Articles | Volume 23, issue 6
Nonlin. Processes Geophys., 23, 447–465, 2016
https://doi.org/10.5194/npg-23-447-2016
Nonlin. Processes Geophys., 23, 447–465, 2016
https://doi.org/10.5194/npg-23-447-2016
Research article
09 Dec 2016
Research article | 09 Dec 2016

Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

Jarmo Mäkelä et al.

Related authors

Technical note: Incorporating expert domain knowledge into causal structure discovery workflows
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
Implementation and initial calibration of carbon-13 soil organic matter decomposition in Yasso model
Jarmo Mäkelä, Laura Arppe, Hannu Fritze, Jussi Heinonsalo, Jari Liski, Markku Oinonen, Petra Straková, and Toni Viskari
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-327,https://doi.org/10.5194/bg-2021-327, 2021
Preprint under review for BG
Short summary
Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation
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
Sensitivity of 21st century simulated ecosystem indicators to model parameters, prescribed climate drivers, RCP scenarios and forest management actions for two Finnish boreal forest sites
Jarmo Mäkelä, Francesco Minunno, Tuula Aalto, Annikki Mäkelä, Tiina Markkanen, and Mikko Peltoniemi
Biogeosciences, 17, 2681–2700, https://doi.org/10.5194/bg-17-2681-2020,https://doi.org/10.5194/bg-17-2681-2020, 2020
Short summary
Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH
Jarmo Mäkelä, Jürgen Knauer, Mika Aurela, Andrew Black, Martin Heimann, Hideki Kobayashi, Annalea Lohila, Ivan Mammarella, Hank Margolis, Tiina Markkanen, Jouni Susiluoto, Tea Thum, Toni Viskari, Sönke Zaehle, and Tuula Aalto
Geosci. Model Dev., 12, 4075–4098, https://doi.org/10.5194/gmd-12-4075-2019,https://doi.org/10.5194/gmd-12-4075-2019, 2019
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Control simulation experiment with Lorenz's butterfly attractor
Takemasa Miyoshi and Qiwen Sun
Nonlin. Processes Geophys., 29, 133–139, https://doi.org/10.5194/npg-29-133-2022,https://doi.org/10.5194/npg-29-133-2022, 2022
Short summary
Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022,https://doi.org/10.5194/npg-29-77-2022, 2022
Short summary
Inferring the instability of a dynamical system from the skill of data assimilation exercises
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021,https://doi.org/10.5194/npg-28-633-2021, 2021
Short summary
Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi
Nonlin. Processes Geophys., 28, 615–626, https://doi.org/10.5194/npg-28-615-2021,https://doi.org/10.5194/npg-28-615-2021, 2021
Short summary
Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz 96 system
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys., 28, 565–583, https://doi.org/10.5194/npg-28-565-2021,https://doi.org/10.5194/npg-28-565-2021, 2021
Short summary

Cited articles

Aalto, T., Ciais, P., Chevillard, A., and Moulin, C.: Optimal determination of the parameters controlling biospheric CO2 fluxes over Europe using eddy covariance fluxes and satellite NDVI measurements, Tellus B, 56, 93–104, https://doi.org/10.3402/tellusb.v56i2.16413, 2004.
Abramowitz, G., Pitman, A., Gupta, H., Kowalczyk, E., and Wang, Y.: Systematic Bias in Land Surface Models, J. Hydrol., 8, 989–1001, https://doi.org/10.1175/JHM628.1, 2007.
Aurela, M.: Carbon dioxide exchange in subarctic ecosystems measured by a micrometeorological technique, Finnish Meteorol. Inst. Contr., 51, 1–39, 2005.
Aurela, M., Lohila, A., Tuovinen, J., Hatakka, J., Riutta, T., and Laurila, T.: Carbon dioxide exchange on a northern boreal fen, Boreal Environ. Res., 14, 699–710, 2009.
Baldocchi, D.: Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future, Global Change Biol., 9, 479–492, https://doi.org/10.1046/j.1365-2486.2003.00629.x, 2003.
Download
Short summary
The land-based hydrological cycle is one of the key processes controlling the growth and wilting of plants and the amount of carbon vegetation can assimilate. Recent studies have shown that many land surface models have biases in this area. We optimized parameters in one such model (JSBACH) and were able to enhance the model performance in many respects, but the response to drought remained unaffected. Further studies into this aspect should include alternative stomatal conductance formulations.