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Routing streamflow through a river network is a fundamental requirement to verify lateral water fluxes simulated by hydrologic and land surface models. River routing is performed at diverse resolutions ranging from few kilometres to 1∘. The presented multiscale routing model mRM calculates streamflow at diverse spatial and temporal resolutions. mRM solves the kinematic wave equation using a finite difference scheme. An adaptive time stepping scheme fulfilling a numerical stability criterion is introduced in this study and compared against the original parameterisation of mRM that has been developed within the mesoscale hydrologic model (mHM). mRM requires a high-resolution river network, which is upscaled internally to the desired spatial resolution. The user can change the spatial resolution by simply changing a single number in the configuration file without any further adjustments of the input data. The performance of mRM is investigated on two datasets: a high-resolution German dataset and a slightly lower resolved European dataset. The adaptive time stepping scheme within mRM shows a remarkable scalability compared to its predecessor. Median Kling–Gupta efficiencies change less than 3 % when the model parameterisation is transferred from 3 to 48 km resolution. mRM also exhibits seamless scalability in time, providing similar results when forced with hourly and daily runoff. The streamflow calculated over the Danube catchment by the regional climate model REMO coupled to mRM reveals that the 50 km simulation shows a smaller bias with respect to observations than the simulation at 12 km resolution. The mRM source code is freely available and highly modular, facilitating easy internal coupling in existing Earth system models.

Distributed environmental models such as land surface models (LSMs) require model parameters in each spatial modeling unit (e.g., grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimensionality of the parameter space in these models is to use regularization techniques. One such highly efficient technique is the multiscale parameter regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of NetCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model (mHM; https://www.ufz.de/mhm, last access: 16 January 2022). By using this tool for the generation of continental-scale soil hydraulic parameters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.

Computer simulations are an essential pillar of knowledge generation in science. Exploring, understanding, reproducing, and sharing the results of simulations relies on tracking and organizing the metadata describing the numerical experiments. The models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata. Here, we present general practices for acquiring and handling metadata that are agnostic to software and hardware, and highly flexible for the user. These consist of two steps: 1) recording and storing raw metadata, and 2) selecting and structuring metadata. As a proof of concept, we develop the Archivist , a Python tool to help with the second step, and use it to apply our practices to distinct high-performance computing use cases from neuroscience and hydrology. Our practices and the Archivist can readily be applied to existing workflows without the need for substantial restructuring. They support sustainable numerical workflows, fostering replicability, reproducibility, data exploration, and data sharing in simulation-based research.

We present a storyline analysis investigating the impacts of background warming on storm Gloria, a severe winter storm that affected the western Mediterranean in January 2020, producing exceptional precipitation, fatalities, and substantial economic damage. The study employs global, ~ 9 km, IFS-FESOM simulations, constrained with spectral nudging to reproduce large-scale conditions under Counterfactual, Actual, and Future climates between 2017 and 2024. The plausibility of the simulated climates is assessed by comparing relative changes in seven-year DJF distributions of precipitable water (PW), integrated vapour transport (IVT) and precipitation between the Counterfactual and Actual simulations against corresponding Past and Present periods in ERA5. The close agreement indicates that the simulated thermodynamic differences are consistent with reanalysis-based differences. We then examine Gloria’s evolution across the three climates. PW and IVT increase with warming rates close to Clausius-Clapeyron scaling. In contrast, precipitation show strong spatial heterogeneity and differences deviate from thermodynamic scaling due to changes in the persistence and organisation of vertical motion. Despite local variability, total event-integrated precipitation increases by 6.3% from the Counterfactual to the Future climate. These findings illustrate the strengths and limitations of spectrally-nudged storylines for dynamically complex storms and their value in separating thermodynamic amplification from dynamical modulation.

In this study, we present a new coupling framework named FINAM (short for “FINAM is not a model”). FINAM is designed to facilitate the coupling of models that were developed as standalone tools in the first place and to enable seamless model extensions by wrapping existing models into components with well-specified interfaces. Although established coupling solutions such as Earth System Modeling Framework (ESMF), Ocean Atmosphere Sea Ice Soil (OASIS), or Yet Another Coupler (YAC) focus on highly parallel workflows, complex data processing, and regridding, FINAM prioritizes usability and flexibility, allowing users to focus on scientific exploration of coupling scenarios rather than technical complexities. FINAM emphasizes ease of use for end users to create, run, and modify model couplings, as well as for model developers to create and maintain components for their models. The framework is particularly suited for applications where rapid prototyping and flexible model extensions are desired. It is primarily targeting environmental models, including ecological models for animal populations, individual-based forest models, field-scale crop models, economical models, and hydrological models. Python's robust interoperability features further enhance FINAM's capabilities, allowing us to wrap and use models written in various programming languages like Fortran, C, C++, Rust, and others. This paper describes the main principles and modules of FINAM and presents example workflows to demonstrate its features. These examples range from simple toy models to well-established models like OpenGeoSys and Bodium covering features like bidirectional dependencies, complex model coupling, and spatiotemporal regridding.

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.

Computer simulations are an essential pillar of knowledge generation in science. Exploring, understanding, reproducing, and sharing the results of simulations relies on tracking and organizing the metadata describing the numerical experiments. The models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata. Here, we present general practices for acquiring and handling metadata that are agnostic to software and hardware, and highly flexible for the user. These consist of two steps: 1) recording and storing raw metadata, and 2) selecting and structuring metadata. As a proof of concept, we develop the Archivist, a Python tool to help with the second step, and use it to apply our practices to distinct high-performance computing use cases from neuroscience and hydrology. Our practices and the Archivist can readily be applied to existing workflows without the need for substantial restructuring. They support sustainable numerical workflows, fostering replicability, reproducibility, data exploration, and data sharing in simulation-based research.

Computer simulations are an essential pillar of knowledge generation in science. Understanding, reproducing, and exploring the results of simulations relies on tracking and organizing metadata describing numerical experiments. However, the models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata. Here, we present general practices for acquiring and handling metadata that are agnostic to software and hardware, and highly flexible for the user. These consist of two steps: 1) recording and storing raw metadata, and 2) selecting and structuring metadata. As a proof of concept, we develop the Archivist, a Python tool to help with the second step, and use it to apply our practices to distinct high-performance computing use cases from neuroscience and hydrology. Our practices and the Archivist can readily be applied to existing workflows without the need for substantial restructuring. They support sustainable numerical workflows, facilitating reproducibility and data reuse in generic simulation-based research.