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Use of biomass data in data assimilation of a global vegetation model

Use of biomass data in data assimilation of a global vegetation model

Biomass is an important variable in the context of climate change. It is crucial to know, how much vegetation can contain carbon in the changing climate.

New remote sensing products enable estimation of plant biomass and thus provide a new benchmark for global vegetation models. In this work we studied possibilities to use different biomass data in data assimilation of a global vegetation model.

Biomass data was added to the data assimilation framework of a French global ORCHIDEE vegetation model, on top of carbon dioxide and water fluxes. This research focused on two forests located in France. In data assimilation the data is fused with model results and new values for the intrinsic model parameters are obtained.

Data assimilation done with Bayesian method showed, that addition of annual biomass increment together with the flux data enabled simultaneous improvements to both flux and biomass increment estimates.

With the new obtained model parameters the ability of model to predict also other biomass associated variables, e.g. fineroot biomass, improved. A future scenario run revealed importance of estimating the net primary production of the vegetation (organic matter produced in photosynthesis) correctly. To reach this aim, measurements that distinguish the components of the carbon dioxide released from the forest, plant respiration and decomposition of the soil organic matter, are needed.

The study also revealed challenges in applying current global vegetation models at canopy level, as they often do not include forest management and storm damages. New vegetation models including forest management allow using of biomass in multiple ways and this study shows possible directions to that work.

This study was done in French LSCE-institute during post doc period of a researcher from the Finnish Meteorological Institute. This research has been funded by European Union and Academy of Finland.

More information:

Researcher Tea Thum, tea.thum@fmi.fi

Thum, T., MacBean, N., Peylin, P.,  Bacour, C., Santaren, D., Longdoz, B., Loustau, D. and Ciais, P., 2017. The potential benefit of using forest biomass data in addition to carbon and water flux measurements to constrain ecosystem model parameters: case studies at two temperate forest sites. Agricultural and Forest Meteorology 234, 48-65.

http://www.sciencedirect.com/science/article/pii/S0168192316307201


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FMI researchers produce some 300 publications annually, the majority of them in international scientific journals. Research results are also published in FMI´ss own publications series. FMI`s own series are:

* FMI Contributions: high-quality peer-reviewed research results, mainly doctoral dissertations

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All publications from 2016 onwards can be found on Helda.