The Danish Meteorological Institute (DMI; Danish: Danmarks Meteorologiske Institut) is the official Danish meteorological institute, administrated by the Ministry of Energy, Utilities and Climate. The institute makes weather forecasts and observations for Denmark, Greenland, and the Faroe Islands.
We present IT-DPC-SRI, the first publicly available long-term archive of Italian weather radar precipitation estimates, spanning 16 years (2010–2025). The dataset contains Surface Rainfall Intensity (SRI) observations from the Italian Civil Protection Department's national radar mosaic, harmonized into a coherent Analysis-Ready Cloud-Optimized (ARCO) Zarr datacube. The archive comprises over one million timesteps at temporal resolutions from 15 to 5 minutes, covering a 1200×1400 kilometer domain at 1 kilometer spatial resolution, compressed from 7TB to 51GB on disk. We address the historical fragmentation of Italian radar data - previously scattered across heterogeneous formats (OPERA BUFR, HDF5, GeoTIFF) with varying spatial domains and projections - by reprocessing the entire record into a unified store. The dataset is accessible as a static versioned snapshot on Zenodo, via cloud-native access on the ECMWF European Weather Cloud, and as a continuously updated live version on the ArcoDataHub platform. This release fills a significant gap in European radar data availability, as Italy does not participate in the EUMETNET OPERA pan-European radar composite. The dataset is released under a CC BY-SA 4.0 license.
The DANish regional atmospheric ReAnalysis (DANRA) is a novel high-resolution (2.5 km) reanalysis dataset covering Denmark and its surrounding regions over a 34-year period (1990–2023). Denmark's complex coastline, with over 400 islands and an extensive 7400 km coastline, means that most municipalities experience mixed land-sea variability. This complexity requires a regional climate reanalysis system that can resolve fine-scale coastal and inland features, as well as their impact on climate variability. DANRA is based on the HARMONIE-AROME Numerical Weather Prediction (NWP) model and assimilates a comprehensive set of observations, with a particular focus on Denmark. Compared to global reanalyses such as the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis v5 (ERA5), DANRA demonstrates superior performance in representing essential climate variables, including near-surface weather parameters during both extreme and ordinary conditions. We illustrate these improvements in the representation of several extreme weather cases over Denmark, such as the December 1999 hurricane-force storm, the July 2022 national temperature record, and the August 2007 cloudburst in South Jutland. DANRA is made to support climate adaptation, impact modelling, and the training of next-generation data-driven atmospheric forecasting models. DANRA is distributed as Zarr dataset freely accessible from an object store (https://doi.org/10.5281/zenodo.17294179, Yang et al., 2025), maximizing its usability for climate adaptation, impact modelling, and data-driven research.
Classical extreme value analysis (EVA) often provides large uncertainties on estimated return levels due to the limited amounts of data available. Marani and Ignaccolo (Adv Water Resour 79:121–126, 2015. https://doi.org/10.1016/j.advwatres.2015.03.001 ) aim to overcome this by the metastatistical extreme value (MEV) approach. Here extremes are treated as large ordinary events described by one common, known distribution, and therefore a much larger pool of data is available for estimation. They performed Monte Carlo simulations with synthetic Weibull-distributed rainfall series and showed that the MEV approach gives unbiased estimates of extremes with a smaller uncertainty than classical EVA does. However, the MEV approach neglects that many complex physical mechanisms influence rainfall and other hydrological processes. This means that the tail behavior of the distribution cannot necessarily be inferred from the ordinary events. We therefore replicated their work but added new Monte Carlo experiments to study the classical EVA and the MEV methodologies with a slightly perturbed tail of the underlying distribution. When applying the MEV approach, i.e. fitting a Weibull distribution to the perturbed Weibull series, we obtained consistently negatively biased estimates with underestimated uncertainty bands. In contrast, classical EVA also produced unbiased estimates here. Finally, we showed that goodness-of-fit tests are not able to provide guidance on whether MEV can provide unbiased and confident return levels. Further Monte Carlo simulations showed that these conclusions seem to be quite general and not dependent on the specific distribution. Consequently, the MEV approach may have limitations that make it less suitable for providing reliable return levels in real-world applications.
Abstract Understanding the coastal zone of the Antarctic Ice Sheet (AIS), where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate and sea level. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the AIS, the Southern Ocean and their global connections in the coming centuries. The AIS remains the largest source of uncertainty in future sea‐level projections. Bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, and is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground‐ and ship‐based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography data sets identifies significant data gaps and their regional distribution, framed in the context of current ice‐sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next‐generation data set of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice‐sheet dynamics, reducing uncertainties in sea‐level rise projections and improving predictions of future ocean and climate changes.
The Atlantic Meridional Overturning Circulation (AMOC), a key component of the Earth’s climate system, has long been considered vulnerable to irreversible weakening or collapse under global warming and related Greenland Ice Sheet (GrIS) melt, yet its resilience remains uncertain. Here, we use a CO2-emission-driven Earth system model with an interactive GrIS to assess AMOC reversibility under idealised CO2 emission pathways that produce near-linear global warming up to 10 K, stabilisation across 1.5-9 K, and subsequent cooling. We find that although the AMOC attains “collapsed” states by commonly used threshold definitions, these weakened states do not represent dynamical tipping: the overturning weakens quasi-linearly with global temperature increase, yet consistently and promptly recovers under cooling. In contrast, GrIS mass loss accelerates with warming, continues through stabilisations, and is only slowed by cooling, committing the planet to long-term sea-level rise. These results reveal a striking asymmetry in Earth-system resilience: under transient CO2 forcing, the AMOC strength remains dynamically reversible even under continued Greenland meltwater input, whereas the GrIS is locked into persistent decline. Our findings underscore the urgency of rapid emission cuts to limit climate overshoot, AMOC weakening, and irreversible ice-sheet loss.
