Reiner Schnur

In Earth system models, three‐dimensional prognostic subsurface hydrodynamics, including groundwater flow, are currently missing at the global scale. In order to prepare Earth system models for kilometer‐scale simulations with integrated continental hydrology, the ParFlow hydrological model has been coupled to the land model of the ICON modeling framework. Global simulations of atmosphere and land were conducted with a two‐way coupling between ParFlow and the soil hydrological scheme of ICON‐Land over the pan‐European region. In this first implementation, ParFlow and ICON‐Land exchange surface moisture fluxes and soil water. The extended summer months of five consecutive years were simulated with 20 km horizontal grid spacing. The explicit treatment of surface and subsurface water in ParFlow has multiple effects. Some notable impacts are presented related to soil moisture variability and lateral redistribution of moisture. Correlations with surface latent heat flux and precipitation show regionally stronger land‐atmosphere coupling. This affects the seasonal change in soil water content in Central Europe during the summer. In addition, the lateral flow of near‐surface groundwater, which is intrinsically linked to the formation of river networks, is shown to influence the evolution of the atmosphere.

While it is clear that vegetation takes part in shaping terrestrial climate through various interactions with the atmosphere, it is not so obvious what role plant functional diversity plays here. So far a tool for investigating this question in land-atmosphere simulations was missing. The new tool JeDi-BACH (version 1.0), described here, closes this gap by combining the Jena Diversity Model (JeDi) with the land component JSBACHv4 of the ICON Earth System Model (version 1.0). In practice, the low-diversity plant parametrization of JSBACH employing plant functional types (PFTs) was replaced by the trait based high-diversity vegetation description of JeDi. The key novelty introduced by the implementation of JeDi into JSBACH is that the composition of terrestrial ecosystems emerges dynamically from environmental filtering based on functional trade-offs. Thereby, in contrast to the PFT approach, a richer set of plant strategies adapted to the ruling environmental conditions is obtained without a priori knowledge of the vegetation distribution. Besides documentation of this new implementation of JeDi, the paper also presents results from first exploratory simulations with interactive land-atmosphere coupling. We find a systematic dependence of simulated terrestrial climate on potential diversity in our experiments. Moreover, when investigating the reaction to changes in trait parameters, we find that at low diversity, simulated local climate varies with the particular composition of vegetation, while at high diversity simulated terrestrial climate proves to be rather insensitive due to a dynamic re-organization of the plant community structure. Apparently, the many more dynamic degrees of freedom of the highly diverse vegetation in JeDi-BACH make this model behave very differently (less tunable) than conventional land components based on only a few PFTs. Besides fundamental research on the relation between diversity and climate, JeDi-BACH may be useful for the investigation of non-analogue climates (e.g., paleoclimate) where we lack knowledge on the structure and distribution of vegetation.

The Climate Change Adaptation Digital Twin (Climate DT), developed as part of the European Commission's Destination Earth (DestinE) initiative, sets up an operational system for producing multi-decadal, multi-model global climate projections and translating climate data into climate impact information to support adaptation efforts. This system delivers data with local granularity at spatial resolutions of 5–10 km and hourly outputs, leading to globally consistent information at scales that matter for decision-making. It also enables the testing of what-if scenarios such as high-resolution storylines, which are physically consistent global simulations of extreme events under different climate conditions and provide contextual insights to support concrete adaptation decisions. They support the generation of more equitable (understood as accessible and relevant across regions) climate information. The Climate DT is built on cutting-edge infrastructure, expert collaboration, and digital innovation. It is designed to support on-demand responses to policy questions, with quantified uncertainty. It will foster interactivity by allowing users to influence simulation design, model output portfolios, and application integration through co-design. AI-based tools, including emulators and chatbots, are being developed in parallel to enhance climate information access. Sector-specific applications are embedded in the system to synchronously translate climate data into tailored climate-impact indicators, with examples provided for energy, water, and forest management. The applications have been co-designed with informed users. A unified, cross-platform workflow defines the orchestration of all components, which is handled by a single workflow manager and relies on containerised components, facilitating automation, portability, maintainability, and traceability. Data management is unified using standard grids (HEALPix), ensuring consistency and easing data usability under a strict governance policy. Streaming enables real-time data use by the data consumers and unlocks access to the unprecedented data wealth produced by the high-resolution simulations. Monitoring tools provide real-time quality control of data and model outputs and enable continuous assessment of the realism of the climate simulations during Climate DT operation. The compute-intensive system is powered by world-class supercomputing capabilities through a strategic partnership with the European High Performance Computing Joint Undertaking (EuroHPC). Despite high computational demands, the Climate DT sets a new benchmark for delivering equitable, credible, and actionable climate information. It complements existing initiatives like CMIP, CORDEX, and national and European climate services, and aligns with global climate science goals to support climate adaptation.

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.

Rising atmospheric CO2 enhances the land carbon (C) uptake, providing a negative feedback mechanism for atmospheric CO2. At the same time, CO2-driven warming of land and air temperature tends to weaken land carbon storage, providing a primarily positive feedback on climate (i.e. intensifying climate change). The magnitude of these effects is, beside others, mediated by the nitrogen (N) content in land, which attenuates the land C response to atmospheric CO2 and climate [Kou-Giesbrecht et al., 2025]. Comprehensive Earth System Models (ESMs) have been developed to project effects from different feedbacks on Earth’s climate change, but to date not all ESMs take into account effects from the coupled C-N cycles.ICON is a state of the art ESM [Jungclaus et al., 2022], yet its initial land surface model (LSM) implementation JSBACHv4.3 [Schneck et al., 2022] does not include a representation of the N-cycle. Recently, the QUINCY model [Thum et al., 2019] was integrated into the ICON framework. While the geophysical processes of the initial LSM JSBACHv4.3 are taken over, the new QUINCY configuration provides an alternative representation of the vegetation and biogeochemical processes, including a more realistically representation of vegetation structure (e.g. by coupling the LAI to the available carbon) and a comprehensive representation of the terrestrial N-cycle processes.In the current study, we apply ICON in its ICON-XPP configuration [Müller et al. 2025] and with QUINCY as configuration for ICON-Land to evaluate the N-effect on land C uptake under conditions of 1%CO2 increase in the atmosphere. For this purpose, two numerical AMIP experiments (sea surface temperature and sea ice are prescribed) were carried out for the period of 1850-2019. In one experiment only C cycle was considered, in another - C and N cycles. The modelling results will be analysed to evaluate a possible N limitation effect under the conditions of increasing atmospheric CO2.

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.

The water and carbon exchange between the land surface and the atmosphere is regulated by meteorological conditions and plant physiological processes. Traditional mechanistic modeling approaches, for example, the Earth system model ICON‐ESM with the land component JSBACH4 , are hampered by relatively rigid parameterizations for stomatal conductance to model land‐atmosphere coupling. We develop a hybrid modeling approach integrating data‐driven flexible parameterizations based on eddy‐covariance flux measurements (FLUXNET) with mechanistic modeling. We replace specific empirical parametrizations of the coupled photosynthesis (gross primary production [GPP]) and transpiration () modules with feed‐forward neural network models pre‐trained on observations. In a proof‐of‐concept, we demonstrate that our approach reconstructs original JSBACH4 parameterizations for stomatal conductance (), maximum carboxylation rates () and the maximum electron transport rates (), that decisively control GPP and . We then replace JSBACH4 's original parametrizations by calling the emulator parameterizations trained on original JSBACH4 output using a Python‐Fortran bridge. Adapting the approach to observational data, Hybrid‐JSBACH4 infers these parametrizations from eddy‐covariance measurements to construct observation‐informed modeling of water and carbon fluxes in JSBACH4 . The mean hourly residuals of in Hybrid‐JSBACH4 with respect to FLUXNET observations vary between −0.1 and 0.15 kg m ⁻² hr ⁻¹ while the JSBACH4 residuals vary between −0.3 and 0.2 kg m ⁻² hr ⁻¹ for forest and grassland sites. The mean hourly residuals for GPP of Hybrid‐JSBACH4 with respect to observations vary between −0.5 and 0.5 gC m ⁻² hr ⁻¹ , compared to the original JSBACH4 with residuals ranging between −1.0 and 0.5 gC m ⁻² hr ⁻¹ , for forest and grassland sites. Our Hybrid‐JSBACH4 model improves the representation of plant physiological responses, and reduces biases in transpiration and GPP simulations under varying atmospheric dryness and water availability conditions.

The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multidecadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only 3 years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: (1) developing km-scale Earth system models with small errors in the energy and water balance, (2) performing km-scale climate simulations with a throughput greater than 1 simulated year per day, and (3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The hackathon participants included experts from climate science, software engineering, and high-performance computing as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations with ICON and IFS-FESOM, spanning the period 2020–2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its time frame, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.

The Climate Change Adaptation Digital Twin (Climate DT), developed as part of the European Commission’s Destination Earth (DestinE) initiative, is a pioneering effort to build an operational climate information system in support of adaptation. This system produces global climate simulations with local granularity, providing information at scales that matter for decision-making. The Climate DT delivers multi-decadal climate simulations at spatial resolutions of 5–10 km, with hourly outputs, offering globally consistent, frequently updated data. The km-scale simulations address some limitations of current climate models, improving local granularity and reducing longstanding biases, supporting more equitable (understood as accessible and relevant across regions) and credible climate information. The Climate DT is built on cutting-edge infrastructure, expert collaboration, and digital innovation. It supports real-time, on-demand responses to policy questions, with quantified uncertainty. It fosters interactivity by allowing users to influence simulation design, model outputs, and applications through co-design. AI-based tools, including emulators and chatbots, are being developed to enhance flexible scenario exploration and ease climate information access. Sector-specific applications are embedded in the system to generate tailored climate-impact indicators, with examples for energy, water, and forest management. The applications have been co-designed with informed users. An important innovation is the use of high-resolution storylines. These are physically consistent simulations of extreme events under different climate conditions that provide contextual insights to support concrete adaptation decisions. A unified workflow across platforms orchestrates all components ensuring automation, containerisation for portability, and traceability. The unified data management ensures consistency and eases the usability of the data. Data is delivered using standard grids (HEALPix) at high-frequency (hourly) and follows a strict governance policy. Streaming enables real-time data use and unlocks access to the unprecedented data produced by the high- resolution simulations. Monitoring tools provide real-time quality control of both data and models and provide diagnostics during the Climate DT operation. The compute-intensive system is powered by world-class supercomputing capabilities through a strategic partnership with the European High Performance Computing Joint Undertaking (EuroHPC). Despite high computational demands, the Climate DT sets a new benchmark for delivering equitable, credible, and actionable climate information. In this way it complements existing initiatives like CMIP, CORDEX, and national and European climate services, and aligns with global climate science goals for climate adaptation.