Articles | Volume 12, issue 9
https://doi.org/10.5194/gmd-12-4075-2019
https://doi.org/10.5194/gmd-12-4075-2019
Model evaluation paper
 | 
20 Sep 2019
Model evaluation paper |  | 20 Sep 2019

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

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Cited articles

Aurela, M., Lohila, A., Tuovinen, J., Hatakka, J., Penttilä, T., and Laurila, T.: Carbon dioxide and energy flux measurements in four northern-boreal ecosystems at Pallas, Boreal Environ. Res., 20, 455–473, 2015. a
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, Ch., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Leek, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw U, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001. a, b
Ball, J., Woodrow, I., and Berry, J.: A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis Under Different Environmental Conditions, Springer, Progress in Photosynthesis Research, edited by: Biggins, J., 221–224, https://doi.org/10.1007/978-94-017-0519-6_48, 1987. a, b
Bergh, J. and Linder, S.: Effects of soil warming during spring on photosynthetic recovery in boreal Norway spruce stands, Global Change Biol., 5, 245–253, https://doi.org/10.1046/j.1365-2486.1999.00205.x, 1999. a
Bergh, J., Mcmurtrie, R., and Linder, S.: Climatic factors controlling the productivity of Norway spruce: A model-based analysis, Forest Ecol. Manag., 110, 127–139, https://doi.org/10.1016/S0378-1127(98)00280-1, 1998. a
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Short summary
We assess the differences of six stomatal conductance formulations, embedded into a land–vegetation model JSBACH, on 10 boreal coniferous evergreen forest sites. We calibrate the model parameters using all six functions in a multi-year experiment, as well as for a separate drought event at one of the sites, using the adaptive population importance sampler. The analysis reveals weaknesses in the stomatal conductance formulation-dependent model behaviour that we are able to partially amend.