## 4 Discussion
### Uncertainties Related to NDVI-NPP Comparison  
NDVI provides a global measure of vegetation canopy responses to environmental conditions. Its strong relationship with the fraction of absorbed photosynthetically active radiation (fPAR) that it has been widely used as a proxy for gross primary productivity to study the terrestrial ecosystems dynamics [3; 4; 40; 41]. However, the strength of this proxy reduces for disentangling of driving factors because NDVI is a one-dimensional measure and integrates the effects of many climatic and non-climatic factors [7]. It also has limitations in densely vegetated regions, because of saturation effects [20] and it may, in certain vegetation types, not fully follow the photosynthetic cycle. NPP models, on the other hand, have uncertainties linked to the parameterization [42], model formulation and underlying assumptions and it is not always clear how these propagate into the model predictions. Therefore, the results presented in this study should be interpreted with appropriate caution, although the interpretation of large-scale patterns and long-term trends can be expected to largely alleviate these concerns.  
Even though this study showed a general agreement between global NPP and NDVI trends (for both ensemble and individual models); the spatial patterns varied significantly. These differences between NPP and NDVI trends can be partly attributed to the identified sources of uncertainties. For example, although several calibration and corrections have been applied to the NDVI data to create the global GIMMS product, some uncertainties associated with multiple satellite sensors, solar zenith angles and atmospheric conditions/corrections remain [4; 43]. Furthermore, several other factors (e.g., atmospheric conditions, scale of imagery, moisture conditions and vegetation cover) can also largely influence the accuracy of NDVI. For example, [PERSON] and [PERSON] [44] found that the soil surface influence on NDVI values was greatest in areas with vegetation cover between 45%-70%, while the NDVI signal may saturate in densely vegetated regions like tropical forest [45]. Discrepancies between NPP models can be partly attributed to the representation of land cover types among models. Some models (e.g., CLM4.5) include highly sophisticated land classification with 17 plant functional types [46] compared to other models that have smaller number of plant functional types (e.g., VEGAS with 6 plant functional types). This diverse representation of land cover/class types result in a variety of different processes and associated parameter settings (relating to carbon allocation and transfer rates among carbon pools) and contribute to differences among the models [47]. Models may use different approaches for representing natural and anthropogenic disturbances (e.g., fire, land use changes, and grazing and harvesting of forest products and croplands) [48]. Benchmarking of the models' response to different disturbances can improve the discrepancies between NDVI and modeled NPP. Climate condition is one of the major factors in regulating the NPP dynamics; therefore, some discrepancies between NDVI and modeled NPP can be attributed to the uncertainties in climate dataset due to temporal and spatial interpolation [49]. Despite the discrepancies between NDVI and modeled NPP, the results of this study provide unique and useful insight for understanding the global and regional vegetation dynamics in response to prevailing climate conditions, and their potential impact on carbon cycle.