## 4 Discussion
Furthermore, we hope that this work will inspire a new generation of forest model intercomparisons in North America, which are sorely absent. Forest models have proven to be a critical testbed for improving the representation of vegetation dynamics in global terrestrial biosphere models [40; 41; 143], given the importance of forests in the global carbon cycle and the increased detail of local- to regional-scale models. Model benchmarking datasets and related results should be publicly shared and regularly updated with version-controlled software repositories (e.g., GitHub or GitLab), as is commonplace in the machine learning research community. Cloud computing providers may provide full reproducibility for cases where compute is limiting. In general, there is a broad disparity between modern software tools and existing forest models. One important new forest model in development is a next-generation model from the SORTIE-PPA family of models, known as SORTIE-NG. This new model combines mechanistic representations of demographic processes, energetic and biogeochemical fluxes, and landscape disturbance dynamics, using hierarchical multiscale modeling with a modular component-based software framework [144]. Along with LM3-PPA [45], SORTIE-NG is among the first of a new class of hybrid models that we term 'cohort-leaf' models for their partitioning of energetic and biogeochemical fluxes amongst dynamic vegetation cohorts, instead of a single vertical 'big-leaf' profile. The SORTIE-NG model includes evolutionary optimality principles as well as phenotype plasticity and intraspecific genetic diversity through first-class support for probabilistic modeling, borrowing design principles from probabilistic programming languages (e.g., [145]). Thus, SORTIE-NG is intended to be the first forest model to bridge the divide between big-leaf, gap, and landscape models, and to be designed from the outset as a probabilistic modeling framework [144]. Future model extensions are in the planning stages, including the first machine learning processes included in an ecosystem model. While implemented in a 'close-to-metal' language (i.e., C++17) and designed for efficiency, SORTIE-NG is more computationally demanding than the PPA-SiBGC model used in this paper. Yet, we anticipate that SORTIE-NG will be able to improved the fidelity to observed fluxes through reliance on detailed process models, which is the major shortcoming of both models considered in this paper. Similarly, there is a new version of the LANDIS-II NECN model in development known as NECN-Hydro, which remains a simplified variant of the Century model, but includes more detailed hydrological processes. The currently presented work provides not only an intercomparison of two current state-of-art models, but also open-source software and wrapper functions for simple and rapid comparison of our results with new models or sites. The selected forested ecosystems modeled in this work are among the best-studied model forests on Earth today. Specifically, the EMS EC flux tower at Harvard Forest is the longest running flux tower in the United States. Extensions of the presentedwork will allow rigorous model comparison methodologies for forest models that will benefit the research community at large. Extensions of this work may also address the robustness of model predictions to variations in parameter values. The parameterization of complex forest biogeochemistry models such as LANDIS-II NECN and PPA-SiBGC is an important problem for consideration. Models such as LANDIS-II NECN operate with an order of magnitude more parameters than PPA-SiBGC, which can each be estimated with different levels of accuracy. Often, we know only the range of parameter values while parameterization can also depend on the statistical approach employed. Meanwhile, authors routinely employ additional model calibration that consists of adjusting parameters in order to obtain improved fitness, which we explicitly avoided in this study.