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Assessing the effect of climate extremes on maternal and infant health is hindered by gaps in exposure data, vulnerability assessments and integration of sociobehavioural dimensions.

This study investigates whether and how climate change affected the compound extreme weather event that caused simultaneous floods in Portugal and an African dust outbreak across the Mediterranean region at the end of March 2024. The event was mainly driven by a large low pressure system located near Western Europe. We use circulation analogs to detect similar weather patterns and assess the possible influence of climate change on this event over the last 44 years. Our findings show that recent similar events are driven by deeper depressions than events further back in time, and follow a different seasonality. In terms of hazards, the more recent events are characterized by both heavier precipitation over the western Iberian Peninsula and larger transport of dust over the central-eastern Mediterranean, pointing to a role of climate change in intensifying the March 2024 event.

The rapid rise of data-driven weather forecasting has prompted increasing interest in how such models perform relative to traditional numerical weather prediction systems. While recent studies have highlighted the formers' superior skill on standard forecast metrics, questions remain regarding their ability to forecast physically complex, derived variables, particularly in the context of extreme events. In this study, we assess the performance of two leading operational data-driven models (GraphCast and Pangu-Weather) in forecasting Integrated Vapour Transport (IVT) and Atmospheric Rivers (ARs), using ECMWF’s IFS-HRES as a reference physics-based forecast. Forecasts are evaluated against ERA5 reanalysis over one year of global data, using three AR detection algorithms and lead times ranging from 1 to 10 days. Results show that data-driven models achieved root-mean-square errors for IVT comparable to or slightly better than IFS-HRES, particularly in the tropics and at shorter lead times. However, they achieved a poorer representation of the higher quantiles of the IVT distribution. A case study of a high-impact AR event revealed that all models could forecast the main event characteristics up to five days in advance. However, AR characteristics and detection performance varied substantially across detection algorithms. Notably, the geometrically stricter detection method highlighted a clearer advantage for IFS-HRES, especially in the midlatitudes and at shorter leads. Overall, while no model systematically outperformed the others across all AR detection algorithms, the results suggest that physics-based models may retain advantages in forecasting geometrically and physically consistent derived features like ARs, particularly under strict detection criteria. These findings underscore the importance of targeted evaluation frameworks for derived and extreme phenomena as data-driven models become more central in operational forecasting.

The CLIMK–WINDS (CLimes IMK – WINDstorm) database is a new, publicly available, database of extreme European windstorm footprints for the extended winter season during 1995–2015. In contrast with previously compiled European windstorm databases, it includes storm footprints derived from four different data sets, rather than a single source: the ERA5 reanalysis, the COSMO-REA6 reanalysis for Europe, the COSMO-Climate Limited-area Mode regional climate model driven by ERA5 on the EURO-CORDEX domain and simulation output from the same model but on an enlarged Germany domain with higher horizontal resolution. The database includes the footprints themselves, expressed as the relative daily maximum wind gusts associated with a storm event, the daily maximum wind gusts in absolute magnitude associated with the footprints and a measure of storm severity. We applied a consistent methodology, the storm loss index, across input data sets for identifying storm footprints and assessing their severity. We identified and included the storm footprints associated with the 50 most severe storms, or top 50 storms, within each of the four input data sets. This enables a direct comparison between the footprints derived from the different input data sets, eases future efforts to extend the time record of the database or to include additional input data sets and enables assessment of uncertainty in the footprints. Moreover, since we derived the top 50 storms from each input data set at its native horizontal resolution, the database also allows us to characterise the impact that horizontal resolution can have on footprint identification and severity assessment. We find that the choice of input data set – including the data's horizontal resolution – can have major effects on extreme storm identification and characterisation. Different storms were identified as belonging to the top 50 storms in the different data sets, and storm footprints for common storms displayed substantial variability across the data sets. A comparison of our database with two existing windstorm databases also highlights the important role of the footprint detection methodology. The CLIMK–WINDS database thus supports both the research community and the insurance industry in exploring the data set, methodology and resolution dependence of assessments of extreme storm hazards. The data presented here can be downloaded from https://doi.org/10.5281/zenodo.10594398 (Flynn et al., 2024).

In recent years, compound climate and weather extremes have received significant attention due to the heightened threat they pose to the environment, human societies, and the economy. This study investigates the impacts of compound drought-flood (CDF) extremes using data from two widely-used disaster databases: the Emergency Events Database (EM-DAT) and its geocoded version (GDIS), along with the DesInventar database. The analysis covers the period from 1960 to 2018, consistent with GDIS' temporal coverage. CDF events are defined as instances where drought and flood hazards occur concurrently or sequentially, with a flood taking place during a drought period or within four months of its end.

Our findings for the global extratropics reveal that the economic losses and the number of affected people resulting from compound drought-flood events are up to eight times higher than those ascribed to isolated droughts or floods, with a confidence interval ranging from one to twelve. Similar qualitative results emerge from DesInventar and EM-DAT, albeit with some quantitative differences. Furthermore, impact ratios have increased in more recent decades compared to earlier periods, emphasizing the increasing impacts of the drought-flood compound events.

These results highlight the amplified negative impacts when droughts and floods occur concomitantly or sequentially, underscoring the need for targeted policies to address their socioeconomic risks, particularly under changing climatic conditions.

Recent work has highlighted the systematic co-occurrence of wintertime climate extremes in Europe and North America. The focus has primarily been on cold spells in Eastern North America and connected wet and windy extremes in Europe. Here, we test whether a similar link can be found between wintertime cold spells in North America and warm or cold spells in Europe. Following previous literature, we consider extreme cold spells in Eastern Canada, Central Canada and Southeastern North America and extreme warm or cold spells in Western, Eastern and Northern Europe, using the NCEP-NCAR reanalysis from 1948 to 2023. We find that cold spells in Eastern Canada co-occur with warm anomalies in Northern Scandinavia. During cold spells in Central Canada, Southwestern Europe records locally above-normal temperatures while Eastern Europe simultaneously experiences significantly below normal temperatures. Additionally, during cold spells in Southeastern North America, Southwestern Europe records above-normal temperatures, while parts of Scandinavia experience below-normal temperatures. Finally, we found that, during warm spells in Southwestern Europe, Southeastern North America records below-normal temperatures. While in most cases only one of the two continents experiences extreme temperature anomalies, we also identify a significant number of concurrent extreme cold and warm spells. In agreement with previous research, we find that the temperature anomalies in North America and Europe are mediated by large-scale atmospheric circulation anomalies in the North Atlantic region. We interpret these anomalies through the lens of weather regimes, showing that specific combinations of North American and Euro-Atlantic regimes correspond to specific sets of concurrent temperature anomalies.

Large-scale atmospheric variability can be summarized by recurring patterns called weather regimes. Their properties, including predictability, have been studied using the local dimension, a geometrical estimate of degrees of freedom from multifractal theory. Local dimension estimates vary across regimes, decrease when a single regime dominates, and increase during transitions, supporting their dynamical significance. However, these variations stem not only from geometry but also from sampling density. We develop a null-hypothesis test using stochastic twins-Gaussian mixture-based surrogates matching atmospheric sampling density but with constant geometry-applied to ERA5 500 hPa fields. Density effects alone explain over 25% of local dimension variance and reproduce the dimension drop near regime peaks, indicating this behavior is density-driven, not geometric. The remaining variability is plausibly geometry-driven. This approach, applicable to any observed system with known sampling distribution, offers a new framework for interpreting local dimension estimates in atmospheric and oceanic data.

Stratospheric wave reflection events involve the upward propagation of planetary waves, which are subsequently reflected downward by the stratospheric polar vortex. This phenomenon establishes a connection between the large-scale circulations in the troposphere and in the stratosphere. Here, we investigate a set of wave reflection events characterized by an enhanced difference between poleward eddy heat flux over the northwestern Pacific and equatorward eddy heat flux over Canada. Previous research has pointed to a link between these events and anomalies in the tropospheric circulation over North America, with an associated abrupt continental-scale surface temperature decrease over the same region. In this study, we elucidate the dynamical mechanisms governing this chain of events.

We find that the evolution of meridional heat flux anomalies over the northwestern Pacific and Canada around reflection events is explained by a westward-propagating geopotential height ridge and by the downstream development of a trough. The trough advects colder-than-average air southward in the lower troposphere over North America, leading to an abrupt temperature decrease close to the surface. The evolution of this large-scale pattern resembles the shift from a Pacific Trough to an Alaskan Ridge weather regime, with approximately one-third to one-half of such transitions associated with reflection events. Furthermore, stratospheric wave reflection events exert a far-reaching influence on the tropospheric circulation across the northern middle and high latitudes. For example, a few days after the reflection-driven temperature decrease across North America, the North Atlantic jet stream becomes unusually intense and zonal, favoring the occurrence of extreme winds over Europe.