Consistent assimilation of multiple data streams in a carbon cycle data assimilation system

Abstract

Abstract. Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. The increase in the number of available datasets in recent years allows us to address different aspects of the model at a variety of spatial and temporal scales. However, combining data streams in a DA system is not a trivial task. In this study we highlight some of the challenges surrounding multiple data stream assimilation for the carbon cycle component of LSMs. We give particular consideration to the assumptions associated with the type of inversion algorithm that are typically used when optimising global LSMs – namely, Gaussian error distributions and linearity in the model dynamics. We explore the effect of biases and inconsistencies between the observations and the model (resulting in non-Gaussian error distributions), and we examine the difference between a simultaneous assimilation (in which all data streams are included in one optimisation) and a step-wise approach (in which each data stream is assimilated sequentially) in the presence of non-linear model dynamics. In addition, we perform a preliminary investigation into the impact of correlated errors between two data streams for two cases, both when the correlated observation errors are included in the prior observation error covariance matrix, and when the correlated errors are ignored. We demonstrate these challenges by assimilating synthetic observations into two simple models: the first a simplified version of the carbon cycle processes represented in many LSMs and the second a non-linear toy model. Finally, we provide some perspectives and advice to other land surface modellers wishing to use multiple data streams to constrain their model parameters.

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

“…The paper also briefly recapitulates the assimilation systems capable of integrating these data: a more comprehensive description of the underlying formalism is given in Rayner et al (2016) while MacBean et al (2016) discuss the implementation strategies for a multiple data assimilation system and their impacts on the results. To take maximum advantage of these data streams in carbon cycle data assimilation studies it is of utmost importance to have the appropriate knowledge of the uncertainty characteristics of the observational data, here with a focus on satellite products.…”

“…On the other hand, a better fit between the posterior maximum likelihood simulation (i.e. using the optimised parameters) and the observations is not necessarily an indication for correct parameters and/or model structure as has been pointed out by MacBean et al (2016).…”

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

“…However, in order to apply modeldata integration for global process-oriented vegetation-fire models, multiple datasets on vegetation, hydrological, and fire-related variables should be used to realistically constrain vegetation-fire interactions. Hence there is a need to develop appropriate observation operators and to extend currently existing model-data integration frameworks of global vegetation models (Forkel et al, 2014;Kaminski et al, 2013;MacBean et al, 2016;Schürmann et al, 2016) to the corresponding fire modules in order to formally assess model structures and to constrain model parameters. In summary, model-data integration frameworks need to be developed that make use of multiple satellite datasets on vegetation and moisture proxies in order to improve the representation of fire in global vegetation models and thus to better understand interactions of fire with ecosystems and the atmosphere within the Earth system.…”

ORCHIDEE-SOM: modeling soil organic carbon (SOC) and dissolved organic carbon (DOC) dynamics along vertical soil profiles in Europe

“…Consequently, prior to the evaluation of the soil module in ORCHIDEE-SOM, we used gross primary production (GPP) measurements from the FLUXNET network to optimize the GPP-related parameters (V cmax , surface leaf area, maximum leaf area index, minimum leaf area index to start photosynthesis, and minimum and maximum photosynthesis temperature sensitivity) in ORCHIDEE in order to ensure that model inputs coming from plant production are correct (Table S1 in the Supplement). The ORCHIDEE data assimilation system, based on a Bayesian optimization scheme, was used for the optimization (MacBean et al, 2016). The optimization approach relies on the iterative minimization of the mismatch between the set of experimental observations and corresponding model outputs by adjusting the model-driving parameters using the L-BFGS-B algorithm (Byrd et al, 1995).…”