## 4 Discussion
This may partially reflect difficulties in accounting for belowground processes, which can contribute disproportionately to C fluxes, and in connecting flux tower NEE to forest stands where the contributing area extent is far greater than a one-hectare stand, as is often the case [138; 139; 140]. This issue can be seen in Figures 6 and 8. In this model intercomparison exercise, ANPP for the PPA-SiBGC model and field data are based on annual woody biomass increment, while ANPP in LANDIS-II NECN includes the Century process model for estimating ANPP. Rather than this basis making the NECN model purely process-based or mechanistic, species-specific growth is tightly constrained by empirical limits in a truncated logistic curve, with LAI and the number of cohorts present used as a proxy for growing space limitations and moisture and temperature used for physiological constraint based on [PERSON]'s Law of the Minimum. In contrast, PPA-SiBGC is parameterized with mean observed growth and mortality rates from field data, which vary depending on the canopy position of a cohort. Understory cohorts assumed to be in full shade face higher mortality and lower growth, as is widely evident in field data, while overstory cohorts assumed to be in full sunlight have higher growth and lower mortality rates. While soil and root processes are explicitly simulated in Century and thus LANDIS-II NECN, PPA-SiBGC relies on simple stoichiometric and allometric relations from field data to model these pools. In other words, PPA-SiBGC is designed primarily to model pools rater than fluxes, as the former are of generally higher interest to foresters. The strong empirical basis of parameterization of both PPA-SiBGC and LANDIS-II NECN explains why the two models are often in better agreement with each other than with observations. The similarity of outputs from the two models is perhaps surprising, given their differences in model architecture and theoretical basis. This shows that, despite any mechanistic process present, both models in their current form are closely fit to field data and are therefore strongly empirical, as evidenced by their representation of growth processes. Meanwhile, this design choice limits the representation of fluxes in both models, as detailed process models are absent. This is expected for PPA-SiBGC, which is intended primarily to be a simple empirical pool model. This work also shows that observations between field and tower measurements are substantially disconnected. We estimate that fluxes are poorly represented by both models because they are tightly coupled to field inventory data rather than to tower-based measurements. Hence, patterns evident in field inventory data are reliably reproduced while fluxes appear wholly uncoupled. The advancement of processor architectures has facilitated the development of increasingly complex forest models. Each new generation of processors allows researchers to conduct large-scale simulations faster and more efficiently than previous designs. As a result, forest models have grown into large, complex, analytically intractable programs. Rigorous intercomparison of models developed by different research groups, as well as the diagnosis of new versions of established models, is therefore a critical step in further advancing ecosystem models. This ensures that models are properly diagnosed and compared in a consistent, reliable, and transparent manner. Too often, model intercomparisons are conducted by each separate research group applying their own model in a manner that is, at best, inconsistent and opaque. In this work, we extended our model intercomparison by further providing wrapper functions that may be used to benchmark additional models or sites through a unified modeling framework. This ensures the consistency and transparency of intercomparison results. The presented research is intended to establish the groundwork for future model intercomparison studies at both sites in order to advance the design of new models.