Abstract
Winter CO(2) fluxes represent an important component of the annual carbon budget in northern ecosystems. Understanding winter respiration processes and their responses to climate change is also central to our ability to assess terrestrial carbon cycle and climate feedbacks in the future. However, the factors influencing the spatial and temporal patterns of winter ecosystem respiration (R(eco)) of northern ecosystems are poorly understood. For this reason, we analyzed eddy covariance flux data from 57 ecosystem sites ranging from similar to 35 degrees N to similar to 70 degrees N. Deciduous forests were characterized by the highest winter R(eco) rates (0.90 +/- 0.39 gCm(-2) d(-1)), when winter is defined as the period during which daily air temperature remains below 0 degrees C. By contrast, arctic wetlands had the lowest winter R(eco) rates (0.02 +/- 0.02 gCm(-2) d(-1)). Mixed forests, evergreen needle-leaved forests, grasslands, croplands and boreal wetlands were characterized by intermediate winter R(eco) rates (g Cm(-2) d(-1)) of 0.70(+/- 0.33), 0.60(+/-0.38), 0.62(+/-0.43), 0.49(+/-0.22) and 0.27(+/-0.08), respectively. Our cross site analysis showed that winter air (T(air)) and soil (T(soil)) temperature played a dominating role in determining the spatial patterns of winter R(eco) in both forest and managed ecosystems (grasslands and croplands). Besides temperature, the seasonal amplitude of the leaf area index (LAI), inferred from satellite observation, or growing season gross primary productivity, which we use here as a proxy for the amount of recent carbon available for R(eco) in the subsequent winter, played a marginal role in winter CO(2) emissions from forest ecosystems. We found that winter R(eco) sensitivity to temperature variation across space (Q(S)) was higher than the one over time (interannual, Q(T)). This can be expected because Q(S) not only accounts for climate gradients across sites but also for (positively correlated) the spatial variability of substrate quantity. Thus, if the models estimate future warming impacts on R(eco) based on Q(S) rather than Q(T), this could overestimate the impact of temperature changes.