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An extreme geomagnetic storm comparable to the Carrington event in 1859 could have a significant impact on modern infrastructure, such as power grids. A previously published simulation by Blake et al. (2021, Space Weather, https://doi.org/10.1029/2020SW002585) reconstructed the magnetic field observations at Colaba, India, during the Carrington storm and provided estimates of the magnetic field variations around the world. We use these results as an input to a first-principle modelling method to estimate the geoelectric field in Fennoscandia based on a 3-dimensional ground conductivity model. We compare the results with the Oct 2003 Halloween storm, which is one of the strongest events in the past 100 years, and of which spatially dense magnetometer recordings are available in Northern Europe. Comparison of the maximum modelled geoelectric field values in Fennoscandia indicates that a Carrington-class storm could generate electric fields 1.4–20.4 times larger than the Halloween storm did, with the Carrington to Halloween ratio having a mean of 6.7 and standard deviation of 2.7.
The Solar Wind Anisotropies all-sky hydrogen Lyman-alpha camera on the Solar and Heliosphere Observatory observed the hydrogen coma of interstellar comet 3I/ATLAS, also called C/2025 N1 (ATLAS), beginning on 2025 November 6, 9 days after perihelion. Water production rates were calculated from each image of 3I/ATLAS using the methodology of J. T. T. Mäkinen and M. R. Combi, and fluorescence rates and g-factors were calculated using the daily solar Lyman-alpha fluxes from the LASP database ( https://lasp.colorado.edu/lisird/data ) corrected for solar rotation and for the comet’s heliocentric velocity. The method has been used for over 90 comet apparitions. A water production rate of 3.17 × 10 ^29 s ^−1 was found on November 6 when the comet was at a heliocentric distance of 1.40 au and at a sufficient solar elongation angle. It decreased over time after that, down to 1–2 × 10 ^28 s ^−1 around 40 days postperihelion (December 9).
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.
Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, radar, gravity, and elemental composition. Yet these datasets remain fragmented across archives, and no benchmark exists for evaluating machine learning on lunar data. We introduce Moonstone, the first multi-modal foundation model benchmark for lunar remote sensing. Our contributions are: (1) a 28-channel, 128 pixels-per-degree ( ∼ 237 m) global lunar pretraining dataset from seven instrument families across five missions, (2) MG-MAE, a modality-grouped masked autoencoder with per-group convolutional tokenizers, a shared Vision Transformer encoder, attention masking for missing modalities, coverage-adaptive masking for heterogeneous spatial coverage, and spectral continuity regularization for physically plausible reconstructions, and (3) a benchmark of six downstream tasks covering classification, regression, and segmentation. MG-MAE pretrained features outperform scratch baselines on all tasks and surpass both ImageNet-pretrained and vanilla MAE baselines by large margins. We release the pretraining dataset, code, and the benchmark suite (Data: https://huggingface.co/datasets/ayushprd/Moonstone Code: https://github.com/ayushprd/Moonstone ).
Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1° × 1° resolution. We apply this framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000–2020 emissions through 2025. In the test data (∼ 30 % of the total dataset), the emulator achieved a global R2 of 0.65 ± 0.003 (mean ± 95 % CI, hereafter) and an RMSE of 5.49±0.12×10-3 Tg CH4 yr−1. The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH4 emissions for 2021–2025 (157.8 ± 2.4 Tg CH4 yr−1) are not significantly higher (∼ 0.05 Tg CH4 yr−1) than the 2000–2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 and 0.35 ± 0.03 Tg CH4 yr−1, respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (−0.95 ± 0.19 and -0.11±0.02 Tg CH4 yr−1, respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).
