecht, Daniel M. Ricciuto, Jeffrey M. Warren, and Charlotte Barbier
Biogeosciences, 14, 861–883, https://doi.org/10.5194/bg-14-861-2017, https://doi.org/10.5194/bg-14-861-2017, 2017
Short summary
Short summary
This paper describes operational methods to achieve whole-ecosystem warming (WEW) for tall-stature, high-carbon, boreal forest peatlands. The methods enable scientists to study immediate and longer-term (1 decade) responses of organisms (microbes to trees) and ecosystem functions (carbon, water and nutrient cycles). The WEW technology allows researchers to have a plausible glimpse of future environmental conditions for study that are not available in the current observational record.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
Short summary
Short summary
Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Jinxin Zhang, Lianhong Gu, Jingbo Zhang, Rina Wu, Feng Wang, Guanghui Lin, Bo Wu, Qi Lu, and Ping Meng
Biogeosciences, 14, 131–144, https://doi.org/10.5194/bg-14-131-2017, https://doi.org/10.5194/bg-14-131-2017, 2017
Short summary
Short summary
Plant nitrogen (N) isotope composition is an indicator of N cycling. How N isotopes are distributed within plants is not well understood. We found intra-plant variations in N isotopes were related to organ N and phosphorous (P) contents and predicted by the N–P interaction. We hypothesized that plant N volatilization, resorption and remobilization of N and P from senescing leaves, and mixing of the re-translocated foliar N and P, are responsible for the observed intra-plant N isotope variations.
Brett Raczka, Henrique F. Duarte, Charles D. Koven, Daniel Ricciuto, Peter E. Thornton, John C. Lin, and David R. Bowling
Biogeosciences, 13, 5183–5204, https://doi.org/10.5194/bg-13-5183-2016, https://doi.org/10.5194/bg-13-5183-2016, 2016
Short summary
Short summary
We use carbon isotopes of CO2 to improve the performance of a land surface model, a component with earth system climate models. We found that isotope observations can provide important information related to the exchange of carbon and water from vegetation driven by environmental stress from low atmospheric moisture and nitrogen limitation. It follows that isotopes have a unique potential to improve model performance and provide insight into land surface model development.
Junyi Liang, Xuan Qi, Lara Souza, and Yiqi Luo
Biogeosciences, 13, 2689–2699, https://doi.org/10.5194/bg-13-2689-2016, https://doi.org/10.5194/bg-13-2689-2016, 2016
Short summary
Short summary
It is unclear how the nitrogen (N) cycle regulates climate change through influencing carbon sequestration. By using meta-analysis, we tested a popular hypothesis, progressive N limitation (PNL), which postulates that greater N sequestration in organisms leads to declining N availability for further plant growth under elevated CO2. Our analyses suggest that extra nitrogen supply by increased biological N fixation and decreased leaching may potentially alleviate PNL.
J. Mao, D. M. Ricciuto, P. E. Thornton, J. M. Warren, A. W. King, X. Shi, C. M. Iversen, and R. J. Norby
Biogeosciences, 13, 641–657, https://doi.org/10.5194/bg-13-641-2016, https://doi.org/10.5194/bg-13-641-2016, 2016
Short summary
Short summary
The aim of this study is to implement, calibrate and evaluate the CLM4 against carbon and hydrology observations from a shading and labeling experiment in a stand of young loblolly pines. We found a combination of parameters measured on-site and calibration targeting biomass, transpiration, and 13C discrimination gave good agreement with pretreatment measurements. We also used observations from the experiment to develop a conceptual model of short-term photosynthate storage and transport.
Gangsheng Wang, Henriette I. Jager, Latha M. Baskaran, Tyler F. Baker, and Craig C. Brandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-34, https://doi.org/10.5194/hess-2016-34, 2016
Manuscript not accepted for further review
J. Zhang, L. Gu, J. Zhang, R. Wu, F. Wang, G. Lin, B. Wu, Q. Lu, and P. Meng
Biogeosciences Discuss., https://doi.org/10.5194/bgd-12-18769-2015, https://doi.org/10.5194/bgd-12-18769-2015, 2015
Revised manuscript not accepted
X. Shi, P. E. Thornton, D. M. Ricciuto, P. J. Hanson, J. Mao, S. D. Sebestyen, N. A. Griffiths, and G. Bisht
Biogeosciences, 12, 6463–6477, https://doi.org/10.5194/bg-12-6463-2015, https://doi.org/10.5194/bg-12-6463-2015, 2015
G. Wohlfahrt, C. Amelynck, C. Ammann, A. Arneth, I. Bamberger, A. H. Goldstein, L. Gu, A. Guenther, A. Hansel, B. Heinesch, T. Holst, L. Hörtnagl, T. Karl, Q. Laffineur, A. Neftel, K. McKinney, J. W. Munger, S. G. Pallardy, G. W. Schade, R. Seco, and N. Schoon
Atmos. Chem. Phys., 15, 7413–7427, https://doi.org/10.5194/acp-15-7413-2015, https://doi.org/10.5194/acp-15-7413-2015, 2015
Short summary
Short summary
Methanol is the second most abundant volatile organic compound in the troposphere and plays a significant role in atmospheric chemistry. While there is consensus about the dominant role of plants as the major source and the reaction with OH as the major sink, global methanol budgets diverge considerably in terms of source/sink estimates. Here we present micrometeorological methanol flux data from eight sites in order to provide a first cross-site synthesis of the terrestrial methanol exchange.
C. Safta, D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams, and P. E. Thornton
Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, https://doi.org/10.5194/gmd-8-1899-2015, 2015
Short summary
Short summary
In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model and focus on the comparison between steady-state and transient
simulation setups. We study model parameters via global sensitivity analysis and employ a Bayesian approach to calibrate these parameters using NEE observations at the Harvard Forest site. The calibration results are then used to assess the predictive skill of the model via posterior predictive checks.
L. Gu, S. G. Pallardy, K. P. Hosman, and Y. Sun
Biogeosciences, 12, 2831–2845, https://doi.org/10.5194/bg-12-2831-2015, https://doi.org/10.5194/bg-12-2831-2015, 2015
Short summary
Short summary
Co-occurring tree species with varying physiologies were continuously monitored for mortality with concurrent observations of key physiological and environmental variables for a decade in a central US forest. New predictors of drought-induced mortality were developed. Time-delayed mortality was shown to be nonlinearly related to drought intensity and species’ capacities in regulating their internal hydraulic status, with elevated risk associated with extreme isohydric and anisohydric behaviors.
J. Zhang, L. Gu, F. Bao, Y. Cao, Y. Hao, J. He, J. Li, Y. Li, Y. Ren, F. Wang, R. Wu, B. Yao, Y. Zhao, G. Lin, B. Wu, Q. Lu, and P. Meng
Biogeosciences, 12, 15–27, https://doi.org/10.5194/bg-12-15-2015, https://doi.org/10.5194/bg-12-15-2015, 2015
Y. Wei, S. Liu, D. N. Huntzinger, A. M. Michalak, N. Viovy, W. M. Post, C. R. Schwalm, K. Schaefer, A. R. Jacobson, C. Lu, H. Tian, D. M. Ricciuto, R. B. Cook, J. Mao, and X. Shi
Geosci. Model Dev., 7, 2875–2893, https://doi.org/10.5194/gmd-7-2875-2014, https://doi.org/10.5194/gmd-7-2875-2014, 2014
S. Jagadamma, M. A. Mayes, J. M. Steinweg, and S. M. Schaeffer
Biogeosciences, 11, 4665–4678, https://doi.org/10.5194/bg-11-4665-2014, https://doi.org/10.5194/bg-11-4665-2014, 2014
X. Yang, P. E. Thornton, D. M. Ricciuto, and W. M. Post
Biogeosciences, 11, 1667–1681, https://doi.org/10.5194/bg-11-1667-2014, https://doi.org/10.5194/bg-11-1667-2014, 2014
D. N. Huntzinger, C. Schwalm, A. M. Michalak, K. Schaefer, A. W. King, Y. Wei, A. Jacobson, S. Liu, R. B. Cook, W. M. Post, G. Berthier, D. Hayes, M. Huang, A. Ito, H. Lei, C. Lu, J. Mao, C. H. Peng, S. Peng, B. Poulter, D. Riccuito, X. Shi, H. Tian, W. Wang, N. Zeng, F. Zhao, and Q. Zhu
Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, https://doi.org/10.5194/gmd-6-2121-2013, 2013
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
Related subject area
Biogeosciences
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
China Wildfire Emission (ChinaWED v1) for the period 2012–2022
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Including the Phosphorus cycle into the LPJ-GUESS Dynamic Global Vegetation Model (v4.1, r10994) – Global patterns and temporal trends of N and P primary production limitation
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Process-based Modeling of Solar-induced Chlorophyll Fluorescence with VISIT-SIF version 1.0
The unicellular NUM v.0.91: A trait-based plankton model evaluated in two contrasting biogeographic provinces
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2592, https://doi.org/10.5194/egusphere-2024-2592, 2024
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they’ve changed from 1901 to 2018. We found that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation has increased, especially in the tropics, while N limitation has decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1914, https://doi.org/10.5194/egusphere-2024-1914, 2024
Short summary
Short summary
Under climate change, the conditions for wildfires to form are becoming more frequent 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 crucial platform for future developments.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
Short summary
Short summary
When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those 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 see what changes on our lands.
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
Short summary
Short summary
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.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
EGUsphere, https://doi.org/10.5194/egusphere-2024-1542, https://doi.org/10.5194/egusphere-2024-1542, 2024
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical process-based model developed to represent the global SIF observed by GOSAT. Our model simulation reproduced the global distribution and seasonal variations of GOSAT SIF. The model can be utilized to improve photosynthetic process through the combination of biogeochemical modeling and GOSAT SIF.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-53, https://doi.org/10.5194/gmd-2024-53, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We describe and test the size-based NUM model, that define organisms by a single set of parameters, on planktonic unicellular ecosystems in a eutrophic and an oligotrophic site. Results show both sites can be modelled with similar parameters, and a robust performance over a wide range of parameters. The study show that the NUM model is useful for non-experts and applicable for modelling domains with limited ecosystem data. It holds promise for evolutionary scenarios and deep-time climate models.
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
Short summary
Short summary
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.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Cited articles
Brooks, R. H. and Corey, A. T.: Hydraulic properties of porous media,
Hydrology Papers, No. 3, Colorado State university, Fort Collins, Colorado,
1964.
Brussaard, L., de Ruiter, P. C., and Brown, G. G.: Soil biodiversity for
agricultural sustainability, Agr. Ecosyst. Environ., 121, 233–244, 2007.
Chapin III, F. S., Matson, P. A., and Vitousek, P.: Principles of
terrestrial ecosystem ecology, Springer, New York, USA, 2011.
Childs, E.: The use of soil moisture characteristics in soil studies, Soil
Sci., 50, 239–252, 1940.
Clapp, R. B. and Hornberger, G. M.: Empirical Equations for Some Soil
Hydraulic-Properties, Water Resour. Res., 14, 601–604, 1978.
Coleman, D. C.: From peds to paradoxes: Linkages between soil biota and
their influences on ecological processes, Soil Biol. Biochem., 40, 271–289,
2008.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A
Statistical Exploration of the Relationships of Soil-Moisture Characteristics
to the Physical-Properties of Soils, Water Resour. Res., 20, 682–690, 1984.
Craine, J. M., Wedin, D. A., and Chapin, F. S.: Predominance of
ecophysiological controls on soil CO2 flux in a Minnesota
grassland, Plant Soil, 207, 77–86, 1999.
De Kauwe, M. G., Medlyn, B. E., Zaehle, S., Walker, A. P., Dietze, M. C.,
Hickler, T., Jain, A. K., Luo, Y. Q., Parton, W. J., Prentice, I. C., Smith,
B., Thornton, P. E., Wang, S. S., Wang, Y. P., Warlind, D., Weng, E. S.,
Crous, K. Y., Ellsworth, D. S., Hanson, P. J., Seok Kim, H., Warren, J. M.,
Oren, R., and Norby, R. J.: Forest water use and water use efficiency at
elevated CO2: a model-data intercomparison at two contrasting
temperate forest FACE sites, Glob. Change Biol., 19, 1759–1779, 2013.
Duarte, H. F., Raczka, B. M., Ricciuto, D. M., Lin, J. C., Koven, C. D.,
Thornton, P. E., Bowling, D. R., Lai, C.-T., Bible, K. J., and Ehleringer, J.
R.: Evaluating the Community Land Model (CLM4.5) at a coniferous forest site
in northwestern United States using flux and carbon-isotope measurements,
Biogeosciences, 14, 4315–4340, https://doi.org/10.5194/bg-14-4315-2017,
2017.
Edwards, N. T. and Riggs, J. S.: Automated monitoring of soil respiration:
A moving chamber design, Soil Sci. Soc. Am. J., 67, 1266–1271, 2003.
E3SM Project: Energy Exascale Earth System Model (E3SM), available at: https://github.com/E3SM-Project/E3SM, last access: 7 March 2017.
Franzluebbers, A. J.: Microbial activity in response to water-filled pore
space of variably eroded southern Piedmont soils, Appl. Soil Ecol., 11,
91–101, 1999.
Fredlund, D. G. and Xing, A. Q.: Equations for the Soil-Water
Characteristic Curve, Can. Geotech. J., 31, 521–532, 1994.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., Bloh, W. V., Brovkin, V.,
Cadule, P., Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C.,
Joos, F., Kato, T., Kawamiya, M., Knorr, W., Lindsay, K., Matthews, H. D.,
Raddatz, T., Rayner, P., Reick, C., Roeckner, E., Schnitzler, K.-G., Schnur,
R., Strassmann, K., Weaver, A. J., Yoshikawa, C., and Zeng, N.:
Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP Model
Intercomparison, J. Climate, 19, 3337–3353, https://doi.org/10.1175/jcli3800.1, 2006.
Ghezzehei, T. A., Sulman, B., Arnold, C. L., Bogie, N. A., and Berhe, A. A.:
On the role of soil water retention characteristic on aerobic microbial
respiration, Biogeosciences, 16, 1187–1209,
https://doi.org/10.5194/bg-16-1187-2019, 2019.
Grandy, A. S., Wieder, W. R., Wickings, K., and Kyker-Snowman, E.: Beyond
microbes: Are fauna the next frontier in soil biogeochemical models?, Soil
Biol. Biochem., 102, 40–44, 2016.
Gu, L., Hanson, P. J., Mac Post, W., and Liu, Q.: A novel approach for
identifying the true temperature sensitivity from soil respiration
measurements, Global Biogeochem. Cy., 22, GB4009, https://doi.org/10.1029/2007GB003164,
2008.
Gu, L. H., Pallardy, S. G., Yang, B., Hosman, K. P., Mao, J. F., Ricciuto,
D., Shi, X. Y., and Sun, Y.: Testing a land model in ecosystem functional
space via a comparison of observed and modeled ecosystem flux responses to
precipitation regimes and associated stresses in a Central US forest, J.
Geophys. Res.-Biogeo., 121, 1884–1902, 2016.
Hanson, P. J., O'Neill, E. G., Chambers, M. L. S., Riggs, J. S., Joslin, J.
D., and Wolfe, M. H.: Soil respiration and litter decomposition, in: North
American temperate deciduous forest responses to changing precipitation
regimes, Springer, 163–189, 2003.
Högberg, P., Nordgren, A., Buchmann, N., Taylor, A. F. S., Ekblad, A.,
Hogberg, M. N., Nyberg, G., Ottosson-Lofvenius, M., and Read, D. J.:
Large-scale forest girdling shows that current photosynthesis drives soil
respiration, Nature, 411, 789–792, 2001.
Holtan, H. N., England, C. B., Lawless, G. P., and Schumaker, G. A.:
Moisture-tension data for selected soils on experimental watersheds, Report ARS 41-144, ARS, US Department of Agriculture, Washington DC,
USA,
1968.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA, 2013.
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E.,
and Schulze, E. D.: A global analysis of root distributions for terrestrial
biomes, Oecologia, 108, 389–411, 1996.
Jones, C., Robertson, E., Arora, V., Friedlingstein, P., Shevliakova, E.,
Bopp, L., Brovkin, V., Hajima, T., Kato, E., Kawamiya, M., Liddicoat, S.,
Lindsay, K., Reick, C. H., Roelandt, C., Segschneider, J., and Tjiputra, J.:
Twenty-First-Century Compatible CO2 Emissions and Airborne Fraction
Simulated by CMIP5 Earth System Models under Four Representative
Concentration Pathways, J. Climate, 26, 4398–4413,
https://doi.org/10.1175/jcli-d-12-00554.1, 2013.
Kelting, D. L., Burger, J. A., and Edwards, G. S.: Estimating root
respiration, microbial respiration in the rhizosphere, and root-free soil
respiration in forest soils, Soil Biol. Biochem., 30, 961–968, 1998.
Kuzyakov, Y.: Separating microbial respiration of exudates from root
respiration in non-sterile soils: a comparison of four methods, Soil Biol.
Biochem., 34, 1621–1631, 2002.
Lasslop, G., Reichstein, M., Papale, D., Richardson, A. D., Arneth, A.,
Barr, A., Stoy, P., and Wohlfahrt, G.: Separation of net ecosystem exchange
into assimilation and respiration using a light response curve approach:
critical issues and global evaluation, Glob. Change Biol., 16, 187–208,
https://doi.org/10.1111/j.1365-2486.2009.02041.x, 2010.
Lawrence, D. M. and Slater, A. G.: Incorporating organic soil into a global
climate model, Clim. Dynam., 30, 145–160, 2008.
Lennon, J. T. and Jones, S. E.: Microbial seed banks: the ecological and
evolutionary implications of dormancy, Nat. Rev. Microbiol., 9, 119–130,
2011.
Linn, D. M. and Doran, J. W.: Effect of Water-Filled Pore-Space on
Carbon-Dioxide and Nitrous-Oxide Production in Tilled and Nontilled Soils,
Soil Sci. Soc. Am. J., 48, 1267–1272, 1984.
Lu, D., Ricciuto, D., Stoyanov, M., and Gu, L.: Calibration of the E3SM Land
Model Using Surrogate Based Global Optimization, J. Adv. Model. Earth Sy., 10,
1337–1356,
https://doi.org/10.1002/2017MS001134, 2018.
Luo, Y. and Zhou, X.: Soil respiration and the environment, Academic press,
2006.
Mao, J., Ricciuto, D. M., Thornton, P. E., Warren, J. M., King, A. W., Shi,
X., Iversen, C. M., and Norby, R. J.: Evaluating the Community Land Model in
a pine stand with shading manipulations and 13CO2 labeling,
Biogeosciences, 13, 641–657, https://doi.org/10.5194/bg-13-641-2016, 2016.
Monard, C., Mchergui, C., Nunan, N., Martin-Laurent, F., and Vieuble-Gonod,
L.: Impact of soil matric potential on the fine-scale spatial distribution
and activity of specific microbial degrader communities, Fems Microbiol.
Ecol., 81, 673–683, 2012.
Montané, F., Fox, A. M., Arellano, A. F., MacBean, N., Alexander, M. R.,
Dye, A., Bishop, D. A., Trouet, V., Babst, F., Hessl, A. E., Pederson, N.,
Blanken, P. D., Bohrer, G., Gough, C. M., Litvak, M. E., Novick, K. A.,
Phillips, R. P., Wood, J. D., and Moore, D. J. P.: Evaluating the effect of
alternative carbon allocation schemes in a land surface model (CLM4.5) on
carbon fluxes, pools, and turnover in temperate forests, Geosci. Model Dev.,
10, 3499–3517, https://doi.org/10.5194/gmd-10-3499-2017, 2017.
Moyano, F. E., Manzoni, S., and Chenu, C.: Responses of soil heterotrophic
respiration to moisture availability: An exploration of processes and models,
Soil Biol. Biochem., 59, 72–85, 2013.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M.,
Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S.,
Thornton, P. E., Bozbiyik, A., R., F., Heald, C. L., Kluzek, E., Lamarque, J.
F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S. P., Ricciuto, D.
M., Sacks, W. J., Sun, Y., Tang, J., and Yang, Z. L.: Technical Description
of Version 4.5 of the Community Land Model (CLM), NCAR Technical Note
NCAR/TN-503+ STR, Boulder, Colorado, 2013.
Powell, T. L., Galbraith, D. R., Christoffersen, B. O., Harper, A.,
Imbuzeiro, H. M. A., Rowland, L., Almeida, S., Brando, P. M., da Costa, A. C.
L., Costa, M. H., Levine, N. M., Malhi, Y., Saleska, S. R., Sotta, E.,
Williams, M., Meir, P., and Moorcroft, P. R.: Confronting model predictions
of carbon fluxes with measurements of Amazon forests subjected to
experimental drought, New Phytol., 200, 350–364, 2013.
Randerson, J. T., Hoffman, F. M., Thornton, P. E., Mahowald, N. M., Lindsay,
K., Lee, Y. H., Nevison, C. D., Doney, S. C., Bonan, G., Stockli, R., Covey,
C., Running, S. W., and Fung, I. Y.: Systematic assessment of terrestrial
biogeochemistry in coupled climate-carbon models, Glob. Change Biol., 15,
2462–2484, 2009.
Rawls, W., Yates, P., and Asmussen, L.: Calibration of selected infiltration
equations for the Georgia Coastal Plain, Report ARS-S-113 July 1976, 110 pp.,
2 fig, 8 tab, 25 ref, 1 append., 1976.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M.,
Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A.,
Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A.,
Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta,
F., Ourcival, J. M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M.,
Tenhunen, J., Seufert, G. n., Vaccari, F., Vesala, T., Yakir, D., and
Valentini, R.: On the separation of net ecosystem exchange into assimilation
and ecosystem respiration: Review and improved algorithm, Glob. Change Biol.,
11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Reichstein, M., Moffat, A. M., Wutzler, T., and Sickel, K.: REddyProc: Data
processing and plotting utilities of (half-)hourly eddy-covariance
measurements, R package version 1.0.0/r18, available at:
https://R-Forge.R-project.org/projects/reddyproc/, last access: 5 June
2017.
Reyes, W. M., Epstein, H. E., Li, X., McGlynn, B. L., Riveros-Iregui, D. A.,
and Emanuel, R. E.: Complex terrain influences ecosystem carbon responses to
temperature and precipitation, Global Biogeochem. Cy., 31, 1306–1317,
https://doi.org/10.1002/2017GB005658, 2017.
Richardson, A. D., Anderson, R. S., Arain, M. A., Barr, A. G., Bohrer, G.,
Chen, G. S., Chen, J. M., Ciais, P., Davis, K. J., Desai, A. R., Dietze, M.
C., Dragoni, D., Garrity, S. R., Gough, C. M., Grant, R., Hollinger, D. Y.,
Margolis, H. A., McCaughey, H., Migliavacca, M., Monson, R. K., Munger, J.
W., Poulter, B., Raczka, B. M., Ricciuto, D. M., Sahoo, A. K., Schaefer, K.,
Tian, H. Q., Vargas, R., Verbeeck, H., Xiao, J. F., and Xue, Y. K.:
Terrestrial biosphere models need better representation of vegetation
phenology: results from the North American Carbon Program Site Synthesis,
Glob. Change Biol., 18, 566–584, 2012.
Sierra, C. A., Malghani, S., and Loescher, H. W.: Interactions among
temperature, moisture, and oxygen concentrations in controlling decomposition
rates in a boreal forest soil, Biogeosciences, 14, 703–710,
https://doi.org/10.5194/bg-14-703-2017, 2017.
Stolpovsky, K., Martinez-Lavanchy, P., Heipieper, H. J., Van Cappellen, P.,
and Thullner, M.: Incorporating dormancy in dynamic microbial community
models, Ecol. Model., 222, 3092–3102, 2011.
Sun, L. J., Ataka, M., Kominami, Y., and Yoshimura, K.: Relationship between
fine-root exudation and respiration of two Quercus species in a Japanese
temperate forest, Tree Physiol. 37, 1011–1020, 2017.
Tian, H. Q., Lu, C. Q., Yang, J., Banger, K., Huntzinger, D. N., Schwalm, C.
R., Michalak, A. M., Cook, R., Ciais, P., Hayes, D., Huang, M. Y., Ito, A.,
Jain, A. K., Lei, H. M., Mao, J. F., Pan, S. F., Post, W. M., Peng, S. S.,
Poulter, B., Ren, W., Ricciuto, D., Schaefer, K., Shi, X. Y., Tao, B., Wang,
W. L., Wei, Y. X., Yang, Q. C., Zhang, B. W., and Zeng, N.: Global patterns
and controls of soil organic carbon dynamics as simulated by multiple
terrestrial biosphere models: Current status and future directions, Global
Biogeochem. Cy., 29, 775–792, 2015.
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.
Todd-Brown, K. E. O., Randerson, J. T., Hopkins, F., Arora, V., Hajima, T.,
Jones, C., Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., and
Allison, S. D.: Changes in soil organic carbon storage predicted by Earth
system models during the 21st century, Biogeosciences, 11, 2341–2356,
https://doi.org/10.5194/bg-11-2341-2014, 2014.
Tuller, M. and Or, D.: Retention of water in soil and the soil water
characteristic curve, Encyclopedia of Soils in the Environment, 4, 278–289,
2004.
van Genuchten, M. T.: A Closed-Form Equation for Predicting the Hydraulic
Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898, 1980.
Verburg, P. S. J., Arnone, J. A., Obrist, D., Schorran, D. E., Evans, R. D.,
Leroux-Swarthout, D., Johnson, D. W., Luo, Y. Q., and Coleman, J. S.: Net
ecosystem carbon exchange in two experimental grassland ecosystems, Glob.
Change Biol., 10, 498–508, 2004.
Verhoef, H. A. and Brussaard, L.: Decomposition and Nitrogen Mineralization
in Natural and Agroecosystems – the Contribution of Soil Animals,
Biogeochemistry, 11, 175–211, 1990.
Walker, A. P., Hanson, P. J., De Kauwe, M. G., Medlyn, B. E., Zaehle, S.,
Asao, S., Dietze, M., Hickler, T., Huntingford, C., Iversen, C. M., Jain, A.,
Lomas, M., Luo, Y. Q., McCarthy, H., Parton, W. J., Prentice, I. C.,
Thornton, P. E., Wang, S. S., Wang, Y. P., Warlind, D., Weng, E. S., Warren,
J. M., Woodward, F. I., Oren, R., and Norby, R. J.: Comprehensive ecosystem
model-data synthesis using multiple data sets at two temperate forest
free-air CO2 enrichment experiments: Model performance at ambient
CO2 concentration, J. Geophys. Res.-Biogeo., 119, 937–964, 2014.
Wan, S. Q. and Luo, Y. Q.: Substrate regulation of soil respiration in a
tallgrass prairie: Results of a clipping and shading experiment, Global
Biogeochem. Cy., 17, 1054, https://doi.org/10.1029/2002GB001971, 2003.
Wang, G., Mayes, M. A., Gu, L., and Schadt, C. W.: Representation of dormant
and active microbial dynamics for ecosystem modeling, PloS one, 9, e89252,
https://doi.org/10.1371/journal.pone.0089252,
2014.
Wang, G., Jagadamma, S., Mayes, M. A., Schadt, C. W., Steinweg, J. M., Gu,
L., and Post, W. M.: Microbial dormancy improves development and experimental
validation of ecosystem model, ISME J., 9, 226–237, 2015.
Wenk, E. S., Callaham, M. A., O'Brien, J. J., and Hanson, P. J.: Soil
Macroinvertebrate Communities across a Productivity Gradient in Deciduous
Forests of Eastern North America, Northeast. Nat., 23, 25–44, 2016.
Wood, J., Knapp, B. O., Muzika, R.-M., Stambaugh, M. C., and Gu, L.: The
importance of drought-pathogen interactions in driving oak mortality events
in the Ozark Border Region, Environ. Res. Lett., 13, 015004,
https://doi.org/10.1088/1748-9326/aa94fa, 2017.
Xia, J., McGuire, A. D., Lawrence, D., Burke, E., Chen, G., Chen, X.,
Delire, C., Koven, C., MacDougall, A., Peng, S., Rinke, A., Saito, K., Zhang,
W., Alkama, R., Bohn, T. J., Ciais, P., Decharme, B., Gouttevin, I., Hajima,
T., Hayes, D. J., Huang, K., Ji, D., Krinner, G., Lettenmaier, D. P., Miller,
P. A., Moore, J. C., Smith, B., Sueyoshi, T., Shi, Z., Yan, L., Liang, J.,
Jiang, L., Zhang, Q., and Luo, Y.: Terrestrial ecosystem model performance in
simulating productivity and its vulnerability to climate change in the
northern permafrost region, J. Geophys. Res.-Biogeo., 122, 430–446,
https://doi.org/10.1002/2016JG003384, 2017.
Yan, Z. F., Bond-Lamberty, B., Todd-Brown, K. E., Bailey, V. L., Li, S. L.,
Liu, C. Q., and Liu, C. X.: A moisture function of soil heterotrophic
respiration that incorporates microscale processes, Nat. Commun., 9, 2562, https://doi.org/10.1038/s41467-018-04971-6,
2018.
Young, F. J., Radatz, C. A., and Marshall, C. A.: Soil survey of Boone
County, Missouri, U.S., Department of Agriculture, Natural Resources Conservation Service, Washington DC, USA,
2001.
Zaehle, S., Medlyn, B. E., De Kauwe, M. G., Walker, A. P., Dietze, M. C.,
Hickler, T., Luo, Y. Q., Wang, Y. P., El-Masri, B., Thornton, P., Jain, A.,
Wang, S. S., Warlind, D., Weng, E. S., Parton, W., Iversen, C. M.,
Gallet-Budynek, A., McCarthy, H., Finzi, A. C., Hanson, P. J., Prentice, I.
C., Oren, R., and Norby, R. J.: Evaluation of 11 terrestrial carbon-nitrogen
cycle models against observations from two temperate Free-Air CO2
Enrichment studies, New Phytol., 202, 803–822, 2014.
Short summary
Soil respiration, the second largest carbon fluxes between the atmosphere and land, is not well represented in global land models. In this study, using long-term observations at a temperate forest, we identified a solution for using better soil water potential simulations to improve predictions of soil respiration in the E3SM land model. In addition, parameter calibration further improved model performance.
Soil respiration, the second largest carbon fluxes between the atmosphere and land, is not well...
