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Terrestrial biosphere models (TBMs) have become an integral tool forextrapolating local observations and understanding of land–atmosphere carbonexchange to larger regions. The North American Carbon Program (NACP)Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP)is a formal model intercomparison and evaluation effort focused on improvingthe diagnosis and attribution of carbon exchange at regional andglobal scales. MsTMIP builds upon current and past synthesis activities, andhas a unique framework designed to isolate, interpret, and informunderstanding of how model structural differences impact estimates of carbonuptake and release. Here we provide an overview of the MsTMIP effort anddescribe how the MsTMIP experimental design enables the assessment andquantification of TBM structural uncertainty. Model structure refers to thetypes of processes considered (e.g., nutrient cycling, disturbance, lateraltransport of carbon), and how these processes are represented (e.g.,photosynthetic formulation, temperature sensitivity, respiration) in themodels. By prescribing a common experimental protocol with standard spin-upprocedures and driver data sets, we isolate any biases and variability in TBMestimates of regional and global carbon budgets resulting from differences inthe models themselves (i.e., model structure) and model-specific parametervalues. An initial intercomparison of model structural differences isrepresented using hierarchical cluster diagrams (a.k.a. dendrograms), whichhighlight similarities and differences in how models account for carboncycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despitethe standardized protocol used to derive initial conditions, models show ahigh degree of variation for GPP, total living biomass, and total soilcarbon, underscoring the influence of differences in model structure andparameterization on model estimates

The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design