## 5 Conclusions

Table 7: Summary of performance metrics for CO\({}_{2}\) concentrations and CO\({}_{2}\) annual growth rates at stations, KUM and SPO. an assimilation approach using both SMOS and FAPAR to constrain the carbon cycle simulation in the terrestrial biosphere model. When comparing simulation results against the in-situ observations, one needs to keep in mind the differences in the spatial representation of the assimilated products with the in-situ observations as mentioned above (see Section 4.3). Nevertheless, the fit against the NEP observations was improved at six of the 10 sites. For soil moisture, we see a reduction of the RMSE error against the in-situ observations of 51.5% and 23.4% for two sites, respectively. Our results from the multi-dataset assimilation (simultaneous assimilation of soil moisture and FAPAR) are consistent with previous studies recommending a combination of these two datasets in constraining parameters in terrestrial biosphere models [18; 19]. Also, the evaluation of the model performance on a global scale against atmospheric CO\({}_{2}\) indicated that optimized parameters result in a better fit as demonstrated by, for instance, a reduction of the RMSE of 10.4% against data from Cape Kumukahi and 20.6% against data from South Pole. However, the deterioration of the seasonal cycle amplitude, as mentioned in Section 4.4 is a sign for still unresolved sub-spaces in the total parameter space, i.e., parts of the biosphere model cannot be constrained by soil moisture and FAPAR data alone. By adding atmospheric CO\({}_{2}\) observations in the assimilation, we expect to constrain also these unresolved sub-spaces, at least partly. In general, assimilating further independent and complementary observations will help to constrain such unresolved sub-spaces in the total parameter space. We demonstrated here that a careful definition of prior parameters and their uncertainties is of importance for an efficient optimization of these parameters. The analysis of the assimilation results, however, revealed that for some sites, the model was not significantly improved. This was especially true for sites covered by grassland and crops, i.e., land cover types that are influenced by management processes, which are not included in the model. Besides a more detailed representation of water and energy balance processes, special attention should be paid to include more sites marked by an even wider spread in climate zones, climate variability, and ecosystem types. Such a wider spread and the addition of additional observations might help to constrain still 'unobserved' (i.e., unobserved by the assimilated soil moisture and FAPAR data) processes in the model as mentioned above. Using remote sensing data has the advantage of providing global data products for the assimilation, which will be further explored in future studies. [PERSON] and [PERSON] conceived and designed this study. [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] contributed to the analysis of the results and all authors co-wrote the manuscript. This research was funded by the SNSB project, grant number Rymdstyrelsen 102/14, and the ESA project, contract number 4000117645/16/NL/SW. The APC was partly funded by Rymdstyrelsen 102/14 and partly by Lund University. We thank three anonymous reviewers for improving the manuscript. We also thank [PERSON] from Department of Physical Geography and Ecosystem Science at Lund University for processing the CRUNCEP v7 meteorological data, [PERSON] from Department of Physical Geography and Ecosystem Science at Lund University for providing Python code for processing and re-gridding remotely sensed soil moisture data and for fruitful discussions. We acknowledge the FLUXNET2015 (www.fluxdata.org) working group for providing the fluxnet dataset used in our analysis.