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Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at

Beliefs, expectations and values are often assumed to drive decisions about climate change adaptation. We tested hypotheses based on this assumption using survey responses from 508 European forest professionals in 10 countries. We used the survey results to identify communication needs and the decision strategies at play, and to develop guidelines on adequate communications about climate change adaptation. We observed polarization in the positive and negative values associated with climate change impacts accepted by survey respondents. We identified a mechanism creating the polarization that we call the 'blocked belief' effect. We found that polarized values did not correlate with decisions about climate change adaptation. Strong belief in the local impacts of climate change on the forest was, however, a prerequisite of decision-making favoring adaptation. Decision-making in favor of adaptation to climate change also correlated with net values of expected specific impacts on the forest and generally increased with the absolute value of these in the absence of "tipping point" behavior. Tipping point behavior occurs when adaptation is not pursued in spite of the strongly negative or positive net value of expected climate change impacts. We observed negative and positive tipping point behavior, mainly in SW Europe and N-NE Europe, respectively. In addition we found that advice on effective adaptation may inhibit adaptation when the receiver is aware of effective adaptation measures unless it is balanced with information explaining how climate change leads to negative impacts. Forest professionals with weak expectations of impacts require communications on climate change and its impacts on forests before any advice on adaptation measures can be effective. We develop evidence-based guidelines on communications using a new methodology which includes Bayesian machine learning modeling of the equivalent of an expected utility function for the adaptation decision problem.

The role of values in climate-related decision-making is a prominent theme of climatecommunication research. The present study examines whether forest professionals are more drivenby values than scientists are, and if this results in value polarization. A questionnaire was designedto elicit and assess the values assigned to expected effects of climate change by forest professionalsand scientists working on forests and climate change in Europe. The countries involved covered anorth-to-south and west-to-east gradient across Europe, representing a wide range of bio-climaticconditions and a mix of economic–social–political structures. We show that European forestprofessionals and scientists do not exhibit polarized expectations about the values of specific impactsof climate change on forests in their countries. In fact, few differences between forest professionalsand scientists were found. However, there are interesting differences in the expected values of orest professionals with regard to climate change impacts across European countries. In NorthernEuropean countries, the aggregated values of the expected effects are more neutral than they are inSouthern Europe, where they are more negative. Expectations about impacts on timber production,economic returns, and regulatory ecosystem services are mostly negative, while expectations aboutbiodiversity and energy production are mostly positive.

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.