Konstantin Gregor

The intensification of disturbance regimes due to climate change is a growing concern in boreal forests, yet the long‐term consequences for forest recovery remain poorly understood, as boreal successional dynamics unfold over a century or longer and exceed the length of available observational records. To overcome this research gap we use the dynamic vegetation model LPJ‐GUESS to investigate future climate change impacts on boreal successional dynamics over the 21st and 22nd centuries. We first confirm that LPJ‐GUESS reproduces historic patterns of post‐disturbance recovery. We then show that under future warming, deciduous transients become more frequent, longer, and more pronounced due to a temperature‐mediated shift in competitive balance emerging in LPJ‐GUESS. Using a random forest framework, we find that the long‐term recovery pathway can be predicted with 90% accuracy from vegetation and environmental conditions in the first decade after disturbance alone. This supports the hypothesis that the changes in post‐disturbance recruitment currently observed in boreal forests are an indicator of more long‐lasting changes in vegetation composition in the future. While we find no evidence for permanent shifts toward deciduous tree cover in LPJ‐GUESS, the combination of prolonged deciduous transients and more frequent disturbances makes an increase in deciduous tree cover a likely future scenario.

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.

Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Extreme events can have severe impacts on ecosystems, their functioning and on the services they provide. For example, extended drought periods and heat waves are occuring more frequently and intensely in the recent past, with severe consequences for forests. My talk will give insights on the impacts of extended drought periods and heat waves on two different forest ecosystem types. First, I show how drought-impacts on forest ecosystems can be detected using the European Forest Condition Monitor and the Amazon Canopy Condition Monitor, which are based on remotely-sensed canopy greenness. Then I exemplify how the fusion of remotely-sensed canopy greenness and model simulation output helps to quantify tree-species specific drought-vulnerabilities. My talk also demonstrates how process-based models can help to assess impacts of extreme events on forest dynamics and composition, and on the carbon and water cycle. I present new developments from the process-based vegetation model LPJ-GUESS regarding the representation of drought-response strategies of different forest types and tree species. Finally, I will discuss how climate change and the impacts of extreme events can lead to different ecosystem recovery trajectories after disturbance.

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.

The complexity of geoscientific models, from pre-processing, model execution, and post-processing, poses major challenges to maintainability, reproducibility, and accessibility, even when FAIR data principles are followed.Based on a survey of the 20 dynamic global vegetation models participating in the Global Carbon Project, we present the current state of, and potential improvements in, practices of software engineering and reproducibility within the community.We also share notable successful practices from the community that could be helpful for all geo-scientists, including- version control- workflow management systems- containerization- automated documentation- continuous integration- automated visualizationsThese approaches enable reproducible, portable, and automated workflows, improve code reliability, and enhance access to scientific results.We conclude with a showcase of a fully reproducible and portable workflow implemented for one model, illustrating how these practices can be implemented by other modeling communities. This example can serve as a practical resource for improving reproducibility, accessibility, and software engineering standards across the geosciences.

Multi-functional forestry is a central objective of recent European policy frameworks, such as the New EU Forest Strategy for 2030 and the EU Biodiversity Strategy for 2030. These strategies, together with the LULUCF regulation, aim to ensure the continued provision of multiple forest ecosystem services under climate change, such as timber production, biodiversity conservation, and climate regulation.These expectations arise alongside increasing demands for wood products, leading to partially conflicting objectives. The uncertainty of future climate changes further complicates the development of multi-functional forestry strategies.Here, we demonstrate our recent work addressing these issues. We used process-based ecosystem modeling combined with robust multi-criteria optimization to derive forest management portfolios for climate-smart forestry under climate uncertainty. Using simplified management options and simulations across four RCPs, we show that regionally optimized portfolios can support the provision of multiple ecosystem services across a wide range of future climates. In particular, higher shares of broad-leaved and unmanaged forests were beneficial for biodiversity and other regulating services, but entailed clear trade-offs with timber provision.We further examined the effects of additional constraints, such as maintaining stable harvest levels and enforcing strict protection on 10% of the land area. These constraints substantially reduced management flexibility and made inter-regional compensation between wood production and forest protection necessary, often at the expense of multi-functionality within regions. Overall, our results highlight the difficulty of fulfilling all demands simultaneously under climate uncertainty. Nonetheless, they illustrate how the methodology can be helpful to derive forward-looking climate-smart strategies.

Computational models play an increasingly vital role in scientific research, by numerically simulating processes that cannot be solved analytically. Such models are fundamental in geosciences and offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of these models, coupled with their integration into broader workflows, also often makes reproducing results, evaluating processes, and building upon them highly challenging. In this paper, we review the current practices within the development processes of the state-of-the-art land surface models used by the Global Carbon Project. By combining the experience of modelers from the respective research groups with the expertise of professional software engineers, we bridge the gap between software development and scientific modeling to outline key principles and tools for improving software quality in research. We explore four main areas: 1) model testing and validation, 2) scientific, technical, and user documentation, 3) version control, continuous integration, and code review, and 4) the portability and reproducibility of workflows. Our review of current models reveals that while modeling communities are incorporating many of the suggested practices, significant room for improvement remains in areas such as automated testing, documentation, and reproducible workflows. For instance, there is limited adoption of automated documentation and testing, and provision of reproducible workflow pipelines remains an exception. This highlights the need to identify and promote essential software engineering practices within the scientific community. Nonetheless, we also discuss numerous examples of practices within the community that can serve as guidelines for other models and could even help streamline processes within the entire community. We conclude with an open-source example implementation of these principles built around the LPJ-GUESS model, showcasing portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Computational models play an increasingly vital role in scientific research, by numerically simulating processes that cannot be solved analytically. Such models are fundamental in geosciences and offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of these models, coupled with their integration into broader workflows, also often makes reproducing results, evaluating processes, and building upon them highly challenging. In this paper, we review the current practices within the development processes of the state-of-the-art land surface models used by the Global Carbon Project. By combining the experience of modelers from the respective research groups with the expertise of professional software engineers, we bridge the gap between software development and scientific modeling to outline key principles and tools for improving software quality in research. We explore four main areas: 1) model testing and validation, 2) scientific, technical, and user documentation, 3) version control, continuous integration, and code review, and 4) the portability and reproducibility of workflows. Our review of current models reveals that while modeling communities are incorporating many of the suggested practices, significant room for improvement remains in areas such as automated testing, documentation, and reproducible workflows. For instance, there is limited adoption of automated documentation and testing, and provision of reproducible workflow pipelines remains an exception. This highlights the need to identify and promote essential software engineering practices within the scientific community. Nonetheless, we also discuss numerous examples of practices within the community that can serve as guidelines for other models and could even help streamline processes within the entire community. We conclude with an open-source example implementation of these principles built around the LPJ-GUESS model, showcasing portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Computational models play an increasingly vital role in scientific research, by numerically simulating processes that cannot be solved analytically. Such models are fundamental in geosciences and offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of these models, coupled with their integration into broader workflows, also often makes reproducing results, evaluating processes, and building upon them highly challenging. In this paper, we review the current practices within the development processes of the state-of-the-art land surface models used by the Global Carbon Project. By combining the experience of modelers from the respective research groups with the expertise of professional software engineers, we bridge the gap between software development and scientific modeling to outline key principles and tools for improving software quality in research. We explore four main areas: 1) model testing and validation, 2) scientific, technical, and user documentation, 3) version control, continuous integration, and code review, and 4) the portability and reproducibility of workflows. Our review of current models reveals that while modeling communities are incorporating many of the suggested practices, significant room for improvement remains in areas such as automated testing, documentation, and reproducible workflows. For instance, there is limited adoption of automated documentation and testing, and provision of reproducible workflow pipelines remains an exception. This highlights the need to identify and promote essential software engineering practices within the scientific community. Nonetheless, we also discuss numerous examples of practices within the community that can serve as guidelines for other models and could even help streamline processes within the entire community. We conclude with an open-source example implementation of these principles built around the LPJ-GUESS model, showcasing portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.