## 1 Introduction  
Forests play a major role in the global carbon (C), nitrogen (N) and water cycle and have great potential to reduce atmospheric greenhouse gases [1]. Especially with regard to atmospheric CO\({}_{2}\), the development and responses of forests to climate change are of major importance due to their C sink capacity [2]. Therefore, understanding forest CO\({}_{2}\) exchange processes (e.g., Gross Primary Production \"GPP\", Terrestrial Ecosystem Respiration \"TER\", Net Ecosystem Exchange \"NEE\") is of major ecological and economic interest, in particular under changing environmental conditions [3]. For process understanding, a number of observational networks have been established (e.g., Ameriflux, Fluxnet, Euroflux, MedeFlux, AsiaFlux, _etc._) using the eddy covariance (EC) methodology with standardized measuring and data processing techniques [4]. This data can be used for regional and global analysis of flux balances as well as for calibrating process-based ecosystem models [5, 6]. Proper calibration of process-based ecosystem models is crucial for assessing the impacts of climate change scenarios on the terrestrial C cycle. A number of forest ecosystem models have recently been developed which do allow estimating ecosystem C cycling under current and future climatic conditions at site and global scales [7, 8, 9, 10, 11, 12]. In Europe, deterministic models such as CASTANEA [13], ORCHIDEE [14], CoupModel [15], MAESTRA [16] and others [12, 17] have been calibrated and evaluated against EC flux records (e.g., GPP, TER, NEE). The degree of compliance between model results and field observations depends both on the level of process implemented (e.g., photosynthesis, phenology, allocation, senescence, mineralization of soil organic C pools) and on respective parameter calibration [18]. [PERSON] _et al._[19] showed that predicted CO\({}_{2}\) fluxes at the regional scale may vary significantly between general and specific parameter calibrations. The reason for such deviations can be twofold. Firstly, the level of process description does not allow for sufficient model sensitivity to changing environmental conditions, and secondly, genetic acclimation of species to surrounding ecosystem properties may substantially vary with the geographical range [19, 20, 21]. Genetic differentiation and adaptation to local environmental conditions is a common phenomenon and has been reported for pine [22], spruce [23] beech [24] and other species [21, 25, 26]. Assuming a sufficient degree of complexity in process description, model parameter calibration to individual ecosystems (site-specific) might be able to address the response of stand acclimation to local biotic and abiotic conditions. Nevertheless, the procurement of site-specific parameters for every individual ecosystem inside a region is currently almost impossible. This is because process-based ecosystem models require detailed input information (climate-vegetation-soil-hydrolog). For regional or continental inventories, a multi-site calibration can be adopted if the model is (a) sensitive to a multitude of environmental and anthropogenic impacts and (b) based on general principles of eco-physiology and biogeochemistry. In this study, we present a general set of parameters that cover CO\({}_{2}\) exchange independently of the geographical location. To thebest of our knowledge, there is no existing study that applies multi-site calibration to represent seasonal patterns of CO\({}_{2}\) fluxes over a crosscut of forest ecosystems in Europe. In this work, the LandscapeDNDC model [27] is used to simulate C exchange processes for 10 forest ecosystems including the species _Pinus sylvestris_ (Scots pine), _Picea abies_ (Norway spruce) and _Fagus sylvatica_ (European beech), which are dominant in Central Europe [28; 29; 30; 31]. We apply site-specific and multi-site calibration for the 27 most important physiological parameters of the plant growth module PnET (derived from the Photosynthetic / EvapoTranspiration model [32]).