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Abstract. Even though dissolved organic carbon (DOC) is the most active carbon (C) cycling in soil organic carbon (SOC) pools, it receives little attention from the global C budget. DOC fluxes are critical to aquatic ecosystem inputs and contribute to the C balance of terrestrial ecosystems, but few ecosystem models have attempted to integrate DOC dynamics into terrestrial C cycling. This study introduces a new process-based model, TRIPLEX-DOC, that is capable of estimating DOC dynamics in forest soils by incorporating both ecological drivers and biogeochemical processes. TRIPLEX-DOC was developed from Forest-DNDC, a biogeochemical model simulating C and nitrogen (N) dynamics, coupled with a new DOC process module that predicts metabolic transformations, sorption/desorption, and DOC leaching in forest soils. The model was validated against field observations of DOC concentrations and fluxes at white pine forest stands located in southern Ontario, Canada. The model was able to simulate seasonal dynamics of DOC concentrations and the magnitudes observed within different soil layers, as well as DOC leaching in the age sequence of these forests. Additionally, TRIPLEX-DOC estimated the effect of forest harvesting on DOC leaching, with a significant increase following harvesting, illustrating that land use change is of critical importance in regulating DOC leaching in temperate forests as an important source of C input to aquatic ecosystems.

Abstract Although fast‐growing Populus species consume a large amount of water for biomass production, there are considerable variations in water use efficiency (WUE) across different poplar species. To compare differences in growth, WUE and anatomical properties of leaf and xylem and to examine the relationship between photosynthesis/WUE and anatomical properties of leaf and xylem, cuttings of six poplar species were grown in a botanical garden. The growth performance, photosynthesis, intrinsic WUE (WUE i ), stable carbon isotope composition (δ 13 C) and anatomical properties of leaf and xylem were analysed in these poplar plants. Significant differences were found in growth, photosynthesis, WUE i and anatomical properties among the examined species. Populus cathayana was the clone with the fastest growth and the lowest WUE i /δ 13 C, whereas P.  ×  euramericana had a considerable growth increment and the highest WUE i /δ 13 C. Among the analysed poplar species, the highest total stomatal density in P. cathayana was correlated with its highest stomatal conductance (g s ) and lowest WUE i /δ 13 C. Moreover, significant correlations were observed between WUE i and abaxial stomatal density and stem vessel lumen area. These data suggest that photosynthesis, WUE i and δ 13 C are associated with leaf and xylem anatomy and there are tradeoffs between growth and WUE i . It is anticipated that some poplar species, e.g. P. × euramericana , are better candidates for water‐limited regions and others, e.g. P. cathayana , may be better for water‐abundant areas.

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.

Abstract. A new process-based model TRIPLEX-GHG was developed based on the Integrated Biosphere Simulator (IBIS), coupled with a new methane (CH4) biogeochemistry module (incorporating CH4 production, oxidation, and transportation processes) and a water table module to investigate CH4 emission processes and dynamics that occur in natural wetlands. Sensitivity analysis indicates that the most sensitive parameters to evaluate CH4 emission processes from wetlands are r (defined as the CH4 to CO2 release ratio) and Q10 in the CH4 production process. These two parameters were subsequently calibrated to data obtained from 19 sites collected from approximately 35 studies across different wetlands globally. Being heterogeneously spatially distributed, r ranged from 0.1 to 0.7 with a mean value of 0.23, and the Q10 for CH4 production ranged from 1.6 to 4.5 with a mean value of 2.48. The model performed well when simulating magnitude and capturing temporal patterns in CH4 emissions from natural wetlands. Results suggest that the model is able to be applied to different wetlands under varying conditions and is also applicable for global-scale simulations.

Abstract. Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales.