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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-160
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 30 Jul 2018

Development and technical paper | 30 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Land surface model parameter optimisation using in-situ flux data: comparison of gradient-based versus random search algorithms

Vladislav Bastrikov1,2, Natasha MacBean1,a, Cédric Bacour3, Diego Santaren1, Sylvain Kuppel4, and Philippe Peylin1 Vladislav Bastrikov et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212 CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
  • 2Institute of Industrial Ecology UB RAS, 620219 Ekaterinburg, Russia
  • 3Noveltis, 153 rue du Lac, 31670 Labège, France
  • 4Northern Rivers Institute, University of Aberdeen, Aberdeen, AB24 3UF, UK
  • anow at: School of Natural Resources and the Environment, University of Arizona, 1064 E Lowell St., 85721, Tucson, AZ, USA

Abstract. Land surface models (LSMs), used within earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the ORCHIDEE land surface model using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods – local gradient-based (the L-BFGS-B algorithm) and global random search (the genetic algorithm) – by evaluating their relative performance in terms of the model–data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently (single-site approach) and when simultaneously optimising the model at all sites for a given PFT using a common set of parameters (multi-site approach). We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model – data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first guess parameters, is much larger with the gradient based method, due to the higher likelihood of being trap in local minima. When using pseudo-observations tests the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameters optimisation.

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In this study, we compare different methods for optimising parameters of the ORCHIDEE land surface model (LSM) using in-situ observations. We use two minimisation methods – local gradient-based and global random search – applied either at each individual site or a group of sites characterised by one plant functional type. We demonstrate the advantages and challenges of different techniques and provide some advice on using it for the LSM parameters optimisation.
In this study, we compare different methods for optimising parameters of the ORCHIDEE land...
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