We present a new synthesis, based on a suite of complementary approaches, of the primary production and carbon sink in forests of the 25 member states of the European Union (EU-25) during 1990–2005. Upscaled terrestrial observations and model-based approaches agree within 25% on the mean net primary production (NPP) of forests, i.e. 520±75 g C m−2 yr−1 over a forest area of 1.32 × 106 km2 to 1.55 × 106 km2 (EU-25). New estimates of the mean long-term carbon forest sink (net biome production, NBP) of EU-25 forests amounts 75±20 g C m−2 yr−1. The ratio of NBP to NPP is 0.15±0.05. Estimates of the fate of the carbon inputs via NPP in wood harvests, forest fires, losses to lakes and rivers and heterotrophic respiration remain uncertain, which explains the considerable uncertainty of NBP. Inventory-based assessments and assumptions suggest that 29±15% of the NBP (i.e., 22 g C m−2 yr−1) is sequestered in the forest soil, but large uncertainty remains concerning the drivers and future of the soil organic carbon. The remaining 71±15% of the NBP (i.e., 53 g C m−2 yr−1) is realized as woody biomass increments. In the EU-25, the relatively large forest NBP is thought to be the result of a sustained difference between NPP, which increased during the past decades, and carbon losses primarily by harvest and heterotrophic respiration, which increased less over the same period

The growth rate of atmospheric CO₂ exhibits large temporal variation that is largely determined by year-to-year fluctuations in land-atmosphere CO₂ fluxes. This land-atmosphere CO₂-flux is driven by large-scale biomass burning and variation in net ecosystem exchange (NEE). Between- and within years, NEE varies due to fluctuations in climate. Studies on climatic influences on inter- and intra-annual variability in gross photosynthesis (GPP) and net carbon uptake in terrestrial ecosystems have shown conflicting results. These conflicts are in part related to differences in methodology and in part to the limited duration of some studies. Here, we introduce an observation-driven methodology that provides insight into the dependence of anomalies in CO₂ fluxes on climatic conditions. The methodology was applied on fluxes from a boreal and two temperate pine forests. Annual anomalies in NEE were dominated by anomalies in GPP, which in turn were correlated with incident radiation and vapor pressure deficit (VPD). At all three sites positive anomalies in NEE (a reduced uptake or a stronger source than the daily sites specific long-term average) were observed on summer days characterized by low incident radiation, low VPD and high precipitation. Negative anomalies in NEE occurred mainly on summer days characterized by blue skies and mild temperatures. Our study clearly highlighted the need to use weather patterns rather than single climatic variables to understand anomalous CO₂ fluxes. Temperature generally showed little direct effect on anomalies in NEE but became important when the mean daily air temperature exceeded 23 degrees C. On such days GPP decreased likely because VPD exceeded 2.0 kPa, inhibiting photosynthetic uptake. However, while GPP decreased, the high temperature stimulated respiration, resulting in positive anomalies in NEE. Climatic extremes in summer were more frequent and severe in the South than in the North, and had larger effects in the South because the criteria to inhibit photosynthesis are more often met

[1] It is estimated that more than 500 eddy covariance sites are operated globally, providing unique information about carbon and energy exchanges between terrestrial ecosystems and the atmosphere. These sites are often organized in regional networks like CarboEurope-IP, which has evolved over the last 15 years without following a predefined network design. Data collected by these networks are used for a wide range of applications. In this context, the representativeness of the current network is an important aspect to consider in order to correctly interpret the results and to quantify uncertainty. This paper proposes a cluster-based tool for quantitative network design, which was developed in order to suggest the best network for a defined number of sites or to assess the representativeness of an existing network to address the scientific question of interest. The paper illustrates how the tool can be used to assess the performance of the current CarboEurope-IP network and to improve its design. The tool was tested and validated with modeled European GPP data as the target variable and by using an empirical upscaling method (Artificial Neural Network (ANN)) to assess the improvements in the ANN prediction with different design scenarios and for different scientific questions, ranging from a simple average GPP of Europe to spatial, temporal, and spatiotemporal variability. The results show how quantitative network design could improve the predictive capacity of the ANN. However, the analysis also reveals a fundamental shortcoming of optimized networks, namely their poor capacity to represent the spatial variability of the fluxes.

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We analyzed the statistical dependence between temperature, the state of functional substances (S), estimated photosynthetic production and the radial growth of Scots pine in northern Finland. Annual sums of these variables were calculated for intervals consisting of consecutive calendar days. For daily mean temperature, all possible intervals between 1 April of the previous year and 31 August of the current year were tested. For S and the daily photosynthetic production, the tested range included days from April to October of the previous year and from April to August of the current year. These sums were compared with tree-ring indices using the Pearson correlation coefficient over the period 1906-2002. The highest correlations (r = 0.64) between daily mean temperature and growth indices were obtained for current-year periods starting 22 June and ending 28 July. For S, a temperaturederived variable describing the instantaneous photosynthetic capacity of Scots pine, the respective interval was from 5 July to 31 July (r = 0.63). The daily photosynthetic production of Scots pine was estimated for 1906-2002 using the PhenPhoto model. The interval during which the estimated daily photosynthetic production of Scots pine produced the highest correlation with growth indices (r = 0.56) was from 5 July to 27 July. Previous-year values of each variable were also significantly correlated to annual growth indices. The intervals with highest correlations were in May-June for each variable, and the correlations were rather low-between 0.3 and 0.4. We also tested selection criteria based on intervals that do not consist of consecutive calendar days, but results did not show notable improvements over the customarily used continuous intervals.