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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.

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.

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.

Humans face various hazards, making it challenging to apply a precautionary approach to all of them. Instead, individuals prioritize risk reduction based on perceived threats and lived experiences, often considering multiple hazards simultaneously. This study explores how public perceptions of multiple hazards are interconnected and change over time in Italy and Sweden, using data from three representative surveys (n = 12,476) conducted in August 2020, November 2020, and August 2021. We assess risk perception across three dimensions (likelihood, impact, and authority knowledge) for nine hazards, including epidemics, climate change, and natural disasters. Findings reveal that recently encountered hazards, such as COVID-19, become more closely connected to other hazards. At the same time, connections among hazards such as wildfires, droughts, and floods remain stable over time. These results indicate a dual component of public risk perception of multiple hazards, offering valuable insights for shaping public health policies and climate adaptation strategies.

Background

Even though HIV-TB co-infection is an emerging public health issue among migrants in European countries, the number of related articles has shown a decreasing trend.

Methods

To better estimate the extent of this problem, we analyzed 34 articles reporting both prevalence and odds ratio for HIV-TB co-infection in migrants in European countries. Heterogeneity analysis was conducted to assess potential bias, and a random-effects model was used to calculate the effect size.

Results

The overall prevalence of HIV-TB co-infection was 9% (95%CI: 7% − 11%) in foreign-born individuals, with higher rates observed in specific subgroups: 14% (95%CI: 5% − 33%) in those from Sub-Saharan Africa, which is higher than the overall average, and 4% (95%CI: 2% − 7%) in those from Latin America, which is lower than the overall average. Compared to the native-born European population, foreign-born individuals had a twofold increased risk of HIV-TB co-infection, with a threefold increased risk for those from Sub-Saharan Africa.

Conclusions

Our meta-analysis results highlight the disproportionate burden of HIV-TB co-infection among foreign-born people in Europe, particularly those from Sub-Saharan Africa.

Extreme weather events increasingly challenge the reliability and resilience of modern power systems. This study presents a novel methodology for assessing weather-related disruptions by integrating two large-scale datasets: outage statistics from Swedish energy companies and meteorological hazard data from the Swedish Meteorological and Hydrological Institute (SMHI). A key contribution is the use of large language models to extract and structure relevant information from unstructured SMHI weather reports, creating a structured hazard database. The datasets are merged and analyzed in Power BI, with a focus on wind-related events. The analysis identifies over 123 000 outages linked to wind and wind-induced treefall, offering valuable insights into spatial and temporal patterns of disruption. A multi-day event window is introduced to improve event-outage matching, and the IEEE Major Event Day (MED) methodology is applied to classify extreme disruption days. The results contribute to a deeper understanding of the impact of weather on power systems and lay the groundwork for future research.

Jet streams are narrow bands of fast-flowing air in the atmosphere. Features of jet streams such as large amplitude waves can be used to quantify teleconnections leading to extreme weather. In this study, we examine extreme cold air outbreaks over three regions of North America, a region influenced by the North Pacific jet. This research provides evidence that representing the jet stream using a jet-core algorithm—a method for extracting the 3-dimensional coordinates of fast-flowing wind—could contribute new information for understanding links between the North Pacific jet and climate patterns. We demonstrate that zonal relationships between atmospheric circulation and extreme weather appear more clearly in short-term fluctations when using methods that explicitly extract the jet stream versus using geopotential, a more common technique. We highlight that spatial relationships between the jet stream and extreme conditions over North America are difficult to examine using only simple indices of climate patterns or jet characteristics.

Europe is a heatwave hotspot: numerous temperature records have been broken in recent summers, and roughly 60,000 and 50,000 heat-related deaths occurred in the summers of 2022 and 2023, respectively. With recent summers, like that of 2022, projected to become the new norm, there is a pressing need to further develop heat-health warning systems to help society adapt to a warming climate. Here, we forecast heat-related mortality by applying a statistical epidemiological framework to temperature forecasts extending up to two weeks in advance. Focusing on two recent and exceptional summers in Europe, namely 2022 and 2023, we evaluate the skill of the daily heat-related mortality forecasts, and assess its association with temperature. For most of Europe, milder temperatures, close to the minimum mortality temperature, are associated with more skilful heat-related mortality forecasts. However, some of the hottest regions in Europe instead showed enhanced forecast skill associated with higher temperatures. This suggests that heat-related mortality forecasts can provide valuable information in European regions associated with high levels of heat-related mortality. Consequently, we advocate for local health authorities to include information from forecasts of heat-related mortality in their heat warning systems.

As data-driven weather forecasting models increasingly come in operational use, questions of fairness and equitable access to forecast improvements are gaining urgency. This paper introduces a conceptual framework for evaluating outcome-based fairness in global data-driven forecasts, drawing on principles from the algorithmic fairness literature. Specifically, we focus on two criteria: statistical parity (i.e. comparable improvements across protected groups) and conditional independence (i.e. no dependence of improvements on protected variables). Using ECMWF's AIFS model as a case study and IFS HRES as a baseline, we assess whether forecast improvements are equitably distributed across different income groups and population densities. We find that although AIFS provides substantial overall improvements in forecast skill, these gains are unevenly distributed: on average, wealthier and more densely populated areas are more likely to experience forecast improvements, violating group fairness and conditional independence criteria. We conclude by discussing how fairness-aware loss functions could be incorporated into data-driven weather forecasting systems and argue for a broader integration of fairness considerations into model development and evaluation.

To better understand how extreme climate events impact society, we need to increase the availability of accurate and comprehensive information about these impacts. We propose a method for building large-scale databases of climate extreme impacts from online textual sources, using LLMs for information extraction in combination with more traditional NLP techniques to improve accuracy and consistency. We evaluate the method against a small benchmark database created by human experts and find that extraction accuracy varies for different types of information. We compare three different LLMs and find that, while the commercial GPT-4 model gives the best performance overall, the open-source models Mistral and Mixtral are competitive for some types of information.