Abstract. This paper introduces Omega, the Ocean Model for E3SM Global Applications. Omega is a new ocean model designed to run efficiently on high performance computing (HPC) platforms, including exascale heterogeneous architectures with accelerators, such as Graphics Processing Units (GPUs). Omega is written in C++ and uses the Kokkos performance portability library. These were chosen because they are well-supported and will help future-proof Omega for upcoming HPC architectures. Omega will eventually replace the Model for Prediction Across Scales-Ocean (MPAS-Ocean) in the US Department of Energy's (DOE's) Energy Exascale Earth System Model (E3SM). Omega runs on unstructured horizontal meshes with variable-resolution capability and implements the same horizontal discretization as MPAS-Ocean. This work documents the design and performance of Omega Version 0.1.0 (Omega-V0), which solves the shallow water equations with passive tracers and is the first step towards the full primitive equation ocean model. On Central Processing Units (CPUs), Omega-V0 is 1.4 times faster than MPAS-Ocean with the same configuration. Omega-V0 is more efficient on GPUs than CPUs on a per-watt basis – by a factor of 5.3 on Frontier and 3.6 on Aurora, two of the world's fastest exascale computers.

Abstract. An accurate representation of tidal dynamics is critical for simulating physical and biogeochemical processes in marginal seas, such as the Yellow Sea, where energetic tides and strong seasonal stratification coexist. This study assessed the impact of vertical coordinate systems on the simulation of barotropic and baroclinic tides using Modular Ocean Model version 6, evaluating two configurations: the z* coordinate system (ZSTAR) and hybrid z*-isopycnal coordinate system (HYBRID). The model outputs were validated against satellite-derived sea surface temperatures, in situ temperature profiles, and TPXO tidal harmonics, with a focus on contrasting winter and summer conditions. HYBRID more accurately reproduced sea surface temperatures and vertical thermal structures, particularly during strongly stratified summers, and maintained a sharper and deeper thermocline, as confirmed by long-term temperature diagnostics and age tracer experiments, indicating reduced vertical mixing and improved stratification. For barotropic tides, HYBRID showed better agreement with TPXO for the dominant M2 constituent (RMSE: 24.19 cm; correlation: 0.76) than ZSTAR (RMSE: 32.55 cm; correlation: 0.67). A similar improvement is found for the K1 constituent, further confirming the superior barotropic tidal performance of HYBRID across multiple tidal frequencies. HYBRID also produced stronger barotropic tidal energy fluxes across key regions, yielding 13.5 %–27.6 % larger fluxes in winter and 17.2 %–51.1 % larger fluxes in summer relative to ZSTAR. Baroclinic tidal dynamics exhibited more contrasting model behavior. In winter, ZSTAR produced 19.5 %–36.8 % greater baroclinic kinetic energy (KE). However, in summer, HYBRID simulated consistently stronger baroclinic KE, with 9.2 %–33.6 % larger magnitudes across all regions, reflecting more realistic baroclinic tide generation under strong stratification. Analysis of the baroclinic energy budget further revealed that, although ZSTAR often yielded greater barotropic-to-baroclinic conversion, a substantial portion of this converted energy was locally dissipated rather than radiated, resulting in a much larger residual dissipation term. This indicates that spurious diapycnal mixing in ZSTAR rapidly removes internal-tide energy, degrading propagation and vertical energy transfer. In contrast, HYBRID preserved baroclinic tidal energy more effectively, enabling more coherent energy radiation away from generation hotspots. These results highlight that vertical coordinate design critically influences tidal energetics and stratification-dependent processes in high-resolution regional models. The improved stratification maintenance enabled by HYBRID offers substantial advantages for accurately representing internal-tide dynamics and associated vertical energy pathways in the Yellow Sea.

Abstract. 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.

Abstract. We describe the operational forecasting system “Norkyst”, now in version 3, which is used for predicting the ocean circulation along the coast of mainland Norway and in the fjords. The forecasting system is based on the Regional Ocean Modeling System (ROMS), and has sub-kilometric horizontal resolution to resolve mesoscale features. Here we describe the basic configuration and report verification statistics of unconstrained model runs. The main features of the circulation and hydrography, including seasonal variations, are well represented in Norkyst v.3, making the forecast system suitable for its intended use as an open service for users in public or private sectors such as aquaculture, fishery, shipping, research, consulting, environmental management, and others who needs detailed predictions of the physical state of the Norwegian coastal ocean.

Abstract. Microbial communities, comprising phototrophic and heterotrophic microorganisms, play a crucial role in global biogeochemical cycles. However, existing biogeochemical models rarely take into account the complex interactions between these groups, usually focusing on competition for resources instead. In this work, we introduce the Microbial Community Model (MCoM), a framework for simulating the dynamics of diverse microbial communities. MCoM incorporates a wide range of interactions, such as cross-feeding, metabolite effects, and competition for nutrients. The model differentiates between dissolved organic nutrients (DON) and carbon (DOC), accounts for phytoplankton and heterotrophic bacterial species-specific organic matter release and uptake profiles, and simulates the impacts of bacterial metabolites on phytoplankton growth. Implemented as a box model, MCoM tracks phototrophic and heterotrophic biomass, active metabolites, DOC, DON, and inorganic nutrients through non-linear differential equations, enabling the exploration of emergent properties and feedbacks. We demonstrate the model's capabilities through simulations of experimental data of pairwise co-cultures of heterotrophic and phototrophic microorganisms, and find overall good agreement. Due to the scalable implementation, interaction matrices for larger, i.e. hundreds, of microbial groups can easily be realised. We show examples for such applications of MCoM in assessing emergent dynamics, including periodic succession patterns and multi-stability. MCoM provides a versatile, scalable, and customizable platform for assessing the range from pairwise interactions to complex microbial communities and their impact on biogeochemical fluxes.

Abstract. 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.

Abstract. Understanding causal relationships in multivariate time series is essential across many scientific domains, especially in climatology and meteorology where complex dependencies drive extreme events. Existing tools often lack intuitive visualization, particularly for heterogeneous Granger causality applied to non-Gaussian data such as time series following exponential distributions. There is a need for an accessible, interpretable tool that helps scientists explore temporal dependencies and uncover causal structure in such settings. We present HMMLVis, an original visualization tool designed for multivariate and heterogeneous Granger causal inference (Heterogeneous Granger causality by Minimum Message Length). The tool supports causal discovery in time series with exponential distributions and offers an intuitive interface for exploring temporal relationships. While HMMLVis is applicable across disciplines dealing with time-series data, this work focuses on its use in climatological and meteorological contexts. We demonstrate HMMLVis on several applications involving meteorological phenomena affecting the upper and lower distributional tails, using datasets from renewable energy (wind and PV), air pollution, and the European Meteorological Network (EUMETNET) post-processing benchmark (EUPPBench) across different temporal horizons. Our results show that HMMLVis successfully recovers known causal relationships and additionally reveals previously unobserved temporal dependencies relevant to the specific cases examined. As an interpretable and user-friendly visualization tool, HMMLVis has strong potential to support climatologists, meteorologists, and other researchers working with complex time-series data. By enabling clearer insight into causal interactions, it contributes to more informed scientific understanding and facilitates knowledge discovery across multiple environmental and atmospheric science applications.

Abstract. We describe an improved method and the associated package for estimating the statistics of temperature extremes in a Bayesian framework. Building on previous work, this method uses a range of climate model simulations to provide a prior of the real-world changes, and then considers observations to derive a posterior estimate of past and future changes. The new version described in this study makes it possible to process several scenarios simultaneously, while keeping one single counterfactual world (i.e., the world without human influence). We offer a free licensed, easy-to-use command-line tool called ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events), which can be used to reproduce the analyses presented here, as well as to process user-defined events. ANKIALE is based on a python code, but is designed to be used from the command line interface. ANKIALE is natively parallel, enabling it to be used on a personal computer as well as on a supercomputer. To derive the posterior, ANKIALE uses state of art MCMC-methods to sample the posterior distribution. The potential of this method and tool is illustrated via an application to maximum temperature over Europe between 1850 and 2100 (the posterior is derived from ERA5, covering the period from 1940 to 2024), at a 0.25° resolution, for a range of four emission scenarios, including a particular focus on the city of Paris (France).

Abstract. We present a fully implicit finite element method for viscous free surface Stokes problems which are common in glaciology and geodynamics. Standard fully implicit iterative solvers diverge for such problems when using large time steps. We address this by incorporating a stabilization term that vanishes upon convergence. This approach enables the use of large time steps, thereby significantly improving the efficiency of simulations. Furthermore, we leverage the fully implicit solver to develop second-order time-stepping schemes that mitigate the high errors associated with first-order methods for large time steps. The methods are tested and evaluated on model domains relevant to geodynamics and glaciology.

Abstract. Efficient and informative air quality modeling in future emission scenarios is vital for effective formulation of emission reduction policies. Traditional chemical transport models (CTMs) struggle with the computational demands required for timely predictions. While advanced emulator techniques greatly accelerate CTM simulating process, they fall short in providing comprehensive estimates of future air quality due to their limited model structure. Additionally, these emulators often have difficulty simultaneously accounting for varying emission variables and the effects of regional transport, which limits their applicability and undermines prediction accuracy. In this study, an informative future air quality prediction model “TGEOS v1.0” based on the Transformer framework is developed as an efficient agent model of GEOS-Chem v14.2.2. TGEOS is able to efficiently estimate key statistical indicators of PM2.5 and O3 concentrations under future emission scenarios and capture potential extreme pollution events, with approximately 2.51 s to execute one-year estimation. The model incorporates sectoral emissions of up to 26 distinct species as well as the impacts of regional emissions and meteorology on pollutant concentrations, enhancing its versatility and predictive accuracy. The spatial and probability distributions predicted by TGEOS are in good agreement with GEOS-Chem, with the correlation coefficients for PM2.5 and O3 exceed 0.98 in high-pollution months. Compared with other machine learning models, TGEOS based on Transformer framework showcases superior performance, underscoring the potential of the Transformer framework in air quality modeling.