Studi recenti di eventi di precipitazioni estreme e alluvioni che hanno interessato l’Italia centro-settentrionale e l’area alpina in particolare hanno rivelato che oltre al contributo locale dovuto all’evaporazione dal Mar Mediterraneo, una quantità rilevante di umidità può giungere da regioni remote per mezzo di un intenso trasporto confinato all’interno di corridoi lunghi e stretti, noti come fiumi atmosferici.

Il progetto nazionale ARMEX, finanziato nell’ambito PRIN2022 dal Ministero dell’Università e della Ricerca, ha l’obiettivo di esplorare i fiumi atmosferici nel Mediterraneo e la loro connessione con eventi idrometeorologici estremi sull’Italia. Il progetto coinvolge competenze sia nella modellistica meteorologica e idrologica, sia nel monitoraggio da satellite. Vengono qui presentati alcuni risultati del progetto.

Utilizzando le rianalisi ERA5 e il dataset di precipitazioni ArCIS, e applicando un algoritmo di identificazione, opportunamente adattato alla peculiare e complessa morfologia della regione, è stato possibile evidenziare le principali caratteristiche climatologiche dei fiumi atmosferici nel Mediterraneo e la loro connessione con eventi idrometeorologici estremi sul centro-nord Italia dal 1960 ad oggi. Inoltre, attraverso l’analisi di diversi casi studio, simulazioni numeriche ad alta risoluzione hanno dimostrato che la presenza di un intenso fiume atmosferico – proveniente dalle aree tropicali dell’Africa o dall’Atlantico – rappresenta un elemento distintivo degli eventi estremi. Gli esperimenti modellistici hanno permesso di investigare le caratteristiche, i meccanismi dinamici e gli impatti dei fiumi atmosferici, i quali si sono rivelati un ingrediente fondamentale per il verificarsi di precipitazioni estreme. Infine, si sta esplorando la predicibilità a scala sub-stagionale di eventi estremi caratterizzati dalla presenza di fiumi atmosferici.

The thermal and moisture regimes of coastal cities arise from the interplay between mesoscale atmospheric circulations and local urban form, creating highly heterogeneous microclimates that directly affect heat exposure, energy demand, and outdoor comfort. This study presents an integrated multi-source approach to evaluate thermo-hygrometric variability in Bari (southern Italy), a representative Mediterranean coastal city characterized by strong land–sea interactions. The analysis combines in situ observations, remote-sensing data, and urban-canopy modelling to disentangle the relative roles of mesoscale forcing and microscale heterogeneity. Eight canyon-level sensors were deployed across districts with distinct morphology, vegetation density, and distance from the coastline, continuously monitoring air temperature and, at five sites, relative humidity during the summer of 2023. These measurements were complemented with high-resolution ECOSTRESS land-surface-temperature (LST) data and numerical simulations from the Multi-Layer Urban Canopy Model (MLUCM BEP+BEM) driven by ERA5 reanalysis. Morphological and radiative parameters around each site were quantified within a 500 m radius following the Local Climate Zone (LCZ) classification. Statistical analyses based on repeated-measures ANOVA quantified temporal and spatial variability in air temperature and humidity, enabling a robust assessment of the significance of observed differences. Results show that nocturnal temperature differences across Bari are relatively limited (< 2 °C), while after sunrise, the development of a sea–land-breeze circulation induces marked divergence among sites. Inland neighbourhoods warm and dry rapidly, reaching up to 5–6 °C higher air temperatures than coastal areas, which remain moderated by maritime ventilation. Conversely, relative humidity exhibits an inverse pattern, increasing along the coastline and decreasing inland. Evening cooling is slower in dense central districts due to greater heat storage, a pattern also captured by ECOSTRESS LST data. These findings highlight the dual control exerted by mesoscale dynamics and local morphology, with compact built-up zones and impervious surfaces amplifying daytime heat accumulation and delaying nocturnal release. Simulations with MLUCM BEP+BEM reproduce the observed intra-urban variability with notable accuracy, outperforming ERA5 near-surface reanalysis fields. Model-sensitivity tests demonstrate that the choice of boundary forcing significantly affects MLUCM BEP+BEM performance: sea-based forcings yield the best agreement near the coastline, while interpolated configurations perform better inland. Across all scenarios, the model captures microscale-driven variability of comparable magnitude to mesoscale contributions, confirming its suitability for reproducing canopy-layer processes under different urban and meteorological conditions. Overall, this research demonstrates that integrating mesoscale and microscale datasets provides a coherent framework for interpreting the spatial structure of thermal and moisture fields in coastal cities. The multi-source methodology—combining field measurements, satellite observations, and a physics-based urban-canopy model—offers a transferable approach for diagnosing urban heat exposure and informing climate adaptation. In particular, the MLUCM BEP+BEM model proves effective in distinguishing coastal–inland gradients and quantifying the influence of urban form and land cover on local climate. These results underscore the need for multiscale assessments to support energy-efficient urban design, improve outdoor comfort, and enhance resilience strategies in Mediterranean coastal environments increasingly exposed to heat stress and climate change.

Acknowledgements: This work is supported by ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – NextGenerationEU (CUP F83C22000740001).

Reference: Pappaccogli, G., Zonato, A., Martilli, A., Buccolieri, R., and Lionello, P.: MLUCM BEP+BEM: An offline one-dimensional Multi-Layer Urban Canopy Model based on the BEP+BEM Scheme, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-219, 2025.

The project “Urban hEat and pollution iSlands inTerAction in Rome and possible mitigation strategies” (RESTART), funded by the Italian Ministry of University and Research within the PRIN program, investigates the interplay between the Urban Heat Island (UHI) and the Urban Pollution Island (UPI) in Rome over the period 2019-2024. At its concluding stage, the project provides new insights into the drivers and feedbacks linking these phenomena and proposes tailored Nature-Based Solutions (NBS) to improve urban liveability and resilience.

High-temporal resolution meteorological and air quality observations from monitoring networks and international observatories were analysed to assess the current state of UHI and UPI and their links under different meteorological conditions. The analysis confirms that their interaction is strongly modulated by meteorological conditions, with combined effects intensifying during heatwaves and calm wind days.

To capture these dynamics, RESTART employed an innovative multiscale modelling framework that couples the Weather Research and Forecasting (WRF) mesoscale model with the ADMS-Urban dispersion model. This chain proved effective in reproducing spatial patterns of meteorological variables and pollutant concentrations, and in evaluating the role of NBS under both current and mitigation scenarios.

In this contribution, the science-based recommendations derived from the project will be discussed. Specifically, the outcomes emphasize the potential of NBS (i.e., urban greening and tree planting) to simultaneously mitigate heat stress and pollution accumulation. By combining high-quality monitoring data with advanced numerical simulations, RESTART contributes to the design of tailored, evidence-based mitigation strategies supporting sustainable and climate-resilient urban planning.

To conclude, the integrated observation–modelling approach developed in RESTART represents not only a valuable decision-support tool for the city of Rome, but also a transferable methodology applicable to other urban areas facing similar climate and air quality challenges.

Le micro- e nanoplastiche (MNP) stanno emergendo come una nuova e pervasiva componente degli aerosol atmosferici, ma le loro origini, i meccanismi di trasporto e il destino finale nell’atmosfera rimangono in gran parte sconosciuti. A differenza delle particelle aerosol convenzionali, esse derivano da una complessa combinazione di sorgenti continentali, marine e secondarie, e il loro comportamento fisico dipende in modo critico da dimensioni, forma e tipo di polimero.
Nonostante la crescente attenzione scientifica, i dati osservativi disponibili sono ancora scarsi e frammentari, raccolti in ambienti eterogenei e mediante metodologie analitiche non uniformi, rendendo difficile ottenere una visione coerente e comparabile del fenomeno.

In questo lavoro vengono analizzate le possibili sorgenti e traiettorie di trasporto atmosferico delle MNP, con l’obiettivo di identificare i processi dominanti che ne controllano la variabilità spaziale e temporale. A partire da osservazioni provenienti da ambienti urbani, suburbani, montani e nuvolosi, lo studio combina l’utilizzo dell’ultima versione del modello di dispersione lagrangiano FLEXPART v11 (adattato per le MNP) con una analisi statistica multi livello, basata su correlazioni e modelli di regressione multipla, per esplorare le relazioni tra le concentrazioni osservate e un insieme di proxy su larga scala rappresentativi delle diverse sorgenti potenziali (suoli agricoli e aridi, aree popolate, spray marino), considerando differenti età di trasporto atmosferico (1–45 giorni).

I risultati indicano che gli indicatori antropici e continentali (popolazione, aree irrigate) presentano correlazioni positive e persistenti soprattutto a brevi tempi di trasporto, mentre le sorgenti a più lungo raggio sembrano essere legate a emissioni di tipo marino e da zone aride. I modelli di regressione evidenziano driver regionali eterogenei e il potenziale contributo del trasporto a lunga distanza evidenziato dai siti montani.

Nel complesso, i risultati dimostrano che, pur in presenza di forti limitazioni osservazionali, è possibile estrarre segnali fisicamente coerenti, fornendo nuove evidenze sui processi che governano la presenza e la trasformazione delle MNP nell’atmosfera, e sottolineando la necessità di miglioramenti nelle tecniche di osservazione e di una maggiore copertura spaziale dei dati.

The rising frequency and severity of compound hydro-meteorological extremes underscore the urgent need to better understand their dynamics and occurrence. Compound events, involving concurrent or sequential natural hazards, often lead to amplified impacts compared to individual events. A recent striking example is the exceptional sequence of heavy rainfall in northern Italy (2023–2024), which triggered widespread flooding in Emilia-Romagna. Flood severity and extent were compounded by prior soil saturation resulting from earlier rainfall, illustrating how antecedent conditions can exacerbate impacts. However, the rarity and unprecedentedness of such events limits their representation in observational records owing to their limited temporal coverage, hence posing substantial challenges for robust statistical characterization. To overcome this, the UNSEEN (Unprecedented Simulated Extremes using ENsembles) approach has recently emerged. UNSEEN is employed by pooling large ensembles of seasonal re-forecasts from numerical weather prediction models to create synthetic time series spanning thousands of years. This enables the analysis of low-probability, high-impact events and the investigation of their dynamical features within a statistically robust framework.

This study applies the UNSEEN methodology to compound flood events within a multivariate framework, focusing on the interaction between precipitation and soil moisture as a key preconditioning driver. Seasonal re-forecasts from the SEAS5 dataset (ECMWF) over 1994-2023 are considered to characterize unprecedented compound floodings in Emilia Romagna. As a first step, the UNSEEN ensemble's ability to represent univariate extremes is assessed to ensure the reliability of the pooled surrogate time series. The surrogate series is then analyzed to distinguish precipitation extremes occurring with and without soil-moisture pre-conditioning, enabling a detailed assessment of their interaction and the associated hydrological responses within the regional river catchments. Results indicate that the UNSEEN ensemble realistically reproduces historical extreme flood events, offering a more robust characterization than observational records alone. Additionally, the river discharge response further differentiates the two event classes, with pre-conditioned events consistently leading to higher river levels, underscoring the amplifying role of antecedent soil moisture to the hydrological system. These findings highlight the value of ensemble-based approaches for better understanding rare compound events and informing more effective adaptation and mitigation strategies in flood-prone areas.

Medicanes are high-impact cyclones whose frequency may decline but whose strongest events may intensify in a warming Mediterranean (Romero et al., 2017; Tous et al., 2016). Medicane Ianos (September 2020) stands out as the strongest event on record, causing severe flooding and coastal damage across the Ionian Sea.

We analyze Medicane Ianos using a vertically integrated Moist Static Energy (MSE) variance budget as a process-based diagnostic of convective–dynamical coupling. ERA5 fields are tracked objectively, phases are classified in Hart phase space, and the budget is evaluated over ~2.5×10⁵ km² along the storm’s track. The intensification of Ianos is explained by a delicate balance between vertical moistening and horizontal advection, with surface latent-heat fluxes and radiative tendencies reinforcing the MSE build-up during the mature stage. The energy structure is tropical-like within ~600 km of the center during peak intensity, supporting medicane classification (Flaounas at al., 2022).

We extend this framework by using MSE variance as a convective metric across ensembles to link energetics to track and phase uncertainty. With the ECMWF IFS ensemble with perturbed physical parameterizations, forecast spread in trajectories and transition timing correlates with early-time MSE-tendency components and with the interaction between upper-level PV streamers and near-surface thermodynamic disequilibrium (Saraceni et al., 2023, ACP). This combined approach—MSE-budget + ensemble diagnostics—clarifies when medicanes behave more “tropical-like” (dominant convective moistening) versus “subtropical” (strong baroclinic/advection control), informing the ongoing classification debate.

References

Saraceni, M., Silvestri, L., & Bongioannini Cerlini, P. (2025). Analyzing the Mediterranean Tropical-like Cyclone Ianos Using the Moist Static Energy Budget. Atmosphere, 16(5), 562. https://doi.org/10.3390/atmos16050562

Saraceni, M., Silvestri, L., Bechtold, P., & Bongioannini Cerlini, P. (2023). Mediterranean tropical-like cyclone forecasts and analysis using the ECMWF ensemble forecasting system with physical parameterization perturbations. Atmospheric Chemistry and Physics, 23, 13883–13909. https://doi.org/10.5194/acp-23-13883-2023

Flaounas, E., Davolio, S., Raveh-Rubin, S., Pantillon, F., Miglietta, M. M., Gaertner, M. A., Hatzaki, M., Homar, V., Khodayar, S., Korres, G., et al. (2022). Mediterranean cyclones: Current knowledge and open questions on dynamics, prediction, climatology and impacts. Weather and Climate Dynamics, 3(1), 173–208. https://doi.org/10.5194/wcd-3-173-2022

Romero, R., Gaertner, M. A., Sánchez, E., Domínguez, M., González-Alemán, J. J., Miglietta, M. M., Walsh, K. J., Gil, V., Padorno, E., Picornell, M. A., & Romero, R. (2017). Climate change projections of medicanes with a large multi-model ensemble of regional climate models. Global and Planetary Change, 151, 134–143. https://doi.org/10.1016/j.gloplacha.2016.10.008

Tous, M., Romero, R., & Ramis, C. (2016). Projected changes in medicanes in the HadGEM3 N512 high-resolution global climate model. Climate Dynamics, 47(3–4), 1357–1372. https://doi.org/10.1007/s00382-015-2901-7