Abstract
Abstract. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land–atmosphere carbon exchange 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 improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g., nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g., photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e., model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.
Highlights
Projections of future climate conditions are based, in part, on the ability to simulate the key drivers and underlying processes that control how atmospheric carbon is exchanged with the terrestrial biosphere
As a result, existing estimates of land– atmosphere carbon exchange from Terrestrial biosphere models (TBMs) vary widely (e.g., Huntzinger et al, 2012), and coupled-carbon-climate models disagree on the strength of the net land sink, and whether the land surface will continue to be a net sink of atmospheric CO2 under changing climatic and environmental conditions (e.g., Friedlingstein et al, 2006)
While the North American Carbon Program (NACP) Regional and Continental Interim Synthesis (RCIS) and Site syntheses efforts provided a unique forum for summarizing the status of terrestrial carbon modeling, they reinforced the need for a consistent and unified model evaluation framework in order to isolate, interpret, understand, and better address differences – primarily structural or process representations – among stateof-the-art TBMs simulating land–atmosphere exchange over continental to global extents
Summary
Projections of future climate conditions are based, in part, on the ability to simulate the key drivers and underlying processes that control how atmospheric carbon (primarily CO2 and CH4) is exchanged with the terrestrial biosphere. While the NACP RCIS and Site syntheses efforts provided a unique forum for summarizing the status of terrestrial carbon modeling, they reinforced the need for a consistent and unified model evaluation framework in order to isolate, interpret, understand, and better address differences – primarily structural or process representations – among stateof-the-art TBMs simulating land–atmosphere exchange over continental to global extents. This manuscript provides an overview of, and describes the experimental protocol for, the Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). We outline some of the key components of the MsTMIP experimental design, focusing on the participating models, key simulations, and spin-up criteria, and show how the initial steady-state results demonstrate the importance of the choices made in the experimental design
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