Abstract. Land surface models (LSMs),
which form the land component of 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 LSM 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, limited memory
Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) 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 trapped in local minima. When using pseudo-observation
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 parameter
optimisation.
Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm
