Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-5959-2020
https://doi.org/10.5194/gmd-13-5959-2020
Methods for assessment of models
 | 
01 Dec 2020
Methods for assessment of models |  | 01 Dec 2020

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

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Latest update: 16 Jul 2024
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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.