Climatological studies of downslope windstorms (DWs) in the Scandinavian mountains (Scandes) are rare. Here we use 20-year long simulations with the kilometre-scale regional climate model HCLIM38-AROME to study DWs in the Scandes in present and future climate, their connection to large-scale atmospheric circulation, and their cooling and warming effect. A DW is identified in a model grid point using a two-step conceptual model based on terrain features, and dynamic and thermodynamic variables. We find that DWs occur most frequently in winter, which is therefore the focal season for the analyses. Normally, DWs are predominantly either warming (foehn type) or cooling (bora type), but our results indicate that this distinction does not hold for the DWs in the Scandes. Even though there is a slight overall tendency towards warming, almost all locations with DWs can experience both cooling and warming. The future simulations using two parent global climate models (GCMs) show an overall decrease in the total number of DW occurrences between 7% and 17% toward the end of the century, with a decrease in the northern and central parts of the Scandes and an increase in the southeastern part. These changes can be attributed to two factors: the frequency change of certain circulation types and internal changes within the circulation types. Despite the agreement in the sign of future changes for both GCM forcings, the difference in the contribution of the two factors indicates that different mechanisms are responsible for the total future change in the DW occurrence. The complexity of DWs in the Scandes and their future change indicates that kilometre-scale or finer climate models are required for a proper depiction of DWs and that a larger ensemble of simulations with different GCM forcing is required for evaluation of different mechanisms of the future change.
Numerical weather prediction and climate models require continuous adaptation to take advantage of advances in high-performance computing hardware. This paper presents the port of the ICON model to GPUs using OpenACC compiler directives for numerical weather prediction applications. In the context of an end-to-end operational forecast application, we adopted a full-port strategy: the entire workflow, from physical parameterizations to data assimilation, was analyzed and ported to GPUs as needed. Performance tuning and mixed-precision optimization yield a 5.5× speed-up compared to the CPU baseline in a socket-to-socket comparison. The ported ICON model meets strict requirements for time-to-solution and meteorological quality, in order for MeteoSwiss to be the first national weather service to run ICON operationally on GPUs with its ICON-CH1-EPS and ICON-CH2-EPS ensemble forecasting systems. We discuss key performance strategies, operational challenges, and the broader implications of transitioning community models to GPU-based platforms.
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.
Workshop on Surface Process Coupling and Its Interactions with the Atmosphere What: This paper reports on the Workshop on surface process coupling and its interactions with the atmosphere that took place within the ECMWF 50th-year celebrations, alongside three other workshops and its Annual Seminar. The purpose of this workshop was to improve the representation of coupled processes to enhance future implementations of global numerical weather prediction models, focusing on coupling strategies and tools, conservation and consistency of the coupling, and differences in coupling strategies between physics-based and machine-learned models. See https:// events.ecmwf.int/event/432/ and https://events.ecmwf.int/event/427/ for more information on these events. When: 9-10 April 2025 Where: Bonn, Germany
Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning (ML) parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in standard climate sensitivity simulations, including two-way interactions of ozone with the Quasi-Biennial Oscillation. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models-the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With atmospheric temperature profile information as the only input, mloz produces stable ozone predictions similar to 31 times faster than the chemistry scheme in UKESM, contributing less than 4% of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON. This highlights mloz's potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, particularly when focusing on climate sensitivity simulations, where ozone trends and variability are known to significantly modulate atmospheric feedback processes.
