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.
Methods for assessment of models
| 
01 Dec 2020
Methods for assessment of models |
| 01 Dec 2020
| 01 Dec 2020
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
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Cited
20 citations as recorded by crossref.
- Predicting potential climate change impacts of bioenergy from perennial grasses in 2050 W. Liu et al. 10.1016/j.resconrec.2022.106818
- Forest residues contribute significantly to climate change in China J. Tian et al. 10.1016/j.resconrec.2023.107157
- Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation T. Viskari et al. 10.5194/gmd-13-5959-2020
- Carbon Farming: Bridging Technology Development with Policy Goals G. Kyriakarakos et al. 10.3390/su16051903
- The Effect of Forest Growth Rate on Climate Change Impacts of Logging Residue Utilization X. Gan et al. 10.3390/atmos14081270
- A Tool for Long-Term Forest Stand Projections of Swedish Forests L. Eriksson & J. Bergh 10.3390/f13060816
- Role of data uncertainty when identifying important areas for biodiversity and carbon in boreal forests H. Kujala et al. 10.1007/s13280-023-01908-2
- Two large-scale forest scenario modelling approaches for reporting CO2 removal: a comparison for the Romanian forests V. Blujdea et al. 10.1186/s13021-021-00188-1
- Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET H. Dokoohaki et al. 10.5194/gmd-15-3233-2022
- Mycelium chemistry differs markedly between ectomycorrhizal and arbuscular mycorrhizal fungi W. Huang et al. 10.1038/s42003-022-03341-9
- Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier B. Ťupek et al. 10.5194/gmd-17-5349-2024
- Carbon discharge from agriculture residues generated in 2020 and left unused in China M. Fang et al. 10.1016/j.biombioe.2025.107897
- Carbon sequestration potential of street tree plantings in Helsinki M. Havu et al. 10.5194/bg-19-2121-2022
- Integration of machine learning and process‐based model outputs via ensemble Kalman filter enhanced space–time modelling of soil organic carbon in a highly human impacted area E. Xie et al. 10.1111/sum.13127
- Effects of Silvicultural Adaptation Measures on Carbon Stock of Austrian Forests T. Ledermann et al. 10.3390/f13040565
- Do mycorrhizal symbionts drive latitudinal trends in photosynthetic carbon use efficiency and carbon sequestration in boreal forests? A. Mäkelä et al. 10.1016/j.foreco.2022.120355
- Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data V. Strîmbu et al. 10.1186/s13021-023-00222-4
- Evaluation of the Terrestrial Ecosystem Model Biome-BGCMuSo for Modelling Soil Organic Carbon under Different Land Uses M. Ostrogović Sever et al. 10.3390/land10090968
- Implementation and initial calibration of carbon-13 soil organic matter decomposition in the Yasso model J. Mäkelä et al. 10.5194/bg-19-4305-2022
- Towards agricultural soil carbon monitoring, reporting, and verification through the Field Observatory Network (FiON) O. Nevalainen et al. 10.5194/gi-11-93-2022
20 citations as recorded by crossref.
- Predicting potential climate change impacts of bioenergy from perennial grasses in 2050 W. Liu et al. 10.1016/j.resconrec.2022.106818
- Forest residues contribute significantly to climate change in China J. Tian et al. 10.1016/j.resconrec.2023.107157
- Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation T. Viskari et al. 10.5194/gmd-13-5959-2020
- Carbon Farming: Bridging Technology Development with Policy Goals G. Kyriakarakos et al. 10.3390/su16051903
- The Effect of Forest Growth Rate on Climate Change Impacts of Logging Residue Utilization X. Gan et al. 10.3390/atmos14081270
- A Tool for Long-Term Forest Stand Projections of Swedish Forests L. Eriksson & J. Bergh 10.3390/f13060816
- Role of data uncertainty when identifying important areas for biodiversity and carbon in boreal forests H. Kujala et al. 10.1007/s13280-023-01908-2
- Two large-scale forest scenario modelling approaches for reporting CO2 removal: a comparison for the Romanian forests V. Blujdea et al. 10.1186/s13021-021-00188-1
- Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET H. Dokoohaki et al. 10.5194/gmd-15-3233-2022
- Mycelium chemistry differs markedly between ectomycorrhizal and arbuscular mycorrhizal fungi W. Huang et al. 10.1038/s42003-022-03341-9
- Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier B. Ťupek et al. 10.5194/gmd-17-5349-2024
- Carbon discharge from agriculture residues generated in 2020 and left unused in China M. Fang et al. 10.1016/j.biombioe.2025.107897
- Carbon sequestration potential of street tree plantings in Helsinki M. Havu et al. 10.5194/bg-19-2121-2022
- Integration of machine learning and process‐based model outputs via ensemble Kalman filter enhanced space–time modelling of soil organic carbon in a highly human impacted area E. Xie et al. 10.1111/sum.13127
- Effects of Silvicultural Adaptation Measures on Carbon Stock of Austrian Forests T. Ledermann et al. 10.3390/f13040565
- Do mycorrhizal symbionts drive latitudinal trends in photosynthetic carbon use efficiency and carbon sequestration in boreal forests? A. Mäkelä et al. 10.1016/j.foreco.2022.120355
- Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data V. Strîmbu et al. 10.1186/s13021-023-00222-4
- Evaluation of the Terrestrial Ecosystem Model Biome-BGCMuSo for Modelling Soil Organic Carbon under Different Land Uses M. Ostrogović Sever et al. 10.3390/land10090968
- Implementation and initial calibration of carbon-13 soil organic matter decomposition in the Yasso model J. Mäkelä et al. 10.5194/bg-19-4305-2022
- Towards agricultural soil carbon monitoring, reporting, and verification through the Field Observatory Network (FiON) O. Nevalainen et al. 10.5194/gi-11-93-2022
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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...
