Environmental variation is directly responsible for short- but not long-term variation in forest-atmosphere carbon exchange

Abstract: Tower-based eddy covariance measurements of forest-atmosphere carbon dioxide (CO₂) exchange from many sites around the world indicate that there is considerable year-to-year variation in net ecosystem exchange (NEE). Here, we use a statistical modeling approach to partition the interannual variability in NEE (and its component fluxes, ecosystem respiration, R_eco, and gross photosynthesis, P_gross) into two main effects: variation in environmental drivers (air and soil temperature, solar radiation, vapor pressure deficit, and soil water content) and variation in the biotic response to this environmental forcing (as characterized by the model parameters). The model is applied to a 9-year data set from the Howland AmeriFlux site, a spruce-dominated forest in Maine, USA. Gap-filled flux measurements at this site indicate that the forest has been sequestering, on average, 190gCm ^-2yr^-1, with a range from 130 to 270 g Cm ^-2 yr^-1. Our fitted model predicts somewhat more uptake (mean 270gCm^-2 yr^-1), but interannual variation is similar, and wavelet variance analyses indicate good agreement between tower measurements and model predictions across a wide range of timescales (hours to years). Associated with the interannual variation in NEE are clear differences among years in model parameters for both R_eco and P_gross. Analysis of model predictions suggests that, at the annual time step, about 40% of the variance in modeled NEE can be attributed to variation in environmental drivers, and 55% to variation in the biotic response to this forcing. As model predictions are aggregated at longer timescales (from individual days to months to calendar year), variation in environmental drivers becomes progressively less important, and variation in the biotic response becomes progressively more important, in determining the modeled flux. There is a strong negative correlation between modeled annual P_gross and R_eco (r =-0.93, P ≤0.001); two possible explanations for this correlation are discussed. The correlation promotes homeostasis of NEE: the interannual variation in modeled NEE is substantially less than that for either P_gross or R_eco.