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The last few years have witnessed the emergence of data-driven weather forecast models capable of competing with – and, in some respects, outperforming – physics-based numerical models. However, recent studies have questioned the capability of data-driven models to provide reliable forecasts of extreme events. Here, we aim to evaluate this claim by comparing the performance of leading data-driven models in a semi-operational setting, focusing on the prediction of near-surface temperature and wind speed extremes globally. We find that data-driven models mostly outperform ECMWF’s physics-based deterministic model in terms of global RMSE for forecasts made 1–10 d ahead and that they can also compete in terms of extreme weather predictions in most regions. However, the performance of data-driven models varies by region, type of extreme event, and forecast lead time. Notably, data-driven models appear to perform best for temperature extremes in regions closer to the tropics and at shorter lead times. We conclude that data-driven models may already be a useful complement to physics-based forecasts in regions where they display superior tail performance but note that some challenges still need to be overcome prior to operational implementation.

Simultaneous occurrences of multiple heatwaves or cold spells in remote geographical regions have drawn considerable attention in the literature due to their potentially far-reaching impacts. We introduce a flexible toolbox to study such concurrent temperature extremes with adjustable parameters that different users can tailor to their specific needs. We then use the toolbox to present a climatological analysis of spatially compounding heatwaves and cold spells in the global midlatitudes. Specific geographical areas, such as western Russia, central Europe, southwestern Eurasia and western North America, emerge as hotspots for concurrent temperature extremes. Concurrent heatwaves are becoming more frequent, longer-lasting and more extended in the Northern Hemisphere, while the opposite holds for concurrent cold spells. Concurrent heatwaves in the Southern Hemisphere are comparatively rare but have been increasing in both number and extent. Notably, several of these trends in concurrent temperature extremes are significantly stronger than the corresponding trends in all temperature extremes.

During the first half of the Holocene (11 000 to 5000 years ago), the Northern Hemisphere experienced a strengthening of the monsoonal regime, with climate reconstructions robustly suggesting a greening of the Sahara region. Palaeoclimate archives also show that this so-called African humid period (AHP) was accompanied by changes in climate conditions at middle to high latitudes. However, inconsistencies still exist in reconstructions of the mid-Holocene (MH) climate at mid-latitudes, and model simulations provide limited support in reducing these discrepancies. In this paper, a set of simulations performed using a climate model are used to investigate the hitherto unexplored impact of Saharan greening on mid-latitude atmospheric circulation during the MH. Numerical simulations show Saharan greening has a year-round impact on the main circulation features in the Northern Hemisphere, especially during boreal summer (when the African monsoon develops). Key findings include a westward shift in the global Walker Circulation, leading to modifications in the North Atlantic jet stream in summer and the North Pacific jet stream in winter. Furthermore, Saharan greening modifies atmospheric synoptic circulation over the North Atlantic, enhancing the effect of orbital forcing on the transition of the North Atlantic Oscillation phase from predominantly positive to negative in winter and summer. Although the prescription of vegetation in the Sahara does not improve the proxy–model agreement, this study provides the first constraint on the influence of Saharan greening on northern mid-latitudes, opening new opportunities for understanding MH climate anomalies in regions such as North America and Eurasia.

The horizontal propagation of Rossby waves in the upper troposphere has been a long-standing topic in dynamical meteorology. The concept of “waveguidability’’, i.e., the capability of the background flow to act as a zonal waveguide for Rossby waves, may prove useful to address this problem, but developing a systematic definition and quantification of such a property remains challenging. With an eye to such issues, the current paper suggests a novel and efficient algorithm to solve the linearized barotropic vorticity equation on a sphere in a forced-dissipative configuration. The algorithm allows one to obtain linear wave solutions resulting from arbitrary combinations of the forcing and the background zonal wind. These solutions can be used to systematically study single- and double-jet configurations and are employed here to show that the latitude of the jet stream does not appear to affect waveguidability. The onset of barotropic instability might hinder the applicability of the linear framework, but it is shown that the nonlinear flow evolution can still be retrieved qualitatively from the linearized solution, both for the stationary component of the wave field and for the temporal evolution of transient waves.

Climate change is a global challenge with multiple far-reaching consequences, including the intensification and increased frequency of many extreme-weather events. In response to this pressing issue, we present ClimaMeter, a platform designed to assess and contextualize extreme-weather events relative to climate change. The platform offers near-real-time insights into the dynamics of extreme events, serving as a resource for researchers and policymakers while also being a science dissemination tool for the general public. ClimaMeter currently analyses heatwaves, cold spells, heavy precipitation, and windstorms. This paper elucidates the methodology, data sources, and analytical techniques on which ClimaMeter relies, providing a comprehensive overview of its scientific foundation. We further present two case studies: the late 2023 French heatwave and the July 2023 Storm Poly. We use two distinct datasets for each case study, namely Multi-Source Weather (MSWX) data, which serve as the reference for our rapid-attribution protocol, and the ERA5 dataset, widely regarded as the leading global climate reanalysis. These examples highlight both the strengths and limitations of ClimaMeter in expounding the link between climate change and the dynamics of extreme-weather events.

This study investigates the role of large-scale atmospheric processes in the development of cyclones causing extreme surface winds over the central North Atlantic basin (30 to 60° N, 10 to 50° W), focusing on the extended winter period (October–March) from 1950 until 2020 in the ERA5 reanalysis product. Extreme surface wind events are identified as footprints of spatio-temporally contiguous 10 m wind exceedances over the local 98th percentile. Cyclones that cause the top 1 % most intense wind footprints are identified. After excluding 16 (14 %) of cyclones that originated as tropical cyclones, further analysis is done on the remaining 99 extratropical cyclones (“top extremes”). These are compared to a set of cyclones yielding wind footprints with exceedances marginally above the 98th percentile (“moderate extremes”). Cyclones leading to top extremes are, from their time of cyclogenesis, characterised by the presence of pre-existing downstream cyclones, a strong polar jet, and positive upper-level potential vorticity anomalies to the north. All these features are absent or much weaker in the case of moderate extremes, implying that they play a key role in the explosive development of top extremes and in the generation of spatially extended wind footprints. There is also an indication of cyclonic Rossby wave breaking preceding the top extremes. Furthermore, analysis of the pressure tendency equation over the cyclones' evolution reveals that, although the leading contributions to surface pressure decrease vary from cyclone to cyclone, top extremes have on average a larger diabatic contribution than moderate extremes.

Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes within the same location and calendar year over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We analyse their co-occurrence on a yearly timescale because some of the climate extremes we consider play out over timescales up to several months. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves and wildfires (+940 % by the end of the century under RCP6.0 relative to present day), followed by river floods and heatwaves (+900 %) and river floods and wildfires (+250 %). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these co-occurring events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that co-occurring extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.

In recent years, deep learning models have rapidly emerged as a stand-alone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts that outperform those from state-of-the-art physics-based models, and operational implementation of data-driven forecasts appears to be drawing near. However, questions remain about the capabilities of deep learning models with respect to providing robust forecasts of extreme weather. This paper provides an overview of recent developments in the field of deep learning weather forecasts and scrutinises the challenges that extreme weather events pose to leading deep learning models. Lastly, it argues for the need to tailor data-driven models to forecast extreme events and proposes a foundational workflow to develop such models.

Windstorms are among the most impacting natural hazards affecting Western and Central Europe. Information on the associated impacts and losses are essential for risk assessment and the development of adaptation and mitigation strategies. In this study, we compare reported and estimated windstorm losses from five datasets belonging to three categories: Indices combining meteorological and insurance aspects, natural hazard databases, and loss reports from insurance companies. We analyse the similarities and differences between the datasets in terms of reported events, the number of storms per dataset and the ranking of specific storm events for the period October 1999 to March 2022 across 21 European countries. A total of 94 individual windstorms were documented. Only 11 of them were reported in all five datasets, while the large majority (roughly 60%) was solely recorded in single datasets. Results show that the total number of storms is different in the various datasets, although for the meteorological indices such number is fixed a priori. Additionally, the datasets often disagree on the storm frequency per winter season. Moreover, the ranking of storms based on reported/estimated losses varies in the datasets. However, these differences are reduced when the ranking is calculated relative to storm events that are common in the various datasets. The results generally hold for losses aggregated at European and at country level. Overall, the datasets provide different views on windstorm impacts. Thus, to avoid misleading conclusions, we use no dataset as “ground truth” but treat all of them as equal. We suggest that these different views can be used to test which features are relevant for calibrating windstorm models in specific regions. Furthermore, it could enable users to assign an uncertainty range to windstorm losses. We conclude that a combination of different datasets is crucial to obtain a representative picture of windstorm associated impacts.

Background: CardioMEMS is a device suitable for telemedicine that is currently being evaluated by the Regional Health Technology Assessment (HTA) Committee of Tuscany. Two detailed HTA reports are available in the specialized literature, the results of which need to be transferred to our regional setting. These decisions in Tuscany are made by the so-called Centro Operativo HTA. Aim: To validate, with local cost-effectiveness data, the decision on CardioMEMS that will be made in the Tuscany region. Methods: Two detailed international HTA reports were rearranged and adapted to our regional setting to generate a simplified analysis that could form the basis of our decision. Two willingness-to-pay (WTP) thresholds of €20,000/quality-adjusted life year (QALY) and €50,000/QALY were considered. Results: Based on epidemiological and regulatory information, the target population in Tuscany for this device is 166 cases. The value-based price of CardioMEMS is estimated to be €4,332 and €16,662 at WTP thresholds of 20,000/QALY and 50,000/QALY, respectively. Its current price in Italy is €12,000. Conclusion: In our region, the introduction of CardioMEMS is likely to be gradual, around 50 patients/year (or €0.60 million/year at current price). This example highlights the need to adapt the information published in the international literature to the local context in which the approval decision is made. In this context, simplified analyses are easier to apply than complex Markov models.

Abstract The possibility of using selectively incorporated 19F nuclei for NMR spectroscopic studies has retrieved increasing interest in recent years. The high gyromagnetic ratio of 19F and its absence in native biomolecular systems make this nucleus an interesting alternative to standard 1H NMR spectroscopy. Here we show how we can attach a label, carrying a 19F atom, to protein tyrosines, through the use of a specific three component Mannich-type reaction. To validate the efficacy and the specificity of the approach, we tested it on two selected systems with the aid of ESI MS measurements.

In Philadelphia chromosome-positive B-cell (Ph+) acute lymphoblastic leukemia (LLA), growing evidence has accumulated regarding the efficacy of low-intensity and chemo-free regimens. Our objective was to analyze all recent trials evaluating these treatments and to compare them in terms of efficacy. We applied the Shiny method, an artificial intelligence technique, to analyze Kaplan–Meier curves and reconstruct patient-level data. Reconstructed patient data were then evaluated through standard survival statistics and subjected to indirect head-to-head treatment comparisons. The endpoint was progression-free survival (PFS). Based on 432 reconstructed patients, eight trials were analyzed. The survival data from these trials were pooled into three types of treatments: (i) treatments based on tyrosine kinase inhibitors (TKIs) combined with reduced-intensity chemotherapy (denoted as TKICHE); (ii) TKIs associated with steroids with no chemotherapy (TKISTE); (iii) chemotherapy-free combinations of blinatumomab plus TKIs (TKIBLI). According to the Shiny method, the three PFS curves were reported in a single Kaplan–Meier graph and subjected to survival statistics. In terms of PFS, TKIBLI ranked first, TKICHE second, and TKISTE third; the differences between these three regimens were statistically significant. This multi-treatment Kaplan–Meier graph, generated through the Shiny method, summarized the current evidence on these treatments in both qualitative and quantitative terms.

Auranofin (AF) is a gold-based compound with a well-known pharmacological and toxicological profile, currently used in the treatment of some severe forms of rheumatoid arthritis. Over the last twenty years, AF has also been repurposed as antiviral, antitumor, and antibacterial drug. In this review we focused on the antibacterial properties of AF, specifically researching the minimal inhibitory concentrations (MIC) of AF in both mono- and diderm bacteria reported so far in literature. AF proves to be highly effective against monoderm bacteria, while diderm are far less susceptible, probably due to the outer membrane barrier. We also reported the current mechanistic hypotheses concerning the antimicrobial properties of AF, although a conclusive description of its antibacterial mode of action is not yet available. Even if its mechanism of action has not been fully elucidated yet and further studies are required to optimize its delivery strategy, AF deserves additional investigation because of its unique mode of action and high efficacy against a wide range of pathogens, which could lead to potential applications in fighting antimicrobial resistance and improving therapeutic outcomes in infectious diseases.