## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Atmosphere Fluxes  
#### 3.2.1 Co\({}_{2}\) Natural Ecosystem Exchange  
More recently, the ECMWF land surface scheme has been extended with a carbon dioxide module based on the A-gs model [147]. The reason for adopting simple vegetation and carbon dioxide schemes, is that these are suitable for the NWP setup where environmental factors are controlled by meteorological forcing and constrained by data assimilation. The model relates photosynthesis to radiation, atmospheric carbon dioxide (CO\({}_{2}\)) concentration, soil moisture and temperature. Ecosystem respiration is based on empirical relations dependent on temperature, soil moisture, snow depth and land use [148]. The CO\({}_{2}\) module parameters are optimised by vegetation type considering the Gross Primary Production (GPP) and the Ecosystem Respiration (Reco). Together, they compose the Net Ecosystem Exchange (NEE) of CO\({}_{2}\) between biosphere and atmosphere. The FLUXNET-based in situ observations from different climate regimes ([[http://www.fluxdata.org/](http://www.fluxdata.org/)]([http://www.fluxdata.org/](http://www.fluxdata.org/))), combined with a benchmarking systems similar to iLAMB [140], was used for parameter optimization by minimizing flux errors. Subsequently, a different year of the FLUXNET data was used for verification. The seasonal cycle of NEE is illustrated in Figure 12 for six sites with different biomes. Two model configurations are shown: the first uses a stomatal resistance formulation for evaporation that is controlled by the photosynthesis module (C-TESSEL), and the second uses the Jarvis-based stomatal resistance for evapotranspiration (CH-TESSEL). In addition, the CASA climatology (Carnegie-Ames-Stanford Approach, [149] ) is shown because it is extensively used in the community and it was previously used as a boundary condition for atmospheric CO\({}_{2}\) in the MACC (Monitoring Atmospheric Composition and Climate) project. Although it is difficult to draw firm conclusions, it is clear that the errors in NEE are large and vary dramatically from site to site, and differences between C-TESSEL and CH-TESSEL are small compared to the errors. The correlation between model NEE and observations averaged over 34 flux tower sites is 0.37 for CASA, 0.68 for C-TESSEL and 0.65 for CH-TESSEL. Both TESSEL versions have a correlation of about 0.80 for sensible and latent heat fluxes [148]. The substantial improvement of C-TESSEL/CH-TESSEL with respect to the CASA climatology is significant because it suggests that the real-time meteorological variability is a key driver of the NEE variability. Figure 11: Relative anomaly [%] with respect to mean (1999–2012) climate of Leaf Area Index (LAI) (**left**) and broadband diffuse albedo (**right**) for (**a**) August 2003; (**b**) July 2010 and (**c**) November 2010. Regions of interest are zoomed in. Correlations coefficients of the two components of NEE, GPP and Reco, compared with tower observations (0.8 for both fluxes, on average over 34 sites) indicate much higher skill and confirm the robustness of these results. NEE is a small residual of GPP and Reco; therefore, a correlation coefficient above 0.6 is highly relevant. Again, C-TESSEL and CH-TESSEL show similar performance, with site to site variability attributed to representativity of the model grid-box [148]. CH-TESSEL has also been evaluated in coupled integration mode for the 2003 to 2008 period. Figure 12: Seasonal cycle (2004) of 10-day averaged offline simulated (lines) and observed (blue dots) Net Ecosystem Exchange \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) for C-TESSEL (with A-gs, black line), CH-TESSEL (with Jarvis-type evaporation, red line) and CASA-GFED3 (green line) at diverse FLUXNET observational sites in the panels (**a–f**) representative of different biomes: (**a**) Southern Great Plains site-Lamont, US; (**b**) Monte Bondone, Italy; (**c**) Tonzi Ranch Sierra Nevada, US; (**d**) Morgan Monroe State Forest, US; (**e**) Saskatchewan Western Boreal, Mature Black Spruce forest, Canada; (**f**) Castelporziano, Italy.