Whole-ecosystem labile carbon production in a north temperate deciduous forest

Using model‐data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling

“…As the process-based model here was optimized to the data, parameter error can be discounted, leaving model structural error, biotic effects, or missing drivers (e.g., diffuse radiation: Moffat et al, 2010) as potential culprits for the poor model performance for inter-annual variability. Lagged effects of climate variability on ecosystem state (e.g., Gough et al, 2009) have been shown to affect model performance on interannual timescales (Keenan et al, in pressGCB), potentially due to inaccurate model allocation structures (Gough et al, 2009). Though it has been suggested that process-based models may effectively reproduce inter-annual variability (Desai, 2010; but see Keenan et al, in pressGCB), both biotic and abiotic factors are known to affect normal between-year variability .…”

Moderate forest disturbance as a stringent test for gap and big-leaf models

“…Average overstory tree age in 2013 was 95 years. NEP in the unmanipulated footprint of the UMBS control tower (US-UMBS) was 0.80-1.98 Mg C ha −1 yr −1 from 1999 to 2006, averaging 1.58 Mg C ha −1 yr −1 with substantial landscape variation (Gough et al, 2009). The forest was heavily logged in the late 1800s and early 1900s and disturbed by fire until 1923; its present-day plant composition is typical of many forests in the upper Great Lakes region (Gough et al, 2007).…”

“…Constraining against observed C stocks can provide significant improvements in model performance (Carvalhais et al, 2010); in this study, slow-turnover soil C, tree stem C, and NPP were used as constraining variables. For the parameter-space search itself we used a variant of the simplex algorithm (Nelder and Mead, 1965) that uses a randomly oriented set of basis vectors instead of fixed coordinate axes, as implemented (gsl_multimin_fminimizer_nmsimplex2rand) in Gnu Scientific Library version GSL-1.16 (Gough, 2009). For each combination of parameter values selected by the algorithm, Biome-BGC was "spun up", i.e., its slow soil pools were brought to equilibrium, and the C pools noted above compared to observed soil C values.…”

Phenological and Temperature Controls on the Temporal Non-Structural Carbohydrate Dynamics of Populus grandidentata and Quercus rubra

“…Seasonal cycles of plant tissue NSC accumulation and depletion are strongly tied to the timing and magnitude of C assimilation and growth [18], indicators of C source and sink demand, respectively. In temperate deciduous trees, tissue NSC concentrations may decline during winter dormancy and early leaf expansion, when current C assimilation is insufficient to meet metabolic and early growth demands [1,2,12,16].…”

“…We focus our analysis on the phenological and environmental regulation of C cycling processes that constrain tissue NSC concentrations, and which may inform predictions of future forest C allocation to NSC. Using standard assays of tissue NSC concentration together with stand-scale assessments of GPP and NPP [18], we demonstrate how these coupled ecosystem C cycling processes interact to regulate highly dynamic, but generally predictable seasonal fluctuations in tissue NSC concentrations, and we consider how these labile C pools are likely to change as temperature increases.…”