Farmers across the world frequently use fire during the winter or dry season, to remove accumulated dead pasture biomass. These fire-management practices have profound effects on vegetation, soil nutrients, and biogeochemical cycles, yet they are rarely represented in process-based fire models embedded within Dynamic Global Vegetation Models (DGVMs). We couple the Chalumeau algorithm, which estimates expected burning dates, with the SPITFIRE module in the DGVM LPJmL and enable the modelling of fire as a grassland management method. Using this model development, we examine the short- and long-term impacts of varying burning strategies, frequencies, and livestock densities across distinct regions, using Brazil as a case study. Our results show that integrating grazing and fire management leads to a gradual decline in vegetation carbon, accompanied by a substantial reduction of the ecosystem and soil nitrogen. This study emphasises the importance of incorporating such practices into DGVMs to enhance the accuracy of impact assessments for pasture management. Furthermore, our findings call for improved data collection describing fire usage methods by farmers, as well as long-term measurements, particularly on vegetation, soil carbon and nitrogen development under burning practices.

Dynamic Global Vegetation Models (DGVMs) with embedded tree demography and flexible plant traits simulate dynamics across tens of thousands of land grid cells, each containing tens of thousands of individual trees, making global simulations computationally expensive. Aggregating cells into climatically coherent clusters and running a single simulation per cluster reduce this cost by orders of magnitude, but cluster quality critically depends on an ecologically meaningful distance metric between cells. We present a framework in which spatially adaptive distance metrics are derived from Köppen–Geiger climate-zone labels via Large Margin Nearest Neighbour (LMNN) metric learning and used for hierarchical clustering. A multilayer perceptron predicts cell-specific positive semi-definite metric matrices, allowing feature weighting to adapt continuously across ecological regimes. We compare a learned metric that captures cross-feature interactions against a simpler variant without interaction terms and the uniform Euclidean metric. Experiments on 67,420 global land cells with 51 climate and soil features, testing cluster counts across five orders of magnitude, show that both learned metrics substantially improve agreement with Köppen–Geiger climate-zone boundaries compared to the Euclidean uniform metric. This improved ecological alignment translates into more accurate biomass reconstruction well beyond the training signal: the learned metrics outperform the Euclidean baseline across cluster counts spanning more than an order of magnitude above the 30-zone signal before all three metrics converge. For cluster counts ≥ 2132, the Euclidean metric performs better, showing that at this scale, different features down-weighted by the metric learning—such as soil properties—become more important. The variant without interaction terms achieves biomass reconstruction accuracy equal to the interaction-based metric with directly interpretable feature weights. Evaluated with the individual-based DGVM LPJmL-FIT, more than 80

Abstract Emerging responses of northern tree cover and composition under climate change have consequences for ecosystem functioning. Satellite-based global tree and land cover datasets have improved our understanding of northern tree cover dynamics. Yet, definitions of forests, design of retrieval algorithms, different spatial resolutions and satellite sensor quality can introduce uncertainties in these data. Here, our objective was to identify consistent patterns of recent change directions of tree cover and tree cover composition over northern lands using multiple datasets. We found large differences in area of tree cover changes in these datasets, ranging from 9% to 82% for increasing trends over North America and Europe. Consequently, we generated a synthesis map that captures consistent trends in tree cover in the four global datasets and found that the synthesis map showed higher agreement with visually interpreted tree cover changes from very high-resolution imagery . This result reflects improved consistency in areas of multi-dataset agreement, rather than improved absolute accuracy. The new synthesis map revealed a substantially larger area of increasing tree cover trends as compared to decreases. This pattern of net increases in tree cover was also relatively consistent among the major northern biomes, whereby large areas of increasing tree cover were particularly evident in boreal regions. Drier boreal and temperate biomes showed more areas with decreasing tree cover in comparison to wetter biomes. Our comparison of changes in the cover of evergreen and deciduous trees based on two land cover products uncovered large disagreements, making it nearly impossible at this point to attribute the identified tree cover changes in regards to forest type. These results may serve as a reference for model simulations of vegetation dynamics. Our findings also call for concerted efforts to produce more consistent tree cover and land cover datasets especially tree cover composition.

Climate and land-use change increasingly influence European fire regimes, creating new challenges for wildfire management. Fuel management strategies, such as prescribed burning and thinning, can alter the composition and distribution of fuels to mitigate wildfire risk. However, the effectiveness of fuel management depends on complex interactions among fuel types and the conditions governing wildfire ignition and spread. Alongside field studies, modelling provides opportunities to explore management priorities under different socioeconomic and climatic conditions. Here, we used a series of fuel-management sensitivity experiments to simulate the response of future wildfire behavior in Europe to five scenarios targeting different fuel size classes (1-, 10-, 100-, and 1000-hour fuels) under alternative land-use and climate futures. Land use was modelled under Shared Socioeconomic Pathways 1 and 3 using CLUMondo, and the resulting scenarios were used as inputs to the fire-enabled dynamic global vegetation model LPJmL-SPITFIRE together with corresponding climate projections. Results show that management targeting 1-hour (fine) fuels produced the largest reductions in burned area, rate of spread, and fire intensity, followed by mixed-fuel management. In contrast, removal of 10-, 100-, and 1000-hour (coarse) fuels did not consistently reduce wildfire behavior within the modelling framework. Treatment responses varied regionally, with southern Europe, the Baltics, and the Carpathian region showing higher sensitivity. We conclude that future wildfire behavior in Europe is particularly sensitive to the management of fine and mixed fuels, and that regionally adapted approaches targeting these fuel classes are likely to provide the greatest potential for reducing wildfire activity under future climate and land-use change.

Extreme fire weather conditions are becoming increasingly unprecedented worldwide, yet the full range of potentially high-impact extreme wildfires remains difficult to assess. Here we generate a large ensemble of wildfire simulations by forcing the process-based model LPJmL-SPITFIRE with a 40-member bias-adjusted and statistically downscaled climate model (ACCESS-ESM1-5). This enables robust sampling of extreme wildfire events and allows comparison against single realizations (using forcing from climate reanalysis GSWP3-W5E and from an individual climate model ensemble member, r1i1p1f1). We show that wildfire ensemble maxima typically exceed single realizations maxima, suggesting that using a single climate forcing misses a substantial portion of the plausible extreme wildfire events due to internal climate variability. Extreme fire impacts (carbon emissions and burned area) respond more strongly to internal climate variability than fire weather conditions, suggesting a strong vegetation-fire feedback sensitivity to the climate forcing. Additionally, the large ensemble simulations capture climate driver-fire relationships not captured by single realizations, where maximum impacts occur without maximum fire danger, and vice-versa, highlighting the critical role of other factors beyond weather conditions that contribute to whether fires become extreme. These findings demonstrate that modelling a large range of possible wildfire events using the full distribution of climate realizations can help identify the mechanisms leading to the most extreme events.

The Cerrado, South America's second largest biome, has been historically underrepresented in Dynamic Global Vegetation Models (DGVMs). Therefore, this study introduces a novel Plant Functional Type (PFT) tailored to the Cerrado biome into the DGVM LPJmL-VR-SPITFIRE. The parametrization of the new PFT, called a Tropical Broadleaved Savanna tree (TrBS), integrates key ecological traits of Cerrado trees, including specific allometric relationships, wood density, specific leaf area (SLA), deep-rooting strategies, and fire-adaptive characteristics. The inclusion of TrBS in LPJmL-VR-SPITFIRE led to notable improvements in simulated vegetation distribution. TrBS became dominant across Brazil's savanna regions, particularly in the Cerrado and Pantanal. The model also better reproduced the above- and belowground biomass patterns, accurately reflecting the "inverted forest" structure of the Cerrado, characterized by a substantial investment in root systems. Moreover, the presence of TrBS improved the simulation of fire dynamics, increasing estimates of burned area and yielding seasonal fire patterns more consistent with observational data. Model validation confirmed the enhanced performance of the model with the new PFT in capturing vegetation structure and fire regimes in Brazil. Additionally, a global-scale test demonstrated reasonable alignment between the simulated and observed global distribution of savannas. In summary, the integration of the TrBS PFT marks a critical advancement for LPJmL-VR-SPITFIRE, offering a more robust framework for investigating the interaction of above- with belowground ecological processes, fire disturbance and the impacts of climate change across the Cerrado and other tropical savanna ecosystems that together account for approximately 30 % of the primary production of all terrestrial vegetation.

Abstract. Understanding the full range of possible extreme wildfire events is crucial for risk assessment and adaptation planning. While the historical record offers only one realization of climate, large ensemble simulations sample a broader range of physically plausible climate trajectories, enabling the assessment of rare but realistic extreme events beyond what the observational record alone can reveal. Here we drive the process-based dynamic vegetation-fire model LPJmL-SPITFIRE at the global scale with different climate inputs to produce three sets of simulations: a 40 member large ensemble (40 members × 36 years sample), a single member drawn from the same ensemble (36 years sample), and a reanalysis-driven simulation (36 years sample), with the latter two each representing only a single trajectory of the climate system. This design enables direct comparison of how these two single realizations (single member and reanalysis) versus large ensemble simulations sample the most extreme fire events. We demonstrate that the single realizations are not suited to study risks associated with the most extreme events in fire danger, burned area, and fire carbon emissions that would be possible under current climate conditions. As expected, the highest values in these runs are typically much lower than those of the large ensemble in most regions. The undersampling of extremes by single realizations is greater for fire impacts (burned area and carbon emissions) than for fire danger, highlighting that vegetation–fire feedbacks interact nonlinearly with internal climate variability. While large ensembles reveal more extreme possible events than those simulated with reanalysis and a single climate model ensemble member, they also enable a more robust analysis of the relationship between extreme fire danger and extreme impacts. In particular, the most extreme burned area and emissions do not always coincide with the most extreme fire danger, underscoring the role of non-climatic factors such as ignitions and fuels. Specifically, years with global maximum impacts may occur in years with global fire danger 4.6 % – 8.4 % lower than the maximum. The findings demonstrate that modeling a broader range of physically plausible wildfire events through large ensemble simulations can help identify the mechanisms leading to the most extreme and high-impact events.

Since its development in 2010, the SPITFIRE global fire model has had a substantial impact on the field of fire modelling using dynamic global vegetation models. It includes process-based representations of fire dynamics, including ignitions, fire spread, and fire effects, resulting in a holistic representation of fire on a global scale. Previously, work had been undertaken to understand the strengths and weaknesses of SPITFIRE and similar models by comparing their outputs against remotely sensed data. We seek to augment this work with new validation methods and extend it by completing a thorough review of the theory underlying the SPITFIRE model to better identify and understand sources of modelling uncertainty. We find several points of improvement in the model, the most impactful being an incorrect implementation of the Rothermel fire spread model that results in large positive biases in fire rate of spread and a live grass moisture parametrization that results in unrealistically dry grasses. The combination of these issues leads to excessively large and intense fires, particularly on the dry modelled grasslands. Because of the tall flames present in these intense fires, which can cause substantial damage to tree crowns, these issues bias SPITFIRE toward high tree mortality. We resolve these issues by correcting the implementation of the Rothermel model and implementing a new live grass moisture parametrization, in addition to several other improvements, including a multi-day fire spread algorithm, and evaluate these changes in the European domain. Our model developments allow SPITFIRE to incorporate more realistic live grass moisture content and result in more accurate burnt area on grasslands and reduced tree mortality. This work provides a crucial improvement to the theoretical basis of the SPITFIRE model and a foundation upon which future model improvements may be built. In addition, this work further supports these model developments by highlighting areas in the model where high amounts of uncertainty remain, based on new analysis and existing knowledge about the SPITFIRE model, and by identifying potential means of mitigating them to a greater extent.

Reconciling biodiversity conservation, food security, and sustainable agriculture at global scale requires a clear understanding of regional social-ecological opportunities and challenges. This understanding helps untap regional contributions to better achieve global policy targets, such as those framed in the Kunming-Montreal Global Biodiversity Framework (GBF). Yet previous global syntheses of social-ecological interlinkages remain limited in thematic and spatial detail, restricting the discussion of regional contributions and targeted policy implementation. Here, we present 25 human-nature archetypes derived from clustering of global social-ecological data revealing regional opportunities and challenges for meeting global policy targets. Our results differentiate regions with large conservation opportunities from those well suited for ecological restoration or ecological intensification. They highlight the widespread need for improving governance to enhance food security and re-design agricultural systems. Overall, our analysis supports international and national decision makers in tailoring GBF targets to regional specificities in order to more effectively achieve global sustainability goals.

This study explores the potential for different fuel management strategies to mitigate future wildfire risks across Europe by leveraging advanced modeling techniques that integrate future climate and land-use change scenarios. Using the Lund-Potsdam-Jena managed Land model (LPJmL), coupled with the SPITFIRE fire model, we simulate the impacts of five fuel management interventions across four fuel classes, ranging from fine fuels (e.g., grasses and leaves) to coarse fuels (e.g., branches and mature trees). These scenarios are based on SSP1 (Shared Socioeconomic Pathway 1; "Sustainability") and SSP3 ("Regional Rivalry") pathways, aligned with Representative Concentration Pathway (RCP) 2.6 and RCP7.0, respectively. The study evaluates fire intensity, surface fire rate of spread, fuel bulk density, and biomass changes to assess how fuel-removal interventions (e.g., prescribed burning and mechanical removal) can influence burned area under varying future conditions.Our findings highlight that fine fuel management is the most effective strategy for reducing wildfire spread in Europe, with especially potential burned area reductions in the Mediterranean. We thus suggest that in temperate and boreal Europe, retaining coarse fuels can contribute to ecosystem health through moisture retention, habitat conservation, and carbon storage. However, managing coarser fuels is critical near wildland-urban interfaces to mitigate fire risks and ensure accessibility for emergency responders in all parts of Europe. This is especially relevant given the large interannual variability in heat and precipitation which can create unpredictable conditions favoring severe fires in the Mediterranean region. We conclude that whether Europe’s future follows a more sustainable trajectory along the lines of SSP1 or a more tumultuous and nationalistic pathway such as SSP3, wildfire will remain a persistent threat with the potential to undermine climate change mitigation efforts. This highlights the need to view landscapes and priorities through a fire-focused lens, emphasizing targeted fuel treatments that optimize resource use and enhance fire resilience.

Human activities have had a significant impact on Earth's systems and processes, leading to a transition of Earth's state from the relatively stable Holocene epoch to the Anthropocene. The planetary boundary framework characterizes major risks of destabilization, particularly in the core dimensions of climate and biosphere change. Land system change, including deforestation and urbanization, alters ecosystems and impacts the water and energy cycle between the land surface and atmosphere, while climate change can disrupt the balance of ecosystems and impact vegetation composition and soil carbon pools. These drivers also interact with each other, further exacerbating their impacts. Earth system models have been used recently to illustrate the risks and interacting effects of transgressing selected planetary boundaries, but a detailed analysis is still missing. Here, we study the impacts of long-term transgressions of the climate and land system change boundaries on the Earth system using an Earth system model with an incorporated detailed dynamic vegetation model. In our centennial-scale simulation analysis, we find that transgressing the land system change boundary results in increases in global temperatures and aridity. Furthermore, this transgression is associated with a substantial loss of vegetation carbon, exceeding 200 Pg C, in contrast to conditions considered safe. Concurrently, the influence of climate change becomes evident as temperatures surge by 2.7–3.1 °C depending on the region. Notably, carbon dynamics are most profoundly affected within the large carbon reservoirs of the boreal permafrost areas, where carbon emissions peak at 150 Pg C. While a restoration scenario to reduce human pressure to meet the planetary boundaries of climate change and land system change proves beneficial for carbon pools and global mean temperature, a transgression of these boundaries could lead to profoundly negative effects on the Earth system and the terrestrial biosphere. Our results suggest that respecting both boundaries is essential for safeguarding Holocene-like planetary conditions that characterize a resilient Earth system and are in accordance with the goals of the Paris Climate Agreement.

Fire interacts with many parts of the Earth system. However, its drivers are myriad and complex, interacting differently in different regions depending on prevailing climate regimes, vegetation types, socioeconomic development, and land use and management. Europe is facing strong increases in projected fire weather danger as a consequence of climate change and has experienced extreme fire seasons and events in recent years. Here, we focus on understanding and simulating burnt area across a European study domain using remote sensing data and generalised linear models (GLMs). We first examined fire occurrence across land cover types and found that all non-cropland vegetation (NCV) types (comprising 26 % of burnt area) burnt with similar spatial and temporal patterns, which were very distinct from those in croplands (74 % of burnt area). We then used GLMs to predict cropland and NCV burnt area at similar to 9x9 km and monthly spatial and temporal resolution, respectively, which together we termed BASE (Burnt Area Simulator for Europe). Compared to satellite burnt area products, BASE effectively captured the general spatial and temporal patterns of burning, explaining 32 % (NCV) and 36 % (cropland) of the deviance, and performed similarly to state-of-the-art global fire models. The most important drivers were fire weather and monthly indices derived from gross primary productivity followed by coarse socioeconomic indicators and vegetation properties. Crucially, we found that the drivers of cropland and NCV burning were very different, highlighting the importance of simulating burning in different land cover types separately. Through the choice of predictor variables, BASE was designed for coupling with dynamic vegetation and Earth system models and thus enabling future projections. The strong model skill of BASE when reproducing seasonal and interannual dynamics of NCV burning and the novel inclusion of cropland burning indicate that BASE is well suited for integration in land surface models. In addition to this, the BASE framework may serve as a basis for further studies using additional predictors to further elucidate drivers of fire in Europe. Through these applications, we suggest BASE may be a useful tool for understanding, and therefore adapting to, the increasing fire risk in Europe.

Forests, critical components of global ecosystems, face unprecedented challenges due to climate change. This study investigates the influence of functional diversity—as a component of biodiversity—to enhance long-term biomass of European forests in the context of changing climatic conditions. Using the next-generation flexible trait-based vegetation model, LPJmL-FIT, we explored the impact of functional diversity on long-term forest biomass under three different climate change scenarios (video abstract: https://www.pik-potsdam.de/~billing/video/2023/video_abstract_billing_et_al_LPJmLFIT.mp4 ). Four model set-ups were tested with varying degrees of functional diversity and best-suited functional traits. Our results show that functional diversity positively influences long-term forest biomass, particularly when climate warming is low (RCP2.6). Under these conditions, high-diversity simulations led to an approximately 18.2% increase in biomass compared to low-diversity experiments. However, as climate change intensity increased, the benefits of functional diversity diminished (RCP8.5). A Bayesian multilevel analysis revealed that both full leaf trait diversity and diversity of plant functional types contributed significantly to biomass enhancement under low warming scenarios in our model simulations. Under strong climate change, the presence of a mixture of different functional groups (e.g. summergreen and evergreen broad-leaved trees) was found more beneficial than the diversity of leaf traits within a functional group (e.g. broad-leaved summergreen trees). Ultimately, this research challenges the notion that planting only the most productive and climate-suited trees guarantees the highest future biomass and carbon sequestration. We underscore the importance of high functional diversity and the potential benefits of fostering a mixture of tree functional types to enhance long-term forest biomass in the face of climate change.

Airborne and ground-based measurements have consistently shown a rise in the seasonal amplitude of atmospheric CO2 concentration since the 1960s, particularly notable in the high northern latitudes. For instance, Barrow (BRW, 71ºN) witnessed a 50% increase in CO2 amplitude from 1960 to 2011, compared to a 15% increase at Mauna Loa (MLO, 20ºN). This trend suggests significant alterations in biosphere-atmosphere interactions and a changing carbon cycle in northern ecosystems. Previous studies suggest that the enhanced amplitude of atmospheric CO2 is mainly caused by the amplified plant productivity in northern ecosystems. However, the major factors that drive the increasing CO2 amplitude in the northern ecosystems are still subjected to debate, reflecting the fact that the underlying mechanisms or processes that govern the changes still remain unclear. Our study aims to understand these changes from both modelling and observational perspectives by using long-term (1980-2018) monthly CO2 concentration records, incorporating climate data, land cover changes, fire emissions, and ecosystem carbon fluxes (including gross primary production and ecosystem respiration). In parallel, we employed the LPJmL dynamic global vegetation model coupled with the TM3 atmospheric transport model (LPJmL+TM3) to simulate the seasonal CO2 concentration shifts and investigate the cause of the increasing CO2 amplitude. Our results show that the LPJmL+TM3 successfully captures both the interannual variability and the rising trend in CO2 amplitude from 1980 to 2018. Utilizing ensemble regression modeling and Shapley value analysis, we assessed the impact of various factors on CO2 amplitude changes. Different to the prevailing view that attributes the increase primarily to photosynthetic carbon uptake, our results suggest that ecosystem respiration plays an important role in driving both the interannual variability and the rising trend in CO2 amplitude in the northern terrestrial ecosystems.

Forage offtake, leaf biomass and soil organic carbon storage are important ecosystem services of permanent grasslands, which are determined by climatic conditions, management and functional diversity. However, functional diversity is not independent of climate and management, and it is important to understand the role of functional diversity and these dependencies for ecosystem services of permanent grasslands, since functional diversity may play a key role in mediating impacts of changing conditions. Large-scale ecosystem models are used to assess ecosystem functions within a consistent framework for multiple climate and management scenarios. However, large-scale models of permanent grasslands rarely consider functional diversity. We implemented a representation of functional diversity based on the competitor, stress-tolerator and ruderal (CSR) theory and the global spectrum of plant form and function into the Lund Potsdam Jena managed Land (LPJmL) dynamic global vegetation model (DGVM) forming LPJmL-CSR. Using a Bayesian calibration method, we parameterised new plant functional types (PFTs) and used these to assess forage offtake, leaf biomass, soil organic carbon storage and community composition of three permanent grassland sites. These are a temperate grassland and a hot and a cold steppe for which we simulated several management scenarios with different defoliation intensities and resource limitations. LPJmL-CSR captured the grassland dynamics well under observed conditions and showed improved results for forage offtake, leaf biomass and/or soil organic carbon (SOC) compared to the original LPJmL 5 version at the three grassland sites. Furthermore, LPJmL-CSR was able to reproduce the trade-offs associated with the global spectrum of plant form and function, and similar strategies emerged independent of the site-specific conditions (e.g. the C and R PFTs were more resource exploitative than the S PFT). Under different resource limitations, we observed a shift in the community composition. At the hot steppe, for example, irrigation led to a more balanced community composition with similar C, S and R PFT shares of aboveground biomass. Our results show that LPJmL-CSR allows for explicit analysis of the adaptation of grassland vegetation to changing conditions while explicitly considering functional diversity. The implemented mechanisms and trade-offs are universally applicable, paving the way for large-scale application. Applying LPJmL-CSR for different climate change and functional diversity scenarios may generate a range of future grassland productivities.

Abstract. Forage supply and soil organic carbon storage are two important ecosystem functions of permanent grasslands, which are determined by climatic conditions, management and functional diversity. However, functional diversity is not independent of climate and management, and it is important to understand the role of functional diversity and these dependencies for ecosystem functions of permanent grasslands. Especially since functional diversity may play a key role in mediating impacts of changing conditions. Large-scale ecosystem models are used to assess ecosystem functions within a consistent framework for multiple climate and management scenarios. However, large-scale models of permanent grasslands rarely consider functional diversity. We implemented a representation of functional diversity based on the CSR theory and the global spectrum of plant form and function into the LPJmL dynamic global vegetation model forming LPJmL-CSR. Using a Bayesian calibration method, we parameterised new plant functional types and used these to assess forage supply, soil organic carbon storage and community composition of three permanent grassland sites. These are a temperate grassland, a hot and a cold steppe for which we simulated several management scenarios with different defoliation intensities and resource limitations. LPJmL-CSR captured the grassland dynamics well under observed conditions and showed improved results for forage supply and/or SOC compared to LPJmL 5.3 at three grassland sites. Furthermore, LPJmL-CSR was able to reproduce the trade-offs associated with the global spectrum of plant form and function and similar strategies emerged independent of the site specific conditions (e.g. the C- and R-PFTs were more resource exploitative than S-PFTs). Under different resource limitations, we observed a shift of the community composition. At the hot steppe for example, irrigation led to a more balanced community composition with similar C-, S- and R-PFT shares of above-ground biomass. Our results show, that LPJmL-CSR allows for explicit analysis of the adaptation of grassland vegetation to changing conditions while explicitly considering functional diversity. The implemented mechanisms and trade-offs are universally applicable paving the way for large-scale application. Applying LPJmL-CSR for different climate change and functional diversity scenarios may generate a range of future grassland productivity.

Temperature targets of the Paris Agreement limit global net cumulative emissions to very tight carbon budgets. The possibility to overshoot the budget and offset near-term excess emissions by net-negative emissions is considered economically attractive as it eases near-term mitigation pressure. While potential side effects of carbon removal deployment are discussed extensively, the additional climate risks and the impacts and damages have attracted less attention. We link six models for an integrative analysis of the climatic, environmental and socio-economic consequences of temporarily overshooting a carbon budget consistent with the 1.5 °C temperature target along the cause-effect chain from emissions and carbon removals to climate risks and impact. Global climatic indicators such as CO 2 -concentration and mean temperature closely follow the carbon budget overshoot with mid-century peaks of 50 ppmv and 0.35 °C, respectively. Our findings highlight that investigating overshoot scenarios requires temporally and spatially differentiated analysis of climate, environmental and socioeconomic systems. We find persistent and spatially heterogeneous differences in the distribution of carbon across various pools, ocean heat content, sea-level rise as well as economic damages. Moreover, we find that key impacts, including degradation of marine ecosystem, heat wave exposure and economic damages, are more severe in equatorial areas than in higher latitudes, although absolute temperature changes being stronger in higher latitudes. The detrimental effects of a 1.5 °C warming and the additional effects due to overshoots are strongest in non-OECD countries (Organization for Economic Cooperation and Development). Constraining the overshoot inflates CO 2 prices, thus shifting carbon removal towards early afforestation while reducing the total cumulative deployment only slightly, while mitigation costs increase sharply in developing countries. Thus, scenarios with carbon budget overshoots can reverse global mean temperature increase but imply more persistent and geographically heterogeneous impacts. Overall, the decision about overshooting implies more severe trade-offs between mitigation and impacts in developing countries.

The Amazon forest is regarded as a tipping element of the Earth system, susceptible to a regime change from tropical forest to savanna and grassland due to anthropogenic land use and climate change. Previous research highlighted the role of fire in amplifying irreversible large-scale Amazon die-back. However, large-scale feedback analyses which integrate the interplay of fire with climate and land-use change are currently lacking. To address this gap, here we applied the fire-enabled Potsdam Earth Model to examine these feedback mechanisms in the Amazon. By studying forest recovery after complete deforestation, we discovered that fire prevents regrowth across 56-82% of the potential natural forest area, contingent on atmospheric carbon dioxide levels. This emphasizes the significant contribution of fire to the irreversible transition, effectively locking the Amazon into a stable grassland state. Introducing fire dynamics into future assessments is vital for understanding climate and land-use impacts in the region.

Nico Bauer1,∗, David P Keller, Julius Garbe, Kristine Karstens, Franziska Piontek, Werner von Bloh, Wim Thiery, Maria Zeitz, Matthias Mengel, Jessica Strefler, Kirsten Thonicke and Ricarda Winkelmann 1 Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany 2 GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany 3 Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany 4 Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium ∗ Author to whom any correspondence should be addressed.

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.