Articles | Volume 11, issue 3
Geosci. Model Dev., 11, 1199–1228, 2018
https://doi.org/10.5194/gmd-11-1199-2018
Geosci. Model Dev., 11, 1199–1228, 2018
https://doi.org/10.5194/gmd-11-1199-2018

Model evaluation paper 29 Mar 2018

Model evaluation paper | 29 Mar 2018

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

Jouni Susiluoto et al.

Related authors

Efficient Bayesian inference for large chaotic dynamical systems
Sebastian Springer, Heikki Haario, Jouni Susiluoto, Aleksandr Bibov, Andrew Davis, and Youssef Marzouk
Geosci. Model Dev., 14, 4319–4333, https://doi.org/10.5194/gmd-14-4319-2021,https://doi.org/10.5194/gmd-14-4319-2021, 2021
Short summary
Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2
Jouni Susiluoto, Alessio Spantini, Heikki Haario, Teemu Härkönen, and Youssef Marzouk
Geosci. Model Dev., 13, 3439–3463, https://doi.org/10.5194/gmd-13-3439-2020,https://doi.org/10.5194/gmd-13-3439-2020, 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
HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands
Maarit Raivonen, Sampo Smolander, Leif Backman, Jouni Susiluoto, Tuula Aalto, Tiina Markkanen, Jarmo Mäkelä, Janne Rinne, Olli Peltola, Mika Aurela, Annalea Lohila, Marin Tomasic, Xuefei Li, Tuula Larmola, Sari Juutinen, Eeva-Stiina Tuittila, Martin Heimann, Sanna Sevanto, Thomas Kleinen, Victor Brovkin, and Timo Vesala
Geosci. Model Dev., 10, 4665–4691, https://doi.org/10.5194/gmd-10-4665-2017,https://doi.org/10.5194/gmd-10-4665-2017, 2017
Short summary
Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements
Jarmo Mäkelä, Jouni Susiluoto, Tiina Markkanen, Mika Aurela, Heikki Järvinen, Ivan Mammarella, Stefan Hagemann, and Tuula Aalto
Nonlin. Processes Geophys., 23, 447–465, https://doi.org/10.5194/npg-23-447-2016,https://doi.org/10.5194/npg-23-447-2016, 2016
Short summary

Related subject area

Biogeosciences
BioRT-Flux-PIHM v1.0: a biogeochemical reactive transport model at the watershed scale
Wei Zhi, Yuning Shi, Hang Wen, Leila Saberi, Gene-Hua Crystal Ng, Kayalvizhi Sadayappan, Devon Kerins, Bryn Stewart, and Li Li
Geosci. Model Dev., 15, 315–333, https://doi.org/10.5194/gmd-15-315-2022,https://doi.org/10.5194/gmd-15-315-2022, 2022
Short summary
Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021,https://doi.org/10.5194/gmd-14-7639-2021, 2021
Short summary
Explicit silicate cycling in the Kiel Marine Biogeochemistry Model version 3 (KMBM3) embedded in the UVic ESCM version 2.9
Karin Kvale, David P. Keller, Wolfgang Koeve, Katrin J. Meissner, Christopher J. Somes, Wanxuan Yao, and Andreas Oschlies
Geosci. Model Dev., 14, 7255–7285, https://doi.org/10.5194/gmd-14-7255-2021,https://doi.org/10.5194/gmd-14-7255-2021, 2021
Short summary
Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
Shannon de Roos, Gabriëlle J. M. De Lannoy, and Dirk Raes
Geosci. Model Dev., 14, 7309–7328, https://doi.org/10.5194/gmd-14-7309-2021,https://doi.org/10.5194/gmd-14-7309-2021, 2021
Short summary
OMEN-SED(-RCM) (v1.1): a pseudo-reactive continuum representation of organic matter degradation dynamics for OMEN-SED
Philip Pika, Dominik Hülse, and Sandra Arndt
Geosci. Model Dev., 14, 7155–7174, https://doi.org/10.5194/gmd-14-7155-2021,https://doi.org/10.5194/gmd-14-7155-2021, 2021
Short summary

Cited articles

Arah, J. R. M. and Stephen, K. D.: A Model of the Processes Leading to Methane Emission from Peatland, Atmos. Environ., 32, 3257–3264, 1998.
Aurela, M., Tuovinen, J.-P., and Laurila, T.: Net CO2 exchange of a subarctic mountain birch ecosystem, Theor. Appl. Climatol., 70, 135–148, https://doi.org/10.1007/s007040170011, 2001.
Aurela, M., Riutta, T., Laurila, T., Tuovinen, J.-V., Vesala, T., Tuittila, E.-S., Jinne, J., Haapanala, S., and Laine, J.: CO2 exchange of a sedge fen in southern Finland–the impact of a drought period, Tellus B, 59, 826–837, https://doi.org/10.1111/j.1600-0889.2007.00309.x, 2007.
Bellisario, L. M., Bubier, J. L., Moore, T. R., and Chanton, J. P.: Controls on CH4 emissions from a northern peatland, Global Biogeochem. Cy., 13, 81–91, https://doi.org/10.1029/1998GB900021, 1999.
Bergman, I., Klarqvist, M., and Nilsson, M.: Seasonal variation in rates of methane production from peat of various botanical origins: effects of temperature and substrate quality, FEMS Microbiol. Ecol., 33, 181–189, https://doi.org/10.1111/j.1574-6941.2000.tb00740.x, 2000.
Download
Short summary
Methane is an important greenhouse gas and methane emissions from wetlands contribute to the warming of the climate. Wetland methane emissions are also challenging to estimate. We analyze the performance of a new wetland emission computer model utilizing mathematical methods and using data from a wetland in southern Finland. The analysis helps to explain how wetlands produce methane and how emission modeling can be improved and uncertainties in the emission estimates reduced in future studies.