Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Mar 2022
Development and technical paper |
| 02 Mar 2022
| 02 Mar 2022
Calibrating the soil organic carbon model Yasso20 with multiple datasets
Finnish Meteorological Institute, Helsinki, 00101, Finland
Janne Pusa
Finnish Meteorological Institute, Helsinki, 00101, Finland
Istem Fer
Finnish Meteorological Institute, Helsinki, 00101, Finland
Anna Repo
Natural Resource Center Finland, Helsinki, 00791, Finland
Julius Vira
Finnish Meteorological Institute, Helsinki, 00101, Finland
Jari Liski
Finnish Meteorological Institute, Helsinki, 00101, Finland
Related authors
Claudia Guidi, Sia Gosheva-Oney, Markus Didion, Roman Flury, Lorenz Walthert, Stephan Zimmermann, Brian J. Oney, Pascal A. Niklaus, Esther Thürig, Toni Viskari, Jari Liski, and Frank Hagedorn
Biogeosciences, 22, 4107–4122, https://doi.org/10.5194/bg-22-4107-2025, https://doi.org/10.5194/bg-22-4107-2025, 2025
Short summary
Short summary
Predicting soil organic carbon (SOC) stocks in forests is crucial to determining the C balance, yet drivers of SOC stocks remain uncertain at large scales. Across a broad environmental gradient in Switzerland, we compared measured SOC stocks with those modeled by Yasso, which is commonly used for greenhouse gas budgets. We show that soil mineral properties and climate are the main controls of SOC stocks, indicating that better accounting of these processes will advance the accuracy of SOC stock predictions.
Jarmo Mäkelä, Laura Arppe, Hannu Fritze, Jussi Heinonsalo, Kristiina Karhu, Jari Liski, Markku Oinonen, Petra Straková, and Toni Viskari
Biogeosciences, 19, 4305–4313, https://doi.org/10.5194/bg-19-4305-2022, https://doi.org/10.5194/bg-19-4305-2022, 2022
Short summary
Short summary
Soils account for the largest share of carbon found in terrestrial ecosystems, and accurate depiction of soil carbon decomposition is essential in understanding how permanent these carbon storages are. We present a straightforward way to include carbon isotope concentrations into soil decomposition and carbon storages for the Yasso model, which enables the model to use 13C as a natural tracer to track changes in the underlying soil organic matter decomposition.
Weilin Huang, Peter M. van Bodegom, Toni Viskari, Jari Liski, and Nadejda A. Soudzilovskaia
Biogeosciences, 19, 1469–1490, https://doi.org/10.5194/bg-19-1469-2022, https://doi.org/10.5194/bg-19-1469-2022, 2022
Short summary
Short summary
This work focuses on one of the essential pathways of mycorrhizal impact on C cycles: the mediation of plant litter decomposition. We present a model based on litter chemical quality which precludes a conclusive examination of mycorrhizal impacts on soil C. It improves long-term decomposition predictions and advances our understanding of litter decomposition dynamics. It creates a benchmark in quantitatively examining the impacts of plant–microbe interactions on soil C dynamics.
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
Short summary
Short summary
Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev., 14, 2603–2633, https://doi.org/10.5194/gmd-14-2603-2021, https://doi.org/10.5194/gmd-14-2603-2021, 2021
Short summary
Short summary
Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare models against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeastern US.
Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski
Geosci. Model Dev., 13, 5959–5971, https://doi.org/10.5194/gmd-13-5959-2020, https://doi.org/10.5194/gmd-13-5959-2020, 2020
Short summary
Short summary
The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
Claudia Guidi, Sia Gosheva-Oney, Markus Didion, Roman Flury, Lorenz Walthert, Stephan Zimmermann, Brian J. Oney, Pascal A. Niklaus, Esther Thürig, Toni Viskari, Jari Liski, and Frank Hagedorn
Biogeosciences, 22, 4107–4122, https://doi.org/10.5194/bg-22-4107-2025, https://doi.org/10.5194/bg-22-4107-2025, 2025
Short summary
Short summary
Predicting soil organic carbon (SOC) stocks in forests is crucial to determining the C balance, yet drivers of SOC stocks remain uncertain at large scales. Across a broad environmental gradient in Switzerland, we compared measured SOC stocks with those modeled by Yasso, which is commonly used for greenhouse gas budgets. We show that soil mineral properties and climate are the main controls of SOC stocks, indicating that better accounting of these processes will advance the accuracy of SOC stock predictions.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, P. Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankararaman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Johann Engelbrecht, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbigniew Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gómez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal L. Weagle, and Xi Zhao
Atmos. Chem. Phys., 25, 4665–4702, https://doi.org/10.5194/acp-25-4665-2025, https://doi.org/10.5194/acp-25-4665-2025, 2025
Short summary
Short summary
Aerosol particles are an important part of the Earth system, but their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Here, we present a new compilation of PM2.5 and PM10 aerosol observations, focusing on the spatial variability across different observational stations, including composition, and demonstrate a method for comparing the data sets to model output.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankarararman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Hannele Hakola, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbiginiw Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gomez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal Weagle, and Xi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-1, https://doi.org/10.5194/essd-2024-1, 2024
Preprint withdrawn
Short summary
Short summary
Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. Here we present a new compilation of aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model.
Jarmo Mäkelä, Laura Arppe, Hannu Fritze, Jussi Heinonsalo, Kristiina Karhu, Jari Liski, Markku Oinonen, Petra Straková, and Toni Viskari
Biogeosciences, 19, 4305–4313, https://doi.org/10.5194/bg-19-4305-2022, https://doi.org/10.5194/bg-19-4305-2022, 2022
Short summary
Short summary
Soils account for the largest share of carbon found in terrestrial ecosystems, and accurate depiction of soil carbon decomposition is essential in understanding how permanent these carbon storages are. We present a straightforward way to include carbon isotope concentrations into soil decomposition and carbon storages for the Yasso model, which enables the model to use 13C as a natural tracer to track changes in the underlying soil organic matter decomposition.
Weilin Huang, Peter M. van Bodegom, Toni Viskari, Jari Liski, and Nadejda A. Soudzilovskaia
Biogeosciences, 19, 1469–1490, https://doi.org/10.5194/bg-19-1469-2022, https://doi.org/10.5194/bg-19-1469-2022, 2022
Short summary
Short summary
This work focuses on one of the essential pathways of mycorrhizal impact on C cycles: the mediation of plant litter decomposition. We present a model based on litter chemical quality which precludes a conclusive examination of mycorrhizal impacts on soil C. It improves long-term decomposition predictions and advances our understanding of litter decomposition dynamics. It creates a benchmark in quantitatively examining the impacts of plant–microbe interactions on soil C dynamics.
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
Short summary
Short summary
Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
Julius Vira, Peter Hess, Money Ossohou, and Corinne Galy-Lacaux
Atmos. Chem. Phys., 22, 1883–1904, https://doi.org/10.5194/acp-22-1883-2022, https://doi.org/10.5194/acp-22-1883-2022, 2022
Short summary
Short summary
Ammonia is one of the main components of nitrogen deposition. Here we use a new model to assess the ammonia emissions from agriculture, the largest anthropogenic source of ammonia. The model results are consistent with earlier estimates over industrialized regions in agreement with observations. However, the model predicts much higher emissions over sub-Saharan Africa compared to earlier estimates. Available observations from surface stations and satellites support these higher emissions.
Laura Heimsch, Annalea Lohila, Juha-Pekka Tuovinen, Henriikka Vekuri, Jussi Heinonsalo, Olli Nevalainen, Mika Korkiakoski, Jari Liski, Tuomas Laurila, and Liisa Kulmala
Biogeosciences, 18, 3467–3483, https://doi.org/10.5194/bg-18-3467-2021, https://doi.org/10.5194/bg-18-3467-2021, 2021
Short summary
Short summary
CO2 and H2O fluxes were measured at a newly established eddy covariance site in southern Finland for 2 years from 2018 to 2020. This agricultural grassland site focuses on the conversion from intensive towards more sustainable agricultural management. The first summer experienced prolonged dry periods, and notably larger fluxes were observed in the second summer. The field acted as a net carbon sink during both study years.
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev., 14, 2603–2633, https://doi.org/10.5194/gmd-14-2603-2021, https://doi.org/10.5194/gmd-14-2603-2021, 2021
Short summary
Short summary
Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare models against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeastern US.
Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski
Geosci. Model Dev., 13, 5959–5971, https://doi.org/10.5194/gmd-13-5959-2020, https://doi.org/10.5194/gmd-13-5959-2020, 2020
Short summary
Short summary
The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
Tea Thum, Julia E. M. S. Nabel, Aki Tsuruta, Tuula Aalto, Edward J. Dlugokencky, Jari Liski, Ingrid T. Luijkx, Tiina Markkanen, Julia Pongratz, Yukio Yoshida, and Sönke Zaehle
Biogeosciences, 17, 5721–5743, https://doi.org/10.5194/bg-17-5721-2020, https://doi.org/10.5194/bg-17-5721-2020, 2020
Short summary
Short summary
Global vegetation models are important tools in estimating the impacts of global climate change. The fate of soil carbon is of the upmost importance as its emissions will enhance the atmospheric carbon dioxide concentration. To evaluate the skill of global vegetation models to model the soil carbon and its responses to environmental factors, it is important to use different data sources. We evaluated two different soil carbon models by using atmospheric carbon dioxide concentrations.
Julius Vira, Peter Hess, Jeff Melkonian, and William R. Wieder
Geosci. Model Dev., 13, 4459–4490, https://doi.org/10.5194/gmd-13-4459-2020, https://doi.org/10.5194/gmd-13-4459-2020, 2020
Short summary
Short summary
Mostly emitted by the agricultural sector, ammonia has an important role in atmospheric chemistry. We developed a model to simulate how ammonia emissions respond to changes in temperature and soil moisture, and we evaluated agricultural ammonia emissions globally. The simulated emissions agree with earlier estimates over many regions, but the results highlight the variability of ammonia emissions and suggest that emissions in warm climates may be higher than previously thought.
Cited articles
Abramoff, R., Xiaofeng, X., Hartmann, M., O'Brien, S., Feng, W., Davidson,
E., Finzi, A., Moorhead, D., Schimel, J., Torn, M., and Mayes, M. A.: The
Millennial model: in search of measurable pools and transformations for
modeling soil carbon in the new century, Biogeochemistry, 137, 51–71, 2018.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais,
N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A.,
Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis,
H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C.,
Woodward, F. I., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake:
Global Distribution and Covariation with Climate, Science, 329, 834–838, 2010.
Berg, B., Hannus, K., Popoff, T., and Theander, P.: Changes in organic
components of litter during decomposition. Long term decomposition in a
Scots pine forest, I. Can. J. Bot., 60, 1310–1319, 1982.
Berg, B., Booltink, H., Breymeyer, A., Ewertsson, A., Gallardo, A., Holm,
B., Johansson, M. B., Koivuoja, S., Meentemeyer, V., Nyman, P., Olofsson, J.,
Pettersson, A.-S., Reurslag, A., Staaf, H., Staaf, I., Uba, L., Berg,
B., Booltink, H., Breymeyer, A., Ewertsson, A., Gallardo, A., Holm, B.,
Johansson, M. B., Koivuoja, S., Meentemeyer, V., Nyman, P., Olofsson, J.,
Pettersson, A.-S., Reurslag, A., Staaf, H., Staaf, I., and Uba, L.: Data on
Needle Litter Decomposition and Soil Climate as Well as Site Characteristics
for Some Coniferous Forest Sites, Part I, Site Characteristics. Report 41,
Swedish University of Agricultural Sciences, Department of Ecology and
Environmental Research, Uppsala, 1991a.
Berg, B., Booltink, H., Breymeyer, A., Ewertsson, A., Gallardo, A., Holm,
B., Johansson, M. B., Koivuoja, S., Meentemeyer, V., Nyman, P., Olofsson, J.,
Pettersson, A. S., Reurslag, A., Staaf, H., Staaf, I., and Uba, L.: Data on
Needle Litter Decomposition and Soil Climate as Well as Site Characteristics
for Some Coniferous Forest Sites, Part II, Decomposition Data. Report 42,
Swedish University of Agricultural Sciences, Department of Ecology and
Environmental Research, Uppsala, 1991b.
Cailleret, M., Bircher, N., Hartig, F., Hulsmann, L., and Bugmann, H.:
Bayesian calibration of a growth-dependent tree mortality model to simulate
the dynamics of European temperate forests, Ecol. Appl., 30, e02021, https://doi.org/10.1002/eap.2021, 2020.
Camino-Serrano, M., Guenet, B., Luyssaert, S., Ciais, P., Bastrikov, V., De Vos, B., Gielen, B., Gleixner, G., Jornet-Puig, A., Kaiser, K., Kothawala, D., Lauerwald, R., Peñuelas, J., Schrumpf, M., Vicca, S., Vuichard, N., Walmsley, D., and Janssens, I. A.: ORCHIDEE-SOM: modeling soil organic carbon (SOC) and dissolved organic carbon (DOC) dynamics along vertical soil profiles in Europe, Geosci. Model Dev., 11, 937–957, https://doi.org/10.5194/gmd-11-937-2018, 2018.
Clemmensen, K. E, Bahr, A., Ovaskainen, O., Dahlberg, A., Ekblad, A.,
Wallander, H., Stenlid, J., Finlay, R. D., Wardle, D. A., and Lindahl, B. D.:
Roots and associated fungi drive long-term carbon sequestration in boreal
forest, Science, 339, 1615–1618, 2013.
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173, 2006.
Davies, J. A. C., Tipping, E., Rowe, E. C., Boyle, J. F., Graf Pannatier, E.,
and Martinsen, V.: Long-term P weathering and recent N deposition control
plant-soil C, N and P, Glob. Biochem. Cycles., 30, 231–249, https://doi.org/10.1002/2015GB005167, 2016.
Fer, I., Shiklomanov, A., Novick, K. A., Gough, C. M., Arain, M. A., Chen, J.,
Murphy, B., Desai, A. R., and Dietze, M. C.: Capturing site-to-site variability
through Hierarchical Bayesian calibration of a process-based dynamic
vegetation model, bioRxiv, 04.28.441243, https://doi.org/10.1101/2021.04.28.441243, 2021.
Gelman, A. G. and Rubin, D. B.: Inference from iterative simulation using
multiple sequences, Stat. Sci., 7, 457e472, https://doi.org/10.1214/ss/1177011136, 1992.
Gholz, H. L., Wedin, D. A., Smitherman, S. M., Harmon, M. E., and Parton, W. J.:
Long- term dynamics of pine and hardwood litter in contrasting environments:
toward a global model of decomposition, Glob. Change Biol., 6, 751e765, https://doi.org/10.1046/j.1365-2486.2000.00349.x, 2000
Goll, D. S., Vuichard, N., Maignan, F., Jornet-Puig, A., Sardans, J., Violette, A., Peng, S., Sun, Y., Kvakic, M., Guimberteau, M., Guenet, B., Zaehle, S., Penuelas, J., Janssens, I., and Ciais, P.: A representation of the phosphorus cycle for ORCHIDEE (revision 4520), Geosci. Model Dev., 10, 3745–3770, https://doi.org/10.5194/gmd-10-3745-2017, 2017.
Haario, H., Saksman, E., and Tamminen, J.: Anadaptive Metropolis algorithm,
Bernoulli, 7, 223–242, https://doi.org/10.2307/3318737,
2001.
Harmon, M. E., Krankina, O. N., and Sexton, J.: Decomposition vectors: a new
approach to estimating woody detritus decomposition dynamics, Can. J. For. Res., 30, 76–84,
2000.
Harmon, M. E., Silver, W. L., Fasth, B., Chen, H., Burke, I. C., Parton, W. J.,
Hart, S. C., Currie, W. S., and the LIDET team: Long-term patterns in of mass
loss in during decomposition of leaf and fine root litter: an intersite
comparison, Global Chang. Biol., 15, 1320–1338, 2009.
Hartig, F., Minunno, F., and Paul, S.: BayesianTools: General-Purpose MCMC
and SMC Samples and Tools for Bayesian Statistics. R package version 0.1.7,
https://CRAN.R-project.org/package=BayesianTools (last access: 28 January 2022), 2019.
Hobbie, S. E.: Contrasting Effects of Substrate and Fertilizer Nitrogen on
the Early Stages of Litter Decomposition, Ecosystems, 8, 644–656, 2005.
Jackson, R. B., Lajtha, K., Crow, S. E., Hugelius, G., Kramer, M. G., and
Pineiro, G.: The ecology of soil carbon: pools, vulnerabilities, and biotic
and abiotic controls. Annual Review of Ecology, Evolution, and Systematics,
48, 419–445, 2017.
Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, C. M., Bampa, F., van
Wesemael, B., Harrison, R. B., Guerrini, I. A., deB Richter, D., Rustad, L.,
Lorenz, K., Chabbi, A., and Miglietta, F.: Current status, uncertainty and
future needs in soil organic carbon monitoring, Sci. Total Environ., 468–469, 376–383, 2014.
Karhu, K., Fritzen, H., Hämäläinen, K., Vanhala, P., Jungner,
P., Oinonen, M., Sonninen, E., Tuomi, M., Spetz, P., Kitunen, V., and Liski,
J.: Temperature sensitivity of soil carbon fractions in boreal forest soil,
Ecology, 91, 370–376, 2010.
Keiluweit, M., Wanzek, T., Kleber, M., Nico, P., and Fendorf, S.: Anaerobic
microsites have an unaccounted role in soil carbon stabilization, Nat. Commun., 8, 1771, https://doi.org/10.1038/s41467-017-01406-6,
2017.
Kyker-Snowman, E., Wieder, W. R., Frey, S. D., and Grandy, A. S.: Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0), Geosci. Model Dev., 13, 4413–4434, https://doi.org/10.5194/gmd-13-4413-2020, 2020.
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of
hydrologic models using multiple-try DREAM (ZS) and high-performance
computing, Water Resour. Res., 48, W01526. https://doi.org/10.1029/2011WR010608, 2012.
Liang, C., Schimel, J. P., and Jastrow, J. D.: The importance of anabolism in
microbial control over soil carbon storage, Nat. Microbiol., 2, 17105, https://doi.org/10.1038/nmicrobiol.2017.105, 2017.
Liski, J. and Westman, C. J.: Density of organic carbon in soil at
coniferous forest sites in southern Finland, Biogeochemistry, 29, 183–197, 1995.
Liski, J., Ilvesniemi, H., Mäkelä, A., and Starr, M.: Model analysis
of the effects of soil age, fires and harvesting on the carbon storage of
boreal forest soils, Eur. J. Soil Sci., 49, 407–416, 1998.
Liski, J., Nissinen, A., Erhard, M., and Taskinen, O.: Climate effects on
litter decomposition from arctic tundra to tropical rainforest, Global
Change Biol., 9, 1–10, 2003.
Liski, J., Palosuo, T., Peltoniemi, M., and Sievänen, R.: Carbon and
decomposition model Yasso for forest soils, Ecol. Modell., 189, 168–182, https://doi.org/10.1016/j.ecolmodel.2005.03.005, 2005.
Lu, D., Ricciuto, D., Walker, A., Safta, C., and Munger, W.: Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods, Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, 2017.
MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.: Consistent assimilation of multiple data streams in a carbon cycle data assimilation system, Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, 2016.
Malhotra, A., Todd-Brown, K., Nave, L. E., Batjes, N. H., Holmquist, J. R.,
Hoyt, A. M., Iversen, C. M., Jackson, R. B., Lajtha, K., Lawrence, C.,
Vinduskova, O., Wieder, W., Williams, M., Hugelius, G., and Harden, J.: The
landscape of soil carbon data: Emerging questions, synergies and questions,
Prog. Phys. Geogr., 43, 707–719, 2019.
Manzoni, S. P. and Porporato, A.: Soil carbon and nitrogen mineralization:
Theory and models across scales, Soil Biol. Biochem., 41, 1355–1379, 2009.
Mayer, M., Prescott, C. E., Abaker, W. E. A., Augusto, L., Cecillon, L.,
Ferreira, G. W. D., James, J., Jandl, R., Katzensteiner, K., Laclau, J.-P.,
Laganiere, J., Nouvellon, Y., Pare, D., Stanturf, J. A., Vanguelova, E. I.,
and Vesterdal, L.: Tamm Review: Influence of forest management activities on
soil organic carbon stocks: A knowledge synthesis, For. Ecol. Manag., 466,
118–127, https://doi.org/10.1016/j.foreco.2020.118127, 2020.
Meentemeyer, V.: Macroclimate and lignin control of litter decomposition
rates, Ecology, 59, 465–472, 1978.
Menichetti, L., Ågren, G. I., Barre, P., Moyano, B., and Kätterer,
T.: Generic parameters of first-order kinetics accurately describe soil
organic matter decay in bare fallow soils over a wide edaphic and climatic
range, Sci. Rep.-UK, 9, 20319, https://doi.org/10.1038/s41598-019-55058-1, 2019.
Mäkinen, H., Hynynen, J., Siitonen, J., and Sievänen, R.: Predicting
the decomposition of scots pine, norway spruce, and birch stems in Finland,
Ecol. Appl., 16, 1865–1879, 2006.
Mäkipää, R., Häkkinen, M., Muukkonen, P., and Peltoniemi,
M.: The costs of monitoring changes in forest soil carbon stocks, Boreal Environ. Res., 13,
120–130, 2008.
Moore, T., Trofymow, J. A., Prescott, C., Titus, B. D., and the CIDET working
group: Can short-term litter-bag measurements predict long-term
decomposition in northern forests, Plant Soil, 416, 419–426, 2017.
Oades, J. M.: The retention of organic matter in soils, Biogeochemistry, 5, 35–70,
1988.
Oberpriller, J., Cameron, D. R., Dietze, M. C., and Hartig, F.: Towards robust
statistical inference for complex computer models, Ecol. Lett., 24, 1251–1261, 2021.
Olson, J. S.: Energy storage and the balance of producers and decomposers in
ecological systems, Ecology, 44, 322–331, 1963.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell,
G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison,
J. C., Loucks, C. J., Allnut, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F.,
Wettengel, W. W., Hedao, P., and Kassem, K. R.: Terrestrial Ecoregions of the
World: A New Map of Life on Earth: A new global map of terrestrial
ecoregions provides an innovative tool for conserving biodiversity,
BioScience, 51, 933–938, 2001.
Palosuo, T., Foereid, B., Svensson, M., Shurpali, N., Lehtonen, A., Herbst,
M., Linkosalo, T., Ortiz, C., Rampazzo Todorovic, G., Marcinkonis, S., Li,
C., and Jandl, R.: A multi-model comparison of soil carbon assessment of a
coniferous forest stand, Environ. Modell. Softw., 35, 38–49, 2012.
Parton, W. J.: The CENTURY model, in: Evaluation of Soil Organic Matter Models, edited by: Powlson, D. S., Smith, P., and Smith,
J. U., NATO ASI Series (Series
I: Global Environmental Change), vol. 38, Springer, Berlin, Heidelberg, https://doi.org/10.2307/1932179, 1996.
Peng, Y., Thomas, S. C., and Tian, D.: For management and soil respiration:
Implications for carbon sequestration, Environ. Rev., 16, 93–111, https://doi.org/10.1139/A08-003, 2008.
Rasmussen, C., Heckman, S., Wieder, W. R., Keiluweit, M., Lawrence, C. R.,
Berhe, A. A., Blankinship, J. C., Crow, S. E., Druhan, J. L., Hicks Pries, C. E.,
Marin-Spiotta, E., Plante, A. F., Schädel, C., Schimel, J. P., Sierra,
C. A., Thompson, A., and Wagai, R.: Beyond Clay: towards an improved set of
variables for predicting soil organic matter content, Biogeochem. Lett., 137, 297–306, 2018.
Schmidt, M. W. I., Torn, M. S., Abiven, S., Dittmar, T., Guggenberger, G.,
Janssens, I., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning,
D. A. C., Nannipier, P., Rasse, D. P., Weiner, S., and Trumbore, S. E.:
Persistence of soil organic matter as an ecosystem property, Nature, 478,
49–56, 2011.
Stevenson, F. J.: Humus Chemistry: Genesis, Composition, Reactions, John
Wiley & Sons, New York, 1982.
Sulman, B. N., Moore, J. A. M., Abramoff, R., Averill, C., Kivlin, S.,
Georgiou, K., Sridhar, B., Hartmann, M. D., Wang, G., Wieder, W., Bradford,
M. A., Luo, Y., Mayer, M. A., Morrison, E., Riley, W. J., Salazar, A., Schimel,
J. P., Tang, J., and Classen, A. T.: Multiple models and experiments underscore
large uncertainty in soil carbon dynamics, Biogeochemistry, 141, 109–123, 2018.
Swift, M. J.: The ecology of wood decomposition, Sci. Prog. Oxf., 64, 175–199,
1977.
Tang, J. and Riley, W. J.: Linear two-pool models are insufficient to infer
soil organic matter decomposition temperature sensitivity from incubations,
Biochemistry, 149, 251–261, 2020.
ter Braak, C. J. F. and Vrugt, J. A.: Differential evolution Markov chain with
snooker updater and fewer chains, Stat. Comput., 18, 435e446,
https://doi.org/10.1007/s11222-008-9104-9, 2008.
Thum, T., Caldararu, S., Engel, J., Kern, M., Pallandt, M., Schnur, R., Yu, L., and Zaehle, S.: A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996), Geosci. Model Dev., 12, 4781–4802, https://doi.org/10.5194/gmd-12-4781-2019, 2019.
Tian, X., Minunno, F., Cao, T., Peltoniemi, M., Kalliokoski, T., and
Mäkelä, A.: Extending the range of applicability of the
semi-empirical ecosystem flux model PRELES for varying forest types and
climate, Glob. Change Biol., 26, 2923–2943, 2020.
Trofymow, J. A. and the CIDET Working Group: The Canadian Intersite
Decomposition Experiment (CIDET): Project and Site Establishment Report,
Information Report BC-X-378, Pacific Forestry Centre, Victoria, Canada, 1998.
Tuomi, M., Vanhala, P., Karhu, K., Fritze, H., and Liski, J.: Heterotrophic
soil respiration – comparison of different models describing its
temperature dependence, Ecol. Modell., 211, 182–190, 2008.
Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M.,
Trofymow, J.A., Sevanto, S., and Liski, J.: Leaf litter decomposition –
Estimates of global variability based on Yasso07 model, Ecol. Modell., 220,
3362–3371, https://doi.org/10.1016/j.ecolmodel.2009.05.016,
2009.
Tuomi, M., Laiho, R., Repo, A., and Liski, J.: Wood decomposition model for
boreal forests, Ecol. Modell., 222, 709–718, 2011a.
Tuomi, M., Rasinmäki, J., Repo, A., Vanhala, P. and Liski, J.: Soil
carbon model Yasso07 graphical user interface, Environ. Model. Softw., 26, 1358–1362, 2011b.
Viskari, T., Pusa, J., Fer, I., Repo, A., Vira, J., and Liski, J.: The impact of calibrating soil organic carbon model Yasso with multiple datasets, Zenodo [code/data set], https://doi.org/10.5281/zenodo.5059909, 2021.
Vrugt, J. A.: Markov chain Monte Carlo simulation using theDREAM software
package: Theory, concepts, and MATLAB im-plementation, Environ. Model.
Softw., 75, 273–316, https://doi.org/10.1016/j.envsoft.2015.08.013, 2016.
Vrugt, J. A., ter Braak, C. J. F., Diks, C. G. H., Higdon, D., Robinson, B. A., and
Hyman, J. M.: Accelerating Markov chain Monte Carlo simulation by
differential evolution with self-adaptive randomized subspace sampling, Int.
J. Nonlinear Sci. Numer. Simul., 10, 273e290, https://doi.org/10.1515/IJNSNS.2009.10.3.273, 2009.
Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M.,
Marin-Spiotta, E., van Wesemael, B., Rabot, E., Liess, Mareike,
Garcia-Franco, N., Wollschläger, U., Vogel, H.-J., and
Kögel-Krabner, I.: Soil organic carbon storage as key function of soils
– A review of drivers and indicators at various scales, Geoderma, 333,
149–162, https://doi.org/10.1016/j.geoderma.2018.07.026, 2019.
Wutzler, T. and Reichstein, M.: Soils apart from equilibrium – consequences for soil carbon balance modelling, Biogeosciences, 4, 125–136, https://doi.org/10.5194/bg-4-125-2007, 2007.
Zaehle, S. and Friend, A. D.: Carbon and nitrogen cycle dynamics in the O-CN
land surface model: 1. Model description, site-scale evaluation, and
sensitivity to parameter estimates, Global Biogeochem. Cy., 24,
GB1005, https://doi.org/10.1029/2009GB003521,
2010.
Zaehle, S., Medlyn, B. E., De Kauwe, M. G., Walker A. P., Dietze, M. C.,
Hickler, T., Luo, Y., Wang, Y.-P., El-Masri, B., Thornton, P., Jain, A.,
Wang, S., Warlind, D., Weng, E., Parton, W., Iverson, C. M., Gallet-Budynek,
A., McCarthy, H., Finzi, A., Hanson, P. J., Prentice, C. I., Oren, R., and
Norby, R. J.: Evaluation of 11 terrestrial carbon–nitrogen cycle models
against observations from two temperate Free-Air CO2 Enrichment studies, N.
Phytol., 202, 803–822, 2014.
Zhang, H., Goll, D. S., Wang, P.-S., Ciais, P., Wieder, W. R., Abramoff, R.,
Huang, Y., Guenet, B., Prescher, A.-K., Viscarra Rossel, R. A., Barre, P.,
Chenu, C., Zhou, G., and Tang, X.: Microbial dynamics and soil
physicochemical properties explain large-scale variations in in soil organic
carbon, Glob. Change Biol., 26, 2668–2685, 2020.
Zinke, P. J., Stangenberger, A. G., Post, W. M., Emanuel, W. R., and Olson,
J. S.: Worldwide organic soil carbon and nitrogen data. NDP-018, Carbon
Dioxide Information Center, Oak Ridge National Laboratory, Oak Ridge,
Tennessee, 1986.
Zobitz, J. M., Desai, A. R., Moore, D. J. P., and Chadwick, M. A.: A primer for
data assimilation with ecological models using Markov Chain Monte Carlo
(MCMC), Oceologia, 167, 599–611, 2011.
Short summary
We wanted to examine how the chosen measurement data and calibration process affect soil organic carbon model calibration. In our results we found that there is a benefit in using data from multiple litter-bag decomposition experiments simultaneously, even with the required assumptions. Additionally, due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.
We wanted to examine how the chosen measurement data and calibration process affect soil organic...
