Aleksi Nummelin

Snow on Arctic sea ice significantly influences regional and global climate processes, yet accurately modelling its spatial and temporal variability remains challenging. We introduce a physics-guided Long Short-Term Memory (LSTM) emulator developed to efficiently simulate pan-Arctic snow density and depth distributions. The emulator is trained on high-resolution SnowModel-3D simulations that resolve wind-driven snow redistribution over sea-ice surface topography, and is applied over the Lagrangian parcel framework of SnowModel-LG for pan-Arctic inference. The topographic input required for inference comes from CryoSat-2 surface roughness estimates at a pan-Arctic scale. We assessed the emulator’s predictive accuracy across multiple years (2014–2021) and various seasonal conditions. Compared with airborne snow observation campaigns, the emulated snow depth has an RMSE of 0.08–0.10 m. By representing the influence of sea-ice topography on snow redistribution, which is not accounted for in SnowModel-LG, the emulator adds a process that brings the simulated snow depth closer to these airborne observations. While seasonal and interannual trends in snow density and depth were effectively captured, emulator accuracy varied during complex melt periods. Our emulator provides an efficient tool for enhancing pan-Arctic snow modelling capabilities in climate studies.

Snow on Arctic sea ice significantly influences regional and global climate processes, yet accurately modelling its spatial and temporal variability remains challenging. We introduce a physics-guided Long Short-Term Memory (LSTM) emulator developed to efficiently simulate pan-Arctic snow density and depth distributions. The emulator is trained on high-resolution SnowModel-3D simulations that resolve wind-driven snow redistribution over sea-ice surface topography, and is applied over the Lagrangian parcel framework of SnowModel-LG for pan-Arctic inference. The topographic input required for inference comes from CryoSat-2 surface roughness estimates at a pan-Arctic scale. We assessed the emulator’s predictive accuracy across multiple years (2014–2021) and various seasonal conditions. Compared with airborne snow observation campaigns, the emulated snow depth has an RMSE of 0.08–0.10 m. By representing the influence of sea-ice topography on snow redistribution, which is not accounted for in SnowModel-LG, the emulator adds a process that brings the simulated snow depth closer to these airborne observations. While seasonal and interannual trends in snow density and depth were effectively captured, emulator accuracy varied during complex melt periods. Our emulator provides an efficient tool for enhancing pan-Arctic snow modelling capabilities in climate studies.

Snow on Arctic sea ice significantly influences regional and global climate processes, yet accurately modelling its spatial and temporal variability remains challenging. We introduce a physics-guided Long Short-Term Memory (LSTM) emulator developed to efficiently simulate pan-Arctic snow density and depth distributions. The emulator is trained on high-resolution SnowModel-3D simulations that resolve wind-driven snow redistribution over sea-ice surface topography, and is applied over the Lagrangian parcel framework of SnowModel-LG for pan-Arctic inference. The topographic input required for inference comes from CryoSat-2 surface roughness estimates at a pan-Arctic scale. We assessed the emulator’s predictive accuracy across multiple years (2014–2021) and various seasonal conditions. Compared with airborne snow observation campaigns, the emulated snow depth has an RMSE of 0.08–0.10 m. By representing the influence of sea-ice topography on snow redistribution, which is not accounted for in SnowModel-LG, the emulator adds a process that brings the simulated snow depth closer to these airborne observations. While seasonal and interannual trends in snow density and depth were effectively captured, emulator accuracy varied during complex melt periods. Our emulator provides an efficient tool for enhancing pan-Arctic snow modelling capabilities in climate studies.

Snow on Arctic sea ice significantly influences regional and global climate processes, yet accurately modelling its spatial and temporal variability remains challenging. We introduce a physics-guided Long Short-Term Memory (LSTM) emulator developed to efficiently simulate pan-Arctic snow density and depth distributions. The emulator is trained on high-resolution SnowModel-3D simulations that resolve wind-driven snow redistribution over sea-ice surface topography, and is applied over the Lagrangian parcel framework of SnowModel-LG for pan-Arctic inference. The topographic input required for inference comes from CryoSat-2 surface roughness estimates at a pan-Arctic scale. We assessed the emulator’s predictive accuracy across multiple years (2014–2021) and various seasonal conditions. Compared with airborne snow observation campaigns, the emulated snow depth has an RMSE of 0.08–0.10 m. By representing the influence of sea-ice topography on snow redistribution, which is not accounted for in SnowModel-LG, the emulator adds a process that brings the simulated snow depth closer to these airborne observations. While seasonal and interannual trends in snow density and depth were effectively captured, emulator accuracy varied during complex melt periods. Our emulator provides an efficient tool for enhancing pan-Arctic snow modelling capabilities in climate studies.

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.

Paleogeography is a key boundary condition for reconstructing Earth’s climatic evolution and habitability. On geological timescales, paleogeographic changes control the latitudinal positioning of environments, governing received and reflected solar radiation and climatic zonation. The distribution and morphology of continents and oceans further control ocean–atmosphere circulation and influence the evolution and dispersal of marine and terrestrial biota.Here we present a new effort to construct a continuous (1 Myr resolution) global paleogeographic digital elevation model for the entire Phanerozoic (540–0 Ma). The reconstructions integrate new and previously published plate models, and global and regional paleo-elevation datasets. Building on and extending methodologies previously applied to the Cenozoic (66–0 Ma), our approach incorporates dynamic topography from mantle circulation (100–0 Ma), oceanic lithospheric ages, sediment thickness, detailed continental margin evolution, parameterized subduction zones, and spatiotemporal interpolation between topographic datasets of different time intervals. The reconstructions focus in detail on key paleogeographic features relevant for ocean circulation, climate, and biogeography, including oceanic gateways, land bridges, and large-scale orogenies.Finally, we present results from a variety of fully coupled Earth system model experiments, mainly with Cenozoic paleogeographic boundary conditions (e.g., present, Eocene–Oligocene, Late Eocene, and the DeepMIP Early Eocene ensemble), to demonstrate how paleogeographic changes influences planetary energy budgets, ocean circulation, and climate sensitivity. These results highlight systematic relationships that offer potential for extrapolation throughout the Phanerozoic.

Climate models are commonly run at resolutions too coarse to resolve mesoscale ocean dynamics, and therefore lack oceanic eddies and fronts that strongly influence air-sea exchange. This leads to an underestimation of the ocean’s role in driving atmosphere–ocean interactions in western boundary current regions, with implications for simulated climate variability and change. We explore whether the effects of mesoscale sea surface temperature (SST) features on large-scale circulation can be represented in a standard resolution climate model using a partially coupled “pacemaker” configuration of the Norwegian Earth System Model version 2 (NorESM2). The setup introduces mesoscale SST features from a high-resolution (0.125°) ocean into the standard-resolution coupled model grid (1° ocean and atmosphere). Focusing on the Kuroshio Current, we find that mesoscale SST features amplify ocean-to-atmosphere turbulent heat fluxes, as expected, and also produce notable free tropospheric responses (a robust local strengthening of the North Pacific storm track at low levels and a poleward shift aloft). The results offer a proof of concept that 1° climate models can capture the broader climate impacts of small-scale oceanic variability without explicitly resolving it, opening promising pathways to improve predictions and projections.