Managed forests occupy a large area of land, with almost 30% of the world’s forests designated primarily for production. Management can alter a forest’s carbon storage to a considerable extent, via tree species selection, planting and thinning density, selective logging, and wood harvesting. Forest management can also impact biophysical variables like canopy roughness, evapotranspiration, and surface albedo, which can produce strong effects on local warming or cooling. In addition, forest management is increasingly seen as a key component of many countries’ climate mitigation planning and a component of Net Zero targets. However, Earth System Models (ESMs) used to produce climate projections have historically contained limited functionality to represent processes relevant to forest management, meaning that the coupled-system consequences of these interventions remain underexplored. In recent years significant advances have been made to simulate forest management in dynamic global vegetation models (DGVMs) and land surface models (LSMs), the offline land surface components of ESMs. These include adding features to represent wood harvest; options to allow users additional control over forest plantation; plant functional types (PFTs) tuned specifically to represent common tree species used in forestry; and more detailed representation of forest demography (allowing for age- or size-specific growth, harvesting and mortality). In this paper, we summarize these developments and make the case for their usage in operational ESM configurations, so that the impacts of forest management on the climate, and the importance of forest management for climate mitigation, are properly represented in next generation climate projections.

European forests provide essential ecosystem services, with current policies focused on four key demands: carbon sequestration, timber provision, bioenergy use, and biodiversity conservation. These policies pursue multiple objectives simultaneously, creating conflicts over forest resources and services that intensify as climate change reduces forests' capacity to deliver multiple services. We synthesize here scientific evidence for these conflicting demands, their interactions and impacts on forests, revealing that current policy frameworks inadequately address fundamental trade-offs between them. Climate change impacts have begun to seriously challenge mitigation targets through negative impacts on the European forest carbon sink. Material substitution benefits face uncertainties in scale and timing as other sectors decarbonize. Bioenergy use conflicts with higher-value applications and biodiversity conservation, while existing policy frameworks inadequately enforce cascade use principles. Climate adaptation towards mixed forests faces implementation barriers including industry infrastructure optimized for softwood, fragmented ownership structures complicating coordination, and local management constraints. While innovative approaches such as Climate-Smart Forestry and landscape-scale triad zoning show potential for integrating multiple demands, they require substantial policy support and institutional capacity. Our review shows that neither technical improvements nor current policies can resolve these fundamental resource conflicts. Sustainable European forest management in the twenty-first century requires enhanced adaptation efforts alongside demand-side management to avoid overexploitation of European forests and environmental impact displacement that could undermine intended policy benefits globally.

Lightning is an important disturbance process in forest ecosystems, affecting trees both directly—when a strike kills a tree—and indirectly by igniting wildfires. While lightning–fire interactions are widely studied, direct lightning-induced tree mortality is not represented in global Earth System Models, limiting our ability to assess the full impact of lightning on forests under a changing climate.To address this gap, we implement lightning-induced tree mortality in the dynamic global vegetation model LPJ-GUESS, using field-derived relationships from a Panamanian forest where lightning mortality has been systematically quantified. The model successfully reproduces observed lightning-induced tree mortality at several sites but simulates lower mortality than estimated at other locations. Running the model globally, we quantify the number of trees and associated biomass directly lost to lightning and compare these losses to biomass losses from lightning-ignited wildfires, highlighting key uncertainties in both pathways.To place these present-day impacts in a future context, we synthesize existing lightning parameterizations used in global chemistry-climate models and assess their skill and projected changes in lightning activity. Applying projections from several well-performing parameterizations, we explore how future changes in lightning may alter both direct and indirect lightning-induced tree mortality. Together, our results demonstrate that lightning is a multifaceted and potentially growing driver of forest change, and that accurately representing lightning mortality is essential for robust projections of future forest dynamics.

Climate change is expected to create a range of impacts on biodiversity, land use, and economic activities, but those sector impacts are rarely analysed together. Here, we assess how climate change and socioeconomic narratives will affect land use and biodiversity in the state of Bavaria, Germany. We apply a multi-sectoral modelling approach with two climate projections (RCP 2.6 and 8.5) downscaled from three different climate models in combination with three land-use scenarios: biodiversity protection, climate change mitigation, and climate change adaptation. We evaluate changes in different sectors such as forestry and agriculture, considering impacts on carbon storage, terrestrial and aquatic biodiversity, and the adaptation of agricultural practices. In our simulations, biodiversity declined sharply under the higher emission scenario, highlighting climate change as a major threat to biodiversity in Bavaria. Prioritising biodiversity through forest conversion and expanding pasture reduced species decline and enhanced carbon storage more effectively than pure climate-focused mitigation. Climate change intensity had minimal impacts on land-use patterns (e.g. allocation of forest types), but it significantly changed farmers’ preferences, increasing their inclination toward more conservative land management practices, i.e. favouring the status quo. We conclude from our findings that policymakers should strategically prioritise biodiversity protection alongside targeted forest-management practices to simultaneously enhance ecosystem health, biodiversity, and carbon storage. Intensified agricultural and land management, on the other hand, should be approached cautiously to avoid biodiversity loss.

Maintaining or increasing forest carbon sinks is considered essential for mitigating the rise in atmospheric CO2 concentrations. In contrast, harvesting trees is perceived as having negative consequences for both the standing biomass stocks and the carbon sink strength. However, the forest carbon sink needs to be examined from a forest stand canopy perspective, where assimilation predominantly occurs in temperate forests. Here we show that a threshold of leaf area exists beyond which additional leaves do not contribute to CO2 uptake. The associated biomass can be harvested without affecting the forest carbon uptake. Based on eddy covariance measurements, we show that CO2 uptake (gross primary production - GPP) and net ecosystem exchange (NEE) in temperate forests are of a similar magnitude in both unmanaged and sustainably managed forests, on the order of 1500-1600 gCm-2yr-1 for GPP and 542-483 gCm-2yr-1 for NEE. A threshold located between 3 and 4.5 m2m-2 LAI (leaf area index) can be used for sustainable harvesting with regard to CO2 uptake. Simulations based on the LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) model reproduce the saturation of GPP and NEP and the convergence on the LAI threshold range. Accordingly, in temperate managed forests, trees can be harvested while maintaining a high tree biomass and carbon sink of the remaining stand. In this case, competition between neighboring trees in unmanaged forests is replaced by harvest management and provision of wood products. No difference in the LAI productivity response was observed between managed and unmanaged sites.

Since its emergence in the 1990s, the science of attributing observed phenomena to human-induced and natural climate drivers has made remarkable progress. To ensure the relevance and uptake of climate impact attribution studies, scientists must effectively engage with stakeholders. This engagement allows stakeholders to pose key questions, which scientists can then substantiate with evidence evaluating the existence of causal links. Although significant advancements have been made in climate impact attribution science, much work remains to understand the varied requirements of different stakeholders for impact attribution findings. This perspective explores the usefulness of stakeholder engagement in climate impact attribution, the challenges it presents, and how it can be made more relevant for addressing societal questions. It advocates for prioritizing stakeholder involvement to achieve greater transparency, legitimacy, and practical application of findings. Such involvement can enhance the societal impact of attribution studies and support informed decision-making in the face of climate change.

In the evergreen boreal forest, field studies show that vegetation does not always regenerate to its previous state after disturbance but instead transitions to systems dominated by deciduous trees or non-forest vegetation. Gaining a better understanding of drivers and impacts of post-disturbance recovery is thus crucial to accurately project future vegetation dynamics and associated impacts on the carbon, water, and energy balance of the region. We here perform simulations with the dynamic vegetation model LPJ-GUESS to investigate (1) if observations of post-disturbance recovery dynamics can be reproduced in the model, (2) which environmental factors control such shifts, and (3) how these in turn influence land surface properties such as albedo and evapotranspiration. We find that post-disturbance recovery trajectories can be clustered into distinct response patterns of recovery and shifts to alternative plant types. These shifts occur even in places where multiple plant types can in theory establish in the model and thus emerge due to shifts in competitive advantage mediated by warming and soil properties. We further find that shifts from forested to non-forested ecosystems have strong impacts on land-surface properties while shifts between different forest types are less impactful. We conclude that LPJ-GUESS is capable of reproducing observed disturbance-induced changes in vegetation dynamics following disturbances. Post-disturbance recovery is a key process driving accelerated vegetation change under climate change, further stressing the importance of accurately representing disturbance impact and recovery processes in land surface and coupled modeling.

Forests play a crucial role in climate change mitigation strategies. They store carbon in biomass, soils, and wood products, and substituting carbon-intensive products with wood products further avoids greenhouse gas emissions. However, substantial uncertainties surround the quantification of their actual mitigation potentials. Using dynamic vegetation modeling, we quantify the impact of various factors on the mitigation potential of forests, namely climate change and nitrogen deposition, disturbances, forest age, forest type, harvesting and wood usage practices, and the decarbonization pace of non-wood products. Our results indicate that reducing sustainable harvest levels is not reasonable within the next decades as wood products will continue to provide substantial substitution effects, even in scenarios with rapid decarbonization. However, increased material usage should be prioritized over using wood as fuel. Climate change, disturbances, and decarbonization introduce critical uncertainties that require novel methods and data to address these uncertainties. Moreover, forests offer many more ecosystem services than climate change mitigation. Their provision needs to be considered in forward-looking, climate-smart management strategies, alongside their adaptation potential to a rapidly changing climate. To this end, we propose a robust multi-criteria optimization approach for developing strategies for multi-functional forestry that are viable across a broad range of climate scenarios and adhere to demands on timber production and EU strategies. Our methodology indicates that all these demands and aims exert strong pressure on European forests. Alleviating this pressure will be necessary to ensure healthy forests that can provide climate change mitigation and other ecosystem services.

Forests play a crucial role in Europe's strategy for achieving carbon neutrality. Carbon turnover time - the time that carbon spends in the ecosystem - is a fundamental component in determining forest potential to mitigate climate change. However, there is a significant knowledge gap regarding how current and future forest management practices will affect carbon turnover time. This study aims to compare the effects of various forest management strategies on carbon turnover time in European forests. To achieve this, we used the dynamic global vegetation model LPJ-GUESS to simulate carbon pools and fluxes under stylised forest management scenarios mainly based on changing species composition. We calculated carbon turnover times under two conditions: first, with constant climate and CO2 concentration to assess the isolated impact of forest management; second, under a climate change scenario (SSP3-RCP7.0) to evaluate the combined effects of forest management and climate change. Our results indicate that unmanaged forests and the transition to broadleaved deciduous forests have a similar ecosystem carbon turnover time, which is the longest among all the management options across all the European climatic zones. Climate change decreases ecosystem carbon turnover time in any forest management, in a similar way, especially in cold climates. This study is the first step to include forest management when modelling carbon turnover time and indicates how the shift towards broadleaved forests, which is seen as an important climate-change adaptation strategy in many European regions, can also provide co-benefits for climate-change mitigation.

Forest disturbances can cause shifts in boreal vegetation cover from predominantly evergreen to deciduous trees or non-forest dominance. This, in turn, impacts land surface properties and, potentially, regional climate. Accurately considering such shifts in future projections of vegetation dynamics under climate change is crucial but hindered (e.g., uncertainties in future disturbance regimes). In this study, we investigate how sensitive future projections of boreal forest dynamics are to additional changes in disturbance regimes. We use the dynamic vegetation model LPJ-GUESS to investigate and disentangle the impacts of climate change and intensifying disturbance regimes in future projections of boreal vegetation cover as well as changes in land surface properties such as albedo and evapotranspiration. Our simulations find that (1) warming alone drives shifts towards more densely forested landscapes, (2) more intense disturbances reduce tree cover in favor of shrubs and grasses, and (3) the interaction between climate and disturbances leads to an expansion of deciduous trees. Our results additionally indicate that warming decreases albedo and increases evapotranspiration, while more intense disturbances have the opposite effect, potentially offsetting climate impacts. Warming and disturbances are thus comparably important agents of change in boreal forests. Our findings highlight future disturbance regimes as a key source of model uncertainty and underscore the necessity of accounting for disturbances-induced effects on vegetation composition and land surface–atmosphere feedback.

Increasingly frequent and intense drought events can jeopardize the current and future productivity and health of forests. Consequently, the ability of dynamic vegetation models (DVMs) to simulate drought impacts is paramount to improving their representation of the carbon cycle. To capture the physiological damage inflicted by drought, many state-of-the-art DVMs have implemented representations of plant hydraulic architecture in recent years. Although the understanding of the underlying processes governing hydrodynamic behavior in plants has steadily increased, the parameterization of hydraulic traits for different plant functional types (PFTs) remains a source of uncertainty in model output – in part due to limited data availability. Here, we use LPJ-GUESS-HYD, an extension of LPJ-GUESS with new parameters and processes to simulate plant hydraulic architecture, isohydrodynamic water-potential regulation, and hydraulic failure mortality. Using latin hypercube sampling we create 6000 sets of hydraulic parameter combinations based on values found in the literature. Based on these parameter sets, we conduct a comprehensive variance-based sensitivity analysis for a set of 12 common European tree species across 37 sites from the FLUXNET 2020 warm winter dataset, encompassing a wide range of European ecosystems. Subsequently, we determine which parameters and parameter interactions contribute the most to variations in model outputs. Our results indicate that of the seven parameters used in the hydraulic architecture model of LPJ-GUESS-HYD, only a few have a significant effect on the model outcomes. More specifically, Ѱ50, the water potential at which 50 percent of conductance is lost, and maximum specific leaf conductance had the largest impact on simulated processes. Parameters related with the isohydric strategy of plants, had a lesser but still substantial role in shaping the model output. These results suggest that certain hydraulic parameters – and combinations thereof – play a disproportionate role in modulating simulated forest fluxes and states in LPJ-GUESS-HYD. Specific parameterization choices can drastically alter model performance, including whether PFTs can survive in a given climate or not. Aside from encouraging careful consideration of the available trait data when parameterizing new PFTs, our results may guide future experiments in choosing which hydraulic traits to focus on.

The recent intensification of hotter droughts due to climate change has resulted in a reduced resilience of forests at global scale. This response is not only mirrored by increasing rates of tree dieback, but also reflected in a reduced canopy greenness (Buras et al., 2021) as well as emerging statistical early-warning signals of declining forest resilience (EWS, Forzieri et al., 2022). Yet, a systematic investigation on how atmospheric water demand, canopy greenness decline, and EWS are linked across European forests and including the most recent extreme droughts of 2022 and 2023 is missing. To overcome this research gap, we 1) deployed time series of remotely sensed canopy greenness (NDVI) at moderate spatial resolution (6.25 ha) over the period 2001-2023 for the European continent, 2) derived three independent statistical indices of forest resilience, and 3) related these data streams to atmospheric water demand (VPD).Over the study period, VPD displayed an increasing trend over most of Europe, which was mirrored in a concurrent decline of forest canopy greenness. Moreover, we found a clear and significant non-linear negative impact of rising VPD on canopy greenness for 75% of European forests. The grid-specific frequency of identified EWS was significantly linked to VPD and featured a record extent in 2023 with about one fifth of European forests being affected. This observation was independently supported by a strong increase in the spatiotemporal memory of canopy greenness since the extreme drought of 2018 (Buras et al., 2020). Finally, in the years following 2018 VPD-based predictions increasingly overestimated canopy greenness, hinting at forests decreasing ability to recover from extreme drought impacts. In conclusion, our study underscores the enhanced vulnerability of European forests during the extraordinarily dry period 2018-2023 with important implications for forestry, land-based mitigation plans, and regional climate feedbacks. Buras, A., Rammig, A., Zang, C.S., 2021. The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline. Frontiers in Plant Science 12, 2355. https://doi.org/10.3389/fpls.2021.689220Buras, A., Rammig, A., Zang, C.S., 2020. Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003. Biogeosciences 17, 1655–1672. https://doi.org/10.5194/bg-17-1655-2020Forzieri, G., Dakos, V., McDowell, N.G., Ramdane, A., Cescatti, A., 2022. Emerging signals of declining forest resilience under climate change. Nature 608, 534–539. https://doi.org/10.1038/s41586-022-04959-9

Lightning is an important yet often overlooked disturbance agent in forest ecosystems. Recent research conducted in Panama suggests that lightning is a major cause of large tree mortality in tropical forests. However, lightning-induced tree mortality is not included in state-of-the-art ecosystem models. Here, we implement a general lightning mortality module in the dynamic global vegetation model LPJ-GUESS to explore the impacts of lightning on forests at local and global scales. Lightning mortality was implemented stochastically in dependency of local cloud-to-ground lightning density and simulated forest structure based on findings from the Panamanian forest. For this site, LPJ-GUESS adequately simulates the average number of trees of different size classes killed per lightning strike, with a total of 2.9 simulated versus 3.2 observed. The model also captures the estimated contribution of lightning to the overall mortality of large trees (21% simulated vs. 24% observed). Applying the new model version to other tropical and temperate forests for which observation-based estimates on lightning mortality exist, LPJ-GUESS reproduces estimated impacts in some forests but simulates substantially lower impacts for others. Global simulations driven by two alternative products of cloud-to-ground lightning densities suggest that lightning kills 301-340 million trees annually, thereby causing 0.21-0.30 GtC yr.-1 of dead biomass (2.1%-2.9% of total killed biomass). The simulations also reveal that the global biomass would be 1.3%-1.7% higher in a world without lightning. Spatially, simulated lightning mortality is largest in the tropical forests of Africa. Although our simulations suggest an important role of lightning in forest ecosystems on a global scale, more data on lightning-induced tree mortality across different forest types would be desirable for more accurate model calibration and evaluation. Given the anticipated increase in future lightning activity, incorporating lightning mortality into ecosystem models is needed to obtain more reliable projections of terrestrial vegetation dynamics and carbon cycling.

Due to climate change, severe-drought events have become increasingly commonplace across Europe in recent decades, with future projections indicating that this trend will likely continue, posing questions about the continued viability of European forests. Observations from the most recent pan-European droughts suggest that these types of “hotter droughts” may acutely alter the carbon balance of European forest ecosystems. However, substantial uncertainty remains regarding the possible future impacts of severe drought on the European forest carbon sink. Dynamic vegetation models can help to shed light on such uncertainties; however, the inclusion of dedicated plant hydraulic architecture modules in these has only recently become more widespread. Such developments intended to improve model performance also tend to add substantial complexity, yet the sensitivity of the models to newly introduced processes is often left undetermined. Here, we describe and evaluate the recently developed mechanistic plant hydraulic architecture version of LPJ-GUESS and provide a parameterization for 12 common European forest tree species. We quantify the uncertainty introduced by the new processes using a variance-based global sensitivity analysis. Additionally, we evaluate the model against water and carbon fluxes from a network of eddy covariance flux sites across Europe. Our results indicate that the new model is able to capture drought-induced patterns of evapotranspiration along an isohydric gradient and manages to reproduce flux observations during drought better than standard LPJ-GUESS does. Further, the sensitivity analysis suggests that hydraulic process related to hydraulic failure and stomatal regulation play the largest roles in shaping the model response to drought.

Increasingly frequent and intense drought events can jeopardize the current and future productivity and health of forests. Consequently, the ability of dynamic vegetation models (DVMs) to simulate drought impacts is paramount to improving their representation of the carbon cycle. To capture the physiological damage inflicted by drought, many state-of-the-art DVMs have implemented representations of plant hydraulic architecture in recent years. Although the understanding of the underlying processes governing hydrodynamic behavior in plants has steadily increased, the parameterization of hydraulic traits for different plant functional types (PFTs) remains a source of uncertainty in model output – in part due to limited data availability.    Here, we use LPJ-GUESS-HYD, an extension of LPJ-GUESS with new parameters and processes to simulate plant hydraulic architecture, isohydrodynamic water-potential regulation, and hydraulic failure mortality. Using latin hypercube sampling we create 6000 sets of hydraulic parameter combinations based on values found in the literature. Based on these parameter sets, we conduct a comprehensive variance-based sensitivity analysis for a set of 12 common European tree species across 37 sites from the FLUXNET 2020 warm winter dataset, encompassing a wide range of European ecosystems. Subsequently, we determine which parameters and parameter interactions contribute the most to variations in model outputs.    Our results indicate that of the seven parameters used in the hydraulic architecture model of LPJ-GUESS-HYD, only a few have a significant effect on the model outcomes. More specifically, Ѱ50, the water potential at which 50 percent of conductance is lost, and maximum specific leaf conductance had the largest impact on simulated processes. Parameters related with the isohydric strategy of plants, had a lesser but still substantial role in shaping the model output.    These results suggest that certain hydraulic parameters – and combinations thereof –  play a disproportionate role in modulating simulated forest fluxes and states in LPJ-GUESS-HYD. Specific parameterization choices can drastically alter model performance, including whether PFTs can survive in a given climate or not. Aside from encouraging careful consideration of the available trait data when parameterizing new PFTs, our results may guide future experiments in choosing which hydraulic traits to focus on.

BACKGROUND:Forests mitigate climate change by reducing atmospheric CO 2 -concentrations through the carbon sink in the forest and in wood products, and substitution effects when wood products replace carbon-intensive materials and fuels. Quantifying the carbon mitigation potential of forests is highly challenging due to the influence of multiple important factors such as forest age and type, climate change and associated natural disturbances, harvest intensities, wood usage patterns, salvage logging practices, and the carbon-intensity of substituted products. Here, we developed a framework to quantify the impact of these factors through factorial simulation experiments with an ecosystem model at the example of central European (Bavarian) forests. RESULTS:Our simulations showed higher mitigation potentials of young forests compared to mature forests, and similar ones in broad-leaved and needle-leaved forests. Long-lived wood products significantly contributed to mitigation, particularly in needle-leaved forests due to their wood product portfolio, and increased material usage of wood showed considerable climate benefits. Consequently, the ongoing conversion of needle-leaved to more broad-leaved forests should be accompanied by the promotion of long-lived products from broad-leaved species to maintain the product sink. Climate change (especially increasing disturbances) and decarbonization were among the most critical factors influencing mitigation potentials and introduced substantial uncertainty. Nevertheless, until 2050 this uncertainty was narrow enough to derive robust findings. For instance, reducing harvest intensities enhanced the carbon sink in our simulations, but diminished substitution effects, leading to a decreased total mitigation potential until 2050. However, when considering longer time horizons (i.e. until 2100), substitution effects became low enough in our simulations due to expected decarbonization such that decreasing harvests often seemed the more favorable solution. CONCLUSION:Our results underscore the need to tailor mitigation strategies to the specific conditions of different forest sites. Furthermore, considering substitution effects, and thoroughly assessing the amount of avoided emissions by using wood products, is critical to determine mitigation potentials. While short-term recommendations are possible, we suggest risk diversification and methodologies like robust optimization to address increasing uncertainties from climate change and decarbonization paces past 2050. Finally, curbing emissions reduces the threat of climate change on forests, safeguarding their carbon sink and ecosystem services.

Balancing increasing demand for wood products while also maintaining forest biodiversity is a paramount challenge. Europe’s Biodiversity and Forest Strategies for 2030 attempt to address this challenge. Together, they call for strict protection of 10

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.

Late-spring frost (LSF) is a critical factor influencing the functioning of temperate forest ecosystems. Frost damage in the form of canopy defoliation impedes the ability of trees to effectively photosynthesize, thereby reducing tree productivity. In recent decades, LSF frequency has increased across Europe, likely intensified by the effects of climate change. With increasing warming, many deciduous tree species have shifted towards earlier budburst and leaf development. The earlier start of the growing season not only facilitates forest productivity but also lengthens the period during which trees are most susceptible to LSF. Moreover, recent forest transformation efforts in Europe intended to increase forest resilience to climate change have focused on increasing the share of deciduous species in forests. To assess the ability of forests to remain productive under climate change, dynamic vegetation models (DVMs) have proven to be useful tools. Currently, however, most state-of-the-art DVMs do not model processes related to LSF and the associated impacts. Here, we present a novel LSF module for integration with the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS). This new model implementation, termed LPJ-GUESS-FROST, provides the ability to directly attribute simulated impacts on forest productivity dynamics to LSF. We use the example of European beech, one of the dominant deciduous species in central Europe, to demonstrate the functioning of our novel LSF module. Using a network of tree-ring observations from past frost events, we show that LPJ-GUESS-FROST can reproduce productivity reductions caused by LSF. Further, to exemplify the effects of including LSF dynamics in DVMs, we run LPJ-GUESS-FROST for a study region in southern Germany for which high-resolution climate observations are available. Here, we show that modeled LSF plays a substantial role in regulating regional net primary production (NPP) and biomass dynamics, emphasizing the need for LSF to be more widely accounted for in DVMs.

The frequency of heatwaves, droughts and their co-occurrence vary greatly in simulations of different climate models. Since these extremes are expected to become more frequent with climate change, it is important to understand how vegetation models respond to different climatologies in heatwave and drought occurrence. In previous work, six climate scenarios featuring different drought-heat signatures have been developed to investigate how single versus compound extremes affect vegetation and carbon dynamics. Here, we use these scenarios to force six dynamic global vegetation models to investigate model agreement in vegetation and carbon cycle response to these scenarios. We find that global responses to different drought-heat signatures vary considerably across models. Models agree that frequent compound hot-dry events lead to a reduction in tree cover and vegetation carbon stocks. However, models show opposite responses in vegetation changes for the scenario with no extremes. We find a strong relationship between the frequency of concurrent hot-dry conditions and the total carbon pool, suggesting a reduction of the natural land carbon sink for increasing occurrence of hot-dry events. The effect of frequent compound hot and dry extremes is larger than the sum of the effects when only one extreme occurs, highlighting the importance of studying compound events. Our results demonstrate that uncertainties in the representation of compound hot-dry event occurrence in climate models propagate to uncertainties in the simulation of vegetation distribution and carbon pools. Therefore, to reduce uncertainties in future carbon cycle projections, the representation of compound events in climate models needs to be improved.