Abstract. Large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr−1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.

SUMMARYAdvanced infrared sounders will provide thousands of radiance data at every observation location. The number of individual pieces of information is not usable in an operational numerical weather-prediction context, and we have investigated the possibilities of choosing an 'optimal' subset of data. These issues have been addressed in the context of optimal linear estimation theory, using simulated Infrared Atmospheric Sounding Interferometer data. Several methods have been tried to select a set of the most useful channels for each individual atmospheric pro le. These are two methods based on the data resolution matrix, one method based on the Jacobian matrix, and one iterative method selecting sequentially the channels with largest information content. The Jacobian method and the iterative method were found to be the most suitable for the problem. The iterative method was demonstrated to always produce the best results, but at a larger cost than the Jacobian method. To test the robustness of the iterative method, a variant has been tried. It consists in building a mean channel selection aimed at optimizing the results over the whole database, and then applying to each pro le this 'constant' selection. Results show that this 'constant' iterative method is very promising, with results of intermediate quality between the ones obtained for the optimal iterative method and the Jacobian method. The practical advantage of this method for operational purposes is that the same set of channels can be used for various atmospheric pro les.

We investigate the benefits of assimilating in situ and satellite data of the fraction of photosynthetically active radiation (FAPAR) relative to eddy covariance flux measurements for the optimization of parameters of the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystem) biosphere model. We focus on model parameters related to carbon fixation, respiration, and phenology. The study relies on two sites-Fontainebleau (deciduous broadleaf forest) and Puechabon (Mediterranean broadleaf evergreen forest)-where measurements of net carbon exchange (NEE) and latent heat (LE) fluxes are available at the same time as FAPAR products derived from ground measurements or derived from spaceborne observations at high (SPOT (Satellite Pour l′Observation de la Terre)) and medium (MERIS (MEdium Resolution Imaging Spectrometer)) spatial resolutions. We compare the different FAPAR products, analyze their consistency with the in situ fluxes, and then evaluate the potential benefits of jointly assimilating flux and FAPAR data. The assimilation of FAPAR data leads to a degradation of the model-data agreement with respect to NEE at the two sites. It is caused by the change in leaf area required to fit the magnitude of the various FAPAR products. Assimilating daily NEE and LE fluxes, however, has a marginal impact on the simulated FAPAR. The results suggest that the main advantage of including FAPAR data is the ability to constrain the timing of leaf onset and senescence for deciduous ecosystems, which is best achieved by normalizing FAPAR time series. The joint assimilation of flux and FAPAR data leads to a model-data improvement across all variables similar to when each data stream is used independently, corresponding, however, to different and likely improved parameter values.

A new method to transform satellite ocean color data into estimates of primary production and carbon fluxes is presented. A one‐dimensional coupled physical‐bio‐geochemical model of the surface ocean is constrained to reproduce the seasonal evolution of surface chlorophyll concentration by adjusting the parameters of the 10‐compartment trophic system using a variational technique. The method is applied to in situ surface chlorophyll data from station Papa, but averaged over the characteristic depth of ocean color measurements, and affected by errors compatible with those of satellite values. Using 35 measurements for the year 1976, it is found that five linear combinations of the trophic parameters can be adjusted. This adjustment is also valid for 1975 chlorophyll data. In general, the adjusted values of the trophic parameters are sensitive to their a priori values, but consistent results are found for the grazing rate (0.35 to 0.6 d−1), the phytoplankton mortality rate (very small), and the minimum concentration of zooplankton in winter (less than 0.16 mmolC m−3). Some carbon fluxes, namely photosynthetic carbon production in the euphotic layer (95 to 110 gC m−2 yr−1), its regeneration by grazing (60 % of the latter), and the recycling efficiency of nitrogen (60%) seem to be robustly constrained, though primary production is apparently underestimated compared to the most recent ones. The export flux amounts to 35 to 40% of primary production, but its value depends on the particle sinking rate, which is not adjustable from chlorophyll data. This study suggests that simplified biological models, compared to the model used here, would be sufficient to achieve this task.

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