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)

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.</p

Solving the equation for the Iberian upwelling biogeochemical dynamics: an optimization experience

1 poster presented at the European Geosciences Union General Assembly 2012, Vienna, Austria, 22 – 27 April 2012Trying to find a set of parameters to properly reproduce the biogeochemical dynamics of the region of study is a major concern in biogeochemical ocean modelling. Model parameters are constant values introduced in the equations that calculate the time and space evolution of the state variables of the biogeochemical model. A good set of parameters allows for a better representation of the biological and chemical processes in the system, and thus to model results more approximated to reality. However, it is not a straightforward task, because many parameters are not well constrained in the literature, or they may be unknown or vary considerably between different regions. Usually, the approach to find the appropriate values is running several simulations, after some sensitivity test to individual parameters, until a satisfactory result is obtained. This may be very time consuming and quite subjective. A more systematic way to find this set of parameters has arisen over the last years by using mathematical optimization techniques. The basic principle under optimization is to minimize the difference between an observed and a simulated data series by using a cost function. We have applied an optimization technique to find an appropriate set of parameters for modelling the biogeochemical dynamics of the western Iberian shelf, off the Atlantic coast of Portugal and Galicia (NW Spain), which is characterized by a conspicuous seasonal upwelling. The ocean model is a high resolution 3D regional configuration of ROMS coupled to a N2PZD2 biogeochemical model. Results using the a priori parameters and the optimized parameters are compared and discussed. The study is the result of a multidisciplinary collaborative effort between the University of Aveiro ocean modelling group (Portugal), the ETHZ (Switzerland) and the IIM-CSIC Vigo oceanography group (Spain)Fundação para a Ciência e a Tecnologia (FCT) for funding this PhD research (SFRH/BD/33388/2008).Peer reviewe

Ecosystem model optimization using in situ flux observations: Benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances

Terrestrial ecosystem models can provide major insights into the responses of Earth's ecosystems to environmental changes and rising levels of atmospheric CO2. To achieve this goal, biosphere models need mechanistic formulations of the processes that drive the ecosystem functioning from diurnal to decadal timescales. However, the subsequent complexity of model equations is associated with unknown or poorly calibrated parameters that limit the accuracy of long-term simulations of carbon or water fluxes and their interannual variations. In this study, we develop a data assimilation framework to constrain the parameters of a mechanistic land surface model (ORCHIDEE) with eddy-covariance observations of CO2 and latent heat fluxes made during the years 2001-2004 at the temperate beech forest site of Hesse, in eastern France. As a first technical issue, we show that for a complex process-based model such as ORCHIDEE with many (28) parameters to be retrieved, a Monte Carlo approach (genetic algorithm, GA) provides more reliable optimal parameter values than a gradient-based minimization algorithm (variational scheme). The GA allows the global minimum to be found more efficiently, whilst the variational scheme often provides values relative to local minima. The ORCHIDEE model is then optimized for each year, and for the whole 2001-2004 period. We first find that a reduced (< 10) set of parameters can be tightly constrained by the eddy-covariance observations, with a typical error reduction of 90 %.We then show that including contrasted weather regimes (dry in 2003 and wet in 2002) is necessary to optimize a few specific parameters (like the temperature dependence of the photosynthetic activity). Furthermore, we find that parameters inverted from 4 years of flux measurements are successful at enhancing the model fit to the data on several timescales (from monthly to interannual), resulting in a typical modeling efficiency of 92% over the 2001-2004 period (Nash-Sutcliffe coefficient). This suggests that ORCHIDEE is able robustly to predict, after optimization, the fluxes of CO2 and the latent heat of a specific temperate beech forest (Hesse site). Finally, it is shown that using only 1 year of data does not produce robust enough optimized parameter sets in order to simulate properly the year-to-year flux variability. This emphasizes the need to assimilate data over several years, including contrasted weather regimes, to improve the simulated flux interannual variability. © Author(s) 2014

The potential benefit of using forest biomass data in addition to carbon and water flux measurements to constrain ecosystem model parameters: Case studies at two temperate forest sites

Biomass as a resource, and as a vulnerable carbon pool, is a key variable to diagnose the impacts of global changes on the terrestrial biosphere, and therefore its proper description in models is crucial. Model-Data Fusion (MDF) or data assimilation methods are useful tools in improving ecosystem models that describe interactions between vegetation and atmosphere. We use a MDF method based on a Bayesian approach, in which data are combined with a process model in order to provide optimized estimates of model parameters and to better quantify model uncertainties, whilst taking into account prior information on the parameters. With this method we are able to use multiple data streams, which allows us to simultaneously constrain modeled variables at site level across different temporal scales. In this study both high frequency eddy covariance flux measurements of net CO2 and evapotranspiration (ET), and low frequency biometric measurements of total aboveground biomass and the annual increment (which includes all compartments), are assimilated with the ORCHIDEE model version “AR5” at a beech (Hesse) and a maritime pine (Le Bray) forest site using four to five years of flux data and nine years of biomass data. When assimilating the observed aboveground annual biomass increment (AGB_inc) together with net CO2 and ET flux, the RMSE of modelled AGB_inc was reduced from the a priori estimates by 37% at Hesse and 69% at Le Bray, without reducing the fit to the net CO2 and ET that can be achieved when assimilating flux data alone. Assimilating biomass increment data also provides insight in the performance of the allocation scheme of the model. Comparison with detailed site-based measurements at Hesse showed that the optimization reduced positive biases in the model, for example in fine root and leaf production. We also investigated how to use stand-scale total aboveground biomass in optimization (AGB_tot). However, this study demonstrated that assimilating AGB_tot measurements in the ORCHIDEE-AR5 model lead to some inconsistencies, particularly for the annual dynamics of the AGB_inc, partly because this version of the model lacked a realistic representation of forest stand processes including management and disturbances. © 2016 Elsevier B.V

Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies with the ORCHIDEE Terrestrial Biosphere Model

International audienc