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First year of Meteosat Third Generation Lightning Imager observations

The Meteosat Third Generation Lightning Imager (MTG-LI) is the first European space mission for monitoring lightning activity over Europe, Africa, and their surrounding areas. During its first year of observations from July 2024 to June 2025, MTG-LI detected a total of 595 × 10⁶ lightning flashes. The highest lightning frequency was found in central Africa. MTG-LI is an excellent instrument for monitoring total lightning activity on operational and climatological scales.

GlaUnTI: a hybrid physics–machine learning model enables transferable glacier surface mass balance estimation

Glaciers are melting worldwide, but direct measurements are spatially sparse. We developed a new model that combines mechanistic physical rules with machine learning to better estimate yearly gains and losses of ice mass across Europe. By learning local corrections to a physical melt model, the machine learning component improves accuracy and works well on glaciers not used for training. This will help produce more reliable glacier assessments and support future climate studies.

Meteosat Third Generation Lightning Imager detection performances

Lightning is a vital meteorological indicator, an Essential Climate Variable, and a key factor in atmospheric chemistry. EUMETSAT’s Meteosat Third Generation Lightning Imager (MTG-LI), operational since July 2024, is Europe’s first space-based geostationary lightning location system devoted to lightning diagnosis and characterization. This manuscript assesses its performance over 18 months, serving as a foundational reference for users.

Efficient ensemble estimation using an MCMC sampler: a reconstruction of the Mediterranean low-frequency variability combining observed and simulated sea level

In this paper, an ensemble method is proposed to estimate directly the low-frequency component of the ocean variability from native observations using statistics from a prior long-term ensemble simulation. The approach is illustrated by the reconstruction of the low-frequency variability of the Mediterranean sea level from along-track altimetric observations over 26 years. The resulting ensemble estimate is assessed against independent observations, showing good reliability.

Propagating meteorological uncertainty in physically based mountain snow modeling

Improving mountain snow prediction requires better representation of meteorological uncertainty. This study generates meteorological ensembles accounting for topography, gauge undercatch, and large-scale atmospheric patterns. Driving a physics-based snow model calibrated using a Large Sample Emulator with these ensembles propagates forcing uncertainty into snow accumulation and melt, producing probablistic estimates for data assimilation and hydrological forecasting.

Temperate marine macrophytes are highly variable sources of Biogenic Volatile Organic Compounds – a comparative study from the Baltic Sea and NE Atlantic

Biogenic volatile organic compounds (BVOCs) emitted by Earth’s ecosystems have profound climate impacts, but emissions from marine benthic sources are poorly quantified. We quantified BVOC emission rates from three macrophytes across two contrasting marine regions and found highly diverse emission profiles with high intra‑ and interspecific variability. Our results underscore the need for more high-quality data, so that macrophyte emissions can eventually be integrated into marine BVOC budgets.

Historical climate extremes in Europe and the connection between spring precipitation and summer heat

We analyse the link between dry springs and hot summers in Europe over the past 600 years. Starting with a study of the heat summer of 1473, which followed dry spring conditions in Southeastern Europe, we use the ModE-RA data set family to statistically analyse the link. We find that the link is strongest in regions where evaporation is moisture-limited. Hot summers are preceded by increased blocking over north-central Europe and a positive East Atlantic pattern in spring.

Invited perspective: Uncertainties in natural systems may be uncomfortable, but ignoring them would be absurd

Natural Hazards and Earth System Sciences
DOI 10.5194/nhess-26-4861-2026
8 October 2026

Natural systems are complex and uncertain, making probabilistic forecasts essential. Representing and communicating all uncertainties is challenging but vital for risk management. A multidisciplinary review identified common challenges across hazards and proposes a shared framework based on a clear hierarchy of uncertainties, complete forecasts, rigorous model evaluation, and effective communication to support decision-making.

From manual classification to transformer-based language models: assessing the quality and consistency of historical convective event records

Before measuring instruments, thunderstorms and hail were recorded only in written reports. We analysed around 7000 reports from 1000 to 1817 and, using a rule-based workflow, linguistically decoded the intensity of the thunderstorm and hail events described. The annual cycle derived from this corresponds well with modern observations. With these data, language models can be trained to reliably classify such events and make large collections of text accessible for climate research.

Pokémon Trading Cards reveal visual stereotypes of natural minerals

Pokémon cards offer an unexpected 30-year record of how popular culture depicts minerals. Analyzing 223 illustrations revealed a "universal crystal template" reducing natural minerals to a shared visual stereotype: giant, translucent, symmetrical crystals inside caves. While these images oversimplify geological reality, their global familiarity and the diverse mineral examples already represented make this collection an excellent educational hook to inspire interest in authentic Earth science.

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