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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 predicting climate impacts and supporting climate mitigation and adaptation measures in forests., Global Change Biology, 28 (23), ISSN:1354-1013, ISSN:1365-2486

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Current changes in the world’s climate increasingly impact a wide variety of sectors globally, from agricul-ture, ecosystems, to water and energy supply or human health. Many impacts of climate on these sectors hap-pen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we pre-sent Climatologies at high resolution for the Earth’s land surface areas - WFDE5 over land merged with ERA5 over the ocean data (CHELSA-W5E5, https://doi.org/10.48364/ISIMIP.836809.3, Karger et al., 2022): a cli-mate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air-temperatures, precipi-tation rates, and downwelling shortwave solar radiation. This dataset is a spatially downscaled version of the 0.5° W5E5 dataset using the CHELSA V2 topographic downscaling algorithm. We show that the downscaling generally increases the accuracy of climate data by decreasing the bias, and increasing the correlation with measurements from meteorological stations. Bias reductions are largest in topographically complex terrain. Limitations arise for minimum near surface air temperatures in regions that are prone to cold air pooling, or at the upper extreme end of surface downwelling shortwave radiation. We further show that our topographically downscaled climate data compare well with the results of dynamical downscaling using the regional climate model Weather Research and Forecasting Model (WRF), as time series from both sources are similarly well correlated to station observations. This is remarkable given the lower computational cost of the CHELSA V2 algorithm compared to WRF and similar models. Overall, we conclude that the downscaling can provide high-er resolution climate data with increased accuracy. Hence, the dataset will be of value for a wide range of climate change impact studies both at global level but also as for applications that cover more than one region and benefit from using a consistent dataset across these regions.

Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches – species distribution models, individual‐based forest models, and dynamic global vegetation models – as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change., Forest models can help understanding the processes that shape forest functioning, structure and diversity, since they can can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations. Here we describe the development of three widely applied but contrasting forest mo−delling approaches — species distribution models, individual‐based models and dynamic global vegetation models. We provide an overview of recent model applications and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.

The anticipated growth and urbanization of the global population over the next several decades will create a vast demand for the construction of new housing, commercial buildings and accompanying infrastructure. The production of cement, steel and other building materials associated with this wave of construction will become a major source of greenhouse gas emissions. Might it be possible to transform this potential threat to the global climate system into a powerful means to mitigate climate change? To answer this provocative question, we explore the potential of mid-rise urban buildings designed with engineered timber to provide long-term storage of carbon and to avoid the carbon-intensive production of mineral-based construction materials. Increasing urbanization will lead to a significant expansion of buildings and related infrastructure, major sources of greenhouse gas emissions. This Perspective discusses the possibility of constructing mid-rise urban buildings with engineered timber for long-term carbon storage and carbon emissions reduction.

International audience; Plant trait variability, emerging from eco-evolutionary dynamics that range from alleles to macroecological scales, is one of the most elusive, but possibly most consequential, aspects of biodiversity. Plasticity, epigenetics, and genetic diversity are major determinants of how plants will respond to climate change, yet these processes are rarely represented in current vegetation models. Here, we provide an overview of the challenges associated with understanding the causes and consequences of plant trait variability, and review current developments to include plasticity and evolutionary mechanisms in vegetation models. We also present a roadmap of research priorities to develop a next generation of vegetation models with flexible traits. Including trait variability in vegetation models is necessary to better represent biosphere responses to global change.

Using engineered wood for construction has been discussed for climate change mitigation. It remains unclear where and in which way the additional demand for wooden construction material shall be fulfilled. Here we assess the global and regional impacts of increased demand for engineered wood on land use and associated CO2 emissions until 2100 using an open-source land system model. We show that if 90% of the new urban population would be housed in newly built urban mid-rise buildings with wooden constructions, 106 Gt of additional CO2 could be saved by 2100. Forest plantations would need to expand by up to 149 Mha by 2100 and harvests from unprotected natural forests would increase. Our results indicate that expansion of timber plantations for wooden buildings is possible without major repercussions on agricultural production. Strong governance and careful planning are required to ensure a sustainable transition to timber cities even if frontier forests and biodiversity hotspots are protected.

Out of 1150 Mha (million hectares) of forest designated primarily for production purposes in 2020, plantations accounted for 11 % (131 Mha) of this area and fulfilled more than 33 % of the global industrial roundwood demand. However, adding additional timber plantations to meet increasing timber demand intensifies competition for scarce land resources between different land uses such as food, feed, livestock and timber production. Despite the significance of plantations with respect to roundwood production, their importance in meeting the long-term timber demand and the implications of plantation expansion for overall land-use dynamics have not been studied in detail, in particular regarding the competition for land between agriculture and forestry in existing land-use models. This paper describes the extension of the modular, open-source land system Model of Agricultural Production and its Impact on the Environment (MAgPIE) using a detailed representation of forest land, timber production and timber demand dynamics. These extensions allow for a better understanding of the land-use dynamics (including competition for land) and the associated land-use change emissions of timber production. We show that the spatial cropland patterns differ when timber production is accounted for, indicating that timber plantations compete with cropland for the same scarce land resources. When plantations are established on cropland, it causes cropland expansion and deforestation elsewhere. Using the exogenous extrapolation of historical roundwood production from plantations, future timber demand and plantation rotation lengths, we model the future spatial expansion of forest plantations. As a result of increasing timber demand, we show a 177 % increase in plantation area by the end of the century (+171 Mha in 1995–2100). We also observe (in our model results) that the increasing demand for timber amplifies the scarcity of land, which is indicated by shifting agricultural land-use patterns and increasing yields from cropland compared with a case without forestry. Through the inclusion of new forest plantation and natural forest dynamics, our estimates of land-related CO2 emissions better match with observed data, in particular the gross land-use change emissions and carbon uptake (via regrowth), reflecting higher deforestation with the expansion of managed land and timber production as well as higher regrowth in natural forests and plantations.

Global aridification is projected to intensify. Yet, our knowledge of its potential impacts on species ranges remains limited. Here, we investigate global aridity velocity and its overlap with three sectors (natural protected areas, agricultural areas, and urban areas) and terrestrial biodiversity in historical (1979 through 2016) and future periods (2050 through 2099), with and without considering vegetation physiological response to rising CO(2). Both agricultural and urban areas showed a mean drying velocity in history, although the concurrent global aridity velocity was on average +0.05/+0.20 km/yr(−1) (no CO(2) effects/with CO(2) effects; “+” denoting wetting). Moreover, in drylands, the shifts of vegetation greenness isolines were found to be significantly coupled with the tracks of aridity velocity. In the future, the aridity velocity in natural protected areas is projected to change from wetting to drying across RCP (representative concentration pathway) 2.6, RCP6.0, and RCP8.5 scenarios. When accounting for spatial distribution of terrestrial taxa (including plants, mammals, birds, and amphibians), the global aridity velocity would be -0.15/-0.02 km/yr(−1) (“-” denoting drying; historical), -0.12/-0.15 km/yr(−1) (RCP2.6), -0.36/-0.10 km/yr(−1) (RCP6.0), and -0.75/-0.29 km/yr(−1) (RCP8.5), with amphibians particularly negatively impacted. Under all scenarios, aridity velocity shows much higher multidirectionality than temperature velocity, which is mainly poleward. These results suggest that aridification risks may significantly influence the distribution of terrestrial species besides warming impacts and further impact the effectiveness of current protected areas in future, especially under RCP8.5, which best matches historical CO(2) emissions [C. R. Schwalm et al., Proc. Natl. Acad. Sci. U.S.A. 117, 19656–19657 (2020)].

Out of 1150 Mha of forests designated primarily for production purposes in 2020, plantations account for 11 % (131 Mha) of area and fulfilled more than 33 % of the global industrial roundwood demand. Yet, adding additional timber plantations to meet increasing timber demand increases competition for scarce land resources between different land-uses for food, feed, livestock and timber production. Despite their significance in roundwood production, the importance of timber plantations in meeting the long-term timber demand and the implications of plantation expansion for overall land-use dynamics have not been studied in detail so far, in particular not the competition for land between agriculture and forestry in existing land-use models. This paper describes the extension of the modular, open-source land-system Model of Agricultural Production and its Impact on the Environment (MAgPIE) by a detailed representation of forest land, timber production and timber demand dynamics. These extensions allow for understanding the land-use dynamics (including competition for land) and associated land-use change emissions of timber production. We show that the spatial cropland patterns differ when timber production is accounted for, indicating that timber plantations compete with cropland for the same scarce land resources. When plantations are established on cropland, it causes cropland expansion and deforestation elsewhere. As a result of increasing timber demand, we show an increase in plantations area by 140 % until the end of the century (+132 Mha in 1995–2100). We also observe in our model results that the increasing demand for timber increases scarcity of land, and causes intensification through yield increasing technological change by 117 % in croplands by 2100 relative to 1995. Through the inclusion of new forest plantation and natural forest dynamics, our estimates of land-related CO2 emissions match better with observed data in particular the gross land-use change emissions and carbon uptake (via regrowth), reflecting higher deforestation for expansion of managed land and timber production, and higher regrowth in natural forests as well as plantations.

Most studies of climate change impacts, adaptation and vulnerability confine their attention to impacts and responses within the same geographical region. However, cross-border climate change impacts that occur remotely from the location of their initial impact can severely disrupt societies and livelihoods (Benzie et al., 2019; Carter et al., under review). In this paper we present a conceptual framework and accompanying terminology for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact are propagated through a response transmission system.The framework recognises and classifies differences in the types of climate trigger, categories of cross-border impacts, scales and dynamics of impact transmission, targets and dynamics of responses and the socio-economic and environmental context. We will demonstrate how the framework can be applied using historical examples of cross-border impacts (e.g. the severe 2011 floods that affected industrial production in Thailand, propagating through the global economy) as well as prospective cases (e.g. multiple cross-border risks and opportunities presented by Arctic sea ice decline).We argue that the framework provides a simple, but flexible, structure to describe and analyse cross-border climate impacts and their consequences. It offers a foundation for consistent comparisons of different patterns of cross-border impacts in different sectors and geographies. It also aids understanding of adaptation strategies and their potential consequences. In particular, with systematic application of the framework it is possible to highlight gaps in our existing understanding of system dynamics, or gain new insights into particular leverage points within the system. These can be targeted in order to find ways of building resilience to climate change in the region of origin, along the impact transmission system and in the recipient region exposed to the propagated risk.AcknowledgementThis work is being undertaken as part of the European Commission Horizon 2020-funded project CASCADES (Cascading climate risks: Towards adaptive and resilient European Societies).ReferencesBenzie M, Carter TR, Carlsen H, Taylor R (2019) Cross-border climate change impacts: implications for the European Union. Regional Environmental Change 19: 763-776, https://doi.org/10.1007/s10113-018-1436-1.Carter TR, Benzie M, Campiglio E, Carlsen H, Fronzek S, Hildén M, Reyer CPO, West C (in review) A conceptual framework for cross-border impacts of climate change.

International audience; The terrestrial carbon sink provides a critical negative feedback to climate warming, yet large uncertainty exists on its long-term dynamics. Here we combined terrestrial biosphere models (TBMs) and climate projections, together with climate-specific land use change, to investigate both the trend and interannual variability (IAV) of the terrestrial carbon sink from 1986 to 2099 under two representative concentration pathways RCP2.6 and RCP6.0. The results reveal a saturation of the terrestrial carbon sink by the end of this century under RCP6.0 due to warming and declined CO2 effects. Compared to 1986–2005 (0.96 ± 0.44 Pg C yr−1), during 2080–2099 the terrestrial carbon sink would decrease to 0.60 ± 0.71 Pg C yr−1 but increase to 3.36 ± 0.77 Pg C yr−1, respectively, under RCP2.6 and RCP6.0. The carbon sink caused by CO2, land use change and climate change during 2080–2099 is −0.08 ± 0.11 Pg C yr−1, 0.44 ± 0.05 Pg C yr−1, and 0.24 ± 0.70 Pg C yr−1 under RCP2.6, and 4.61 ± 0.17 Pg C yr−1, 0.22 ± 0.07 Pg C yr−1, and -1.47 ± 0.72 Pg C yr−1 under RCP6.0. In addition, the carbon sink IAV shows stronger variance under RCP6.0 than RCP2.6. Under RCP2.6, temperature shows higher correlation with the carbon sink IAV than precipitation in most time, which however is the opposite under RCP6.0. These results suggest that the role of terrestrial carbon sink in curbing climate warming would be weakened in a no-mitigation world in future, and active mitigation efforts are required as assumed under RCP2.6.

Amplified climate warming has led to permafrost degradation and a shortening of the winter season, both impacting cost-effective overland travel across the Arctic. Here we use, for the first time, four state-of-the-art Land Surface Models that explicitly consider ground freezing states, forced by a subset of bias-adjusted CMIP5 General Circulation Models to estimate the impact of different global warming scenarios (RCP2.6, 6.0, 8.5) on two modes of winter travel: overland travel days (OTDs) and ice road construction days (IRCDs). We show that OTDs decrease by on average −13% in the near future (2021–2050) and between −15% (RCP2.6) and −40% (RCP8.5) in the far future (2070–2099) compared to the reference period (1971–2000) when 173 d yr−1 are simulated across the Pan-Arctic. Regionally, we identified Eastern Siberia (Sakha (Yakutia), Khabarovsk Krai, Magadan Oblast) to be most resilient to climate change, while Alaska (USA), the Northwestern Russian regions (Yamalo, Arkhangelsk Oblast, Nenets, Komi, Khanty-Mansiy), Northern Europe and Chukotka are highly vulnerable. The change in OTDs is most pronounced during the shoulder season, particularly in autumn. The IRCDs reduce on average twice as much as the OTDs under all climate scenarios resulting in shorter operational duration. The results of the low-end global warming scenario (RCP2.6) emphasize that stringent climate mitigation policies have the potential to reduce the impact of climate change on winter mobility in the second half of the 21st century. Nevertheless, even under RCP2.6, our results suggest substantially reduced winter overland travel implying a severe threat to livelihoods of remote communities and increasing costs for resource exploration and transport across the Arctic. Met Office Hadley Centre Climate Programme Bundesministerium für Bildung und Forschunghttp://dx.doi.org/10.13039/501100002347 Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661 Uniscientia Foundation

Highlights • We define cross-border climate impacts and identify challenges they may pose. • We develop a conceptual framework to help understand them and their dynamics. • The framework can organise and offer useful insights into past and projected cases. • To demonstrate we use examples of the 2011 Thai floods and Arctic sea ice decline. • The complexity of impact dynamics suggests a need for diverse adaptation responses. Climate change impacts, adaptation and vulnerability studies tend to confine their attention to impacts and responses within the same geographical region. However, this approach ignores cross-border climate change impacts that occur remotely from the location of their initial impact and that may severely disrupt societies and livelihoods. We propose a conceptual framework and accompanying nomenclature for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact propagate through a response transmission system. A key to understanding cross-border impacts and responses is a recognition of different types of climate triggers, categories of cross-border impacts, the scales and dynamics of impact transmission, the targets and dynamics of responses and the socio-economic and environmental context that also encompasses factors and processes unrelated to climate change. These insights can then provide a basis for identifying relevant causal relationships. We apply the framework to the floods that affected industrial production in Thailand in 2011, and to projected Arctic sea ice decline, and demonstrate that the framework can usefully capture the complex system dynamics of cross-border climate impacts. It also provides a useful mechanism to identify and understand adaptation strategies and their potential consequences in the wider context of resilience planning. The cross-border dimensions of climate impacts could become increasingly important as climate changes intensify. We conclude that our framework will allow for these to be properly accounted for, help to identify new areas of empirical and model-based research and thereby support climate risk management.

As the impacts of climate change begin to take hold, increased attention is being paid to the consequences that might occur remotely from the location of the initial climatic impact, where impacts and responses are transmitted across one or more borders. As an economy that is highly connected to other regions and countries of the world, the European Union (EU) is potentially exposed to such cross-border impacts. Here, we undertake a macro-scale, risk-focused literature and data review to explore the potential impact transmission pathways between the EU and other world regions and countries. We do so across three distinct domains of interest - trade, human security and finance - which are part of complex socio-economic, political and cultural systems and may contribute to mediate or exacerbate risk exposure. Across these domains, we seek to understand the extent to which there has been prior consideration of aspects of climate-related risk exposure relevant to developing an understanding of cross-border impacts. We also pro-vide quantitative evidence of the extent and strength of connectivity between the EU and other world regions. Our analysis reveals that - within this nascent area of research - there is uncer-tainty about the dynamics of cross-border impact that will affect whether the EU is in a relatively secure or vulnerable position in comparison with other regions. However, we reveal that risk is likely to be focused in particular ‘hotspots’; defined geographies, for example, that produce materials for EU consumption (e.g. Latin American soybean), hold financial investments (e.g. North America), or are the foci for EU external action (e.g. the Middle East and North Africa region). Importantly, these domains will also interact, and - via the application of a conceptual example of soybean production in Argentina based on a historical drought event - we illustrate that impact and response pathways linked to EU risk exposure may be complex, further heightening the challenge of developing effective policy responses within an uncertain climatic and socioeconomic future.

Summarization: The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. Presented on: Earth's Future

International audience

Arctic ecosystems are particularly vulnerable to climate change because of Arctic amplification. Here, we assessed the climatic impacts of low-end, 1.5 °C, and 2.0 °C global temperature increases above pre-industrial levels, on the warming of terrestrial ecosystems in northern high latitudes (NHL, above 60 °N including pan-Arctic tundra and boreal forests) under the framework of the Inter-Sectoral Impact Model Intercomparison Project phase 2b protocol. We analyzed the simulated changes of net primary productivity, vegetation biomass, and soil carbon stocks of eight ecosystem models that were forced by the projections of four global climate models and two atmospheric greenhouse gas pathways (RCP2.6 and RCP6.0). Our results showed that considerable impacts on ecosystem carbon budgets, particularly primary productivity and vegetation biomass, are very likely to occur in the NHL areas. The models agreed on increases in primary productivity and biomass accumulation, despite considerable inter-model and inter-scenario differences in the magnitudes of the responses. The inter-model variability highlighted the inadequacies of the present models, which fail to consider important components such as permafrost and wildfire. The simulated impacts were attributable primarily to the rapid temperature increases in the NHL and the greater sensitivity of northern vegetation to warming, which contrasted with the less pronounced responses of soil carbon stocks. The simulated increases of vegetation biomass by 30–60 Pg C in this century have implications for climate policy such as the Paris Agreement. Comparison between the results at two warming levels showed the effectiveness of emission reductions in ameliorating the impacts and revealed unavoidable impacts for which adaptation options are urgently needed in the NHL ecosystems.

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

International audience; Carbon fluxes at the land-atmosphere interface are strongly influenced by weather and climate conditions. Yet what is usually known as “climate extremes” does not always translate into very high or low carbon fluxes or so-called “carbon extremes.” To reveal the patterns of how climate extremes influence terrestrial carbon fluxes, we analyzed the interannual variations in ecosystem carbon fluxes simulated by the Terrestrial Biosphere Models (TBMs) in the Inter-Sectoral Impact Model Intercomparison Project. At the global level, TBMs simulated reduced ecosystem net primary productivity (NPP; 18.5 ± 9.3 g C m−2 yr−1), but enhanced heterotrophic respiration (Rh; 7 ± 4.6 g C m−2 yr−1) during extremely hot events. TBMs also simulated reduced NPP (60.9 ± 24.4 g C m−2 yr−1) and reduced Rh (16.5 ± 11.4 g C m−2 yr−1) during extreme dry events. Influences of precipitation extremes on terrestrial carbon uptake were larger in the arid/semiarid zones than other regions. During hot extremes, ecosystems in the low latitudes experienced a larger reduction in carbon uptake. However, a large fraction of carbon extremes did not occur in concert with either temperature or precipitation extremes. Rather these carbon extremes are likely to be caused by the interactive effects of the concurrent temperature and precipitation anomalies. The interactive effects showed considerable spatial variations with the largest effects on NPP in South America and Africa. Additionally, TBMs simulated a stronger sensitivity of ecosystem productivity to precipitation than satellite estimates. This study provides new insights into the complex ecosystem responses to climate extremes, especially the emergent properties of carbon dynamics resulting from compound climate extremes.

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 ten 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.

Knowledge of how climate impacts occurring outside Europe might affect the continent is poor, and even less is known about what measures national governments and the European Union (EU) should take to address them. It is essential that policy-makers consider the risks of cross-border and cascading impacts and the Union’s range of influence for mitigating and preparing for them

Ongoing climate change affects growth and increases biotic and abiotic threats to Germany's forests. We analysed how these risks develop through the mid-century under a variety of climate change scenarios using the process-based forest model 4C. This model allows the calculation of indicators for fire danger, late frost risk for beech and oak, drought stress and nun moth risk. 4C was driven by a set of 4 simulations of future climate generated with the statistical model STARS and with 10 simulations of future climate based on EURO-CORDEX model simulations for the RCP2.6, RCP4.5 and RCP8.5 pathways. A set of about 70000 forest stands (Norway spruce, Scots pine, beech, oak, birch), based on the national forest inventory describing 98.4 % of the forest in Germany, was used together with data from a digital soil map. The changes and the range of changes were analysed by comparing results of a recent time period (1971–2005) and a scenario time period (2011–2045). All indicators showed higher risks for the scenario time period compared to the recent time period, except the late frost risk indicators, if averaged over all climate scenarios. The late frost risk for beech and oaks decreased for the main forest sites. Under recent climate conditions, the highest risk with regard to all five indicators was found to be in the Southwest Uplands and the northern part of Germany. The highest climate-induced uncertainty regarding the indicators for 2011–2045 is projected for the East Central Uplands and Northeast German Plain.

Process-based vegetation models are crucial tools to better understand biosphere-atmosphere exchanges and ecophysiological responses to climate change. In this contribution the performance of two global dynamic vegetation models, i.e. CARAIB and ISBACC, and one stand-scale forest model, i.e. 4C, was compared to long-term observed net ecosystem carbon exchange (NEE) time series from eddy covariance monitoring stations at three old-grown European beech (Fagus sylvatica L.) forest stands. Residual analysis, wavelet analysis and singular spectrum analysis were used beside conventional scalar statistical measures to assess model performance with the aim of defining future targets for model improvement. We found that the most important errors for all three models occurred at the edges of the observed NEE distribution and the model errors were correlated with environmental variables on a daily scale. These observations point to possible projection issues under more extreme future climate conditions. Recurrent patterns in the residuals over the course of the year were linked to the approach to simulate phenology and physiological evolution during leaf development and senescence. Substantial model errors occurred on the multi-annual time scale, possibly caused by the lack of inclusion of management actions and disturbances. Other crucial processes defined were the forest structure and the vertical light partitioning through the canopy. Further, model errors were shown not to be transmitted from one time scale to another. We proved that models should be evaluated across multiple sites, preferably using multiple evaluation methods, to identify processes that request reconsideration.

Forest disturbances are sensitive to climate. However, our understanding of disturbance dynamics in response to climatic changes remains incomplete, particularly regarding large-scale patterns, interaction effects and dampening feedbacks. Here we provide a global synthesis of climate change effects on important abiotic (fire, drought, wind, snow and ice) and biotic (insects and pathogens) disturbance agents. Warmer and drier conditions particularly facilitate fire, drought and insect disturbances, while warmer and wetter conditions increase disturbances from wind and pathogens. Widespread interactions between agents are likely to amplify disturbances, while indirect climate effects such as vegetation changes can dampen long-term disturbance sensitivities to climate. Future changes in disturbance are likely to be most pronounced in coniferous forests and the boreal biome. We conclude that both ecosystems and society should be prepared for an increasingly disturbed future of forests. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

This article provides a review of recent scientific literature on social vulnerability to climate change, aiming to determine which social and demographic groups, across a wide range of geographical locations, are the most vulnerable to climate change impacts within four well-being dimensions: health, safety, food security, and displacement. We analyze how vulnerability changes over time and ask whether there is evidence of critical thresholds beyond which social vulnerability drastically changes. The review finds that climate change is expected to exacerbate current vulnerabilities and inequalities. The findings confirm concerns about climate justice, especially its intergenerational dimensions. For example, deficiencies in early childhood may limit future educational and income generation opportunities. Evidence of clear thresholds is rare and is mainly related to the vulnerability of different age groups, household income level, and the impacts of different degrees of global warming.

Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand-level, as well as remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction, and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a SQLite relational database or ASCII flat file version (at https://doi.org/10.5880/PIK.2019.008). The data policies of the individual, contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R-package (https://github.com/COST-FP1304-PROFOUND/ProfoundData), which provides basic functions to explore, plot, and extract the data for model set-up, calibration and evaluation.

The parameter uncertainty of process-based models has received little attention in climate change impact studies. This paper aims to integrate parameter uncertainty into simulations of climate change impacts on forest net primary productivity (NPP). We used either prior (uncalibrated) or posterior (calibrated using Bayesian calibration) parameter variations to express parameter uncertainty, and we assessed the effect of parameter uncertainty on projections of the process-based model 4C in Scots pine (Pinus sylvestris) stands under climate change. We compared the uncertainty induced by differences between climate models with the uncertainty induced by parameter variability and climate models together. The results show that the uncertainty of simulated changes in NPP induced by climate model and parameter uncertainty is substantially higher than the uncertainty of NPP changes induced by climate model uncertainty alone. That said, the direction of NPP change is mostly consistent between the simulations using the standard parameter setting of 4C and the majority of the simulations including parameter uncertainty. Climate change impact studies that do not consider parameter uncertainty may therefore be appropriate for projecting the direction of change, but not for quantifying the exact degree of change, especially if parameter combinations are selected that are particularly climate sensitive. We conclude that if a key objective in climate change impact research is to quantify uncertainty, parameter uncertainty as a major factor driving the degree of uncertainty of projections should be included.

Russia’s boreal forests provide numerous important ecosystem functions and services, but they are being increasingly affected by climate change. This review presents an overview of observed and potential future climate change impacts on those forests with an emphasis on their aggregate carbon balance and processes driving changes therein. We summarize recent findings highlighting that radiation increases, temperature-driven longer growing seasons and increasing atmospheric CO2 concentrations generally enhance vegetation productivity, while heat waves and droughts tend to decrease it. Estimates of major carbon fluxes such as net biome production agree that the Russian forests as a whole currently act as a carbon sink, but these estimates differ in terms of the magnitude of the sink due to different methods and time periods used. Moreover, models project substantial distributional shifts of forest biomes, but they may overestimate the extent to which the boreal forest will shift poleward as past migration rates have been slow. While other impacts of current climate change are already substantial, and projected impacts could be both large-scale and disastrous, the likelihood for a tipping point behavior of Russia’s boreal forest is still unquantified. Other substantial research gaps include the large-scale effect of (climate-driven) disturbances such as fires and insect outbreaks, which are expected to increase in the future. We conclude that the impacts of climate change on Russia’s boreal forest are often superimposed by other environmental and societal changes in a complex way, and the interaction of these developments could exacerbate both existing and projected future challenges. Hence, development of adaptation and mitigation strategies for Russia’s forests is strongly advised.

International audience; Masting is the highly variable and synchronous production of seeds by plants. Masting can have cascading effects on plant population dynamics and forest properties such as tree growth, carbon stocks, regeneration, nutrient cycling, or future species composition. However, masting has often been missing from forest models. Those few that simulate masting have done so using relatively simple empirical rules, and lack an implementation of process-based mechanisms that control such events. Here we review more than 200 published papers on mechanistic formulations of masting, and summarize how the main processes involved in masting and their related patterns can be incorporated in forest models at different degrees of complexity. Our review showed that, of all proximate causes of masting, resource acquisition, storage and allocation were the processes studied most often. Hormonal and genetic regulation of bud formation, floral induction, and anthesis were less frequently addressed. We outline the building blocks of a general process-based model of masting that can be used to improve the oversimplified functions in different types of forest models, and to implement them where missing. A complete implementation of masting in forest models should include functions for resource allocation and depletion, and for pollination, as regulated by both forest structure and weather in the years prior to seed production. When models operate at spatio-temporal scales mismatched with the main masting processes, or if calibration data are not available, simulation can be based on parameterizing masting patterns (variability, synchrony, or frequency). Also, observed masting patterns have the potential to be used as “reality checks” for more process-based forest models wishing to accurately reproduce masting as an emergent phenomenon.

Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here we assess the potential impact of thinning on projected carbon cycle dynamics and implications for forest CUE and its components (i.e., gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process‐based forest ecosystem model forced by climate outputs of five Earth System Models under four representative climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO2 concentration, and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development, and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations lead the forests to grow faster, mature earlier, and also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody pools, and reduce physiological‐climate‐induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest‐based mitigation strategies and should be carefully considered within a portfolio of mitigation options., Key Points How will C‐fluxes, CUE, and C‐stocks of the major European forest types may respond to elevated atmospheric CO2, warming, and management in the future?Results show that managed forests left unthinned will reduce their CUE and their C‐stocks capability faster under climate change because of accelerated developmentResults show that thinning may have a large influence on C‐sequestration improving forest efficiency in stocking C as also in preventing risks of forest dieback

European temperate and boreal forests sequester up to 12% of Europe’s annual carbon emissions. Forest carbon density can be manipulated through management to maximize its climate mitigation potential, and fast-growing tree species may contribute the most to Climate Smart Forestry (CSF) compared to slow-growing hardwoods. This type of CSF takes into account not only forest resource potentials in sequestering carbon, but also the economic impact of regional forest products and discounts both variables over time. We used the process-based forest model 4 C to simulate European commercial forests’ growth conditions and coupled it with an optimization algorithm to simulate the implementation of CSF for 18 European countries encompassing 68.3 million ha of forest (42.4% of total EU-28 forest area). We found a European CSF policy that could sequester 7.3–11.1 billion tons of carbon, projected to be worth 103 to 141 billion euros in the 21st century. An efficient CSF policy would allocate carbon sequestration to European countries with a lower wood price, lower labor costs, high harvest costs, or a mixture thereof to increase its economic efficiency. This policy prioritized the allocation of mitigation efforts to northern, eastern and central European countries and favored fast growing conifers Picea abies and Pinus sylvestris to broadleaves Fagus sylvatica and Quercus species.

This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Latin America and the Caribbean (LAC). Projections show increasing mean temperatures by up to 4.5 °C compared to pre-industrial by the end of this century across LAC. Associated physical impacts include altered precipitation regimes, a strong increase in heat extremes, higher risks of droughts and increasing aridity. Moreover, the mean intensity of tropical cyclones, as well as the frequency of the most intense storms, is projected to increase while sea levels are expected to rise by ~0.2–1.1 mm depending on warming level and region. Tropical glacier volume is found to decrease substantially, with almost complete deglaciation under high warming levels. The much larger glaciers in the southern Andes are less sensitive to warming and shrink on slower timescales. Runoff is projected to be reduced in Central America, the southern Amazon basin and southernmost South America, while river discharge may increase in the western Amazon basin and in the Andes in the wet season. However, in many regions, there is uncertainty in the direction of these changes as a result of uncertain precipitation projections and differences in hydrological models. Climate change will also reduce agricultural yields, livestock and fisheries, although there may be opportunities such as increasing rice yield in several LAC countries or higher fish catch potential in the southernmost South American waters. Species range shifts threaten terrestrial biodiversity, and there is a substantial risk of Amazon rainforest degradation with continuing warming. Coral reefs are at increasing risk of annual bleaching events from 2040 to 2050 onwards irrespective of the climate scenario. These physical and biophysical climate change impacts challenge human livelihoods through, e.g., decreasing income from fisheries, agriculture or tourism. Furthermore, there is evidence that human health, coastal infrastructures and energy systems are also negatively affected. This paper concludes that LAC will be severely affected by climate change, even under lower levels of warming, due to the potential for impacts to occur simultaneously and compound one another., This research has been funded through the World Bank Project “Turn Down the Heat: Confronting the New Climate Normal,” and we are grateful to everybody involved in this activity for making it a success.

Climate change, including increasing atmospheric CO2 concentrations ([CO2]), nitrogen deposition, and recovery from past management have led to changes in forest productivity in many parts of the world. Process-based forest models have been widely used to project productivity changes under changing environmental conditions into the future. Based on a review of published simulation results from a large number of process-based models, a synthesis of impacts of environmental change on forest productivity and carbon pools is presented. This synthesis shows that most stand-scale process-based model studies have been carried out in temperate and boreal forests, focusing mostly on monospecific forests with tree species that are relevant for forestry and on analyses of the impacts of climate change and of increasing [CO2] rather than that of other environmental drivers. Forest productivity and biomass carbon pools in these forests mainly respond positively to environmental change especially if the effects of increasing [CO2] are included. If climate change is considered in isolation 61 % of the simulations show positive responses, but 35 % of the simulations show decreasing forest productivity and declining biomass carbon pools. Boreal forests mostly become more productive and sequester more carbon under climate change and increasing [CO2], while temperate and especially Mediterranean forests show more mixed responses depending on the importance of individual environmental driving variables. It is recommended that future modeling studies should increasingly strive to incorporate mixed stands and tropical forests, and include other environmental drivers besides climate and [CO2] to better capture the totality of future changes in forest productivity and carbon pools.

Summary Anthropogenic global change compromises forest resilience, with profound impacts to ecosystem functions and services. This synthesis paper reflects on the current understanding of forest resilience and potential tipping points under environmental change and explores challenges to assessing responses using experiments, observations and models. Forests are changing over a wide range of spatio-temporal scales, but it is often unclear whether these changes reduce resilience or represent a tipping point. Tipping points may arise from interactions across scales, as processes such as climate change, land-use change, invasive species or deforestation gradually erode resilience and increase vulnerability to extreme events. Studies covering interactions across different spatio-temporal scales are needed to further our understanding. Combinations of experiments, observations and process-based models could improve our ability to project forest resilience and tipping points under global change. We discuss uncertainties in changing CO2 concentration and quantifying tree mortality as examples. Synthesis. As forests change at various scales, it is increasingly important to understand whether and how such changes lead to reduced resilience and potential tipping points. Understanding the mechanisms underlying forest resilience and tipping points would help in assessing risks to ecosystems and presents opportunities for ecosystem restoration and sustainable forest management.

The objective of this paper is to assess how much carbon (C) is currently stored in a forest district in Thuringia, Germany, and how the carbon stocks will develop up to the year 2099 with a changing climate and under various management regimes (including no management), with different assumptions about carbon dioxide (CO2) fertilization effects. We applied the process-based model 4C and a wood product model to a forest district in Germany and evaluated both models for the period from 2002 to 2010, based on forest inventory data for the stands in the district. Then, we simulated the growth of the stands in the forest district under three different realizations of a climate change scenario, combined with different management regimes. Our simulations show that in 2099, between 630 and 1149 t C ha−1 will be stored in this district. The simulations also showed that climate change affects carbon sequestration. The no management strategy sequestered the highest amount of carbon (8.7 t C ha−1 year−1), which was greater than the management regimes. In the model, the possible fertilization effect of CO2 is an important factor. However, forest management remains the determining factor in this forest district.

Simulating vegetation photosynthetic productivity (or gross primary production, GPP) is a critical feature of the biome models used for impact assessments of climate change. We conducted a benchmarking of global GPP simulated by eight biome models participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a) with four meteorological forcing datasets (30 simulations), using independent GPP estimates and recent satellite data of solar-induced chlorophyll fluorescence as a proxy of GPP. The simulated global terrestrial GPP ranged from 98 to 141 Pg C yr−1 (1981–2000 mean); considerable inter-model and inter-data differences were found. Major features of spatial distribution and seasonal change of GPP were captured by each model, showing good agreement with the benchmarking data. All simulations showed incremental trends of annual GPP, seasonal-cycle amplitude, radiation-use efficiency, and water-use efficiency, mainly caused by the CO2 fertilization effect. The incremental slopes were higher than those obtained by remote sensing studies, but comparable with those by recent atmospheric observation. Apparent differences were found in the relationship between GPP and incoming solar radiation, for which forcing data differed considerably. The simulated GPP trends co-varied with a vegetation structural parameter, leaf area index, at model-dependent strengths, implying the importance of constraining canopy properties. In terms of extreme events, GPP anomalies associated with a historical El Niño event and large volcanic eruption were not consistently simulated in the model experiments due to deficiencies in both forcing data and parameterized environmental responsiveness. Although the benchmarking demonstrated the overall advancement of contemporary biome models, further refinements are required, for example, for solar radiation data and vegetation canopy schemes.

Recent studies projecting future climate change impacts on forests mainly consider either the effects of climate change on productivity or on disturbances. However, productivity and disturbances are intrinsically linked because 1) disturbances directly affect forest productivity (e.g. via a reduction in leaf area, growing stock or resource-use efficiency), and 2) disturbance susceptibility is often coupled to a certain development phase of the forest with productivity determining the time a forest is in this specific phase of susceptibility. The objective of this paper is to provide an overview of forest productivity changes in different forest regions in Europe under climate change, and partition these changes into effects induced by climate change alone and by climate change and disturbances. We present projections of climate change impacts on forest productivity from state-of-the-art forest models that dynamically simulate forest productivity and the effects of the main European disturbance agents (fire, storm, insects), driven by the same climate scenario in seven forest case studies along a large climatic gradient throughout Europe. Our study shows that, in most cases, including disturbances in the simulations exaggerate ongoing productivity declines or cancel out productivity gains in response to climate change. In fewer cases, disturbances also increase productivity or buffer climate-change induced productivity losses, e.g. because low severity fires can alleviate resource competition and increase fertilization. Even though our results cannot simply be extrapolated to other types of forests and disturbances, we argue that it is necessary to interpret climate change-induced productivity and disturbance changes jointly to capture the full range of climate change impacts on forests and to plan adaptation measures., published version, peerReviewed

This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Central Asia. Projections show mean temperatures increasing by up to 6.5 °C compared to pre-industrial by the end of this century across the region. Associated physical impacts include altered precipitation regimes, more frequent heat extremes and increasing aridity. Increasing rates of glacial and snow melt could lead to greater river runoff, but also to greater seasonality of runoff in the short term and to decreasing water availability in the medium term to long term. These changes have negative implications for the water availability in the region and for conflicting water demands between agriculture and hydropower. Climate change could mostly decrease crop yields, challenging food security, but in more northern regions there could also be positive effects. Studies on climate change impacts on energy systems are scarce and yield conflicting results, but the more regional study shows decreasing prospects for hydropower. The health of the population is already sensitive to heat extremes and is projected to be exposed to more frequent and prolonged heat waves in the future, among other potential health impacts. While the evidence for a link between climate and migration is weak, the rural-to-urban migration can be especially expected to intensify. The paper concludes that Central Asia will be severely affected by climate change even if the global mean temperature increase is limited to 2 °C above pre-industrial levels, due to the potential for impacts to occur simultaneously and compound one another as well as interactions with wider development challenges, while risks will be strongly amplified if this threshold is crossed., This research has been funded through the World Bank Project “Turn Down the Heat: Confronting the New Climate Normal,” and we are grateful to everybody involved in this activity for making it a success.