Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-5959-2020
© Author(s) 2020. 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-13-5959-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation
Finnish Meteorological Institute, Helsinki, 00101, Finland
Maisa Laine
Finnish Meteorological Institute, Helsinki, 00101, Finland
Liisa Kulmala
Finnish Meteorological Institute, Helsinki, 00101, Finland
Department of Forest Sciences, University of Helsinki, P.O. Box 27,
00014 Helsinki, Finland
Institute for Atmospheric Sciences and Earth System Research,
University of Helsinki, Helsinki, Finland
Jarmo Mäkelä
Finnish Meteorological Institute, Helsinki, 00101, Finland
Istem Fer
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.
Toni Viskari, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski
Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
Short summary
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.
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.
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.
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025, https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
Short summary
The balance between CO2 uptake and emissions from urban green areas is still not well understood. This study evaluated for the first time the urban park CO2 exchange simulations with four different types of biosphere model by comparing them with observations. Even though some advantages and disadvantages of the different model types were identified, there was no strong evidence that more complex models performed better than simple ones.
Laura Thölix, Leif Backman, Minttu Havu, Esko Karvinen, Jesse Soininen, Justine Trémeau, Olli Nevalainen, Joyson Ahongshangbam, Leena Järvi, and Liisa Kulmala
Biogeosciences, 22, 725–749, https://doi.org/10.5194/bg-22-725-2025, https://doi.org/10.5194/bg-22-725-2025, 2025
Short summary
Short summary
Cities aim for carbon neutrality and seek to understand urban vegetation's role as a carbon sink. Direct measurements are challenging, so models are used to estimate the urban carbon cycle. We evaluated model performance at estimating carbon sequestration in lawns, park trees, and urban forests in Helsinki, Finland. Models captured seasonal and annual variations well. Trees had higher sequestration rates than lawns, and irrigation often enhanced carbon sinks.
Tuula Aalto, Aki Tsuruta, Jarmo Mäkelä, Jurek Müller, Maria Tenkanen, Eleanor Burke, Sarah Chadburn, Yao Gao, Vilma Mannisenaho, Thomas Kleinen, Hanna Lee, Antti Leppänen, Tiina Markkanen, Stefano Materia, Paul A. Miller, Daniele Peano, Olli Peltola, Benjamin Poulter, Maarit Raivonen, Marielle Saunois, David Wårlind, and Sönke Zaehle
Biogeosciences, 22, 323–340, https://doi.org/10.5194/bg-22-323-2025, https://doi.org/10.5194/bg-22-323-2025, 2025
Short summary
Short summary
Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in northern Europe using ecosystem models, atmospheric inversions, and upscaled flux observations. The ecosystem models differed in their responses to temperature and precipitation and in their seasonality. However, multi-model means, inversions, and upscaled fluxes had similar seasonality, and they suggested co-limitation by temperature and precipitation.
Esko Karvinen, Leif Backman, Leena Järvi, and Liisa Kulmala
SOIL, 10, 381–406, https://doi.org/10.5194/soil-10-381-2024, https://doi.org/10.5194/soil-10-381-2024, 2024
Short summary
Short summary
We measured and modelled soil respiration, a key part of the biogenic carbon cycle, in different urban green space types to assess its dynamics in urban areas. We discovered surprisingly similar soil respiration across the green space types despite differences in some of its drivers and that irrigation of green spaces notably elevates soil respiration. Our results encourage further research on the topic and especially on the role of irrigation in controlling urban soil respiration.
Helena Rautakoski, Mika Korkiakoski, Jarmo Mäkelä, Markku Koskinen, Kari Minkkinen, Mika Aurela, Paavo Ojanen, and Annalea Lohila
Biogeosciences, 21, 1867–1886, https://doi.org/10.5194/bg-21-1867-2024, https://doi.org/10.5194/bg-21-1867-2024, 2024
Short summary
Short summary
Current and future nitrous oxide (N2O) emissions are difficult to estimate due to their high variability in space and time. Several years of N2O fluxes from drained boreal peatland forest indicate high importance of summer precipitation, winter temperature, and snow conditions in controlling annual N2O emissions. The results indicate increasing year-to-year variation in N2O emissions in changing climate with more extreme seasonal weather conditions.
Justine Trémeau, Beñat Olascoaga, Leif Backman, Esko Karvinen, Henriikka Vekuri, and Liisa Kulmala
Biogeosciences, 21, 949–972, https://doi.org/10.5194/bg-21-949-2024, https://doi.org/10.5194/bg-21-949-2024, 2024
Short summary
Short summary
We studied urban lawns and meadows in the Helsinki metropolitan area, Finland. We found that meadows are more resistant to drought events but that they do not increase carbon sequestration compared with lawns. Moreover, the transformation from lawns to meadows did not demonstrate any negative climate effects in terms of greenhouse gas emissions. Even though social and economic aspects also steer urban development, these results can guide planning to consider carbon-smart options.
Joyson Ahongshangbam, Liisa Kulmala, Jesse Soininen, Yasmin Frühauf, Esko Karvinen, Yann Salmon, Anna Lintunen, Anni Karvonen, and Leena Järvi
Biogeosciences, 20, 4455–4475, https://doi.org/10.5194/bg-20-4455-2023, https://doi.org/10.5194/bg-20-4455-2023, 2023
Short summary
Short summary
Urban vegetation is important for removing urban CO2 emissions and cooling. We studied the response of urban trees' functions (photosynthesis and transpiration) to a heatwave and drought at four urban green areas in the city of Helsinki. We found that tree water use was increased during heatwave and drought periods, but there was no change in the photosynthesis rates. The heat and drought conditions were severe at the local scale but were not excessive enough to restrict urban trees' functions.
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.
Minttu Havu, Liisa Kulmala, Pasi Kolari, Timo Vesala, Anu Riikonen, and Leena Järvi
Biogeosciences, 19, 2121–2143, https://doi.org/10.5194/bg-19-2121-2022, https://doi.org/10.5194/bg-19-2121-2022, 2022
Short summary
Short summary
The carbon sequestration potential of two street tree species and the soil beneath them was quantified with the urban land surface model SUEWS and the soil carbon model Yasso. The street tree plantings turned into a modest sink of carbon from the atmosphere after 14 years. Overall, the results indicate the importance of soil in urban carbon sequestration estimations, as soil respiration exceeded the carbon uptake in the early phase, due to the high initial carbon loss from the soil.
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, and Kai Puolamäki
Biogeosciences, 19, 2095–2099, https://doi.org/10.5194/bg-19-2095-2022, https://doi.org/10.5194/bg-19-2095-2022, 2022
Short summary
Short summary
Causal structure discovery algorithms have been making headway into Earth system sciences, and they can be used to increase our understanding on biosphere–atmosphere interactions. In this paper we present a procedure on how to utilize prior knowledge of the domain experts together with these algorithms in order to find more robust causal structure models. We also demonstrate how to avoid pitfalls such as over-fitting and concept drift during this process.
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.
Toni Viskari, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski
Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
Short summary
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.
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).
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.
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.
Cited articles
Abramoff, R., Xu, X., Hartman, 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,
2017.
Anderson, J. L.: An ensemble adjustment Kalman Filter for data assimilation,
Mon. Weather Rev., 129, 2884–2903, 2001.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm
for ensemble filters, Tellus A, 2, 210–224, 2006.
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Arellano, A.: The Data Assimilation Research Testbed: A community facility,
B. Am. Meteorol. Soc., 90, 1283–1296, 2009.
Barré, P., Eglin, T., Christensen, B. T., Ciais, P., Houot, S., Kätterer, T., van Oort, F., Peylin, P., Poulton, P. R., Romanenkov, V., and Chenu, C.: Quantifying and isolating stable soil organic carbon using long-term bare fallow experiments, Biogeosciences, 7, 3839–3850, https://doi.org/10.5194/bg-7-3839-2010, 2010.
Barré, P., Quénéa, K., Vidal, A., Cecilloin, L., Christensen, B. T., Kätterer, T., Macdonald, A., Petit, L., Plante, A. F., van Oort, F., and Chenu, C.: Microbial and plant-derived
compounds both contribute to persistent soil organic carbon in temperate
soils, Biogeochemistry, 140, 81–92, https://doi.org/10.1007/s10533-018-0475-5, 2018.
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, Departnent 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, Departnent of Ecology and
Environmental Research, Uppsala, 1991b.
Bradford, M. A., Wieder, W. R., Bonan, G. B., Fierer, N., Raymond, P. A., and
Crowther, T. W.: Managing uncertainty in soil carbon feedbacks to climate
change, Nat. Clim. Change, 6, 751–758, 2016.
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.
Chapin, F. S., Matson, P. A., and Vitousek, P.: Principles of Terrestrial
Ecosystem Ecology, 2nd edn., Springer-Verlag, New York, USA, 2011.
Christensen, B. T.: Effect of cropping system on the soil organic matter
content II, Field experiments on a sandy loam 1956–1985, Tidsskrift for
Planteavl 94, 161–169, 1990 (in Danish with English summary).
Christensen, B. T. and Johnston, A. E.: Soil organic matter and soil quality
– lessons learned from long-term experiments at Askov and Rothamsted, Dev.
Soil Sci., 25, 399–430, 1997.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quere, C., Myneni, R.B., Piao, S., Thornton, P., Ahlström, A., Anav, A., Andrews, O., Archer, D., Arora, V., Bonan, G., Borges, A. V., Bousquet, P., Bouwman, L., Bruhwiler, L. M., Caldeira, K., Cao, L., Chappelaz, J., Chevallier, F., Cleveland, C., Cox, P., Dentener, F. J., Doney, S. C., Erisman, J. W., Eurkirchen, E.S., Friedlingstein, P., Gruber, N., Gurney, K., Holland, E.A., Hopwood, B., Houghton, R.A., House, J. I., Houweling, S., Hunter, S., Hurtt, G., Jacobson, A. D., Jain, A., Joos, F., Jungclaus, J., Kaplan, J. O., Kato, E., Keeling, R., Khatiwala, S., Kirsche, S., Goldewijk, K. K., Kloster, S., Koven, C., Kroeze, C., Lamarque, J.-F., Lassey, K., Law, R. M., Lenton, A., Lomas, M. A., Luo, Y., Maki, T., Marland, G., Matthews, H. D., Mayorga, E., Melton, J. R., Metzl, N., Munhoven, G., Niwa, Y., Norby, R. J., O'Connor, F., Orr, J., Park, G.-H., Patra, R., Peregon, A., Peters, W., Peylin, P., Piper, S., Pongratz, J., Poulter, B., Raymond, P. A., Rayner, P., Ridgwell, A., Ringewell, B., Rödenbeck, C., Saunois, M., Schmittner, A., Schuur, E., Sitch, S., Spahni, R., Stocker, B., Takahashi, T., Thompson, R. L., Tjiputra, J., van der Werf, G., van Vuuren, G., Voulgarakis, A., Wania, R., Zaehle, S., and Zeng, N.: Carbon and Other Biogeochemical Cycles, in: Climate Change
2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by:
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung,
J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
2013.
Cornwell, W. K., Cornelissen, J. H. C., Amatangelo, K., Dorrepaal, E., Eviner,
V. T., Godoy, O., Hobbie, S. E., Hoorens, B., Kurokawa, H.,
Perez-Harguindeguy, N., Quested, H. M., Santiage, L. S., Wardle, D. A., Wright,
I. J., Aerts, R., Allison, S. D., van Bodegom, P., Brovkin, V., Chatain, A.,
Callaghan, T. V., Diaz, S., Garnier, E., Gurvich, D. E., Kazakou, E., Klein,
J. A., Read, J., Reich, P. B., Soudzilovskaia, N. A., Vaieretti, M. V., and
Westoby, M.: Plant species traits are the predominant control on litter
decomposition rates within biomes worldwide, Ecol. Lett., 11, 1065–1071,
2008.
Del Grosso, S., Parton, W., Stohlgren, T., Zheng, D., Bachelet, D., Prince,
S., Hibbard, K., and Olson, R.: Global potential Net Primary Production
predicted from vegetation class, precipitation and temperature, Ecology,
89, 2117–2126, 2008.
Dietze, M. C.: Ecological Forecasting, Princeton Univ. Press, Princeton, 2017.
Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M. B.,
Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C. M., Larsen, L. G.,
Loescher, H. W., Lunch, C. K., Pijanowski, B. C., Randerson, J. T., Read, E. K.,
Tredennick, A. T., Vargas, R., Weathers, K. C., and White, E. P.: Iterative
near-term ecological forecasting, P. Natl. Acad.
Sci. USA, 115, 1424–1432, https://doi.org/10.1073/pnas.1710231115, 2018.
Elbern, H., Schimdt, H., Talagrand, O., and Ebel, A.: 4D-variational data
assimilation with an adjoint air quality model for emission analysis,
Environ. Mod. Softw., 15, 539–548, 2000.
Evensen, G.: Sampling strategies and square root analysis schemes for EnKF,
Ocean Dyn., 54, 539–560, 2004.
Evensen, G.: Data Assimilation: The Ensemble Kalman Filter, Springer, New
York, 2009.
Gao, C., Wang, H., Weng, E., Lakshmivaran, S., Zhang, Y., and Luo, Y.:
Assimilation of multiple data sets with Kalman filter to improve forecasts
of forest carbon dynamics, Ecol. Appl., 21, 1461–1473,
https://doi.org/10.1890/09-1234.1, 2011.
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, Global Change Biol., 6, 751e765, https://doi.org/10.1046/j.1365-2486.2000.00349.x, 2000.
Haario, H., Saksman, E., and Tamminen, J.: An adaptive Metropolis algorithm,
Bernoulli, 7, 223–242, 2001.
Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-dependent filtering of
background error covariance estimates in an ensemble Kalman filter, Mon.
Weather Rev., 129, 2776–2790, https://doi.org/10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2, 2001.
Hararuk, O., Xia, J., and Luo, Y.: Evaluation and improvement of a global
land model against soil carbon data using a Bayesian Markov chain Monte
Carlo method, J. Geophys. Res.-Biogeo., 119, 403–417, https://doi.org/10.1002/2013JG002535, 2014.
He, Y., Trumbore, S. E., Torn, M. S., Harden, J. W., Vaughn, L. J. S., Allison,
S. D., and Randerson, J. T.: Radiocarbon constraints imply reduced carbon
uptake by soils during the 21st century, Science, 353, 1419–1424,
2016.
Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, M. F., Bampa, F., van
Wesemael, Harrison, R. B., Guerrini, I. A., Richter Jr., 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.
Kalman, R. E.: A new approach to linear filtering and prediction problems, J.
Basic Eng., 82, 35–45, 1960.
Karhu, K., Gärdenäs, A. I., Heikkinen, J., Vanhala, P., Tuomi, M., and
Liski, J.: Impacts of organic amendments on carbon stock of an agricultural
soil – Comparison of model-simulations to measurements, Geoderma, 189–190,
606–616, 2012.
Karhu, K., Auffret, M. D., Dungait, J. A. J., Hopkins, D. W., Prosser, J. I.,
Singh, B. K., Subke, J.-A., Wookey, P. A., Ågren, G. I., Sebastia, M.-T.,
Gouriveau, F., Bergkvist, G., Meir, P., Nottingham, A. T., Salinas, N., and
Hartley, I. P.: Temperature sensitivity of soil respiration rates enhanced by
microbial community response, Nature, 513, 81–84, 2014.
Kulmala, L. and Liski, J.: Bare fallow experiments highlight the importance
of long-term history on soil carbon decomposition rate on agricultural
lands, Report series in aerosol science, 215, 225–227, available at:
http://www.faar.fi/wp-content/uploads/2019/12/RS215_proceedings_2018.pdf (last access: 24 November 2020), 2018.
Le Dimet, F.-X. and Talagrang, O.: Variational algorithms for analysis and
assimilation of meteorological observations: theoretical aspects, Tellus,
38A, 97–110, 1986.
Lehmann, J. and Kleber, M.: The contentious nature of soil organic matter,
Nature, 528, 60–68, 2015.
Li, H., Kalnay, E., and Miyoshi, T.: Simultaneous estimation of covariance
inflation and observation errors within an ensemble Kalman filter, Q. J. Roy.
Meteor. Soc., 123, 523–533, 2009.
Liang, C., Schimel, J., and Jastrow, J.: The importance of anabolism in microbial control over soil carbon storage, Nat. Microbiol., 2, 17105, https://doi.org/10.1038/nmicrobiol.2017.105, 2017.
Manzoni, S. P. and Porporato, A.: Soil carbon and nitrogen mineralization:
Theory and models across scales, Soil Biol. Biochem., 41, 1355–1379, 2009.
Mao, Z., Derrien, D., Didion, M., Liski, J., Eglin, T., Nicolas, M., Jonard, M., and Saint-André, L.: Modeling soil organic carbon dynamics in temperate forests with Yasso07, Biogeosciences, 16, 1955–1973, https://doi.org/10.5194/bg-16-1955-2019, 2019.
Menichetti, L., Ågren, G. I., Barré, P., Moyano, F., 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.
Orchard, V. A. and Cook, F. J.: Relationship between soil respiration and
soil moisture, Soil Biol. Biochem., 15, 447–453, 1983.
Ortiz, C. A., Liski, J., Gärdenäs, A. I., Lehtonen, A., Lundblad, M.,
Stendahl, J., Ågren, G. I., and Karltun, E.: Soil organic carbon stock
changes in Swedish forest soils – A comparison of uncertainties and their
sources through a national inventory and two simulation models, Ecol.
Model., 251, 221–231, 2013.
Palosuo, T., Foereid, B., Svensson, M., Shurpali, N., Lehtonen, A., Herbst,
M., Linkosalo, T., Ortiz, C., Todorovic, G. R., Marcinkonis, S., Li, C., and
Jandl, R.: A multi-model comparison of soil carbon assessment of a
coniferous forest stand, Environ. Model. 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), 38, Springer, Berlin, Heidelberg, 1996.
Schlee, F. H., Standish, C. J., and Toda, N. F.: Divergence in the Kalman
filter, AIAA J., 5, 1114–1120, 1967.
Smith, P., Soussana, J.-F., Angers, D., Schipper, L., Chenu, C., Rasse, D. P., Batjes, N. H., van Egmond, F., McNeill, S., Kuhnert, M., Arias-Navarro, C., Olesen, J. E., Chirinda, N., Fornara, D., Wollenberg, E., Alvaro-Fuentes, J., Sanz-Cobena, A., and Klumpp, K.: How to measure, report and verify soil carbon change to realize the
potential of soil sequestration for atmospheric greenhouse gas removal, Global
Change Biol., 26, 219–241, 2020.
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.
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.
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.
Trudinger, C. M., Raupach, M. R., Rayner, P. J., and Enting, I. G.: Using
the Kalman filterfor parameter estimation in biogeochemical models,
Environmetrics, 19, 849–870, https://doi.org/10.1002/env.910, 2008.
Tuomi, M., Vanhala, P., Karhu, K., Fritze, H., and Liski, J.: Heterotrophic
soil respiration-Comparison of different models describing its temperature
dependence, Ecol. Model., 211, 182–190, https://doi.org/10.1016/j.ecolmodel.2007.09.003,
2008.
Tuomi, M., Laiho, R., Repo, A., and Liski, J.: Wood decomposition model for
boreal forests, Ecol Model., 222, 709–718, 2011.
van Oijen, M.: Bayesian Methods for Quantifying and Reducing Uncertainty and
Error in Forest Models, Curr. Forest. Rep., 3, 269–280,
https://doi.org/10.1007/s40725-017-0069-9, 2017.
van Oort, F., Paradelo, R., Proix, N., Delarue, G., Baize, D., and Monna, F.: Centennial fertilization-induced soil processes control trace metal
dynamics. Lessons from a long-term bare fallow experiment, Soil Systems, 2,
23, https://doi.org/10.3390/soilsystems2020023,
2018.
Viskari, T., Asmi, E., Virkkula, A., Kolmonen, P., Petäjä, T., and Järvinen, H.: Estimation of aerosol particle number distribution with Kalman Filtering – Part 2: Simultaneous use of DMPS, APS and nephelometer measurements, Atmos. Chem. Phys., 12, 11781–11793, https://doi.org/10.5194/acp-12-11781-2012, 2012.
Viskari, T., Hardiman, B., Desari, A. R., and Dietze, M. C.: Model-data
assimilation of multiple phenological observations to constrain and predict
leaf area index, Ecol. Appl., 25, 546–558, 2015.
Viskari, T., Laine, M., Kulmala, L., Mäkelä, J., Fer, I., and Liski, J.: Improving Yasso15 soil carbon model estimates with Ensemble Adjustment Kalman Filter state data assimilation, Zenodo, https://doi.org/10.5281/zenodo.4041038, 2020.
Vogel, C., Heister, K., Buegger, F., Tanuwidjaja, I., Haug, S., Schloter, M.,
and Kögel-Krabner, I.: Clay mineral composition modifies decomposition
and sequestration of organic carbon and nitrogen in fine soil fractions,
Biol. Ferti. Soils., 51, 427–442, 2015.
Weaver, A. T., Vialard, J., and Anderson, D. L. T.: Three- and Four-Dimensional
Variational Assimilation with a General Circulation Model of the Tropical
Pacific Ocean. Part I: Formulation, Internal Diagnostics, and Consistency
Checks, Mon. Weather Rev., 131, 1360–1378, 2003.
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.
Yan, M., Li, Z., Tian, X., Zhang, L., and Zhou, Y.: Improved simulation of
carbon and waterfluxes by assimilating multi-layer soil temperature and
moisture into process-basedbiogeochemical model, Forest Ecosyst., 6,
12, https://doi.org/10.1186/s40663-019-0171-5, 2019.
Yang, Y., Dunham, E., Barnier, G., and Almquist, M.: Tsunami Wavefield
Reconstruction and Forecasting using the Ensemble Kalman Filter, Geophys. Res.
Lett., 46, 853–860, 2019.
Ziche, D., Gruneberg, E., Hilbrig, L., Höhle, J., Kompa, T., Liski, J.,
Repo, A., and Wellbrock, N.: Comparing soil inventory with modelling: Carbon
balance in central European forest soils varies among forest types, Sci.
Total Environ., 647, 1573–1585, 2019.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(686 KB) - Full-text XML
- Corrigendum
-
Supplement
(266 KB) - BibTeX
- EndNote
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.
The research here established whether a Bayesian statistical method called state data...