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. Land surface models (LSMs),which form the land component of earth system models, rely on numerous processes fordescribing carbon, water and energy budgets, often associated with highly uncertainparameters. Data assimilation (DA) is a useful approach for optimising the most criticalparameters in order to improve model accuracy and refine future climate predictions. Inthis study, we compare two different DA methods for optimising the parameters of sevenplant functional types (PFTs) of the ORCHIDEE LSM using daily averaged eddy-covarianceobservations 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 – localgradient-based (the L-BFGS-B algorithm, limited memoryBroyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) and global randomsearch (the genetic algorithm) – by evaluating their relative performance in terms ofthe model–data fit and the difference in retrieved parameter values. We examine theperformance of each method for two cases: when optimising parameters at each siteindependently (“single-site” approach) and when simultaneously optimising the model atall sites for a given PFT using a common set of parameters (“multi-site” approach). Wefind that for the single site case the random search algorithm results in lower values ofthe cost function (i.e. lower model–data root mean square differences) than thegradient-based method; the difference between the two methods is smaller for themulti-site optimisation due to a smoothing of the cost function shape with a greaternumber of observations. The spread of the cost function, when performing the same testswith 16 random first-guess parameters, is much larger with the gradient-based method, dueto the higher likelihood of being trapped in local minima. When using pseudo-observationtests, the genetic algorithm results in a closer approximation of the true posteriorparameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challengesof different DA techniques and provide some advice on using it for the LSM parameteroptimisation.

geoscientific model development

Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)