A dilemma of the uniqueness of weather and climate model closure parameters

Janne Hakkarainen*, Antti Solonen, Alexander Ilin, Jouni Susiluoto, Marko Laine, Heikki Haario, Heikki Järvinen

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    10 Citations (Scopus)


    Parameterisation schemes of subgrid-scale physical processes in atmospheric models contain so-called closure parameters. Their precise values are not generally known; thus, they are subject to fine-tuning for achieving optimal model performance. In this article, we show that there is a dilemma concerning the optimal parameter values: an identical prediction model formulation can have two different optimal closure parameter value settings depending on the level of approximations made in the data assimilation component of the prediction system. This result tends to indicate that the prediction model re-tuning in large-scale systems is not only needed when the prediction model undergoes a major change, but also when the data assimilation component is updated. Moreover, we advocate an accurate albeit expensive method based on so-called filter likelihood for the closure parameter estimation that is applicable in fine-tuning of both prediction model and data assimilation system parameters. In this article, we use a modified Lorenz-95 system as a prediction model and extended Kalman filter and ensemble adjustment Kalman filter for data assimilation. With this setup, we can compute the filter likelihood for the chosen parameters using the output of the two versions of the Kalman filter and apply a Markov chain Monte Carlo algorithm to explore the parameter posterior distributions.

    Original languageEnglish
    Article number20147
    Publication statusPublished - 2013
    MoE publication typeA1 Journal article-refereed


    • Filter formulation
    • Likelihood
    • Markov chain monte carlo
    • Model tuning


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