Abstract
Abstract. The seasonality of the NEE of the northern boreal coniferous forests was investigated by means of inversion modelling using eddy covariance data. Eddy covariance data was used to optimize the biochemical model parameters. Our study sites consisted of three Scots pine (l. Pinus sylvestris) forests and one Norway spruce (l. Picea abies) forest that were located in Finland and Sweden. We obtained temperature and seasonal dependence for the biochemical model parameters: the maximum rate of carboxylation (Vc(max)) and the maximum rate of electron transport (Jmax). Both of the parameters were optimized without assumptions about their mutual magnitude. The values obtained for the biochemical model parameters were similar at all the sites during summer time. To describe seasonality, different temperature fits were made for the spring, summer and autumn periods. During summer, average Jmax across the sites was 54.0 μmol m−2 s−1 (variance 31.2 μmol m−2 s−1) and Vc(max) was 12.0 μmol m−2 s−1 (variance 6.6 μmol m−2 s−1) at 17°C. The sensitivity of the model to LAI and atmospheric soil water stress was also studied. The impact of seasonality on annual GPP was 17% when only summertime parameterization was used throughout the year compared to seasonally changing parameterizations.
Highlights
According to scenarios of future climate, the boreal forest zone is expected to experience a larger increase in temperature than other regions (Trenberth et al, 2007)
Comparison across the latitudinal spread of boreal forest is important, so that it is possible to predict how the northern forests will behave in the future, and what effects the future climate might have on their carbon balance
Our results indicated exponential temperature response of the biochemical model parameters at our sites, justifying the use of Eq (5)
Summary
According to scenarios of future climate, the boreal forest zone is expected to experience a larger increase in temperature than other regions (Trenberth et al, 2007). Comparison across the latitudinal spread of boreal forest is important, so that it is possible to predict how the northern forests will behave in the future, and what effects the future climate might have on their carbon balance. In order to obtain estimates for large-scale carbon sinks, it is important that the global and regional models are parameterized using a sufficiently good method. Large-scale models often use photosynthesis parameters that have been estimated at the leaf level and scaled to the canopy level (Sellers et al, 1996). It is essential to parameterise photosynthesis models on the larger scale, taking advantage of the widespread eddy covariance flux tower network
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