Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process‐oriented biosphere model

Bacour, Cédric; Peylin, P.; MacBean, Natasha; Rayner, P. J.; Delage, François; Chevallier, F.; Weiss, Marie; Demarty, Jérôme; Santaren, Diego; Baret, Frédéric; Berveiller, Daniel; Dufrêne, Éric; Prunet, Pascal

doi:10.1002/2015jg002966

Cited by 41 publications

(52 citation statements)

References 73 publications

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“…For the assimilation, the half-hourly observations have been averaged on a daily basis and smoothed with a 15-days running mean, following Bacour et al (2015). This allows us to focus the optimisation on the seasonal and annual time scales, 30 excluding the influence of short-term flux variations.…”

Section: Assimilated Data: Carbon and Water Fluxesmentioning

confidence: 99%

“…More than 650 sites, operating on a long-term and continuous basis and covering major ecosystem of the world, are available (Baldocchi et al, 2001; http://fluxnet.fluxdata.org). A large number of studies have used these flux measurements to optimise ecosystem 30 model parameters, either using the data from one single site (SS) (Reichstein et al, 2003;Braswell et al, 2005;Moore et al, 2008;Ricciuto et al, 2011;Santaren et al, 2014;Kato et al, 2015;Bacour et al, 2015) or from multiple sites (MS) simultaneously usually at the level of PFT (Groenendijk et al, 2011;Kuppel et al, 2014;Raoult et al, 2016). These studies Geosci.…”

Section: Introductionmentioning

confidence: 99%

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

Bastrikov

MacBean

Bacour

et al. 2018

Preprint

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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 15 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 20 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 25 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.Geosci. Model Dev. Discuss., https://doi

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Section: Assimilated Data: Carbon and Water Fluxesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bastrikov

MacBean

Bacour

et al. 2018

Preprint

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“…Previous studies with these systems focussed on the effect of different (in situ and satellite) FAPAR observations at selected sites on simulated phenology with the ORCHIDEE model (e.g. Bacour et al, 2015) or on the joint use of site-level carbon flux and FAPAR observations . At the global scale, Forkel et al (2014) investigated the use of long-term FAPAR data to constrain long-term trends in vegetation greenness simulated by the LPJmL model, whereas Kaminski et al (2012) focussed on the joint assimilation of FAPAR and atmospheric CO 2 observations.…”

Section: Introductionmentioning

confidence: 99%

Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0

et al. 2016

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Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the JSBACH land-surface scheme, which is part of the MPI-Earth System Model v1. The simulated phenology and net land carbon balance were constrained by globally distributed observations of the fraction of absorbed photosynthetically active radiation (FAPAR, using the TIP-FAPAR product) and atmospheric CO 2 at a global set of monitoring stations for the years 2005 to 2009. When constrained by FAPAR observations alone, the system successfully, and computationally efficiently, improved simulated growing-season average FAPAR, as well as its seasonality in the northern extra-tropics. When constrained by atmospheric CO 2 observations alone, global net and gross carbon fluxes were improved, despite a tendency of the system to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO 2 ) equally well as the single data stream assimilation cases, thereby increasing the overall appropriateness of the simulated biosphere dynamics and underlying parameter values. Our study thus demonstrates the value of multiple-data-stream assimilation for the simulation of terrestrial biosphere dynamics. It further highlights the potential role of remote sensing data, here the TIP-FAPAR product, in stabilising the strongly underdetermined atmospheric inversion problem posed by atmospheric transport and CO 2 observations alone. Notwithstanding these advances, the constraint of the observations on regional gross and net CO 2 flux patterns on the MPI-CCDAS is limited through the coarse-scale parametrisation of the biosphere model. We expect improvement through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

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“…The model-data fusion framework used in this study is called the ORCHIDEE Data Assimilation System (ORCHIDAS, https://orchidas.lsce.ipsl.fr/index.php), which consists of the ORCHIDEE TEM and a Bayesian inversion framework [e.g., Kuppel et al, 2014;Bacour et al, 2015;MacBean et al, 2015;Peylin et al, 2016]. The ORCHIDEE model includes processes of the terrestrial carbon cycle, vegetation dynamics, and the Global Biogeochemical Cycles 10.1002/2017GB005714 energy, water, and momentum exchange between the atmosphere and biosphere [Krinner et al, 2005].…”

Section: The Orchidee Data Assimilation Systemmentioning

confidence: 99%

“…The cost function is minimized through a gradient-based algorithm called L-BFGS [Byrd et al, 1995], with its setting following Kuppel et al [2012Kuppel et al [ , 2014. Following previous studies [Kuppel et al, 2014;Bacour et al, 2015], we define the observation errors as the RMSE of fluxes between observations and the prior simulations during the optimization year. At each site, daily NEE, LE, and partitioned GPP for the year of optimization (i.e., the optimization year, Table S1) are flagged as the good quality data if the daily value is computed from the half-hourly data with less than 50% gaps within a day (see section 2.4.2).…”

Section: The Orchidee Data Assimilation Systemmentioning

confidence: 99%

Reducing the uncertainty of parameters controlling seasonal carbon and water fluxes in Chinese forests and its implication for simulated climate sensitivities

Yang

Wang

et al. 2017

Global Biogeochemical Cycles

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Reducing parameter uncertainty of process‐based terrestrial ecosystem models (TEMs) is one of the primary targets for accurately estimating carbon budgets and predicting ecosystem responses to climate change. However, parameters in TEMs are rarely constrained by observations from Chinese forest ecosystems, which are important carbon sink over the northern hemispheric land. In this study, eddy covariance data from six forest sites in China are used to optimize parameters of the ORganizing Carbon and Hydrology In Dynamics EcosystEms TEM. The model‐data assimilation through parameter optimization largely reduces the prior model errors and improves the simulated seasonal cycle and summer diurnal cycle of net ecosystem exchange, latent heat fluxes, and gross primary production and ecosystem respiration. Climate change experiments based on the optimized model are deployed to indicate that forest net primary production (NPP) is suppressed in response to warming in the southern China but stimulated in the northeastern China. Altered precipitation has an asymmetric impact on forest NPP at sites in water‐limited regions, with the optimization‐induced reduction in response of NPP to precipitation decline being as large as 61% at a deciduous broadleaf forest site. We find that seasonal optimization alters forest carbon cycle responses to environmental change, with the parameter optimization consistently reducing the simulated positive response of heterotrophic respiration to warming. Evaluations from independent observations suggest that improving model structure still matters most for long‐term carbon stock and its changes, in particular, nutrient‐ and age‐related changes of photosynthetic rates, carbon allocation, and tree mortality.

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Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process‐oriented biosphere model

Cited by 41 publications

References 73 publications

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

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

Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0

Reducing the uncertainty of parameters controlling seasonal carbon and water fluxes in Chinese forests and its implication for simulated climate sensitivities

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