[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.

[1] The land surface model (LSM) ISBA-A-gs (Interactions between Soil, Biosphere and Atmosphere, CO 2 -reactive) is specifically designed to simulate leaf stomatal conductance and leaf area index (LAI) in response to climate, soil properties, and atmospheric carbon dioxide concentration. The model is run at the global scale, forced by the GSWP-2 meteorological data at a resolution of 1°for the period of 1986-1995. We test the model by comparing the simulated LAI values against three satellite-derived data sets (ISLSCP Initiative II data, MODIS data and ECOCLIMAP data) and find that the model reproduces the major patterns of spatial and temporal variability in global vegetation. As a result, the mean of the maximum annual LAI estimates of the model falls within the range of the various satellite data sets. Despite no explicit representation of phenology, the model captures the seasonal cycle in LAI well and shows realistic variations in start of the growing season as a function of latitude. The interannual variability is also well reported for numerous regions of the world, particularly where precipitation controls photosynthesis. The comparison also reveals that some processes need to be improved or introduced in the model, in particular the snow dynamics and the treatment of vegetation in cultivated areas, respectively. The overall comparisons demonstrate the potential of ISBA-A-gs model to simulate LAI in a realistic fashion at the global scale.

A multimodel comparison of the performance of land surface parameterization schemes increases understanding of the land-atmosphere feedback mechanisms over West Africa.

The AMMA-CATCH Gourma observatory site in Mali: 7The experimental strategy includes deployment of a variety of instruments, from local to 8 meso-scale, dedicated to monitoring and documentation of the major variables characterizing 9 the climate forcing, and the spatio-temporal variability of surface processes and state 10 variables such as vegetation mass, leaf area index (LAI), soil moisture and surface fluxes. 11This paper describes the Gourma site, its associated instrumental network and the research 12 activities that have been carried out since 1984. In the AMMA project, emphasis is put on the 13 relations between climate, vegetation and surface fluxes. However, the Gourma site is also 14 important for development and validation of satellite products, mainly due to the existence of 15 large and relatively homogeneous surfaces. The social dimension of the water resource uses 16 and governance is also briefly analyzed, relying on field enquiry and interviews. 18The climate of the Gourma region is semi-arid, daytime air temperatures are always high and 29Land surface in the Gourma is characterized by rapid response to climate variability, strong

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97 % (κ = 0.85) for MOD10A1 and 96 % (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96 %, κ = 0.77; MYD10A1: 95 %, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50 % of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Root-zone soil moisture constitutes an important variable for hydrological and weather forecast models. Microwave radiometers like the L-band instrument on board the European Space Agency’s (ESA) future Soil Moisture and Ocean Salinity (SMOS) mission are being designed to provide estimates of near-surface soil moisture (0–5 cm). This quantity is physically related to root-zone soil moisture through diffusion processes, and both surface and root-zone soil layers are commonly simulated by land surface models (LSMs). Observed time series of surface soil moisture may be used to analyze the root-zone soil moisture using data assimilation systems. In this paper, various assimilation techniques derived from Kalman filters (KFs) and variational methods (VAR) are implemented and tested. The objective is to correct the modeled root-zone soil moisture deficiencies of the newest version of the Interaction between Soil, Biosphere, and Atmosphere scheme (ISBA) LSM, using the observations of the surface soil moisture of the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) over a 4-yr period (2001–04). This time period includes contrasting climatic conditions. Among the different algorithms, the ensemble Kalman filter (EnKF) and a simplified one-dimensional variational data assimilation (1DVAR) show the best performances. The lower computational cost of the 1DVAR is an advantage for operational root-zone soil moisture analysis based on remotely sensed surface soil moisture observations at a global scale.

No abstract

No abstract