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The Arctic has warmed almost four times faster than the rest of the globe during the past four decades. This has led to multiple impacts in the Arctic such as the melting of glaciers and the Greenland ice sheet, sea ice retreat, permafrost thaw, altered species distribution and abundance, changes in hydrology and snow conditions, and altered wildfire regimes. These documented and projected impacts in the region can also propagate across borders, creating risks and opportunities requiring adaptation responses well beyond the Arctic. By undertaking a systematic literature review that uses a conceptual framework for cross‐border climate change impacts, we demonstrate how local impacts of the type described above, which are often analyzed separately in the literature, may initiate knock‐on effects that can be transmitted and transformed across borders. We illustrate examples of six categories of cross‐border risks resulting from this impact transmission and potentially requiring adaptation. These concern biophysical impacts, trade, infrastructure, finance, geopolitical relationships and human security and social justice. We examine potential adaptation options for responding to such cross‐border risks that are of relevance for Europe. The systemic approach taken in this paper promotes improved understanding of trade‐offs between potential benefits and risks, assists priority‐setting for targeting adaptation interventions, and can account for the important role of non‐climatic drivers in amplifying or dampening the cross‐border risks of climate change impacts in the Arctic. This article is categorized under:Trans‐Disciplinary Perspectives > Regional ReviewsAssessing Impacts of Climate Change > Observed Impacts of Climate ChangeAssessing Impacts of Climate Change > Evaluating Future Impacts of Climate ChangeVulnerability and Adaptation to Climate Change > Learning from Cases and Analogies [ABSTRACT FROM AUTHOR]

Forests provide important ecosystem services (ESs), including climate change mitigation, local climate regulation, habitat for biodiversity, wood and non‐wood products, energy, and recreation. Simultaneously, forests are increasingly affected by climate change and need to be adapted to future environmental conditions. Current legislation, including the European Union (EU) Biodiversity Strategy, EU Forest Strategy, and national laws, aims to protect forest landscapes, enhance ESs, adapt forests to climate change, and leverage forest products for climate change mitigation and the bioeconomy. However, reconciling all these competing demands poses a tremendous task for policymakers, forest managers, conservation agencies, and other stakeholders, especially given the uncertainty associated with future climate impacts. Here, we used process‐based ecosystem modeling and robust multi‐criteria optimization to develop forest management portfolios that provide multiple ESs across a wide range of climate scenarios. We included constraints to strictly protect 10% of Europe's land area and to provide stable harvest levels under every climate scenario. The optimization showed only limited options to improve ES provision within these constraints. Consequently, management portfolios suffered from low diversity, which contradicts the goal of multi‐functionality and exposes regions to significant risk due to a lack of risk diversification. Additionally, certain regions, especially those in the north, would need to prioritize timber provision to compensate for reduced harvests elsewhere. This conflicts with EU LULUCF targets for increased forest carbon sinks in all member states and prevents an equal distribution of strictly protected areas, introducing a bias as to which forest ecosystems are more protected than others. Thus, coordinated strategies at the European level are imperative to address these challenges effectively. We suggest that the implementation of the EU Biodiversity Strategy, EU Forest Strategy, and targets for forest carbon sinks require complementary measures to alleviate the conflicting demands on forests. [ABSTRACT FROM AUTHOR]

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, http://www.isimip.org , last access: 2 November 2023) 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 CH 4 and CO 2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6. [ABSTRACT FROM AUTHOR]

Current changes in the world's climate increasingly impact a wide variety of sectors globally, from agriculture and ecosystems to water and energy supply or human health. Many impacts of climate on these sectors happen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we present CHELSA-W5E5 (10.48364/ISIMIP.836809.3, Karger et al., 2022): a climate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air temperatures, precipitation 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 Weather Research and Forecasting (WRF) regional climate model, 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 higher-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 and for applications that cover more than one region and benefit from using a consistent dataset across these regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Current changes in the world's climate increasingly impact a wide variety of sectors globally, from agriculture, ecosystems, to water and energy supply or human health. Many impacts of climate on these sectors happen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we present 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 climate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air-temperatures, precipitation 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 higher resolution climate data with in- creased 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. [ABSTRACT FROM AUTHOR]

Forests mitigate climate change by storing carbon and reducing emissions via substitution effects of wood products. Additionally, they provide many other important ecosystem services (ESs), but are vulnerable to climate change; therefore, adaptation is necessary. Climate‐smart forestry combines mitigation with adaptation, whilst facilitating the provision of many ESs. This is particularly challenging due to large uncertainties about future climate. Here, we combined ecosystem modeling with robust multi‐criteria optimization to assess how the provision of various ESs (climate change mitigation, timber provision, local cooling, water availability, and biodiversity habitat) can be guaranteed under a broad range of climate futures across Europe. Our optimized portfolios contain 29% unmanaged forests, and implicate a successive conversion of 34% of coniferous to broad‐leaved forests (11% vice versa). Coppices practically vanish from Southern Europe, mainly due to their high water requirement. We find the high shares of unmanaged forests necessary to keep European forests a carbon sink while broad‐leaved and unmanaged forests contribute to local cooling through biogeophysical effects. Unmanaged forests also pose the largest benefit for biodiversity habitat. However, the increased shares of unmanaged and broad‐leaved forests lead to reductions in harvests. This raises the question of how to meet increasing wood demands without transferring ecological impacts elsewhere or enhancing the dependence on more carbon‐intensive industries. Furthermore, the mitigation potential of forests depends on assumptions about the decarbonization of other industries and is consequently crucially dependent on the emission scenario. Our findings highlight that trade‐offs must be assessed when developing concrete strategies for climate‐smart forestry. Plain Language Summary: Forests help mitigate climate change by storing carbon and via avoided emissions when wood products replace more carbon‐intensive materials. At the same time, forests provide many other "ecosystem services (ESs)" to society. For example, they provide timber, habitat for various species, and they cool their surrounding regions. They are, however, also vulnerable to ongoing climate change. Forest management must consider all these aspects, which is particularly challenging considering the uncertainty about future climate. Here, we propose how this may be tackled by computing optimized forest management portfolios for Europe for a broad range of future climate pathways. Our results show that changes to forest composition are necessary. In particular, increased shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs. However, these increased shares also lead to decreases in harvest rates, posing a conflict between wood supply and demand. We further show that the mitigation potential of forests strongly depends on how carbon‐intensive the replaced materials are. Consequently, should these materials become "greener" due to new technologies, the importance of wood products in terms of climate change mitigation decreases. Our study highlights that we cannot optimize every aspect, but that trade‐offs between ESs need to be made. Key Points: Strategies for climate‐smart forestry under a range of climate scenarios always lead to trade‐offs between different ecosystem services (ESs)Higher shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs, but lead to decreased timber provisionThe mitigation potential of forests strongly relies on substitution effects which depend on the carbon‐intensity of the alternative products [ABSTRACT FROM AUTHOR]

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. Key Points: Elevated temperature has negative impacts on terrestrial carbon sinkCO2 effects on terrestrial carbon sink saturate at high CO2 concentrationInterannual variability of terrestrial carbon sink is more correlated with temperature under RCP2.6 but precipitation under RCP6.0 [ABSTRACT FROM AUTHOR]

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 CO2. 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 CO2 effects/with CO2 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 CO2 emissions [C. R. Schwalm et al., Proc. Natl. Acad. Sci. U.S.A. 117, 19656-19657 (2020)]. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Global warming changes the frequency, intensity, and spatial distribution of extreme events. We analyze computer simulations of river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves under past, present‐day, and potential future climate conditions. Our results show that global warming increases the number of people around the world that are affected by these events each year, both for all event types combined and each type individually. Changes in the chance of being affected by extreme events are unevenly distributed in space. Particularly large increases are simulated for tropical and subtropical regions. Key Points: We quantify the pure effect of climate change on the exposure to extreme climate impact events, for both historical and future time periodsGlobal warming increases the global population exposure to river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwavesThe largest increases in exposure are projected for tropical and subtropical regions [ABSTRACT FROM AUTHOR]

The process-based model 4C (FORESEE) has been developed over the past 20 years to study climate impacts on forests and is now freely available as an open-source tool. The objective of this paper is to provide a comprehensive description of this 4C version (v2.2) for scientific users of the model and to present an evaluation of 4C at four different forest sites across Europe. The evaluation focuses on forest growth as well as carbon (net ecosystem exchange, gross primary production), water (actual evapotranspiration, soil water content), and heat fluxes (soil temperature) using data from the PROFOUND database. We applied different evaluation metrics and compared the daily, monthly, and annual variability of observed and simulated values. The ability to reproduce forest growth (stem diameter and biomass) differs from site to site and is best for a pine stand in Germany (Peitz, model efficiency ME=0.98). 4C is able to reproduce soil temperature at different depths in Sorø and Hyytiälä with good accuracy (for all soil depths ME > 0.8). The dynamics in simulating carbon and water fluxes are well captured on daily and monthly timescales (0.51 < ME < 0.983) but less so on an annual timescale (ME < 0). This model–data mismatch is possibly due to the accumulation of errors because of processes that are missing or represented in a very general way in 4C but not with enough specific detail to cover strong, site-specific dependencies such as ground vegetation growth. These processes need to be further elaborated to improve the projections of climate change on forests. We conclude that, despite shortcomings, 4C is widely applicable, reliable, and therefore ready to be released to the scientific community to use and further develop the model. [ABSTRACT FROM AUTHOR]

Soil organic carbon changes (ΔSOC) are regulated by climate and land use change. Here, we analyze regional and global ΔSOC from 1861 to 2099 based on five terrestrial biosphere model (TBM) simulations of the Inter‐Sectoral Impact Model Intercomparison Project Phase 2b. The TBMs were driven by harmonized gridded land use change and bias‐adjusted climate forcing data from different general circulation models (GCMs) for climate scenarios RCP 2.6 and RCP 6.0. Between 2005 and the end of this century, we estimated an increase of SOC for two scenarios with large uncertainty, which is dominated by differences between TBMs. We present a new emergent constraint approach to constrain future modeled ΔSOC over natural vegetation from RCP 6.0 simulations using recent observed trends of net primary productivity as a proxy of litter inputs to soil pools. Our results showed that the uncertainties in constrained ΔSOC can be reduced in comparison with the original model ensemble, but constrained values of ΔSOC depend on the choice of a GCM and climate regions. For the reduction of the SOC density in areas where cropland expanded (Δsoccropland expansion) over natural vegetation as a result of land use change, the constrained Δsoccropland expansion still features large uncertainties due to uncertain observed data. Our proposed emergent constraint approach appears to be valuable to reduce uncertainty on SOC projections, but it is limited here by the small number of models (five) and by the uncertainty in the observational data. Applications to larger ensembles from Earth System Models should be tested for the future. Key Points: The uncertainty in soil organic carbon (SOC) change is dominated by differences between model structure rather than by climate forcingSoil input changes explain most variations in projected SOC change for natural vegetation across models at global and regionThe effective reduction in constrained SOC change depends on climate forcing and region considered [ABSTRACT FROM AUTHOR]

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 on European forests 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, and 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 10.5880/PIK.2020.006/; Reyer et al., 2020). 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://CRAN.R-project.org/package=ProfoundData ; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation. [ABSTRACT FROM AUTHOR]

Carbon allocation plays a key role in ecosystem dynamics and plant adaptation to changing environmental conditions. Hence, proper description of this process in vegetation models is crucial for the simulations of the impact of climate change on carbon cycling in forests. Here we review how carbon allocation modelling is currently implemented in 31 contrasting models to identify the main gaps compared with our theoretical and empirical understanding of carbon allocation. A hybrid approach based on combining several principles and/or types of carbon allocation modelling prevailed in the examined models, while physiologically more sophisticated approaches were used less often than empirical ones. The analysis revealed that, although the number of carbon allocation studies over the past 10 years has substantially increased, some background processes are still insufficiently understood and some issues in models are frequently poorly represented, oversimplified or even omitted. Hence, current challenges for carbon allocation modelling in forest ecosystems are (i) to overcome remaining limits in process understanding, particularly regarding the impact of disturbances on carbon allocation, accumulation and utilization of nonstructural carbohydrates, and carbon use by symbionts, and (ii) to implement existing knowledge of carbon allocation into defence, regeneration and improved resource uptake in order to better account for changing environmental conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C, including a potential overshoot and longterm impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the lowemissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0.With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity). [ABSTRACT FROM AUTHOR]

To assess how the effects of drought could be better captured in process-based models, this study simulated and contrasted two water uptake approaches in Scots pine and Scots pine-Sessile oak stands. The first approach consisted of an empirical function for root water uptake (WU1). The second approach was based on differences of soil water potential along a soil-plant-atmosphere continuum (WU2) with total root resistance varying at low, medium and high total root resistance levels. Three data sets on different time scales relevant for tree growth were used for model evaluation: Two short-term datasets on daily transpiration and soil water content as well as a long-term dataset on annual tree ring increments. Except WU2 with high total root resistance, all transpiration outputs exceeded observed values. The strongest correlation between simulated and observed annual tree ring width occurred with WU2 and high total root resistance. The findings highlighted the importance of severe drought as a main reason for small diameter increment. However, if all three data sets were taken into account, no approach was superior to the other. We conclude that accurate projections of future forest productivity depend largely on the realistic representation of root water uptake in forest model simulations. [ABSTRACT FROM AUTHOR]

Adapting the management of forest resources to climate change involves addressing several crucial aspects to provide a valid basis for decision making. These include the knowledge and belief of decision makers, the mapping of management options for the current as well as anticipated future bioclimatic and socioeconomic conditions, and the ways decisions are evaluated and made. We investigate the adaptive management process and develop a framework including these three aspects, thus providing a structured way to analyze the challenges and opportunities of managing forests in the face of climate change. We apply the framework for a range of case studies that differ in the way climate and its impacts are projected to change, the available management options, and how decision makers develop, update, and use their beliefs about climate change scenarios to select among adaptation options, each being optimal for a certain climate change scenario. We describe four stylized types of decision-making processes that differ in how they (1) take into account uncertainty and new information on the state and development of the climate and (2) evaluate alternative management decisions: the "no-change," the "reactive," the "trend-adaptive," and the "forward-looking adaptive" decision-making types. Accordingly, we evaluate the experiences with alternative management strategies and recent publications on using Bayesian optimization methods that account for different simulated learning schemes based on varying knowledge, belief, and information. Finally, our proposed framework for identifying adaptation strategies provides solutions for enhancing forest structure and diversity, biomass and timber production, and reducing climate change-induced damages. They are spatially heterogeneous, reflecting the diversity in growing conditions and socioeconomic settings within Europe. [ABSTRACT FROM AUTHOR]