## 5 Discussion
### Model Evaluation  
Accurate modeling of net primary productivity (NPP) and GPP is essential for predicting carbon-climate feedback in the future. There are many models without coupling the P cycle, which have been evaluated for GPP or NPP. The IBIS model used 19 sites globally and found reasonable agreement between simulated and observed NPP (R\({}^{2}\) = 0.52) [91]. BGC and DNDC models have been used to examine potential impacts of climatic and CO\({}_{2}\) changes as well as N deposition or fertilization, but these models do not incorporate the P cycle, and they have been validated only for the regions North America, Australia, or Europe. The recently developed model, such as CLM-CNP model has been validated using NPP data from two forest sites in Hawaii and the Amazon [56], and the model captured the shift of nutrients from N to P limitation in a tropical forest ecosystem. N14 CP that integrated P weathering and N deposition was validated using data from 88 sites in northern Europe [92]. In this study, we achieved an R\({}^{2}\) = 0.85 for GPP using TRIPLEX-CNP, which incorporates the P cycle and was validated for different plant functional types (PFTs) using data from 116 sites. Our model simulated GPP 8% better than the predecessor model TRIPLEX-GHG, which does not contain the P cycle. We are aware of a few of global land surface models that have fully integrated C-N-P cycles and have been evaluated for different PFTs on global scale, CASA-CNP and JSBACH-CNP were two good examples. However, the ability of CASA-CNP to model P processes has been validated only with respect to leaf N:P ratios at absolute latitudes, while the ability of JSBACH-CNP has not been validated in detail but only through indirect comparisons to the literature [54]. JSBACH-CNP simulated mean P stock of 0.23 Gt P in a vegetation pool from 1970 to 1999, smaller than the range of 0.39-3.0 [53], but much larger than the 0.08 Gt [93]. JSBACH-CNP estimated soil organic P of 5.74 Gt, which fell at the low end of the 5-10 Gt P [94]. In addition, it provided updated information regarding to the need to incorporate P cycle processes to estimate global C stocks under climate change [95]. They tested and evaluated ORCHIDEE-CNP model at global scale using remote sensing data, instead of site-by-site observation data. For biomass evaluation, we found that a few models, which have been coupled with the P cycle, have a global validation of biomass. For example, the ORCHIDEE-CNP [59] used the GlobBiomass dataset to evaluate the simulated global forest above-ground biomass. The CoupModel (v6.0) showed the simulated and measured plant biomass of four regions in Sweden [96]. In addition, simulated biomass response to fertilizer addition were consistent with field observations in tropical forests [97]. SOC is an important soil variable to evaluate the effect of the P cycle on the soil C cycle. Accurate modeling of the soil C cycle is essential for predicting C-N-P cycles and climate feedback in the future. Current models provide SOC estimates with large uncertainty: SOC simulations by 11 Earth system models from the fifth Coupled Model Intercomparison Project [98] varied 6-fold, despite the similarities in model structures [99], and only six of the models gave estimates within the range of 890-1660 Pg C in the HWSD. The spatial correlation coefficient between modeled and observed SOC values did not exceed 0.4 for any of the models [100; 101]. Compared with other validation results, the simulated SOC result is less accurate. It might be related several reasons, including that most models lack of the microbe process for simulating soil organic matter (SOM), and very few models have explicitly represented soil microorganisms at global scale [102]. In addition, the SOC data used for model validation in this study is not site by site point data, resulting in deviations due to scaling problems, due to the lack of SOC observation--on a global scale.