Abstract. SURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surfaces: nature, town, inland water and ocean. It is mostly based on pre-existing, well-validated scientific models that are continuously improved. The motivation for the building of SURFEX is to use strictly identical scientific models in a high range of applications in order to mutualise the research and development efforts. SURFEX can be run in offline mode (0-D or 2-D runs) or in coupled mode (from mesoscale models to numerical weather prediction and climate models). An assimilation mode is included for numerical weather prediction and monitoring. In addition to momentum, heat and water fluxes, SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. The main principles of the organisation of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally, the main applications of the code are summarised. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage.

[1] The European Centre for Medium-Range Weather Forecasts land surface model has been extended to include a carbon dioxide module. This relates photosynthesis to radiation, atmospheric carbon dioxide (CO 2 ) concentration, soil moisture, and temperature. Furthermore, it has the option of deriving a canopy resistance from photosynthesis and providing it as a stomatal control to the transpiration formulation. Ecosystem respiration is based on empirical relations dependent on temperature, soil moisture, snow depth, and land use. The CO 2 model is designed for the numerical weather prediction (NWP) environment where it benefits from good quality meteorological input (i.e., radiation, temperature, and soil moisture). This paper describes the CO 2 model formulation and the way it is optimized making use of off-line simulations for a full year of tower observations at 34 sites. The model is then evaluated against the same observations for a different year. A correlation coefficient of 0.65 is obtained between model simulations and observations based on 10 day averaged CO 2 fluxes. For sensible and latent heat fluxes there is a correlation coefficient of 0.80. To study the impact on atmospheric CO 2 , coupled integrations are performed for the 2003 to 2008 period. The global atmospheric growth is well reproduced. The simulated interannual variability is shown to reproduce the observationally based estimates with a correlation coefficient of 0.70. The main conclusions are (i) the simple carbon dioxide model is highly suitable for the numerical weather prediction environment where environmental factors are controlled by data assimilation, (ii) the use of a carbon dioxide model for stomatal control has a positive impact on evapotranspiration, and (iii) even using a climatological leaf area index, the interannual variability of the global atmospheric CO 2 budget is well reproduced due to the interannual variability in the meteorological forcing (i.e., radiation, precipitation, temperature, humidity, and soil moisture) despite the simplified or missing processes. This highlights the importance of meteorological forcing but also cautions the use of such a simple model for process attribution.

Abstract. The performance of the joint assimilation in a land surface model of a Soil Wetness Index (SWI) product provided by an exponential filter together with Leaf Area Index (LAI) is investigated. The data assimilation is evaluated with different setups using the SURFEX modeling platform, for a period of seven years (2001)(2002)(2003)(2004)(2005)(2006)(2007), at the SMOSREX grassland site in southwestern France. The results obtained with a Simplified Extended Kalman Filter demonstrate the effectiveness of a joint data assimilation scheme when both SWI and Leaf Area Index are merged into the ISBA-A-gs land surface model. The assimilation of a retrieved Soil Wetness Index product presents several challenges that are investigated in this study. A significant improvement of around 13 % of the root-zone soil water content is obtained by assimilating dimensionless root-zone SWI data. For comparison, the assimilation of in situ surface soil moisture is considered as well. A lower impact on the root zone is noticed. Under specific conditions, the transfer of the information from the surface to the root zone was found not accurate. Also, our results indicate that the assimilation of in situ LAI data may correct a number of deficiencies in the model, such as low LAI values in the senescence phase by using a seasonal-dependent error definition for background and observations. In order to verify the specification of the errors for SWI and LAI products, a posteriori diagnostics are employed. This approach highlights the importance of the assimilation design on the quality of the analysis. The impact of data assimilation scheme on CO 2 fluxes is also quantified by using measurements of net CO 2 fluxes gathered at the SMOSREX site from 2005 to 2007. An improvement of about 5 % in terms of rms error is obtained.

SURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surface: nature, town, inland water and ocean. It can be run either coupled or in offline mode. It is mostly based on pre-existing, well validated scientific models. It can be used in offline mode (from point scale to global runs) or fully coupled with an atmospheric model. SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. It also includes a data assimilation module. The main principles of the organization of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally the main applications of the code are summarized. The current applications are extremely diverse, ranging from surface monitoring and hydrology to numerical weather prediction and global climate simulations. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage

[1] Optimum daily light-use efficiency (LUE) and normalized canopy photosynthesis (GEE*) rate, a proxy for LUE, have been derived from eddy covariance CO 2 flux measurements obtained at a range of sites located in the mid to high latitudes. These two variables were analyzed with respect to environmental conditions, plant functional types (PFT) and leaf nitrogen concentration, in an attempt to characterize their variability and their potential drivers. LUE averaged 0.0182 mol/mol with a coefficient of variation of 37% (42% for GEE*). Foliar nitrogen N of the dominant plant species was found to explain 71% of LUE (n = 26) and 62% of GEE* (n = 44) variance, across all PFTs and sites. Mean Annual Temperature, MAT, explained 27% of LUE variance, and the two factors (MAT and N) combined in a simple linear model explain 80% of LUE and 76% GEE* variance. These results showed that plant canopies in the temperate, boreal and arctic zones fit into a general scheme closely related to the one, which had been established for plant leaves worldwide. The N-MAT-LUE relationships offer perspectives for LUE-based models of terrestrial photosynthesis based on remote sensing. On a continental scale, the decrease of LUE from the temperate to the arctic zone found in the data derived from flux measurements is not in line with LUE resulting from inversion of atmospheric CO 2 .