## 4 Discussion  
According to [PERSON] _et al._[38], there are two direct dependencies of GPP on drought: structural changes in the vegetation, and physiological responses of the vegetation. In this study we only consider the latter ones. Depending on the physiological responses of stomata to soil moisture, plant species can be roughly divided into two types, _i.e._, isohydric and anisohydric species [39]. The isohydric species close their stomata when soil moisture decreases or VPD increases, while the anisohydric species are insensitive to soil moisture but close their stomata only responding to high VPD. Therefore, modeling drought response using VPD or soil moisture is similar to assuming that vegetation is composed by either isohydric or anisohydric species. The TOPS model structure assumes an isohydric behavior of vegetation, whereas models in which drought is simulated through VPD controls, such as the MODIS 17 algorithm, assume an anisohydric behavior of vegetation. Although plants cannot be clearly divided into isohydric or anisohydric by species [40], forest trees are predominantly of isohydric nature [41; 42; 43]. Thus, ecosystem models should have both VPD and soil moisture sub-models to properly represent the drought effect on GPP. Although both VPD and precipitation are required for modeling physiological processes an exception can be made for short-term analyses when VPD and precipitation tend to be closely related. Our simulation in Figure 1 showed similar trend with MODIS17 analysis in 2000s [6]. Caution should be exerted, however, in extending the interpretation of short-term effects of drought effects on GPP to long-term trends. Our 30-year simulation clearly showed different trends between soil moisture-driven and VPD-driven simulations. Dynamic global vegetation models (DGVMs) also showed model-dependent sensitivities to increased VPD in correspondence to increased temperature in the Amazon during the 21 st century [44]. Though this study focused on the global scale variability and trends in GPP, we need more studies dealing with the differential controls on a regional scale. For example, [PERSON] _et al._[11] reported the decoupling between precipitation and VPD caused a failure in GPP simulation by MODIS 17 algorithm in monsoon-controlled China. It is also known that variations in VPD sometimes fail to capture severe droughts at a watershed scale [45]. Therefore, assessing long-term trend in GPP in regional scale is more difficult by VPD-only model. In addition to climate variability, other factors, not accounted here, such as CO\({}_{2}\) fertilization, nitrogen deposition [46], and diffuse radiation [37], affect the interannual variation in GPP. These effects are difficult to quantify and complicate the bottom-line GPP trend through combined effects [47]. In this study, by focusing on the difference of after-2000 and before-2000, we ignored these effects on the interannual variation in GPP. CO\({}_{2}\) concentration and nitrogen deposition have a smaller interannual variability compared to the climate variables [5; 48], and the effect of diffuse radiation is marginal over the three decades studied here [37]. The differences in GPP after 2,000 simulated by different models were also found in time series of estimated evapotranspiration [49]. [PERSON] _et al._[49] showed that most of the ecosystem models displayed an increasing trend in modeled evapotranspiration from 1982 to 1998, but after that trends diverged among models. [PERSON] _et al._[49] concluded that the decreasing trend in evapotranspiration found in some models after 1998 was due to the limited soil moisture supply. However, similarly to the divergent GPP trends simulated for the 2000s, the diversion after 1998 can be explained by therelative sensitivity of the model structure to VPD compared to other climate components. The observed global warming trend over the past few decades causes the VPD to increase.